linkedin profiles of people who work at OpenAI
https://www.linkedin.com/in/adamperelman/
Adam Perelman - OpenAI | LinkedIn
2024-01-01
Adam Perelman
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 Experience &amp; Education
 
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 OpenAI
 
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<h2>View Adam’s full experience</h2>
<h3>See their title, tenure and more.</h3>
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 Honors &amp; Awards
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 Henry Ford II Scholar
 
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 Stanford University
 
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 Awarded for having the highest GPA of graduating seniors in the School of Engineering.
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 Phi Beta Kappa
 
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 Stanford University
 
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 Boothe Prize for Excellence in Writing
 
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 Stanford University
 
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 Awarded to the top research essay for each academic quarter of the Program in Writing and Rhetoric.
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 Elementary proficiency
 
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 OpenAI
 Stanford University
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 Experience & Education

 * OpenAI

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 2011 - 2015

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 Publications

 * DunceCap: Compiling Worst-Case Optimal Query Plans

 SIGMOD '15 2015

 Other authors
 + 
 See publication

 Honors & Awards

 * Henry Ford II Scholar

 Stanford University

 2015

 Awarded for having the highest GPA of graduating seniors in the School of Engineering.

 * Phi Beta Kappa

 Stanford University

 2014

 * Boothe Prize for Excellence in Writing

 Stanford University

 2011

 Awarded to the top research essay for each academic quarter of the Program in Writing and Rhetoric.

 Languages

 * English

 Native or bilingual proficiency

 * Spanish

 Native or bilingual proficiency

 * Hebrew

 Elementary proficiency

 * Japanese

 Elementary proficiency

 Recommendations received

 * Nick Laskowski

 “Adam is an incredibly adept learner with an intense ability to adapt quickly to a variety of social situations. I had the privilege of coaching Adam in some of his first forays into teaching upper elementary students, and his approach to the challenging task showed a level of mental dedication to a complex task that very few people display so readily. Adam (not surprisingly) is a fantastic teacher and has a wonderfully positive, supportive approach to working with students and children. More broadly, Adam is a very thoughtful and productive person who learns quickly and responds very strongly to a good challenge, and I give him my unqualified recommendation.”

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Add new skills with these courses

 * 49m

 Wavelet Analysis: Concepts with Wolfram Language

 * 1h 49m

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Experience &amp; Education
 
 
 
 OpenAI
 
 
View Adam’s full experience
See their title, tenure and more.
 Publications
 
 Honors &amp; Awards
 
 
 Henry Ford II Scholar
 
 
 
 Stanford University
 
 
  
 Awarded for having the highest GPA of graduating seniors in the School of Engineering.
 
 
 Phi Beta Kappa
 
 
 
 Stanford University
 
 
 
 Boothe Prize for Excellence in Writing
 
 
 
 Stanford University
 
 
  
 Awarded to the top research essay for each academic quarter of the Program in Writing and Rhetoric.
 
 Languages
 
 
 English
 
 
 
 Native or bilingual proficiency
 
 
 
 Spanish
 
 
 
 Native or bilingual proficiency
 
 
 
 Hebrew
 
 
 
 Elementary proficiency
 
 
 
 Japanese
 
 
 
 Elementary proficiency
 
 
 Recommendations received
 
 Other similar profiles
 
 
 Sherwin Wu
 
 
 San Francisco, CA
 
 Connect
 
 
 Arvind Neelakantan
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Haozhun Jin
 
 
 Mountain View, CA
 
 Connect
 
 
 Wesam Manassra
 
 
 San Francisco, CA
 
 Connect
 
 
 David Schnurr
 
 
 San Francisco, CA
 
 Connect
 
 
 Rohan Sahai
 
 
 San Francisco, CA
 
 Connect
 
 
 Rachel Lim
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Yash Dani
 
 
 San Francisco, CA
 
 Connect
 
 
 Hunter L.
 
 
 San Francisco, CA
 
 Connect
 
 
 Ted Sanders
 
 
 San Francisco, CA
 
 Connect
 
 
 Evan Morikawa
 
 
 San Francisco, CA
 
 Connect
 
 
 Akila Welihinda
 
 
 San Francisco, CA
 
 Connect
 
 
 Thomas Dimson
 
 
 Palo Alto, CA
 
 Connect
 
 
 Tarun Gogineni
 
 
 San Francisco, CA
 
 Connect
 
 
 Simón Fishman
 
 
 San Francisco, CA
 
 Connect
 
 
 Alex Karpenko
 
 
 Palo Alto, CA
 
 Connect
 
 
 Abraham Bachrach
 
 
 San Francisco Bay Area
 
 Connect
 
 Connect
 
 
 Dirk Harms-Merbitz
 
 
 Half Moon Bay, CA
 
 Connect
 
 
 Dmitri Dolgov
 
 
 Mountain View, CA
 
 Connect
 
 Explore collaborative articles
 
 We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
 
 Explore More
 
 Others named Adam Perelman
 2 others named Adam Perelman are on LinkedIn
 
 
 See others named Adam Perelman
 
 Add new skills with these courses
Adam Perelman - OpenAI | LinkedIn
https://www.linkedin.com/in/adamperelman/
https://www.linkedin.com/in/rajeevnayak/
Rajeev Nayak - OpenAI | LinkedIn
2024-01-01
Rajeev Nayak
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Rajeev Nayak - OpenAI | LinkedIn
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Clemens Winter - OpenAI | LinkedIn
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Clemens Winter
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 517 followers 391 connections

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 * We are thrilled to announce that #GrantScout has successfully closed a $3.4 million seed funding round, led by Adverb Ventures and Abstract Ventures,…

 We are thrilled to announce that #GrantScout has successfully closed a $3.4 million seed funding round, led by Adverb Ventures and Abstract Ventures,…

 Liked by Clemens Winter

 * Short blogpost featuring a new tool I hacked on recently that creates neat visualizations of the inner workings of one of my favorite algorithms:…

 Short blogpost featuring a new tool I hacked on recently that creates neat visualizations of the inner workings of one of my favorite algorithms:…

 Shared by Clemens Winter

 * Excited to share my blogpost on a new set of libraries that aim to dramatically reduce the engineering effort, computational cost, and expertise…

 Excited to share my blogpost on a new set of libraries that aim to dramatically reduce the engineering effort, computational cost, and expertise…

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 * How to Analyze Billions of Records per Second on a Single Desktop PC

 July 9, 2018

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 * Excited to share our new results about thermodynamically consistent property prediction using neural networks. Have you ever struggled to find…

 Excited to share our new results about thermodynamically consistent property prediction using neural networks. Have you ever struggled to find…

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 * Who would have thought that a #SMILE is all it takes? Our new paper shows how physical models, experimental data, and machine learning can be…

 Who would have thought that a #SMILE is all it takes? Our new paper shows how physical models, experimental data, and machine learning can be…

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 * Many of OpenAI's breakthrough results -- such as our language model, GPT-2, which has over 1.5B parameters -- have been enabled by exponential…

 Many of OpenAI's breakthrough results -- such as our language model, GPT-2, which has over 1.5B parameters -- have been enabled by exponential…

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Aravind Srinivas - Perplexity | LinkedIn
2024-01-01
Aravind Srinivas
<div><div>
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<h2>
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 Aravind Srinivas

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 * Excited to be speaking about Perplexity & AI 🤝 Business at the 32nd Annual Tech Conference at Harvard Business School on February 4th. DM if you'd…

 Excited to be speaking about Perplexity & AI 🤝 Business at the 32nd Annual Tech Conference at Harvard Business School on February 4th. DM if you'd…

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 * Aravind Srinivas seems like the kind of founder that you'd want to rally behind, regardless of what they're working on. This interview is an exercise…

 Aravind Srinivas seems like the kind of founder that you'd want to rally behind, regardless of what they're working on. This interview is an exercise…

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 * Visiting my alma matter tomorrow, for a leadership lecture! 6:30 pm IST!

 Visiting my alma matter tomorrow, for a leadership lecture! 6:30 pm IST!

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 Honors & Awards

 * Viterbi India Fellowship

 Indo-US Science and Technology Forum

 May 2015

 https://www.iusstf.org/assets/sitesfile/image/download_links/pdf/pdf_2068068638.pdf

 * Indian National Mathematics Olympiad Merit Awardee

 National Board of Higher Mathematics, India

 * Kishore Vaigyanik Protsahan Yojana

 Department of Science and Technology, Government of India

 * National Talent Search Scholarship

 Government of India

 More activity by Aravind

 * Bright lights, big news... 👏 Perplexity on the Nasdaq big screen. Aravind Srinivas Cack Wilhelm Shravan N.

 Bright lights, big news... 👏 Perplexity on the Nasdaq big screen. Aravind Srinivas Cack Wilhelm Shravan N.

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 * Surprise: Perplexity on Nasdaq billboard!

 Surprise: Perplexity on Nasdaq billboard!

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 * What retention looks like

 What retention looks like

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 * Android users, we’ve heard you loud and clear! You can now add the Perplexity widget to your home screen. Enjoy quick access to Copilot, ask anything…

 Android users, we’ve heard you loud and clear! You can now add the Perplexity widget to your home screen. Enjoy quick access to Copilot, ask anything…

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 * Congratulations to our partner Perplexity on their recent Series B raise! 🎉 Hot tip: Brex users can get 6 free months of Perplexity from our…

 Congratulations to our partner Perplexity on their recent Series B raise! 🎉 Hot tip: Brex users can get 6 free months of Perplexity from our…

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 * Excited about Perplexity's partnership with Brex!

 Excited about Perplexity's partnership with Brex!

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 * Great way to start the year for Perplexity! Well done, Aravind Srinivas and team!

 Great way to start the year for Perplexity! Well done, Aravind Srinivas and team!

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 * Retro vibes

 Retro vibes

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 * Perplexity is hiring a Mobile Design Lead. Henry Modisett has incredibly good taste in product design. Perplexity is one of the rare products in the…

 Perplexity is hiring a Mobile Design Lead. Henry Modisett has incredibly good taste in product design. Perplexity is one of the rare products in the…

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2024-04-01
Keren Zhou
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 * I am happy to announce that I defended my Ph.D. Thesis on Monday, April 1st, 2024! 🎓🥳 🚀 🥂 This journey took 4 years and 9 months and enabled me…

 I am happy to announce that I defended my Ph.D. Thesis on Monday, April 1st, 2024! 🎓🥳 🚀 🥂 This journey took 4 years and 9 months and enabled me…

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 * Jinjun Xiong, Chair of the Institute for Artificial Intelligence and Data Science and SUNY Empire Innovation Professor in the Department of Computer…

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 * Our deep learning performance tutorial continues with José Cano from University of Glasgow talking about development of FPGA-based DNN accelerators…

 Our deep learning performance tutorial continues with José Cano from University of Glasgow talking about development of FPGA-based DNN accelerators…

 Liked by Keren Zhou

 * We've raised $117M from Nat Friedman and others to build an AI software engineer. Code generation is both a product and a path to AGI, requiring new…

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 * Join our Team at OpenAI! We're expanding our Research Platform Team and looking for talented individuals. If you're passionate about AI and eager…

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 Liked by Keren Zhou

 * 🎆My New Year’s Resolution for 2024: no more OOMs in my model training jobs.🎆 Just in time for the New Year, the PyTorch team dropped the second…

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 * Do you know that there is a man in Serbia who gained world fame with a patent that is used in all magnetic disks all over the world? Doctor…

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 * The NeurIPS LLM efficiency challenge is heating-up with more prizes and GPU credits from Mozilla.ai, Amazon Web Services (AWS), Microsoft, Lightning…

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 * 💚💛🥳

 💚💛🥳

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 * Today, we’re announcing that Amazon will invest up to $4 billion in Anthropic. The agreement is part of a broader collaboration to develop reliable…

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 * We are very excited to be hosting our next guest speaker, Dr. Hyung Won Chung from OpenAI ChatGPT team. Please tune in to join us at 12:10 CT this…

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 * A quick technical note for my AI systems and computer architecture friends. I have been learning and exploring ideas related to speculative decoding…

 A quick technical note for my AI systems and computer architecture friends. I have been learning and exploring ideas related to speculative decoding…

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Activity
 
 
 Experience &amp; Education
 
 
 
 OpenAI
 
 
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See their title, tenure and more.
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 Our deep learning performance tutorial continues with José Cano from University of Glasgow talking about development of FPGA-based DNN accelerators…
 
 
 
 Liked by Keren Zhou
 
 
 
 We've raised $117M from Nat Friedman and others to build an AI software engineer.
Code generation is both a product and a path to AGI, requiring new…
 
 
 
 Liked by Keren Zhou
 
 
 
 Join our Team at OpenAI! 
We're expanding our Research Platform Team and looking for talented individuals. If you're passionate about AI and eager…
 
 
 
 Liked by Keren Zhou
 
 
 
 🎆My New Year’s Resolution for 2024: no more OOMs in my model training jobs.🎆
Just in time for the New Year, the PyTorch team dropped the second…
 
 
 
 Liked by Keren Zhou
 
 
 
 Shruti Sekar was today's senior speaker at the George Mason graduation. Offering wise and touching words about living through times marked by climate…
 
 
 
 Liked by Keren Zhou
 
 
 
 Do you know that there is a man in Serbia who gained world fame with a patent that is used in all magnetic disks all over the world?
Doctor…
 
 
 
 Liked by Keren Zhou
 
 
 
 Today, AMD has entered a definitive agreement to acquire nod.ai. Together, we will help advance AI by strengthening our open-source software…
 
 
 
 Liked by Keren Zhou
 
 
 
 The NeurIPS LLM efficiency challenge is heating-up with more prizes and GPU credits from Mozilla.ai, Amazon Web Services (AWS), Microsoft, Lightning…
 
 
 
 Liked by Keren Zhou
 
 
 
 💚💛🥳
 
 
 
 Liked by Keren Zhou
 
 
 
 Today, we’re announcing that Amazon will invest up to $4 billion in Anthropic. The agreement is part of a broader collaboration to develop reliable…
 
 
 
 Liked by Keren Zhou
 
 
 
 We are very excited to be hosting our next guest speaker, Dr. Hyung Won Chung from OpenAI ChatGPT team. Please tune in to join us at 12:10 CT this…
 
 
 
 Liked by Keren Zhou
 
 
 
 I am hiring GPU engineers to come work with me at Magic. We have to make an efficient training and inference stack for new ML architectures from the…
 
 
 
 Liked by Keren Zhou
 
 
 
 A quick technical note for my AI systems and computer architecture friends. I have been learning and exploring ideas related to speculative decoding…
 
 
 
 Liked by Keren Zhou
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Keren Zhou - OpenAI | LinkedIn
https://www.linkedin.com/in/kerenzhou/
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Adrien Ecoffet - OpenAI | LinkedIn
2024-01-01
Adrien Ecoffet
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 Google search quality has been going down. Even for simple things like the abbreviation of a state like Nevada. Left: Google, Right: Perplexity AI.…
 
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This multi-year, multi-billion dollar investment from Microsoft…
 
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 The OpenAI Residency offers a pathway to a full-time role at OpenAI for engineers and researchers who are interested in learning more about the…
 
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 Check out text-davinci-003! It's a model that I'm quite fond of — I hope you enjoy it too.
https://lnkd.in/g43-i8JJ
 
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 Pretty big news, today i can announce that a robot simulation that I have been working on for a while is going public.
For the last 6 months I have…
 
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 In order to make AI that benefits all of humanity, we believe it’s essential to have a team with a diverse set of skills and backgrounds that reflect…
 
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 As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative…
 
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 Happy to announce I have joined the Vector Institute as a Faculty Member! I remain a prof at UBC too. 
https://lnkd.in/gDdv4VHz
 
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 Excited to release my most recent research project with the Multi-Agent team at OpenAI!
https://lnkd.in/e8ENVZhw
We trained a neural network to…
 
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 Excited to share that I recently started in a new role as Director of Data Science at Monzo! #newmonzonaut
 
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 I founded Predibase last year together with Travis Addair (Uber AI, Horovod), Devvret Rishi (Firebase, Google AI, Kaggle) and Professor Chris Ré from…
 
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By the way I represent International British owned company with headquarter in Czech Republic. Even I’m Russian I live…
 
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 * Google search quality has been going down. Even for simple things like the abbreviation of a state like Nevada. Left: Google, Right: Perplexity AI.…

 Google search quality has been going down. Even for simple things like the abbreviation of a state like Nevada. Left: Google, Right: Perplexity AI.…

 Liked by Adrien Ecoffet

 * We have some job openings at Perplexity AI: 1. Backend Engineer: Search and Ranking models for Perplexity Ask 2. Data Engineer: Tooling and Data…

 We have some job openings at Perplexity AI: 1. Backend Engineer: Search and Ranking models for Perplexity Ask 2. Data Engineer: Tooling and Data…

 Liked by Adrien Ecoffet

 * “We’re happy to announce that OpenAI and Microsoft are extending our partnership. This multi-year, multi-billion dollar investment from Microsoft…

 “We’re happy to announce that OpenAI and Microsoft are extending our partnership. This multi-year, multi-billion dollar investment from Microsoft…

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 The OpenAI Residency offers a pathway to a full-time role at OpenAI for engineers and researchers who are interested in learning more about the…

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 * Check out text-davinci-003! It's a model that I'm quite fond of — I hope you enjoy it too. https://lnkd.in/g43-i8JJ

 Check out text-davinci-003! It's a model that I'm quite fond of — I hope you enjoy it too. https://lnkd.in/g43-i8JJ

 Liked by Adrien Ecoffet

 * Pretty big news, today i can announce that a robot simulation that I have been working on for a while is going public. For the last 6 months I have…

 Pretty big news, today i can announce that a robot simulation that I have been working on for a while is going public. For the last 6 months I have…

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 * In order to make AI that benefits all of humanity, we believe it’s essential to have a team with a diverse set of skills and backgrounds that reflect…

 In order to make AI that benefits all of humanity, we believe it’s essential to have a team with a diverse set of skills and backgrounds that reflect…

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 * As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative…

 As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative…

 Liked by Adrien Ecoffet

 * Happy to announce I have joined the Vector Institute as a Faculty Member! I remain a prof at UBC too. https://lnkd.in/gDdv4VHz

 Happy to announce I have joined the Vector Institute as a Faculty Member! I remain a prof at UBC too. https://lnkd.in/gDdv4VHz

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 * Excited to release my most recent research project with the Multi-Agent team at OpenAI! https://lnkd.in/e8ENVZhw We trained a neural network to…

 Excited to release my most recent research project with the Multi-Agent team at OpenAI! https://lnkd.in/e8ENVZhw We trained a neural network to…

 Liked by Adrien Ecoffet

 * Excited to share that I recently started in a new role as Director of Data Science at Monzo! #newmonzonaut

 Excited to share that I recently started in a new role as Director of Data Science at Monzo! #newmonzonaut

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 * I founded Predibase last year together with Travis Addair (Uber AI, Horovod), Devvret Rishi (Firebase, Google AI, Kaggle) and Professor Chris Ré from…

 I founded Predibase last year together with Travis Addair (Uber AI, Horovod), Devvret Rishi (Firebase, Google AI, Kaggle) and Professor Chris Ré from…

 Liked by Adrien Ecoffet

 * Xenophobia, Ready set Go By the way I represent International British owned company with headquarter in Czech Republic. Even I’m Russian I live…

 Xenophobia, Ready set Go By the way I represent International British owned company with headquarter in Czech Republic. Even I’m Russian I live…

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 * We’ve just updated our logo to better reflect our focus on sharing knowledge. Please let us know what you think!

 We’ve just updated our logo to better reflect our focus on sharing knowledge. Please let us know what you think!

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 Google search quality has been going down. Even for simple things like the abbreviation of a state like Nevada. Left: Google, Right: Perplexity AI.…
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 We have some job openings at Perplexity AI:
1. Backend Engineer: Search and Ranking models for Perplexity Ask
2. Data Engineer: Tooling and Data…
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 “We’re happy to announce that OpenAI and Microsoft are extending our partnership.
This multi-year, multi-billion dollar investment from Microsoft…
 
 
 
 Liked by Adrien Ecoffet
 
 Join now to see all activity
 
 
 
 Experience &amp; Education
 
 
 
 OpenAI
 
 
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See their title, tenure and more.
 or
 
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 More activity by Adrien
 
 
 
 
 The OpenAI Residency offers a pathway to a full-time role at OpenAI for engineers and researchers who are interested in learning more about the…
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 Check out text-davinci-003! It's a model that I'm quite fond of — I hope you enjoy it too.
https://lnkd.in/g43-i8JJ
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 Pretty big news, today i can announce that a robot simulation that I have been working on for a while is going public.
For the last 6 months I have…
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 In order to make AI that benefits all of humanity, we believe it’s essential to have a team with a diverse set of skills and backgrounds that reflect…
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative…
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 Happy to announce I have joined the Vector Institute as a Faculty Member! I remain a prof at UBC too. 
https://lnkd.in/gDdv4VHz
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 Excited to release my most recent research project with the Multi-Agent team at OpenAI!
https://lnkd.in/e8ENVZhw
We trained a neural network to…
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 Excited to share that I recently started in a new role as Director of Data Science at Monzo! #newmonzonaut
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 I founded Predibase last year together with Travis Addair (Uber AI, Horovod), Devvret Rishi (Firebase, Google AI, Kaggle) and Professor Chris Ré from…
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 Xenophobia, Ready set Go
By the way I represent International British owned company with headquarter in Czech Republic. Even I’m Russian I live…
 
 
 
 Liked by Adrien Ecoffet
 
 
 
 We’ve just updated our logo to better reflect our focus on sharing knowledge. Please let us know what you think!
 
 
 
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 By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
 
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 Other similar profiles
 
 
 Prafulla Dhariwal
 
 
 San Francisco, CA
 
 Connect
 
 
 Wojciech Zaremba
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Lilian Weng
 
 
 United States
 
 Connect
 
 
 Greg Brockman
 
 
 San Francisco, CA
 
 Connect
 
 
 Jonathan Gordon
 
 
 San Francisco, CA
 
 Connect
 
 
 Jerry Tworek
 
 
 San Francisco, CA
 
 Connect
 
 
 Ted Sanders
 
 
 San Francisco, CA
 
 Connect
 
 
 Karl Cobbe
 
 
 Stanford, CA
 
 Connect
 
 
 Hunter L.
 
 
 San Francisco, CA
 
 Connect
 
 
 Paul Baltescu
 
 
 San Francisco Bay Area
 
 Connect
 
 Explore collaborative articles
 
 We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
 
 Explore More
 
 Add new skills with these courses
 
1h 21m
 
 PyTorch Essential Training: Deep Learning
 
 
1h 13m
 
 Deep Learning: Getting Started
 
 
1h 33m
 
 Giving Computers Vision
 
 
 
 See all courses
Adrien Ecoffet - OpenAI | LinkedIn
https://www.linkedin.com/in/aecoffet/
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          Dirk-Jan van Veen
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          One of the best things about being in San Francisco 🌉  is the ability to drop in on great talks. Last week I had a chance to see NVIDIA's CEO Jensen Huang (JH) being interviewed by Stripe's CEO Patrick Collison. 

Jensen is among the longest-serving founder-CEOs in Silicon Valley at the moment (30+ years). With that comes a special clarity of mind and some hot takes. 🌶👇 

🗣 Jensen doesn't believe in 'praise in public and criticism in private'. Instead, he reasons that most information in the organization should not be privy to certain people. By giving feedback in public, you allow everyone to learn. 'What is better than learning from your own mistakes? It's learning from someone else's mistakes!!' 

🔥 JH also doesn't like to fire people. He would rather "torture them into greatness". He invests in people and he doesn't like to write off his investments.

👨‍💻 Asked whether he likes his job every day, he answers "no". The perfect job doesn't give you happiness 100% of the time. "No great things were done, that were easy all the way." ... "Even if I come home from a day where I did things I didn't enjoy, it could still be have been a great day."

👩‍👧‍👦 Jensen famously has 60 direct reports. "This allowed us to cut out probably 7 levels of management". His subordinates don't necessarily have such large teams. "It doesn't scale down, less senior people need more guidance". Looking at the numbers, this approach allowed NVidia to reach ~2T market cap with ~28.000 people vs. Microsoft's 3T with ~220.000 people and Apple's 2.6T with 160.000 people. 

❔ Jensen answers almost every question in the same way: "Let's break it down, let's reason about it". This is a great response on so many levels. First, it allows him to not be put on the spot. Second, it allows him to teach his workforce how he reasons through a problem so that they can emulate it and create alignment. Third, it gives people a chance to point out flaws in his reasoning. He admits he is not right all the time. 

ON AI 

👨‍🔧 "You will not lose your job to AI, your company will go out of business because another company used AI." "If you're not using AI you're doing it wrong."

⏱   The extent to which AI will enter many processes is largely underestimated. Right now we are focused on language - sounds in a time sequence. "Any time sequence can be tokenized." This will include robotic movement. 

🥛 JH also expects that we won't be satisfied with foundation models "You're going to want to tune it into perfection because you care so deeply. Say if it's already at 99% you'll want to get it to say 99.3%"

This last piece is also why we created Query Vary (YC W22). AI will never feel truly intelligent until it completely understands your data and context. It needs to be taught, regardless of how powerful the foundation model is. 

Finally, besides insights, it was remarkable how funny Jensen was, cracking a joke at roughly a 1 per 3 min rate. It was a very entertaining talk. 

#ai #sf

        
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          Christine Coutolenc
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          🚗💡 Exciting Experience with Autonomous Driving! 💡🚗
Recently, my son and I had the opportunity to take a ride in an autonomous vehicle, and it was nothing short of amazing! 🌟

Feeling perfectly safe throughout the journey, we were blown away by how the car made incredibly safe choices through the bustling streets of San Francisco, filled with pedestrians, scooters, bicyclists, and cars. It detected risks that even my eyes didn't catch (albeit I was in the back seat), reacting to them swiftly and appropriately. 🚦

What impressed me most was that this cutting-edge technology will soon be accessible to everyone at a cost comparable to that of an Uber or Lyft ride (I was on a list as part of a Waymo's pilot group for this ride). It's incredible to think that with the rise of AI developments, we're on the brink of a transportation revolution that promises both safety and convenience. 🌐

As someone deeply supportive of these AI advancements, I'm excited about the endless possibilities they hold for the future. 🚀  🌍

#AutonomousDriving #AI #Innovation #FutureTech #TransportationRevolution #Waymo
        
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          Vibhor Kumar
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          In a recent interview, NVIDIA's CEO, Jensen Huang highlighted Tesla's leading position in self-driving technology. 

I have been using Tesla's #ModelY in Full Self-Driving mode for over a month now, and I couldn't agree more with the CEO's assessment. The experience truly feels like something out of a sci-fi movie.

On highways, #FSD excels at lane changes, safe distance maintenance, and merges. Its navigation through complex interchanges and adaptability to varying traffic conditions impressed me, making long drives more relaxing and enjoyable.

In neighborhood settings, #FSD's attention to detail in recognizing stop signs, traffic lights, pedestrians, and cyclists stood out. It navigated turns and intersections smoothly, ensuring a safe driving experience even on busy streets.

The technology's advanced capabilities in navigation, lane changes, and automated parking have significantly enhanced my driving experience. The seamless integration with real-time data and intuitive interface instilled a sense of confidence and safety.

Beyond the autonomy, driving a Tesla is exhilarating in itself. The instant torque and rapid acceleration deliver a thrilling experience, with a pickup comparable to racing cars. This powerful performance, combined with cutting-edge technology, makes every drive an adventure

While there is room for improvement, Tesla's #FSD undeniably represents a substantial advancement in automotive technology.

Highly recommend exploring this innovative and transformative technology!

#Tesla #FSD #Innovation #AutonomousDriving, #ElonMusk
        
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          Apratim Banerjee
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          Its sometimes hard to believe that we live in an era where driverless vehicles aren't just a vision anymore but a reality. Waymo driverless cabs for example, are transforming the San Francisco streets forever! 

It was a wonderful experience working on an application of autonomous vehicles as part of my final project in the course CS267: Applications of Parallel Computing at the University of California, Berkeley.

Our team, comprising Terence N., Zeid Solh, and myself, focused on parallelizing stereo vision for driverless applications. Stereo vision is crucial for self-driving cars as it builds a 3D understanding of the environment by using two cameras. However, real-time processing of these images is computationally expensive, posing a significant challenge for autonomous vehicles.

We tackled this by exploring various parallel computing techniques to improve the speed of calculating the Disparity Map algorithm. We used popular techniques such as #SIMD (Single Instruction, Multiple Data), #OpenMP (multi-threading), and CUDA (NVIDIA GPU acceleration), and benchmarked the performance. 

By distributing the computational load across multiple cores and processors, we achieved substantial performance improvements, enabling real-time processing of high-resolution images and videos. The results were promising! Utilizing CUDA, we saw significant speedups, demonstrating the potential for efficient, real-time depth perception in autonomous systems.

This project signified the importance of parallel computing in advancing autonomous driving technologies. I'm incredibly proud of our team's hard work and the insights we gained through this project.

Check out our GitHub repo: https://lnkd.in/gYHrEU9z for more details on our implementation! 

#UCberkeley #CS267 #ParallelComputing #StereoVision #AutonomousDriving #DeepLearning #ComputerVision
        
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          Ken Schmahl, PE
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          I'm starting to worry about the insurgence of AI in hardware design cycles.  The claim is AI will "solve systems problems and offer chips-to-system intelligent design acceleration."

First, many small company management teams still fail to see the need to bring on-board, *in a timely manner*, the experienced staff needed to get it right first-time. The industry thinks moving to a model dependent on ODM houses is a good idea, but like a stubborn mule ODM houses need to be checked, poked and prodded to produce good designs.  Like a good mule skinner, experienced in-house staff are needed to keep them in line.
Second, finding good package/board designers is getting harder since new college grads are going into ML and Data Science, and leaving the physical sciences needed for electric circuit design.  The consequence of this will be seen in new products which must go thru the school of hard-knocks, i.e. they won't work first-time.
Third, vendors are now offering to solve these problems by bring this "expertise" in-house through AI, promising to "accelerate design" with "artificial intelligence".

To me, looking forward, this looks like a recipe for disaster.  Execs holding the purse will ask why they need experienced engineering staff when AI will show the new kids how to do it. The new kids won't know AI is as dumb as a bag of hammers, won't know how to vet its answers, suggestions and directions, won't know when to call BS. And the savior - AI - will screw it up with *no accountability*.  Schedules will slip, budgets will be blown, market opportunities will be lost.  There will be weeping and gnashing of teeth.

Meanwhile the experienced package/board designers are retiring, leaving a gaping hole in the industry.  No amount of AI will resolve this in the next few years.  

        
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      <a href="https://il.linkedin.com/in/natishalom?trk=public_profile_relatedPosts_face-pile-cta">
        
      
        <p>
          Nati Shalom
        </p>
          
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      <a href="https://www.linkedin.com/posts/natishalom_start-up-nation-deep-dive-with-dan-senor-activity-7210557120994258945-dPxD?trk=public_profile_relatedPosts">
        


      

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          Good listen: Mark Suster, the guest host, is joined by Dan Senor to discuss the remarkable growth of NVIDIA in #Israel, which now boasts over 4,000 employees and a global CTO who is Israeli. 

The conversation shifts to Israel’s startup ecosystem, highlighting its impressive statistics: 500 venture capital firms, 120 accelerators, 35 incubators, 400 multinationals with major R&amp;D operations, and 7,000 startups. These figures underscore Israel’s position as having the highest density of startups globally, attracting significant global venture capital, leading in R&amp;D spending relative to GDP, and ranking fourth in NASDAQ-listed public companies.

Regardless - I'll recommend following This Week in Startups podcasts which brings interesting conversations with leaders from the startup community...


https://lnkd.in/eGv3fr-H

#iwork4dell
        
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        Karl Cobbe

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        * Dirk-Jan van Veen

            One of the best things about being in San Francisco 🌉 is the ability to drop in on great talks. Last week I had a chance to see NVIDIA's CEO Jensen Huang (JH) being interviewed by Stripe's CEO Patrick Collison. Jensen is among the longest-serving founder-CEOs in Silicon Valley at the moment (30+ years). With that comes a special clarity of mind and some hot takes. 🌶👇 🗣 Jensen doesn't believe in 'praise in public and criticism in private'. Instead, he reasons that most information in the organization should not be privy to certain people. By giving feedback in public, you allow everyone to learn. 'What is better than learning from your own mistakes? It's learning from someone else's mistakes!!' 🔥 JH also doesn't like to fire people. He would rather "torture them into greatness". He invests in people and he doesn't like to write off his investments. 👨‍💻 Asked whether he likes his job every day, he answers "no". The perfect job doesn't give you happiness 100% of the time. "No great things were done, that were easy all the way." ... "Even if I come home from a day where I did things I didn't enjoy, it could still be have been a great day." 👩‍👧‍👦 Jensen famously has 60 direct reports. "This allowed us to cut out probably 7 levels of management". His subordinates don't necessarily have such large teams. "It doesn't scale down, less senior people need more guidance". Looking at the numbers, this approach allowed NVidia to reach ~2T market cap with ~28.000 people vs. Microsoft's 3T with ~220.000 people and Apple's 2.6T with 160.000 people. ❔ Jensen answers almost every question in the same way: "Let's break it down, let's reason about it". This is a great response on so many levels. First, it allows him to not be put on the spot. Second, it allows him to teach his workforce how he reasons through a problem so that they can emulate it and create alignment. Third, it gives people a chance to point out flaws in his reasoning. He admits he is not right all the time. ON AI 👨‍🔧 "You will not lose your job to AI, your company will go out of business because another company used AI." "If you're not using AI you're doing it wrong." ⏱ The extent to which AI will enter many processes is largely underestimated. Right now we are focused on language - sounds in a time sequence. "Any time sequence can be tokenized." This will include robotic movement. 🥛 JH also expects that we won't be satisfied with foundation models "You're going to want to tune it into perfection because you care so deeply. Say if it's already at 99% you'll want to get it to say 99.3%" This last piece is also why we created Query Vary (YC W22). AI will never feel truly intelligent until it completely understands your data and context. It needs to be taught, regardless of how powerful the foundation model is. Finally, besides insights, it was remarkable how funny Jensen was, cracking a joke at roughly a 1 per 3 min rate. It was a very entertaining talk. #ai #sf

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        * Ridwan Arefeen

            If we can learn from history, why don't we learn from other people's mistake in a company? Personally, I believe it so that discussing mistakes in a group setting helps everyone learn and grow, fostering a culture of transparency and continuous improvement. That doesn't mean we need to do address the person who made the mistake. One form of doing so is conducting *post-mortem* every once in a while with everyone. It helps everyone in a company grow regardless he/she is part of a project or not! [listening to background videos while doing something else has become more of a habit. I stumbled upon the attached video and felt like sharing it and hopefully it might help someone/group.] https://lnkd.in/gCDWdD7W

        * Monowar Anjum

            From the experience of professionally working in compiler engineering and performance engineering role for the last six months, I can say with quite a bit of confidence that ... NOBODY, absolutely NOBODY is taking over NVIDIA's market share for the next decade. It's kind of unbelievable how bad the closest competitor's software stack is. It does not really matter how much compute you offer in your hardware if there is no way to harness that power through your software stack. Even if it was barely usable, the complete lack of documentation makes it impossible to even tweak the most basic of things like which compiler to point at or which runtime driver to use to offload to the device from the host side. So, I guess this is why the closest competitor to NVIDIA sponsors an F1 team instead of fixing their codebase. After all, they have something in common: Neither of them have good drivers.

                  18

        * Christine Coutolenc

            🚗💡 Exciting Experience with Autonomous Driving! 💡🚗 Recently, my son and I had the opportunity to take a ride in an autonomous vehicle, and it was nothing short of amazing! 🌟 Feeling perfectly safe throughout the journey, we were blown away by how the car made incredibly safe choices through the bustling streets of San Francisco, filled with pedestrians, scooters, bicyclists, and cars. It detected risks that even my eyes didn't catch (albeit I was in the back seat), reacting to them swiftly and appropriately. 🚦 What impressed me most was that this cutting-edge technology will soon be accessible to everyone at a cost comparable to that of an Uber or Lyft ride (I was on a list as part of a Waymo's pilot group for this ride). It's incredible to think that with the rise of AI developments, we're on the brink of a transportation revolution that promises both safety and convenience. 🌐 As someone deeply supportive of these AI advancements, I'm excited about the endless possibilities they hold for the future. 🚀 🌍 #AutonomousDriving #AI #Innovation #FutureTech #TransportationRevolution #Waymo

                  2

        * Jeffrey Sutherland

            Widespread deployment of AI may be in trouble. John Loeffler, writing on the web site Techradar (https://lnkd.in/gDHWKEjU) published one of the more thoughtful assessments of Nvidia CEO Jensen Huang's keynote speech June 3 at Taipei's Computex 2024. But I think John need not be overly concerned about the speed of deployment of AI. I attended Computex this year and the hype surrounding the coming of AI seems significantly out of touch with a simple reality: there's not enough electrical generating or grid transmission capacity to run all the proposed deployments of new data centers, or "AI factories". Virtually lost among all the hype over Nvidia's new Blackwell GPU is the massive amount of power required to run it. Can someone who was there help fact check this for me? Marc Hamilton from Nvidia while showing an example of a dual Blackwell server blade said with a straight face that a server rack of 72 Blackwell processors required 100KW, along with liquid cooling. I thought I heard wrong at first but then started doing some back of the envelope math. If every Blackwell GPU was running at full tilt in a rack the peak energy consumption needed for the rack plus cooling system just might approach 100KW. Now how many racks will be in a "AI factory"? It will need its own multi-megawatt power plant out back just to run the place. Some other fun facts about data center power consumption today: 30% of the electricity generated in Ireland goes to run data centers there. In 2022 data centers worldwide consumed 460 TeraWatt/hrs. By 2026 data centers at current deployment rates will need 1050 TeraWatt/hrs annually. That's more energy than the entire country of Japan uses today. Where is all this power going to come from? Jensen Huang has proposed building a large AI factory here in Taiwan, where we barely have enough energy in the summer to run all our air conditioners. How is that going to work?

                  7
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        * John Heilbronn

            Lots of interesting perspectives in this discussion. A few that stood out to me: Huang doesn't believe in 1 on 1s for his direct staff of 60. His thought is the focus should be on transparency. If someone makes a mistake, everyone should learn from it. Everyone should know what the goals and challenges are all at once. But he also said this does not "scale down" because junior people still need supervision. 60 directs is a lot of people, but it also eliminates 7 layers of management in his view. Conceptually this sounds the same as Ray Dalio's transparency approach at Bridgewater Associates. His rejection of 1 on 1s for his staff suggests a very high level of trust within his executive ranks. That must have developed over time and according to his management style. As he puts it: "You can achieve operational excellence through process, but craft can only be achieved with tenure". Extraordinary things require craft. Other insights: Attends meetings all day but not regular meetings. E-staff are pinch hitters. Problems, idea, brainstorming or creation meetings only. "0-billion dollar markets." If you try to do something very difficult and create something that has never been done before, the effort provides huge opportunities. More fun to create then take share. Also, no one will notice your failures if you are the only one doing something new. If you are not using AI, you will be replaced by someone who is or by a company that is using it. (It might sound like he's talking his own book, but I find AI to provide significant productivity boosts. I agree with his assessment.) Huang talks about the need for an aesthetic in high quality work. Collison agrees. Other examples of people I can think of who said this are Steve Jobs and Jim Simons. I think it is more than coincidence when extremely accomplished individuals have similar principles.

        * Titus Winters

            Come see this talk, which I view as the culmination of several years of discussions and co-teaching with George Fairbanks. It's a bit of an overview, a bit of an explainer, a bit of a how-to, but mostly a sneaky way to make some important vocabulary from design become load-bearing in how you think about software testing.

                  86
                  1 Comment

        * Vibhor Kumar

            In a recent interview, NVIDIA's CEO, Jensen Huang highlighted Tesla's leading position in self-driving technology. I have been using Tesla's #ModelY in Full Self-Driving mode for over a month now, and I couldn't agree more with the CEO's assessment. The experience truly feels like something out of a sci-fi movie. On highways, #FSD excels at lane changes, safe distance maintenance, and merges. Its navigation through complex interchanges and adaptability to varying traffic conditions impressed me, making long drives more relaxing and enjoyable. In neighborhood settings, #FSD's attention to detail in recognizing stop signs, traffic lights, pedestrians, and cyclists stood out. It navigated turns and intersections smoothly, ensuring a safe driving experience even on busy streets. The technology's advanced capabilities in navigation, lane changes, and automated parking have significantly enhanced my driving experience. The seamless integration with real-time data and intuitive interface instilled a sense of confidence and safety. Beyond the autonomy, driving a Tesla is exhilarating in itself. The instant torque and rapid acceleration deliver a thrilling experience, with a pickup comparable to racing cars. This powerful performance, combined with cutting-edge technology, makes every drive an adventure While there is room for improvement, Tesla's #FSD undeniably represents a substantial advancement in automotive technology. Highly recommend exploring this innovative and transformative technology! #Tesla #FSD #Innovation #AutonomousDriving, #ElonMusk

                  12

        * Chad Vavra

            as a hearing impaired, hearing-aid augmented person, @MikeKuniavsky nails it on the topic of distributed cognition. In some aspects technology can add to your inputs, but in others like noise-cancellation it can subtract so you can focus. With hearing-aids going OTC there is a ripe landscape for AI developers to lean in.

                  1

        * Akihide Narita

            For all of you striving for greatness. Here's a great conversation between Stripe co-founder, Patrick Collison and co-founder and CEO of Nvidia, Jensen Huang. I especially thought Jensen Huang's explanation on what people misunderstand about happiness and building something great was insightful. In the media we really only see the hype and excitement of those who have achieved something great in life, whatever that may be, but we rarely see the hardships and struggles that are part of this journey. It was encouraging for me as there are definitely times when its tough and not easy, as Jensen Huang says here, "I don’t love every day of my job… And every day I’m not happy… But I love the company every single second. And so I think what people misunderstand is [they think] the best jobs are the ones that bring you happiness all the time. I don’t think that’s right. You have to suffer. You have to struggle. You have to endeavor. You have to do those hard things and work through it in order to really appreciate what you’ve done. There’s no such thing that is great that was easy to do.” https://lnkd.in/gWEwkMRj #greatness #life #building #journey #entrepreneurship

                  9
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        * Deepak Shankar

            🚀 Exciting advancements in embedded system design! I'm thrilled to share our latest research, co-authored with Wesley Skeffington, PE from AMD. We tackle the intricate challenge faced by today’s embedded system application architects: efficiently mapping applications to diverse compute resources like CPUs, AIEs, and FPGA accelerators. Our work highlights the current gaps in SoC tools for supporting system architects in evaluating application mapping trade spaces. We introduce the “application explorer” tool, designed for early-stage analysis of application mapping using a system-level stochastic model simulation. This model-based system engineering tool allows architects to explore different design mappings, providing detailed scope definitions for downstream implementation teams. Join us as we present a prototype of this concept at DAC 2024, integrated into the Mirabilis VisualSim Architect tool, demonstrating its application to a signal processing algorithm on a Versal FPGA SoC. Visit: https://lnkd.in/g87fReq3 for more on DAC & Mirabilis Design Inc. Exciting times ahead for system architecture innovation! #EmbeddedSystems #SystemArchitecture #FPGA #SoC #Innovation #Research #Engineering

                  10

        * Stefan Brenner

            Level up Chip Design Innovation with MATLAB and Silicon Catalyst! #MathWorks News & Stories brings you an exciting collaboration between MathWorks and Silicon Catalyst. This article explores how MATLAB an Simulink empowers chip-based semiconductor startups to develop cutting-edge solutions in medicine and wireless technology. Key Takeaways: >> Richard Curtin, Managing Partner at Silicon Catalyst, highlights the growing demand for "specialized design tools to overcome the complexities of modern chip design." >> Raphael Guimond, Antenna Designer at SPARK Microsystems, emphasizes how MATLAB's capabilities streamline the antenna design process, allowing engineers to "focus on innovation rather than repetitive tasks." ➡️ Dive deeper and discover how MATLAB can: >> Shorten design cycles through its powerful simulation and modeling tools. ⏱️➡️ >> Optimize performance with efficient algorithms and code generation capabilities. >> Facilitate collaboration with a robust platform for data sharing and version control. Don't miss out! Read the full story here: https://lnkd.in/eAv7JhYV #engineering #semiconductor #wireless #innovation #MATLAB #medicine #Simulink #Probius #SPARK #Microsystems #SiliconCatalyst

                  4

        * Apratim Banerjee

            Its sometimes hard to believe that we live in an era where driverless vehicles aren't just a vision anymore but a reality. Waymo driverless cabs for example, are transforming the San Francisco streets forever! It was a wonderful experience working on an application of autonomous vehicles as part of my final project in the course CS267: Applications of Parallel Computing at the University of California, Berkeley. Our team, comprising Terence N., Zeid Solh, and myself, focused on parallelizing stereo vision for driverless applications. Stereo vision is crucial for self-driving cars as it builds a 3D understanding of the environment by using two cameras. However, real-time processing of these images is computationally expensive, posing a significant challenge for autonomous vehicles. We tackled this by exploring various parallel computing techniques to improve the speed of calculating the Disparity Map algorithm. We used popular techniques such as #SIMD (Single Instruction, Multiple Data), #OpenMP (multi-threading), and CUDA (NVIDIA GPU acceleration), and benchmarked the performance. By distributing the computational load across multiple cores and processors, we achieved substantial performance improvements, enabling real-time processing of high-resolution images and videos. The results were promising! Utilizing CUDA, we saw significant speedups, demonstrating the potential for efficient, real-time depth perception in autonomous systems. This project signified the importance of parallel computing in advancing autonomous driving technologies. I'm incredibly proud of our team's hard work and the insights we gained through this project. Check out our GitHub repo: https://lnkd.in/gYHrEU9z for more details on our implementation! #UCberkeley #CS267 #ParallelComputing #StereoVision #AutonomousDriving #DeepLearning #ComputerVision

                  50
                  3 Comments

        * Swayambhoo Jain

            Checkout our latest report on how just eight SN40L chips run 1 Trillion parameter model at blazing fast speed https://lnkd.in/grwdFVAi. It was great to collaborate with some of the most talented folks in this area. Here is the link to the demo operating on similar principles https://fast.snova.ai/

                  28

        * Caleb Robey

            LLama3 passed the super-proprietary, advanced test that we designed for modern LLMs! almost... You can read about that test here: https://lnkd.in/gPG8WndR When I first saw the response generating, it looked like I was going to have to come back here and apologize for slandering these machines when they were just one iteration away from solving very basic prompt injection issues. But then I looked a bit closer... can you see the mistake? #promptinjection #countingishard #LLMs #LLama3

                  13
                  4 Comments

        * Diamond Assets LLC

            TECH TRANSITION: As Apple moves on from Intel, your aged devices could depreciate even faster. In our latest blog, we detail how the shift from Intel to Apple-designed chips impacts your operations, budgets, and productivity. Here’s how you can stay ahead of the curve and safeguard your business: https://lnkd.in/gCpB9X5p

                  7

        * Ken Schmahl, PE

            I'm starting to worry about the insurgence of AI in hardware design cycles. The claim is AI will "solve systems problems and offer chips-to-system intelligent design acceleration." First, many small company management teams still fail to see the need to bring on-board, *in a timely manner*, the experienced staff needed to get it right first-time. The industry thinks moving to a model dependent on ODM houses is a good idea, but like a stubborn mule ODM houses need to be checked, poked and prodded to produce good designs. Like a good mule skinner, experienced in-house staff are needed to keep them in line. Second, finding good package/board designers is getting harder since new college grads are going into ML and Data Science, and leaving the physical sciences needed for electric circuit design. The consequence of this will be seen in new products which must go thru the school of hard-knocks, i.e. they won't work first-time. Third, vendors are now offering to solve these problems by bring this "expertise" in-house through AI, promising to "accelerate design" with "artificial intelligence". To me, looking forward, this looks like a recipe for disaster. Execs holding the purse will ask why they need experienced engineering staff when AI will show the new kids how to do it. The new kids won't know AI is as dumb as a bag of hammers, won't know how to vet its answers, suggestions and directions, won't know when to call BS. And the savior - AI - will screw it up with *no accountability*. Schedules will slip, budgets will be blown, market opportunities will be lost. There will be weeping and gnashing of teeth. Meanwhile the experienced package/board designers are retiring, leaving a gaping hole in the industry. No amount of AI will resolve this in the next few years.

                  38
                  7 Comments

        * Hadi Abroshan

            I'll be presenting two talks at SID 2024 in San Jose on the topic of digital chemistry for display technology. Let's connect and discuss the exciting developments and opportunities. #DigitalChemistry #DisplayTechnology #Innovation #machinelearning #materialsscience #oled #optoelectronics #datascience #vr #ar #optics #displays #microled #lcd

                  36

        * Todd Remington

            🚀 Engineers, ready to master Team-Based Image Labeling? Join our webinar on June 13th. Learn how to label ground truth data in a collection of images using the Image Labeler app, as well as how to increase the scale of labeling projects by distributing tasks across a range of team members. Register now 👉 #AI #MATLAB #Engineering

        * Nati Shalom

            Good listen: Mark Suster, the guest host, is joined by Dan Senor to discuss the remarkable growth of NVIDIA in #Israel, which now boasts over 4,000 employees and a global CTO who is Israeli. The conversation shifts to Israel’s startup ecosystem, highlighting its impressive statistics: 500 venture capital firms, 120 accelerators, 35 incubators, 400 multinationals with major R&D operations, and 7,000 startups. These figures underscore Israel’s position as having the highest density of startups globally, attracting significant global venture capital, leading in R&D spending relative to GDP, and ranking fourth in NASDAQ-listed public companies. Regardless - I'll recommend following This Week in Startups podcasts which brings interesting conversations with leaders from the startup community... https://lnkd.in/eGv3fr-H #iwork4dell

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          One of the best things about being in San Francisco 🌉  is the ability to drop in on great talks. Last week I had a chance to see NVIDIA's CEO Jensen Huang (JH) being interviewed by Stripe's CEO Patrick Collison. 
Jensen is among the longest-serving founder-CEOs in Silicon Valley at the moment (30+ years). With that comes a special clarity of mind and some hot takes. 🌶👇 
🗣 Jensen doesn't believe in 'praise in public and criticism in private'. Instead, he reasons that most information in the organization should not be privy to certain people. By giving feedback in public, you allow everyone to learn. 'What is better than learning from your own mistakes? It's learning from someone else's mistakes!!' 
🔥 JH also doesn't like to fire people. He would rather "torture them into greatness". He invests in people and he doesn't like to write off his investments.
👨‍💻 Asked whether he likes his job every day, he answers "no". The perfect job doesn't give you happiness 100% of the time. "No great things were done, that were easy all the way." ... "Even if I come home from a day where I did things I didn't enjoy, it could still be have been a great day."
👩‍👧‍👦 Jensen famously has 60 direct reports. "This allowed us to cut out probably 7 levels of management". His subordinates don't necessarily have such large teams. "It doesn't scale down, less senior people need more guidance". Looking at the numbers, this approach allowed NVidia to reach ~2T market cap with ~28.000 people vs. Microsoft's 3T with ~220.000 people and Apple's 2.6T with 160.000 people. 
❔ Jensen answers almost every question in the same way: "Let's break it down, let's reason about it". This is a great response on so many levels. First, it allows him to not be put on the spot. Second, it allows him to teach his workforce how he reasons through a problem so that they can emulate it and create alignment. Third, it gives people a chance to point out flaws in his reasoning. He admits he is not right all the time. 
ON AI 
👨‍🔧 "You will not lose your job to AI, your company will go out of business because another company used AI." "If you're not using AI you're doing it wrong."
⏱   The extent to which AI will enter many processes is largely underestimated. Right now we are focused on language - sounds in a time sequence. "Any time sequence can be tokenized." This will include robotic movement. 
🥛 JH also expects that we won't be satisfied with foundation models "You're going to want to tune it into perfection because you care so deeply. Say if it's already at 99% you'll want to get it to say 99.3%"
This last piece is also why we created Query Vary (YC W22). AI will never feel truly intelligent until it completely understands your data and context. It needs to be taught, regardless of how powerful the foundation model is. 
Finally, besides insights, it was remarkable how funny Jensen was, cracking a joke at roughly a 1 per 3 min rate. It was a very entertaining talk. 
#ai #sf
        
            
            
        
          
  
      
    
      
  
  
  
    
  
              
              
                
    
  
              
              
                
    
  
              
              
                
    
      
      
    
      
        
      
        
          Christine Coutolenc
        
          
      
  
  
    
    
      
        
      
        
          
            
              
          🚗💡 Exciting Experience with Autonomous Driving! 💡🚗
Recently, my son and I had the opportunity to take a ride in an autonomous vehicle, and it was nothing short of amazing! 🌟
Feeling perfectly safe throughout the journey, we were blown away by how the car made incredibly safe choices through the bustling streets of San Francisco, filled with pedestrians, scooters, bicyclists, and cars. It detected risks that even my eyes didn't catch (albeit I was in the back seat), reacting to them swiftly and appropriately. 🚦
What impressed me most was that this cutting-edge technology will soon be accessible to everyone at a cost comparable to that of an Uber or Lyft ride (I was on a list as part of a Waymo's pilot group for this ride). It's incredible to think that with the rise of AI developments, we're on the brink of a transportation revolution that promises both safety and convenience. 🌐
As someone deeply supportive of these AI advancements, I'm excited about the endless possibilities they hold for the future. 🚀  🌍
#AutonomousDriving #AI #Innovation #FutureTech #TransportationRevolution #Waymo
        
            
            
        
      
    
      
  
  
  
    
  
              
              
                
    
  
              
              
                
    
  
              
              
                
    
  
              
              
                
    
      
      
    
      
        
      
        
          Vibhor Kumar
        
          
      
  
  
    
    
      
        
      
        
          
            
              
          In a recent interview, NVIDIA's CEO, Jensen Huang highlighted Tesla's leading position in self-driving technology. 
I have been using Tesla's #ModelY in Full Self-Driving mode for over a month now, and I couldn't agree more with the CEO's assessment. The experience truly feels like something out of a sci-fi movie.
On highways, #FSD excels at lane changes, safe distance maintenance, and merges. Its navigation through complex interchanges and adaptability to varying traffic conditions impressed me, making long drives more relaxing and enjoyable.
In neighborhood settings, #FSD's attention to detail in recognizing stop signs, traffic lights, pedestrians, and cyclists stood out. It navigated turns and intersections smoothly, ensuring a safe driving experience even on busy streets.
The technology's advanced capabilities in navigation, lane changes, and automated parking have significantly enhanced my driving experience. The seamless integration with real-time data and intuitive interface instilled a sense of confidence and safety.
Beyond the autonomy, driving a Tesla is exhilarating in itself. The instant torque and rapid acceleration deliver a thrilling experience, with a pickup comparable to racing cars. This powerful performance, combined with cutting-edge technology, makes every drive an adventure
While there is room for improvement, Tesla's #FSD undeniably represents a substantial advancement in automotive technology.
Highly recommend exploring this innovative and transformative technology!
#Tesla #FSD #Innovation #AutonomousDriving, #ElonMusk
        
            
            
        
          
  
      
    
      
  
  
  
    
  
              
              
                
    
  
              
              
                
    
  
              
              
                
    
  
              
              
                
    
  
              
              
                
    
      
      
    
      
        
      
        
          Apratim Banerjee
        
          
      
  
  
    
    
      
        
      
        
          
            
              
          Its sometimes hard to believe that we live in an era where driverless vehicles aren't just a vision anymore but a reality. Waymo driverless cabs for example, are transforming the San Francisco streets forever! 
It was a wonderful experience working on an application of autonomous vehicles as part of my final project in the course CS267: Applications of Parallel Computing at the University of California, Berkeley.
Our team, comprising Terence N., Zeid Solh, and myself, focused on parallelizing stereo vision for driverless applications. Stereo vision is crucial for self-driving cars as it builds a 3D understanding of the environment by using two cameras. However, real-time processing of these images is computationally expensive, posing a significant challenge for autonomous vehicles.
We tackled this by exploring various parallel computing techniques to improve the speed of calculating the Disparity Map algorithm. We used popular techniques such as #SIMD (Single Instruction, Multiple Data), #OpenMP (multi-threading), and CUDA (NVIDIA GPU acceleration), and benchmarked the performance. 
By distributing the computational load across multiple cores and processors, we achieved substantial performance improvements, enabling real-time processing of high-resolution images and videos. The results were promising! Utilizing CUDA, we saw significant speedups, demonstrating the potential for efficient, real-time depth perception in autonomous systems.
This project signified the importance of parallel computing in advancing autonomous driving technologies. I'm incredibly proud of our team's hard work and the insights we gained through this project.
Check out our GitHub repo: https://lnkd.in/gYHrEU9z for more details on our implementation! 
#UCberkeley #CS267 #ParallelComputing #StereoVision #AutonomousDriving #DeepLearning #ComputerVision
        
            
            
        
          
  
      
    
      
  
  
  
    
  
              
              
                
    
  
              
              
                
    
  
              
              
                
    
  
              
              
                
    
      
      
    
      
        
      
        
          Ken Schmahl, PE
        
          
      
  
  
    
    
      
        
      
        
          
            
              
          I'm starting to worry about the insurgence of AI in hardware design cycles.  The claim is AI will "solve systems problems and offer chips-to-system intelligent design acceleration."
First, many small company management teams still fail to see the need to bring on-board, *in a timely manner*, the experienced staff needed to get it right first-time. The industry thinks moving to a model dependent on ODM houses is a good idea, but like a stubborn mule ODM houses need to be checked, poked and prodded to produce good designs.  Like a good mule skinner, experienced in-house staff are needed to keep them in line.
Second, finding good package/board designers is getting harder since new college grads are going into ML and Data Science, and leaving the physical sciences needed for electric circuit design.  The consequence of this will be seen in new products which must go thru the school of hard-knocks, i.e. they won't work first-time.
Third, vendors are now offering to solve these problems by bring this "expertise" in-house through AI, promising to "accelerate design" with "artificial intelligence".
To me, looking forward, this looks like a recipe for disaster.  Execs holding the purse will ask why they need experienced engineering staff when AI will show the new kids how to do it. The new kids won't know AI is as dumb as a bag of hammers, won't know how to vet its answers, suggestions and directions, won't know when to call BS. And the savior - AI - will screw it up with *no accountability*.  Schedules will slip, budgets will be blown, market opportunities will be lost.  There will be weeping and gnashing of teeth.
Meanwhile the experienced package/board designers are retiring, leaving a gaping hole in the industry.  No amount of AI will resolve this in the next few years.  
        
            
            
        
      
    
      
  
  
  
    
  
              
              
                
    
  
              
              
                
    
  
              
              
                
    
      
      
    
      
        
      
        
          Nati Shalom
        
          
      
  
  
    
    
      
        
      
        
          
            
              
          Good listen: Mark Suster, the guest host, is joined by Dan Senor to discuss the remarkable growth of NVIDIA in #Israel, which now boasts over 4,000 employees and a global CTO who is Israeli. 
The conversation shifts to Israel’s startup ecosystem, highlighting its impressive statistics: 500 venture capital firms, 120 accelerators, 35 incubators, 400 multinationals with major R&amp;D operations, and 7,000 startups. These figures underscore Israel’s position as having the highest density of startups globally, attracting significant global venture capital, leading in R&amp;D spending relative to GDP, and ranking fourth in NASDAQ-listed public companies.
Regardless - I'll recommend following This Week in Startups podcasts which brings interesting conversations with leaders from the startup community...
https://lnkd.in/eGv3fr-H
#iwork4dell
        
            
            
        
          
  
      
    
      
  
  
  
    
  
              
          
        
            
      
  
  
      
      
      
        
         
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Andrej Karpathy - San Francisco, California, United States | Professional Profile | LinkedIn
2023-01-01
Andrej Karpathy
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(Former) Director of AI at Tesla, OpenAI, PhD @ Stanford
 
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Andrej Karpathy - San Francisco, California, United States | Professional Profile | LinkedIn
https://www.linkedin.com/in/andrej-karpathy-9a650716/
https://openai.com/
OpenAI
2022-03-15
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<div><div><div><ul><li><a href="/about"><div><h3>Creating safe AGI that benefits all of humanity</h3></div></a></li><li><a href="/research/overview"><div><h3>Pioneering research on the path to AGI</h3></div></a></li><li><a href="/product"><div><h3>Transforming work and creativity with AI</h3></div></a></li><li><a href="/careers"><div><h3>Join us in shaping the future of technology</h3></div></a></li></ul></div><div><p></p><h2>Safety &amp; responsibility</h2><p></p><div><p>Our work to create safe and beneficial AI requires a deep understanding of the potential risks and benefits, as well as careful consideration of the impact.<br /></p><div><p><a href="/safety">Learn about safety</a></p></div></div></div><div><figure><p></p><figcaption></figcaption></figure></div><div><p></p><h2>Research</h2><p></p><div><p>We research generative models and how to align them with human values.</p><div><p><a href="/research/overview">Learn about our research</a></p></div></div></div><div><ul><li><a href="/research/gpt-4"><div><p></p></div><div><h3>GPT-4</h3><p>March 14, 2023</p></div></a></li><li><a href="/research/forecasting-misuse"><div><p></p></div><div><h3>Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk</h3><p>January 11, 2023</p></div></a></li><li><a href="/research/point-e"><div><p></p></div><div><h3>Point-E: A system for generating 3D point clouds from complex prompts</h3><p>December 16, 2022</p></div></a></li><li><a href="/research/whisper"><div><p></p></div><div><h3>Introducing Whisper</h3><p>September 21, 2022</p></div></a></li></ul></div><div><p></p><h2>Products</h2><p></p><div><p>Our API platform offers our latest models and guides for safety best practices.<br /></p><div><p><a href="/product">Explore our products</a></p></div></div></div><div><ul><li><a href="/blog/new-and-improved-embedding-model"><div><p></p></div><div><h3>New and improved embedding model</h3><p>December 15, 2022</p></div></a></li><li><a href="/blog/dall-e-now-available-without-waitlist"><div><p></p></div><div><h3>DALLÂ·E now available without waitlist</h3><p>September 28, 2022</p></div></a></li><li><a href="/blog/new-and-improved-content-moderation-tooling"><div><p></p></div><div><h3>New and improved content moderation tooling</h3><p>August 10, 2022</p></div></a></li><li><a href="/blog/gpt-3-edit-insert"><div><p></p></div><div><h3>New GPT-3 capabilities: Edit &amp; insert</h3><p>March 15, 2022</p></div></a></li></ul></div><div><p></p><h2>Careers at OpenAI</h2><p></p><div><p>Developing safe and beneficial AI requires people from a wide range of disciplines and backgrounds.<br /></p><div><p><a href="/careers">View careers</a></p></div></div></div><div><figure><p></p><figcaption></figcaption></figure></div><div><div><figure><blockquote><p>I encourage my team to keep learning. Ideas in different topics or fields can often inspire new ideas and broaden the potential solution space.</p></blockquote><figcaption>Lilian WengApplied AI at OpenAI</figcaption></figure></div><div><p></p></div></div></div></div>||||I|||| Close
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Creating safe AGI that benefits all of humanity       Pioneering research on the path to AGI       Transforming work and creativity with AI       Join us in shaping the future of technology         Safety &amp; responsibility    Our work to create safe and beneficial AI requires a deep understanding of the potential risks and benefits, as well as careful consideration of the impact.    Learn about safety                Research    We research generative models and how to align them with human values.   Learn about our research              GPT-4 March 14, 2023           Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk January 11, 2023           Point-E: A system for generating 3D point clouds from complex prompts December 16, 2022           Introducing Whisper September 21, 2022         Products    Our API platform offers our latest models and guides for safety best practices.    Explore our products              New and improved embedding model December 15, 2022           DALLÂ·E now available without waitlist September 28, 2022           New and improved content moderation tooling August 10, 2022           New GPT-3 capabilities: Edit &amp; insert March 15, 2022         Careers at OpenAI    Developing safe and beneficial AI requires people from a wide range of disciplines and backgrounds.    View careers                 I encourage my team to keep learning. Ideas in different topics or fields can often inspire new ideas and broaden the potential solution space.  Lilian WengApplied AI at OpenAI
OpenAI
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<div><div><div><div><a href="https://wellfound.com/company/openai"><p></p></a></div><div><h2>Discovering and enacting the path to safe artificial general intelligence</h2></div></div><section><div><header>Team</header><div><div><p></p></div><div><p></p></div><div><div><h4>Angie Luo</h4><p><span>Employee<span>San Francisco</span></span></p></div><p></p></div><div><p></p></div><div><p></p></div><div><div><h4>Red Avila</h4><p><span>Employee<span>San Francisco</span></span></p></div><p></p></div><div><div><h4>Justin Loi</h4><p><span>Technical Sourcer • 4 years<span>Millbrae</span></span></p></div><p></p></div><div><div><h4>Luke Miller</h4><p><span>Product Manager • 5 years<span>California</span></span></p></div><p></p></div><div><div><h4>Elena Chatziathanasiadou</h4><p><span>Employee<span>Cheney</span></span></p></div><p></p></div><div><div><div><h4>Ben Barry</h4><p><span>Design Director • 6 years<span>Portland</span></span></p></div><p></p></div><p>Visual designer and illustrator.</p></div><div><div><div><h4>Sam McCandlish</h4><p><span>Member of the Technical Staff (Research Fellow) • 6 years<span>San Francisco Bay Area</span></span></p></div><p></p></div><p>Research Fellow at OpenAI / Theoretical Physics PhD</p></div><div><div><div><h4>Peter Welinder</h4><p><span>Research and Engineering Lead • 7 years<span>San Francisco</span></span></p></div><p></p></div><p>Research and Engineering Lead OpenAI Robotics. Head of Machine Learning at Dropbox. Previously Caltech, co-founder Anchovi Labs.</p></div><div><div><h4>Richard Chen</h4><p><span>Employee • 7 years<span>San Francisco</span></span></p></div><p></p></div></div></div><div><header>Former team</header><div><div><a href="https://wellfound.com/p/rob-mallery"><p></p></a></div><div><div><h4>Karson Elmgren</h4><p><span>People Operations • 1 year</span></p></div><p></p></div><div><div><h4>Filip Wolski</h4><p><span>Member of Technical Staff • 3 years</span></p></div><p></p></div><div><div><h4>Vicki Cheung</h4><p><span>Head of Infrastructure • 2 years</span></p></div><p></p></div><div><div><h4>Kate Miltenberger</h4><p><span>Operations Manager • 1 year</span></p></div><p></p></div></div></div></section></div></div>||||I||||        DiscoverFind JobsFor Recruiters
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                      OpenAI

                Discovering and enacting the path to safe artificial general intelligence

              Website
                    www.openai.com/
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              Company size201-500 peopleTotal raised$11BCompany type Artificial Intelligence Markets Artificial Intelligence Online Gaming Non Profit
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                    People at OpenAI

                        Founders

                                  Greg Brockman

                                  CTO • 9 years San Francisco
                                Co-Founder & CTO @ OpenAI , ex-CTO @ Stripe

                            Background

                                  * OpenAI

                                  CTO • 9 years

                                  * Stripe

                                  CTO • 5 years

                      Team

                                  Vikki Lampton

                                  Employee

                                  Phuong Vu

                                  Employee

                                  Angie Luo

                                  Employee San Francisco

                                  Tifa Chen

                                  Employee

                                  Evan Morikawa

                                  Employee

                                  Red Avila

                                  Employee San Francisco

                                  Justin Loi

                                  Technical Sourcer • 4 years Millbrae

                                  Luke Miller

                                  Product Manager • 5 years California

                                  Elena Chatziathanasiadou

                                  Employee Cheney

                                  Ben Barry

                                  Design Director • 6 years Portland
                                Visual designer and illustrator.

                                  Sam McCandlish

                                  Member of the Technical Staff (Research Fellow) • 6 years San Francisco Bay Area
                                Research Fellow at OpenAI / Theoretical Physics PhD

                                  Peter Welinder

                                  Research and Engineering Lead • 7 years San Francisco
                                Research and Engineering Lead OpenAI Robotics. Head of Machine Learning at Dropbox. Previously Caltech, co-founder Anchovi Labs.

                                  Richard Chen

                                  Employee • 7 years San Francisco
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                    About OpenAI

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                      OpenAI
                Discovering and enacting the path to safe artificial general intelligence
              Website
                    www.openai.com/
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              Company size201-500 peopleTotal raised$11BCompany type Artificial Intelligence Markets Artificial Intelligence Online Gaming Non Profit
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                    People at OpenAI
                        Founders
                                  Greg Brockman
                                  CTO • 9 years San Francisco
                                Co-Founder & CTO @ OpenAI , ex-CTO @ Stripe
                            Background
                                  * OpenAI
                                  CTO • 9 years
                                  * Stripe
                                  CTO • 5 years
                      Team
                                  Vikki Lampton
                                  Employee
                                  Phuong Vu
                                  Employee
                                  Angie Luo
                                  Employee San Francisco
                                  Tifa Chen
                                  Employee
                                  Evan Morikawa
                                  Employee
                                  Red Avila
                                  Employee San Francisco
                                  Justin Loi
                                  Technical Sourcer • 4 years Millbrae
                                  Luke Miller
                                  Product Manager • 5 years California
                                  Elena Chatziathanasiadou
                                  Employee Cheney
                                  Ben Barry
                                  Design Director • 6 years Portland
                                Visual designer and illustrator.
                                  Sam McCandlish
                                  Member of the Technical Staff (Research Fellow) • 6 years San Francisco Bay Area
                                Research Fellow at OpenAI / Theoretical Physics PhD
                                  Peter Welinder
                                  Research and Engineering Lead • 7 years San Francisco
                                Research and Engineering Lead OpenAI Robotics. Head of Machine Learning at Dropbox. Previously Caltech, co-founder Anchovi Labs.
                                  Richard Chen
                                  Employee • 7 years San Francisco
                      Former team
                                  Rob Mallery
                                  Member of Talent Staff • 1 year
                                  Karson Elmgren
                                  People Operations • 1 year
                                  Filip Wolski
                                  Member of Technical Staff • 3 years
                                  Vicki Cheung
                                  Head of Infrastructure • 2 years
                                  Kate Miltenberger
                                  Operations Manager • 1 year
                    About OpenAI
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                        www.openai.com/
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OpenAI People
https://angel.co/company/openai/people
https://www.technologyreview.com/2020/02/17/844721/ai-openai-moonshot-elon-musk-sam-altman-greg-brockman-messy-secretive-reality
The messy, secretive reality behind OpenAI’s bid to save the world
2020-02-17
Karen Hao
<div><div><div> <p>Every year, OpenAI’s employees vote on when they believe artificial general intelligence, or AGI, will finally arrive. It’s mostly seen as a fun way to bond, and their estimates differ widely. But in a field that still debates whether human-like autonomous systems are even possible, half the lab bets it is likely to happen within 15 years.</p> <p>In the four short years of its existence, OpenAI has become one of the leading AI research labs in the world. It has made a name for itself producing consistently headline-grabbing research, alongside other AI heavyweights like Alphabet’s DeepMind. It is also a darling in Silicon Valley, counting Elon Musk and legendary investor Sam Altman among its founders.</p> </div><div> <p>Above all, it is lionized for its mission. Its goal is to be the first to create AGI—a machine with the learning and reasoning powers of a human mind. The purpose is not world domination; rather, the lab wants to ensure that the technology is developed safely and its benefits distributed evenly to the world.</p> <p>The implication is that AGI could easily run amok if the technology’s development is left to follow the path of least resistance. Narrow intelligence, the kind of clumsy AI that surrounds us today, has already served as an example. We now know that algorithms are <a href="https://www.technologyreview.com/s/612876/this-is-how-ai-bias-really-happensand-why-its-so-hard-to-fix/">biased</a> and <a href="https://www.technologyreview.com/s/614497/military-artificial-intelligence-can-be-easily-and-dangerously-fooled/">fragile</a>; they can perpetrate <a href="https://www.technologyreview.com/s/613898/an-ai-app-that-undressed-women-shows-how-deepfakes-harm-the-most-vulnerable/">great abuse</a> and <a href="https://www.technologyreview.com/s/611810/fake-america-great-again/">great deception</a>; and the expense of developing and running them tends to <a href="https://www.technologyreview.com/f/614056/ai-research-has-an-environment-climate-toll/">concentrate their power</a> in the hands of a few. By extrapolation, AGI could be catastrophic without the careful guidance of a benevolent shepherd.</p> 
 <p>OpenAI wants to be that shepherd, and it has carefully crafted its image to fit the bill. In a field dominated by wealthy corporations, it was founded as a nonprofit. Its <a href="https://openai.com/blog/introducing-openai/">first announcement</a> said that this distinction would allow it to “build value for everyone rather than shareholders.” Its <a href="https://openai.com/charter/">charter</a>—a document so sacred that employees’ pay is tied to how well they adhere to it—further declares that OpenAI’s “primary fiduciary duty is to humanity.” Attaining AGI safely is so important, it continues, that if another organization were close to getting there first, OpenAI would stop competing with it and collaborate instead. This alluring narrative plays well with investors and the media, and in July Microsoft injected the lab with a fresh $1 billion.</p> <figure><figcaption>OpenAI's logo hanging in its office.<p>Christie Hemm Klok</p></figcaption></figure> <p>But three days at OpenAI’s office—and nearly three dozen interviews with past and current employees, collaborators, friends, and other experts in the field—suggest a different picture. There is a misalignment between what the company publicly espouses and how it operates behind closed doors. Over time, it has allowed a fierce competitiveness and mounting pressure for ever more funding to erode its founding ideals of transparency, openness, and collaboration. Many who work or worked for the company insisted on anonymity because they were not authorized to speak or feared retaliation. Their accounts suggest that OpenAI, for all its noble aspirations, is obsessed with maintaining secrecy, protecting its image, and retaining the loyalty of its employees.</p> 
 <hr /> <p>Since its earliest conception, AI as a field has strived to understand human-like intelligence and then re-create it. In 1950, Alan Turing, the renowned English mathematician and computer scientist, began <a href="https://www.csee.umbc.edu/courses/471/papers/turing.pdf">a paper</a> with the now-famous provocation “Can machines think?” Six years later, captivated by the nagging idea, a group of scientists gathered at Dartmouth College to formalize the discipline.</p> <p>“It is one of the most fundamental questions of all intellectual history, right?” says Oren Etzioni, the CEO of the Allen Institute for Artificial Intelligence (AI2), a Seattle-based nonprofit AI research lab. “It’s like, do we understand the origin of the universe? Do we understand matter?”</p> <p>The trouble is, AGI has always remained vague. No one can really describe what it might look like or the minimum of what it should do. It’s not obvious, for instance, that there is only one kind of general intelligence; human intelligence could just be a subset. There are also differing opinions about what purpose AGI could serve. In the more romanticized view, a machine intelligence unhindered by the need for sleep or the inefficiency of human communication could help solve complex challenges like climate change, poverty, and hunger.</p> <p>But the resounding consensus within the field is that such advanced capabilities would take decades, even centuries—if indeed it’s possible to develop them at all. Many also fear that pursuing this goal overzealously could backfire. In the 1970s and again in the late ’80s and early ’90s, the field overpromised and underdelivered. Overnight, funding dried up, leaving deep scars in an entire generation of researchers. “The field felt like a backwater,” says Peter Eckersley, until recently director of research at the industry group Partnership on AI, of which OpenAI is a member.</p> </div><div> <figure><figcaption>A conference room on the first floor named Infinite Jest.<p>Christie Hemm Klok</p></figcaption></figure> <p>Against this backdrop, OpenAI entered the world with a splash on December 11, 2015. It wasn’t the first to openly declare it was pursuing AGI; DeepMind had done so five years earlier and had been acquired by Google in 2014. But OpenAI seemed different. For one thing, the sticker price was shocking: the venture would start with $1 billion from private investors, including Musk, Altman, and PayPal cofounder Peter Thiel.</p> <p>The star-studded investor list stirred up a media frenzy, as did the impressive list of initial employees: Greg Brockman, who had run technology for the payments company Stripe, would be chief technology officer; Ilya Sutskever, who had studied under AI pioneer Geoffrey Hinton, would be research director; and seven researchers, freshly graduated from top universities or plucked from other companies, would compose the core technical team. (Last February, Musk <a href="https://www.bloomberg.com/news/articles/2019-02-17/elon-musk-left-openai-on-disagreements-about-company-pathway">announced</a> that he was parting ways with the company over disagreements about its direction. A month later, Altman <a href="https://techcrunch.com/2019/05/18/sam-altmans-leap-of-faith/">stepped down</a> as president of startup accelerator Y Combinator to become OpenAI’s CEO.)</p> <p>But more than anything, OpenAI’s nonprofit status made a statement. “It’ll be important to have a leading research institution which can prioritize a good outcome for all over its own self-interest,” the <a href="https://openai.com/blog/introducing-openai/">announcement said</a>. “Researchers will be strongly encouraged to publish their work, whether as papers, blog posts, or code, and our patents (if any) will be shared with the world.” Though it never made the criticism explicit, the implication was clear: other labs, like DeepMind, could not serve humanity because they were constrained by commercial interests. While they were closed, OpenAI would be <em>open</em>.</p> <p>In a research landscape that had become increasingly privatized and focused on short-term financial gains, OpenAI was offering a new way to fund progress on the biggest problems. “It was a beacon of hope,” says Chip Huyen, a machine learning expert who has closely followed the lab’s journey.</p> 
 <hr /> <p>At the intersection of 18th and Folsom Streets in San Francisco, OpenAI’s office looks like a mysterious warehouse. The historic building has drab gray paneling and tinted windows, with most of the shades pulled down. The letters “PIONEER BUILDING”—the remnants of its bygone owner, the Pioneer Truck Factory—wrap around the corner in faded red paint.</p> <p>Inside, the space is light and airy. The first floor has a few common spaces and two conference rooms. One, a healthy size for larger meetings, is called A Space Odyssey; the other, more of a glorified phone booth, is called Infinite Jest. This is the space I’m restricted to during my visit. I’m forbidden to visit the second and third floors, which house everyone’s desks, several robots, and pretty much everything interesting. When it’s time for their interviews, people come down to me. An employee trains a watchful eye on me in between meetings.</p> <div><figure><figcaption>The Pioneer Building.<p>wikimedia commons / tfinc</p></figcaption></figure></div> <p>On the beautiful blue-sky day that I arrive to meet Brockman, he looks nervous and guarded. “We’ve never given someone so much access before,” he says with a tentative smile. He wears casual clothes and, like many at OpenAI, sports a shapeless haircut that seems to reflect an efficient, no-frills mentality.</p> <p>Brockman, 31, grew up on a hobby farm in North Dakota and had what he describes as a “focused, quiet childhood.” He milked cows, gathered eggs, and fell in love with math while studying on his own. In 2008, he entered Harvard intending to double-major in math and computer science, but he quickly grew restless to enter the real world. He dropped out a year later, entered MIT instead, and then dropped out again within a matter of months. The second time, his decision was final. Once he moved to San Francisco, he never looked back.</p> </div><div> <p>Brockman takes me to lunch to remove me from the office during an all-company meeting. In the café across the street, he speaks about OpenAI with intensity, sincerity, and wonder, often drawing parallels between its mission and landmark achievements of science history. It’s easy to appreciate his charisma as a leader. Recounting memorable passages from the books he’s read, he zeroes in on the Valley’s favorite narrative, America’s race to the moon. (“One story I really love is the story of the janitor,” he says, referencing a famous yet probably apocryphal tale. “Kennedy goes up to him and asks him, ‘What are you doing?’ and he says, ‘Oh, I’m helping put a man on the moon!’”) There’s also the transcontinental railroad (“It was actually the last megaproject done entirely by hand … a project of immense scale that was totally risky”) and Thomas Edison’s incandescent lightbulb (“A committee of distinguished experts said ‘It’s never gonna work,’ and one year later he shipped”).</p> <figure><figcaption>Greg Brockman, co-founder and CTO.<p>Christie Hemm Klok</p></figcaption></figure> <p>Brockman is aware of the gamble OpenAI has taken on—and aware that it evokes cynicism and scrutiny. But with each reference, his message is clear: People can be skeptical all they want. It’s the price of daring greatly.</p> <hr /> <p>Those who joined OpenAI in the early days remember the energy, excitement, and sense of purpose. The team was small—formed through a tight web of connections—and management stayed loose and informal. Everyone believed in a flat structure where ideas and debate would be welcome from anyone.</p> <p>Musk played no small part in building a collective mythology. “The way he presented it to me was ‘Look, I get it. AGI might be far away, but what if it’s not?’” recalls Pieter Abbeel, a professor at UC Berkeley who worked there, along with several of his students, in the first two years. “‘What if it’s even just a 1% or 0.1% chance that it’s happening in the next five to 10 years? Shouldn’t we think about it very carefully?’ That resonated with me,” he says.</p> 
 <p>But the informality also led to some vagueness of direction. In May 2016, Altman and Brockman received a visit from Dario Amodei, then a Google researcher, who told them no one understood what they were doing. In an account published in <a href="https://www.newyorker.com/magazine/2016/10/10/sam-altmans-manifest-destiny">the New Yorker</a>, it wasn’t clear the team itself knew either. “Our goal right now … is to do the best thing there is to do,” Brockman said. “It’s a little vague.”</p> <p>Nonetheless, Amodei joined the team a few months later. His sister, Daniela Amodei, had previously worked with Brockman, and he already knew many of OpenAI’s members. After two years, at Brockman’s request, Daniela joined too. “Imagine—we started with nothing,” Brockman says. “We just had this ideal that we wanted AGI to go well.”</p> 
 <figure><blockquote><p>Throughout our lunch, Brockman recites the charter like scripture, an explanation for every aspect of the company’s existence.</p></blockquote></figure> <p>By March of 2017, 15 months in, the leadership realized it was time for more focus. So Brockman and a few other core members began drafting an internal document to lay out a path to AGI. But the process quickly revealed a fatal flaw. As the team studied trends within the field, they realized staying a nonprofit was financially untenable. The computational resources that others in the field were using to achieve breakthrough results were doubling <a href="https://openai.com/blog/ai-and-compute/">every 3.4 months</a>. It became clear that “in order to stay relevant,” Brockman says, they would need enough capital to match or exceed this exponential ramp-up. That required a new organizational model that could rapidly amass money—while somehow also staying true to the mission.</p> <p>Unbeknownst to the public—and most employees—it was with this in mind that OpenAI released <a href="https://openai.com/charter/">its charter</a> in April of 2018. The document re-articulated the lab’s core values but subtly shifted the language to reflect the new reality. Alongside its commitment to “avoid enabling uses of AI or AGI that harm humanity or unduly concentrate power,” it also stressed the need for resources. “We anticipate needing to marshal substantial resources to fulfill our mission,” it said, “but will always diligently act to minimize conflicts of interest among our employees and stakeholders that could compromise broad benefit.”</p> </div><div> <p>“We spent a long time internally iterating with employees to get the whole company bought into a set of principles,” Brockman says. “Things that had to stay invariant even if we changed our structure.”</p> <figure><figcaption>From left to right: Daniela Amodei, Jack Clark, Dario Amodei, Jeff Wu (technical staff member), Greg Brockman, Alec Radford (technical language team lead), Christine Payne (technical staff member), Ilya Sutskever, and Chris Berner (head of infrastructure).<p>Christie Hemm Klok</p></figcaption></figure> <p>That structure change happened in March 2019. OpenAI shed its purely nonprofit status by setting up a “capped profit” arm—a for-profit with a 100-fold limit on investors’ returns, albeit overseen by a board that's part of a nonprofit entity. Shortly after, it <a href="https://www.technologyreview.com/f/613994/microsoft-is-investing-1-billion-in-openai-to-create-brain-like-machines/">announced</a> Microsoft’s billion-dollar investment (though it didn’t reveal that this was split between cash and credits to Azure, Microsoft’s cloud computing platform).</p> <p>Predictably, the move set off a wave of accusations that OpenAI was going back on its mission. In a post on <a href="https://news.ycombinator.com/item?id=19360709">Hacker News</a> soon after the announcement, a user asked how a 100-fold limit would be limiting at all: “Early investors in Google have received a roughly 20x return on their capital,” they wrote. “Your bet is that you’ll have a corporate structure which returns orders of magnitude more than Google ... but you don’t want to ‘unduly concentrate power’? How will this work? What exactly is power, if not the concentration of resources?”</p> <p>The move also rattled many employees, who voiced similar concerns. To assuage internal unrest, the leadership wrote up an FAQ as part of a series of highly protected transition docs. “Can I trust OpenAI?” one question asked. “Yes,” began the answer, followed by a paragraph of explanation.</p> 
 <hr /> <p><a href="https://openai.com/charter/">The charter</a> is the backbone of OpenAI. It serves as the springboard for all the lab’s strategies and actions. Throughout our lunch, Brockman recites it like scripture, an explanation for every aspect of the company’s existence. (“By the way,” he clarifies halfway through one recitation, “I guess I know all these lines because I spent a lot of time really poring over them to get them exactly right. It’s not like I was reading this before the meeting.”)</p> <p><em>How will you ensure that humans continue to live meaningful lives as you develop more advanced capabilities?</em> “As we wrote, we think its impact should be to give everyone economic freedom, to let them find new opportunities that aren’t imaginable today.” <em>How will you structure yourself to evenly distribute AGI?</em> “I think a utility is the best analogy for the vision that we have. But again, it’s all subject to the charter.” <em>How do you compete to reach AGI first without compromising safety?</em> “I think there is absolutely this important balancing act, and our best shot at that is what’s in the charter.”</p> <div><figure><figcaption>APRIL 9, 2018 5 MINUTE READ<p>OpenAI</p></figcaption></figure></div> <p>For Brockman, rigid adherence to the document is what makes OpenAI’s structure work. Internal alignment is treated as paramount: all full-time employees are required to work out of the same office, with few exceptions. For the policy team, especially Jack Clark, the director, this means a life divided between San Francisco and Washington, DC. Clark doesn’t mind—in fact, he agrees with the mentality. It’s the in-between moments, like lunchtime with colleagues, he says, that help keep everyone on the same page.</p> <p>In many ways, this approach is clearly working: the company has an impressively uniform culture. The employees work long hours and talk incessantly about their jobs through meals and social hours; many go to the same parties and subscribe to the rational philosophy of “<a href="https://www.effectivealtruism.org/">effective altruism</a>.” They crack jokes using machine-learning terminology to describe their lives: “What is your life a function of?” “What are you optimizing for?” “Everything is basically a minmax function.” To be fair, other AI researchers also love doing this, but people familiar with OpenAI agree: more than others in the field, its employees treat AI research not as a job but as an identity. (In November, Brockman <a href="https://twitter.com/gdb/status/1194293326452625408">married</a> his girlfriend of one year, Anna, in the office against a backdrop of flowers arranged in an OpenAI logo. Sutskever acted as the officiant; a robot hand was the ring bearer.)</p> 
</div><div> <p>But at some point in the middle of last year, the charter became more than just lunchtime conversation fodder. Soon after switching to a capped-profit, the leadership instituted a new pay structure based in part on each employee’s absorption of the mission. Alongside columns like “engineering expertise” and “research direction” in a spreadsheet tab titled “Unified Technical Ladder,” the last column outlines the culture-related expectations for every level. Level 3: “You understand and internalize the OpenAI charter.” Level 5: “You ensure all projects you and your team-mates work on are consistent with the charter.” Level 7: “You are responsible for upholding and improving the charter, and holding others in the organization accountable for doing the same.”</p> <hr /> <p>The first time most people ever heard of OpenAI was on February 14, 2019. That day, the lab <a href="https://openai.com/blog/better-language-models/">announced</a> impressive new research: a model that could generate convincing essays and articles at the push of a button. Feed it a sentence from <em>The Lord of the Rings</em> or the start of a (fake) news story about Miley Cyrus shoplifting, and it would spit out paragraph after paragraph of text in the same vein.</p> <p>But there was also a catch: the model, called GPT-2, was too dangerous to release, the researchers said. If such powerful technology fell into the wrong hands, it could easily be <a href="https://www.technologyreview.com/s/612960/an-ai-tool-auto-generates-fake-news-bogus-tweets-and-plenty-of-gibberish/">weaponized</a> to produce disinformation at immense scale.</p> <p>The backlash among scientists was immediate. OpenAI was pulling a publicity stunt, some said. GPT-2 was not nearly advanced enough to be a threat. And if it was, why announce its existence and then preclude public scrutiny? “It seemed like OpenAI was trying to capitalize off of panic around AI,” says Britt Paris, an assistant professor at Rutgers University who studies AI-generated disinformation.</p> <figure><figcaption>Jack Clark, policy director.<p>Christie Hemm Klok</p></figcaption></figure> <p>By May, OpenAI had revised its stance and announced plans for a “<a href="https://www.technologyreview.com/s/614237/openai-released-its-fake-news-ai-gpt-2/">staged release</a>.” Over the following months, it successively dribbled out more and more powerful versions of GPT-2. In the interim, it also engaged with several research organizations to scrutinize the algorithm’s potential for abuse and develop countermeasures. Finally, it released the full code in November, having found, <a href="https://openai.com/blog/gpt-2-1-5b-release/">it said</a>, “no strong evidence of misuse so far.”</p> <p>Amid continued accusations of publicity-seeking, OpenAI insisted that GPT-2 hadn’t been a stunt. It was, rather, a carefully thought-out experiment, agreed on after a series of internal discussions and debates. The consensus was that even if it had been slight overkill this time, the action would set a precedent for handling more dangerous research. Besides, the charter had predicted that “safety and security concerns” would gradually oblige the lab to “reduce our traditional publishing in the future.”</p> <p>This was also the argument that the policy team carefully laid out in its <a href="https://openai.com/blog/gpt-2-6-month-follow-up/">six-month follow-up blog post</a>, which they discussed as I sat in on a meeting. “I think that is definitely part of the success-story framing,” said Miles Brundage, a policy research scientist, highlighting something in a Google doc. “The lead of this section should be: We did an ambitious thing, now some people are replicating it, and here are some reasons why it was beneficial.”</p> <p>But OpenAI’s media campaign with GPT-2 also followed a well-established pattern that has made the broader AI community leery. Over the years, the lab’s big, splashy research announcements have been repeatedly accused of fueling the AI hype cycle. More than once, critics have also accused the lab of talking up its results to the point of mischaracterization. For these reasons, many in the field have tended to keep OpenAI at arm’s length.</p> </div><div> <figure><figcaption>Cover images of OpenAI's research releases hang on its office wall.<p>Christie Hemm Klok</p></figcaption></figure> <p>This hasn’t stopped the lab from continuing to pour resources into its public image. As well as research papers, it publishes its results in highly produced company blog posts for which it does everything in-house, from writing to multimedia production to design of the cover images for each release. At one point, it also began developing a documentary on one of its projects to rival <a href="https://www.youtube.com/watch?v=jGyCsVhtW0M">a 90-minute movie about DeepMind’s AlphaGo</a>. It eventually spun the effort out into an independent production, which Brockman and his wife, Anna, are now partially financing. (I also agreed to appear in the documentary to provide technical explanation and context to OpenAI’s achievement. I was not compensated for this.)</p> <p>And as the blowback has increased, so have internal discussions to address it. Employees have grown frustrated at the constant outside criticism, and the leadership worries it will undermine the lab’s influence and ability to hire the best talent. An internal document highlights this problem and an outreach strategy for tackling it: “In order to have government-level policy influence, we need to be viewed as the most trusted source on ML [machine learning] research and AGI,” says a line under the “Policy” section. “Widespread support and backing from the research community is not only necessary to gain such a reputation, but will amplify our message.” Another, under “Strategy,” reads, "Explicitly treat the ML community as a comms stakeholder. Change our tone and external messaging such that we only antagonize them when we intentionally choose to."</p> <hr /> <p>There was another reason GPT-2 had triggered such an acute backlash. People felt that OpenAI was once again walking back its earlier promises of openness and transparency. With news of the for-profit transition a month later, the withheld research made people even more suspicious. Could it be that the technology had been kept under wraps in preparation for licensing it in the future?</p> <div><figure><figcaption>Ilya Sutskever, co-founder and chief scientist.<p>Christie Hemm Klok</p></figcaption></figure></div> <p>But little did people know this wasn’t the only time OpenAI had chosen to hide its research. In fact, it had kept another effort entirely secret.</p> <p>There are two prevailing technical theories about what it will take to reach AGI. In one, all the necessary techniques already exist; it’s just a matter of figuring out how to scale and assemble them. In the other, there needs to be an entirely new paradigm; deep learning, the current dominant technique in AI, won’t be enough.</p> <p>Most researchers fall somewhere between these extremes, but OpenAI has consistently sat almost exclusively on the scale-and-assemble end of the spectrum. Most of its breakthroughs have been the product of sinking dramatically greater computational resources into technical innovations developed in other labs.</p> <p>Brockman and Sutskever deny that this is their sole strategy, but the lab’s tightly guarded research suggests otherwise. A team called “Foresight” runs experiments to test how far they can push AI capabilities forward by training existing algorithms with increasingly large amounts of data and computing power. For the leadership, the results of these experiments have confirmed its instincts that the lab’s all-in, compute-driven strategy is the best approach.</p> <p>For roughly six months, these results were hidden from the public because OpenAI sees this knowledge as its primary competitive advantage. Employees and interns were explicitly instructed not to reveal them, and those who left signed nondisclosure agreements. It was only in January that the team, without the usual fanfare, quietly posted <a href="https://arxiv.org/pdf/2001.08361.pdf">a paper</a> on one of the primary open-source databases for AI research. People who experienced the intense secrecy around the effort didn’t know what to make of this change. Notably, <a href="https://arxiv.org/pdf/1909.12673.pdf">another paper</a> with similar results from different researchers had been posted a few months earlier.</p> </div><div> <figure><figcaption><p>Christie Hemm Klok</p></figcaption></figure> <p>In the beginning, this level of secrecy was never the intention, but it has since become habitual. Over time, the leadership has moved away from its original belief that openness is the best way to build beneficial AGI. Now the importance of keeping quiet is impressed on those who work with or at the lab. This includes never speaking to reporters without the express permission of the communications team. After my initial visits to the office, as I began contacting different employees, I received an email from the head of communications reminding me that all interview requests had to go through her. When I declined, saying that this would undermine the validity of what people told me, she instructed employees to keep her informed of my outreach. A Slack message from Clark, a former journalist, later commended people for keeping a tight lid as a reporter was “sniffing around.”</p> <p>In a statement responding to this heightened secrecy, an OpenAI spokesperson referred back to a section of its charter. “We expect that safety and security concerns will reduce our traditional publishing in the future,” the section states, “while increasing the importance of sharing safety, policy, and standards research.” The spokesperson also added: “Additionally, each of our releases is run through an infohazard process to evaluate these trade-offs and we want to release our results slowly to understand potential risks and impacts before setting loose in the wild.”</p> <p>One of the biggest secrets is the project OpenAI is working on next. Sources described it to me as the culmination of its previous four years of research: an AI system trained on images, text, and other data using massive computational resources. A small team has been assigned to the initial effort, with an expectation that other teams, along with their work, will eventually fold in. On the day it was announced at an all-company meeting, interns weren’t allowed to attend. People familiar with the plan offer an explanation: the leadership thinks this is the most promising way to reach AGI.</p> <hr /> <p>The man driving OpenAI’s strategy is Dario Amodei, the ex-Googler who now serves as research director. When I meet him, he strikes me as a more anxious version of Brockman. He has a similar sincerity and sensitivity, but an air of unsettled nervous energy. He looks distant when he talks, his brows furrowed, a hand absentmindedly tugging his curls.</p> <p>Amodei divides the lab’s strategy into two parts. The first part, which dictates how it plans to reach advanced AI capabilities, he likens to an investor’s “portfolio of bets.” Different teams at OpenAI are playing out different bets. The language team, for example, has its money on a theory postulating that AI can develop a significant understanding of the world through mere language learning. The robotics team, in contrast, is advancing an opposing theory that intelligence requires a physical embodiment to develop.</p> <p>As in an investor’s portfolio, not every bet has an equal weight. But for the purposes of scientific rigor, all should be tested before being discarded. Amodei points to GPT-2, with its remarkably realistic auto-generated texts, as an instance of why it’s important to keep an open mind. “Pure language is a direction that the field and even some of us were somewhat skeptical of,” he says. “But now it's like, ‘Wow, this is really promising.’”</p> <p>Over time, as different bets rise above others, they will attract more intense efforts. Then they will cross-pollinate and combine. The goal is to have fewer and fewer teams that ultimately collapse into a single technical direction for AGI. This is the exact process that OpenAI’s latest top-secret project has supposedly already begun.</p> <figure><figcaption>Dario Amodei, research director.<p>Christie Hemm Klok</p></figcaption></figure> <p>The second part of the strategy, Amodei explains, focuses on how to make such ever-advancing AI systems safe. This includes making sure that they reflect human values, can explain the logic behind their decisions, and can learn without harming people in the process. Teams dedicated to each of these safety goals seek to develop methods that can be applied across projects as they mature. Techniques developed by the explainability team, for example, may be used to expose the logic behind GPT-2’s sentence constructions or a robot’s movements.</p> </div><div> <p>Amodei admits this part of the strategy is somewhat haphazard, built less on established theories in the field and more on gut feeling. “At some point we’re going to build AGI, and by that time I want to feel good about these systems operating in the world,” he says. “Anything where I don’t currently feel good, I create and recruit a team to focus on that thing.”</p> <p>For all the publicity-chasing and secrecy, Amodei looks sincere when he says this. The possibility of failure seems to disturb him.</p> <p>“We’re in the awkward position of: we don’t know what AGI looks like,” he says. “We don’t know when it’s going to happen.” Then, with careful self-awareness, he adds: “The mind of any given person is limited. The best thing I’ve found is hiring other safety researchers who often have visions which are different than the natural thing I might’ve thought of. I want that kind of variation and diversity because that’s the only way that you catch everything.”</p> <hr /> <p>The thing is, OpenAI actually has little “variation and diversity”—a fact hammered home on my third day at the office. During the one lunch I was granted to mingle with employees, I sat down at the most visibly diverse table by a large margin. Less than a minute later, I realized that the people eating there were not, in fact, OpenAI employees. Neuralink, Musk’s startup working on computer-brain interfaces, shares the same building and dining room.</p> <div><figure><figcaption>Daniela Amodei, head of people operations.<p>Christie Hemm Klok</p></figcaption></figure></div> <p>According to a lab spokesperson, out of the over 120 employees, 25% are female or nonbinary. There are also two women on the executive team and the leadership team is 30% women, she said, though she didn’t specify who was counted among these teams. (All four C-suite executives, including Brockman and Altman, are white men. Out of over 112 employees I identified on LinkedIn and other sources, the overwhelming number were white or Asian.)</p> <p>In fairness, this lack of diversity is typical in AI. Last year a <a href="https://ainowinstitute.org/discriminatingsystems.pdf">report</a> from the New York–based research institute AI Now found that women accounted for only 18% of authors at leading AI conferences, 20% of AI professorships, and 15% and 10% of research staff at Facebook and Google, respectively. “There is definitely still a lot of work to be done across academia and industry,” OpenAI’s spokesperson said. “Diversity and inclusion is something we take seriously and are continually working to improve by working with initiatives like WiML, Girl Geek, and our Scholars program.”</p> <p>Indeed, OpenAI has tried to broaden its talent pool. It began its remote Scholars program for underrepresented minorities in 2018. But only two of the first eight scholars became full-time employees, even though they reported positive experiences. The most common reason for declining to stay: the requirement to live in San Francisco. For Nadja Rhodes, a former scholar who is now the lead machine-learning engineer at a New York–based company, the city just had too little diversity.</p> <p>But if diversity is a problem for the AI industry in general, it’s something more existential for a company whose mission is to spread the technology evenly to everyone. The fact is that it lacks representation from the groups most at risk of being left out.</p> </div><div> <p>Nor is it at all clear just <em>how</em> OpenAI plans to “distribute the benefits” of AGI to “all of humanity,” as Brockman frequently says in citing its mission. The leadership speaks of this in vague terms and has done little to flesh out the specifics. (In January, the Future of Humanity Institute at Oxford University <a href="https://www.fhi.ox.ac.uk/wp-content/uploads/Windfall-Clause-Report.pdf">released a report</a> in collaboration with the lab proposing to distribute benefits by distributing a percentage of profits. But the authors cited “significant unresolved issues regarding … the way in which it would be implemented.”) “This is my biggest problem with OpenAI,” says a former employee, who spoke on condition of anonymity.</p> <figure><figcaption><p>Christie Hemm Klok</p></figcaption></figure> <p>“They are using sophisticated technical practices to try to answer social problems with AI,” echoes Britt Paris of Rutgers. “It seems like they don’t really have the capabilities to actually understand the social. They just understand that that’s a sort of a lucrative place to be positioning themselves right now.”</p> <p>Brockman agrees that both technical and social expertise will ultimately be necessary for OpenAI to achieve its mission. But he disagrees that the social issues need to be solved from the very beginning. “How exactly do you bake ethics in, or these other perspectives in? And when do you bring them in, and how? One strategy you could pursue is to, from the very beginning, try to bake in everything you might possibly need,” he says. “I don’t <em>think</em> that that strategy is likely to succeed.”</p> <p>The first thing to figure out, he says, is what AGI will even look like. Only then will it be time to “make sure that we are understanding the ramifications.”</p> <hr /> <p>Last summer, in the weeks after the switch to a capped-profit model and the $1 billion injection from Microsoft, the leadership assured employees that these updates wouldn’t functionally change OpenAI’s approach to research. Microsoft was well aligned with the lab’s values, and any commercialization efforts would be far away; the pursuit of fundamental questions would still remain at the core of the work.</p> <p>For a while, these assurances seemed to hold true, and projects continued as they were. Many employees didn’t even know what promises, if any, had been made to Microsoft.</p> <p>But in recent months, the pressure of commercialization has intensified, and the need to produce money-making research no longer feels like something in the distant future. In sharing his 2020 vision for the lab privately with employees, Altman’s message is clear: OpenAI needs to make money in order to do research—not the other way around.</p> <p>This is a hard but necessary trade-off, the leadership has said—one it had to make for lack of wealthy philanthropic donors. By contrast, Seattle-based AI2, a nonprofit that ambitiously advances fundamental AI research, receives its funds from a self-sustaining (at least for the foreseeable future) pool of money left behind by the late Paul Allen, a billionaire best known for cofounding Microsoft.</p> </div><div><p>But the truth is that OpenAI faces this trade-off not only because it’s not rich, but also because it made the strategic choice to try to reach AGI before anyone else. That pressure forces it to make decisions that seem to land farther and farther away from its original intention. It leans into hype in its rush to attract funding and talent, guards its research in the hopes of keeping the upper hand, and chases a computationally heavy strategy—not because it’s seen as the only way to AGI, but because it seems like the fastest.</p> <p>Yet OpenAI is still a bastion of talent and cutting-edge research, filled with people who are sincerely striving to work for the benefit of humanity. In other words, it still has the most important elements, and there’s still time for it to change.</p> <p>Near the end of my interview with Rhodes, the former remote scholar, I ask her the one thing about OpenAI that I shouldn’t omit from this profile. “I guess in my opinion, there’s problems,” she begins hesitantly. “Some of them come from maybe the environment it faces; some of them come from the type of people that it tends to attract and other people that it leaves out.”</p> <p>“But to me, it feels like they are doing something a little bit right,” she says. “I got a sense that the folks there are earnestly trying.”</p> <p><em><strong>Update:</strong> We made some changes to this story after OpenAI asked us to clarify that when Greg Brockman said he didn’t think it was possible to “bake ethics in… from the very beginning” when developing AI, he intended it to mean that ethical questions couldn’t be solved from the beginning, not that they couldn’t be addressed from the beginning. Also, that after dropping out of Harvard he transferred straight to MIT rather than waiting a year. Also, that he was raised not “on a farm,” but "on a hobby farm." Brockman considers this distinction important.</em></p> <p><em>In addition, we have clarified that while OpenAI did indeed "shed its nonprofit status," a board that is part of a nonprofit entity still oversees it, and that OpenAI publishes its research in the form of company blog posts as well as, not in lieu of, research papers. We’ve also corrected the date of publication of a paper by outside researchers and the affiliation of Peter Eckersley (former, not current, research director of Partnership on AI, which he recently left).</em> </p></div></div></div>||||I||||You need to enable JavaScript to view this site.
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 Artificial intelligence

 The messy, secretive reality behind OpenAI’s bid to save the world

 The AI moonshot was founded in the spirit of transparency. This is the inside story of how competitive pressure eroded that idealism.

 By
 * Karen Hao archive page
 February 17, 2020

 Greg Brockman, co-founder and CTO; Ilya Sutskever, co-founder and chief scientist; and Dario Amodei, research director. Christie Hemm Klok

 Every year, OpenAI’s employees vote on when they believe artificial general intelligence, or AGI, will finally arrive. It’s mostly seen as a fun way to bond, and their estimates differ widely. But in a field that still debates whether human-like autonomous systems are even possible, half the lab bets it is likely to happen within 15 years.

 In the four short years of its existence, OpenAI has become one of the leading AI research labs in the world. It has made a name for itself producing consistently headline-grabbing research, alongside other AI heavyweights like Alphabet’s DeepMind. It is also a darling in Silicon Valley, counting Elon Musk and legendary investor Sam Altman among its founders.

 Above all, it is lionized for its mission. Its goal is to be the first to create AGI—a machine with the learning and reasoning powers of a human mind. The purpose is not world domination; rather, the lab wants to ensure that the technology is developed safely and its benefits distributed evenly to the world.

 The implication is that AGI could easily run amok if the technology’s development is left to follow the path of least resistance. Narrow intelligence, the kind of clumsy AI that surrounds us today, has already served as an example. We now know that algorithms are biased and fragile; they can perpetrate great abuse and great deception; and the expense of developing and running them tends to concentrate their power in the hands of a few. By extrapolation, AGI could be catastrophic without the careful guidance of a benevolent shepherd.

 OpenAI wants to be that shepherd, and it has carefully crafted its image to fit the bill. In a field dominated by wealthy corporations, it was founded as a nonprofit. Its first announcement said that this distinction would allow it to “build value for everyone rather than shareholders.” Its charter—a document so sacred that employees’ pay is tied to how well they adhere to it—further declares that OpenAI’s “primary fiduciary duty is to humanity.” Attaining AGI safely is so important, it continues, that if another organization were close to getting there first, OpenAI would stop competing with it and collaborate instead. This alluring narrative plays well with investors and the media, and in July Microsoft injected the lab with a fresh $1 billion.

 OpenAI's logo hanging in its office.
 Christie Hemm Klok

 But three days at OpenAI’s office—and nearly three dozen interviews with past and current employees, collaborators, friends, and other experts in the field—suggest a different picture. There is a misalignment between what the company publicly espouses and how it operates behind closed doors. Over time, it has allowed a fierce competitiveness and mounting pressure for ever more funding to erode its founding ideals of transparency, openness, and collaboration. Many who work or worked for the company insisted on anonymity because they were not authorized to speak or feared retaliation. Their accounts suggest that OpenAI, for all its noble aspirations, is obsessed with maintaining secrecy, protecting its image, and retaining the loyalty of its employees.

 Since its earliest conception, AI as a field has strived to understand human-like intelligence and then re-create it. In 1950, Alan Turing, the renowned English mathematician and computer scientist, began a paper with the now-famous provocation “Can machines think?” Six years later, captivated by the nagging idea, a group of scientists gathered at Dartmouth College to formalize the discipline.

 “It is one of the most fundamental questions of all intellectual history, right?” says Oren Etzioni, the CEO of the Allen Institute for Artificial Intelligence (AI2), a Seattle-based nonprofit AI research lab. “It’s like, do we understand the origin of the universe? Do we understand matter?”

 The trouble is, AGI has always remained vague. No one can really describe what it might look like or the minimum of what it should do. It’s not obvious, for instance, that there is only one kind of general intelligence; human intelligence could just be a subset. There are also differing opinions about what purpose AGI could serve. In the more romanticized view, a machine intelligence unhindered by the need for sleep or the inefficiency of human communication could help solve complex challenges like climate change, poverty, and hunger.

 But the resounding consensus within the field is that such advanced capabilities would take decades, even centuries—if indeed it’s possible to develop them at all. Many also fear that pursuing this goal overzealously could backfire. In the 1970s and again in the late ’80s and early ’90s, the field overpromised and underdelivered. Overnight, funding dried up, leaving deep scars in an entire generation of researchers. “The field felt like a backwater,” says Peter Eckersley, until recently director of research at the industry group Partnership on AI, of which OpenAI is a member.

 A conference room on the first floor named Infinite Jest.
 Christie Hemm Klok

 Against this backdrop, OpenAI entered the world with a splash on December 11, 2015. It wasn’t the first to openly declare it was pursuing AGI; DeepMind had done so five years earlier and had been acquired by Google in 2014. But OpenAI seemed different. For one thing, the sticker price was shocking: the venture would start with $1 billion from private investors, including Musk, Altman, and PayPal cofounder Peter Thiel.

 The star-studded investor list stirred up a media frenzy, as did the impressive list of initial employees: Greg Brockman, who had run technology for the payments company Stripe, would be chief technology officer; Ilya Sutskever, who had studied under AI pioneer Geoffrey Hinton, would be research director; and seven researchers, freshly graduated from top universities or plucked from other companies, would compose the core technical team. (Last February, Musk announced that he was parting ways with the company over disagreements about its direction. A month later, Altman stepped down as president of startup accelerator Y Combinator to become OpenAI’s CEO.)

 But more than anything, OpenAI’s nonprofit status made a statement. “It’ll be important to have a leading research institution which can prioritize a good outcome for all over its own self-interest,” the announcement said. “Researchers will be strongly encouraged to publish their work, whether as papers, blog posts, or code, and our patents (if any) will be shared with the world.” Though it never made the criticism explicit, the implication was clear: other labs, like DeepMind, could not serve humanity because they were constrained by commercial interests. While they were closed, OpenAI would be open.

 In a research landscape that had become increasingly privatized and focused on short-term financial gains, OpenAI was offering a new way to fund progress on the biggest problems. “It was a beacon of hope,” says Chip Huyen, a machine learning expert who has closely followed the lab’s journey.

 At the intersection of 18th and Folsom Streets in San Francisco, OpenAI’s office looks like a mysterious warehouse. The historic building has drab gray paneling and tinted windows, with most of the shades pulled down. The letters “PIONEER BUILDING”—the remnants of its bygone owner, the Pioneer Truck Factory—wrap around the corner in faded red paint.

 Inside, the space is light and airy. The first floor has a few common spaces and two conference rooms. One, a healthy size for larger meetings, is called A Space Odyssey; the other, more of a glorified phone booth, is called Infinite Jest. This is the space I’m restricted to during my visit. I’m forbidden to visit the second and third floors, which house everyone’s desks, several robots, and pretty much everything interesting. When it’s time for their interviews, people come down to me. An employee trains a watchful eye on me in between meetings.

 The Pioneer Building.
 wikimedia commons / tfinc

 On the beautiful blue-sky day that I arrive to meet Brockman, he looks nervous and guarded. “We’ve never given someone so much access before,” he says with a tentative smile. He wears casual clothes and, like many at OpenAI, sports a shapeless haircut that seems to reflect an efficient, no-frills mentality.

 Brockman, 31, grew up on a hobby farm in North Dakota and had what he describes as a “focused, quiet childhood.” He milked cows, gathered eggs, and fell in love with math while studying on his own. In 2008, he entered Harvard intending to double-major in math and computer science, but he quickly grew restless to enter the real world. He dropped out a year later, entered MIT instead, and then dropped out again within a matter of months. The second time, his decision was final. Once he moved to San Francisco, he never looked back.

 Brockman takes me to lunch to remove me from the office during an all-company meeting. In the café across the street, he speaks about OpenAI with intensity, sincerity, and wonder, often drawing parallels between its mission and landmark achievements of science history. It’s easy to appreciate his charisma as a leader. Recounting memorable passages from the books he’s read, he zeroes in on the Valley’s favorite narrative, America’s race to the moon. (“One story I really love is the story of the janitor,” he says, referencing a famous yet probably apocryphal tale. “Kennedy goes up to him and asks him, ‘What are you doing?’ and he says, ‘Oh, I’m helping put a man on the moon!’”) There’s also the transcontinental railroad (“It was actually the last megaproject done entirely by hand … a project of immense scale that was totally risky”) and Thomas Edison’s incandescent lightbulb (“A committee of distinguished experts said ‘It’s never gonna work,’ and one year later he shipped”).

 Greg Brockman, co-founder and CTO.
 Christie Hemm Klok

 Brockman is aware of the gamble OpenAI has taken on—and aware that it evokes cynicism and scrutiny. But with each reference, his message is clear: People can be skeptical all they want. It’s the price of daring greatly.

 Those who joined OpenAI in the early days remember the energy, excitement, and sense of purpose. The team was small—formed through a tight web of connections—and management stayed loose and informal. Everyone believed in a flat structure where ideas and debate would be welcome from anyone.

 Musk played no small part in building a collective mythology. “The way he presented it to me was ‘Look, I get it. AGI might be far away, but what if it’s not?’” recalls Pieter Abbeel, a professor at UC Berkeley who worked there, along with several of his students, in the first two years. “‘What if it’s even just a 1% or 0.1% chance that it’s happening in the next five to 10 years? Shouldn’t we think about it very carefully?’ That resonated with me,” he says.

 But the informality also led to some vagueness of direction. In May 2016, Altman and Brockman received a visit from Dario Amodei, then a Google researcher, who told them no one understood what they were doing. In an account published in the New Yorker, it wasn’t clear the team itself knew either. “Our goal right now … is to do the best thing there is to do,” Brockman said. “It’s a little vague.”

 Nonetheless, Amodei joined the team a few months later. His sister, Daniela Amodei, had previously worked with Brockman, and he already knew many of OpenAI’s members. After two years, at Brockman’s request, Daniela joined too. “Imagine—we started with nothing,” Brockman says. “We just had this ideal that we wanted AGI to go well.”

 Throughout our lunch, Brockman recites the charter like scripture, an explanation for every aspect of the company’s existence.

 By March of 2017, 15 months in, the leadership realized it was time for more focus. So Brockman and a few other core members began drafting an internal document to lay out a path to AGI. But the process quickly revealed a fatal flaw. As the team studied trends within the field, they realized staying a nonprofit was financially untenable. The computational resources that others in the field were using to achieve breakthrough results were doubling every 3.4 months. It became clear that “in order to stay relevant,” Brockman says, they would need enough capital to match or exceed this exponential ramp-up. That required a new organizational model that could rapidly amass money—while somehow also staying true to the mission.

 Unbeknownst to the public—and most employees—it was with this in mind that OpenAI released its charter in April of 2018. The document re-articulated the lab’s core values but subtly shifted the language to reflect the new reality. Alongside its commitment to “avoid enabling uses of AI or AGI that harm humanity or unduly concentrate power,” it also stressed the need for resources. “We anticipate needing to marshal substantial resources to fulfill our mission,” it said, “but will always diligently act to minimize conflicts of interest among our employees and stakeholders that could compromise broad benefit.”

 “We spent a long time internally iterating with employees to get the whole company bought into a set of principles,” Brockman says. “Things that had to stay invariant even if we changed our structure.”

 From left to right: Daniela Amodei, Jack Clark, Dario Amodei, Jeff Wu (technical staff member), Greg Brockman, Alec Radford (technical language team lead), Christine Payne (technical staff member), Ilya Sutskever, and Chris Berner (head of infrastructure).
 Christie Hemm Klok

 That structure change happened in March 2019. OpenAI shed its purely nonprofit status by setting up a “capped profit” arm—a for-profit with a 100-fold limit on investors’ returns, albeit overseen by a board that's part of a nonprofit entity. Shortly after, it announced Microsoft’s billion-dollar investment (though it didn’t reveal that this was split between cash and credits to Azure, Microsoft’s cloud computing platform).

 Predictably, the move set off a wave of accusations that OpenAI was going back on its mission. In a post on Hacker News soon after the announcement, a user asked how a 100-fold limit would be limiting at all: “Early investors in Google have received a roughly 20x return on their capital,” they wrote. “Your bet is that you’ll have a corporate structure which returns orders of magnitude more than Google ... but you don’t want to ‘unduly concentrate power’? How will this work? What exactly is power, if not the concentration of resources?”

 The move also rattled many employees, who voiced similar concerns. To assuage internal unrest, the leadership wrote up an FAQ as part of a series of highly protected transition docs. “Can I trust OpenAI?” one question asked. “Yes,” began the answer, followed by a paragraph of explanation.

 The charter is the backbone of OpenAI. It serves as the springboard for all the lab’s strategies and actions. Throughout our lunch, Brockman recites it like scripture, an explanation for every aspect of the company’s existence. (“By the way,” he clarifies halfway through one recitation, “I guess I know all these lines because I spent a lot of time really poring over them to get them exactly right. It’s not like I was reading this before the meeting.”)

 How will you ensure that humans continue to live meaningful lives as you develop more advanced capabilities? “As we wrote, we think its impact should be to give everyone economic freedom, to let them find new opportunities that aren’t imaginable today.” How will you structure yourself to evenly distribute AGI? “I think a utility is the best analogy for the vision that we have. But again, it’s all subject to the charter.” How do you compete to reach AGI first without compromising safety? “I think there is absolutely this important balancing act, and our best shot at that is what’s in the charter.”

 APRIL 9, 2018 5 MINUTE READ
 OpenAI

 For Brockman, rigid adherence to the document is what makes OpenAI’s structure work. Internal alignment is treated as paramount: all full-time employees are required to work out of the same office, with few exceptions. For the policy team, especially Jack Clark, the director, this means a life divided between San Francisco and Washington, DC. Clark doesn’t mind—in fact, he agrees with the mentality. It’s the in-between moments, like lunchtime with colleagues, he says, that help keep everyone on the same page.

 In many ways, this approach is clearly working: the company has an impressively uniform culture. The employees work long hours and talk incessantly about their jobs through meals and social hours; many go to the same parties and subscribe to the rational philosophy of “effective altruism.” They crack jokes using machine-learning terminology to describe their lives: “What is your life a function of?” “What are you optimizing for?” “Everything is basically a minmax function.” To be fair, other AI researchers also love doing this, but people familiar with OpenAI agree: more than others in the field, its employees treat AI research not as a job but as an identity. (In November, Brockman married his girlfriend of one year, Anna, in the office against a backdrop of flowers arranged in an OpenAI logo. Sutskever acted as the officiant; a robot hand was the ring bearer.)

 But at some point in the middle of last year, the charter became more than just lunchtime conversation fodder. Soon after switching to a capped-profit, the leadership instituted a new pay structure based in part on each employee’s absorption of the mission. Alongside columns like “engineering expertise” and “research direction” in a spreadsheet tab titled “Unified Technical Ladder,” the last column outlines the culture-related expectations for every level. Level 3: “You understand and internalize the OpenAI charter.” Level 5: “You ensure all projects you and your team-mates work on are consistent with the charter.” Level 7: “You are responsible for upholding and improving the charter, and holding others in the organization accountable for doing the same.”

 The first time most people ever heard of OpenAI was on February 14, 2019. That day, the lab announced impressive new research: a model that could generate convincing essays and articles at the push of a button. Feed it a sentence from The Lord of the Rings or the start of a (fake) news story about Miley Cyrus shoplifting, and it would spit out paragraph after paragraph of text in the same vein.

 But there was also a catch: the model, called GPT-2, was too dangerous to release, the researchers said. If such powerful technology fell into the wrong hands, it could easily be weaponized to produce disinformation at immense scale.

 The backlash among scientists was immediate. OpenAI was pulling a publicity stunt, some said. GPT-2 was not nearly advanced enough to be a threat. And if it was, why announce its existence and then preclude public scrutiny? “It seemed like OpenAI was trying to capitalize off of panic around AI,” says Britt Paris, an assistant professor at Rutgers University who studies AI-generated disinformation.

 Jack Clark, policy director.
 Christie Hemm Klok

 By May, OpenAI had revised its stance and announced plans for a “staged release.” Over the following months, it successively dribbled out more and more powerful versions of GPT-2. In the interim, it also engaged with several research organizations to scrutinize the algorithm’s potential for abuse and develop countermeasures. Finally, it released the full code in November, having found, it said, “no strong evidence of misuse so far.”

 Amid continued accusations of publicity-seeking, OpenAI insisted that GPT-2 hadn’t been a stunt. It was, rather, a carefully thought-out experiment, agreed on after a series of internal discussions and debates. The consensus was that even if it had been slight overkill this time, the action would set a precedent for handling more dangerous research. Besides, the charter had predicted that “safety and security concerns” would gradually oblige the lab to “reduce our traditional publishing in the future.”

 This was also the argument that the policy team carefully laid out in its six-month follow-up blog post, which they discussed as I sat in on a meeting. “I think that is definitely part of the success-story framing,” said Miles Brundage, a policy research scientist, highlighting something in a Google doc. “The lead of this section should be: We did an ambitious thing, now some people are replicating it, and here are some reasons why it was beneficial.”

 But OpenAI’s media campaign with GPT-2 also followed a well-established pattern that has made the broader AI community leery. Over the years, the lab’s big, splashy research announcements have been repeatedly accused of fueling the AI hype cycle. More than once, critics have also accused the lab of talking up its results to the point of mischaracterization. For these reasons, many in the field have tended to keep OpenAI at arm’s length.

 Cover images of OpenAI's research releases hang on its office wall.
 Christie Hemm Klok

 This hasn’t stopped the lab from continuing to pour resources into its public image. As well as research papers, it publishes its results in highly produced company blog posts for which it does everything in-house, from writing to multimedia production to design of the cover images for each release. At one point, it also began developing a documentary on one of its projects to rival a 90-minute movie about DeepMind’s AlphaGo. It eventually spun the effort out into an independent production, which Brockman and his wife, Anna, are now partially financing. (I also agreed to appear in the documentary to provide technical explanation and context to OpenAI’s achievement. I was not compensated for this.)

 And as the blowback has increased, so have internal discussions to address it. Employees have grown frustrated at the constant outside criticism, and the leadership worries it will undermine the lab’s influence and ability to hire the best talent. An internal document highlights this problem and an outreach strategy for tackling it: “In order to have government-level policy influence, we need to be viewed as the most trusted source on ML [machine learning] research and AGI,” says a line under the “Policy” section. “Widespread support and backing from the research community is not only necessary to gain such a reputation, but will amplify our message.” Another, under “Strategy,” reads, "Explicitly treat the ML community as a comms stakeholder. Change our tone and external messaging such that we only antagonize them when we intentionally choose to."

 There was another reason GPT-2 had triggered such an acute backlash. People felt that OpenAI was once again walking back its earlier promises of openness and transparency. With news of the for-profit transition a month later, the withheld research made people even more suspicious. Could it be that the technology had been kept under wraps in preparation for licensing it in the future?

 Ilya Sutskever, co-founder and chief scientist.
 Christie Hemm Klok

 But little did people know this wasn’t the only time OpenAI had chosen to hide its research. In fact, it had kept another effort entirely secret.

 There are two prevailing technical theories about what it will take to reach AGI. In one, all the necessary techniques already exist; it’s just a matter of figuring out how to scale and assemble them. In the other, there needs to be an entirely new paradigm; deep learning, the current dominant technique in AI, won’t be enough.

 Most researchers fall somewhere between these extremes, but OpenAI has consistently sat almost exclusively on the scale-and-assemble end of the spectrum. Most of its breakthroughs have been the product of sinking dramatically greater computational resources into technical innovations developed in other labs.

 Brockman and Sutskever deny that this is their sole strategy, but the lab’s tightly guarded research suggests otherwise. A team called “Foresight” runs experiments to test how far they can push AI capabilities forward by training existing algorithms with increasingly large amounts of data and computing power. For the leadership, the results of these experiments have confirmed its instincts that the lab’s all-in, compute-driven strategy is the best approach.

 For roughly six months, these results were hidden from the public because OpenAI sees this knowledge as its primary competitive advantage. Employees and interns were explicitly instructed not to reveal them, and those who left signed nondisclosure agreements. It was only in January that the team, without the usual fanfare, quietly posted a paper on one of the primary open-source databases for AI research. People who experienced the intense secrecy around the effort didn’t know what to make of this change. Notably, another paper with similar results from different researchers had been posted a few months earlier.

 Christie Hemm Klok

 In the beginning, this level of secrecy was never the intention, but it has since become habitual. Over time, the leadership has moved away from its original belief that openness is the best way to build beneficial AGI. Now the importance of keeping quiet is impressed on those who work with or at the lab. This includes never speaking to reporters without the express permission of the communications team. After my initial visits to the office, as I began contacting different employees, I received an email from the head of communications reminding me that all interview requests had to go through her. When I declined, saying that this would undermine the validity of what people told me, she instructed employees to keep her informed of my outreach. A Slack message from Clark, a former journalist, later commended people for keeping a tight lid as a reporter was “sniffing around.”

 In a statement responding to this heightened secrecy, an OpenAI spokesperson referred back to a section of its charter. “We expect that safety and security concerns will reduce our traditional publishing in the future,” the section states, “while increasing the importance of sharing safety, policy, and standards research.” The spokesperson also added: “Additionally, each of our releases is run through an infohazard process to evaluate these trade-offs and we want to release our results slowly to understand potential risks and impacts before setting loose in the wild.”

 One of the biggest secrets is the project OpenAI is working on next. Sources described it to me as the culmination of its previous four years of research: an AI system trained on images, text, and other data using massive computational resources. A small team has been assigned to the initial effort, with an expectation that other teams, along with their work, will eventually fold in. On the day it was announced at an all-company meeting, interns weren’t allowed to attend. People familiar with the plan offer an explanation: the leadership thinks this is the most promising way to reach AGI.

 The man driving OpenAI’s strategy is Dario Amodei, the ex-Googler who now serves as research director. When I meet him, he strikes me as a more anxious version of Brockman. He has a similar sincerity and sensitivity, but an air of unsettled nervous energy. He looks distant when he talks, his brows furrowed, a hand absentmindedly tugging his curls.

 Amodei divides the lab’s strategy into two parts. The first part, which dictates how it plans to reach advanced AI capabilities, he likens to an investor’s “portfolio of bets.” Different teams at OpenAI are playing out different bets. The language team, for example, has its money on a theory postulating that AI can develop a significant understanding of the world through mere language learning. The robotics team, in contrast, is advancing an opposing theory that intelligence requires a physical embodiment to develop.

 As in an investor’s portfolio, not every bet has an equal weight. But for the purposes of scientific rigor, all should be tested before being discarded. Amodei points to GPT-2, with its remarkably realistic auto-generated texts, as an instance of why it’s important to keep an open mind. “Pure language is a direction that the field and even some of us were somewhat skeptical of,” he says. “But now it's like, ‘Wow, this is really promising.’”

 Over time, as different bets rise above others, they will attract more intense efforts. Then they will cross-pollinate and combine. The goal is to have fewer and fewer teams that ultimately collapse into a single technical direction for AGI. This is the exact process that OpenAI’s latest top-secret project has supposedly already begun.

 Dario Amodei, research director.
 Christie Hemm Klok

 The second part of the strategy, Amodei explains, focuses on how to make such ever-advancing AI systems safe. This includes making sure that they reflect human values, can explain the logic behind their decisions, and can learn without harming people in the process. Teams dedicated to each of these safety goals seek to develop methods that can be applied across projects as they mature. Techniques developed by the explainability team, for example, may be used to expose the logic behind GPT-2’s sentence constructions or a robot’s movements.

 Amodei admits this part of the strategy is somewhat haphazard, built less on established theories in the field and more on gut feeling. “At some point we’re going to build AGI, and by that time I want to feel good about these systems operating in the world,” he says. “Anything where I don’t currently feel good, I create and recruit a team to focus on that thing.”

 For all the publicity-chasing and secrecy, Amodei looks sincere when he says this. The possibility of failure seems to disturb him.

 “We’re in the awkward position of: we don’t know what AGI looks like,” he says. “We don’t know when it’s going to happen.” Then, with careful self-awareness, he adds: “The mind of any given person is limited. The best thing I’ve found is hiring other safety researchers who often have visions which are different than the natural thing I might’ve thought of. I want that kind of variation and diversity because that’s the only way that you catch everything.”

 The thing is, OpenAI actually has little “variation and diversity”—a fact hammered home on my third day at the office. During the one lunch I was granted to mingle with employees, I sat down at the most visibly diverse table by a large margin. Less than a minute later, I realized that the people eating there were not, in fact, OpenAI employees. Neuralink, Musk’s startup working on computer-brain interfaces, shares the same building and dining room.

 Daniela Amodei, head of people operations.
 Christie Hemm Klok

 According to a lab spokesperson, out of the over 120 employees, 25% are female or nonbinary. There are also two women on the executive team and the leadership team is 30% women, she said, though she didn’t specify who was counted among these teams. (All four C-suite executives, including Brockman and Altman, are white men. Out of over 112 employees I identified on LinkedIn and other sources, the overwhelming number were white or Asian.)

 In fairness, this lack of diversity is typical in AI. Last year a report from the New York–based research institute AI Now found that women accounted for only 18% of authors at leading AI conferences, 20% of AI professorships, and 15% and 10% of research staff at Facebook and Google, respectively. “There is definitely still a lot of work to be done across academia and industry,” OpenAI’s spokesperson said. “Diversity and inclusion is something we take seriously and are continually working to improve by working with initiatives like WiML, Girl Geek, and our Scholars program.”

 Indeed, OpenAI has tried to broaden its talent pool. It began its remote Scholars program for underrepresented minorities in 2018. But only two of the first eight scholars became full-time employees, even though they reported positive experiences. The most common reason for declining to stay: the requirement to live in San Francisco. For Nadja Rhodes, a former scholar who is now the lead machine-learning engineer at a New York–based company, the city just had too little diversity.

 But if diversity is a problem for the AI industry in general, it’s something more existential for a company whose mission is to spread the technology evenly to everyone. The fact is that it lacks representation from the groups most at risk of being left out.

 Nor is it at all clear just how OpenAI plans to “distribute the benefits” of AGI to “all of humanity,” as Brockman frequently says in citing its mission. The leadership speaks of this in vague terms and has done little to flesh out the specifics. (In January, the Future of Humanity Institute at Oxford University released a report in collaboration with the lab proposing to distribute benefits by distributing a percentage of profits. But the authors cited “significant unresolved issues regarding … the way in which it would be implemented.”) “This is my biggest problem with OpenAI,” says a former employee, who spoke on condition of anonymity.

 Christie Hemm Klok

 “They are using sophisticated technical practices to try to answer social problems with AI,” echoes Britt Paris of Rutgers. “It seems like they don’t really have the capabilities to actually understand the social. They just understand that that’s a sort of a lucrative place to be positioning themselves right now.”

 Brockman agrees that both technical and social expertise will ultimately be necessary for OpenAI to achieve its mission. But he disagrees that the social issues need to be solved from the very beginning. “How exactly do you bake ethics in, or these other perspectives in? And when do you bring them in, and how? One strategy you could pursue is to, from the very beginning, try to bake in everything you might possibly need,” he says. “I don’t think that that strategy is likely to succeed.”

 The first thing to figure out, he says, is what AGI will even look like. Only then will it be time to “make sure that we are understanding the ramifications.”

 Last summer, in the weeks after the switch to a capped-profit model and the $1 billion injection from Microsoft, the leadership assured employees that these updates wouldn’t functionally change OpenAI’s approach to research. Microsoft was well aligned with the lab’s values, and any commercialization efforts would be far away; the pursuit of fundamental questions would still remain at the core of the work.

 For a while, these assurances seemed to hold true, and projects continued as they were. Many employees didn’t even know what promises, if any, had been made to Microsoft.

 But in recent months, the pressure of commercialization has intensified, and the need to produce money-making research no longer feels like something in the distant future. In sharing his 2020 vision for the lab privately with employees, Altman’s message is clear: OpenAI needs to make money in order to do research—not the other way around.

 This is a hard but necessary trade-off, the leadership has said—one it had to make for lack of wealthy philanthropic donors. By contrast, Seattle-based AI2, a nonprofit that ambitiously advances fundamental AI research, receives its funds from a self-sustaining (at least for the foreseeable future) pool of money left behind by the late Paul Allen, a billionaire best known for cofounding Microsoft.

 But the truth is that OpenAI faces this trade-off not only because it’s not rich, but also because it made the strategic choice to try to reach AGI before anyone else. That pressure forces it to make decisions that seem to land farther and farther away from its original intention. It leans into hype in its rush to attract funding and talent, guards its research in the hopes of keeping the upper hand, and chases a computationally heavy strategy—not because it’s seen as the only way to AGI, but because it seems like the fastest.

 Yet OpenAI is still a bastion of talent and cutting-edge research, filled with people who are sincerely striving to work for the benefit of humanity. In other words, it still has the most important elements, and there’s still time for it to change.

 Near the end of my interview with Rhodes, the former remote scholar, I ask her the one thing about OpenAI that I shouldn’t omit from this profile. “I guess in my opinion, there’s problems,” she begins hesitantly. “Some of them come from maybe the environment it faces; some of them come from the type of people that it tends to attract and other people that it leaves out.”

 “But to me, it feels like they are doing something a little bit right,” she says. “I got a sense that the folks there are earnestly trying.”

 Update: We made some changes to this story after OpenAI asked us to clarify that when Greg Brockman said he didn’t think it was possible to “bake ethics in… from the very beginning” when developing AI, he intended it to mean that ethical questions couldn’t be solved from the beginning, not that they couldn’t be addressed from the beginning. Also, that after dropping out of Harvard he transferred straight to MIT rather than waiting a year. Also, that he was raised not “on a farm,” but "on a hobby farm." Brockman considers this distinction important.

 In addition, we have clarified that while OpenAI did indeed "shed its nonprofit status," a board that is part of a nonprofit entity still oversees it, and that OpenAI publishes its research in the form of company blog posts as well as, not in lieu of, research papers. We’ve also corrected the date of publication of a paper by outside researchers and the affiliation of Peter Eckersley (former, not current, research director of Partnership on AI, which he recently left).

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Every year, OpenAI’s employees vote on when they believe artificial general intelligence, or AGI, will finally arrive. It’s mostly seen as a fun way to bond, and their estimates differ widely. But in a field that still debates whether human-like autonomous systems are even possible, half the lab bets it is likely to happen within 15 years. In the four short years of its existence, OpenAI has become one of the leading AI research labs in the world. It has made a name for itself producing consistently headline-grabbing research, alongside other AI heavyweights like Alphabet’s DeepMind. It is also a darling in Silicon Valley, counting Elon Musk and legendary investor Sam Altman among its founders.   Above all, it is lionized for its mission. Its goal is to be the first to create AGI—a machine with the learning and reasoning powers of a human mind. The purpose is not world domination; rather, the lab wants to ensure that the technology is developed safely and its benefits distributed evenly to the world. The implication is that AGI could easily run amok if the technology’s development is left to follow the path of least resistance. Narrow intelligence, the kind of clumsy AI that surrounds us today, has already served as an example. We now know that algorithms are biased and fragile; they can perpetrate great abuse and great deception; and the expense of developing and running them tends to concentrate their power in the hands of a few. By extrapolation, AGI could be catastrophic without the careful guidance of a benevolent shepherd. 
 OpenAI wants to be that shepherd, and it has carefully crafted its image to fit the bill. In a field dominated by wealthy corporations, it was founded as a nonprofit. Its first announcement said that this distinction would allow it to “build value for everyone rather than shareholders.” Its charter—a document so sacred that employees’ pay is tied to how well they adhere to it—further declares that OpenAI’s “primary fiduciary duty is to humanity.” Attaining AGI safely is so important, it continues, that if another organization were close to getting there first, OpenAI would stop competing with it and collaborate instead. This alluring narrative plays well with investors and the media, and in July Microsoft injected the lab with a fresh $1 billion.  OpenAI's logo hanging in its office.Christie Hemm Klok   But three days at OpenAI’s office—and nearly three dozen interviews with past and current employees, collaborators, friends, and other experts in the field—suggest a different picture. There is a misalignment between what the company publicly espouses and how it operates behind closed doors. Over time, it has allowed a fierce competitiveness and mounting pressure for ever more funding to erode its founding ideals of transparency, openness, and collaboration. Many who work or worked for the company insisted on anonymity because they were not authorized to speak or feared retaliation. Their accounts suggest that OpenAI, for all its noble aspirations, is obsessed with maintaining secrecy, protecting its image, and retaining the loyalty of its employees. 
  Since its earliest conception, AI as a field has strived to understand human-like intelligence and then re-create it. In 1950, Alan Turing, the renowned English mathematician and computer scientist, began a paper with the now-famous provocation “Can machines think?” Six years later, captivated by the nagging idea, a group of scientists gathered at Dartmouth College to formalize the discipline. “It is one of the most fundamental questions of all intellectual history, right?” says Oren Etzioni, the CEO of the Allen Institute for Artificial Intelligence (AI2), a Seattle-based nonprofit AI research lab. “It’s like, do we understand the origin of the universe? Do we understand matter?” The trouble is, AGI has always remained vague. No one can really describe what it might look like or the minimum of what it should do. It’s not obvious, for instance, that there is only one kind of general intelligence; human intelligence could just be a subset. There are also differing opinions about what purpose AGI could serve. In the more romanticized view, a machine intelligence unhindered by the need for sleep or the inefficiency of human communication could help solve complex challenges like climate change, poverty, and hunger. But the resounding consensus within the field is that such advanced capabilities would take decades, even centuries—if indeed it’s possible to develop them at all. Many also fear that pursuing this goal overzealously could backfire. In the 1970s and again in the late ’80s and early ’90s, the field overpromised and underdelivered. Overnight, funding dried up, leaving deep scars in an entire generation of researchers. “The field felt like a backwater,” says Peter Eckersley, until recently director of research at the industry group Partnership on AI, of which OpenAI is a member.    A conference room on the first floor named Infinite Jest.Christie Hemm Klok   Against this backdrop, OpenAI entered the world with a splash on December 11, 2015. It wasn’t the first to openly declare it was pursuing AGI; DeepMind had done so five years earlier and had been acquired by Google in 2014. But OpenAI seemed different. For one thing, the sticker price was shocking: the venture would start with $1 billion from private investors, including Musk, Altman, and PayPal cofounder Peter Thiel. The star-studded investor list stirred up a media frenzy, as did the impressive list of initial employees: Greg Brockman, who had run technology for the payments company Stripe, would be chief technology officer; Ilya Sutskever, who had studied under AI pioneer Geoffrey Hinton, would be research director; and seven researchers, freshly graduated from top universities or plucked from other companies, would compose the core technical team. (Last February, Musk announced that he was parting ways with the company over disagreements about its direction. A month later, Altman stepped down as president of startup accelerator Y Combinator to become OpenAI’s CEO.) But more than anything, OpenAI’s nonprofit status made a statement. “It’ll be important to have a leading research institution which can prioritize a good outcome for all over its own self-interest,” the announcement said. “Researchers will be strongly encouraged to publish their work, whether as papers, blog posts, or code, and our patents (if any) will be shared with the world.” Though it never made the criticism explicit, the implication was clear: other labs, like DeepMind, could not serve humanity because they were constrained by commercial interests. While they were closed, OpenAI would be open. In a research landscape that had become increasingly privatized and focused on short-term financial gains, OpenAI was offering a new way to fund progress on the biggest problems. “It was a beacon of hope,” says Chip Huyen, a machine learning expert who has closely followed the lab’s journey. 
  At the intersection of 18th and Folsom Streets in San Francisco, OpenAI’s office looks like a mysterious warehouse. The historic building has drab gray paneling and tinted windows, with most of the shades pulled down. The letters “PIONEER BUILDING”—the remnants of its bygone owner, the Pioneer Truck Factory—wrap around the corner in faded red paint. Inside, the space is light and airy. The first floor has a few common spaces and two conference rooms. One, a healthy size for larger meetings, is called A Space Odyssey; the other, more of a glorified phone booth, is called Infinite Jest. This is the space I’m restricted to during my visit. I’m forbidden to visit the second and third floors, which house everyone’s desks, several robots, and pretty much everything interesting. When it’s time for their interviews, people come down to me. An employee trains a watchful eye on me in between meetings.   The Pioneer Building.wikimedia commons / tfinc    On the beautiful blue-sky day that I arrive to meet Brockman, he looks nervous and guarded. “We’ve never given someone so much access before,” he says with a tentative smile. He wears casual clothes and, like many at OpenAI, sports a shapeless haircut that seems to reflect an efficient, no-frills mentality. Brockman, 31, grew up on a hobby farm in North Dakota and had what he describes as a “focused, quiet childhood.” He milked cows, gathered eggs, and fell in love with math while studying on his own. In 2008, he entered Harvard intending to double-major in math and computer science, but he quickly grew restless to enter the real world. He dropped out a year later, entered MIT instead, and then dropped out again within a matter of months. The second time, his decision was final. Once he moved to San Francisco, he never looked back.   Brockman takes me to lunch to remove me from the office during an all-company meeting. In the café across the street, he speaks about OpenAI with intensity, sincerity, and wonder, often drawing parallels between its mission and landmark achievements of science history. It’s easy to appreciate his charisma as a leader. Recounting memorable passages from the books he’s read, he zeroes in on the Valley’s favorite narrative, America’s race to the moon. (“One story I really love is the story of the janitor,” he says, referencing a famous yet probably apocryphal tale. “Kennedy goes up to him and asks him, ‘What are you doing?’ and he says, ‘Oh, I’m helping put a man on the moon!’”) There’s also the transcontinental railroad (“It was actually the last megaproject done entirely by hand … a project of immense scale that was totally risky”) and Thomas Edison’s incandescent lightbulb (“A committee of distinguished experts said ‘It’s never gonna work,’ and one year later he shipped”).  Greg Brockman, co-founder and CTO.Christie Hemm Klok   Brockman is aware of the gamble OpenAI has taken on—and aware that it evokes cynicism and scrutiny. But with each reference, his message is clear: People can be skeptical all they want. It’s the price of daring greatly.  Those who joined OpenAI in the early days remember the energy, excitement, and sense of purpose. The team was small—formed through a tight web of connections—and management stayed loose and informal. Everyone believed in a flat structure where ideas and debate would be welcome from anyone. Musk played no small part in building a collective mythology. “The way he presented it to me was ‘Look, I get it. AGI might be far away, but what if it’s not?’” recalls Pieter Abbeel, a professor at UC Berkeley who worked there, along with several of his students, in the first two years. “‘What if it’s even just a 1% or 0.1% chance that it’s happening in the next five to 10 years? Shouldn’t we think about it very carefully?’ That resonated with me,” he says. 
 But the informality also led to some vagueness of direction. In May 2016, Altman and Brockman received a visit from Dario Amodei, then a Google researcher, who told them no one understood what they were doing. In an account published in the New Yorker, it wasn’t clear the team itself knew either. “Our goal right now … is to do the best thing there is to do,” Brockman said. “It’s a little vague.” Nonetheless, Amodei joined the team a few months later. His sister, Daniela Amodei, had previously worked with Brockman, and he already knew many of OpenAI’s members. After two years, at Brockman’s request, Daniela joined too. “Imagine—we started with nothing,” Brockman says. “We just had this ideal that we wanted AGI to go well.” 
   Throughout our lunch, Brockman recites the charter like scripture, an explanation for every aspect of the company’s existence.   By March of 2017, 15 months in, the leadership realized it was time for more focus. So Brockman and a few other core members began drafting an internal document to lay out a path to AGI. But the process quickly revealed a fatal flaw. As the team studied trends within the field, they realized staying a nonprofit was financially untenable. The computational resources that others in the field were using to achieve breakthrough results were doubling every 3.4 months. It became clear that “in order to stay relevant,” Brockman says, they would need enough capital to match or exceed this exponential ramp-up. That required a new organizational model that could rapidly amass money—while somehow also staying true to the mission. Unbeknownst to the public—and most employees—it was with this in mind that OpenAI released its charter in April of 2018. The document re-articulated the lab’s core values but subtly shifted the language to reflect the new reality. Alongside its commitment to “avoid enabling uses of AI or AGI that harm humanity or unduly concentrate power,” it also stressed the need for resources. “We anticipate needing to marshal substantial resources to fulfill our mission,” it said, “but will always diligently act to minimize conflicts of interest among our employees and stakeholders that could compromise broad benefit.”   “We spent a long time internally iterating with employees to get the whole company bought into a set of principles,” Brockman says. “Things that had to stay invariant even if we changed our structure.”  From left to right: Daniela Amodei, Jack Clark, Dario Amodei, Jeff Wu (technical staff member), Greg Brockman, Alec Radford (technical language team lead), Christine Payne (technical staff member), Ilya Sutskever, and Chris Berner (head of infrastructure).Christie Hemm Klok   That structure change happened in March 2019. OpenAI shed its purely nonprofit status by setting up a “capped profit” arm—a for-profit with a 100-fold limit on investors’ returns, albeit overseen by a board that's part of a nonprofit entity. Shortly after, it announced Microsoft’s billion-dollar investment (though it didn’t reveal that this was split between cash and credits to Azure, Microsoft’s cloud computing platform). Predictably, the move set off a wave of accusations that OpenAI was going back on its mission. In a post on Hacker News soon after the announcement, a user asked how a 100-fold limit would be limiting at all: “Early investors in Google have received a roughly 20x return on their capital,” they wrote. “Your bet is that you’ll have a corporate structure which returns orders of magnitude more than Google ... but you don’t want to ‘unduly concentrate power’? How will this work? What exactly is power, if not the concentration of resources?” The move also rattled many employees, who voiced similar concerns. To assuage internal unrest, the leadership wrote up an FAQ as part of a series of highly protected transition docs. “Can I trust OpenAI?” one question asked. “Yes,” began the answer, followed by a paragraph of explanation. 
   The charter is the backbone of OpenAI. It serves as the springboard for all the lab’s strategies and actions. Throughout our lunch, Brockman recites it like scripture, an explanation for every aspect of the company’s existence. (“By the way,” he clarifies halfway through one recitation, “I guess I know all these lines because I spent a lot of time really poring over them to get them exactly right. It’s not like I was reading this before the meeting.”)  How will you ensure that humans continue to live meaningful lives as you develop more advanced capabilities? “As we wrote, we think its impact should be to give everyone economic freedom, to let them find new opportunities that aren’t imaginable today.” How will you structure yourself to evenly distribute AGI? “I think a utility is the best analogy for the vision that we have. But again, it’s all subject to the charter.” How do you compete to reach AGI first without compromising safety? “I think there is absolutely this important balancing act, and our best shot at that is what’s in the charter.”   APRIL 9, 2018 5 MINUTE READOpenAI    For Brockman, rigid adherence to the document is what makes OpenAI’s structure work. Internal alignment is treated as paramount: all full-time employees are required to work out of the same office, with few exceptions. For the policy team, especially Jack Clark, the director, this means a life divided between San Francisco and Washington, DC. Clark doesn’t mind—in fact, he agrees with the mentality. It’s the in-between moments, like lunchtime with colleagues, he says, that help keep everyone on the same page. In many ways, this approach is clearly working: the company has an impressively uniform culture. The employees work long hours and talk incessantly about their jobs through meals and social hours; many go to the same parties and subscribe to the rational philosophy of “effective altruism.” They crack jokes using machine-learning terminology to describe their lives: “What is your life a function of?” “What are you optimizing for?” “Everything is basically a minmax function.” To be fair, other AI researchers also love doing this, but people familiar with OpenAI agree: more than others in the field, its employees treat AI research not as a job but as an identity. (In November, Brockman married his girlfriend of one year, Anna, in the office against a backdrop of flowers arranged in an OpenAI logo. Sutskever acted as the officiant; a robot hand was the ring bearer.) 
  But at some point in the middle of last year, the charter became more than just lunchtime conversation fodder. Soon after switching to a capped-profit, the leadership instituted a new pay structure based in part on each employee’s absorption of the mission. Alongside columns like “engineering expertise” and “research direction” in a spreadsheet tab titled “Unified Technical Ladder,” the last column outlines the culture-related expectations for every level. Level 3: “You understand and internalize the OpenAI charter.” Level 5: “You ensure all projects you and your team-mates work on are consistent with the charter.” Level 7: “You are responsible for upholding and improving the charter, and holding others in the organization accountable for doing the same.”  The first time most people ever heard of OpenAI was on February 14, 2019. That day, the lab announced impressive new research: a model that could generate convincing essays and articles at the push of a button. Feed it a sentence from The Lord of the Rings or the start of a (fake) news story about Miley Cyrus shoplifting, and it would spit out paragraph after paragraph of text in the same vein. But there was also a catch: the model, called GPT-2, was too dangerous to release, the researchers said. If such powerful technology fell into the wrong hands, it could easily be weaponized to produce disinformation at immense scale. The backlash among scientists was immediate. OpenAI was pulling a publicity stunt, some said. GPT-2 was not nearly advanced enough to be a threat. And if it was, why announce its existence and then preclude public scrutiny? “It seemed like OpenAI was trying to capitalize off of panic around AI,” says Britt Paris, an assistant professor at Rutgers University who studies AI-generated disinformation.  Jack Clark, policy director.Christie Hemm Klok   By May, OpenAI had revised its stance and announced plans for a “staged release.” Over the following months, it successively dribbled out more and more powerful versions of GPT-2. In the interim, it also engaged with several research organizations to scrutinize the algorithm’s potential for abuse and develop countermeasures. Finally, it released the full code in November, having found, it said, “no strong evidence of misuse so far.” Amid continued accusations of publicity-seeking, OpenAI insisted that GPT-2 hadn’t been a stunt. It was, rather, a carefully thought-out experiment, agreed on after a series of internal discussions and debates. The consensus was that even if it had been slight overkill this time, the action would set a precedent for handling more dangerous research. Besides, the charter had predicted that “safety and security concerns” would gradually oblige the lab to “reduce our traditional publishing in the future.” This was also the argument that the policy team carefully laid out in its six-month follow-up blog post, which they discussed as I sat in on a meeting. “I think that is definitely part of the success-story framing,” said Miles Brundage, a policy research scientist, highlighting something in a Google doc. “The lead of this section should be: We did an ambitious thing, now some people are replicating it, and here are some reasons why it was beneficial.” But OpenAI’s media campaign with GPT-2 also followed a well-established pattern that has made the broader AI community leery. Over the years, the lab’s big, splashy research announcements have been repeatedly accused of fueling the AI hype cycle. More than once, critics have also accused the lab of talking up its results to the point of mischaracterization. For these reasons, many in the field have tended to keep OpenAI at arm’s length.    Cover images of OpenAI's research releases hang on its office wall.Christie Hemm Klok   This hasn’t stopped the lab from continuing to pour resources into its public image. As well as research papers, it publishes its results in highly produced company blog posts for which it does everything in-house, from writing to multimedia production to design of the cover images for each release. At one point, it also began developing a documentary on one of its projects to rival a 90-minute movie about DeepMind’s AlphaGo. It eventually spun the effort out into an independent production, which Brockman and his wife, Anna, are now partially financing. (I also agreed to appear in the documentary to provide technical explanation and context to OpenAI’s achievement. I was not compensated for this.) And as the blowback has increased, so have internal discussions to address it. Employees have grown frustrated at the constant outside criticism, and the leadership worries it will undermine the lab’s influence and ability to hire the best talent. An internal document highlights this problem and an outreach strategy for tackling it: “In order to have government-level policy influence, we need to be viewed as the most trusted source on ML [machine learning] research and AGI,” says a line under the “Policy” section. “Widespread support and backing from the research community is not only necessary to gain such a reputation, but will amplify our message.” Another, under “Strategy,” reads, "Explicitly treat the ML community as a comms stakeholder. Change our tone and external messaging such that we only antagonize them when we intentionally choose to."  There was another reason GPT-2 had triggered such an acute backlash. People felt that OpenAI was once again walking back its earlier promises of openness and transparency. With news of the for-profit transition a month later, the withheld research made people even more suspicious. Could it be that the technology had been kept under wraps in preparation for licensing it in the future?   Ilya Sutskever, co-founder and chief scientist.Christie Hemm Klok    But little did people know this wasn’t the only time OpenAI had chosen to hide its research. In fact, it had kept another effort entirely secret. There are two prevailing technical theories about what it will take to reach AGI. In one, all the necessary techniques already exist; it’s just a matter of figuring out how to scale and assemble them. In the other, there needs to be an entirely new paradigm; deep learning, the current dominant technique in AI, won’t be enough. Most researchers fall somewhere between these extremes, but OpenAI has consistently sat almost exclusively on the scale-and-assemble end of the spectrum. Most of its breakthroughs have been the product of sinking dramatically greater computational resources into technical innovations developed in other labs. Brockman and Sutskever deny that this is their sole strategy, but the lab’s tightly guarded research suggests otherwise. A team called “Foresight” runs experiments to test how far they can push AI capabilities forward by training existing algorithms with increasingly large amounts of data and computing power. For the leadership, the results of these experiments have confirmed its instincts that the lab’s all-in, compute-driven strategy is the best approach. For roughly six months, these results were hidden from the public because OpenAI sees this knowledge as its primary competitive advantage. Employees and interns were explicitly instructed not to reveal them, and those who left signed nondisclosure agreements. It was only in January that the team, without the usual fanfare, quietly posted a paper on one of the primary open-source databases for AI research. People who experienced the intense secrecy around the effort didn’t know what to make of this change. Notably, another paper with similar results from different researchers had been posted a few months earlier.     Christie Hemm Klok   In the beginning, this level of secrecy was never the intention, but it has since become habitual. Over time, the leadership has moved away from its original belief that openness is the best way to build beneficial AGI. Now the importance of keeping quiet is impressed on those who work with or at the lab. This includes never speaking to reporters without the express permission of the communications team. After my initial visits to the office, as I began contacting different employees, I received an email from the head of communications reminding me that all interview requests had to go through her. When I declined, saying that this would undermine the validity of what people told me, she instructed employees to keep her informed of my outreach. A Slack message from Clark, a former journalist, later commended people for keeping a tight lid as a reporter was “sniffing around.” In a statement responding to this heightened secrecy, an OpenAI spokesperson referred back to a section of its charter. “We expect that safety and security concerns will reduce our traditional publishing in the future,” the section states, “while increasing the importance of sharing safety, policy, and standards research.” The spokesperson also added: “Additionally, each of our releases is run through an infohazard process to evaluate these trade-offs and we want to release our results slowly to understand potential risks and impacts before setting loose in the wild.” One of the biggest secrets is the project OpenAI is working on next. Sources described it to me as the culmination of its previous four years of research: an AI system trained on images, text, and other data using massive computational resources. A small team has been assigned to the initial effort, with an expectation that other teams, along with their work, will eventually fold in. On the day it was announced at an all-company meeting, interns weren’t allowed to attend. People familiar with the plan offer an explanation: the leadership thinks this is the most promising way to reach AGI.  The man driving OpenAI’s strategy is Dario Amodei, the ex-Googler who now serves as research director. When I meet him, he strikes me as a more anxious version of Brockman. He has a similar sincerity and sensitivity, but an air of unsettled nervous energy. He looks distant when he talks, his brows furrowed, a hand absentmindedly tugging his curls. Amodei divides the lab’s strategy into two parts. The first part, which dictates how it plans to reach advanced AI capabilities, he likens to an investor’s “portfolio of bets.” Different teams at OpenAI are playing out different bets. The language team, for example, has its money on a theory postulating that AI can develop a significant understanding of the world through mere language learning. The robotics team, in contrast, is advancing an opposing theory that intelligence requires a physical embodiment to develop. As in an investor’s portfolio, not every bet has an equal weight. But for the purposes of scientific rigor, all should be tested before being discarded. Amodei points to GPT-2, with its remarkably realistic auto-generated texts, as an instance of why it’s important to keep an open mind. “Pure language is a direction that the field and even some of us were somewhat skeptical of,” he says. “But now it's like, ‘Wow, this is really promising.’” Over time, as different bets rise above others, they will attract more intense efforts. Then they will cross-pollinate and combine. The goal is to have fewer and fewer teams that ultimately collapse into a single technical direction for AGI. This is the exact process that OpenAI’s latest top-secret project has supposedly already begun.  Dario Amodei, research director.Christie Hemm Klok   The second part of the strategy, Amodei explains, focuses on how to make such ever-advancing AI systems safe. This includes making sure that they reflect human values, can explain the logic behind their decisions, and can learn without harming people in the process. Teams dedicated to each of these safety goals seek to develop methods that can be applied across projects as they mature. Techniques developed by the explainability team, for example, may be used to expose the logic behind GPT-2’s sentence constructions or a robot’s movements.   Amodei admits this part of the strategy is somewhat haphazard, built less on established theories in the field and more on gut feeling. “At some point we’re going to build AGI, and by that time I want to feel good about these systems operating in the world,” he says. “Anything where I don’t currently feel good, I create and recruit a team to focus on that thing.” For all the publicity-chasing and secrecy, Amodei looks sincere when he says this. The possibility of failure seems to disturb him. “We’re in the awkward position of: we don’t know what AGI looks like,” he says. “We don’t know when it’s going to happen.” Then, with careful self-awareness, he adds: “The mind of any given person is limited. The best thing I’ve found is hiring other safety researchers who often have visions which are different than the natural thing I might’ve thought of. I want that kind of variation and diversity because that’s the only way that you catch everything.”  The thing is, OpenAI actually has little “variation and diversity”—a fact hammered home on my third day at the office. During the one lunch I was granted to mingle with employees, I sat down at the most visibly diverse table by a large margin. Less than a minute later, I realized that the people eating there were not, in fact, OpenAI employees. Neuralink, Musk’s startup working on computer-brain interfaces, shares the same building and dining room.   Daniela Amodei, head of people operations.Christie Hemm Klok    According to a lab spokesperson, out of the over 120 employees, 25% are female or nonbinary. There are also two women on the executive team and the leadership team is 30% women, she said, though she didn’t specify who was counted among these teams. (All four C-suite executives, including Brockman and Altman, are white men. Out of over 112 employees I identified on LinkedIn and other sources, the overwhelming number were white or Asian.) In fairness, this lack of diversity is typical in AI. Last year a report from the New York–based research institute AI Now found that women accounted for only 18% of authors at leading AI conferences, 20% of AI professorships, and 15% and 10% of research staff at Facebook and Google, respectively. “There is definitely still a lot of work to be done across academia and industry,” OpenAI’s spokesperson said. “Diversity and inclusion is something we take seriously and are continually working to improve by working with initiatives like WiML, Girl Geek, and our Scholars program.” Indeed, OpenAI has tried to broaden its talent pool. It began its remote Scholars program for underrepresented minorities in 2018. But only two of the first eight scholars became full-time employees, even though they reported positive experiences. The most common reason for declining to stay: the requirement to live in San Francisco. For Nadja Rhodes, a former scholar who is now the lead machine-learning engineer at a New York–based company, the city just had too little diversity. But if diversity is a problem for the AI industry in general, it’s something more existential for a company whose mission is to spread the technology evenly to everyone. The fact is that it lacks representation from the groups most at risk of being left out.   Nor is it at all clear just how OpenAI plans to “distribute the benefits” of AGI to “all of humanity,” as Brockman frequently says in citing its mission. The leadership speaks of this in vague terms and has done little to flesh out the specifics. (In January, the Future of Humanity Institute at Oxford University released a report in collaboration with the lab proposing to distribute benefits by distributing a percentage of profits. But the authors cited “significant unresolved issues regarding … the way in which it would be implemented.”) “This is my biggest problem with OpenAI,” says a former employee, who spoke on condition of anonymity.   Christie Hemm Klok   “They are using sophisticated technical practices to try to answer social problems with AI,” echoes Britt Paris of Rutgers. “It seems like they don’t really have the capabilities to actually understand the social. They just understand that that’s a sort of a lucrative place to be positioning themselves right now.” Brockman agrees that both technical and social expertise will ultimately be necessary for OpenAI to achieve its mission. But he disagrees that the social issues need to be solved from the very beginning. “How exactly do you bake ethics in, or these other perspectives in? And when do you bring them in, and how? One strategy you could pursue is to, from the very beginning, try to bake in everything you might possibly need,” he says. “I don’t think that that strategy is likely to succeed.” The first thing to figure out, he says, is what AGI will even look like. Only then will it be time to “make sure that we are understanding the ramifications.”  Last summer, in the weeks after the switch to a capped-profit model and the $1 billion injection from Microsoft, the leadership assured employees that these updates wouldn’t functionally change OpenAI’s approach to research. Microsoft was well aligned with the lab’s values, and any commercialization efforts would be far away; the pursuit of fundamental questions would still remain at the core of the work. For a while, these assurances seemed to hold true, and projects continued as they were. Many employees didn’t even know what promises, if any, had been made to Microsoft. But in recent months, the pressure of commercialization has intensified, and the need to produce money-making research no longer feels like something in the distant future. In sharing his 2020 vision for the lab privately with employees, Altman’s message is clear: OpenAI needs to make money in order to do research—not the other way around. This is a hard but necessary trade-off, the leadership has said—one it had to make for lack of wealthy philanthropic donors. By contrast, Seattle-based AI2, a nonprofit that ambitiously advances fundamental AI research, receives its funds from a self-sustaining (at least for the foreseeable future) pool of money left behind by the late Paul Allen, a billionaire best known for cofounding Microsoft.   But the truth is that OpenAI faces this trade-off not only because it’s not rich, but also because it made the strategic choice to try to reach AGI before anyone else. That pressure forces it to make decisions that seem to land farther and farther away from its original intention. It leans into hype in its rush to attract funding and talent, guards its research in the hopes of keeping the upper hand, and chases a computationally heavy strategy—not because it’s seen as the only way to AGI, but because it seems like the fastest. Yet OpenAI is still a bastion of talent and cutting-edge research, filled with people who are sincerely striving to work for the benefit of humanity. In other words, it still has the most important elements, and there’s still time for it to change. Near the end of my interview with Rhodes, the former remote scholar, I ask her the one thing about OpenAI that I shouldn’t omit from this profile. “I guess in my opinion, there’s problems,” she begins hesitantly. “Some of them come from maybe the environment it faces; some of them come from the type of people that it tends to attract and other people that it leaves out.” “But to me, it feels like they are doing something a little bit right,” she says. “I got a sense that the folks there are earnestly trying.”   Update: We made some changes to this story after OpenAI asked us to clarify that when Greg Brockman said he didn’t think it was possible to “bake ethics in… from the very beginning” when developing AI, he intended it to mean that ethical questions couldn’t be solved from the beginning, not that they couldn’t be addressed from the beginning. Also, that after dropping out of Harvard he transferred straight to MIT rather than waiting a year. Also, that he was raised not “on a farm,” but "on a hobby farm." Brockman considers this distinction important.   In addition, we have clarified that while OpenAI did indeed "shed its nonprofit status," a board that is part of a nonprofit entity still oversees it, and that OpenAI publishes its research in the form of company blog posts as well as, not in lieu of, research papers. We’ve also corrected the date of publication of a paper by outside researchers and the affiliation of Peter Eckersley (former, not current, research director of Partnership on AI, which he recently left).
The messy, secretive reality behind OpenAI’s bid to save the world
https://www.technologyreview.com/2020/02/17/844721/ai-openai-moonshot-elon-musk-sam-altman-greg-brockman-messy-secretive-reality/
https://www.linkedin.com/in/fryzner/
Filip Ryzner - Klarity | LinkedIn
2024-01-01
Filip Ryzner
<div><div>
<section>
<section>
<figure>
</figure>
</section>
<section>
<h2>
 About
 </h2>
<div><p>
 Machine Learning Engineer focusing on NLP (LLM) at Klarity Intelligence (YC…
 
 
 
 
 
 </p>
</div>
</section>
<div>
<h2>
 Activity
 </h2>
</div>
<section>
<h2>
 
 Experience &amp; Education
 
 </h2>
<div>
<ul>
<li>
<div>
<h3>
 
 Klarity
 
 </h3>
<h4>
</h4>
</div>
</li>
</ul>
<div>
<h2>View Filip’s full experience</h2>
<h3>See their title, tenure and more.</h3>
</div>
</div>
</section>
<section>
<h2>
 Licenses &amp; Certifications
 </h2>
<div>
<ul>
<li>
<a href="https://uk.linkedin.com/company/ielts-official?trk=public_profile_profile-section-card_image-click">
</a>
<div>
<h3>
 
 IELTS Academic, Band 8.5 (C2)
 
 </h3>
<h4>
<a href="https://uk.linkedin.com/company/ielts-official?trk=public_profile_profile-section-card_subtitle-click">
 
 IELTS Official
 
 </a>
</h4>
<div>
<p>
</p>
<p>
 Credential ID 19CZ000730RYZF001A
 </p>
</div>
</div>
</li>
</ul>
</div>
</section>
<section>
<h2>
 Volunteer Experience
 </h2>
<div>
<ul>
<li>
<a href="https://cz.linkedin.com/company/futureport?trk=public_profile_volunteering-position_profile-section-card_image-click">
</a>
<div>
<h3>
 
 Webmaster and Automatization Guy
 
 </h3>
<h4>
<a href="https://cz.linkedin.com/company/futureport?trk=public_profile_volunteering-position_profile-section-card_subtitle-click">
 
 Future Port
 
 </a>
</h4>
<div>
<p>
</p>
<p>
 Education
 </p>
<div><p>
 Helping to prepare and run one of the most exciting conferences about technology for High School students in Europe. </p><p>Responsible for setting up the conference website, all registration forms, automating data flows between utilized platforms and monitoring website performance.</p><p>Tools Utilized: Squarespace, Mailchimp, ClickUp, Make (Integromat), Smartlook</p><p>More at - https://futureportyouth.com/
</p></div>
</div>
</div>
</li>
<li>
<a href="https://cz.linkedin.com/company/people-in-need?trk=public_profile_volunteering-position_profile-section-card_image-click">
</a>
<div>
<h3>
 
 Tutor
 
 </h3>
<h4>
<a href="https://cz.linkedin.com/company/people-in-need?trk=public_profile_volunteering-position_profile-section-card_subtitle-click">
 
 People in Need
 
 </a>
</h4>
<div>
<p>
</p>
<p>
 Social Services
 </p>
<p>
 Tutoring a young student from a disadvantaged background once a week. Helping with the management of school workload and development of better learning techniques
 </p>
</div>
</div>
</li>
</ul>
</div>
</section>
<section>
<h2>
 Courses
 </h2>
<div>
<ul>
<li>
<div>
<h3>
 
 Advances in Computer Vision
 
 </h3>
<h4>
 
 6.869
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 Analytics of Finance
 
 </h3>
<h4>
 
 15.450
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 Bayesian Modeling and Inference
 
 </h3>
<h4>
 
 6.435
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 EC201 – Microeconomics Principles – the top 5%
 
 </h3>
<h4>
 
 78/100, distinction
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 FM300 - Corporate Finance, Investment and Financial Markets - the top 5% in 2016/17
 
 </h3>
<h4>
 
 86/100, distinction
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 MA207 – Further Quantitative Methods
 
 </h3>
<h4>
 
 85/100, distinction
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 Machine Learning
 
 </h3>
<h4>
 
 6.867
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 Market Research: The Integrated approach - 80/100, distinction, the best result in 2016/17
 
 </h3>
<h4>
 
 ST327
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 Network Science and Models
 
 </h3>
<h4>
 
 6.268
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 Optimization Methods
 
 </h3>
<h4>
 
 6.255
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 ST211 - Applied Regression with R - the best result in 2016/17
 
 </h3>
<h4>
 
 81/100, distinction
 
 </h4>
</div>
</li>
</ul>
</div>
</section>
<section>
<h2>
 Honors &amp; Awards
 </h2>
<div>
<ul>
<li>
<div>
<h3>
 
 Dean's Award 2020
 
 </h3>
<h4>-</h4>
<div>
<p></p><p>
 - Awarded for academic excellence throughout undergraduate studies
 </p>
</div>
</div>
</li>
<li>
<div>
<h3>
 
 KPMG Internation Case Competition (KICC) 2018 - National Round winner
 
 </h3>
<h4>
 
 KPMG
 
 </h4>
<div>
<p></p><p>
 - Winner of the National Round, Participant in the International Round held in Malaysia<br/>- Led a team of 4 university student
 </p>
</div>
</div>
</li>
</ul>
</div>
</section>
<section>
<h2>
 Languages
 </h2>
<div>
<ul>
<li>
<div>
<h3>
 
 English
 
 </h3>
<h4>
 
 Full professional proficiency
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 French
 
 </h3>
<h4>
 
 Limited working proficiency
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 Czech
 
 </h3>
<h4>
 
 Native or bilingual proficiency
 
 </h4>
</div>
</li>
<li>
<div>
<h3>
 
 Slovak
 
 </h3>
<h4>
 
 Native or bilingual proficiency
 
 </h4>
</div>
</li>
</ul>
</div>
</section>
<section>
<h2>
 Recommendations received
 </h2>
</section>
<section>
<p>
<h2>
 More activity by Filip
 </h2>
</p>
<div>
<ul>
<li>
<div>
<p><a href="https://www.linkedin.com/posts/fryzner_%C4%8Desko-je-pro-protivn%C3%ADky-atraktivn%C3%AD-ter%C4%8D-activity-7189740626849017857-goOV?trk=public_profile">
</a></p>
<p>
<h3>
 
 Extremely proud of Berta Jarosova! You should go read about her recent accomplishments and role at seznamzpravy.cz
https://lnkd.in/eUPT6FK6
 
 </h3>
<h4>
 
 Shared by <a href="https://www.linkedin.com/in/fryzner?trk=public_profile_actor-name">Filip Ryzner</a>
</h4>
</p>
</div>
</li>
<li>
</li>
<li>
</li>
<li>
</li>
<li>
</li>
<li>
</li>
<li>
</li>
<li>
</li>
<li>
</li>
<li>
<div>
<p><a href="https://www.linkedin.com/posts/fryzner_if-you-are-tinkering-with-agents-in-sf-then-activity-7186750703871152128-6Nyi?trk=public_profile">
</a></p>
<p>
<h3>
 
 If you are tinkering with agents in SF, then you need to be here on April 27 😉
 
 </h3>
<h4>
 
 Shared by <a href="https://www.linkedin.com/in/fryzner?trk=public_profile_actor-name">Filip Ryzner</a>
</h4>
</p>
</div>
</li>
<li>
</li>
</ul>
</div>
</section>
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<section>
<section>
<h2>
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</section>
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 * No two invoices are created the same and your AI should be able to learn and adapt instead of attempting to standardize invoices from your vendors…

 No two invoices are created the same and your AI should be able to learn and adapt instead of attempting to standardize invoices from your vendors…

 Liked by Filip Ryzner

 * Adam Zvada will be sharing his experience as a startup builder on 13/5 … if you are thinking about building a startup and want to learn from one of…

 Adam Zvada will be sharing his experience as a startup builder on 13/5 … if you are thinking about building a startup and want to learn from one of…

 Shared by Filip Ryzner

 * To understand recursion, you must first understand recursion. #programming #softwareengineering #softwaredevelopment

 To understand recursion, you must first understand recursion. #programming #softwareengineering #softwaredevelopment

 Liked by Filip Ryzner

 Join now to see all activity

 Experience & Education

 * Klarity

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 * *** ********** ******* ** *********

 ******** *********

 * ********* **

 ******* ******** **********

 * ************* ********* ** **********

 ******'* ****** ******* *********** *.*/*.*

 2021 - 2023

 * ******* ** ********** ***********, ***** ********* ********** ** ******

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 2020 - 2021

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 Licenses & Certifications

 * IELTS Academic, Band 8.5 (C2)

 IELTS Official

 Issued Oct 2019 Expires Oct 2021
 Credential ID 19CZ000730RYZF001A

 Volunteer Experience

 * Webmaster and Automatization Guy

 Future Port

 Jun 2022 - Nov 2022 6 months

 Education

 Helping to prepare and run one of the most exciting conferences about technology for High School students in Europe.

 Responsible for setting up the conference website, all registration forms, automating data flows between utilized platforms and monitoring website performance.

 Tools Utilized: Squarespace, Mailchimp, ClickUp, Make (Integromat), Smartlook

 More at - https://futureportyouth.com/

 * Tutor

 People in Need

 Sep 2019 - Aug 2020 1 year

 Social Services

 Tutoring a young student from a disadvantaged background once a week. Helping with the management of school workload and development of better learning techniques

 Courses

 * Advances in Computer Vision

 6.869

 * Analytics of Finance

 15.450

 * Bayesian Modeling and Inference

 6.435

 * EC201 – Microeconomics Principles – the top 5%

 78/100, distinction

 * FM300 - Corporate Finance, Investment and Financial Markets - the top 5% in 2016/17

 86/100, distinction

 * MA207 – Further Quantitative Methods

 85/100, distinction

 * Machine Learning

 6.867

 * Market Research: The Integrated approach - 80/100, distinction, the best result in 2016/17

 ST327

 * Network Science and Models

 6.268

 * Optimization Methods

 6.255

 * ST211 - Applied Regression with R - the best result in 2016/17

 81/100, distinction

 Honors & Awards

 * Dean's Award 2020

 -

 Jun 2020

 - Awarded for academic excellence throughout undergraduate studies

 * KPMG Internation Case Competition (KICC) 2018 - National Round winner

 KPMG

 Feb 2018

 - Winner of the National Round, Participant in the International Round held in Malaysia
 - Led a team of 4 university student

 Languages

 * English

 Full professional proficiency

 * French

 Limited working proficiency

 * Czech

 Native or bilingual proficiency

 * Slovak

 Native or bilingual proficiency

 Recommendations received

 * Petr Zmatlo

 “Filip is enthusiastic person, he has lot of energy which he puts in anything he does. There is no task too big for Filip and he can go into very detail to make sure he delivers what is expected and needed. His skills cover wide range of knowledge, he is multilingual, hard working person with good skills working in international environment. Filip was part of my team only four couple weeks during his summer work, but I would be happy to employ him on full time immediately.”

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 More activity by Filip

 * Extremely proud of Berta Jarosova! You should go read about her recent accomplishments and role at seznamzpravy.cz https://lnkd.in/eUPT6FK6

 Extremely proud of Berta Jarosova! You should go read about her recent accomplishments and role at seznamzpravy.cz https://lnkd.in/eUPT6FK6

 Shared by Filip Ryzner

 * 'We support Ukraine, we are a strong partner to Israel and we cooperate with Taiwan. That can make us an attractive target for our adversaries in…

 'We support Ukraine, we are a strong partner to Israel and we cooperate with Taiwan. That can make us an attractive target for our adversaries in…

 Liked by Filip Ryzner

 * We'll hit 100k sandboxes in April today Come join us, we're hiring! More info in the comments

 We'll hit 100k sandboxes in April today Come join us, we're hiring! More info in the comments

 Liked by Filip Ryzner

 * Attending #xponential2024 in San Diego this week? Make sure to stop by the Czech national booth #3343 to meet UpVision UDX research and Workswell to…

 Attending #xponential2024 in San Diego this week? Make sure to stop by the Czech national booth #3343 to meet UpVision UDX research and Workswell to…

 Liked by Filip Ryzner

 * I've taken a break from my ETH Zürich CS Masters this semester to focus on building. I’ve been building almost every day from 9am to midnight for…

 I've taken a break from my ETH Zürich CS Masters this semester to focus on building. I’ve been building almost every day from 9am to midnight for…

 Liked by Filip Ryzner

 * This paper (https://lnkd.in/dNjzjTgE) introduces pfl-research, a fast, modular, and easy-to-use Python framework for simulating FL. It supports…

 This paper (https://lnkd.in/dNjzjTgE) introduces pfl-research, a fast, modular, and easy-to-use Python framework for simulating FL. It supports…

 Liked by Filip Ryzner

 * Llama 3 hosted on Groq with E2B code interpreter is super fast 🏎️

 Llama 3 hosted on Groq with E2B code interpreter is super fast 🏎️

 Liked by Filip Ryzner

 * A picture is worth a thousand words.

 A picture is worth a thousand words.

 Liked by Filip Ryzner

 * If you know of lots of insurance documents that you want to do KYC (Know Your Customer) confirmation on, or make sure info matches across documents…

 If you know of lots of insurance documents that you want to do KYC (Know Your Customer) confirmation on, or make sure info matches across documents…

 Liked by Filip Ryzner

 * If you are tinkering with agents in SF, then you need to be here on April 27 😉

 If you are tinkering with agents in SF, then you need to be here on April 27 😉

 Shared by Filip Ryzner

 * Pražský nástroj pro scraping webu Apify po světě využívá přes 200 tisíc lidí, jeho obrat se pomalu blíží 200 milionům a zároveň je v zisku. Pro…

 Pražský nástroj pro scraping webu Apify po světě využívá přes 200 tisíc lidí, jeho obrat se pomalu blíží 200 milionům a zároveň je v zisku. Pro…

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About
 
 
 Machine Learning Engineer focusing on NLP (LLM) at Klarity Intelligence (YC…
 
 
 
 
 
 
 Activity
 
 
 Experience &amp; Education
 
 
 
 Klarity
 
 
View Filip’s full experience
See their title, tenure and more.
 Licenses &amp; Certifications
 
 
 IELTS Academic, Band 8.5 (C2)
 
 
 
 IELTS Official
 
 
 Credential ID 19CZ000730RYZF001A
 
 Volunteer Experience
 
 
 Webmaster and Automatization Guy
 
 
 
 Future Port
 
 
 Education
 
 
 Helping to prepare and run one of the most exciting conferences about technology for High School students in Europe.  Responsible for setting up the conference website, all registration forms, automating data flows between utilized platforms and monitoring website performance. Tools Utilized: Squarespace, Mailchimp, ClickUp, Make (Integromat), Smartlook More at - https://futureportyouth.com/
 
 
 Tutor
 
 
 
 People in Need
 
 
 Social Services
 
 Tutoring a young student from a disadvantaged background once a week. Helping with the management of school workload and development of better learning techniques
 
 Courses
 
 
 Advances in Computer Vision
 
 
 
 6.869
 
 
 
 Analytics of Finance
 
 
 
 15.450
 
 
 
 Bayesian Modeling and Inference
 
 
 
 6.435
 
 
 
 EC201 – Microeconomics Principles – the top 5%
 
 
 
 78/100, distinction
 
 
 
 FM300 - Corporate Finance, Investment and Financial Markets - the top 5% in 2016/17
 
 
 
 86/100, distinction
 
 
 
 MA207 – Further Quantitative Methods
 
 
 
 85/100, distinction
 
 
 
 Machine Learning
 
 
 
 6.867
 
 
 
 Market Research: The Integrated approach - 80/100, distinction, the best result in 2016/17
 
 
 
 ST327
 
 
 
 Network Science and Models
 
 
 
 6.268
 
 
 
 Optimization Methods
 
 
 
 6.255
 
 
 
 ST211 - Applied Regression with R - the best result in 2016/17
 
 
 
 81/100, distinction
 
 
 Honors &amp; Awards
 
 
 Dean's Award 2020
 
 
-
  
 - Awarded for academic excellence throughout undergraduate studies
 
 
 KPMG Internation Case Competition (KICC) 2018 - National Round winner
 
 
 
 KPMG
 
 
  
 - Winner of the National Round, Participant in the International Round held in Malaysia- Led a team of 4 university student
 
 Languages
 
 
 English
 
 
 
 Full professional proficiency
 
 
 
 French
 
 
 
 Limited working proficiency
 
 
 
 Czech
 
 
 
 Native or bilingual proficiency
 
 
 
 Slovak
 
 
 
 Native or bilingual proficiency
 
 
 Recommendations received
 
 More activity by Filip
 
 
 
 
 Extremely proud of Berta Jarosova! You should go read about her recent accomplishments and role at seznamzpravy.cz
https://lnkd.in/eUPT6FK6
 
 
 
 Shared by Filip Ryzner
 
 
 
 If you are tinkering with agents in SF, then you need to be here on April 27 😉
 
 
 
 Shared by Filip Ryzner
 People also viewed
 
 Explore collaborative articles
 
 We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
 
 Explore More
 
 Add new skills with these courses
Filip Ryzner - Klarity | LinkedIn
https://www.linkedin.com/in/fryzner/
https://www.linkedin.com/in/davidgarry1/
David Garry - Google | LinkedIn
2024-01-01
David Garry
<div><div>
<a href="https://www.linkedin.com/in/jacobelledge?trk=public_profile_recommendations">
<div>
<h3>
 
 Jacob Elledge
 
 </h3>
<p>
 “David did a great job as a full-stack engineer this past summer. He implemented a seamless RESTful API that transformed raw JSON files from out database into an intuitive and responsive platform. It utilizes Bootstrap, jQuery, and AJAX, with filtering and sorting options that even work in Internet Explorer. This new website will allow multiple users, on multiple platforms, to track work progress and improve cross-functional collaboration across Raytheon's many business units. He also developed C++ and JavaScript tools for custom file processing and conversion for file types including JSON, XML, WKT, and SVGs. The last thing he worked on was a camera movement functionality for our 3D simulation environment that greatly improved visual transitions inside of the environment.”
 </p>
</div>
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 “David did a great job as a full-stack engineer this past summer. He implemented a seamless RESTful API that transformed raw JSON files from out database into an intuitive and responsive platform. It utilizes Bootstrap, jQuery, and AJAX, with filtering and sorting options that even work in Internet Explorer. This new website will allow multiple users, on multiple platforms, to track work progress and improve cross-functional collaboration across Raytheon's many business units. He also developed C++ and JavaScript tools for custom file processing and conversion for file types including JSON, XML, WKT, and SVGs. The last thing he worked on was a camera movement functionality for our 3D simulation environment that greatly improved visual transitions inside of the environment.”
David Garry - Google | LinkedIn
https://www.linkedin.com/in/davidgarry1/
https://www.linkedin.com/in/dumitruerhan/
Dumitru Erhan - Google DeepMind | LinkedIn
2024-01-01
Dumitru Erhan
<div><div>
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<section>
<h2>
 About
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<h2>
 
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 * Achievement unlocked! I just bench pressed my body weight (165lb) for the first time in my life! At 43, I'm stronger than I've ever been. So, why am…

 Achievement unlocked! I just bench pressed my body weight (165lb) for the first time in my life! At 43, I'm stronger than I've ever been. So, why am…

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 * Microsoft Research Montreal is recruiting interns, postdocs and permanent researchers. The intern posting is up (https://lnkd.in/ee2SnGmp), the…

 Microsoft Research Montreal is recruiting interns, postdocs and permanent researchers. The intern posting is up (https://lnkd.in/ee2SnGmp), the…

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 * We're hiring ML Research Scientists in the Apple Machine Learning Research Team. If you have a strong background in Machine Learning, have a strong…

 We're hiring ML Research Scientists in the Apple Machine Learning Research Team. If you have a strong background in Machine Learning, have a strong…

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 * One more fitness goal nailed! Crushed it on 10 pull ups for the first time. I've since repeated this easily the past few days. I'm down to 12.8% body…

 One more fitness goal nailed! Crushed it on 10 pull ups for the first time. I've since repeated this easily the past few days. I'm down to 12.8% body…

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 * Check out our work on multi-resolution (time and space) diffusion models called Matryoshka Diffusion Models (https://lnkd.in/gPajw6b2). MDM can…

 Check out our work on multi-resolution (time and space) diffusion models called Matryoshka Diffusion Models (https://lnkd.in/gPajw6b2). MDM can…

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 * Nice recent work coming from our team at Apple MLR.

 Nice recent work coming from our team at Apple MLR.

 Liked by Dumitru Erhan

 * At 380 lbs during the Pandemic, I was winded with my pulse racing with barely a 1-mile brisk walk. The thought of being active - running, swimming…

 At 380 lbs during the Pandemic, I was winded with my pulse racing with barely a 1-mile brisk walk. The thought of being active - running, swimming…

 Liked by Dumitru Erhan

 * After many years of hard work, we are proud to kick off Soft Launch in Canada and officially begin our road to worldwide release! Thank you to all…

 After many years of hard work, we are proud to kick off Soft Launch in Canada and officially begin our road to worldwide release! Thank you to all…

 Liked by Dumitru Erhan

 * It was in 2021 at 380 lbs fearing pre-Stroke symptoms being out of breath, poor vision, losing balance on my feet, and with dangerously high BP at…

 It was in 2021 at 380 lbs fearing pre-Stroke symptoms being out of breath, poor vision, losing balance on my feet, and with dangerously high BP at…

 Liked by Dumitru Erhan

 * This week’s #TogetherTuesday is focused on the Mila AI Techaide conference — https://lnkd.in/eDC7TKnX happening this Friday, April 14 by Mila -…

 This week’s #TogetherTuesday is focused on the Mila AI Techaide conference — https://lnkd.in/eDC7TKnX happening this Friday, April 14 by Mila -…

 Liked by Dumitru Erhan

 * Hi all, time for a new chapter! I’m joining Helsing as VP of Artificial Intelligence. During my nine years at FAIR, it has been fantastic to work…

 Hi all, time for a new chapter! I’m joining Helsing as VP of Artificial Intelligence. During my nine years at FAIR, it has been fantastic to work…

 Liked by Dumitru Erhan

 * I’m the Google SRE who made sure to hand off the pager in the minutes after I got laid off on 2023-01-20. If you’ve worked at Google (or maybe even…

 I’m the Google SRE who made sure to hand off the pager in the minutes after I got laid off on 2023-01-20. If you’ve worked at Google (or maybe even…

 Liked by Dumitru Erhan

 * For the love of amazing women... Today, I am celebrating a fellow Moldovan who is shaking up the pastry world and serving as an inspiration for many…

 For the love of amazing women... Today, I am celebrating a fellow Moldovan who is shaking up the pastry world and serving as an inspiration for many…

 Liked by Dumitru Erhan

 * Appreciate the recognition, although I have to admit I initially thought SDV meant 'self driving vehicle'. :) For over 4 years at Waymo Research, we…

 Appreciate the recognition, although I have to admit I initially thought SDV meant 'self driving vehicle'. :) For over 4 years at Waymo Research, we…

 Liked by Dumitru Erhan

 * The ML team at Microsoft Research Montreal is hiring interns for summer 2023! Looking for candidates interested in robust, adaptive, modular ML /…

 The ML team at Microsoft Research Montreal is hiring interns for summer 2023! Looking for candidates interested in robust, adaptive, modular ML /…

 Liked by Dumitru Erhan

 * As of today, I have officially been a Distinguished Member of the Typing Pool and Chief Cost Center at Google for 15 years!

 As of today, I have officially been a Distinguished Member of the Typing Pool and Chief Cost Center at Google for 15 years!

 Liked by Dumitru Erhan

 * We are proud to announce a new partnership with Boehringer Ingelheim to research and develop SZN-413 for treatment of retinal diseases. Learn more in…

 We are proud to announce a new partnership with Boehringer Ingelheim to research and develop SZN-413 for treatment of retinal diseases. Learn more in…

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About
 
 
 I'm part of the Google DeepMind, trying to build intelligent models of the world using…
 
 
 
 
 
 
 Activity
 
 
 Experience &amp; Education
 
 
 
 Google DeepMind
 
 
View Dumitru’s full experience
See their title, tenure and more.
 Languages
 
 
 Romanian
 
 
 
 Native or bilingual proficiency
 
 
 
 French
 
 
 
 Full professional proficiency
 
 
 
 Russian
 
 
 
 Professional working proficiency
 
 
 
 German
 
 
 
 Limited working proficiency
 
 
 
 English
 
 
 
 Native or bilingual proficiency
 
 
 More activity by Dumitru
 
 People also viewed
 
 Connect
 
 
 Yasaman Bahri
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Isabelle Guyon
 
 
 San Francisco, CA
 
 Connect
 
 
 Christian Szegedy
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Abhishek Kumar
 
 
 Mountain View, CA
 
 Connect
 
 Connect
 
 Connect
 
 
 Razvan Pascanu
 
 
 
 Research Scientist at Google DeepMind, EEML
 
 
 United Kingdom
 
 Connect
 
 
 Yoshua Bengio
 
 
 Montreal, QC
 
 Connect
 
 
 Hanie Sedghi
 
 
 Mountain View, CA
 
 Connect
 
 Connect
 
 
 Xingyou Song
 
 
 United States
 
 Connect
 
 
 James Bergstra
 
 
 Toronto, ON
 
 Connect
 
 
 Jaehoon Lee
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Ross Girshick
 
 
 Seattle, WA
 
 Connect
 
 
 Amr Ahmed
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Douglas Eck
 
 
 Palo Alto, CA
 
 Connect
 
 
 Lu Jiang
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Liangliang Cao
 
 
 Sunnyvale, CA
 
 Connect
 
 
 Maryam Najafian
 
 
 Cambridge, MA
 
 Connect
 
 Explore collaborative articles
 
 We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
 
 Explore More
 
 Others named Dumitru Erhan
 1 other named Dumitru Erhan is on LinkedIn
 
 
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Dumitru Erhan - Google DeepMind | LinkedIn
https://www.linkedin.com/in/dumitruerhan/