I'm a smart undergraduate studying computer science. here is a top industry AI lab to do research at:
https://openai.com/
OpenAI
2022-03-15
None
<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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 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.

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 Research

 We research generative models and how to align them with human values.

 Learn about our research

 * GPT-4

 Mar 14, 2023 March 14, 2023

 * Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk

 Jan 11, 2023 January 11, 2023

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 Dec 16, 2022 December 16, 2022

 * Introducing Whisper

 Sep 21, 2022 September 21, 2022

 Products

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 Explore our products

 * New and improved embedding model

 Dec 15, 2022 December 15, 2022

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 Sep 28, 2022 September 28, 2022

 * New and improved content moderation tooling

 Aug 10, 2022 August 10, 2022

 * New GPT-3 capabilities: Edit & insert

 Mar 15, 2022 March 15, 2022

 Careers at OpenAI

 Developing safe and beneficial AI requires people from a wide range of disciplines and backgrounds.

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 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 Weng Applied AI at OpenAI

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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
https://openai.com/
https://research.google/teams/brain/
Brain Team â Google Research
2021-11-01
Jacob Devlin; Ming-Wei Chang; Kenton Lee; Kristina N Toutanova
<div><div>
<div>
 
 <p>Make machines intelligent. Improve peopleâs lives.</p>
 </div><div>
 <p>
 </p><h2>
 About the team
 </h2>
 <p></p>
 <div>
 <h4>History of Research Breakthroughs</h4>
 <p>Google Brain started in 2011 at X as an exploratory lab and was founded by Jeff Dean, Greg Corrado and Andrew Ng, along with other engineers and is now part of Google Research. Since then, we continually rethink our approach to machine learning and are proud of our breakthroughs, which include:</p>
<ul>
<li>AI infrastructure (developing TensorFlow)</li>
<li>Sequence-to-sequence learning, leading to Transformers and BERT</li>
<li>AutoML, pioneering automated machine learning for production use</li>
</ul>
<p>Our research breakthroughs enable Googleâs mission to organize the world's information and make it universally accessible and useful.</p>
 <h4>Google Impact</h4>
 <p>As part of Google and Alphabet, the Brain team has access to resources and unparalleled collaboration opportunities that have a positive impact on products and society. Our broad and fundamental research goals allow us to collaborate with and contribute to many teams across Alphabet, which deploy our cutting-edge technology into products used by billions of users, positively impacting society and the research community.</p>
 <h4>Open and Bottom-Up Culture</h4>
 <p>We believe that openly disseminating research is critical to a healthy exchange of ideas, leading to rapid progress in the field.</p>
 <p>As such, we regularly publish our research at top academic conferences and journals, and release our tools, such as TensorFlow and Jax, as open-source projects.</p>
 <p>Team members are encouraged to set their own research goals, allowing the Brain team to maintain a portfolio of projects across varied time horizons, research areas and levels of risk.</p>
 </div>
 </div><div>
 <p>
 </p><h2>
 Research areas
 </h2>
 <p></p>
 </div><section>
 <div>
 <p>
 </p><h2>
 Team focus summaries
 </h2>
 <p></p>
 </div>
 <div>
 <div>
 <a>
 <h4>Machine learning</h4>
 
 </a>
 <div>
 
 <p>Optimization, reinforcement learning, generative modeling, uncertainty and robustness, theory, etc.</p>
 </div>
</div>
 <div>
 <a>
 <h4>Systems</h4>
 
 </a>
 <div>
 
 <p>Programming languages, privacy, security, hardware, etc.</p>
 </div>
</div>
 <div>
 <a>
 <h4>Computer Vision</h4>
 
 </a>
 <div>
 
 <p>Image classification, object detection, image generation, video compression, etc.</p>
 </div>
</div>
 <div>
 <a>
 <h4>Natural Language</h4>
 
 </a>
 <div>
 
 <p>Machine translation, document summarization, speech recognition, dialog systems, etc.</p>
 </div>
</div>
 
 
 <div>
 <a>
 <h4>Health</h4>
 
 </a>
 <div>
 
 <p>We are working to create new machine learning tools and discover opportunities to increase the availability and accuracy of healthcare globally.</p>
 </div>
</div>
 </div>
</section><section>
 <div>
 <p>
 </p><h2>
 Highlighted projects
 </h2>
 <p></p>
 </div>
 <div>
 
 <div>
 <p><a href="/teams/brain/magenta/">Magenta</a></p>
<div>
 
 <p>An open source research project exploring the role of machine learning as a tool in the creative process.</p>
 </div> </div>
 <div>
 <p><a href="https://ai.googleblog.com/2021/11/metnet-2-deep-learning-for-12-hour.html">MetNet</a></p>
<div>
 
 <p>Hyper local weather forecasts using deep learning</p>
 </div> </div>
 <div>
 <p><a href="/pubs/pub47647/">RL for Recommendations improvement</a></p>
<div>
 <div>
 <p>
 RL for Recommendations improvement
 </p>
 </div>
 <p>Optimize long-term values of Google recommendation products using reinforcement learning and other ML techniques.</p>
 </div> </div>
 <div>
 <p><a href="https://ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html">LaMDA</a></p>
<div>
 
 <p>LaMDA (Language Model For Dialog Applications) is a language model tasked with generating sensible, specific, interesting, and appropriate dialog on open-domain, multi-turn conversations.</p>
 </div> </div>
 <div>
 <p><a href="/pubs/pub51340/">AutoML</a></p>
<div>
 
 <p>Approaching automated machine learning and âlearning to learnâ.</p>
 </div> </div>
 <div>
 <p><a href="https://ai.googleblog.com/2022/03/using-deep-learning-to-annotate-protein.html">Protein function prediction with AI</a></p>
<div>
 <div>
 <p>
 Protein function prediction with AI
 </p>
 </div>
 <p>Machine learning can help read the language of life by reading the chemical formulas of proteins and telling us their purpose.</p>
 </div> </div>
 <div>
 <p><a href="/pubs/?collection=learning-for-code">Learning for Code</a></p>
<div>
 
 <p>Using AI research to radically improve the software development process</p>
 </div> </div>
 </div>
</section><div>
 <div>
 <p>
 </p><h2>
 Featured publications
 </h2>
 <p></p>
 </div>
 <div>
 <div>
 <p><a href="/pubs/pub51348/">Towards a Human-like Open-Domain Chatbot</a></p>
 <div>
 <p>Preview Abstract</p>
 <div>
 <p>We present Meena, a multi-turn end-to-end open-domain chatbot trained on data mined from public social media and filtered. The model was trained to minimize perplexity of the next token, but we have found evidence that this metric correlates with human judgement of quality. We propose a human judgement metric called Sensibleness and Specificity Average (SSA) which captures key elements of good...</p>
 <p><a href="/pubs/pub51348/">View details</a>
 </p></div>
 </div>
 <div>
 <p>
 Apoorv Kulshreshtha, Daniel De Freitas Adiwardana, <a href="/people/107601/">David Richard So</a>, Gaurav Nemade, Jamie Hall, <a href="/people/NoahFiedel/">Noah Fiedel</a>, <a href="/people/QuocLe/">Quoc V. Le</a>, Romal Thoppilan, <a href="/people/105176/">Thang Luong</a>, Yifeng Lu, Zi Yang </p>
 <p>arXiv (2020)</p>
 </div>
 </div>
 <div>
 <p><a href="/pubs/pub48643/">Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer</a></p>
 <div>
 <p>Preview Abstract</p>
 <div>
 <p>Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a lower-resource downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning for NLP by...</p>
 <p><a href="/pubs/pub48643/">View details</a>
 </p></div>
 </div>
 <div>
 <p>
 <a href="/people/104881/">Adam Roberts</a>, Colin Raffel, <a href="/people/105565/">Katherine Lee</a>, Michael Matena, Noam Shazeer, <a href="/people/105182/">Peter J. Liu</a>, Sharan Narang, <a href="/people/106528/">Wei Li</a>, <a href="/people/107323/">Yanqi Zhou</a> </p>
 <p>Google (2019)</p>
 </div>
 </div>
 <div>
 <p><a href="/pubs/pub46201/">Attention is All You Need</a></p>
 <div>
 <p>Preview Abstract</p>
 <div>
 <p>The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely....</p>
 <p><a href="/pubs/pub46201/">View details</a>
 </p></div>
 </div>
 <div>
 <p>
 Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin </p>
 <p>NIPS (2017)</p>
 </div>
 </div>
 
 <div>
 <p><a href="/pubs/pub51346/">Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies</a></p>
 <div>
 <p>Preview Abstract</p>
 <div>
 <p>Unrolled computation graphs arise in many scenarios, including training RNNs, tuning hyperparameters through unrolled optimization, and training learned optimizers. Current approaches to optimizing parameters in such computation graphs suffer from high variance gradients, bias, slow updates, or large memory usage. We introduce a method called Persistent Evolution Strategies (PES), which divides...</p>
 <p><a href="/pubs/pub51346/">View details</a>
 </p></div>
 </div>
 <div>
 
 <p>International Conference on Machine Learning (2021)</p>
 </div>
 </div>
 <div>
 <p><a href="/pubs/pub50764/">Oops I Took A Gradient: Scalable Sampling for Discrete Distributions</a></p>
 <div>
 <p>Preview Abstract</p>
 <div>
 <p>We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables. Our approach uses gradients of the likelihood function with respect to its discrete inputs to propose updates in a Metropolis-Hastings sampler. We show empirically that this approach outperforms generic samplers in a number of difficult settings including Ising models, Potts models,...</p>
 <p><a href="/pubs/pub50764/">View details</a>
 </p></div>
 </div>
 <div>
 <p>
 Christopher Joseph Maddison, David Duvenaud, <a href="/people/105739/">Kevin Jordan Swersky</a>, <a href="/people/MiladHashemi/">Milad Hashemi</a>, Will Grathwohl </p>
 <p>ICML (2021)</p>
 </div>
 </div>
 <div>
 <p><a href="/pubs/pub51347/">Score-based generative modeling through stochastic differential equations</a></p>
 <div>
 <p>Preview Abstract</p>
 <div>
 <p>Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the...</p>
 <p><a href="/pubs/pub51347/">View details</a>
 </p></div>
 </div>
 
 </div>
 <div>
 <p><a href="/pubs/pub51349/">Poisoning the Unlabeled Dataset of Semi-Supervised Learning</a></p>
 <div>
 <p>Preview Abstract</p>
 <div>
 <p>Semi-supervised machine learning models learn from a (small) set of labeled training examples, and a (large) set of unlabeled training examples. State-of-the-art models can reach within a few percentage points of fully-supervised training, while requiring 100x less labeled data. We study a new class of vulnerabilities: poisoning attacks that modify the unlabeled dataset. In order to be useful,...</p>
 <p><a href="/pubs/pub51349/">View details</a>
 </p></div>
 </div>
 <div>
 <p>
 <a href="/people/NicholasCarlini/">Nicholas Carlini</a> </p>
 <p>USENIX Security (2021)</p>
 </div>
 </div>
 <div>
 <p><a href="/pubs/pub48831/">Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport</a></p>
 <div>
 <p>Preview Abstract</p>
 <div>
 <p>Estimating the Wasserstein distance between two measures living in high-dimensional spaces is a challenging task. Indeed, that problem suffers from the curse of dimensionality: a number of samples that is exponential in the dimension of the ambient space is required to obtain good approximations. Regularizing the optimal transport (OT) problem is therefore crucial to use Wasserstein distances...</p>
 <p><a href="/pubs/pub48831/">View details</a>
 </p></div>
 </div>
 <div>
 <p>
 FranÃ§ois-Pierre Paty, Alexandre d'Aspremont, Marco Cuturi </p>
 <p>Proc. AISTATS (2020) (to appear)</p>
 </div>
 </div>
 </div>
 </div><div>
 <p>
 </p><h2>
 Some of our locations
 </h2>
 <p></p>
 </div><div>
 <p>
 </p><h2>
 Some of our people
 </h2>
 <p></p>
 </div>
<div>
 <p>
 Join us
 </p><p>
 </p><h3>
 We're looking for talented people to join our team
 </h3>
 <p></p>
 </div> </div>||||I|||| Google Research
 Google Research
 Philosophy
 Research Areas
 Publications
 People
 Resources
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 Careers
 Blog
 Teams ›

 Brain Team

 Make machines intelligent. Improve peopleâs lives.

 About the team

 History of Research Breakthroughs

 Google Brain started in 2011 at X as an exploratory lab and was founded by Jeff Dean, Greg Corrado and Andrew Ng, along with other engineers and is now part of Google Research. Since then, we continually rethink our approach to machine learning and are proud of our breakthroughs, which include:

 * AI infrastructure (developing TensorFlow)
 * Sequence-to-sequence learning, leading to Transformers and BERT
 * AutoML, pioneering automated machine learning for production use

 Our research breakthroughs enable Googleâs mission to organize the world's information and make it universally accessible and useful.

 Google Impact

 As part of Google and Alphabet, the Brain team has access to resources and unparalleled collaboration opportunities that have a positive impact on products and society. Our broad and fundamental research goals allow us to collaborate with and contribute to many teams across Alphabet, which deploy our cutting-edge technology into products used by billions of users, positively impacting society and the research community.

 Open and Bottom-Up Culture

 We believe that openly disseminating research is critical to a healthy exchange of ideas, leading to rapid progress in the field.

 As such, we regularly publish our research at top academic conferences and journals, and release our tools, such as TensorFlow and Jax, as open-source projects.

 Team members are encouraged to set their own research goals, allowing the Brain team to maintain a portfolio of projects across varied time horizons, research areas and levels of risk.

 Research areas

 Machine Intelligence

 Machine Perception

 Machine Translation

 Natural Language Processing

 Robotics

 Software Systems

 Team focus summaries

 Machine learning

 Optimization, reinforcement learning, generative modeling, uncertainty and robustness, theory, etc.

 Systems

 Programming languages, privacy, security, hardware, etc.

 Computer Vision

 Image classification, object detection, image generation, video compression, etc.

 Natural Language

 Machine translation, document summarization, speech recognition, dialog systems, etc.

 Human/Machine interaction

 Interpretability, visualization, HCI, recommender systems, etc.

 ML for health and science

 Drug design, environmental modeling, etc.

 Health

 We are working to create new machine learning tools and discover opportunities to increase the availability and accuracy of healthcare globally.

 Learn more

 Highlighted projects

 Project Euphonia
 Project Euphonia

 Improving communication for people with speech impairments.

 Magenta
 Magenta

 An open source research project exploring the role of machine learning as a tool in the creative process.

 MetNet
 MetNet

 Hyper local weather forecasts using deep learning

 RL for Recommendations improvement
 RL for Recommendations improvement

 Optimize long-term values of Google recommendation products using reinforcement learning and other ML techniques.

 LaMDA
 LaMDA

 LaMDA (Language Model For Dialog Applications) is a language model tasked with generating sensible, specific, interesting, and appropriate dialog on open-domain, multi-turn conversations.

 AutoML
 AutoML

 Approaching automated machine learning and âlearning to learnâ.

 Protein function prediction with AI
 Protein function prediction with AI

 Machine learning can help read the language of life by reading the chemical formulas of proteins and telling us their purpose.

 Learning for Code
 Learning for Code

 Using AI research to radically improve the software development process

 Featured publications

 See all Brain Team publications
 Towards a Human-like Open-Domain Chatbot
 Preview Abstract

 We present Meena, a multi-turn end-to-end open-domain chatbot trained on data mined from public social media and filtered. The model was trained to minimize perplexity of the next token, but we have found evidence that this metric correlates with human judgement of quality. We propose a human judgement metric called Sensibleness and Specificity Average (SSA) which captures key elements of good...

 View details
 Towards a Human-like Open-Domain Chatbot

 Apoorv Kulshreshtha , Daniel De Freitas Adiwardana , David Richard So , Gaurav Nemade , Jamie Hall , Noah Fiedel , Quoc V. Le , Romal Thoppilan , Thang Luong , Yifeng Lu , Zi Yang

 arXiv (2020)

 Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
 Preview Abstract

 Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a lower-resource downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning for NLP by...

 View details
 Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

 Adam Roberts , Colin Raffel , Katherine Lee , Michael Matena , Noam Shazeer , Peter J. Liu , Sharan Narang , Wei Li , Yanqi Zhou

 Google (2019)

 Attention is All You Need
 Preview Abstract

 The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely....

 View details
 Attention is All You Need

 Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N. Gomez , Lukasz Kaiser , Illia Polosukhin

 NIPS (2017)

 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
 Preview Abstract

 We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned...

 View details
 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

 Jacob Devlin , Ming-Wei Chang , Kenton Lee , Kristina N. Toutanova

 NAACL 2019 (2018)

 Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
 Preview Abstract

 Unrolled computation graphs arise in many scenarios, including training RNNs, tuning hyperparameters through unrolled optimization, and training learned optimizers. Current approaches to optimizing parameters in such computation graphs suffer from high variance gradients, bias, slow updates, or large memory usage. We introduce a method called Persistent Evolution Strategies (PES), which divides...

 View details
 Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies

 Jascha Sohl-dickstein , Luke Shekerjian Metz , Paul Vicol

 International Conference on Machine Learning (2021)

 Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
 Preview Abstract

 We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables. Our approach uses gradients of the likelihood function with respect to its discrete inputs to propose updates in a Metropolis-Hastings sampler. We show empirically that this approach outperforms generic samplers in a number of difficult settings including Ising models, Potts models,...

 View details
 Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

 Christopher Joseph Maddison , David Duvenaud , Kevin Jordan Swersky , Milad Hashemi , Will Grathwohl

 ICML (2021)

 Score-based generative modeling through stochastic differential equations
 Preview Abstract

 Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the...

 View details
 Score-based generative modeling through stochastic differential equations

 Yang Song , Jascha Sohl-dickstein , Diederik P. Kingma , Abhishek Kumar , Stefano Ermon , Ben Poole

 ICLR 2021 (2021) (to appear)

 Poisoning the Unlabeled Dataset of Semi-Supervised Learning
 Preview Abstract

 Semi-supervised machine learning models learn from a (small) set of labeled training examples, and a (large) set of unlabeled training examples. State-of-the-art models can reach within a few percentage points of fully-supervised training, while requiring 100x less labeled data. We study a new class of vulnerabilities: poisoning attacks that modify the unlabeled dataset. In order to be useful,...

 View details
 Poisoning the Unlabeled Dataset of Semi-Supervised Learning

 Nicholas Carlini

 USENIX Security (2021)

 Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport
 Preview Abstract

 Estimating the Wasserstein distance between two measures living in high-dimensional spaces is a challenging task. Indeed, that problem suffers from the curse of dimensionality: a number of samples that is exponential in the dimension of the ambient space is required to obtain good approximations. Regularizing the optimal transport (OT) problem is therefore crucial to use Wasserstein distances...

 View details
 Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport

 FranÃ§ois-Pierre Paty , Alexandre d'Aspremont , Marco Cuturi

 Proc. AISTATS (2020) (to appear)

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Make machines intelligent. Improve peopleâs lives.
  
 
  
 About the team
 
  
 
 History of Research Breakthroughs
 Google Brain started in 2011 at X as an exploratory lab and was founded by Jeff Dean, Greg Corrado and Andrew Ng, along with other engineers and is now part of Google Research. Since then, we continually rethink our approach to machine learning and are proud of our breakthroughs, which include:
AI infrastructure (developing TensorFlow)
Sequence-to-sequence learning, leading to Transformers and BERT
AutoML, pioneering automated machine learning for production use
Our research breakthroughs enable Googleâs mission to organize the world's information and make it universally accessible and useful.
 Google Impact
 As part of Google and Alphabet, the Brain team has access to resources and unparalleled collaboration opportunities that have a positive impact on products and society. Our broad and fundamental research goals allow us to collaborate with and contribute to many teams across Alphabet, which deploy our cutting-edge technology into products used by billions of users, positively impacting society and the research community.
 Open and Bottom-Up Culture
 We believe that openly disseminating research is critical to a healthy exchange of ideas, leading to rapid progress in the field.
 As such, we regularly publish our research at top academic conferences and journals, and release our tools, such as TensorFlow and Jax, as open-source projects.
 Team members are encouraged to set their own research goals, allowing the Brain team to maintain a portfolio of projects across varied time horizons, research areas and levels of risk.
 
  
 
  
 Research areas
 
  
  
 
 
  
 Team focus summaries
 
  
 
 
 
 
 Machine learning
 
 
 
 
 Optimization, reinforcement learning, generative modeling, uncertainty and robustness, theory, etc.
 
 
 
 Systems
 
 
 
 
 Programming languages, privacy, security, hardware, etc.
 
 
 
 Computer Vision
 
 
 
 
 Image classification, object detection, image generation, video compression, etc.
 
 
 
 Natural Language
 
 
 
 
 Machine translation, document summarization, speech recognition, dialog systems, etc.
 
 
 
 
 
 Health
 
 
 
 
 We are working to create new machine learning tools and discover opportunities to increase the availability and accuracy of healthcare globally.
 
 
 
 
 
  
 Highlighted projects
 
  
 
 
 
 
  Magenta 
 
 An open source research project exploring the role of machine learning as a tool in the creative process.
  
 
  MetNet 
 
 Hyper local weather forecasts using deep learning
  
 
  RL for Recommendations improvement 
 
 
 RL for Recommendations improvement
 
 
 Optimize long-term values of Google recommendation products using reinforcement learning and other ML techniques.
  
 
  LaMDA 
 
 LaMDA (Language Model For Dialog Applications) is a language model tasked with generating sensible, specific, interesting, and appropriate dialog on open-domain, multi-turn conversations.
  
 
  AutoML 
 
 Approaching automated machine learning and âlearning to learnâ.
  
 
  Protein function prediction with AI 
 
 
 Protein function prediction with AI
 
 
 Machine learning can help read the language of life by reading the chemical formulas of proteins and telling us their purpose.
  
 
  Learning for Code 
 
 Using AI research to radically improve the software development process
  
 
 
 
 
  
 Featured publications
 
  
 
 
 
  Towards a Human-like Open-Domain Chatbot 
 
 Preview Abstract
 
 We present Meena, a multi-turn end-to-end open-domain chatbot trained on data mined from public social media and filtered. The model was trained to minimize perplexity of the next token, but we have found evidence that this metric correlates with human judgement of quality. We propose a human judgement metric called Sensibleness and Specificity Average (SSA) which captures key elements of good...
  View details
  
 
 
 
 Apoorv Kulshreshtha, Daniel De Freitas Adiwardana, David Richard So, Gaurav Nemade, Jamie Hall, Noah Fiedel, Quoc V. Le, Romal Thoppilan, Thang Luong, Yifeng Lu, Zi Yang 
 arXiv (2020)
 
 
 
  Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer 
 
 Preview Abstract
 
 Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a lower-resource downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning for NLP by...
  View details
  
 
 
 
 Adam Roberts, Colin Raffel, Katherine Lee, Michael Matena, Noam Shazeer, Peter J. Liu, Sharan Narang, Wei Li, Yanqi Zhou 
 Google (2019)
 
 
 
  Attention is All You Need 
 
 Preview Abstract
 
 The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely....
  View details
  
 
 
 
 Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin 
 NIPS (2017)
 
 
 
 
  Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies 
 
 Preview Abstract
 
 Unrolled computation graphs arise in many scenarios, including training RNNs, tuning hyperparameters through unrolled optimization, and training learned optimizers. Current approaches to optimizing parameters in such computation graphs suffer from high variance gradients, bias, slow updates, or large memory usage. We introduce a method called Persistent Evolution Strategies (PES), which divides...
  View details
  
 
 
 
 International Conference on Machine Learning (2021)
 
 
 
  Oops I Took A Gradient: Scalable Sampling for Discrete Distributions 
 
 Preview Abstract
 
 We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables. Our approach uses gradients of the likelihood function with respect to its discrete inputs to propose updates in a Metropolis-Hastings sampler. We show empirically that this approach outperforms generic samplers in a number of difficult settings including Ising models, Potts models,...
  View details
  
 
 
 
 Christopher Joseph Maddison, David Duvenaud, Kevin Jordan Swersky, Milad Hashemi, Will Grathwohl 
 ICML (2021)
 
 
 
  Score-based generative modeling through stochastic differential equations 
 
 Preview Abstract
 
 Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the...
  View details
  
 
 
 
 
  Poisoning the Unlabeled Dataset of Semi-Supervised Learning 
 
 Preview Abstract
 
 Semi-supervised machine learning models learn from a (small) set of labeled training examples, and a (large) set of unlabeled training examples. State-of-the-art models can reach within a few percentage points of fully-supervised training, while requiring 100x less labeled data. We study a new class of vulnerabilities: poisoning attacks that modify the unlabeled dataset. In order to be useful,...
  View details
  
 
 
 
 Nicholas Carlini 
 USENIX Security (2021)
 
 
 
  Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport 
 
 Preview Abstract
 
 Estimating the Wasserstein distance between two measures living in high-dimensional spaces is a challenging task. Indeed, that problem suffers from the curse of dimensionality: a number of samples that is exponential in the dimension of the ambient space is required to obtain good approximations. Regularizing the optimal transport (OT) problem is therefore crucial to use Wasserstein distances...
  View details
  
 
 
 
 FranÃ§ois-Pierre Paty, Alexandre d'Aspremont, Marco Cuturi 
 Proc. AISTATS (2020) (to appear)
 
 
 
  
 
  
 Some of our locations
 
  
  
 
  
 Some of our people
 
  
 
 
 Join us
  
  
 We're looking for talented people to join our team
Brain Team â Google Research
https://research.google/teams/brain/
https://www.deepmind.com/
DeepMind
2022-11-22
None
<div><div><div><div><p></p></div><div><p>Meta-Learning Black-Box Optimization via Black-Box Optimization</p><p>Robert Lange,
Tom Schaul,
Tom Zahavy,
Yutian Chen,
Valentin Dalibard,
Chris Lu,
Satinder Baveja,
Sebastian Flennerhag</p><p>ICLR</p></div></div><div><div><p></p></div><div><p>Is forgetting less a good inductive bias for forward transfer?</p><p>Arslan Chaudhry,
Jiefeng Chen,
Timothy Nguyen,
Dilan Gorur</p><p>ICLR</p></div></div><div><div><p></p></div><div><p>Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation</p><p>Xu Han *,
Xiaohui Chen *,
Francisco Ruiz,
Liping Liu *</p><p>JMLR</p></div></div><div><div><p></p></div><div><p>Fast exploration and learning of latent graphs with aliased observations</p><p>Miguel Lazaro-Gredilla,
Ishan Deshpande,
Siva Swaminathan,
Meet Dave,
Dileep George</p><p>arXiv</p></div></div><div><div><p></p></div><div><p>Denoising diffusion samplers</p><p>Francisco Vargas,
Will Grathwohl,
Arnaud Doucet</p><p>ICLR</p></div></div><div><div><p></p></div><div><p>Graph schemas as abstractions for transfer learning, inference, and planning</p><p>Swaroop Guntupalli,
Rajkumar Vasudeva Raju,
Shrinu Kushagra,
Carter Wendelken,
Danny Sawyer,
Ishan Deshpande,
Stannis Zhou,
Miguel Lazaro-Gredilla,
Dileep George</p><p>arXiv</p></div></div></div></div>||||I|||| Research
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 Competitive programming with AlphaCode
 December 8, 2022
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 Publication
 Meta-Learning Black-Box Optimization via Black-Box Optimization
 Robert Lange, Tom Schaul, Tom Zahavy, Yutian Chen, Valentin Dalibard, Chris Lu, Satinder Baveja, Sebastian Flennerhag
 ICLR
 Download
 Publication
 Is forgetting less a good inductive bias for forward transfer?
 Arslan Chaudhry, Jiefeng Chen, Timothy Nguyen, Dilan Gorur
 ICLR
 Download
 Publication
 Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation
 Xu Han *, Xiaohui Chen *, Francisco Ruiz, Liping Liu *
 JMLR
 Download
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 Fast exploration and learning of latent graphs with aliased observations
 Miguel Lazaro-Gredilla, Ishan Deshpande, Siva Swaminathan, Meet Dave, Dileep George
 arXiv
 Download
 Publication
 Denoising diffusion samplers
 Francisco Vargas, Will Grathwohl, Arnaud Doucet
 ICLR
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 Graph schemas as abstractions for transfer learning, inference, and planning
 Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra, Carter Wendelken, Danny Sawyer, Ishan Deshpande, Stannis Zhou, Miguel Lazaro-Gredilla, Dileep George
 arXiv
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 Alphabet Inc.
Meta-Learning Black-Box Optimization via Black-Box Optimization Robert Lange,
Tom Schaul,
Tom Zahavy,
Yutian Chen,
Valentin Dalibard,
Chris Lu,
Satinder Baveja,
Sebastian Flennerhag ICLR         Is forgetting less a good inductive bias for forward transfer? Arslan Chaudhry,
Jiefeng Chen,
Timothy Nguyen,
Dilan Gorur ICLR         Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation Xu Han *,
Xiaohui Chen *,
Francisco Ruiz,
Liping Liu * JMLR         Fast exploration and learning of latent graphs with aliased observations Miguel Lazaro-Gredilla,
Ishan Deshpande,
Siva Swaminathan,
Meet Dave,
Dileep George arXiv         Denoising diffusion samplers Francisco Vargas,
Will Grathwohl,
Arnaud Doucet ICLR         Graph schemas as abstractions for transfer learning, inference, and planning Swaroop Guntupalli,
Rajkumar Vasudeva Raju,
Shrinu Kushagra,
Carter Wendelken,
Danny Sawyer,
Ishan Deshpande,
Stannis Zhou,
Miguel Lazaro-Gredilla,
Dileep George arXiv
DeepMind
https://www.deepmind.com/
https://ai.facebook.com/
Meta AI
2022-11-01
None
<div><div><div><p></p><h4>NLP</h4><p></p></div><p></p><h4>LLaMA: Open and Efficient Foundation Language Models</h4><p></p><p>Faisal Azhar, Hugo Touvron, Armand Joulin, Aurelien Rodriguez, Baptiste Rozière, Eric Hambro, Gautier Izacard, Guillaume Lample, Marie-Anne Lachaux, Naman Goyal, Thibaut Lavril, Timothee Lacroix, Xavier Martinet, Edouard Grave</p></div>||||I|||| Research
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 Meet CICERO

 Meta AI researchers met a grand challenge of AI by creating and training an agent to play Diplomacy with humans, like humans, and win.

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 Meet CICERO

 Meta AI researchers met a grand challenge of AI by creating and training an agent to play Diplomacy with humans, like humans, and win.

 Learn More

 Latest News

 RESEARCH

 CICERO: An AI agent that negotiates, persuades, and cooperates with people

 November 22, 2022

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 November 18, 2022

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 The first view of the ‘dark matter’ of the protein universe

 November 01, 2022

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 Casual Conversations Dataset is Now Available for Download

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 GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce

 Sean Bell

 Yiqun Liu

 Sami Alsheikh

 Yina Tang...

 KDD

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 Live Face De-Identification in Video

 Oran Gafni

 Lior Wolf

 Yaniv Taigman

 International Conference on Computer Vision (ICCV)

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 Single-Network Whole-Body Pose Estimation

 Gines Hidalgo

 Yaadhav Raaj

 Haroon Idrees

 Donglai Xiang...

 International Conference on Computer Vision (ICCV)

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 SPEECH & AUDIO

 A Universal Music Translation Network

 Noam Mor

 Lior Wolf

 Adam Polyak

 Yaniv Taigman

 International Conference on Learning Representations (ICLR)

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 February 23, 2023

 NLP

 LLaMA: Open and Efficient Foundation Language Models

 Faisal Azhar, Hugo Touvron, Armand Joulin, Aurelien Rodriguez, Baptiste Rozière, Eric Hambro, Gautier Izacard, Guillaume Lample, Marie-Anne Lachaux, Naman Goyal, Thibaut Lavril, Timothee Lacroix, Xavier Martinet, Edouard Grave

 February 23, 2023

 Read the Paper

 December 12, 2022

 NLP

 COMPUTER VISION

 Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language

 Michael Auli, Alexei Baevski, Arun Babu, Wei-Ning Hsu

 December 12, 2022

 Read the Paper

 November 27, 2022

 CORE MACHINE LEARNING

 Neural Attentive Circuits

 Nicolas Ballas, Bernhard Schölkopf, Chris Pal, Francesco Locatello, Li Erran, Martin Weiss, Nasim Rahaman, Yoshua Bengio

 November 27, 2022

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NLP      LLaMA: Open and Efficient Foundation Language Models   Faisal Azhar, Hugo Touvron, Armand Joulin, Aurelien Rodriguez, Baptiste Rozière, Eric Hambro, Gautier Izacard, Guillaume Lample, Marie-Anne Lachaux, Naman Goyal, Thibaut Lavril, Timothee Lacroix, Xavier Martinet, Edouard Grave
Meta AI
https://ai.facebook.com/
https://ailabs.com.tr/
ailabs
2020-01-01
None
<div><div>
 <div><p></p><h3><strong>We make it possible!</strong><br /></h3>
 <p><br />We come up with great ideas that bring solutions to challenging real
 world problems that are normally deemed to be not that possible. Here's the motto: Don't
 follow innovations, make innovations! This is the art.<br /></p>
 </div>
 <div><p></p><h3><strong>We make it work!</strong><br /></h3>
 <div><p>We set up a hypothesis based on our educated guess, test it in
 experiments and learn from failures until it really works. Never give up! this is the
 science.</p></div>
 </div>
 <div><p></p><h3><strong>We make it optimized!</strong><br /></h3>
 <p>We make use of advanced techniques to acheve high-performance computing on
 tiny machines that is needed by large intelligence in deep learning. Always think something
 better might be out there! This is the engineering.<br /></p>
 </div>
 </div></div>||||I|||| * About (current)
 * Philosophy
 * Products & Projects
 * Contact
 1. 
 2. 
 3. 

 WELCOME TO AI LABS

 Machines Can See

 AI & Computer Vision

 We come up with innovative solutions for the retail, industry and individuals by utilizing the power of artificial intelligence and computer vision.

 OUR SOLUTION

 Deep Learning • B2C Projects • B2B Projects • Usable Statistics and Actionable insights • Identify your targets better

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 WHAT WE DO


 We, AI Labs, develop innovative artificial intelligence and computer vision solutions that revolutionize industries. Machines that can see: We pack our solutions in small yet intelligent devices that can be easily integrated to your existing data flow. Computer vision for everyone: Our devices can recognize faces, estimate age and gender, classify clothing types and colors, identify everyday objects and detect motion. Technical consultancy: We help you identify use cases of artificial intelligence and computer vision in your industry. Artificial intelligence is the technology of today, not the future.

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 CONTACT

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 We compress large intelligence into tiny things.

 We make it possible!


 We come up with great ideas that bring solutions to challenging real world problems that are normally deemed to be not that possible. Here's the motto: Don't follow innovations, make innovations! This is the art.

 We make it work!


 We set up a hypothesis based on our educated guess, test it in experiments and learn from failures until it really works. Never give up! this is the science.


 We make it optimized!

 We make use of advanced techniques to acheve high-performance computing on tiny machines that is needed by large intelligence in deep learning. Always think something better might be out there! This is the engineering.

 AI LABS CREATE ARTIFICIAL INTELLIGENCE FOR THE RETAIL, INDUSTRY AND INDIVIDUALS...

 PRODUCTS & PROJECTS


 MonReader

 MonReader is a new mobile document digitalization experience for the blind, for researchers and for everyone else in need for fully automatic, highly fast and high-quality document scanning in bulk. It is composed of a mobile app and all the user needs to do is flip pages and everything is handled by MonReader: it detects page flips from low-resolution camera preview and takes a high-resolution picture of the document, recognizing its corners and crops it accordingly, and it dewarps the cropped document to obtain a bird's eye view, sharpens the contrast between the text and the background and finally recognizes the text with formatting kept intact, being further corrected by MonReader's ML powered redactor.

 MonSight

 MonSight is a wearable assistive technology device for the blind. A mini camera mounted on a pair of glasses transmits video stream to the main board, which can process video frames with artificial intelligence techniques to produce audable notifications for the user. Current capabilities of the device include accurate and efficient object detection, face detection, trainable face recognition, scene text recognition, and a special algorithm to map arbitrary images to sounds.

 Monatis

 Monatis is a set of AI models for efficient and accurate face recognition, age and gender estimation, and emotion recognition from faces in images and camera streams. We developed innovative techniques and optimized the whole pipeline to run smoothly even on modest hardware with high performance and accuracy. These models might be deployed collectively or individually to gather statistics or tighten the security so long as to abide by the legal and ethical considerations.

 MonTongue

 MonTongue is a set of NLP and NLU models, tools and utilities primarily to help machines better understand Turkish. These goodies are powered by innovative techniques developed with the quite challenging Turkish morphology in consideration. We mostly deploy them in our friendly chat bots and automatic information extraction pipelines, an so can you.

 AI Labs

 AI Labs - Machines Can See © 2020

 Cyberpark Tepe Binası Z-48 Bilkent, Ankara
 Turkey

 ai@ailabs.com.tr

 About the company

 AI Labs, develop innovative artificial intelligence and computer vision solutions that revolutionize industries.

We make it possible!  
  We come up with great ideas that bring solutions to challenging real
 world problems that are normally deemed to be not that possible. Here's the motto: Don't
 follow innovations, make innovations! This is the art. 
 
     We make it work!  
  We set up a hypothesis based on our educated guess, test it in
 experiments and learn from failures until it really works. Never give up! this is the
 science. 
 
     We make it optimized!  
 We make use of advanced techniques to acheve high-performance computing on
 tiny machines that is needed by large intelligence in deep learning. Always think something
 better might be out there! This is the engineering.
ailabs
https://ailabs.com.tr/
https://www.microsoft.com/en-us/ai/ai-lab
AI Lab & Artificial Intelligence Development – Microsoft AI
2022-09-15
None
<div><div>
 <div>
 <section>
 
 
 
 <strong>New</strong>
<h3>Responsible AI dashboard</h3> <p>How can we empower ML practitioners and business stakeholders to operationalize responsible AI in practice? We created a dashboard that brings together responsible AI tools for model debugging and decision-making.</p>
<a href="https://www.microsoft.com/en-us/ai/ai-lab-responsible-ai-dashboard">
 Learn about AI dashboard
 </a>
 
 </section>
 <section>
 
 
 
 <strong>ML, Cognitive</strong>
<h3>Platform for Situated Intelligence</h3> <p>How can we develop AI systems that support seamless interactive experiences? Platform for Situated Intelligence is a framework that makes it easier for developers to build AI that can perceive, understand, and act in our human-filled world in real time.</p>
<a href="https://www.microsoft.com/en-us/ai/ai-lab-platform-for-situated-intelligence">
 Learn about Platform for Situated Intelligence
 </a>
 
 </section>
 </div>
 <div>
 <section>
 
 
 
 <strong>Natural language, ML</strong>
<h3>AI at Scale</h3> <p>We create the systems and platforms for training and deploying large AI models at scale. These advancements can make it faster and easier for developers and data scientists to infuse AI into their applications.</p>
<a href="https://www.microsoft.com/en-us/ai/ai-lab-aiatscale">
 Learn about AI at Scale
 </a>
 
 </section>
 <section>
 
 
 
 <strong>Cognitive, ML</strong>
<h3>Datamations</h3> <p>Data visualizations are an important part of data storytelling but sometimes they tell just a part of the whole story. Datamations help bring data to life by making it easy to create animations that show the process behind a plot or table.</p>
<a href="https://www.microsoft.com/en-us/ai/ai-lab-Datamations">
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 </a>
 
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 New

 Responsible AI dashboard

 How can we empower ML practitioners and business stakeholders to operationalize responsible AI in practice? We created a dashboard that brings together responsible AI tools for model debugging and decision-making.

 Learn about AI dashboard
 ML, Cognitive

 Platform for Situated Intelligence

 How can we develop AI systems that support seamless interactive experiences? Platform for Situated Intelligence is a framework that makes it easier for developers to build AI that can perceive, understand, and act in our human-filled world in real time.

 Learn about Platform for Situated Intelligence
 Natural language, ML

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Stanford AI Lab
2022-10-21
Stanford AI Lab
<div><div>
	<div>
	<div>
	<h2>Stanford AI Lab</h2>
	<p>The Stanford Artificial Intelligence Laboratory (SAIL) has been a center of excellence for Artificial Intelligence research, teaching, theory, and practice since its founding in 1963.</p>
	</div>
	<div>
	<p></p><h2>Latest News</h2><p></p>
	<div>
	<div><h3>Congratulations to Oussama Khatib on Receiving the Title of Knight of the National Order of Merit of France!</h3><p>Congratulations to Oussama Khatib on receiving the title of Chevalier de l’Ordre National du Mérite (English: Knight of the National Order of Merit) from the government of France.  The French order of merit was established in 1963 with support from Charles de Gaulle.  Membership in this order is awarded by the President of France. </p></div><div><h3>SAIL Releases a New Video on the History of AI at Stanford</h3><p>The Stanford Artificial Intelligence Lab has a rich history which you can learn about in a new video on AI at Stanford from 1962 to 2022. Click “Read More” below to see it.</p></div><div><h3>Congratulations to Prof. Manning, SAIL Director,  for his Honorary Doctorate at UvA!</h3><p>Professor Manning is being awarded by the the University of Amsterdam for his pioneering research contributions in the field of Natural Language Processing (NLP).</p></div><div><h3>AI Lab celebrates 50th Anniversary of Intergalactic “Spacewar!” Olympics</h3><p>Stanford’s Artificial Intelligence Laboratory celebrated the 50th anniversary of the Intergalactic “Spacewar!” Olympics, the world’s first video game tournament on Wednesday, October 20th.</p></div><div><h3>Chelsea Finn Explains Moravec’s Paradox in 5 Levels of Difficulty in WIRED Video</h3><p>Prof. Finn explains Moravec’s paradox, which is the observation that many things that are difficult for robots are easy for humans and vice versa, to a child, a teenager, an undergrad, a grad student and finally to a colleague.</p></div><div><h3>Prof. Oussama Khatib’s Journey with Ocean OneK</h3><p>A robotic diving system that looks like a Transformer? Ocean OneK can go to depths that would kill a human diver. WIRED spoke with Professor Oussama Khatib to understand how he and his team designed, built and tested this robotic diver.</p></div>	</div>
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	<div>
	<p></p><h2>Latest Tweets</h2><p></p>
	<div>
 
 <div>
 
<div>
 	<p>We are proud to offer many of our best AI courses free on YouTube. Want to study with a cohort and course assistants? You can do that in the AI Professional Program via @StanfordOnline.<br />
New this year: Deep Multi-Task and Meta Learning with @chelseabfinn <br />
https://online.stanford.edu/programs/artificial-intelligence-professional-program </p>
	
 
	 
 
 </div>
<div><p>🧵We’re excited to announce MegaBlocks, our system for efficient “dropless” MoE training! 🤖</p><p>
MegaBlocks outperforms Tutel by up to 40% by reformulating MoEs as block-sparse operations, which allows us to avoid token dropping without sacrificing hardware efficiency 🚀. </p></div>
<div>
 	<p>Thank you @DorsaSadigh for visiting CMU and giving a great talk. It was a full house both in-person and zoom! Thoroughly enjoyed all the discussions. </p>
	
 
	 
 
 </div>
<div>
 	<p>a short teaser of what we’ve been up to lately ⁦@stanford⁩: here is an end-to-end policy running on low-cost arms! </p>
	
 
	 
 
 </div>
<div><p>Attention is all you need... but how much of it do you need?</p><p>
Announcing H3 - a new generative language models that outperforms GPT-Neo-2.7B with only *2* attention layers! Accepted as a *spotlight* at #ICLR2023! 📣 w/ @tri_dao </p><p>
📜 https://arxiv.org/abs/2212.14052 1/n</p></div>
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	<p><strong>Diyi Yang</strong> who focuses on Computational Social Science and Natural Language Processing</p>
<p><strong>Sanmi Koyejo </strong>who focuses on Trustworthy Machine Learning for Healthcare and Neuroscience</p>
<p><a href="/faculty">See the Entire Faculty</a></p>
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	<p>Sanmi KoyejoTrustworthy Machine Learning for Healthcare and Neuroscience</p>
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<p>Stanford AI Lab faculty and students enjoy chances to understand and solve the not-yet-doable pain points of industry. Get a chance to support and interact with SAIL’s brightest minds.</p>	<p><a href="/ai-affiliates-program/">Learn More</a>
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 Stanford AI Lab

 The Stanford Artificial Intelligence Laboratory (SAIL) has been a center of excellence for Artificial Intelligence research, teaching, theory, and practice since its founding in 1963.

 Latest News

 Congratulations to Oussama Khatib on Receiving the Title of Knight of the National Order of Merit of France!

 Congratulations to Oussama Khatib on receiving the title of Chevalier de l’Ordre National du Mérite (English: Knight of the National Order of Merit) from the government of France. The French order of merit was established in 1963 with support from Charles de Gaulle. Membership in this order is awarded by the President of France.

 Read More

 SAIL Releases a New Video on the History of AI at Stanford

 The Stanford Artificial Intelligence Lab has a rich history which you can learn about in a new video on AI at Stanford from 1962 to 2022. Click “Read More” below to see it.

 Read More

 Congratulations to Prof. Manning, SAIL Director, for his Honorary Doctorate at UvA!

 Professor Manning is being awarded by the the University of Amsterdam for his pioneering research contributions in the field of Natural Language Processing (NLP).

 Read More

 AI Lab celebrates 50th Anniversary of Intergalactic “Spacewar!” Olympics

 Stanford’s Artificial Intelligence Laboratory celebrated the 50th anniversary of the Intergalactic “Spacewar!” Olympics, the world’s first video game tournament on Wednesday, October 20th.

 Read More

 Chelsea Finn Explains Moravec’s Paradox in 5 Levels of Difficulty in WIRED Video

 Prof. Finn explains Moravec’s paradox, which is the observation that many things that are difficult for robots are easy for humans and vice versa, to a child, a teenager, an undergrad, a grad student and finally to a colleague.

 Read More

 Prof. Oussama Khatib’s Journey with Ocean OneK

 A robotic diving system that looks like a Transformer? Ocean OneK can go to depths that would kill a human diver. WIRED spoke with Professor Oussama Khatib to understand how he and his team designed, built and tested this robotic diver.

 Read More

 Latest Tweets

 @StanfordAILab
 ; Stanford AI Lab @StanfordAILab ·
 January 31 1620525931411705858

 We are proud to offer many of our best AI courses free on YouTube. Want to study with a cohort and course assistants? You can do that in the AI Professional Program via @StanfordOnline.
 New this year: Deep Multi-Task and Meta Learning with @chelseabfinn
 https://online.stanford.edu/programs/artificial-intelligence-professional-program

 Twitter feed image.
 Reply on Twitter 1620525931411705858 Retweet on Twitter 1620525931411705858 141 Like on Twitter 1620525931411705858 787 Twitter 1620525931411705858
 Retweet on Twitter Stanford AI Lab Retweeted
 ; Trevor Gale @Tgale96 ·
 January 27 1619027806087946242

 🧵We’re excited to announce MegaBlocks, our system for efficient “dropless” MoE training! 🤖

 MegaBlocks outperforms Tutel by up to 40% by reformulating MoEs as block-sparse operations, which allows us to avoid token dropping without sacrificing hardware efficiency 🚀.

 Twitter feed image.
 Reply on Twitter 1619027806087946242 Retweet on Twitter 1619027806087946242 18 Like on Twitter 1619027806087946242 97 Twitter 1619027806087946242
 Retweet on Twitter Stanford AI Lab Retweeted
 ; Deepak Pathak @pathak2206 ·
 January 24 1617947992727891968

 Thank you @DorsaSadigh for visiting CMU and giving a great talk. It was a full house both in-person and zoom! Thoroughly enjoyed all the discussions.

 Twitter feed image.
 Reply on Twitter 1617947992727891968 Retweet on Twitter 1617947992727891968 8 Like on Twitter 1617947992727891968 120 Twitter 1617947992727891968
 Retweet on Twitter Stanford AI Lab Retweeted
 ; Tony Z. Zhao @tonyzzhao ·
 January 24 1618020964918517762

 a short teaser of what we’ve been up to lately ⁦@stanford⁩: here is an end-to-end policy running on low-cost arms!

 Twitter feed video.
 Reply on Twitter 1618020964918517762 Retweet on Twitter 1618020964918517762 90 Like on Twitter 1618020964918517762 792 Twitter 1618020964918517762
 Retweet on Twitter Stanford AI Lab Retweeted
 ; Dan Fu @realDanFu ·
 January 23 1617605971395891201

 Attention is all you need... but how much of it do you need?

 Announcing H3 - a new generative language models that outperforms GPT-Neo-2.7B with only *2* attention layers! Accepted as a *spotlight* at #ICLR2023! 📣 w/ @tri_dao

 📜 https://arxiv.org/abs/2212.14052 1/n

 Reply on Twitter 1617605971395891201 Retweet on Twitter 1617605971395891201 283 Like on Twitter 1617605971395891201 1720 Twitter 1617605971395891201
 Retweet on Twitter Stanford AI Lab Retweeted
 ; Marco Pavone @drmapavone ·
 January 20 1616574150591795200

 We are organizing a workshop on
 bridging learning and algorithmic fairness in the operation of urban infrastructure systems at the next CPS-IoT Week on May 9. More info here: (including call for papers). #CPSIoTWeek @alexandrebayen @DevanshJalota @jr_laz

 AFL2023

 This workshop aims to bring together researchers from across operations research, computer science, and transportation t...

 sites.google.com

 Reply on Twitter 1616574150591795200 Retweet on Twitter 1616574150591795200 4 Like on Twitter 1616574150591795200 12 Twitter 1616574150591795200
 Load More...

 We Are Pleased to Welcome New Members of Our Faculty

 Diyi Yang who focuses on Computational Social Science and Natural Language Processing

 Sanmi Koyejo who focuses on Trustworthy Machine Learning for Healthcare and Neuroscience

 See the Entire Faculty

 Diyi Yang Computational Social Science, Natural Language Processing
 Sanmi Koyejo Trustworthy Machine Learning for Healthcare and Neuroscience

 Affiliates Program

 Stanford AI Lab faculty and students enjoy chances to understand and solve the not-yet-doable pain points of industry. Get a chance to support and interact with SAIL’s brightest minds.

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Stanford AI Lab
	The Stanford Artificial Intelligence Laboratory (SAIL) has been a center of excellence for Artificial Intelligence research, teaching, theory, and practice since its founding in 1963.
	
	
	  Latest News  
	
	 Congratulations to Oussama Khatib on Receiving the Title of Knight of the National Order of Merit of France! Congratulations to Oussama Khatib on receiving the title of Chevalier de l’Ordre National du Mérite (English: Knight of the National Order of Merit) from the government of France.  The French order of merit was established in 1963 with support from Charles de Gaulle.  Membership in this order is awarded by the President of France.    SAIL Releases a New Video on the History of AI at Stanford The Stanford Artificial Intelligence Lab has a rich history which you can learn about in a new video on AI at Stanford from 1962 to 2022. Click “Read More” below to see it.   Congratulations to Prof. Manning, SAIL Director,  for his Honorary Doctorate at UvA! Professor Manning is being awarded by the the University of Amsterdam for his pioneering research contributions in the field of Natural Language Processing (NLP).   AI Lab celebrates 50th Anniversary of Intergalactic “Spacewar!” Olympics Stanford’s Artificial Intelligence Laboratory celebrated the 50th anniversary of the Intergalactic “Spacewar!” Olympics, the world’s first video game tournament on Wednesday, October 20th.   Chelsea Finn Explains Moravec’s Paradox in 5 Levels of Difficulty in WIRED Video Prof. Finn explains Moravec’s paradox, which is the observation that many things that are difficult for robots are easy for humans and vice versa, to a child, a teenager, an undergrad, a grad student and finally to a colleague.   Prof. Oussama Khatib’s Journey with Ocean OneK A robotic diving system that looks like a Transformer? Ocean OneK can go to depths that would kill a human diver. WIRED spoke with Professor Oussama Khatib to understand how he and his team designed, built and tested this robotic diver. 	
	
	
	  Latest Tweets  
	
 
 
 
 	We are proud to offer many of our best AI courses free on YouTube. Want to study with a cohort and course assistants? You can do that in the AI Professional Program via @StanfordOnline.
New this year: Deep Multi-Task and Meta Learning with @chelseabfinn 
https://online.stanford.edu/programs/artificial-intelligence-professional-program 
	
 
	 
 
 
 🧵We’re excited to announce MegaBlocks, our system for efficient “dropless” MoE training! 🤖 
MegaBlocks outperforms Tutel by up to 40% by reformulating MoEs as block-sparse operations, which allows us to avoid token dropping without sacrificing hardware efficiency 🚀.  
 	Thank you @DorsaSadigh for visiting CMU and giving a great talk. It was a full house both in-person and zoom! Thoroughly enjoyed all the discussions. 
	
 
	 
 
 
 	a short teaser of what we’ve been up to lately ⁦@stanford⁩: here is an end-to-end policy running on low-cost arms! 
	
 
	 
 
 
 Attention is all you need... but how much of it do you need? 
Announcing H3 - a new generative language models that outperforms GPT-Neo-2.7B with only *2* attention layers! Accepted as a *spotlight* at #ICLR2023! 📣 w/ @tri_dao  
📜 https://arxiv.org/abs/2212.14052 1/n 
 
 Load More...
 
	
	
	
	We Are Pleased to Welcome New Members of Our Faculty
	 Diyi Yang who focuses on Computational Social Science and Natural Language Processing
 Sanmi Koyejo who focuses on Trustworthy Machine Learning for Healthcare and Neuroscience
 See the Entire Faculty 
	
	
	
	 
	Diyi YangComputational Social Science, Natural Language Processing
	
	
	 
	Sanmi KoyejoTrustworthy Machine Learning for Healthcare and Neuroscience
	
	
	
	
	Affiliates Program
Stanford AI Lab faculty and students enjoy chances to understand and solve the not-yet-doable pain points of industry. Get a chance to support and interact with SAIL’s brightest minds.	 Learn More
Stanford AI Lab
https://ai.stanford.edu/
https://www.bluewaveailabs.com/
Front Page
2022-06-03
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<div><div><div><h5>WHAT WE DO</h5><h2>Conventional approaches are becoming inadequate for solving <i><b>high-value</b></i>, <i><b>complex problems</b></i></h2><p>Operational challenges for safety, efficiency, and resource allocation are becoming more impactful and complex.</p><div><p>Specialized and innovative approaches are crucial to making informed decisions. We can successfully incorporate Artificial Intelligence and Machine Learning (AI/ML) to properly evaluate and mitigate risks, drive more efficient operations, and solve complex challenges. </p><p> Our proprietary solutions successfully incorporate your domain knowledge and data into concrete solutions which increase efficiency, save time, money, and optimize performance. Industry leaders partner with us to find solutions to previously intractable problems, predict and prevent catastrophic operational delays, and drive better methods for resource allocation and safety. </p><p> Our team of industry leaders, engineers, and scientists integrate decades of expertise with the most advanced techniques in AI/ML to (1) transform and safeguard military information and economic superiority by assessing the situational balance of power, using reconnaissance data, and gaining strategic advantage; and (2) establish nuclear power plants as a reliable, carbon-free, clean-energy source by increasing efficiency and reducing downtime with our preventative maintenance models via BWnuclear.ai. </p><p> Blue Wave has proven to be the trusted leader in AI solutions for the nuclear industry, already serving over half the U.S. domestic fleet of boiling water reactors and making a significant difference in their operational efficiency…</p></div> <p><a href="https://www.bluewaveailabs.com/about-blue-wave-ai-labs">LEARN MORE</a></p></div><div><div><h5>OUR POWERFUL AI-CENTRIC PRODUCTS FOR THE NUCLEAR POWER INDUSTRY</h5><h2>BWnuclear.ai</h2><p>Energize reload core design with the BWnuclear.ai software suite. Secured on the Gov Cloud, BWnuclear.ai is a powerful AI platform that integrates seamlessly into established processes, whether it be for reload core design or cycle management applications. Its predictive power has enabled game-changing improvements in the reload planning process, resulting in millions of dollars in savings per fuel cycle.</p></div><div><div><div><p></p></div><div><h5>MCO.ai</h5><p>MCO.ai Platform enables visibility into moisture carryover, reduces exposure risk, ensures long-term viability of key plant assets, enhances core efficiency, and reduces reload fuel costs in Boiling Water Reactors.﻿ </p></div></div><div><div><p></p></div><div><h5>eigenvalue.ai</h5><p>eigenvalue.ai is a state-of-the-art SaaS platform for predicting the eigenvalue in BWRs enables powerful predictive capability of one of the most fundamental parameters in nuclear engineering, reduces reload fuel costs, and ensures fuel cycle energy requirements are met.</p></div></div><div><div><p></p></div><div><h5>Thermal Limits.ai</h5><p>Thermal Limits.ai is a prediction platform for Thermal Limit Bias reduction in BWRs that will be launched soon and will perfectly complement your reload planning process. You can become more efficient, reduce unexpected downtime, and gain invaluable insight with our powerful AI-based proprietary predictive capabilities and technological expertise.Thermal Limits.ai will be launched soon. Become more efficient and reduce unexpected downtime with AI and machine learning. </p></div></div></div></div><div><h5>Industries</h5><h2>Nuclear Energy</h2><p>By utilizing your existing plant data our Machine Learning predictive models can help reduce fuel and O&amp;M costs, allowing nuclear energy to better compete as a cost-effective, sustainable energy source to power our carbon neutral, clean energy future.</p><p>Our solutions integrate your domain knowledge and data into concrete solutions which save millions in reload fuel costs, eliminate operational challenges, predict component failure, and optimize scheduled downtime.</p> <p><a href="https://www.bluewaveailabs.com/nuclear-energy-case-studies">LEARN MORE</a></p></div><div><p></p><h2>Use your data and our expertise to mitigate risk and increase efficiency.</h2><p></p></div><div><h5>Industries</h5><h2>Military Defense and National Security</h2><p>AI and Machine Learning have the potential to be a transformative national security technology comparable to the impacts that integrated circuits and biotech have had on strategy, organization, priorities, and allocation of resources.</p><p>Learn how your data can be transformed into critical intelligence and utilized to recognize patterns, derive correlations, and effectively assess complex, chaotic situations to mitigate national security risks.</p> <p><a href="https://www.bluewaveailabs.com/military-ai-and-machine-learning-case-studies">LEARN MORE</a></p></div><div><div><h5>DOE AWARDS $7 MILLION TO BLUE WAVE AI LABS</h5> <p><a href="https://www.bluewaveailabs.com/doe-awards-nearly-7-million-to-purdue-based-startup-to-advance-nuclear-technology-using-artificial-intelligence-machine-learning">LEARN MORE</a></p></div><div><h5>BLUE WAVE AI LABS AND CONSTELLATION FORM LONG-TERM STRATEGIC PARTNERSHIP</h5> <p><a href="https://www.bluewaveailabs.com/blue-wave-ai-labs-and-constellation-strike-long-term-strategic-agreement-for-nuclear-ai-tool-suite">LEARN MORE</a></p></div><div><h5>BLUE WAVE AI LABS RECEIVES TIP AWARD AT NEI CONFERENCE</h5> <p><a href="https://www.bluewaveailabs.com/blue-wave-ai-labs-receives-tip-award-at-nei-conference">LEARN MORE</a></p></div><div><h5>inTechBrew ANNOUNCE BLUE WAVE AI LABS AND DIGITAL SIG NUCLEAR INSTITUTE WEBINAR</h5> <p><a href="https://www.bluewaveailabs.com/intechbrew-announce-joint-blue-wave-ai-labs-and-digital-sig-nuclear-institute-webinar-ai-for-nuclear-reactors">LEARN MORE</a></p></div><div><h5>STUDSVIK SCANDPOWER AND BLUE WAVE AI LABS ANNOUNCE STRATEGIC PARTNERSHIP TO DELIVEER NEXT GENERATION ACUURACY </h5> <p><a href="https://www.bluewaveailabs.com/studsvik-scandpower-and-blue-wave-ai-labs-announce-strategic-partnership-to-deliver-next-generation-accuracy-to-nuclear-power-analytics">LEARN MORE</a></p></div><div><h5>READ OUR PUBLISHED NUCLEAR NEWS FEATURE ARTICLE POWERING OUR NUCLEAR FLEET WITH ARTIFICIAL INTELLIGENCE</h5> <p><a href="https://www.bluewaveailabs.com/powering-our-nuclear-fleet-with-artificial-intelligence">LEARN MORE</a></p></div></div><div><h2>Through collaborative and innovative partnerships, we’re able to identify and solve real-world, complex problems.</h2><p><a href="https://www.bluewaveailabs.com/about-blue-wave-ai-labs/partnerships">LEARN MORE</a></p></div><div><h2>Latest from Our Newsroom</h2><p>Learn how AI and Machine Learning are impacting safety, efficiency, strategic planning, and society.  Explore our industry news and updates to see how our technology empowers industry leaders to solve complex, high-value problems.</p></div><div><p></p><h2>Subscribe to our Newsletter</h2><p></p></div><div><p></p><h2>We solve previously unsolvable problems﻿</h2><p></p></div></div></div>||||I||||Skip to content
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 Transform your data into unprecedented visibility and game-changing solutions ﻿

 BLUE WAVE AI LABS
 Industry Focused AI

 We build actionable, ethical, and responsible AI solutions that evaluate and mitigate risk, increase efficiency to save time and money, and optimize performance for the nuclear energy and defense industries. Contact us to solve your most complex operational and strategic challenges.

 CONTACT US

 WHAT WE DO

 Conventional approaches are becoming inadequate for solving high-value , complex problems

 Operational challenges for safety, efficiency, and resource allocation are becoming more impactful and complex.

 Specialized and innovative approaches are crucial to making informed decisions. We can successfully incorporate Artificial Intelligence and Machine Learning (AI/ML) to properly evaluate and mitigate risks, drive more efficient operations, and solve complex challenges.

 Our proprietary solutions successfully incorporate your domain knowledge and data into concrete solutions which increase efficiency, save time, money, and optimize performance. Industry leaders partner with us to find solutions to previously intractable problems, predict and prevent catastrophic operational delays, and drive better methods for resource allocation and safety.

 Our team of industry leaders, engineers, and scientists integrate decades of expertise with the most advanced techniques in AI/ML to (1) transform and safeguard military information and economic superiority by assessing the situational balance of power, using reconnaissance data, and gaining strategic advantage ; and (2) establish nuclear power plants as a reliable, carbon-free, clean-energy source by increasing efficiency and reducing downtime with our preventative maintenance models via BWnuclear.ai .

 Blue Wave has proven to be the trusted leader in AI solutions for the nuclear industry , already serving over half the U.S. domestic fleet of boiling water reactors and making a significant difference in their operational efficiency…

 LEARN MORE

 OUR POWERFUL AI-CENTRIC PRODUCTS FOR THE NUCLEAR POWER INDUSTRY

 BWnuclear.ai

 Energize reload core design with the BWnuclear.ai software suite. Secured on the Gov Cloud, BWnuclear.ai is a powerful AI platform that integrates seamlessly into established processes, whether it be for reload core design or cycle management applications. Its predictive power has enabled game-changing improvements in the reload planning process, resulting in millions of dollars in savings per fuel cycle.

 LEARN MORE

 MCO.ai

 MCO.ai Platform enables visibility into moisture carryover, reduces exposure risk, ensures long-term viability of key plant assets, enhances core efficiency, and reduces reload fuel costs in Boiling Water Reactors. ﻿



 LEARN MORE

 eigenvalue.ai

 eigenvalue.ai is a state-of-the-art SaaS platform for predicting the eigenvalue in BWRs enables powerful predictive capability of one of the most fundamental parameters in nuclear engineering, reduces reload fuel costs, and ensures fuel cycle energy requirements are met.

 LEARN MORE

 Thermal Limits.ai

 Thermal Limits.ai is a prediction platform for Thermal Limit Bias reduction in BWRs that will be launched soon and will perfectly complement your reload planning process. You can become more efficient, reduce unexpected downtime, and gain invaluable insight with our powerful AI-based proprietary predictive capabilities and technological expertise.Thermal Limits.ai will be launched soon. Become more efficient and reduce unexpected downtime with AI and machine learning.




 LEARN MORE

 Industries

 Nuclear Energy

 By utilizing your existing plant data our Machine Learning predictive models can help reduce fuel and O&M costs , allowing nuclear energy to better compete as a cost-effective, sustainable energy source to power our carbon neutral, clean energy future.

 Our solutions integrate your domain knowledge and data into concrete solutions which save millions in reload fuel costs, eliminate operational challenges, predict component failure, and optimize scheduled downtime.

 LEARN MORE

 Use your data and our expertise to mitigate risk and increase efficiency.

 Industries

 Military Defense and National Security

 AI and Machine Learning have the potential to be a transformative national security technology comparable to the impacts that integrated circuits and biotech have had on strategy, organization, priorities, and allocation of resources.

 Learn how your data can be transformed into critical intelligence and utilized to recognize patterns, derive correlations, and effectively assess complex, chaotic situations to mitigate national security risks.

 LEARN MORE

 DOE AWARDS $7 MILLION TO BLUE WAVE AI LABS

 LEARN MORE

 BLUE WAVE AI LABS AND CONSTELLATION FORM LONG-TERM STRATEGIC PARTNERSHIP

 LEARN MORE

 BLUE WAVE AI LABS RECEIVES TIP AWARD AT NEI CONFERENCE

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WHAT WE DO Conventional approaches are becoming inadequate for solving  high-value ,  complex problems   Operational challenges for safety, efficiency, and resource allocation are becoming more impactful and complex.  Specialized and innovative approaches are crucial to making informed decisions. We can successfully incorporate Artificial Intelligence and Machine Learning (AI/ML) to properly evaluate and mitigate risks, drive more efficient operations, and solve complex challenges.   Our proprietary solutions successfully incorporate your domain knowledge and data into concrete solutions which increase efficiency, save time, money, and optimize performance. Industry leaders partner with us to find solutions to previously intractable problems, predict and prevent catastrophic operational delays, and drive better methods for resource allocation and safety.   Our team of industry leaders, engineers, and scientists integrate decades of expertise with the most advanced techniques in AI/ML to (1) transform and safeguard military information and economic superiority by assessing the situational balance of power, using reconnaissance data, and gaining strategic advantage; and (2) establish nuclear power plants as a reliable, carbon-free, clean-energy source by increasing efficiency and reducing downtime with our preventative maintenance models via BWnuclear.ai.   Blue Wave has proven to be the trusted leader in AI solutions for the nuclear industry, already serving over half the U.S. domestic fleet of boiling water reactors and making a significant difference in their operational efficiency…   LEARN MORE     OUR POWERFUL AI-CENTRIC PRODUCTS FOR THE NUCLEAR POWER INDUSTRY BWnuclear.ai Energize reload core design with the BWnuclear.ai software suite. Secured on the Gov Cloud, BWnuclear.ai is a powerful AI platform that integrates seamlessly into established processes, whether it be for reload core design or cycle management applications. Its predictive power has enabled game-changing improvements in the reload planning process, resulting in millions of dollars in savings per fuel cycle.         MCO.ai MCO.ai Platform enables visibility into moisture carryover, reduces exposure risk, ensures long-term viability of key plant assets, enhances core efficiency, and reduces reload fuel costs in Boiling Water Reactors.﻿          eigenvalue.ai eigenvalue.ai is a state-of-the-art SaaS platform for predicting the eigenvalue in BWRs enables powerful predictive capability of one of the most fundamental parameters in nuclear engineering, reduces reload fuel costs, and ensures fuel cycle energy requirements are met.         Thermal Limits.ai Thermal Limits.ai is a prediction platform for Thermal Limit Bias reduction in BWRs that will be launched soon and will perfectly complement your reload planning process. You can become more efficient, reduce unexpected downtime, and gain invaluable insight with our powerful AI-based proprietary predictive capabilities and technological expertise.Thermal Limits.ai will be launched soon. Become more efficient and reduce unexpected downtime with AI and machine learning.       Industries Nuclear Energy By utilizing your existing plant data our Machine Learning predictive models can help reduce fuel and O&amp;M costs, allowing nuclear energy to better compete as a cost-effective, sustainable energy source to power our carbon neutral, clean energy future. Our solutions integrate your domain knowledge and data into concrete solutions which save millions in reload fuel costs, eliminate operational challenges, predict component failure, and optimize scheduled downtime.  LEARN MORE      Use your data and our expertise to mitigate risk and increase efficiency.     Industries Military Defense and National Security AI and Machine Learning have the potential to be a transformative national security technology comparable to the impacts that integrated circuits and biotech have had on strategy, organization, priorities, and allocation of resources. Learn how your data can be transformed into critical intelligence and utilized to recognize patterns, derive correlations, and effectively assess complex, chaotic situations to mitigate national security risks.  LEARN MORE     DOE AWARDS $7 MILLION TO BLUE WAVE AI LABS  LEARN MORE    BLUE WAVE AI LABS AND CONSTELLATION FORM LONG-TERM STRATEGIC PARTNERSHIP  LEARN MORE    BLUE WAVE AI LABS RECEIVES TIP AWARD AT NEI CONFERENCE  LEARN MORE    inTechBrew ANNOUNCE BLUE WAVE AI LABS AND DIGITAL SIG NUCLEAR INSTITUTE WEBINAR  LEARN MORE    STUDSVIK SCANDPOWER AND BLUE WAVE AI LABS ANNOUNCE STRATEGIC PARTNERSHIP TO DELIVEER NEXT GENERATION ACUURACY   LEARN MORE    READ OUR PUBLISHED NUCLEAR NEWS FEATURE ARTICLE POWERING OUR NUCLEAR FLEET WITH ARTIFICIAL INTELLIGENCE  LEARN MORE     Through collaborative and innovative partnerships, we’re able to identify and solve real-world, complex problems.  LEARN MORE    Latest from Our Newsroom Learn how AI and Machine Learning are impacting safety, efficiency, strategic planning, and society.  Explore our industry news and updates to see how our technology empowers industry leaders to solve complex, high-value problems.     Subscribe to our Newsletter       We solve previously unsolvable problems
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AI Labs | Publicis Sapient
2019-12-20
None
<div><div>
<p>The data models and prototypes you create will address various issues and concerns, including customer intelligence, operational efficiency, business insights, supply chain optimization and compliance.</p>
<p>With access to one the world’s largest collections of data, collected from our sister companies, we build and adopt state-of-the-art machine learning models to produce a variety of solutions, including predictive analytics, recommendation &amp; personalization, anomaly detection, natural language processing, computer vision and simulation-based optimization.</p>
<p>Our data scientists develop these solutions with our deep knowledge of key technologies: machine learning frameworks (Keras &amp; Tensorflow, PyTorch, Scikit-learn), programming languages (Python, R, Julia) and industry platforms (GCP, Azure, AWS, Watson, Dataiku, Domino Datalabs, Anaconda, H2O.ai, etc.)   </p>
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 Generative AI: Using artificial intelligence to make human impact. Learn how

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 Publicis Sapient + Elder Research

 With the enormous opportunity to help our clients change the way they interact and understand their customers, Publicis Sapient, a digital business transformation partner, has teamed up with Elder Research, a data science consultancy, to create PS AI Labs. The joint venture fuses Publicis Sapient’s 30 years of global delivery and experience capabilities with Elder Research’s 25 years of expertise in the multidisciplinary field of data science.

 By 2021, 80 percent of emerging technologies will have AI foundations. (Source: Gartner)

 Our ambition is to create an environment where data scientists and artificial-intelligence experts can tackle some of today’s most complex, strategic operational challenges. This new venture will deliver predictive analytics solutions to leading businesses. It’s an opportunity to design, build and implement practical, end-to-end platforms that help the world’s most recognized brands to make smarter decisions and provide more valuable services to their customers.

 With mentorship from both companies and the agility of a startup, we’re building something from the ground up with deep resources, alongside a team of the world’s premier talent in data science.

 World-class brands, real-world impact

 We grade ourselves solely on the value we bring to our clients.

 Our talented data and decision scientists thrive in a continuous-learning culture. You will team up with other gifted professionals on a variety of emerging AI projects. Our rigorous process includes intensive training curricula and ongoing peer review to ensure our methodology is scientifically sound.

 You will have the opportunity to capitalize on the latest technologies to create real business impact for our clients. This is a chance to take part in artificial intelligence solutions for some of the biggest Fortune 1000 companies – affecting five billion people in the next decade alone.

 PS AI Labs data scientists will leverage, support and help fuel the intellectual property of advanced AI applications and technologies created by Publicis Sapient and Elder Research such as the Applied Innovation Platform, Glyphic, KaaS™, the Machine Learning Platform, and NUI™.

 Our alliance partners

 You’ll work in collaboration with our alliance partners such as Google, AWS and Microsoft, leveraging their AI and machine learning technologies to develop additional solutions and platforms.

 * 
 * 
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 Why join PS AI Labs?

 There’s no better time for data and decision scientists to tackle the most challenging problems facing our clients. Joining PS AI Labs will expose you to a variety of the most complex issues in data science, across various industries: financial services, travel & hospitality, energy & commodities, retail, media and healthcare.

 A continuous learning culture

 We want you to stay abreast the field’s latest developments, reading academic journals, attending conferences and asking how the insights apply to your work. You’ll have dedicated time to focus on learning and growing as a practitioner. If you love data science, you’ll never be bored! There’s always an exciting challenge.

 Do what you love

 You’ll have the opportunity to focus on what you do best–data science. You will have access to diverse, multidisciplinary teams working in a collegial environment to deliver solutions to the problems you’re tackling.

 True to our principles

 We value humility, curiosity, resourcefulness and imagination. A commitment to lifetime learning is in our DNA.

 Access to the latest technologies

 The data models and prototypes you create will address various issues and concerns, including customer intelligence, operational efficiency, business insights, supply chain optimization and compliance.

 With access to one the world’s largest collections of data, collected from our sister companies, we build and adopt state-of-the-art machine learning models to produce a variety of solutions, including predictive analytics, recommendation & personalization, anomaly detection, natural language processing, computer vision and simulation-based optimization.

 Our data scientists develop these solutions with our deep knowledge of key technologies: machine learning frameworks (Keras & Tensorflow, PyTorch, Scikit-learn), programming languages (Python, R, Julia) and industry platforms (GCP, Azure, AWS, Watson, Dataiku, Domino Datalabs, Anaconda, H2O.ai, etc.)

 Could we be a good fit?

 If you are interested in a data science role with PS AI Labs’ U.S. or U.K. offices, join our talent network by sending us your resume and cover letter.

 Contact us if you have questions.

 If you would like to know about what we do:
 Case Studies
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 Check in here periodically for updates as we grow our team.
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The data models and prototypes you create will address various issues and concerns, including customer intelligence, operational efficiency, business insights, supply chain optimization and compliance.
With access to one the world’s largest collections of data, collected from our sister companies, we build and adopt state-of-the-art machine learning models to produce a variety of solutions, including predictive analytics, recommendation &amp; personalization, anomaly detection, natural language processing, computer vision and simulation-based optimization.
Our data scientists develop these solutions with our deep knowledge of key technologies: machine learning frameworks (Keras &amp; Tensorflow, PyTorch, Scikit-learn), programming languages (Python, R, Julia) and industry platforms (GCP, Azure, AWS, Watson, Dataiku, Domino Datalabs, Anaconda, H2O.ai, etc.)
AI Labs | Publicis Sapient
https://www.publicissapient.com/services/data-artificial-intelligence/ai-labs
https://paiml.com/
Pragmatic AI Labs
2023-01-01
None
<div><div>
 
 
 
 <article><p></p>
<p>Pragmatic AI Labs creates content and training for the top educational brands in the world, including <a href="https://learning.oreilly.com/library/publisher/pragmatic-ai-solutions/">O’Reilly</a>, <a href="https://www.udacity.com/course/machine-learning-engineer-for-microsoft-azure-nanodegree--nd00333">Udacity</a>, <a href="https://www.coursera.org/learn/cloud-computing-foundations-duke">Coursera</a>, <a href="https://www.datacamp.com/courses/command-line-automation-in-python">DataCamp</a>, Skillsoft, and <a href="https://www.pearsonitcertification.com/authors/bio/5582808d-a2c5-4bc8-8587-ef9bf469f551">Pearson</a>.</p>
<p>We have worked and taught at leading universities globally, including <a href="https://datascience.duke.edu/noah-gift">Duke</a>, <a href="https://sps.northwestern.edu/masters/data-science/faculty.php">Northwestern</a>, <a href="https://gsm.ucdavis.edu/profile/noah-gift">UC Davis</a>, <a href="https://www.ischool.berkeley.edu/people/noah-gift">UC Berkeley</a>, <a href="https://haslam.utk.edu/speakers/business-analytics-forum/noah-gift">University of Tennessee</a> and the University of North Carolina. We have consulted, trained, and worked with organizations like NASA, PayPal, <a href="https://techcommunity.microsoft.com/t5/educator-developer-blog/jupyter-notebook-workflow-devops-for-data-science/ba-p/1169468">Microsoft</a>, <a href="https://aws.amazon.com/developer/community/heroes/noah-gift/">AWS</a> and <a href="https://githubuniverse.com/speakers/Alfredo-Deza/">Github</a>.</p>
<p>Our mission is to educate the world by training millions on vocational skills that directly lead to jobs or upskilling. All <a href="docs/home/">books and courses</a> are available via Creative Commons <a href="books/cloud-computing-for-data/">Attribution-NonCommercial license</a>. These books and classes can be taught at the vocational level through advanced graduate work at top universities.</p>
<ul>
<li>Subscribe to the <a href="https://www.youtube.com/c/PragmaticAILabs">Pragmatic AI Labs YouTube Channel</a> to get the latest content!</li>
<li>Subscribe to the <a href="https://podcast.paiml.com/">52 Weeks of Cloud Podcast (Focused initially on AWS)</a></li>
<li>Subscribe to <a href="https://discord.gg/7epak2bY5A">Pragmatic AI Labs Discord server</a></li>
</ul>
<h2>
 Latest Content
 <a href="#latest-content">#</a>
</h2>
<h5>
 MLOps (Specialization: 4 Courses)
 <a href="#mlops-specialization--4-courses">#</a>
</h5>
<h6>
 Publisher: Coursera + Duke
 <a href="#publisher--coursera--duke">#</a>
</h6>
<h6>
 Release Date: 1/1/2023
 <a href="#release-date--112023">#</a>
</h6>
<ul>
<li>
<p><a href="https://www.coursera.org/specializations/mlops-machine-learning-duke">MLOps | Machine Learning Operations Specialization</a></p>
</li>
<li>
<p><a href="https://www.coursera.org/learn/devops-dataops-mlops-duke">DevOps, DataOps, MLOps</a></p>
</li>
<li>
<p><a href="https://www.coursera.org/learn/mlops-aws-azure-duke">MLOps Platforms: Amazon SageMaker and Azure ML</a></p>
</li>
<li>
<p><a href="https://www.coursera.org/learn/open-source-mlops-platforms-duke">Open Source Platforms for MLOps</a></p>
</li>
<li>
<p><a href="https://www.coursera.org/learn/python-essentials-mlops-duke">Python Essentials for MLOps</a></p>
</li>
<li>
<p><a href="https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale">Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization</a></p>
</li>
<li>
<p><a href="https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke">Python, Bash, and SQL Essentials for Data Engineering Specialization</a></p>
</li>
<li>
<p><a href="https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true">AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity</a></p>
</li>
<li>
<p><a href="https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017">O’Reilly Book: Practical MLOps</a></p>
</li>
<li>
<p><a href="https://www.amazon.com/gp/product/B082P97LDW/">O’Reilly Book: Python for DevOps</a></p>
</li>
<li>
<p><a href="https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877">O’Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform</a></p>
</li>
<li>
<p><a href="https://www.amazon.com/gp/product/B07FB8F8QP/">Pragmatic AI: An Introduction to Cloud-based Machine Learning</a></p>
</li>
<li>
<p><a href="https://www.amazon.com/gp/product/B0855FSFYZ">Pragmatic AI Labs Book: Python Command-Line Tools</a></p>
</li>
<li>
<p><a href="https://www.amazon.com/gp/product/B0992BN7W8">Pragmatic AI Labs Book: Cloud Computing for Data Analysis</a></p>
</li>
<li>
<p><a href="https://www.amazon.com/gp/product/B0855NSRR7">Pragmatic AI Book: Minimal Python</a></p>
</li>
<li>
<p><a href="https://www.amazon.com/gp/product/B0855NSRR7">Pragmatic AI Book: Testing in Python</a></p>
</li>
</ul>
<h2>
 ðº Latest YouTube Videos
 <a href="#-latest-youtube-videos">#</a>
</h2>
<p></p>
<ul>
<li><a href="https://www.youtube.com/watch?v=hpfCOmZL6XM">Setting up your Rust development environment</a></li>
<li><a href="https://www.youtube.com/watch?v=cFAhdgwg5nc">Deploy Rust on Azure Functions</a></li>
<li><a href="https://www.youtube.com/watch?v=qYIZqMhvgVc">Free! Develop on Linux with Codespaces</a></li>
<li><a href="https://www.youtube.com/watch?v=auQAXH0wtjo">Introduction to Azure AutoML using regression</a></li>
<li><a href="https://www.youtube.com/watch?v=AsL7MI8b0m4">How to deploy PostgreSQL on Azure</a></li>
<li><a href="https://www.youtube.com/watch?v=ldFJBzSH5cM">Create Azure Functions with Flask and automation</a></li>
<li><a href="https://www.youtube.com/watch?v=UfFpMJSDdGQ">Azure Functions for the AZ-900 certification</a></li>
<li><a href="https://www.youtube.com/watch?v=UZ2oOJe9SP0">MLOps packaging: HuggingFace and GitHub Container Registry</a></li>
<li><a href="https://www.youtube.com/watch?v=CDuXK8du26w">Ultimate development environment with VSCode web and Codespaces</a></li>
<li><a href="https://www.youtube.com/watch?v=rXwR7LqDreg">MLOps deployment to Azure Container Apps</a></li>
<li><a href="https://www.youtube.com/watch?v=_n8JKdRFgog">MLOps packaging: HuggingFace and Azure Container Registry</a></li>
<li><a href="https://www.youtube.com/watch?v=Jo98GLhw6BM">MLOps packaging: HuggingFace and Docker Hub</a></li>
<li><a href="https://www.youtube.com/watch?v=H4B37bZdYsk">GitHub Codespaces with custom dotfiles</a></li>
<li><a href="https://www.youtube.com/watch?v=Nd4cwxROq30">Get Azure spending under control!</a></li>
<li><a href="https://www.youtube.com/watch?v=Z06OyG4i18w">Azure Login in GitHub Actions</a></li>
</ul>
<ul>
<li><a href="https://www.youtube.com/watch?v=crtU6mGydXc">Rust for MLOps with Amazon Sagemaker</a></li>
<li><a href="https://www.youtube.com/watch?v=7kvEsCBO3Jg">Using VS Code, Copilot and Codespaces to Level Up to Rust from Python</a></li>
<li><a href="https://www.youtube.com/watch?v=Oeu-ytx7Lfw">Rust Distroless PyTorch Computer Vision Microservice Walkthrough</a></li>
<li><a href="https://www.youtube.com/watch?v=2g2mAEWNeBs">Assimilate-Go</a></li>
<li><a href="https://www.youtube.com/watch?v=1Mb48uJrYhI">Assimilate-Zig</a></li>
<li><a href="https://www.youtube.com/watch?v=-86h9DX-3Jo">Assimilate-Bash</a></li>
<li><a href="https://www.youtube.com/watch?v=4GUGvGXIzAs">Assimilate-Haskell</a></li>
<li><a href="https://www.youtube.com/watch?v=uHk3F8VZMHA">Assimilate-Rust</a></li>
<li><a href="https://www.youtube.com/watch?v=lTJEVlWkbZg">Pair Programming a Rust Chatbot using ChatGPT 4 and GitHub Copilot</a></li>
<li><a href="https://www.youtube.com/watch?v=OKZLABfzL7k">Quick demo of GitHub Copilot CLI Tool that helps you write shell commands</a></li>
<li><a href="https://www.youtube.com/watch?v=F67rjOjEQE4">Quick Demo: GitHub Copilot CLI</a></li>
<li><a href="https://www.youtube.com/watch?v=rBhMUAlC9TM">Assimilate OpenAI w/ Rust</a></li>
<li><a href="https://www.youtube.com/watch?v=72CqJAAv7KQ">Assimilate GitHub</a></li>
<li><a href="https://www.youtube.com/watch?v=W4j2C3Jw6Rk">MLOPs Projects with Rust</a></li>
<li><a href="https://www.youtube.com/watch?v=NIkDXzTyvVg">Data Science Projects With Rust</a></li>
</ul>
</article>
 
 
 
 
 
 
 
 
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Pragmatic AI Labs creates content and training for the top educational brands in the world, including O’Reilly, Udacity, Coursera, DataCamp, Skillsoft, and Pearson.
We have worked and taught at leading universities globally, including Duke, Northwestern, UC Davis, UC Berkeley, University of Tennessee and the University of North Carolina. We have consulted, trained, and worked with organizations like NASA, PayPal, Microsoft, AWS and Github.
Our mission is to educate the world by training millions on vocational skills that directly lead to jobs or upskilling. All books and courses are available via Creative Commons Attribution-NonCommercial license. These books and classes can be taught at the vocational level through advanced graduate work at top universities.
Subscribe to the Pragmatic AI Labs YouTube Channel to get the latest content!
Subscribe to the 52 Weeks of Cloud Podcast (Focused initially on AWS) 
Subscribe to Pragmatic AI Labs Discord server 
 Latest Content
 #
 MLOps (Specialization: 4 Courses)
 #
 Publisher: Coursera + Duke
 #
 Release Date: 1/1/2023
 #
 MLOps | Machine Learning Operations Specialization 
 DevOps, DataOps, MLOps 
 MLOps Platforms: Amazon SageMaker and Azure ML 
 Open Source Platforms for MLOps 
 Python Essentials for MLOps 
 Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization 
 Python, Bash, and SQL Essentials for Data Engineering Specialization 
 AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity 
 O’Reilly Book: Practical MLOps 
 O’Reilly Book: Python for DevOps 
 O’Reilly Book: Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform 
 Pragmatic AI: An Introduction to Cloud-based Machine Learning 
 Pragmatic AI Labs Book: Python Command-Line Tools 
 Pragmatic AI Labs Book: Cloud Computing for Data Analysis 
 Pragmatic AI Book: Minimal Python 
 Pragmatic AI Book: Testing in Python 
 ðº Latest YouTube Videos
 #
 
 Setting up your Rust development environment 
 Deploy Rust on Azure Functions 
 Free! Develop on Linux with Codespaces 
 Introduction to Azure AutoML using regression 
 How to deploy PostgreSQL on Azure 
 Create Azure Functions with Flask and automation 
 Azure Functions for the AZ-900 certification 
 MLOps packaging: HuggingFace and GitHub Container Registry 
 Ultimate development environment with VSCode web and Codespaces 
 MLOps deployment to Azure Container Apps 
 MLOps packaging: HuggingFace and Azure Container Registry 
 MLOps packaging: HuggingFace and Docker Hub 
 GitHub Codespaces with custom dotfiles 
 Get Azure spending under control! 
 Azure Login in GitHub Actions 
 Rust for MLOps with Amazon Sagemaker 
 Using VS Code, Copilot and Codespaces to Level Up to Rust from Python 
 Rust Distroless PyTorch Computer Vision Microservice Walkthrough 
 Assimilate-Go 
 Assimilate-Zig 
 Assimilate-Bash 
 Assimilate-Haskell 
 Assimilate-Rust 
 Pair Programming a Rust Chatbot using ChatGPT 4 and GitHub Copilot 
 Quick demo of GitHub Copilot CLI Tool that helps you write shell commands 
 Quick Demo: GitHub Copilot CLI 
 Assimilate OpenAI w/ Rust 
 Assimilate GitHub 
 MLOPs Projects with Rust 
 Data Science Projects With Rust
Pragmatic AI Labs
https://paiml.com/
https://ailab.criteo.com
Criteo AI Lab (Criteo AI Lab): Machine Learning for Computational Advertising
2016-07-26
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 25 Jan 2023</p><p>In this new year 2023, we are happy to share that Criteos...</p>
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 Papers at NeurIPS 2022 Alexandre Ramé (Sorbonne Université), Matthieu Kirchmeyer (Sorbonne Université...

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Company 
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	   About us
	 Research team 
 Publications 
 Blog posts 
 Datasets 
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	 &gt; Laplace’s Causal Demon 
	 &gt; Bayesian ML at Scale 
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	 &gt;&gt; 2019 Edition 
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	 &gt;&gt; 2017 Edition 
	 &gt;&gt; 2016 Edition 
	
 Programs 
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 Latest @ Criteo AI Lab
 
 
 
  Papers @AISTATS (x4), @ICLR, @TMLR, @AAAI, @AAMAS
 25 Jan 2023 In this new year 2023, we are happy to share that Criteos...
 
  More
  
 
 
  Papers from Criteos at NeurIPS (x 7), TMLR
 20 Sep 2022 Papers at NeurIPS 2022 Alexandre Ramé (Sorbonne Université), Matthieu Kirchmeyer (Sorbonne Université...
 
  More
  
 
 
  Criteo 1TB Click logs dataset for MLPerf
 22 Jun 2022 CRITEO DATA TERMS OF USE FOR MLPERF Users By exercising the Licensed...
 
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  Papers @ICML, @COLT, @UAI, @ICLR, and more
 23 May 2022 A few papers co-authored by Criteos recently accepted at many premiere ML...
 
  More
Criteo AI Lab (Criteo AI Lab): Machine Learning for Computational Advertising
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