

From my story in MIT Technology Review:




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From my Forbes blog:






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With IdeaMarket, Idealab’s Bill Gross Wants To Create 1 Million Startups

From my Forbes blog:






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Interview: Inside Google Brain Founder Andrew Ng’s Plans To Transform Baidu

Baidu Chief Scientist Andrew Ng

Baidu Chief Scientist Andrew Ng

From my Forbes blog:

Little known outside China, the Chinese search engine Baidu scored a coup earlier this year when it hired Andrew Ng to be chief scientist and open a new artificial intelligence lab in Silicon Valley. Ng, a Stanford computer science professor who headed the Google Brain AI project and then cofounded the online education startup Coursera, is the foundation for Baidu’s plan to transform itself into a global power.

In two wide-ranging conversations at Baidu’s still mostly empty Silicon Valley Artificial Intelligence Lab in Sunnyvale, adjacent to the rocket scientists at NASA’s Ames Research Center, Ng and his lab chief Adam Coates recently outlined their plans at Baidu and their vision of what AI can accomplish. That, as I outlined in a story on those plans, includes everything from improved speech recognition to much smarter robots to truly intelligent personal assistants.

Ng, who speaks in an extraordinarily gentle voice that compels close attention by the listener, seems to realize how much he has to prove, both vs. fast-rising Chinese rivals such as soon-to-go-public Alibaba and global forces such as Google and Facebook that are also betting big on AI, in particular the fast-emerging branch of AI called deep learning. Even before being asked, Ng sought to quash what he called the “stereotype” of Chinese companies as mere copycats of U.S. and other technology companies.

In Baidu’s case, at least, the stereotype may be superficial. But it also seems clear that Ng’s hiring is part of an attempt by Baidu, often called “China’s Google,” to create world-beating technologies that will elevate it to the top tier of global innovators. In this edited version of the interview, he reveals plenty of details about how he plans to help make that happen.

Q: How did you get interested in artificial intelligence?

A: I just thought making machines intelligent was the coolest thing you could do. I had a summer internship in AI in high school, writing neural networks at National University of Singapore–early versions of deep learning algorithms. I thought it was amazing you could write software that would learn by itself and make predictions.

If we can make computers more intelligent–and I want to be careful of AI hype–and understand the world and the environment better, it can make life so much better for many of us. Just as the Industrial Revolution freed up a lot of humanity from physical drudgery, I think AI has the potential to free up humanity from a lot of the mental drudgery. …

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Startup Ersatz Labs Launches Deep Learning AI In The Cloud (Or In A Box)

From my Forbes blog:

Deep learning, a branch of artificial intelligence that has led to recent breakthroughs in automated image and speech recognition, is the hot new technology among tech giants from Google and Facebook to Microsoft and China’s Baidu. They’ve been spending hundreds of millions of dollars to buy companies and vacuum up talent from universities that are all working on deep learning neural networks, which attempt to mimic how the brain works to improve computing performance on tasks humans do with ease.

Now, a San Francisco startup called Ersatz Labs is formally launching what it calls the first deep learning platform, one that it says any company or researcher can use to do deep learning on the (relatively) cheap. It’s being offered as a service in the cloud and, for companies that want or need to do what can often be mission-critical work inside their corporate network firewall, as a hardware appliance with software installed.

Either way, the upshot of the service is that a whole lot of companies may be able to apply deep learning to their own services to achieve similar breakthroughs to Google’s, Microsoft’s, and others’. The service has been in beta for the past year with 2,200 customers, from Wall Street traders to researchers looking to detect tumors on mammograms to energy companies analyzing seismic data to an iPhone app maker using accelerometers to determine if you’re exercising correctly. “The deep learning methods are established enough that you don’t have to build this all yourself,” says Ersatz Labs CEO Dave Sullivan, cofounder with Chairman Ronjon Nag, who also provided $250,000 in funding. …

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Meet The Guy Who Helped Google Beat Apple’s Siri

Google's Jeff Dean

Google’s Jeff Dean

From my Forbes blog:

For all the attention lavished on Siri, the often-clever voice-driven virtual assistant on Apple’s iPhone, Google’s mobile search app lately has impressed a lot more people. That’s partly thanks to Google Now, its own virtual assistant that’s part of that app, which some observers think is more useful than Siri.

But the success of Google’s mobile search stems at least as much from a big improvement over the past year in Google’s speech recognition efforts. That’s the result of research by legendary Google Fellow Jeff Dean and others in applying a fast-emerging branch of artificial intelligence called deep learning to recognizing speech in all its ambiguity and in noisy environments. Replacing part of Google’s speech recognition system last July with one based on deep learning cut error rates by 25% in one fell swoop.

As I wrote in a recent article on deep learning neural networks, the technology tries to emulate the way layers of neurons in the human neocortex recognize patterns and ultimately engage in what we call thinking. Improvements in mathematical formulas coupled with the rise of powerful networks of computers are helping machines get noticeably closer to humans in their ability to recognize speech and images.

Making the most of Google’s vast network of computers has been Dean’s specialty since he joined Google an almost inconceivable 14 years ago, when the company employed only 20 people. He helped create a programming tool called MapReduce that allowed software developers to process massive amounts of data across many computers, as well as BigTable, a distributed storage system that can handle millions of gigabytes of data (known in technical terms as “bazillions.”) Although conceptual breakthroughs in neural networks have a huge role in deep learning’s success, sheer computer power is what has made deep learning practical in a Big Data world.

Dean’s extreme geekitude showed in a recent interview, when he gamely tried to help me understand how deep learning works, in much more detail than most of you will ever want to know. Nonetheless, I’ll warn you that some of this edited interview still gets pretty deep, as it were. Even more than the work of Ray Kurzweil, who joined Google recently to improve the ability of computers to understand natural language, Dean’s work is focused on more basic advances in how to use smart computer and network design to make AI more effective, not on the application to advertising.

Still, Google voice search seems certain to change the way most people find things, including products. So it won’t hurt for marketers and users alike to understand a bit more about how this technology will transform marketing, which after all boils down to how to connect people with products and services they’re looking for. Here’s a deeply edited version of our conversation:

Q: What’s “deep” about deep learning?

A: “Deep” typically refers to the fact that you have many layers of neurons in neural networks. It’s been very hard to train networks with many layers. In the last five years, people have come up with techniques that allow training of networks with more layers than, say, three. So in a sense it’s trying to model how human neurons respond to stimuli.

We’re trying to model not at the detailed molecular level, but abstractly we understand there are these lower-level neurons that construct very primitive features, and as you go higher up in the network, it’s learning more and more complicated features.

Q: What has happened in the last five years to make deep learning a more widely used technique?

A: In the last few years, people have figured out how to do layer-by-layer pre-training [of the neural network]. So you can train much deeper networks than was possible before. The second thing is the use of unsupervised training, so you can actually feed it any image you have, even if you don’t know what’s in it. That really expands the set of data you can consider because now, it’s any image you get your hands on, not just one where you have a true label of what that image is [such as an image you know is a cheetah]. The third thing is just more computational power. …

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Interview: How Ray Kurzweil Plans To Revolutionize Search At Google

Google's Ray Kurzweil (Photo: Wikipedia)

Google’s Ray Kurzweil (Photo: Wikipedia)

From my Forbes blog:










Q: How will you apply that theory at Google?



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This Is How Google (And Its Advertisers) Will Really Get Inside Your Head


From my Forbes blog:

Google cofounder Sergey Brin said only half-jokingly back in 2002 that his company aimed to create the equivalent of the intelligent computer HAL 9000 in 2001: A Space Odyssey, but without the bug that resulted in it, you know, killing people.

More than a decade later, Google isn’t nearly there, for better or worse. But lately, it has been aiming much more directly at building HAL, or what’s sometimes called the Google Brain. As I wrote in a recent article, a fast-emerging branch of artificial intelligence called deep learning is helping Google and other companies and researchers produce significant advances in machines that at least approach the way we think. It won’t be long–for better or worse–before their work also has a profound impact on marketing and advertising as well. …

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Deep Learning: Artificial Intelligence Finally Gets Smart


From my feature story in Technology Review:




All this has normally cautious AI researchers hopeful that intelligent machines may finally escape the pages of science fiction. Indeed, machine intelligence is starting to transform everything from communications and computing to medicine, manufacturing, and transportation. The possibilities are apparent in IBM’s Jeopardy!-winning Watson computer, which uses some deep-learning techniques and is now being trained to help doctors make better decisions. Microsoft has deployed deep learning in its Windows Phone and Bing voice search. Says Peter Lee, head of Microsoft Research USA, “Deep learning has reignited some of the grand challenges in artificial intelligence.” …

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