AI News, Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter

Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter

At the end of 2013, when Mark Zuckerberg decided to create Facebook AI Research, the organization I direct, Facebook was about to turn 10 years old.

The company thought about what “connecting people” would entail 10 years in the future, and realized that AI would play a key role.

Facebook can potentially show each person on Facebook about 2,000 items per day: posts, pictures, videos, etc.

Doing a good job at this requires understanding people, their tastes, interests, relationships, aspirations and even goals in life.

It also requires understanding content: understanding what a post or a comment talks about, what an image or a video contains, etc.

In a way, doing a perfect job at this is an “AI-complete” problem: it requires understanding people, emotions, culture, art.

Much of our work at Facebook AI focuses on devising new theories, principles, methods, and systems to make machines understand images, video, speech, and language—and then to reason about them.

Facebook recently announced a face-verification algorithm called “DeepFace,” with results that were widely reported to involve near-human accuracy in facial recognition.

Whereas lay people might think the computers are looking at the same sorts of random pictures that people look at every day.

Spectrum: How well will Deep Learning do in areas beyond image recognition, especially with issues associated with generalized intelligence, like natural language?

How do we combine the advantages of Deep Learning, with its ability to represent the world through learning, with things like accumulating knowledge from a temporal signal, which happens with language, with being able to do reasoning, with being able to store knowledge in a different way than current Deep Learning systems store it.

There’s a lot of work at Facebook, at Google, and at various other places where we’re trying to have a neural net on one side, and then a separate module on the other side that is used as a memory.

These systems are based on the idea of representing words and sentences with continuous vectors, transforming these vectors through layers of a deep architecture, and storing them in a kind of associative memory.

A lot of people are working on what’s called “recurrent neural nets.” These are networks where the output is fed back to the input, so you can have a chain of reasoning.

Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter

At the end of 2013, when Mark Zuckerberg decided to create Facebook AI Research, the organization I direct, Facebook was about to turn 10 years old.

The company thought about what “connecting people” would entail 10 years in the future, and realized that AI would play a key role.

Facebook can potentially show each person on Facebook about 2,000 items per day: posts, pictures, videos, etc.

Doing a good job at this requires understanding people, their tastes, interests, relationships, aspirations and even goals in life.

It also requires understanding content: understanding what a post or a comment talks about, what an image or a video contains, etc.

In a way, doing a perfect job at this is an “AI-complete” problem: it requires understanding people, emotions, culture, art.

Much of our work at Facebook AI focuses on devising new theories, principles, methods, and systems to make machines understand images, video, speech, and language—and then to reason about them.

Facebook recently announced a face-verification algorithm called “DeepFace,” with results that were widely reported to involve near-human accuracy in facial recognition.

Whereas lay people might think the computers are looking at the same sorts of random pictures that people look at every day.

Spectrum: How well will Deep Learning do in areas beyond image recognition, especially with issues associated with generalized intelligence, like natural language?

How do we combine the advantages of Deep Learning, with its ability to represent the world through learning, with things like accumulating knowledge from a temporal signal, which happens with language, with being able to do reasoning, with being able to store knowledge in a different way than current Deep Learning systems store it.

There’s a lot of work at Facebook, at Google, and at various other places where we’re trying to have a neural net on one side, and then a separate module on the other side that is used as a memory.

These systems are based on the idea of representing words and sentences with continuous vectors, transforming these vectors through layers of a deep architecture, and storing them in a kind of associative memory.

A lot of people are working on what’s called “recurrent neural nets.” These are networks where the output is fed back to the input, so you can have a chain of reasoning.

Why Deep Learning Is Suddenly Changing Your Life

Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies.

To gather up dog pictures, the app must identify anything from a Chihuahua to a German shepherd and not be tripped up if the pup is upside down or partially obscured, at the right of the frame or the left, in fog or snow, sun or shade.

Medical startups claim they’ll soon be able to use computers to read X-rays, MRIs, and CT scans more rapidly and accurately than radiologists, to diagnose cancer earlier and less invasively, and to accelerate the search for life-saving pharmaceuticals.

They’ve all been made possible by a family of artificial intelligence (AI) techniques popularly known as deep learning, though most scientists still prefer to call them by their original academic designation: deep neural networks.

Programmers have, rather, fed the computer a learning algorithm, exposed it to terabytes of data—hundreds of thousands of images or years’ worth of speech samples—to train it, and have then allowed the computer to figure out for itself how to recognize the desired objects, words, or sentences.

“You essentially have software writing software,” says Jen-Hsun Huang, CEO of graphics processing leader Nvidia nvda , which began placing a massive bet on deep learning about five years ago.

What’s changed is that today computer scientists have finally harnessed both the vast computational power and the enormous storehouses of data—images, video, audio, and text files strewn across the Internet—that, it turns out, are essential to making neural nets work well.

“We’re now living in an age,” Chen observes, “where it’s going to be mandatory for people building sophisticated software applications.” People will soon demand, he says, “ ‘Where’s your natural-language processing version?’ ‘How do I talk to your app?

The increased computational power that is making all this possible derives not only from Moore’s law but also from the realization in the late 2000s that graphics processing units (GPUs) made by Nvidia—the powerful chips that were first designed to give gamers rich, 3D visual experiences—were 20 to 50 times more efficient than traditional central processing units (CPUs) for deep-learning computations.

Its chief financial officer told investors that “the vast majority of the growth comes from deep learning by far.” The term “deep learning” came up 81 times during the 83-minute earnings call.

I think five years from now there will be a number of S&P 500 CEOs that will wish they’d started thinking earlier about their AI strategy.” Even the Internet metaphor doesn’t do justice to what AI with deep learning will mean, in Ng’s view.

Earlier this week, Joaquin held a Q&A session on Quora in which he explained the role of AML, the relationship with FAIR, and the awesome projects his group has worked on and deployed.

I would say FAIR is 75% research and 25% engineering, while AML is perhaps 75% engineering and 25% research.

When it's time for their project to move from research to development, some research engineers choose to follow their project to fruition and transfer to AML.

The Race To Build An AI Chip For Everything Just Got Real

He and several other researchers designed this chip to run deep neural networks—complex mathematical systems that can learn tasks on their own by analyzing vast amounts of data—but ANNA never reached the mass market.

As CNBC revealed last week, several of the original engineers behind the Google TPU are now working to build similar chips at a stealth startup called Groq, and the big-name commercial chip makers, including Intel, IBM, and Qualcomm, are pushing in the same direction.

Neural networks can run faster and consume less power when paired with chips specifically designed to handle the massive array of mathematical calculations these AI systems require.

Now, as companies like Google and Facebook push neural networks onto phones and VR headsets—so they can eliminate the delay that comes when shuttling images to distant data centers—they need AI chips that can run on personal devices, too.

And more recently, Qualcomm has started building chips specifically for executing neural networks, according to LeCun, who is familiar with Qualcomm's plans because Facebook is helping the chip maker develop technologies related to machine learning.

Just last month, the Silicon Valley chip maker hired Clément Farabet, who explored this kind of chip architecture while studying under LeCun at NYU and went on to found a notable deep learning startup called Madbits, which was acquired by Twitter in 2014.

But about five years ago, companies like Google and Facebook started using them for neural network training, just because they were the best option for the task, and LeCun believes they will continue to play this role.

But he also believes that a new breed of AI chips will significantly change the way the big internet companies execute neural networks, both in the data center and on consumer devices—everything from phones to smart lawn mowers and vacuum cleaners.

Public Workshop: A Framework for Regulatory Use of Real-World Evidence

Marcus Weldson, Dr. Renee Armour, J. Randy MacDonald

Marcus Weldon, President of Bell Labs, explains how people can run their lives more effectively using technology to perform mundane tasks. Steve Adubato ...