Voices in AI – Episode 67: A Conversation with Amir Khosrowshahi


About this Episode

Episode 67 of Voices in AI features host Byron Reese and Amir Khosrowshahi talk about the explainability, privacy, and other implications of using AI for business. Amir Khosrowshahi is VP and CTO at Intel. He holds a Bachelor’s Degree from Harvard in Physics and Math, a Master’s Degree from Harvard in Physics, and a PhD in Computational Neuroscience from UC Berkeley.
Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm. I’m Byron Reese. Today I’m so excited that my guest is Amir Khosrowshahi. He is a VP and the CTO of AI products over at Intel. He holds a Bachelor’s Degree from Harvard in Physics and Math, a Master’s Degree from Harvard in Physics, and a PhD in Computational Neuroscience from UC Berkeley. Welcome to the show, Amir.
Amir Khosrowshahi: Thank you, thanks for having me.
I can’t imagine someone better suited to talking about the kinds of things we talk about on this show, because you’ve got a PhD in Computational Neuroscience, so, start off by just telling us what is Computational Neuroscience?
So neuroscience is a field, the study of the brain, and it is mostly a biologically minded field, and of course there are aspects of the brain that are computational and there’s aspects of the brain that are opening up the skull and peering inside and sticking needles into areas and doing all sorts of different kinds of experiments. Computational neuroscience is a combination of these two threads, the thread that there [are] computer science statistics and machine learning and mathematical aspects to intelligence, and then there’s biology, where you are making an attempt to map equations from machine learning to what is actually going on in the brain.
I have a theory which I may not be qualified to have and you certainly are, and I would love to know your thoughts on it. I think it’s very interesting that people are really good at getting trained with a sample size of one, like draw a made up alien you’ve never seen before and then I can show you a series of photographs, and even if that alien’s upside down, underwater, behind a tree, whatever, you can spot it.
Further, I think it’s very interesting that people are so good at transfer learning, I could give you two objects like a trout swimming in a river, and that same trout in a jar of formaldehyde in a laboratory and I could ask you a series of questions: Do they weigh the same, are they the same color, do they smell the same, are they the same temperature? And you would instantly know, and yet, likewise, if you were to ask me if hitting your thumb with a hammer hurts, and I would say “yes,” and then somebody would say, “Well, have you ever done it?” And I’m like, “yeah,” and they would say, “when?” And it’s like, I don’t really remember, I know I have. Somehow we take data and throw it out, and remember metadata and yet the fact a hammer hurts your thumb is stored in some little part of your brain that you could cut it out and somehow forget that. And so when I think of all of those things that seem so different than computers to me, I kind of have a sense that human intelligence doesn’t really tell us anything about how to build artificial intelligence. What do you say?
Okay, those are very deep questions and actually each one of those items is a separate thread in the field of machine learning and artificial intelligence. There are lots of people working on things, so the first thing you mentioned I think, was one shot learning where you have, you see as something that’s novel. From the first time you see it, you recognize it as something that’s singular and you retain that knowledge to then identify if it occurs again—such as for a child it would be like a chair, for you it’s potentially an alien. So, how do you learn from single examples?
That’s an open problem in machine learning and is very actively studied because you want to be able to have a parsimonious strategy for learning and the current ways that—it’s a good problem to have—the current ways that we’re doing learning in, for example, online services that sort photos and recognize objects and images. It’s very computationally wasteful and it’s actually wasteful in usage of data. You have to see many examples of chairs to have an understanding of a chair, and it’s actually not clear if you actually have an understanding of a chair, because the models that we have today for chairs, they do make mistakes. When you peer into where the mistakes were made, it seems like there the machine learning model doesn’t actually have an understanding of a chair, it doesn’t have a semantic understanding of a scene or of grammar, or of languages that are translated, and we’re noticing these efficiencies and we’re trying to address them.
You mentioned some other things, such as how do you transfer knowledge from one domain to the next. Humans are very good at generalizing. We see an example of something in one context, and it’s amazing that you can extrapolate or transfer it to a completely different context. That’s also something that we’re working on quite actively, and we have some initial success in that we can take a statistical model that was trained on one set of data and then we can then apply to another set of data by using that previous experience as a warm start, and then moving away from that old domain to the new domain. This is also possible to do in continuous time.
Much of the things we experience in the real world—they’re not stationary, and that’s a statistics change with time. We need to have models that can also change. For a human it’s easy to do that, it’s very good at going from… it’s good at handling non-stationary statistics, so we need to build that into our models, be cognizant of it, we’re working on it. And then [for] other things you mentioned—that intuition is very difficult. It’s potentially one of the most difficult things for us to translate from human intelligence to machines, and remembering things and having kind of a hazy idea of having done something bad to yourself with a hammer, that I’m not actually sure where that falls in into the various subdomains of machine learning.
Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com
Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

MindMaze’s headset brings your brainwaves into virtual reality

Swiss startup MindMaze is moving its technology from the medical field to the mainstream with a virtual reality headset that reads the wearer’s brain waves and uses the data to help them relax and play. The team announced $8.5 million in angel funding today, which it will use to help bring several products to market by the end of the year.

At the Game Developer Conference in San Francisco, I tried four prototypes that demonstrated how MindMaze’s technology works on different platforms. Inside a virtual reality headset MindMaze calls “NeuroGoggles,” I saw virtual fire spring from the tips of my fingers in an augmented reality mode. Plastic strips placed EKG sensors all over my head, allowing a TV next to me to livestream my brain activity.

A prototype version of MindMaze's NeuroGoggles.

A prototype version of MindMaze’s NeuroGoggles.

At another table, a sweatband studded with sensors measured my relaxation and allowed me to power up a glowing ball on the screen in front of me. I furrowed my brow to release its energy and battle a MindMaze employee in a reverse tug-of-war.

MindMaze also has a Kinect-like camera that tracked my movement in 3D. The company has plans for it similar to Leap Motion; a gamer could use it to integrate their motions into their virtual avatar, for example.

A prototype MindMaze  headband reads brainwaves and incorporates them into games.

A prototype MindMaze headband reads brainwaves and incorporates them into games.

MindMaze got started by using its 3D tracking technology to help stroke, amputation and spinal cord and brain injury patients. There’s an episode of “House” where Hugh Laurie discovers a man’s anger stems from pain in his amputated hand. He alleviates the man’s pain with a box split down the middle by a mirror. One arm goes on each side of the mirror, and when the patient moves their intact hand it appears their other hand is moving too.

Studies have not found conclusive evidence that mirror boxes actually alleviates phantom limb pain, but it has been shown to help stroke patients regain control of their limbs. MindMaze replicates the same treatment with a virtual limb. The patient moves their intact hand, and a mirrored virtual hand can perform the same action.

A rendering of the proposed design for MindMaze's NeuroGoggles.

A rendering of the proposed design for MindMaze’s NeuroGoggles.

When MindMaze begins selling its devices to consumers, CEO and founder Tej Tadi said he sees people using it both to manage their mental health and for gaming.

“The first thing we want to do short term is enable a whole new gaming platform,” Tadi said. “It’s more enriched than real life.”

In the tug-of-war, it was nearly impossible to relax and power up because journalists don’t relax and the room was buzzing with people. I’ve tried the Muse headband in the past and wasn’t particularly impressed. Feedback reminding me I’m not doing well at relaxing doesn’t exactly make me more relaxed.

A rendering of the proposed design for MindMaze's motion capturing camera.

A rendering of the proposed design for MindMaze’s motion capturing camera.

But I like the idea of relaxing inside virtual reality. It is especially suited to blocking out the world and putting you in a space where it is possible to be calm. MindMaze’s prototype goggles were decently made and had the unusual feature of displaying 180 degrees of your view in virtual reality and the other 180 in augmented reality, making it easy to switch back and forth between the virtual and real worlds. The company has plans to make its headset and 3D camera smaller and wireless, and both are meant to look a whole lot spiffier before their consumer release. I’m interested to see what other applications MindMaze dreams up.

Why deep learning is at least inspired by biology, if not the brain

As deep learning continues gathering steam among researchers, entrepreneurs and the press, there’s a loud-and-getting-louder debate about whether its algorithms actually operate like the human brain does.

The comparison might not make much of a difference to developers who just want to build applications that can identify objects or predict the next word you’ll text, but it does make a difference. Researchers leery of another “AI winter” or trying to refute worries of a forthcoming artificial superintelligence worry that the brain analogy is setting people up for disappointment, if not undue stress. When people hear “brain,” they think about machines that can think like us.

On this week’s Structure Show podcast, we dove into the issue with Ahna Girschick, an accomplished neuroscientist, visual artist and senior data scientist at deep learning startup Enlitic. Girschick’s colleague, Enlitic Founder and CEO (and former Kaggle chief scientist) Jeremy Howard, also joined us for what turned out to be a rather insightful discussion.

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Below are some of the highlights, focused on Girshick and Howard view the brain analogy. (They take a different tack than Google researcher Greg Corrado, who recently called the analogy “officially overhyped.”). But we also talk at length about deep learning, in general, and how Enlitic is using it to analyze medical images and hopefully help overcome a global shortage of doctors.

If you’re interested in hearing more from Girshick, Enlitic and deep learning, come to our Structure Data conference next month, where she’ll be accepting a startup award and joining me on stage for an in-depth talk about how artificial intelligence can improve the health care system. If you want two full days of all AI, all the time, start making plans for our Structure Intelligence conference in September.

Ahna Girshick, Enlitic's senior data scientist.

Ahna Girshick

Natural patterns at work in deep learning systems

“It’s true, deep learning was inspired by how the human brain works,” Girshick said on the Structure Show, “but it’s definitely very different.”

Just like with our vision systems, deep learning systems for computer vision process stuff in layers, if you will. They start with edges and then get more abstract with each layer, focusing on faces or perhaps whole objects, she explained. “That said, our brain has many different types of neurons,” she added. “Everywhere we look in the brain we see diversity. In these artificial networks, every node is trying to basically do the same thing.”

This is why our brains are able to navigate a dynamic world and do many things, while deep learning systems are usually focused on one task with a clear objective. Still, Girshick said, “From a computer vision standpoint, you can learn so much by looking at the brain that why not.”

She explained some of these connections by discussing a research project she worked on at NYU:

“We were interested in, kind of, the statistics of the of the world around us, the visual world around us. And what that means is basically the patterns in the visual world around us. If you were to take a bunch of photos of the world and run some statistics on them, you’ll find some patterns — things like more horizontals than verticals. . . . And then we look inside the brain and we see,  ‘Gee, wow, there’s all these neurons that are sensitive to edges and there’s more of them that are sensitive to horizontals than verticals!’ And then we measured . . . the behavioral response in a type of psychology experiment and we see, ‘Gee, people are biased to perceive things as more horizontal or more vertical than they actually are!'”

Asked if computer vision has been such a big focus of deep learning research so far because of those biological parallels, or because that’s companies such as Google and Facebook have the most need for, Girshick suggested it’s a bit of both. “It’s the same in the neuroscience department at a university,” she said. “The reason that people focus on vision is because a third of our cortex is devoted to vision — it’s a major chunk of our brain. . . . It’s also something that’s easier for us to think about, because we see it.”

Structure Data 2012: Ryan Kim – Staff Writer, GigaOM, Eric Huls – VP, Allstate Insurance Company, Jeremy Howard – President and Chief Scientist, Kaggle

Jeremy Howard (left) at Structure: Data 2012.

Howard noted that the team at Enlitic keeps finding more connections between Girshick’s research and the cutting edge of deep learning, and suggested that attempts to distance the two fields are sometimes insincere. “I think it’s kind of fashionable for people to say how deep learning is just math and these people who are saying ‘brain-like’ are crazy, but the truth is … it absolutely is inspired by the brain,” he said. “It’s a massive simplification, but we keep on finding more and more inspirations.”

The issue probably won’t be resolved any time soon — in part because it’s so easy for journalists and others to take the easy way out when explaining deep learning — but Girshick offered a solution.

“Maybe they should say ‘inspired by biology’ instead of ‘inspired by the brain,'” she said. “. . . Yes, deep learning is kind of amazing and very flexible compared to other generations of algorithms, but it’s not like the intelligent system I was studying when I studied the brain — at all.”

Enter the Matrix: The rise of brain-computer interfaces

In 2012, a paralyzed woman with an investigational 96-electrode sensor the size of a baby aspirin implanted onto the surface of her brain was able to think about steering a robotic arm toward a canister with a straw in it, move the canister toward her mouth, tilt it so the straw fell into her mouth, and take a sip.

Can an app help improve your vision? Science says yes.

Yes, you can improve your vision through an app although what you’re really training is your brain. A team of baseball players used UltimEyes and can on average see clearly 31 percent farther than before.

StarCraft makes you smarter, according to research

While scholars and pundits are concerned about the negative affects gaming can have on brain development, a study conducted at University of Texas at Austin proves that it can also expand the mind. Students were divided into two groups — one played strategy game StarCraft while the other played The Sims. After two months of playing, the StarCraft gamers showed increased mental flexibility and multitasking abilities compared to The Sims players. Picking strategy games may be the right way to go for some brain-boosting exercises.