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Voices in AI - Naveen Rao

Voices in AI – Episode 79: A Conversation with Naveen Rao

About this Episode

Episode 79 of Voices in AI features host Byron Reese and Naveen Rao discussing intelligence, the mind, consciousness, AI, and what the day-to-day looks like at Intel.

Byron and Naveen also delve into the implications of an AI future.

Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

Transcript Excerpt

Q-Byron Reese:

This is Voices in AI brought to you by GigaOm, and I’m Byron Reese.

Today I’m excited that our guest is Naveen Rao.

He is the Corporate VP and General Manager of the Artificial Intelligence Products Group at Intel.

He holds a Bachelor of Science in Electrical Engineering from Duke and a Ph.D. in Neuroscience from Brown University.

Welcome to the show, Naveen.

A-Naveen Rao:

Thank you. Glad to be here.

Q-1:

You’re going to give me a great answer to my standard opening question, which is: What is intelligence?

A-1:

That is a great question. It really doesn’t have an agreed-upon answer.

My version of this is about potential and capability.

What I see as an intelligent system is a system that is capable of decomposing structure within data.

By my definition, I would call a newborn human baby intelligent, because the potential is there, but the system is not yet trained with real experience.

I think that’s different than other definitions, where we talk about the phenomenology of intelligence, where you can categorize things, and all of this.

I think that’s where the outcropping of having actually learned the inherent structure of the world is.

AI Tech

Q-2:

So, in what sense by that definition is artificial intelligence actually artificial?

Is it artificial because we built it, or is it artificial because it’s not real intelligence?

It’s like artificial turf; it just looks like intelligence.

A-2:

No. I think it’s artificial because we built it. That’s all.

There’s nothing artificial about it.

The term intelligence doesn’t have to be on biological mush, it can be implemented on any kind of substrate.

In fact, there’s even research on how slime mold, actually…

Artificial Intelligence

Q-3:

Right. It can work mazes…

A-3:

… can solve computational problems, Yeah.

 

Q-4:

How does it do that, by the way? That’s really a pretty staggering thing.

A-4:

There’s a concept that we call gradients. Gradients are just how information gets more crystalized.

If I feel like I’m going to learn something by going in one direction, that direction is the gradient.

It’s sort of a pointer in the way I should go.

That can exist in the chemical world as well, and things like slime mold actually use chemical gradients that translate into information processing and actually learn the dynamics of a system.

Our neurons do that. Deep neural networks do that in a computer system.

They’re all based on something similar at one level.

AI System in Robot

Q-5:

So, let’s talk about the nematode worm for a minute.

A-5:

Okay.

 

Q-6:

You’ve got this worm, the most successful creature on the planet.

Seventy percent of all animals are nematode worms.

He’s got 302 neurons and exhibits certain kinds of complex behavior.

There have been a bunch of people in the OpenWorm Project, who spent 20 years trying to model those 302 neurons in a computer, just to get it to duplicate what the nematode does.

Even among them, they say: “We’re not even sure if this is possible.”

So, why are we having such a hard time with such a simple thing as a nematode worm?

Neural System

A-6:

Well, I think this is a bit of a fallacy of reductive thinking here, that, “Hey, if I can understand the 302 neurons, then I can understand the 86 billion neurons in the human brain.”

I think that fallacy falls apart because there are different emergent properties that happen when we go from one size system to another.

It’s like running a company of 50 people is not the same as running a company of 50,000. It’s very different.

 

Q-7:

But, to jump in there… my question wasn’t, “Why doesn’t the nematode worm tell us something about human intelligence?”

My question was simply, “Why don’t we understand how a nematode worm works?”

 

A-7:

Right. I was going to get to that. I think there are a few reasons for that.

One is, the interaction of any complex system – hundreds of elements – is extremely complicated.

There’s a concept in physics called the three-body problem, where if I have two pool balls on a pool table, I can actually 100 percent predict where the balls will end up if I know the initial state and I know how much energy I’m injecting when I hit one of the balls in one direction with a certain force.

If you make that three, I cannot do that in a closed-form system.

I have to simulate steps along the way.

That is called a three-body problem, and it’s computationally intractable to compute that.

So, you can imagine when it gets to 302, it gets even more difficult.

And what we see in big systems like in mammalian brains, where we have billions of neurons, and 300 neurons, is that you actually have pockets of closely interacting pieces in a big brain that interact at a higher level.

That’s what I was getting at when I talked about these emergent properties.

So, you still have that 302-body problem, if you will, in a big brain as you do in a small brain.

That complexity hasn’t gone away, even though it seemingly is a much simpler system.

The interaction between 302 different things, even when you know precisely how each one of them is connected, is just a very complex matter.

If you try to model all the interactions and you’re off by just a little bit on any one of those things, the entire system may not work.

That’s why we don’t understand it, because you can’t characterize every piece of this, like every synapse… you can’t mathematically characterize it.

And if you don’t get it perfect, you won’t get a system that functions properly.

Human Brain & Neuron Model

Q-8:

So, do you say that suggesting by extension that the Human Brain Project in Europe, which really is… You’re laughing and nodding.

What’s your take on that?

A-8:

I am not a fan of the Human Brain Project for this exact reason.

The complexity of the system is just incredibly high, and if you’re off by one tiny parameter, by a tiny little amount, it’s sort of like the butterfly effect.

It can have huge consequences on the operation of the system, and you really haven’t learned anything.

All you’ve learned how to do is model some micro dynamics of a system.

You haven’t really gotten any true understanding of how the system really works.

Data Warehouse

Q-9:

You know, I had a guest on the show, Nova Spivack, who said that a single neuron may turn out to be as complicated as a supercomputer, and it may even operate down at the Planck level.

It’s an incredibly complex thing.

A-9:

Yeah.

 

Q-10:

Is that possible?

A-10:

It is a physical system – a physical device.

One could argue the same thing about a single transistor as well.

We engineer these things to act within certain bounds… and I believe the brain actually takes advantage of that as well.

So, a neuron… to completely, accurately describe everything a neuron is doing, you’re absolutely right.

It could take a supercomputer to do so, but we don’t necessarily need to abstract a supercomputer’s worth of value from each neuron.

I think that’s a fallacy.

There are lots of nonlinear effects and all this kind of crazy stuff that are happening that really aren’t useful to the overall function of the brain.

Just like an individual neuron can do very complicated things, when we put a whole bunch of [transistors] together to build a processor, we’re exploiting one piece of the way that transistor behaves to make that processor work.

We’re not exploiting everything in the realm of possibility that the transistor can do.

Q-11:

We’re going to get to artificial intelligence in a minute.

It’s always great to have a neuroscientist on the show.

So, we have these brains, and you said they exhibit emergent properties.

Emergence is of course the phenomenon where the whole of something takes on characteristics that none of the components have. And it’s often thought of in two variants.

One is weak emergence, where once you see the emergent behavior, with enough study you can kind of reverse engineer… “Ah, I see why that happened.”

And one is a much more controversial idea of strong emergence that may not be discernible.

The emergent property may not be derivable from the component.

Do you think human intelligence is a weak emergent property, or do you believe in strong emergence?

 

AQ-11:

I do in some ways believe in strong emergence.

Let me give you the subtlety of that.

I don’t necessarily think it can be analytically solved because the system is so complex.

What I do believe is that you can characterize the system within certain bounds.

It’s much like how a human may solve a problem like playing chess.

We don’t actually pre-compute every possibility.

We don’t do that sort of a brute force kind of thing.

But we do come up with heuristics that are accurate most of the time.

And I think the same thing is true with the bounds of a very complex system like the brain.

We can come up with bounds of these emergent properties that are accurate 95 percent of the time, but we won’t be accurate 100 percent of the time.

It’s not going to be as beautiful as some of the physics we have that can describe the world.

In fact, even physics might fall into this category as well.

So, I guess the short answer to your question is: I do believe in strong emergence that will never actually 100 percent describe…

Prepare your mind

Q-12:

But, do you think fundamentally intelligence could, given an infinitely large computer, be understood in a reductionist format?

Or is there some break-in cause and effect along the way, where it would be literally impossible?

Are you saying it’s practically impossible or literally impossible?

 

AQ-12:

…To understand the whole system top to bottom, from the emerging…?

 

Q-13:

Well, to start with, this is a neuron.

AQ-13:

Yeah.

 

Q-14:

And it does this, and you put 86 billion together and voilà, you have Naveen Rao.

AQ-14:

I think it’s literally impossible.

 

Q-15:

Okay, I’ll go with that. That’s interesting. Why is it literally impossible?

AQ-15:

Because the complexity is just too high, and the amount of energy and effort required to get to that level of understanding is many orders of magnitude more complicated than what you’re trying to understand.

 

Q-16:

So now, let’s talk about the mind for a minute.

We talked about the brain, which is physics. To use a definition that most people I think wouldn’t have trouble with, I’m going to call the mind all the capabilities of the brain that seem a little beyond what three pounds of goo should be able to do… like creativity and a sense of humor.

Your liver presumably doesn’t have a sense of humor, but your brain does.

So where do you think the mind comes from?

Or are you going to just say it’s an emergent property?

AQ-16:

I do kind of say it’s an emergent property, but it’s not just an emergent property.

It’s an emergent property that is actually the coordination of the physics of our brain – the way the brain itself works – and the environment.

I don’t believe that a mind exists without the world.

You know, a newborn baby, I called intelligent because it has the potential to decompose the world and find meaningful structure within it in which it can act.

But if it doesn’t actually do that, it doesn’t have a mind. You can see that… if you had kids yourself.

I actually had a newborn while I was studying neuroscience, and it was actually quite interesting to see.

I don’t think a newborn baby is really quite sentient yet.

That sort of emerges over time as the system interacts with the real world.

So, I think the mind is an emergent property of the brain plus environments interacting.

 

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.

Source: gigaom.com

Voices in AI – Episode 77: A Conversation with Nicholas Thompson

About this Episode

Episode 77 of Voices in AI features host Byron Reese and Nicholas Thompson discussing AI, humanity, social credit, as well as information bubbles.

Nicholas Thompson is the editor in chief of WIRED magazine, contributing editor at CBS, co-founder of The Atavist, and also worked at The New Yorker and authored a Cold War-era biography.

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 my guest is Nicholas Thompson. He is the editor-in-chief of WIRED magazine. He’s also a contributing editor at CBS which means you’ve probably seen him on the air talking about tech stories and trends. He also co-founded The Atavist, a digital magazine publishing platform. Prior to being at WIRED, he was a senior editor at The New Yorker and editor of NewYorker.com. He also published a book called The Hawk and the Dove, which is about the history of the Cold War. Welcome to the show Nicholas.

Nicholas Thompson: Thanks, Byron.

How are you doing?

I’m doing great. So… artificial intelligence, what’s that all about?

(Laughs) It’s one of the most important things happening in technology right now.

So do you think it really is intelligent, or is it just faking it?

What is it like from your viewpoint?

Is it actually smart or not?

Oh, I think it’s definitely smart.

I think that the premise of artificial intelligence, which if you define it as machines making independent decisions, is very smart right now and soon to get even smarter.

Data Warehouse

Well, it always sounds like I’m just playing what they call semantic gymnastics or something.

But does the machine actually make a decision, or is it just no more than your clock makes a decision to advance the minute hand one minute?

The computer is as deterministic as that clock. It doesn’t really decide anything it just is a giant clockwork, isn’t it?

Right.

I mean that gets you into about 19 layers of a really complicated discussion.

I would say ‘yes’ in a way it is like a clock.

Keep Watch on Your Time

But in other ways, machines are making decisions that are totally independent of the instructions or the data that was initially fed it, are finding patterns that the humans won’t see, and couldn’t be coded in.

So in that way, it becomes quite different from a clock.

I’m intrigued by that.

I mean the compass points to the north.

It doesn’t know which way north is.

That would be giving it too much credit.

But it does something that we can’t do, called magnetic north. So how is that really is the compass intelligent by the way you see the world?

Is the compass intelligent by the way I see the world?

Well, the compass is…

Compass

I mean one of the issues here is that artificial intelligence uses two words that have very complicated meanings and their definition evolves as we learn more about artificial intelligence.

And not only that but the definition of artificial intelligence and the way it’s used changes constantly both as our technology evolves as it learns to do new things and as it develops its brand value.

So back to your initial question, “Is a compass that points to the north intelligent?”

It is intelligent in the sense that it’s adding information to our world, but it’s not doing anything independent of the person who created it, who built the tools, and who imagined what it would do.

You build a compass you know that it’s going to point north, you put the pieces inside of it, [and] you know it will do that.

It’s not breaking outside of the box of the initial rules that were given to it and the promise of artificial intelligence is that it is breaking out of that box.

So. I’d like to really understand that a little more.

Like if I buy a NEST learning thermometer and over time I’m like, ‘oh I’m too hot, I’m too cold, I’m too cold,’ and it “figures it out” but how is it breaking out of what it knows?

Well, what would be interesting about a NEST thermometer, (I don’t know the details of how a NEST thermometer works, but) a NEST thermometer is looking at all the patterns of when you turn on your heat and when you don’t….

Thermometer Gun

If you program in a NEST thermometer and you say please make the house hotter between 6:00 in the morning and 10:00 o’clock at night, that’s relatively simple.

If you just install a NEST thermometer and then it watches you and follows your patterns and then reaches the same conclusion, it’s ended up at the same output, but it’s done it in a different way which is more intelligent right?

Well that’s really the question isn’t it?

The reason I dwell on these things is not too kind of count angels dancing on heads of pins.

But to me this kind of speaks to the ultimate limit of what this technology can do.

Like if it is just a giant clockwork, then you have to come to the question, ‘Is that what we are?

Are we just a giant clockwork?’ If we’re not and it is, then there are limits to what it can do.

If we are and it is or we’re not and it’s not, then maybe someday it can do everything we can do.

Do you think that someday it can do everything we can to do?

Yes. I thought this might be where you were going and this is where it gets so interesting.

And that was where in my initial answer I was starting to head in this direction, but my instinct is that we are like a giant clock, an extremely complex clock and a clock that’s built on rules that we don’t understand and won’t understand for a long time,

Moving Time

and that is built on rules that defy the way we normally programmed rules into clocks and calculators, but that essentially we are reducible to some form of math,

and with infinite wisdom, we could reach that that there isn’t a special spiritual unknowable element in the box…

Let me pause right there.

Let’s put a pin in that word ‘spiritual’ for a minute, but I want to draw attention to when I asked you if AI is just a clockwork, you said “No it’s more than that,” and if I ask you if a human’s a clockwork, you say “yeah I think so.”

Well that’s because I was taking your definition of the clock, right?

So I think what you said a minute ago is really where it’s at — which is: either we are clocks and the machines are clocks, or we are machines, we are clocks and they’re not clocks, there are four possibilities there.

data protection

And my instinct is that if we’re going to define it that way, I’m going to define clocks in an incredibly broad sense meaning mathematical reasoning including mathematics we don’t understand today, I’ll make the argument that both humans and machines you’re creating are clocks.

If we’re thinking of clocks in a much narrower sense, which is just a set of simple instructions input/output, then machines can go beyond that and humans can go beyond that too.
But no matter how we define the clocks, I’m putting the humans and the machines in the same category.

Data Management Strategy
So I either agree depending on what your base definitions are that humans and machines both are category A or they’re both not category A, that there isn’t any fundamental difference between humans and machines.

 

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.

Source: gigaom.com

Voices in AI – Episode 75: A Conversation with Kevin Kelly

About this Episode

Episode 75 of Voices in AI features host Byron Reese and Kevin Kelly discuss the brain, the mind, what it takes to make AI, and Kevin’s thoughts on its inevitability.

Kevin has written books such as ‘The New Rules for a New Economy, ‘What Technology Wants’, and ‘The Inevitable’. Kevin also started Wired Magazine, an internet and print magazine of tech and culture.

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, and I’m Byron Reese.

Today I am so excited we have as our guest, Kevin Kelly.

You know when I was writing the biography for Kevin, I didn’t even know where to start or where to end.

He’s perhaps best known for a quarter of a century ago, starting Wired magazine, but that is just one of many many things on an amazing career [path].

He has written a number of books, The New Rules for a New EconomyWhat Technology Wants, and most recently, The Inevitable, where he talks about the immediate future.

I’m super excited to have him on the show, welcome Kevin.

 

Kevin Kelly: It’s a real delight to be here, thanks for inviting me.

data protection

So what is inevitable?

There’s a hard version and a soft version, and I kind of adhere to the soft version.

The hard version is kind of a total deterministic world in which if we rewound the tape of life, it all unfolds exactly as it has, and we still have Facebook and Twitter, and we have the same president and so forth.

The soft version is to say that there are biases in the world, in biology as well as its extension into technology and that these biases tend to shape some of the large forms that we see in the world, still leaving the particulars, the specifics, the species to be completely, inherently, unpredictable and stochastic and random.

So that would say that things like you’re going to find on any planet that has water, you’ll find fish, it has life and in water, you’ll find fish, or will things, if you rewound the tape of life you’d probably get flying animals again and again, but you’ll never, but I mean, a specific bird, a robin is not inevitable.

And the same thing with technology. Any planet that discovers electricity and mixed wires will have telephones.

So telephones are inevitable, but the iPhone is not. And the internet’s inevitable, but Google’s not.

AI’s inevitable, but the particular variety of character, the specific species of AI is not.

That’s what I mean by inevitable—that there are these biases that are built by the very nature of chemistry and physics, that will bend things in certain directions.

Human Brain & Neuron Model

And what are some examples of those that you discuss in your book?

So, technology’s basically an extension of the same forces that drive life, and a kind of accelerated evolution is what technology is.

So if you ask the question about what are the larger forces in evolution, we have this movement towards complexity.

We have a movement towards diversity; we have a movement towards specialization; we have a movement towards mutualism.

Those also are happening in technology, which means that all things being equal, technology will tend to become more and more complex.

The idea that there’s any kind of simplification going on in technology is completely erroneous, there isn’t.

It’s not that the iPhone is any simpler.

Phone Notification

There’s a simple interface.

It’s like you have an egg, it’s a very simple interface but inside it’s very complex.

The inside of an iPhone continues to get more and more complicated, so there is a drive that, all things being equal, technology will be more complex and then next year it will be more and more specialized.

So, the history of technology in photography was there was one camera, one kind of camera.

Then there was a special kind of camera you could do for high speed; maybe there’s another kind of camera that could do underwater; maybe there was a kind that could do infrared; and then eventually we would do a high speed, underwater, infrared camera.

So, all these things become more and more specialized and that’s also going to be true about AI, we will have more and more specialized varieties of AI.

Data Virtualization

So let’s talk a little bit about [AI].

Normally the question I launch this with—and I heard your discourse on it—is: What is intelligence?

And in what sense is AI artificial?

Yes. So the big hairy challenge for that question is, we humans collectively as a species at this point in time, have no idea what intelligence really is.

We think we know when we see it, but we don’t really, and as we try to make artificial synthetic versions of it, we are, again and again, coming up to the realization that we don’t really know how it works and what it is.

Their best guess right now is that there are many different subtypes of cognition that collectively interact with each other and are codependent on each other, form the total output of our minds and of course other animal minds, and so.

I think the best way to think of this is we have a ‘zoo’ of different types of cognition, different types of solving things, of learning, of being smart, and that collection varies a little bit by person-to-person and a lot between different animals in the natural world and so…

AI Tech

That collection is still being mapped, and we know that there’s something like symbolic reasoning.

We know that there’s a kind of deductive logic, that there’s something about spatial navigation as a kind of intelligence.

We know that there’s mathematical type thinking; we know that there’s emotional intelligence; we know that there’s perception; and so far, all the AI that we have been ‘wowed’ by in the last 5 years is really all a synthesis of only one of those types of cognition, which is perception.

So all the deep learning neural net stuff that we’re doing is really just varieties of perception of perceiving patterns, and whether there’s audio patterns or image patterns, that’s really as far as we’ve gotten.

But there are all these other types, and in fact, we don’t even know what all the varieties of types [are].

Data Warehouse

We don’t know how we think, and I think one of the consequences of AI, trying to make AI, is that AI is going to be the microscope that we need to look into our minds to figure out how they work.

So it’s not just that we’re creating artificial minds, it’s the fact that that creation—that process—is the scope that we’re going to use to discover what our minds are made of.

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.

Source: gigaom.com

Use of Robots in War

The Case For and Against AGI

The following is an excerpt from GigaOm publisher Byron Reese’s new book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity. You can purchase the book here.

The Fourth Age explores the implications of automation and AI on humanity, and has been described by Ethernet inventor and 3Com founder Bob Metcalfe as framing “the deepest questions of our time in clear language that invites the reader to make their own choices.

Using 100,000 years of human history as his guide, he explores the issues around artificial general intelligence, robots, consciousness, automation, the end of work, abundance, and immortality.”

One of those deep questions of our time:

Is artificial general intelligence, or AGI, even possible?

Most people working in the field of AI are convinced that an AGI is possible, though they disagree about when it will happen.

In this excerpt from The Fourth Age, Byron Reese considers it an open question and explores if it is possible.

 

The Case for AGI

Robot in Wars

Those who believe we can build an AGI operate from a single core assumption.

While granting that no one understands how the brain works, they firmly believe that it is a machine, and therefore our mind must be a machine as well.

Thus, ever more powerful computers eventually will duplicate the capabilities of the brain and yield intelligence. As Stephen Hawking explains:

I believe there is no deep difference between what can be achieved by a biological brain and what can be achieved by a computer. It, therefore, follows that computers can, in theory, emulate human intelligence—and exceed it.

As this quote indicates, Hawking would answer our foundational question about the composition of the universe as a monist, and therefore someone who believes that AGI is certainly possible.

If nothing happens in the universe outside the laws of physics, then whatever makes us intelligent must obey the laws of physics. And if that is the case, we can eventually build something that does the same thing.

He would presumably answer the foundational question of “What are we?” with “machines,” thus again believing that AGI is clearly possible. Can a machine be intelligent? Of course! You are just such a machine.

Consider this thought experiment: What if we built a mechanical neuron that worked exactly like the organic kind.

And what if we then duplicated all the other parts of the brain mechanically as well. This isn’t a stretch, given that we can make other artificial organs.

Then, if you had a scanner of incredible power, it could make a synthetic copy of your brain right down to the atomic level. How in the world can you argue that won’t have your intelligence?

The only way, the argument goes, you get away from AGI being possible is by invoking some mystical, magical feature of the brain that we have no proof exists.

In fact, we have a mountain of evidence that it doesn’t. Every day we learn more and more about the brain, and not once have the scientists returned and said, “Guess what!

We discovered a magical part of the brain that defies all laws of physics, and which therefore requires us to throw out all the science we have based on that physics for the last four hundred years.”

No, one by one, the inner workings of the brain are revealed. And yes, the brain is a fantastic organ, but there is nothing magical about it. It is just another device.

Since the beginning of the computer age, people have come up with lists of things that computers will supposedly never be able to do. One by one, computers have done them.

And even if there were some magical part of the brain (which there isn’t), there would be no reason to assume that it is the mechanism by which we are intelligent.

Even if you proved that this magical part is the secret sauce in our intelligence (which it isn’t), there would be no reason to assume we can’t find another way to achieve intelligence.

Thus, this argument concludes, of course, we can build an AGI. Only mystics and spiritualists would say otherwise.

 

The Case against AGI

Let’s now explore the other side.

Warrier AI Robot

A brain, as was noted earlier, contains a hundred billion neurons with a hundred trillion connections among them.

But just as music is the space between the notes, you exist not in those neurons, but in the space between them. Somehow, your intelligence emerges from these connections.

We don’t know how the mind comes into being, but we do know that computers don’t operate anything at all like a mind, or even a brain for that matter.

They simply do what they have been programmed to do. The words they output mean nothing to them. They have no idea if they are talking about coffee beans or cholera.

They know nothing, they think nothing, they are as dead as fried chicken.

A computer can do only one simple thing: manipulate abstract symbols in memory. So what is incumbent on the “for” camp is to explain how such a device, no matter how fast it can operate, could, in fact, “think.”

We casually use language about computers as if they are creatures like us. We say things like, “When the computer sees someone repeatedly type in the wrong password, it understands what this means and interprets it as an attempted security breach.”

But the computer does not actually “see” anything. Even with a camera mounted on top, it does not see. It may detect something, just like a lawn system uses a sensor to detect when the lawn is dry. Further, it does not understand anything. It may compute something, but it has no understanding.

We use language that treats computers as alive colloquially, but we should keep in mind it is not really true. It is important now to make the distinction because with AGI we are talking about machines going from computing something to understanding something.

Joseph Weizenbaum, an early thinker about AI, built a simple computer program in 1966, ELIZA, which was a natural language program that roughly mirrored what a psychologist might say.

You make a statement like “I am sad” and ELIZA would ask, “What do you think made you sad?” Then you might say, “I am sad because no one seems to like me.”

ELIZA might respond “Why do you think that no one seems to like you?” And so on. This approach will be familiar to anyone who has spent much time with a four-year-old who continually and recursively asks why, why, why to every statement.

When Weizenbaum saw that people were actually pouring out their hearts to ELIZA, even though they knew it was a computer program, he turned against it. He said that in effect, when the computer says “I understand,” it tells a lie. There is no “I” and there is no understanding.

His conclusion is not simply linguistic hairsplitting. The entire question of AGI hinges on this point of understanding something.

To get at the heart of this argument, consider the thought experiment offered up in 1980 by the American philosopher John Searle. It is called the Chinese room argument. Here it is in the broad form:

There is a giant room, sealed off, with one person in it. Let’s call him the Librarian. The Librarian doesn’t know any Chinese. However, the room is filled with thousands of books that allow him to look up any question in Chinese and produce an answer in Chinese.

Someone outside the room, a Chinese speaker, writes a question in Chinese and slides it under the door. The Librarian picks up the piece of paper and retrieves a volume we will call book 1.

He finds the first symbol in book 1, and written next to that symbol is the instruction “Look up the next symbol in book 1138.”

He looks up the next symbol in book 1138. Next to that symbol, he is given the instruction to retrieve book 24,601 and look up the next symbol. This goes on and on.

When he finally makes it to a final symbol on the piece of paper, the final book directs him to copy a series of symbols down. He copies the cryptic symbols and passes them under the door.

The Chinese speaker outside picks up the paper and reads the answer to his question. He finds the answer to be clever, witty, profound, and insightful. In fact, it is positively brilliant.

Again, the Librarian does not speak any Chinese. He has no idea what the question was or what the answer said. He simply went from book to book as the books directed and copied what they directed him to copy.

Now, here is the question: Does the Librarian understand Chinese?

Searle uses this analogy to show that no matter how complex a computer program is, it is doing nothing more than going from book to book. There is no understanding of any kind.

And it is quite hard to imagine how there can be true intelligence without any understanding whatsoever.

He states plainly, “In the literal sense, the programmed computer understands what the car and the adding machine understand, namely, exactly nothing.”

Some try to get around the argument by saying that the entire system understands Chinese. While this seems plausible at first, it doesn’t really get us very far.

Say the Librarian memorized the contents of every book, and further could come up with the response from these books so quickly that as soon as you could write a question down, he could write the answer.

But still, the Librarian has no idea what the characters he is writing mean. He doesn’t know if he is writing about dishwater or doorbells. So again, does the Librarian understand Chinese?

So that is the basic argument against the possibility of AGI. First, computers simply manipulate ones and zeros in memory.

No matter how fast you do that, that doesn’t somehow conjure up intelligence. Second, the computer just follows a program that was written for it, just like the Chinese Room.

So no matter how impressive it looks, it doesn’t really understand anything. It is just a party trick.

It should be noted that many people in the AI field would most likely scratch their heads at the reasoning of the case against AGI and find it all quite frustrating.

They would say that of course, a brain is a machine—what else could it be? Sure, computers can only manipulate abstract symbols, but the brain is just a bunch of neurons that send electrical and chemical signals to each other.

Who would have guessed that would have given us intelligence? It is true that brains and computers are made of different stuff, but there is no reason to assume they can’t do the same exact things.

The only reason, they would say, that we think brains are not machines is because we are uncomfortable thinking we are only machines.

They would also be quick to offer rebuttals of the Chinese room argument. There are several, but the one most pertinent to our purposes is what I call the “quacks like a duck” argument.

If it walks like a duck, swims like a duck, and quacks like a duck, I am going to assume it is a duck. It doesn’t really matter if in your opinion there is no understanding, for if you can ask it questions in Chinese and it responds with good answers in Chinese, then it understands Chinese.

If the room can act as it understands, then it understands. End of story. This was in fact Turing’s central thesis in his 1950 paper on the question of whether computers can think.

He states, “May, not machines carry out something which ought to be described as thinking but which is very different from what a human does?”

Turing would have seen no problem at all in saying the Chinese room can think. Of course, it can. It is obvious. The idea that it can answer questions in Chinese but doesn’t understand Chinese is self-contradictory.

To read more of GigaOm publisher Byron Reese’s new book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity, you can purchase it here.

Source: gigaom.com

School

Interview with Dean Kamen

Dean Kamen is an engineer, inventor, and businessman. He holds hundreds of U.S. and foreign patents, many of them for medical devices including the iBOTTM mobility device, the first wearable infusion pump, the first wearable insulin pump for diabetics. He is perhaps best known for his invention of the Segway® Human Transporter.

He founded DEKA Research & Development Corporation as well as FIRST (For Inspiration and Recognition of Science and Technology), a global organization dedicated to helping young people understand and enjoy science and technology.

Kamen has received many awards including the National Medal of Technology in 2000, the Lemelson-MIT Prize in 2002, and he was inducted into the National Inventors Hall of Fame in May 2005.

What follows is an interview between Dean Kamen and Byron Reese, publisher of GigaOm, and author of the new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity. They discuss artificial intelligence and future technology, and their impact on jobs, education, innovation.

 

QA: 1

Byron Reese: I want to start off by saying this show is about artificial intelligence. Let’s just start with that piece of technology.

When people say, “Well what is it?” or “How should I feel about it?” What would you say?

Dean Kamen: I think the first thing people should do is not be afraid of putting those words together. The word ‘artificial’ to some people sounds bad. You don’t want to have artificial stuff in your food. You don’t want artificial stuff elsewhere.

And the word ‘intelligence’ means very different things to very different people. So, putting those two words together makes a mess of language and thought.

But I would say to maybe make it a more constructive conversation, we can say that people, for the first few thousand years after we climbed out of the primordial ooze, would try to build things with their hands, which would get pretty tough I guess.

And then somebody invented the first tool: a hammer or a shovel. Ok, those were artificial muscles. Then we, through the industrial revolution, created things substantially more capable than the shovel, like a bulldozer.

And it eliminated all those jobs of those ditch diggers — because one bulldozer could do the job of a thousand ditch diggers. I’d call a bulldozer artificial muscle. And it probably gave you a thousand to one — or more — leverage over what you could do with your back-breaking work.

But it didn’t eliminate jobs, because when you didn’t have a bulldozer, you might spend a whole life digging a hole big enough to make a house. Once you had a bulldozer, you didn’t eliminate the careers for a thousand people that might dig a hole.

You created the plausible possibility of paving, for instance, North America with a superhighway system.

So as we developed more artificial muscle, we built more and more things because we could, from roads to skyscrapers.

And those people that somehow believe that as we use computers or computing technologies to eliminate the work that people used to do that’s drudgery or boring work, the idea that we’re going to eliminate jobs is equally silly as saying the bulldozer eliminated a career.

No, it eliminated the horrible work that nobody wanted to do and gave people time to do way more magnificent things.

So I will call out artificial intelligence as the ability of the engineering community to move its capability to amplify not what your muscles used to do by building us the industrial revolution, but by amplifying what your thinking and problem-solving capabilities are because you’ve added the equivalent of an electronic bulldozer to eliminate the bulldozer, did something to the shovel, and the computer did something to the adding machine.

In each case, it opens up new opportunities to do more and more great stuff and to more and more young people, especially if they develop the skill sets to use these tools. And I hope they do it through my first robotics competition.

I hope that more and more young people see “artificial intelligence” as nothing more than saying, I don’t do back-breaking physical work due to the industrial revolution, I won’t be doing boring mental work due to artificial intelligence. I’ll leverage these technologies to build a better future and more capable career for myself. And that’s what it is.

 

 

QA: 2

Anybody who listens to the Voices in AI show knows I agree with every word you’ve just said. But the arguments that are sometimes brought against that viewpoint, believe me, I hear them a lot.

The first one is that, well, the change that is coming is going to come so fast, that it’s going to be severely disrupting. Do you think that there’s any merit to that?

I think every major change that has been wrought by technology permeates two things through the culture that’s experiencing it. To the pessimist, it brings fear, because people don’t like change. To the optimists, it brings excitement.

I’m sure when those first machines were made that could knit so quickly that all the people that knew how to make cloth by hand were terrified by the textile industry. I am again sure that the industrial revolution and steam engines and locomotives terrified all the people that were doing things that existed without the leverage of those technologies.

And I’m sure equally that other people saw those technologies as opportunities to do more and better things for themselves and their community in the world. So, I think you are right.

There are certainly lots of people that are appropriately concerned that the skill sets they now have are going to be displaced by more efficient, more cost-effective, maybe more accurate, more reliable, more stable systems, all lumped into something we call artificial intelligence.

But those very same people ought to say, if I learn about these systems, I learn how to use them, design them, develop them, deploy them, it will give me opportunities to advance my career and my sights about the future, and be part of something that’s bigger and better than the past. That’s called innovation, that’s called progress.

I think it comes down in every generation to the fact that as the world changes, you have a choice. You can be on that bus, or you could be off that bus, and if you embrace technology, you have the opportunity to be on a bus that’s moving further and moving faster.

Now having said that, by the way, you get on to a bus that’s bigger, heavier, and faster-moving than the one that you used to have and the scale of the impact of the accidents that can be caused are terrifying. But, again, this is not a new problem.

I’m sure the first tools that we made, using a rock as a hammer, can help you build something. Using the first hammer, you could break your thumb. Figuring out how to control fire, gave us the capability to stay warm and cook our food, and have light at night. It also gave us the capability to burn down our houses.

So, there is no technology that has only upside. In fact, I’d argue that the more potential upside that any new technology has, the more it can amplify what we do, by definition, it is just an amplifier. It’s not good, it’s not bad, it’s not immoral, it’s amoral. It’s an amplifier. It can help us do more good better; it can help people with nefarious goals do worse, or better.

And we need to deal with that. I think one way we deal with that is when we start teaching kids at an ever-earlier age about the power of technology, they also have to be taught not to use it as a weapon but to use it as a tool and not to simply do what you can do with technology, but to focus our efforts and do what we should with technology.

And to kids that are lucky enough, that have the privilege to have access to advanced technologies, they should understand that with every privilege comes responsibility.

I know in this country everybody runs around, saying “I’ve got my rights because of the Bill of Rights.” Well, maybe the founding fathers should have put right next to the Bill of Rights, the bill of responsibilities. Those people with capability need to use it wisely and prudently and help the rest.

So to those people that now claim the next big evolution in technology, which isn’t about amplifying muscles, it’s amplifying brains, it is going to be terrifying. I’m sure they were terrified by the locomotive, by the sewing machine.

I think smart people have to recognize that there’s always a risk when things change, and we need to continue to make sure that the changes net us all out to be in a better place, and we use those changes responsibly.

 

 

QA: 3

And then the second concern people have, and I think it’s one you share as well, is, do people have the education to do the jobs of the future?  Talk a little bit about that, and what you’re doing in that regard.

So, now you hit one where I think there is a problem, but again, in any rapidly changing environment, where you displace the status quo, some people fall out of the bottom. I think we now have the changes happening not at what was typically the worker base in most industries, but these are changes that affect everything.

And what is called white collared jobs or professional jobs are going to be potentially hugely impacted, and for some people in a negative way by ‘artificial intelligence’ because it was those white-collar or professional jobs that required a lot of sophisticated thinking that now might be displaced by programs that can get to better results more quickly than the manual process of thinking was capable of doing, even a decade ago.

So I started this program called FIRST, for inspiration and recognition of Science and Technology, a few decades ago recognizing that the jobs of the future are going to need kids that have a much more sophisticated skillset as they get through even their junior high school and high school years to be ready for these career options.

And the rate of change in the skill sets that will be necessary to have really interesting, exciting career opportunities over the next decade or two, is going to require a major change in our education system.

Keeping kids lined up in rows, having them memorize facts that used to be important, because if you didn’t know it, where were you going to find it? [is an obsolete approach].

Now, every kid in the country is carrying around on his or her belt every fact known to man, in a very well-organized way to find those facts.

So, education should no longer be giving you the disciplines and the toolsets that you used to need to go become a factory worker, learn how to follow instructions, learn how to do the same repetitive thing over and over again.

Education has to now be a much more sophisticated process of giving kids the toolsets and the understanding of how, for instance, to use ‘artificial intelligence,’ how to leverage the fact that information is now virtually free and what they need is to learn how to be systems innovators that add innovative ways for taking all this information and creating new opportunities to solve old problems.

And that’s what I tried to do in the FIRST community and that’s what I think schools need to quickly embrace so that school, as we knew it, can remain relevant to kids and it could be worth them spending so much of their lives in these locations.

 

 

QA: 4

Well, put a little flesh on the bones. How would you do schools?

Well, one of the things I’ve urged every school in this country to do, is incorporate the FIRST program. I mean, we’ve known now for decades that kids will sit in class and for 45 minutes once a week do phonics or spelling, but then every day during the season, every day after school, for three hours they practice [sports], whether it’s the football season, or the baseball season, or the basketball season, or soccer.

Kids, in a free country, you get the best of what you celebrate, and we have great programs that turn kids into great athletes because they put more time and effort and passion into that than they do in ‘academic’ stuff.

We justify all of that, by the way, putting so much into our physical school environment, whether it’s the parquet floors on the basketball court or the side lawns for the football and baseball.

We justify sports even though kids run the risk of being physically hurt. We justify it, almost exclusively by saying it’s critical that kids at an early age learn teamwork, and learn how to work together, compete in a positive way.

Well, really, if teamwork is all that important, why when they do it in the classroom, do we still call it cheating?

So I said look, we have a model that works, that gets kids inspired. It’s called sports. What if we could take that kind of model, that kind of essentially…a program that’s an interactive project base like building a sports team, what if we could take that model, but make the content not bouncing a ball or kicking a ball or throwing.

What if we could make the content developing the muscle hanging between their ears? What if we could create an opportunity within the school environment where kids could all participate in something where unlike in the other sport, every kid on a first-team could turn pro.

There simply aren’t millions of jobs in the NBA, the NFL, or Hollywood.

There are millions of not just jobs, but there are millions of career opportunities to create whole new industries that you and I haven’t even conceived of yet, that will be created by, and available to the next generation of kids that understand technology, that understands how to work together, that understand how to stand on the shoulders of the giants that have delivered, e.g., microprocessors that have essentially now made computation free and memory are essentially free, and the speed and the power of these devices have now turned them all into commodities.

We need kids that know how to leverage those commodities to solve the world’s problems, to create the new industries, and I think schools should be giving kids the toolset and the environment to do that, and I think rather than line them up with twenty, or thirty-year-old textbooks were science to them is putting pins in a frog, I think FIRST has the real potential to change the environment and the culture in schools to turn them into places where kids are excited to participate and come away with opportunities to create careers that they wouldn’t have imagined without FIRST.

 

 

QA: 5

So, take a step back just for the readers who may not be familiar with it. Describe what FIRST is.

FIRST, well the name stands for, For Inspiration and Recognition of Science and Technology. Notice the word ‘education’ isn’t in there.

The same way as the word ‘education’ isn’t in little league baseball. I said look, let’s create an institution that we can offer to schools, that will give kids kits and parts, cutting edge technologies, almost exclusively donated by massive, fantastic corporate supporters we have across the country, across every industry, to give kids access to the most cutting-edge technologies, and software development tools.

Let them take those kits or parts into their school and in a very exciting competitive short intense season, like any other sporting season, the schools will have these FIRST kits working between the kids, the teachers, the parents and the magic is the outside mentors from my 3700 corporate sponsors, pretty much every high tech company in this country and in the world now, embraces us because they need these kids more than these kids need them.

So, it’s a win, win, win for everybody.

The kids win, the teachers win, the parents win, the companies win, but basically FIRST is a program that brings together all of these different entities and says, we’re going to not give you quizzes and tests, but we’re going to give you this aspirational, extracurricular activity during which you learn how to do programming, how do you do electrical engineering, mechanical engineering, systems, controls, teamwork, build your company, build your little team, get it out there, go compete in these tournaments.

And again, you could say it’s all about teaching kids how to build robots, but I’ve been saying for years, we have never started FIRST… the goal was never to figure out how to get to use robots as the output product that we measure.

What we’ve now shortened it out to, is hey, everybody, we are not using kids to build robots. That would be slave labor. We are not using kids to build robots. We are using robots to build kids.

We are using these robots as an example to kids of what happens when you give people a sophisticated tactical challenge of an open-ended set of tools and inspire them to just try to do it.

There are no answers in the back of the book, there’s no one right way to do it. We’re not asking you to recall what we told you yesterday, that you took notes on when you were sitting in your classroom.

We’re saying, there is this problem. And every different school is going to get the same kit of parts, and you’re all going to have six or eight weeks to turn that kit of parts into an operating system.

And you’re going to put it out on that playing field, our playing fields are smaller than a basketball court, and you’re going to go head-on in battles in a double-elimination tournament against other kids and other schools that had other ideas on what’s the best way to accomplish this goal.

And in the end, again, [there are] no quizzes, no tests. It brings the school band, brings the mascots, brings the cheerleaders, let’s go celebrate what we’ve all accomplished.

Let’s go celebrate the creativity we’ve demonstrated, let’s go celebrate what we’ve learned about science and technology and engineering and problem-solving.

It went from twenty-three teams the first year we did it, to this year, we have over 61,000 schools from 83 countries, will be represented back here in Texas at the end of next month at the World Championship and then a couple of days later, because it was so big, we couldn’t get everybody here, a couple of days later we will do the second half of our World Championship, in Detroit, with thousands of these teams.

The passion that you see in these kids lets you know that despite all the crummy news we’re always hearing about technology — we’re running out of water and food, and the polar caps are melting and the environment is being… You know, news typically loves to make a spotlight around big problems — these kids just beam when you talk to them about these problems.

They are showing self-confidence and their ability to innovate and their ability to deal with these issues in a positive way, and it renews your confidence, that, while the world has gotten better and better at being negative and pessimistic if you give kids the right tools and the right mentorship, and first, if you inspire them, to recognize science and technology, you can walk away from our events believing that the future is going to be better than the past.

 

 

QA: 6

And how much of your time does all of that take?

Well, if I’m awake I’m working, so I probably work a normal forty or fifty or sixty-hour week in my day job. I have 500 engineers and in our day job, we design lots of critical systems for medical needs.

Let’s talk about DEKA. Let’s do that. You started it in 1982, it’s in New Hampshire, it’s focused on R & D.

Yeah, DEKA mostly does the front-end development of what we’d like to do, which takes advanced technologies as they get developed and come to scale, and then figure out how to apply them into a world where most people don’t apply them.

As processors got better and better, and faster and faster, you had ever more realistic violence on video games. Well, that’s great because we can do it, it’s easy.

But, once these big processors and faster processors and lower power processors got developed, we said, could we use those to make a better dialysis machine, one that might actually be so capable of monitoring itself through artificial intelligence that patients could do life support at home in their own bedroom.

It’s more comfortable, there’s more dignity, it’s lower cost,  it’s better outcomes. So, we didn’t invent microprocessors or sensors, or lithium batteries or solid-state gyros, or things like iBots and Segways.

We are a company of systems integrators.

We’re always looking at the world and saying, what new technologies have been developed, because some industry sees a need for huge amounts of them, whether it’s the gaming industry, or the defense industry, or the automotive industry, and we look at those technologies and say, now that they exist, could we system integrate them to make a better, simpler, smaller wearable drug delivery system, so somebody isn’t tied to a hospital.

Or, can we make an iBot so that a paraplegic or a quadriplegic who hasn’t been able to look somebody in the eye, or go up a curb or a flight of stairs since their accident, or whatever [has mobility again].

Can we give them back that capability by bringing these technologies to the field of human health? And I now have 500 engineers working on various projects to do that. Just a couple of weeks ago, we put a pair of prosthetic arms on a guy that had lost both of his arms. This guy was bilateral without arms.

 

 

QA: 7

Is this the Luke?

This is the Luke Arm.

Do you hold it above your head?

Yes, you sure can. And each one of the Luke Arms gives somebody substantially more capability than they had frankly with a plastic stick with the hook on the end of it, that they’d been using now for decades.

But as I said a couple of weeks ago, with a lot of support from the Veterans Administration and DARPA and the military, we ended up putting onto a guy for the first time ever a pair of our prosthetic arms, and within a very short time, this guy stands up in front of frankly our Senator, they had to see it in New Hampshire, and he spreads both of his arms and looks at her and says, I’m ambidextrous now.

And it was a great moment. So, I have a lot of engineers, as I said about 500 technical people in Manchester. We work mostly on systems to improve health care.

 

 

QA: 8

So, how does that work? In a way, you’ve done a fantastic job of systematizing the productization of technology through innovation. How do you go all the way from “we know how to do this really cool thing,” to “and there’s a business here?”

How do you do that, because usually things get handed off multiple times and different people have different skillsets, but you kind of do both ends of it, and how have you managed to do that?

So, I mean technically I guess it was specifically, I’m really not an engineer. I studied physics, I love mathematics and logic. I have, I think some of the best engineers in the world in each of the disciplines you need to do these very multi-disciplinary projects that we do.

We need mechanical engineers and electrical engineers, and systems engineers and controls engineers, but to your point, in most companies, they’re very vertical, and then they hand it off to a manufacturing group and then they hand it off to sales and marketing.

And at DEKA, we’re a little bit different from that, and we look at the whole problem from end to end, and say, “Look, let’s be good systems integrators and let’s figure out how to take all the stuff that really did take, in many cases, decades to develop core technologies, but now that they’re here and they’re ready, let’s figure out how we can,– across all the engineering disciplines that would be necessary to do it, — create a new class of solution to an old problem.”

The most exciting one we’re doing right now and hopefully we’re going to get a lot of support from IEEE for this one is we were just given at the end of the last administration, $80 million by the Department of Defense with the specific goal of, ok let’s take all those miracles happening in med schools and research labs, called regenerative medicine, let’s take those Petri dishes and roller bottles which have these little miracles in them and bring it to scale were 400,000 people that are right now waiting for a liver or a kidney or a lung are going to get one before, frankly, they die waiting.

And the researchers that have literally broken down the problem of understanding life, how does a liver be a liver, how does a kidney do what it does, how does a pancreas make insulin.

They know the answers to these questions, but they’re doing it in laboratory-scale environments and we said, I am as unlikely tomorrow to wake up and suddenly understand all the cellular biologies as they are unlikely tomorrow to wake up and say, oh, verification, validation, process control, regulatory standards.

These things aren’t going to jump out of these roller bottles and Petri dishes and suddenly become an industry themselves.  We need standards. We need the expertise and scale of companies that know how to make a prototype and make lots and lots of them, and if what we’re making lots and lots of are human organs, and human tissue, man you better get it right, because you’re putting it in somebody.

So, we said, look, as systems integrators, we think we can bring together probably dozens of them. We ended up bringing more than 80 companies: engineering companies, and manufacturing companies. In fact, there’s only one giant company I know who has the name ‘automation’ in their name, Rockwell Automation.

Well, we went to Rockwell and their Chairman, Blake Moret, said, “Dean, I’ll not only support you, but I’ll also join [your] board” of this new entity called Advanced Regenerative Manufacturing Institute.

He kind of sat there and said, we, like anybody else, don’t know a whole lot about manufacturing whole human organs, because nobody has ever done it, but Dean, if you can bring the medical community and these researchers, and these guys that have or will win the Nobel Prize for their contributions to medicine if you can bring those guys to the table, and you at that table can bring your systems integrators and the rest of the engineering industry.

And people that understand artificial intelligence and robotics, and if we can bring, for instance, IEEE and ASME, and NIST, the National Institute of Standards and Technology all together, so that when we show up at the Food and Drug Administration (FDA), and say, trust us, this is a real organ, it needs a standard; we know what the quality is, we can make these things, and make them safe, and we can make them in volume and we can make them affordable, that’s what Army is going to do.

And if it succeeds we will create a new industry, and that industry will be able to start supplying spare parts to humans and assure them of a higher quality of life than we now can offer people who find out their kidneys failed and we put them on dialysis.

I built a lot of dialysis equipment. We are very proud that we’re helping to keep these people alive while they wait for a cure, but you wouldn’t want to be on dialysis, trust me.

We make lots of stuff to keep people going, but how much better could it be, if instead of keeping them alive with chronic treatment, how much better would it be if we could cure their condition.

Somebody suffering from Macular Degeneration and they’re sitting there saying, I see less and less, and soon I’m not going to see at all. And maybe it’s your Mom or your Dad, what would it be like to say, oh, we’ll go to this place and they’ll give you a new eye.

What would it be like to see that little kid that has to take insulin three times a day and say, oh, we’re going to give you a new pancreas?

Imagine a world where you can safely and reliably replace organs that have stopped working in people and give them a new way to start. That’s what Army’s going to do.

 

 

QA: 9

You say “if” it works, but it isn’t going to be a binary outcome, right? Some things will work, and some things won’t.

Fair enough. When I said, “if it works,” I didn’t mean that there is any chance whatsoever that we won’t eventually do it.

Right.

I should have said, “We’re doing this now, going down this path that we’ve created, and what I’ve promised a whole lot of people, is that within five years there’ll be at least some evidence of some of these things that have gotten far enough that they’re actually now meeting realistic clinical needs.”

Certainly, we’re not going to wake up one day and be able to do double click and say, send me a liver, send me a kidney. That’s not going to happen digitally, instantly.

We’ll start with simple things. Maybe it won’t be whole organs. It’ll be cells, it’ll be pieces of tissue, it’ll be cartilage, it’ll be skin, it’ll be bone, and then it will grow, no pun intended, into full organs, and in some cases those full organs, we will integrate the process again, not that we invented, but we know very well now that in a laboratory environment, we can take cells from an individual and through a very, very elegant process, turn them into what are called IPSCs, Induced pluripotent stem cells.

We could take a cell from your body because all the cells in your body have the same information, [but] then why did one of them become an eyeball and one of them become a toenail? Well, they got differentiated.

But what if we could get a cell out of you and say, we’re going to put it back to what it looked like when you were an embryo. And what if we could take that cell from you and put it into a structure that would manufacture on a 3D printer that wants to be a kidney or a liver.

And what if I could make these IPSC cells from you and that cell essentially can grow up to be whatever it wants. It could be a liver cell, it could be a hepatocyte, it could be an Islet cell, and it could make insulin.

What if we could, through the engineering capabilities, grow the physical structure and at the same time, develop a scalable process to take your cells and at just the right moment, coming down a ‘manufacturing line,’ take those cells from you, put them into this organ that we just grew, have those cells become a fully operating organ of that type, a liver, a lung, or a kidney, have it in a sterile environment, essentially delivered to the surgical suite, where the first time a human touched this manufactured product, it gets taken and it’s assembled back into you replacing the defective one, in the same way, you take your car to the dealership and they take the old noisy cracked muffler off and put on a new muffler or a new started motor, or a new generator.

You’ve got this whole beautiful car, but if those spark plugs don’t work, the car is useless. If that starter motor won’t crank, the car is useless. Well, you’ve got this whole beautiful car, we have learned a long time ago to figure out how to replace the one or two parts that needed to be replaced to make the whole car work again.

What if we could do that for you, and if we did it by putting an organ back in you that was built from your own cells, and it won’t be rejected? You won’t need to spend the rest of your life taking immunosuppressive drugs to prevent it from being rejected, which gives you a higher quality of life, a lower-cost medical system. Everybody wins.

So, my “if,” was not if that could be done. My if, as if our plan and the support we’re getting from the government, from researchers, from the engineering community, from the standards community, from NIST, from IEEE, from the FDA…if all of those things come together to create what might be the most sophisticated, vertically integrated, manufacturing process the world has ever seen, we could transform medicine in two ways.

We could give people a way better quality of life, and we will dramatically lower the costs that right now look like they’re going to bankrupt this country unless we come up with some great innovations.

 

 

QA: 10

Another thing I guess DEKA’s been working on that we’re interested in, is computer vision.

Yes.

Can you talk a little bit about that?

Sure. As an example of two places where we’re in desperate need of better computer vision, in this Army advanced regenerative manufacturing, we’re trying to manufacture organs, but those organs have had some of their sub-components, cells, literally cells, we’ve got to be able to see where they are, how are they moving, how are they duplicated, where are they putting themselves.

So, if we could create an environment in which some of the systems we’re developing to bring these things out of the laboratory don’t need a postdoc sitting glued to a microscope and manually doing things.

What if we could automate through vision systems, some of the processes of monitoring and controlling the manufacture of things that have components that are literally smaller than human cells? That would be a huge win for us and we’re working on that.

And another place we want great vision systems is, I’m excited to say, we just got our next generation of iBot approved through the Food and Drug Administration and we’ve proven that’s it’s a very safe, reliable system to keep a person standing up.

But just like anybody that stands up, you and I, if we’re not paying attention, we could trip, we can slip, we can step into that pothole, we miss that curb, and then we fall down.

Well, what if we could add to our iBot systems that allow us to do the local mapping to make sure that the device is even safer when the person that’s using it isn’t paying attention.

What if we could help prevent some of those slips and trips? So, now the computer vision has gotten to where the actual cameras are so small and so inexpensive and require so little power consumption. I mean everybody is walking around, and now on their smartphone they have a camera that has a super high resolution, can operate in very low light, it’s very small, it’s very low powered.

What if we now integrate that with again, some of the very sophisticated software that can look at images coming through these cameras and help us map the environment and make sure that what we’re doing is safe?

So, I think vision systems, because the hardware has gotten so much better and smaller and cheaper, and because the algorithms to take the data coming out of these cameras has gotten so much better, whether it’s a simple camera or a lidar system or an ultrasonic system, or some combination of all sorts of others, giving machines the capability to interact with the outside world that they’re in, and gather data in real-time without physical locations and things are going hugely improve the capability of machines to do things we’d like them to do.

Hence, the self-driving car.

 

 

QA: 11

And last question. You’re clearly an optimistic person about the future in a world that is full of people who aren’t. How are you seeing things differently than people who are more down on the future?

Well, I just think the world works like this: you never solve a problem until you can identify and define it. So naturally as we’ve gotten better and better with technology, we’re able to define more and more problems. That could be depressing to people. To me, it’s just a transition stage. Now that we’ve identified that problem, let’s go about solving it.

So there are those people that look at the world, and now ironically through technology, through those satellite images, we can see that, wow, we’re impacting the environment, we can prove to ourselves the polar caps are melting.

We can take tests now that say, “Wow, you’re predisposed that you will have Parkinson’s disease” or “You will have Alzheimer’s,” and we develop more and more tools to help define problems. That’s always the first step.

So people see those tools, and they get obsessed with…oh my gosh, I’m going to have this disease or that disease. But an optimist says, oh, now that I’ve defined that problem, I’ll put some grey matter to it; I’m going to solve that problem.

I’m sure sixty or seventy or eighty years ago, when they figured out, oh my gosh, Polio, this virus, it’s gone through the country and it’s just wreaking havoc. My grandparents were terrified that this disease would come and what if their kids got Polio.

And then somebody figured out how to invent the iron lung so that if the Polio extended past your legs it would get up to your lungs which normally killed you.

Then people, those pessimists, those guys that know how to extrapolate into the future are probably the same guys that are telling us that our health care system is going to bankrupt the country.

They probably sat there and said, wait a minute, what if a few million kids next year get Polio and it gets so bad they need an iron lung.

Well, it used to be as tragic as that; they would just die. But now, I’m sure they would have extrapolated in the next ten years, half the population of America is going to be kept alive lying in an iron lung and the other half of the population is going to be stuck to take care of them and we’ll all be bankrupt.

Because they looked at the current state of data, the current state of what we knew, the current state of the problem. But they didn’t say, oh, don’t worry, in about ten years this visionary guy named Jonas Salk is going to come along, he’s going to realize that if you take the virus, kill it and then scratch it under somebody’s skin, they not only will not need an iron lung, they won’t even need the little things on the…they’ll never get Polio.

And kids today are not only not afraid of Polio, but they also don’t know Polio, it’s gone, it’s over. And it didn’t bankrupt the country. It’s about the cheapest thing you can do. Kids are born; they get a bunch of vaccines, Smallpox, Polio.

So I think the people today that are similar to the ones that must have been worried back then about that problem, will always see the cost of everything and the value of nothing.

They will always see this insurmountable set of problems and I’ve heard as a very young person that phrase, well every problem represents an opportunity.

I think we are surrounded by merely insurmountable opportunities and I think smart people will dice up those problems, those opportunities into different pieces and if we can create an army, a large enough army of young kids…

Back to FIRST, if we can create millions of kids that can spread themselves across all these opportunities, the generation of people alive today as the pessimists about health care, global warming, you name it, that generation of pessimists is going to see the next generation of smart, passionate kids, one by one, say, oh, well we’ve eliminated this problem on the global warming, we’ve eliminated this health care problem, we’ve now just created a vaccine against this cancer or Alzheimer’s or that.

Or, we’ve just figured out a way to make new organs for this, so you won’t need dialysis. I think the smart optimistic kids with the right toolsets will always stay one step ahead and in this constant race that we have in our society between the fear of catastrophe and the opportunity of success, the kids that are focused, that are optimistic, that work hard, that embrace technology will be the kids that make sure in that race between catastrophe and success we will succeed.

And we will succeed if we invest in these kids and if we have policies that allow us to embrace innovation, and I’m hoping that our government leaders, our industry leaders, our parents, our schools, and most of all our kids will embrace innovation, will embrace hard work, will take reasonable risks and create a better world in the future, as has happened in every generation since we climbed out of the primordial ooze.

 

 

QA: 12

Well, what a fantastic message and thank you for sharing it with our audience and thank you for taking the time.

You’re very welcome.

Source: gigaom.com