Today’s leading minds talk AI with host Byron Reese
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.

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…

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.

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?

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.

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.

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…

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
Today’s leading minds talk AI with host Byron Reese
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 Economy, What 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.

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.

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.

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.

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…

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].

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
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

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.

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