the future of artificial intelligence, robots and beyond. I'm Amy Alving, and I'll be your

moderator for today. We have a very distinguished panel here, and in your information, you have

a detailed bio on everybody on the stage, so we won't go into those specifics.

But briefly, let me introduce Professor Peter Bock, emeritus from George Washington University,

who has decades of experience in building and developing artificial intelligence systems.

Next to me we have Paul Cohen, also an academic from the University of Arizona, who is now

at my alma mater, DARPA, working for the Defense Department's most advanced research and development

organization. And we also have Andy McAfee from MIT, who comes to this from a business

and economic background with long experience looking at the impact of artificial intelligence

from an economic perspective. So we'll start today with thirty minutes of

moderated discussion amongst the panelists, and then we'll turn it over to the audience

for Q&A. I think in this area, it's important to make

sure that we have some common understanding of what we're talking about when we say artificial

intelligence. And so I'll ask Peter to start off by describing to us, what is artificial

intelligence more than just smart software? BOCK: Yeah, in my TED talk, I described people

who come up to me and say that AI is really the field that tries to solve very, very,

very hard problems, and I always found that definition a bit smarmy, because all of us

here are involved in solving very, very, very hard problems. That's not it at all.

It's a general purpose problem-solving engine that has a more or less broad domain of applications

so that a single solution can apply to many different situations even in different fields.

That's beginning -- a beginning definition for AI, and also probably a longer definition,

an engine that can eventually be broadened into beginning to imitate, shall we say, or,

in fact, emulate the cognition of our own thinking patterns.

I think I'll stop there and let the rest jump in.

ALVING: OK. So, Paul, I know that from your perspective, artificial intelligence is about

more than just crunching a lot of numbers. You know, the buzzword in -- out in the world

today is big data, big data is going to solve all our problems. But big data isn't sufficient,

is that correct? COHEN: That's right. So do you want me to

talk about what's AI or why big data isn't sufficient?

ALVING: Both. How does -- how does AI go beyond simply crunching a lot of numbers for big

data? COHEN: Let me give an example. I'm working

on a program -- managing a program at DARPA now called Big Mechanism. Big Mechanism is

sort of a poke in the eye to big data. But it's actually -- it's based on exactly this

distinction between crunching numbers and understanding what the data is telling you.

So the purpose of the Big Mechanism program is for machines to read the primary literature

in cancer biology, assemble models of cell signaling pathways in cancer biology that

are much bigger or more detailed than any human can comprehend, and then figure out

from those models how to attack and suppress cancer.

Now, data certainly is an important part of that, but I think the difference between big

data and Big Mechanism is that we seek causal models of something really complicated. Data

informs those models, but the understanding comes from those models. And AI has always

been about understanding, understanding the visual world, understanding speech, understanding

-- it's always been about understanding. ALVING: And so is the artificial intelligence

creating the understanding? COHEN: Yeah.

ALVING: Or are you building in the understanding... COHEN: No, no, the machine will read the literature.

I mean, you know, you see it in the papers. The papers say things like, well, you know,

we suppressed this gene and the following stuff happened, and so you take that little

piece of causal knowledge and you put it into your big, complicated model. And as the model

gets bigger and more complicated, you get a more and more refined understanding of how

cancer works. ALVING: So, Andy, I know you look at this

from more of an economic impact perspective. Where do you see this play between model-based

understanding and big data playing out in the market today?

MCAFEE: And this is the Jets versus the Sharks of the artificial intelligence world. There

are these two camps that have been going at it for as long as we've been thinking about

these problems. There's the model first camp. You need to understand cause and effect. You

need to understand the world before we can think about simulating it in a piece of -- or

embedding it in a piece of technology. There's the other part that says, No, actually.

And the best distinction I ever heard between those two approaches -- the one that brought

it home to me -- was the way a child learns language versus the way and adult learns language.

So if I were to start learning a language tomorrow, I would do it the model-based way.

I'd sit down with a grammar textbook. I'd try to understand how to conjugate the verbs.

I'd understand if there are masculine and feminine. I'd go through this arduous model-based

process of trying to acquire a new language. And like we all know, that's not how a two-year-old

does it. She just sits around and listens to the adults around her talking and talking

to her, and she builds up a very much data-first understanding of the world to the point that

she acquires language flawlessly, without having a single grammar lesson.

And what's further interesting about that is that if you ask her, why did you add the

S to that word? Why is it "I go" but "he goes"? She would say, "I have no idea -- I've never

heard the word conjugation before. I just know that's how language works."

So this divide is a really, really fundamentally important divide. The news from the trenches

that I can bring you is that in the world of -- in real-world applications, the data

side is winning. And there's almost a dismissive term for the model-based view these days among

a lot of the AI geeks that are doing work at Google and Apple and Facebook and putting

things in front of us. They call the model-based view "feature engineering," and they put kind

of air quotes around it, and they're almost quite dismissive about it.

And in general, in head-to-head competitions among different approaches in areas that we

care about, image recognition, natural language processing, artificial speech, things like

that, the model-based approach is the one that's winning these competitions and, therefore,

is being embedded in the commercial technologies that we're using.

COHEN: You meant to say the data... MCAFEE: I meant to say the data-based -- thank

you -- the data-based side is winning. My single favorite example of that -- this was

a crazy demonstration -- a team out of -- that founded a start-up called DeepMind built a

completely data-first learning system, and they asked it to play old-fashioned Atari

videogames from the 1980s. And they said, we're not going to even try to teach you the

rules of Pac-Man or Battlezone or anything like that. All you're going to do is try to

minimize this thing in the upper-right-hand corner called the score. You figure it out

from there. They pointed it at seven different Atari games.

On three of those games, the system eventually got better than any human player. So I'm not

-- I personally am not taking a stand on the model versus data. I'm just saying, over and

over again these days, the data world is winning the competitions.

ALVING: Peter? BOCK: I couldn't agree with Andrew more. I'm

in that... MCAFEE: Boring panel.

(LAUGHTER) BOCK: I'm in the same camp that he describes

as being data-driven, not rule-driven. I have been developing, since 1980, programs that

he describes called collective learning systems that play games and get better than humans

at them, simple games, but it soon became obvious to me that it's interesting to play

games, but none of you out there is spending much of your life playing games, and do you

really want an opponent who is not a natural opponent to play games with you? I think you

should be talking to your therapist about that.

The ultimate result of that, which is a totally data-driven game -- that is, it's all -- it

doesn't have any rules at all -- is the art that you see being generated out in the place

where we were having lunch on the screen. That is trained by the masters. Simply we

show where all of these -- all the paintings of Van Gogh or Renoir or Rembrandt and so

forth, and then we say, here's a photograph. Render it in the style of that.

And when it's all through, you -- if you were to say to ELIZA, so what does this mean over

here? She would say, "I don't understand the question," because she doesn't know how to

answer questions like that. She just knows how to paint. Probably Van Gogh would be incensed

with the remark or at least simply turn away and walk into the fields to paint again.

And one last thing. It was Fu at Purdue University who said many years ago, if you have the right

features, almost any decision-making apparatus will work. If you don't have the right features,

no apparatus will work. So, once again, we have to say that the data side people do have

to pay attention to the extremely important aspect of extracting what you're looking at

and what the important aspects of that are or listening to or smelling or feeling and

use that, those features, as the basis for assembling a lot of statistical data.

Three of my graduate students are now building exactly the system that Andrew just described,

a natural language understanding system. They have read 11,000 English novels -- not the

students, the machine -- they haven't read any of them...

(LAUGHTER) ... which disturbs me a bit. And it can carry

on a coherent conversation. It's still under development, so I'm not ready to show it yet,

but it can carry on a coherent conversation with somebody who cares to converse with it.

It tends to wander around a bit, sort of like people who've had perhaps a few beers and

are talking about who knows what, but nonetheless, it is coherent and that's a step in the right

direction. It is understanding based on learning and experience.

COHEN: So we don't entirely agree about everything. Let's go back to your game example for just

a moment, because I don't want people to think that the distinction between model-based approaches

and data-based approaches is quite so cut and dried.

Humans who have played -- who have learned to play one videogame will learn to play the

next more quickly. That's not true of computers. It'll be a fixed cost per game. And the rate

at which the machine learns, the next game will be unaffected by anything it learned

in the first game. Now, why is that not true for humans? Well,

it's obviously because, as you learn games or, in fact, learn anything at all, they abstract

general principles from what they're learning. Call that models, if you like. It's as good

a word as any, but it is something that we know machines don't do very well. In fact,

DARPA has sunk vast amounts of money into programs with names like transfer learning,

right, where the goal is to try and transfer knowledge acquired in one context to another.

Can't do it. I also don't want to leave anyone with the

impression that we're trying to have humans build models of anything, right? We're trying

to have machines... BOCK: This is important.

COHEN: ... build -- trying to have machines build models of things by reading.

MCAFEE: With data, build the model. COHEN: With the data, build the model. And

it's not any old model. It has to be a causal model.

MCAFEE: Good. COHEN: Right? Because only when you have a

causal model does it make sense to say, push this button and the following thing will happen.

If it's not a causal model -- and so that sort of brings me to the fundamental limitation

of data-driven science as it is practiced today is that really what you've got there

is correlation versus cause. I mean, what it all boils down to is machines

are exceptionally good at finding associations between things that co-vary -- that's correlation

-- and they're exceptionally bad at figuring out what causes what, right? And, you know,

if you want to do science, if you want to change the world, it's important to understand

why things are the way they are, not just that if you, you know, profile your customer

in the following way, you can increase the probability that they'll buy some product.

BOCK: My experience is quite different, Paul. My experience is that these games that learn

how -- these machines that learn how to play games can be built to understand the general

purpose roles that hold by expanding their knowledge base so that one of the features

they have is, what is the game like? And they bring in and they notice the similarity. They

don't bother re-learning that lesson that greed is good and red is bad. They know that

already, and, in fact, a disadvantage of that is that in some instances, green is bad and

red is good, and they have to unlearn that. Does that sound familiar to any of you?

ALVING: So it sounds like there's some agreement that there's a heavy emphasis on data-driven

autonomous systems today. They'll get better when they have more of a model-based understanding

that includes a causal... (CROSSTALK)

BOCK: And more resources. MCAFEE: And part of -- yep, more data is always

good, but part of I think what we're all excited about is a move away from what we -- what

some people call pure black box models. In other words, purely data-driven. And if you

ask the model, how did you arrive at that? It says, I have no idea.

We're getting better at building data-driven models that are not purely about black box.

So my colleagues who do image recognition using these -- the most popular toolkit today

is called deep learning. It's a class of algorithms that's just cleaning up in all these competitions.

If you do a deep learning algorithm to do image recognition, for example, it can get

pretty good at it, quite good at it. What's encouraging to me is if you -- you can then

-- my language is horribly informal -- you can query the algorithm to say, how are you

doing it? And it will say, well, you have to do edge detection, you have to do gradient