The following is a conversation with David Ferrochi.
He led the team that built Watson,
the IBM question answering system
that beat the top humans in the world
at the game of jeopardy.
For spending a couple hours with David,
I saw a genuine passion,
not only for abstract understanding of intelligence,
but for engineering it to solve real world problems
under real world deadlines and resource constraints.
Where science meets engineering
is where brilliant simple ingenuity emerges.
People who work and joining it to have a lot of wisdom
earned through failures and eventual success.
David is also the founder, CEO
and chief scientist of Elemental Cognition,
a company working to engineer AI systems
that understand the world the way people do.
This is the Artificial Intelligence podcast.
If you enjoy it, subscribe on YouTube,
give it five stars on iTunes,
support it on Patreon or simply connect with me on Twitter.
Alex Friedman spelled F-R-I-D-M-A-N.
And now here's my conversation with David Ferrochi.
Your undergrad was in biology
with an eye toward medical school
before you went on for the PhD in computer science.
So let me ask you an easy question.
What is the difference between biological systems
and computer systems?
In your, when you sit back,
look at the stars and think philosophically?
I often wonder,
I often wonder whether or not there is a substantive difference.
I mean, I think the thing that got me into computer science
and into artificial intelligence
was exactly this presupposition
that if we can get machines to think,
or I should say this question, this philosophical question,
if we can get machines to think,
to understand, to process information the way we do,
so if we can describe a procedure, describe a process,
even if that process were the intelligence process itself,
then what would be the difference?
So from a philosophical standpoint,
I'm not trying to convince that there is.
I mean, you can go in the direction of spirituality,
you can go in the direction of a soul,
but in terms of what we can experience
from an intellectual and physical perspective,
I'm not sure there is.
Clearly, there are different implementations,
but if you were to say,
is a biological information processing system
fundamentally more capable
than one we might be able to build out of silicon
or some other substrate,
I don't know that there is.
How distant do you think is the biological implementation?
So fundamentally, they may have the same capabilities,
but is it really a far mystery
where a huge number of breakthroughs are needed
to be able to understand it,
or is it something that for the most part
in the important aspects,
echoes of the same kind of characteristics?
Yeah, that's interesting.
I mean, so your question presupposes
that there's this goal to recreate
what we perceive as biological intelligence.
I'm not sure that's the,
I'm not sure that's how I would state the goal.
I mean, I think that's studying.
What is the goal?
Good, so I think there are a few goals.
I think that understanding the human brain
and how it works is important for us
to be able to diagnose and treat issues
for us to understand our own strengths and weaknesses,
both intellectual, psychological, and physical.
So neuroscience and understanding the brain
from that perspective, there's a clear, clear goal there.
From the perspective of saying,
I want to mimic human intelligence.
That one's a little bit more interesting.
Human intelligence certainly has a lot of things we envy.
It's also got a lot of problems too.
So I think we're capable of sort of stepping back
and saying, what do we want out of an intelligence?
How do we want to communicate with that intelligence?
How do we want it to behave?
How do we want it to perform?
Now, of course, it's somewhat of an interesting argument
because I'm sitting here as a human with a biological brain
and I'm critiquing the strengths and weaknesses
of human intelligence and saying
that we have the capacity to step back
and say, gee, what is intelligence
and what do we really want out of it?
And that even in and of itself suggests
that human intelligence is something quite enviable,
that it can introspect that way.
And the flaws, you mentioned the flaws,
the humans have flaws.
Yeah, I think that flaws that human intelligence has
is extremely prejudicial and bias
in the way it draws many inferences.
Do you think those are sorry to interrupt?
Do you think those are features or are those bugs?
Do you think the prejudice, the forgetfulness,
the fear, what are the flaws?
List them all, what love, maybe that's a flaw.
You think those are all things
that can get in the weight of intelligence
or the essential components of intelligence?
Well, again, if you go back and you define intelligence
as being able to sort of accurately, precisely,
rigorously reason, develop answers
and justify those answers in an objective way,
yeah, then human intelligence has these flaws
and that it tends to be more influenced
by some of the things you said.
And it's largely an inductive process,
meaning it takes past data, uses that to predict the future,
very advantageous in some cases,
but fundamentally biased and prejudicial in other cases
because it's gonna be strongly influenced by its priors,
whether they're right or wrong
from some objective reasoning perspective,
you're gonna favor them because those are the decisions
or those are the paths that succeeded in the past.
And I think that mode of intelligence
makes a lot of sense for when your primary goal
is to act quickly and survive and make fast decisions.
And I think those create problems
when you wanna think more deeply
and make more objective and reasoned decisions.
Of course, humans capable of doing both,
they do sort of one more naturally than they do the other,
but they're capable of doing both.
You're saying they do the one that responds quickly
and more naturally?
Right.
Because that's the thing we kinda need to not be eaten
by the predators in the world.
For example, but then we've learned to reason
through logic, we've developed science,
we train people to do that.
I think that's harder for the individual to do.
I think it requires training and teaching.
I think we are, the human mind certainly is capable of it,
but we find it more difficult.
And then there are other weaknesses, if you will,
as you mentioned earlier, just memory capacity
and how many chains of inference
can you actually go through without losing your way?
So just focus and...
So the way you think about intelligence,
and we're really sort of floating
in this philosophical space,
but I think you're the perfect person to talk about this
because we'll get to Jeopardy and beyond.
That's like an incredible,
one of the most incredible accomplishments in AI,
in the history of AI,
but hence the philosophical discussion.
So let me ask, you've kind of alluded to it,
but let me ask again, what is intelligence?
Underlying the discussion we'll have
with Jeopardy and beyond,
how do you think about intelligence?
Is it a sufficiently complicated problem
being able to reason your way through solving that problem?
Is that kind of how you think about
what it means to be intelligent?
So I think of intelligence to primarily two ways.
One is the ability to predict.
So in other words, if I have a problem,
what's gonna, can I predict what's gonna happen next?
Whether it's to predict the answer of a question
or to say, look, I'm looking at all the market dynamics
and I'm gonna tell you what's gonna happen next
or you're in a room and somebody walks in
and you're gonna predict what they're gonna do next
or what they're gonna say next.
So in a highly dynamic environment
full of uncertainty, be able to predict.
Lots of, the more variables, the more complex,
the more possibilities, the more complex,
but can I take a small amount of prior data
and learn the pattern and then predict
what's gonna happen next accurately and consistently?
That's certainly a form of intelligence.
What do you need for that, by the way?
You need to have an understanding
of the way the world works
in order to be able to unroll it into the future, right?
That, what do you think is needed to predict?
Depends what you mean by understanding.
I need to be able to find that function
and this is very much what deep learning does,
machine learning does, is if you give me enough prior data
and you tell me what the output variable is that matters,
I'm gonna sit there and be able to predict it.
And if I can predict it accurately
so that I can get it right more often than not,
I'm smart, if I can do that with less data
and less training time, I'm even smarter.
If I can figure out what's even worth predicting,
I'm smarter, meaning I'm figuring out
what path is gonna get me toward a goal.
What about picking a goal?
Sorry to interrupt again.
Well, that's interesting about picking a goal,
sort of an interesting thing.
And I think that's where you bring in
what do you pre-program to do?
We talk about humans and well,
humans are pre-programmed to survive.
So it's sort of their primary driving goal.
What do they have to do to do that?
And that can be very complex, right?
So it's not just figuring out that you need to run away
from the ferocious tiger,
but we survive in a social context as an example.
So understanding the subtleties of social dynamics
becomes something that's important for surviving,
finding a mate, reproducing, right?
So we're continually challenged with complex,
excessive variables, complex constraints,
rules, if you will, or patterns.
And we learn how to find the functions
and predict the things.
In other words, represent those patterns efficiently
and be able to predict what's gonna happen.
And that's a form of intelligence.
That doesn't really require anything specific,
other than the ability to find that function
and predict that right answer.
That's certainly a form of intelligence.
But then when we say, well, do we understand each other?
In other words, would you perceive me as intelligent
beyond that ability to predict?
So now I can predict, but I can't really articulate
how I'm going through that process,
what my underlying theory is for predicting.
And I can't get you to understand what I'm doing
so that you can figure out how to do this yourself
if you did not have, for example,
the right pattern-managing machinery that I did.
And now we potentially have this breakdown
where in effect, I'm intelligent,
but I'm sort of an alien intelligence relative to you.
You're intelligent, but nobody knows about it.
Well, I can see the output.
So you're saying, let's sort of separate the two things.
One is you explaining why you were able to predict
the future and the second is me being able to,
like impressing me that you're intelligent,
me being able to know
that you successfully predicted the future.
Do you think that's-
Well, it's not impressing you that I'm intelligent.
In other words, you may be convinced
that I'm intelligent in some form.
So how, what would-
Because of my ability to predict.
So I would look at the metrics.
And I'd say, wow, you're right.
You're right more times than I am.
You're doing something interesting.
That's a form of intelligence.
But then what happens is, if I say, how are you doing that?
And you can't communicate with me
and you can't describe that to me.
Now I may label you a savant.
I may say, well, you're doing something weird
and it's just not very interesting to me
because you and I can't really communicate.
And so this is interesting, right?
Because now you're in this weird place
where for you to be recognized
as intelligent the way I'm intelligent,
then you and I sort of have to be able to communicate.
And then we start to understand each other
and then my respect and my appreciation,
my ability to relate to you starts to change.
So now you're not an alien intelligence anymore.
You're a human intelligence now
because you and I can communicate.
And so I think when we look at animals, for example,
animals can do things we can't quite comprehend.
We don't quite know how they do them,
but they can't really communicate with us.
They can't put what they're going through in our terms.
And so we think of them as sort of low.
They're these alien intelligences
and they're not really worth necessarily what we're worth.
We don't treat them the same way as a result of that.
But it's hard because who knows what's going on.
So just a quick elaboration on that,
the explaining that you're intelligent,
explaining the reasoning that went into the prediction
is not some kind of mathematical proof.
If we look at humans, look at political debates
and discourse on Twitter,
it's mostly just telling stories.
So your task is not to tell an accurate depiction
of how you reason, but to tell a story real or not
that convinces me that there was a mechanism
by which you-
Well, ultimately, that's what a proof is.
I mean, even a mathematical proof is that
because ultimately the other mathematicians
have to be convinced by your proof, otherwise.
In fact, there have been-
That's the metric of success, yeah.
There have been several proofs out there
where mathematicians would study for a long time
before they were convinced
that it actually proved anything, right?
You never know if it proved anything
until the community of mathematicians decided that it did.
So I mean, so, but it's a real thing.
And that's sort of the point, right?
Is that ultimately, this notion of understanding us,
understanding something is ultimately a social concept.
In other words, I have to convince enough people
that I did this in a reasonable way.
I could do this in a way that other people
can understand and replicate
and that it makes sense to them.
So our human intelligence is bound together in that way.
We're bound up in that sense.
We sort of never really get away with it
until we can sort of convince others
that our thinking process makes sense.
Did you think the general question of intelligence
is then also a social construct?
So if we ask questions
of an artificial intelligence system,
is this system intelligent?
The answer will ultimately be a socially constructed concept.
I think, I think, so I think I'm making two statements.
I'm saying we can try to define intelligence
in a super objective way that says, here's this data.
I wanna predict this type of thing, learn this function.
And then if you get it right, often enough,
we consider you intelligent.
But that's more like a savant.
I think it is.
It doesn't mean it's not useful.
It could be incredibly useful.
It could be solving a problem we can't otherwise solve
and can solve it more reliably than we can.
But then there's this notion of
can humans take responsibility
for the decision that you're making?
Can we make those decisions ourselves?
Can we relate to the process that you're going through?
And now you as an agent,
whether you're a machine or another human, frankly,
are now obliged to make me understand
how it is that you're arriving at that answer
and allow me, me or obviously a community
or a judge of people to decide
whether or not that makes sense.
And by the way, that happens with humans as well.
You're sitting down with your staff, for example,
and you ask for suggestions about what to do next.
And someone says, well, I think you should buy
and I should think you should buy this much
or whatever or sell or whatever it is.
Or I think you should launch the product today or tomorrow
or launch this product versus that product,
whatever the decision may be.
And you ask why?
And the person said, I just have a good feeling about it.
And it's not, you're not very satisfied.
Now, that person could be, you know,
you might say, well, you've been right, you know, before,
but I'm going to put the company on the line.
Can you explain to me why I should believe this?
All right, and that explanation
may have nothing to do with the truth.
You just, the-
It's got to convince the other person.
You'll still be wrong, you'll still be wrong.
You just got to be convincing.
It's ultimately got to be convincing.
And that's why I'm saying it's, we're bound together, right?
Our intelligences are bound together in that sense.
We have to understand each other.
And if, for example, you're giving me an explanation,
and this is a very important point, right?
You're giving me an explanation.
And I'm not good at reasoning well and being objective
and following logical paths and consistent paths.
And I'm not good at measuring
and sort of computing probabilities across those paths.
What happens is collectively, we're not going to do,
we're not going to do well.
How hard is that problem, the second one?
So I think we'll talk quite a bit about the first
on a specific objective metric benchmark performing well,
but being able to explain the steps, the reasoning,
how hard is that problem?
I think that's very hard.
I mean, I think that's, well, it's hard for humans.
The thing that's hard for humans, as you know,
may not necessarily be hard for computers
and vice versa.
So, sorry, so how hard is that problem for computers?
I think it's hard for computers.
And the reason why I related to,
or saying that it's also hard for humans
is because I think when we step back
and we say we want to design computers to do that,
one of the things we have to recognize is
we're not sure how to do it well.
I'm not sure we have a recipe for that.
And even if you wanted to learn it,
it's not clear exactly what data we use
and what judgments we use to learn that well.
And so what I mean by that is,
if you look at the entire enterprise of science,
science is supposed to be at about objective reason
and reason, right?
So we think about,
who's the most intelligent person
or group of people in the world?
Do we think about the savants who can close their eyes
and give you a number?
We think about the think tanks
or the scientists or the philosophers
who kind of work through the details
and write the papers and come up
with the thoughtful logical proves
and use the scientific method.
And I think it's the latter.
And my point is that,
how do you train someone to do that?
And that's what I mean by it's hard.
How do you, what's the process of training people to do that well?
That's a hard process.
We work as a society,
we work pretty hard to get other people
to understand our thinking
and to convince them of things.
Now we could persuade them,
obviously we talked about this,
like human flaws or weaknesses,
we can persuade them through emotional means.
But to get them to understand and connect to
and follow a logical argument is difficult.
We try it, we do it as scientists,
we try to do it as journalists,
we try to do it as,
even artists in many forms as writers, as teachers,
we go through a fairly significant training process
to do that.
And then we could ask, well, why is that so hard?
But it's hard.
And for humans, it takes a lot of work.
And when we step back and say,
well, how do we get a machine to do that?
It's a vexing question.
How would you begin to try to solve that?
And maybe just a quick pause
because there's an optimistic notion
in the things you're describing,
which is being able to explain something through reason.
But if you look at algorithms
that recommend things that we look at next,
whether it's Facebook, Google,
advertisement-based companies, their goal
is to convince you to buy things based on anything.
So that could be reason
because the best of advertisement is showing you things
that you really do need and explain why you need it.
But it could also be through emotional manipulation.
The algorithm that describes
why a certain decision was made,
how hard is it to do it through emotional manipulation?
And why is that a good or a bad thing?
So you've kind of focused on reason, logic,
really showing in a clear way why something is good.
One, is that even a thing that us humans do?
And two, how do you think of the difference
in the reasoning aspect and the emotional manipulation?
So you call it emotional manipulation,
but more objectively is essentially saying,
there are certain features of things
that seem to attract your attention.
I mean, it kind of give you more of that stuff.
Manipulation is a bad word.
Yeah, I mean, I'm not saying it's good, right or wrong.
It works to get your attention
and it works to get you to buy stuff.
And when you think about algorithms
that look at the patterns of features
that you seem to be spending your money on
and say, I'm gonna give you something
with a similar pattern.
So I'm gonna learn that function
because the objective is to get you to click on
and to get you to buy it or whatever it is.
I don't know, I mean, it is what it is.
I mean, that's what the algorithm does.
You can argue whether it's good or bad.
It depends what your goal is.
I guess this seems to be very useful
for convincing, for telling a story.
For convincing humans, it's good
because again, this goes back to how does a human,
what is the human behavior like?
How does a human brain respond to things?
I think there's a more optimistic view of that too,
which is that if you're searching
for certain kinds of things,
you've already reasoned that you need them.
And these algorithms are saying, look, that's up to you.
The reason whether you need something or not,
that's your job.
You may have an unhealthy addiction to this stuff
or you may have a reasoned and thoughtful explanation
for why it's important to you.
And the algorithms are saying, hey, that's like whatever,
like that's your problem.
All I know is you're buying stuff like that,
you're interested in stuff like that,
could be a bad reason, could be a good reason,
that's up to you.
I'm gonna show you more of that stuff.
And I think that it's not good or bad.
It's not reasoned or not reasoned.
The algorithm is doing what it does,
which is saying, you seem to be interested in this,
I'm gonna show you more of that stuff.
And I think we're seeing this not just in buying stuff,
but even in social media.
You're reading this kind of stuff.
I'm not judging on whether it's good or bad.
I'm not reasoning at all.
I'm just saying, I'm gonna show you other stuff
with similar features.
And like, and that's it.
And I wash my hands from it.
And I say, that's all, that's all that's going on.
You know, there is, people are so harsh on AI systems.
So one, the bar of performance is extremely high.
And yet we also ask them to, in the case of social media,
to help find the better angels of our nature
and help make a better society.
So what do you think about the role of AI there?
So that, I agree with you.
That's the interesting dichotomy, right?
Because on one hand, we're sitting there
and we're sort of doing the easy part,
which is finding the patterns.
We're not building, the system's not building a theory
that is consumable and understandable to other humans
that can be explained and justified.
And so on one hand to say, oh, you know, AI is doing this.
Why isn't doing this other thing?
Well, this other thing is a lot harder.
And it's interesting to think about why it's harder.
And because you're interpreting the data
in the context of prior models,
in other words, understandings of what's important
in the world, what's not important.
What are all the other abstract features
that drive our decision making?
What's sensible, what's not sensible, what's good,
what's bad, what's moral, what's valuable, what isn't?
Where is that stuff?
No one's applying the interpretation.
So when I see you clicking on a bunch of stuff
and I look at these simple features, the raw features,
the features that are there in the data,
like what words are being used, or how long the material is,
or other very superficial features,
what colors are being used in the material.
Like I don't know why you're clicking
on this stuff you're clicking,
or if it's products, what the price is,
or what the categories and stuff like that.
And I just feed you more of the same stuff.
That's very different than kind of getting in there
and saying, what does this mean?
The stuff you're reading, like why are you reading it?
What assumptions are you bringing to the table?
Are those assumptions sensible?
Does the material make any sense?
Does it lead you to thoughtful, good conclusions?
Again, there's this interpretation
and judgment involved in that process
that isn't really happening in the AI today.
That's harder.
Because you have to start getting
at the meaning of the stuff of the content.
You have to get at how humans interpret the content
relative to their value system and deeper thought processes.
So that's what meaning means,
is not just some kind of deep, timeless, semantic thing
that the statement represents,
but also how a large number of people
are likely to interpret.
So that's again, even meaning is a social construct.
So you have to try to predict
how most people would understand this kind of statement.
Yeah, meaning is often relative,
but meaning implies that the connections
go beneath the surface of the artifacts.
If I show you a painting,
it's a bunch of colors on a canvas,
what does it mean to you?
And it may mean different things
to different people because of their different experiences.
It may mean something even different
to the artist who painted it.
As we try to get more rigorous with our communication,
we try to really nail down that meaning.
So we go from abstract art to precise mathematics,
precise engineering drawings and things like that.
We're really trying to say,
I want to narrow that space of possible interpretations
because the precision of the communication
ends up becoming more and more important.
And so that means that I have to specify,
and I think that's why this becomes really hard.
Because if I'm just showing you an artifact
and you're looking at it superficially,
whether it's a bunch of words on a page
or whether it's brushstrokes on a canvas
or pixels on a photograph,
you can sit there and you can interpret lots
of different ways at many, many different levels.
But when I want to align our understanding of that,
I have to specify a lot more stuff
that's actually not directly in the artifact.
Now I have to say, how are you interpreting this image
and that image and what about the colors
and what do they mean to you?
What perspective are you bringing to the table?
What are your prior experiences with those artifacts?
What are your fundamental assumptions and values?
What is your ability to kind of reason to chain together
logical implication as you're sitting there and saying,
well, if this is the case, then I would conclude this.
And if that's the case, then I would conclude that.
So your reasoning processes and how they work,
your prior models and what they are,
your values and your assumptions,
all those things now come together into the interpretation.
Getting in sync with that is hard.
And yet humans are able to intuit some of that
without any pre-
Because they have the shared experience.
Me and we're not talking about shared,
two people having shared experience as a society.
That's correct.
We have the shared experience and we have similar brains.
So we tend to, in other words,
part of our shared experience is our shared local experience.
Like we may live in the same culture,
we may live in the same society
and therefore we have similar educations.
We have similar, what we like to call prior models
about the world prior experiences.
And we use that as a, think of it as a wide collection
of interrelated variables and they're all bound
to similar things.
And so we take that as our background
and we start interpreting things similarly.
But as humans, we have a lot of shared experience.
We do have similar brains, similar goals,
similar emotions under similar circumstances
because we're both humans.
So now one of the early questions you asked,
how is biological and computer information systems
fundamentally different?
Well, one is humans come with a lot of pre-programmed stuff,
a ton of pro-programmed stuff
and they're able to communicate
because they have a lot of,
because they share that stuff.
Do you think that shared knowledge,
if we can maybe escape the hardware question,
how much is encoded in the hardware?
Just the shared knowledge in the software,
the history, the many centuries of wars
and so on that came to today, that shared knowledge.
How hard is it to encode?
Do you have a hope?
Can you speak to how hard is it to encode that knowledge
systematically in a way that could be used by a computer?
So I think it is possible to learn for a machine,
to program a machine, to acquire that knowledge
with a similar foundation,
in other words, a similar interpretive foundation
for processing that knowledge.
What do you mean by that?
So in other words, we view the world in a particular way.
So in other words, we have, if you will,
as humans, we have a framework
for interpreting the world around us.
So we have multiple frameworks
for interpreting the world around us,
but if you're interpreting, for example,
social-political interactions,
you're thinking about, well, there's people,
there's collections in groups of people,
they have goals, goals are largely built around survival
and quality of life, they're fundamental economics
around scarcity of resources.
And when humans come and start interpreting a situation
like that, because you brought up historical events,
they start interpreting situations like that.
They apply a lot of this fundamental framework
for interpreting that.
Well, who are the people?
What were their goals?
What resources did they have?
How much power influence did they have over the other?
Like this fundamental substrate, if you will,
for interpreting and reasoning about that.
So I think it is possible to imbue a computer
with that stuff that humans take for granted
when they go and sit down and try to interpret things.
And then with that foundation, they acquire,
they start acquiring the details,
the specifics and the given situation,
are then able to interpret it with regard to that framework.
And then given that interpretation,
they can do what?
They can predict.
But not only can they predict,
they can predict now with an explanation
that can be given in those terms,
in the terms of that underlying framework
that most humans share.
Now you could find humans that come and interpret events
very differently than other humans
because they're like using a different framework.
The movie Matrix comes to mind
where they decided humans were really just batteries.
And that's how they interpreted the value of humans
as a source of electrical energy.
So, but I think that for the most part,
we have a way of interpreting the events
or the social events around us
because we have this shared framework.
It comes from, again, the fact that we're similar beings
that have similar goals, similar emotions,
and we can make sense out of these.
These frameworks make sense to us.
So how much knowledge is there, do you think?
So you said it's possible.
Well, there's a tremendous amount
of detailed knowledge in the world.
You can imagine effectively infinite number
of unique situations and unique configurations
of these things.
But the knowledge that you need,
what I referred to as like the frameworks,
for you need for interpreting them, I don't think.
I think those are finite.
You think the frameworks are more important
than the bulk of the, so like framing.
Yeah, because what the frameworks do
is they give you now the ability to interpret and reason.
And to interpret and reason it,
to interpret and reason over the specifics
in ways that other humans would understand.
What about the specifics?
Were you acquired the specifics by reading
and by talking to other people?
So mostly actually, just even if you can focus
on even the beginning, the common sense stuff,
the stuff that doesn't even require reading
or it almost requires playing around with the world
or something, just being able to sort of manipulate objects,
drink water and so on, all of that.
Every time we try to do that kind of thing
in robotics or AI, it seems to be like an onion.
You seem to realize how much knowledge is really required
to perform even some of these basic tasks.
Do you have that sense as well?
And so how do we get all those details?
Are they written down somewhere?
Do they have to be learned through experience?
So I think when, like if you're talking about sort of
the physics, the basic physics around us,
for example, acquiring information about,
acquiring how that works.
Yeah, I mean, I think there's a combination of things going,
I think there's a combination of things going on.
I think there is like fundamental pattern matching
like what we were talking about before,
where you see enough examples, enough data about something
you start assuming that and with similar input,
I'm gonna predict similar outputs.
You can't necessarily explain it at all.
You may learn very quickly that when you let something go,
it falls to the ground.
That's such a- But you can't necessarily explain that.
But that's such a deep idea
that if you let something go, like the idea of gravity.
I mean, people are letting things go and counting
on them falling well before they understood gravity.
But that seems to be, that's exactly what I mean,
is before you take a physics class
or study anything about Newton,
just the idea that stuff falls to the ground
and then be able to generalize that all kinds of stuff
falls to the ground, it just seems like a non,
if without encoding it, like hard coding it in,
it seems like a difficult thing to pick up.
It seems like you have to have a lot of different knowledge
to be able to integrate that into the framework,
sort of into everything else.
So both know that stuff falls to the ground
and start to reason about sociopolitical discourse.
So both like the very basic
and the high-level reasoning decision-making.
I guess my question is, how hard is this problem?
And sorry to linger on it because again,
and we'll get to it for sure,
as what Watson with Jeopardy did is take on a problem
that's much more constrained,
but has the same hugeness of scale,
at least from the outsider's perspective.
So I'm asking the general life question
of to be able to be an intelligent being
and reasoning in the world
about both gravity and politics, how hard is that problem?
So I think it's solvable.
Okay, now beautiful.
So what about time travel?
Okay, I'm not as convinced yet, okay.
No, I think it is solvable.
I mean, I think that it's, first of all,
it's about getting machines to learn.
Learning is fundamental.
And I think we're already in a place
that we understand, for example,
how machines can learn in various ways.
Right now our learning stuff is sort of primitive
in that we haven't sort of taught machines
to learn the frameworks.
We don't communicate our frameworks
because of how shared there are some cases we do,
but we don't annotate, if you will,
all the data in the world with the frameworks
that are inherent or underlying our understanding.
Instead, we just operate with the data.
So if we wanna be able to reason over the data
in similar terms in the common frameworks,
we need to be able to teach the computer,
or at least we need to,
program the computer to acquire,
to have access to and acquire,
learn the frameworks as well
and connect the frameworks to the data.
I think this can be done.
I think we can start, I think machine learnings,
for example, with enough examples,
can start to learn these basic dynamics.
Will they relate them necessarily to gravity
not unless they can also acquire those theories as well
and put the experiential knowledge
and connect it back to the theoretical knowledge?
I think if we think in terms of these class of architectures
that are designed to both learn the specifics,
find the patterns, but also acquire the frameworks
and connect the data to the frameworks,
if we think in terms of robust architectures like this,
I think there is a path toward getting there.
In terms of encoding architectures like that,
do you think systems that are able to do this
will look like neural networks
or representing, if you look back to the 80s and 90s
of the expert systems,
so more like graphs, systems that are based in logic,
able to contain a large amount of knowledge
where the challenge was the automated acquisition
of that knowledge.
I guess the question is,
when you collect both the frameworks
and the knowledge from the data,
what do you think that thing will look like?
Yeah, so I mean, I think asking the question,
they look like neural networks is a bit of a red herring.
I mean, I think that they will certainly do inductive
or pattern-matched based reasoning,
and I've already experimented with architectures
that combine both that use machine learning
and neural networks to learn certain classes of knowledge
in those words to find repeated patterns
in order for it to make good inductive guesses,
but then ultimately to try to take those learnings
and marry them, in other words,
connect them to frameworks
so that it can then reason over that
in terms other humans understand.
So for example, at Elemental Cognition, we do both.
We have architectures that do both,
but both those things,
but also have a learning method
for acquiring the frameworks themselves
and saying, look, ultimately I need to take this data.
I need to interpret it in the form of these frameworks
so they can reason over it.
So there is a fundamental knowledge representation,
like what you're saying, like these graphs of logic,
if you will.
There are also neural networks
that acquire certain class of information.
Then they then align them with these frameworks,
but there's also a mechanism
to acquire the frameworks themselves.
Yeah, so it seems like the idea of frameworks
requires some kind of collaboration with humans.
Absolutely.
So do you think of that collaboration as-
Well, and let's be clear.
Only for the express purpose
that you're designing machine,
you're designing an intelligence
that can ultimately communicate with humans
in the terms of frameworks
that help them understand things.
Right, so now to be really clear,
you can independently create
a machine learning system and intelligence
that I might call an alien intelligence
that does a better job than you with some things,
but can't explain the framework to you.
That doesn't mean it might be better than you at the thing.
It might be that you cannot comprehend the framework
that it may have created for itself
that is inexplicable to you.
That's a reality.
But you're more interested in a case where you can.
I am, yeah.
My sort of approach to AI is because
I've set the goal for myself.
I want machines to be able
to ultimately communicate understanding with humans.
I want to be able to acquire and communicate,
acquire knowledge from humans
and communicate knowledge to humans.
They should be using what inductive
machine learning techniques are good at,
which is to observe patterns of data,
whether it be in language or whether it be in images
or videos or whatever,
to acquire these patterns,
to induce the generalizations from those patterns,
but then ultimately to work with humans,
to connect them to frameworks,
interpretations, if you will,
that ultimately make sense to humans.
Of course, the machine is gonna have the strength
that it has the richer and longer memory,
but it has the more rigorous reasoning abilities,
the deeper reasoning abilities.
So it'll be an interesting, complimentary relationship
between the human and the machine.
Do you think that ultimately needs explainability,
like a machine?
So if you look, we study, for example,
Tesla autopilot a lot, where humans,
I don't know if you've driven the vehicle
or are aware of what it did.
So you're basically the human
and machine are working together there,
and the human is responsible for their own life
to monitor the system.
And the system fails every few miles.
And so there's millions of those failures a day.
And so that's like a moment of interaction.
Do you see?
Yeah, that's exactly right.
That's a moment of interaction where the machine
has learned some stuff, it has a failure.
Somehow the failure is communicated.
The human is now filling in the mistake, if you will,
or maybe correcting or doing something
that is more successful in that case,
the computer takes that learning.
So I believe that the collaboration
between human and machine, I mean,
that's sort of a primitive example
and sort of a more, another example
is where the machine is literally talking to you
and saying, look, I'm reading this thing.
I know that like the next word might be this or that,
but I don't really understand why.
I have my guess, can you help me understand
the framework that supports this
and then can kind of acquire that,
take that and reason about it and reuse it.
The next time it's reading to try to understand something,
not unlike a human student might do.
I mean, I remember when my daughter was in first grade
and she had a reading assignment about electricity
and somewhere in the text it says,
and electricity is produced by water flowing over turbines
or something like that.
And then there's a question that says,
well, how is electricity created?
And so my daughter comes to me and says,
I mean, I could create it and produce
or kind of send it in this case
so I can go back to the text
and I can copy by water flowing over turbines,
but I have no idea what that means.
Like I don't know how to interpret water flowing
over turbines and what electricity even is.
I mean, I can get the answer right by matching the text,
but I don't have any framework
for understanding what this means at all.
And framework really is, I mean, it's a set of
not to be mathematical, but axioms of ideas
that you bring to the table and interpreting stuff.
And then you build those up somehow.
You build them up with the expectation
that there's a shared understanding of what they are.
Sure, yeah, it's the social that us humans,
do you have a sense that humans on earth
in general share a set of like how many frameworks are there?
I mean, it depends on how you bound them, right?
So in other words, how big or small,
like their individual scope, but there's lots
and there are new ones.
I think the way I think about is kind of in a layer.
I think that the architectures are being layered in that.
There's a small set of primitives
that allow you the foundation to build frameworks.
And then there may be many frameworks,
but you have the ability to acquire them.
And then you have the ability to reuse them.
I mean, one of the most compelling ways of thinking about this
is a reasoning by analogy where I can say,
oh, wow, I've learned something very similar.
You know, I never heard of this,
I never heard of this game soccer,
but if it's like basketball in the sense
that the goal's like the hoop,
and I have to get the ball in the hoop,
and I have guards and I have this and I have that,
like where are the similarities and where the difference is?
And I have a foundation now
for interpreting this new information.
And then the different groups,
like the millennials will have a framework,
and then the Democrats and Republicans,
millennials, nobody wants that framework, I think.
Well, I mean, I think,
I don't understand that.
Right, I mean, we're talking about political and social ways
of interpreting the world around them.
And I think these frameworks are still largely,
largely similar.
I think they differ in maybe
what some fundamental assumptions and values are.
Now, from a reasoning perspective,
like the ability to process the framework
might not be that different.
The implications of different fundamental values
or fundamental assumptions in those frameworks
may reach very different conclusions.
So from a social perspective,
the conclusions may be very different.
From an intelligence perspective,
I just followed where my assumptions took me.
Yeah, the process itself looks similar,
but that's a fascinating idea
that frameworks really help carve
how a statement will be interpreted.
I mean, having a Democrat and a Republican framework
and read the exact same statement
and the conclusions that you derive
will be totally different
from an AI perspective is fascinating.
What we would want out of the AI
is to be able to tell you that this perspective,
one perspective, one set of assumptions
is gonna lead you here,
another set of assumptions is gonna lead you there.
And in fact, to help people reason and say,
oh, I see where our differences lie.
I have this fundamental belief about that.
I have this fundamental belief about that.
Yeah, that's quite brilliant.
From my perspective and NLP,
there's this idea that there's one way
to really understand a statement,
but there probably isn't.
There's probably an infinite number of ways
to understand a statement.
Well, there's lots of different interpretations
and the broader the content, the richer it is.
And so, you and I can have very different experiences
with the same text, obviously.
And if we're committed to understanding each other,
and that's the other important point,
like if we're committed to understanding each other,
we start decomposing and breaking down our interpretation
towards more and more primitive components
until we get to that point where we say,
oh, I see why we disagree.
And we try to understand how fundamental
that disagreement really is.
But that requires a commitment
to breaking down that interpretation
in terms of that framework in a logical way.
Otherwise, you know, and this is why,
like I think of AI is really complimenting
and helping human intelligence
to overcome some of its biases
and its predisposition to be persuaded by more shallow reasoning
in the sense that like we get over this idea,
well, you know, I'm right because I'm Republican
or I'm right because I'm Democratic
and someone labeled this as Democratic point of view
or it has to follow in keywords in it.
And if the machine can help us break that argument down
and say, wait a second, you know,
what do you really think about this, right?
So essentially holding us accountable
to doing more critical thinking.
I'm gonna have to sit and think about that as fast.
That's, I love that.
I think that's really empowering use of AI
for the public discourse
that's completely disintegrating currently.
As we learn how to do it on social media.
That's right.
So one of the greatest accomplishments
in the history of AI is Watson competing
in the game of Jeopardy Against Humans.
And you were a lead in that, a critical part of that.
Let's start at the very basics.
What is the game of Jeopardy?
The game for us humans, human versus human.
Right, so it's to take a question
and answer it.
The game of Jeopardy.
Well, actually, well, no, but it's not, right?
It's really not, it's really to get a question and answer.
But it's what we call a factoid question.
So this notion of like it's,
it really relates to some fact that every,
few people would argue whether the facts are true or not.
In fact, most people, what in Jeopardy?
Kind of counts on the idea that these statements
have factual answers.
And the idea is to, first of all,
determine whether or not you know the answer,
which is sort of an interesting twist.
So first of all, understand the question.
You have to understand the question.
What is it asking?
And that's a good point because the questions
are not asked directly, right?
They're all like, the way the questions are asked
is non-linear.
It's like, it's a little bit witty.
It's a little bit playful sometimes.
It's a little bit tricky.
Yeah, they're asked in exactly in numerous witty,
tricky ways, exactly what they're asking is not obvious.
It takes inexperienced humans a while to go,
what is it even asking?
And it's sort of an interesting realization that you have
when somebody says, oh, what's the,
Jeopardy is a question answering show.
And then he's like, oh, like I know a lot.
And then you read it and you're still
trying to process the question and the champions
have answered and moved on.
There are three questions ahead,
but at the time you figured out
what the question even meant.
So there's definitely an ability there
to just parse out what the question even is.
So that was certainly challenging.
It's interesting historically though,
if you look back at the Jeopardy games,
much earlier, you know,
like 60s, 70s, that kind of thing.
The questions were much more direct.
They weren't quite like that.
They got sort of more and more interesting.
The way they asked them,
that sort of got more and more interesting
and subtle and nuanced and humorous and witty over time,
which really required the human
to kind of make the right connections
in figuring out what the question was even asking.
So yeah, you have to figure out the questions even asking.
Then you have to determine whether or not
you think you know the answer.
And because you have to buzz in really quickly,
you sort of have to make that determination
as quickly as you possibly can.
Otherwise you lose the opportunity to buzz in.
You may even before you really know if you know the answer.
I think a lot of humans will assume
they'll look at, they'll look at it.
They're processed it very superficially.
In other words, what's the topic?
What are some keywords?
And just say, do I know this area or not?
Before they actually know the answer,
then they'll buzz in and think about it.
So it's interesting what humans do.
Now some people who know all things,
like Ken Jennings or something
or the more recent big Jeopardy player,
they'll just assume they know all the Jeopardy
and they'll just buzz in.
Watson interestingly didn't even come close
to knowing all of Jeopardy, right?
Watson really-
Even at the peak, even at the expense.
Yeah, so for example, I mean,
we had this thing called recall,
which is like how many of all the Jeopardy questions,
how many could we even find the right answer for anywhere?
Like can we come up with if we had a big body of knowledge
from the order of several terabytes?
I mean, from a web scale was actually very small,
but from like a book scale,
I was talking about millions of books, right?
So they're quoting millions of books,
and encyclopedias, dictionaries, books.
So it's still a ton of information.
And I think it was only 85% was the answer
anywhere to be found.
So you're ready down at that level
just to get started, right?
So it was important to get up a very quick sense of
do you think you know the right answer to this question?
So we had to compute that confidence
as quickly as we possibly could.
So in effect, we had to answer it
and at least spend some time essentially answering it
and then judging the confidence that our answer was right
and then deciding whether or not
we were confident enough to buzz in.
And that would depend on what else was going on in the game
because it was a risk.
So like if you're really in a situation
where I have to take a guess, I have very little to lose,
then you'll buzz in with less confidence.
So that was a counter for the financial standings
of the different competitors?
Correct.
How much of the game was left?
How much time was left?
Where you were in the standing, things like that.
What, how many hundreds of milliseconds
that we're talking about here?
Do you have a sense of what is-
We targeted, yeah, we targeted.
So I mean, we targeted answering
in under three seconds and-
Buzzing in.
So the decision to buzz in
and then the actual answering, are those two different-
Yeah, they were two different things.
In fact, we had multiple stages
whereas like we would say, let's estimate our confidence,
which was sort of a shallow answering process.
And then ultimately decide to buzz in
and then we may take another second or something
to kind of go in there and do that.
But by and large, we're saying like, we can't play the game.
We can't even compete if we can't, on average,
answer these questions in around three seconds or less.
So you stepped in.
So there's these three humans playing a game
and you stepped in with the idea that IBM Watson
would replace one of the humans and compete against two.
Can you tell the story of Watson taking on this game?
Sure.
That seems exceptionally difficult.
Yeah, so the story was that it was coming up,
I think, to the 10-year anniversary of Big Blue.
Not Big Blue.
Deep Blue.
IBM wanted to do sort of another kind of really,
fun challenge, public challenge
that can bring attention to IBM research
and the kind of the cool stuff that we were doing.
I had been working in AI at IBM for some time.
I had a team doing what's called
open domain factoid question answering,
which is, we're not going to tell you what the questions are.
We're not even going to tell you what they're about.
Can you go off and get accurate answers to these questions?
And it was an area of AI research that I was involved in.
And so it was a very specific passion of mine.
Language understanding had always been a passion of mine.
One sort of narrow slice on whether or not
you could do anything with language
was this notion of open domain,
meaning I could ask anything about anything.
Factoid, meaning it essentially had an answer.
And being able to do that accurately and quickly.
So that was a research area that my team had already been in.
And so completely independently,
several IBM executives were like,
what are we going to do?
What's the next cool thing to do?
And Ken Jennings was on his winning streak.
This was like whatever it was, 2004, I think,
was on his winning streak.
And someone thought, hey, that would be really cool
if the computer could play Jeopardy.
And so this was like in 2004,
they were shopping this thing around.
And everyone was telling the research execs, no way.
Like, this is crazy.
And we had some pretty senior people
in the field saying, no, this is crazy.
And it would come across my desk and I was like,
but that's kind of what I'm really interested in doing.
But there was such this prevailing sense of,
this is nuts, we're not going to risk IBM's reputation
on this, we're just not doing it.
And this happened in 2004, it happened in 2005.
At the end of 2006, it was coming around again.
And I was coming off of a,
I was doing the open domain question answering stuff,
but I was coming off a couple other projects,
had a lot more time to put into this.
And I argued that it could be done.
And I argued it would be crazy not to do this.
Can I, you could be honest at this point.
So even though you argued for it,
what's the confidence that you had yourself privately
that this could be done?
Was, we just told the story,
how you tell stories to convince others.
How confident were you?
What was your estimation of the problem at that time?
So I thought it was possible.
And a lot of people thought it was impossible.
I thought it was possible.
A reason why I thought it was possible
was because I did some brief experimentation.
I knew a lot about how we were approaching open domain,
factoid question answering,
we've been doing it for some years.
I looked at the Jafferty stuff.
I said, this is going to be hard
for a lot of the points that we mentioned earlier,
hard to interpret the question,
hard to do it quickly enough,
hard to compute an accurate confidence.
None of this stuff had been done well enough before.
But a lot of the technologies we're building
with the kinds of technologies that should work.
But more to the point, what was driving me was,
I was an IBM research,
I was a senior leader in IBM research,
and this is the kind of stuff we were supposed to do.
In other words, we were basically supposed to take-
This is the moonshot.
This is the- I mean, we were supposed to take things
and say, this is an active research area.
It's our obligation to kind of,
if we have the opportunity, to push it to the limits.
And if it doesn't work,
to understand more deeply why we can't do it.
And so I was very committed to that notion saying,
folks, this is what we do.
It's crazy not to do it.
This is an active research area.
We've been in this for years.
Why wouldn't we take this grand challenge
and push it as hard as we can?
At the very least, we'd be able to come out and say,
here's why this problem is way hard.
Here's what we tried and here's how we failed.
So I was very driven as a scientist from that perspective.
And then I also argued,
based on what we did a feasibility study,
of why I thought it was hard but possible.
And I showed examples of where it succeeded,
where it failed, why it failed,
and sort of a high-level architectural approach
for why we should do it.
But for the most part, at that point,
the execs really were just looking for someone crazy enough
to say yes, because for several years at that point,
everyone had said no,
I'm not willing to risk my reputation and my career
on this thing.
Clearly you did not have such fears.
Okay. I did not.
So you dived right in and yet, for what I understand,
it was performing very poorly in the beginning.
So what were the initial approaches
and why did they fail?
Well, there were lots of hard aspects to it.
I mean, one of the reasons why prior approaches
that we had worked on in the past failed was because of,
because the questions were difficult to interpret.
Like what are you even asking for, right?
Very often, like if the question was very direct,
like what city, you know, or what, you know,
even then it could be tricky, but you know,
what city or what person,
often women would name it very clearly,
you would know that.
And if there was just a small set of them,
in other words, we're going to ask about these five types.
Like it's going to be an answer
and the answer will be a city in this state
or a city in this country.
The answer will be a person of this type, right?
Like an actor or whatever it is.
But turns out that in Jeopardy,
there were like tens of thousands of these things
and it was a very, very long tail.
Meaning, you know, it just went on and on.
And so even if you focused on trying to encode the types
at the very top, like there's five that were the most,
let's say five of the most frequent,
you still cover a very small percentage of the data.
So you couldn't take that approach of saying,
I'm just going to try to collect facts
about these five or 10 types or 20 types
or 50 types or whatever.
So that was like one of the first things,
like what do you do about that?
And so we came up with an approach toward that
and the approach to look promising
and we continue to improve our ability
to handle that problem throughout the project.
The other issue was that right from the outside,
I said, we're not going to,
I committed to doing this in three to five years.
So we did it in four.
So I got lucky.
But one of the things that that,
putting that like stake in the ground was I,
and I knew how hard the language understanding problem was.
I said, we're not going to actually understand language
to solve this problem.
We are not going to interpret the question
and the domain of knowledge that the question refers to
in reason over that to answer these questions.
Obviously we're not going to be doing that.
At the same time, simple search wasn't good enough
to confidently answer with a single correct answer.
First of all, that's like brilliant.
That's such a great mix of innovation
in practical engineering, three, three, four, eight.
So you're not trying to solve the general NLU problem.
You're saying, let's solve this in any way possible.
Oh, yeah.
No, I was committed to saying,
look, we're going to solving the open domain
question answering problem.
We're using Jeopardy as a driver for that.
Big benchmark.
Hard enough, big benchmark exactly.
And now we're- How do we do it?
We could just like whatever,
like just figure out what works
because I want to be able to go back
to the academic and scientific community
and say, here's what we tried,
here's what worked, here's what didn't work.
I don't want to go in and say,
oh, I only have one technology,
I have a hammer and I'm only going to use this.
I'm going to do whatever it takes.
I'm like, I'm going to think out of the box
and do whatever it takes.
One, and I also, there was another thing I believed.
I believed that the fundamental NLP technologies
and machine learning technologies would be adequate.
And this was an issue of how do we enhance them?
How do we integrate them?
How do we advance them?
So I had one researcher and came to me,
who had been working on question answering with me
for a very long time who had said,
we're going to need Maxwell's equations
for question answering.
And I said, if we need some fundamental formula
that breaks new ground and how we understand language,
we're screwed, we're not going to get there from here.
Like, I am not counting.
My assumption is I'm not counting
on some brand new invention.
What I'm counting on is the ability to take
everything that has done before
to figure out an architecture on how to integrate it well
and then see where it breaks
and make the necessary advances we need to make
until this thing works.
Yeah, push it hard to see where it breaks
and then pass it up.
I mean, that's how people change the world.
I mean, that's the Elon Musk approach
with rockets, SpaceX, that's the Henry Ford and so on.
I love it.
And in this case, I happen to be right,
but like we didn't know,
but you kind of have to put a stake in the ground
and say, how are you going to run the project?
So yeah, and backtracking to search.
So if you were to do, what's the brute force solution?
What would you search over?
So you have a question,
how would you search the possible space of answers?
And look, web searches come a long way even since then.
But at the time, like, you know, you first of all,
I mean, there are a couple of other constraints
around the problem, which is interesting.
So you couldn't go out to the web.
You couldn't search the internet.
In other words, the AI experiment was,
we want a self-contained device.
The device, if the device is as big as a room,
fine, it's as big as a room,
but we want a self-contained device.
You're not going out to the internet.
You don't have a lifeline to anything.
So it had to kind of fit in a shoebox, if you will,
or at least a size of a few refrigerators,
whatever it might be.
But also you couldn't just get out there.
You couldn't go off network to kind of go.
So there was that limitation.
But then we did,
but the basic thing was go do web search.
Problem was, even when we went and did a web search,
I don't remember exactly the numbers,
but someone in the order of 65% of the time,
the answer would be somewhere in the top 10 or 20 documents.
So first of all,
that's not even good enough to play Jeopardy.
You know, in other words, even if you could pull the,
Evian, if you could perfectly pull the answer out
of the top 20 documents,
top 10 documents, whatever it was,
which we didn't know how to do,
but even if you could do that,
you'd be, and you knew it was right,
unless we had enough confidence in it, right?
So you'd have to pull out the right answer.
You'd have to have confidence it was the right answer.
And then you'd have to do that fast enough
to now go buzz in
and you'd still only get 65% of them right,
which doesn't even put you in the winner circle.
Winner circle, you have to be up over 70
and you have to do it really quickly.
But now the problem is, well,
even if I had somewhere in the top 10 documents,
how do I figure out where in the top 10 documents
that answer is?
And how do I compute a confidence
of all the possible candidates?
So it's not like I go in knowing the right answer
and have to pick it.
I don't know the right answer.
I have a bunch of documents
somewhere in there is the right answer.
How do I, as a machine, go out and figure out
which one's right?
And then how do I score it?
So, and now how do I deal with the fact
that I can't actually go out to the web?
First of all, if you pause on that, just think about it.
If you could go to the web,
do you think that problem is solvable
if you just pause on it?
Just thinking even beyond jeopardy.
Do you think the problem of reading text
to find where the answer is?
Well, we solved that in some definition of solved
given the jeopardy challenge.
How did you do it for jeopardy?
So how did you take a body of work
in a particular topic
and extract the key pieces of information?
So now forgetting about the huge volumes
that are on the web, right?
So now we have to figure out,
we did a lot of source research.
In other words, what body of knowledge
is gonna be small enough but broad enough
to answer jeopardy?
And we ultimately did find the body of knowledge
that did that.
We included Wikipedia and a bunch of other stuff.
So like encyclopedia type of stuff.
I don't know if you can speak to it.
Encyclopedia, dictionaries, different times
of semantic resources like WordNet
and other types of semantic resources like that
as well as like some web crawls.
In other words, where we went out and took that content
and then expanded it based on producing statistically
producing seeds using those seeds for other searches
and then expanding that.
So using these like expansion techniques,
we went out and had found enough content
and we're like, okay, this is good.
And even up until the end,
we had a thread of research
that's always trying to figure out
what content could we efficiently include?
I mean, there's a lot of popular,
like what is the church lady?
Well, I think it was one of the, like, what?
Where do you, I guess that's probably an encyclopedia, so.
So that's an encyclopedia but then we would take that stuff
and we would go out and we would expand.
In other words, we go find other content
that wasn't in the core resources and expanded.
The amount of content grew it by an order of magnitude,
but still, again, from a web scale perspective,
this is very small amount of content.
It's very select.
We then took all that content.
We pre-analyzed the crap out of it,
meaning we parsed it,
broke it down into all those individual words
and then we did semantic,
statistic and semantic parses on it,
had computer algorithms that annotated it
and we indexed that.
In a very rich and very fast index.
So we have a relatively huge amount of,
let's say the equivalent of,
for the sake of argument, two to five million bucks.
We've now analyzed all that,
blowing up its size even more
because now it boasts metadata
and then we richly indexed all of that.
And by the way, in a giant in-memory cache.
So Watson did not go to disk.
So the infrastructure component there,
if you could just speak to it, how tough it,
I mean, I know 2000, maybe this is 2008, nine,
you know, that's kind of a long time ago.
Right.
How hard is it to use multiple machines?
How hard is the infrastructure component,
the hardware component?
We used IBM, so we used IBM hardware.
We had something like, I forget exactly,
but 2000, close to 3000 cores, completely connected.
So you had a switch where, you know,
every CPU was connected to every other CPU.
And you were sharing memory in some kind of way.
Large, kind of clever.
Shared memory, right?
And all this data was pre-analyzed
and put into a very fast indexing structure
that was all in memory.
And then we took that question,
we would analyze the question.
So all the content was now pre-analyzed.
So if I went and tried to find a piece of content,
it would come back with all the metadata
that we had pre-computed.
How do you shove that question?
How do you connect the big knowledge base
of the metadata and that's indexed
to the simple little witty, confusing question?
Right.
So therein lies, you know, the Watson architecture, right?
So we would take the question,
we would analyze the question.
So which means that we would parse it
and interpret it a bunch of different ways.
We'd try to figure out what is it asking about.
So we would come, we had multiple strategies
to kind of determine what was it asking for.
That might be represented as a simple string,
a character string, or something we would connect back
to different semantic types
that were from existing resources.
So anyway, the bottom line is we would do
a bunch of analysis in the question.
And question analysis had to finish
and had to finish fast.
So we do the question analysis
because then from the question analysis,
we would now produce searches.
So we would, and we had built,
using open source search engines, we modified them.
We had a number of different search engines we would use
that had different characteristics.
We went in there and engineered
and modified those search engines,
ultimately to now take our question analysis,
produce multiple queries based
on different interpretations of the question
and fire out a whole bunch of searches in parallel.
And they would come back with passages.
So these were passage search algorithms.
They would come back with passages.
And so now let's say you had 1,000 passages.
Now for each passage, you parallelize again.
So you went out and you parallelized the search.
Each search would now come back with a whole bunch of passages.
Maybe you had a total of 1,000 or 5,000, whatever passages.
For each passage now, you'd go and figure out
whether or not there was a candidate,
we'd call it candidate answer in there.
So you had a whole bunch of other algorithms
that would find candidate answers,
possible answers to the question.
And so you had candidate answers,
cold candidate answers generators,
a whole bunch of those.
So for every one of these components,
the team was constantly doing research coming up,
better ways to generate search queries from the questions,
better ways to analyze the question,
better ways to generate candidates.
And speed, so better is accuracy and speed.
Correct.
So, right, speed and accuracy for the most part were separated.
We handle that sort of in separate ways.
Like I focus purely on accuracy and to inaccuracy.
Are we ultimately getting more questions
and producing more accurate confidences?
And then a whole another team
that was constantly analyzing the workflow to find the bottlenecks.
And then figuring out how to both parallelize
and drive the algorithm speed.
But anyway, so now think of it like,
again, you have this big fan out now, right?
Because you have, you had multiple queries.
Now you have thousands of candidate answers.
For each candidate answer, you're gonna score it.
So you're gonna use all the data that built up.
You're gonna use the question analysis.
You're gonna use how the query was generated.
You're gonna use the passage itself.
And you're gonna use the candidate answer that was generated.
And you're gonna score that.
So now we have a group of researchers
coming up with scores.
There are hundreds of different scores.
So now you're getting a fan at it again
from however many candidate answers you have
to all the different scores.
So if you have a 200 different scores
and you have a thousand candidates,
now you have 200,000 scores.
And so now you gotta figure out,
how do I now rank these answers
based on the scores that came back?
And I wanna rank them based on the likelihood
that they're a correct answer to the question.
So every score was its own research project.
What do you mean by score?
So is that the annotation process
of basically a human being saying that this answer
has quality of-
Think of it, if you wanna think of it,
what you're doing,
if you wanna think about what a human would be doing,
human would be looking at a possible answer.
They'd be reading the, Emily Dickinson,
they'd be reading the passage in which that occurred.
They'd be looking at the question
and they'd be making a decision
of how likely it is that Emily Dickinson,
given this evidence in this passage,
is the right answer to that question.
Got it.
So that's the annotation task.
That's the annotation process.
That's the scoring task.
But scoring implies zero to one kind of continuous-
That's right, you give it a zero to one score.
Since this is not a binary-
No, you give it a score.
Give it a zero, yeah, exactly.
So humans give different scores
so that you have to somehow normalize
and all that kind of stuff that deal with all that.
Depends on what your strategy is.
We both, we-
It could be relative too.
It could be-
We actually looked at the raw scores as well,
standardized scores,
because humans are not involved in this.
Humans are not involved.
Sorry, so I'm misunderstanding the process here.
There's just passages.
Where is the ground truth coming from?
Grand truth is only the answers to the questions.
So it's end to end.
It's end to end.
So we all, so I was always driving end to end performance.
Very interesting, a very interesting engineering approach
and ultimately scientific and research approach
were always driving end to end now.
That's not to say we wouldn't make hypotheses
that individual component performance
was related in some way to end to end performance.
Of course we would,
because people would have to build individual components.
But ultimately, to get your component integrated
into the system, you had to show impact
on end to end performance, question answering performance.
So there's many very smart people working on this
and they're basically trying to sell their ideas
as a component that should be part of the system.
That's right.
And they would do research on their component
and they would say things like,
I'm gonna improve this as a candidate generator.
I'm gonna improve this as a question score
or as a passive score.
I'm gonna improve this or as a parser.
And I can improve it by 2% on its component metric,
like a better parse or better candidate
or a better type estimation or whatever it is.
And then I would say,
I need to understand how the improvement
on that component metric
is gonna affect the end to end performance.
If you can't estimate that and can't do experiments
to demonstrate that, it doesn't get in.
That's like the best run AI project I've ever heard.
That's awesome.
Like what breakthrough would you say?
Like I'm sure there's a lot of day-to-day breakthroughs,
but was there like a breakthrough
that really helped improve performance?
Like wait, where people began to believe
or is it just a gradual process?
Well, I think it was a gradual process,
but one of the things that I think gave people confidence
that we can get there was that as we follow this,
as we follow this procedure of different ideas,
build different components,
plug them into the architecture,
run the system, see how we do,
do the error analysis,
start off new research projects to improve things.
And the very important idea that
the individual component work
did not have to deeply understand
everything that was going on with every other component.
And this is where we leveraged machine learning
in a very important way.
So while individual components could be
statistically driven machine learning components,
some of them were heuristic,
some of them were machine learning components,
the system has a whole combined all the scores
using machine learning.
This was critical because that way
you can divide and conquer.
So you can say, okay, you work on your candidate generator
or you work on this approach to answer scoring,
you work on this approach to type scoring,
you work on this approach to passage search
or to passage selection and so forth.
But when we just plug it in
and we had enough training data to say,
now we can train and figure out how do we weigh
all the scores relative to each other
based on predicting the outcome
which is right or wrong on jeopardy?
And we had enough training data to do that.
So this enabled people to work independently
and to let the machine learning do the integration.
Beautiful, so the machine learning is doing the fusion
and then it's a human orchestrated ensemble
with different approaches.
That's great.
Still impressive that you're able to get it done
in a few years.
That's not obvious to me that it's doable.
If I just put myself in that mindset.
But when you look back at the Jeopardy Challenge,
again, when you're looking up at the stars,
what are you most proud of?
It's looking back at those days.
I'm most proud of my,
my commitment and my team's commitment
to be true to the science, to not be afraid to fail.
That's beautiful because there's so much pressure
because it is a public event.
It is a public show that you were dedicated to the idea.
That's right.
Do you think it was a success?
In the eyes of the world, it was a success.
By your, I'm sure, exceptionally high standards.
Is there something you regret you would do differently?
It was a success.
It was a success for our goal.
Our goal was to build the most advanced,
open the main question answering system.
We went back to the old problems that we used to try to solve
and we did dramatically better on all of them
as well as we beat Jeopardy.
So we won at Jeopardy.
So it was a success.
It was, I worked on it,
it was, I worry that the world would not understand it
as a success because it came down to only one game.
And I knew statistically speaking,
this can be a huge technical success
and we could still lose that one game.
And that's a whole nother theme of the journey.
But it was a success.
It was not a success in natural language understanding,
but that was not the goal.
Yeah, that was the, but I would argue,
I understand what you're saying in terms of the science,
but I would argue that the inspiration of it, right?
The, not a success in terms of solving natural language
understanding, but it was a success of being an inspiration
to future challenges.
Absolutely.
That drive future efforts.
What's the difference between how human being compete
in Jeopardy and how Watson does it?
That's important in terms of intelligence.
Yeah.
So that actually came up very early on in the project also.
In fact, I had people who wanted to be on the project
who were early on who sort of approached me
once I committed to do it,
had wanted to think about how humans do it.
And they were, you know, from a cognition perspective,
like human cognition and how that should play.
And I would not take them on the project
because another assumption or another stake
I put in the ground was I don't really care
how humans do this.
At least in the context of this project.
I need to build, in the context of this project,
in NLU and in building an AI that understands how
it needs to ultimately communicate with humans,
I very much care.
So it wasn't that I didn't care in general.
In fact, as an AI scientist, I care a lot about that,
but I'm also a practical engineer.
And I committed to getting this thing done.
And I wasn't gonna get distracted.
I had to kind of say, like, if I'm gonna get this done,
I'm gonna chart this path.
And this path says, we're gonna engineer a machine
that's gonna get this thing done.
And we know what search and NLP can do.
We have to build on that foundation.
If I come in and take a different approach
and start wondering about how the human mind
might or might not do this,
I'm not gonna get there from here in the time frame.
I think that's a great way to lead the team.
But now there's done, and there's one, when you look back,
analyze what's the difference, actually.
So I was a little bit surprised, actually,
to discover over time, as this would come up
from time to time, and we'd reflect on it.
And talking to Ken Jennings a little bit,
and hearing Ken Jennings talk about how he answered questions,
that it might have been closer to the way humans
answered questions than I might have imagined previously.
Because humans are probably in the game of Jeopardy
at the level of Ken Jennings,
are probably also cheating their weight to winning, right?
Well, they're doing shallow analysis.
They're doing the fastest possible.
They're doing shallow analysis.
So they are very quickly analyzing the question
coming up with some key vectors
or cues, if you will.
And they're taking those cues,
and they're very quickly going through
their library of stuff,
not deeply reasoning about what's going on.
And then a lot's of different,
like what we call these scores,
what's kind of score in a very shallow way.
And then say, oh, boom, that's what it is.
And so it's interesting, as we reflected on that,
so we may be doing something that's not too far off
from the way humans do it.
But we certainly didn't approach it by saying,
how would a human do this?
Now, in elemental cognition,
like the project I'm leading now,
we ask those questions all the time,
because ultimately we're trying to do something
that is to make the intelligence of the machine
and the intelligence of the human very compatible.
Well, compatible in the sense they can communicate
with one another, and they can reason
with their shared understanding.
So how they think about things
and how they build answers, how they build explanations
becomes a very important question to consider.
So what's the difference between this open domain,
but cold constructed question answering of Jeopardy
and more something that requires understanding
for shared communication with humans and machines?
Yeah, well, this goes back to the interpretation
of what we were talking about before.
Jeopardy, the system's not trying to interpret the question
and it's not interpreting the content that's reusing
and with regard to any particular framework.
I mean, it is parsing it and parsing the content
and using grammatical cues and stuff like that.
So if you think of grammar as a human framework
in some sense it has that,
but when you get into the Richard Semantic frameworks,
what do people, how do they think, what motivates them,
what are the events that are occurring
and why they're occurring and what causes what else
to happen and where are things in time and space?
And like when you started thinking about
how humans formulate and structure the knowledge
that they acquire in their head
and wasn't doing any of that.
What do you think are the essential challenges
of free flowing communication, free flowing dialogue
versus question answering even with a framework
or the interpretation dialogue?
Yep.
Do you see free flowing dialogue
as a fundamentally more difficult
than question answering even with shared interpretation?
Yeah, so dialogue is important in a number of different ways.
I mean, it's a challenge.
So first of all, when I think about the machine that,
when I think about a machine that understands language
and ultimately can reason in an objective way
that can take the information that it perceives
through language or other means
and connect it back to these frameworks,
reason and explain itself,
that system ultimately needs to be able to talk to humans
or it needs to be able to interact with humans.
So in some sense it needs to dialogue.
That doesn't mean that it,
sometimes people talk about dialogue and they think,
you know, how do humans talk to each other
in a casual conversation?
Then you can mimic casual conversations.
Well, we're not trying to mimic casual conversations.
We're really trying to produce a machine
whose goal is to help you think
and help you reason about your answers and explain why.
So instead of like talking to your friend down the street
about having a small talk conversation
with your friend down the street,
this is more about like you would be communicating
to the computer on Star Trek
where like, what do you wanna think about?
Like, what do you wanna reason about?
I'm gonna tell you the information I have.
I'm gonna have to summarize it.
I'm gonna ask you questions.
You're gonna answer those questions.
I'm gonna go back and forth with you.
I'm gonna figure out what your mental model is.
I'm gonna now relate that to the information I have
and present it to you in a way that you can understand it
and then we can ask follow-up questions.
So it's that type of dialogue that you wanna construct.
It's more structured, it's more goal-oriented,
but it needs to be fluid.
In other words, it can't, it can't,
it has to be engaging and fluid.
It has to be productive and not distracting.
So there has to be a model of,
in other words, the machine has to have a model
of how humans think through things and discuss them.
So basically a productive, rich conversation,
unlike this podcast.
What, what?
Oh, I'd like to think it's more similar to this podcast.
I was just joking.
I'll ask you about humor as well, actually.
But what's the hardest part of that?
Because it seems we're quite far away
as a community from that still to be able to,
so one is having a shared understanding.
That's, I think a lot of the stuff you said
with frameworks is quite brilliant,
but just creating a smooth discourse.
Yeah, it feels clunky right now.
Which aspects of this whole problem
that you just specified
of having a productive conversation is the hardest?
And that were, or maybe,
maybe any aspect of it you can comment on
because it's so shrouded in mystery.
So I think to do this,
you kind of have to be creative in the following sense.
If I were to do this as purely a machine learning approach
and someone said learn how to have a good,
fluent, structured knowledge acquisition conversation,
I'd go out and say, okay,
I have to collect a bunch of data of people doing that.
People reasoning well,
having a good structured conversation
that both acquires knowledge efficiently
as well as produces answers and explanations
as part of the process.
And you struggle.
I don't know to collect the data
because I don't know how much data is like that.
Okay, this one, there's a humorous comment
around the lack of rational discourse.
But also, even if it's out there, say it was out there,
how do you actually, and it, like, how do you,
I think like an successful example.
Right, so I think any problem like this
where you don't have enough data
to represent the phenomenon you want to learn,
in other words, if you have enough data,
you could potentially learn the pattern.
In an example like this, it's hard to do.
It's sort of a human sort of thing to do.
What recently came out of IBM
was the debate or project sort of interesting, right?
Because now you do have these structured dialogues,
these debate things,
where they did use machine learning techniques
to generate these debates.
Dialogues are a little bit tougher in my opinion
than generating a structured argument
where you have lots of other structured arguments like this.
You could potentially annotate that data
and you could say this is a good response,
this is a bad response in a particular domain.
Here, I have to be responsive
and I have to be opportunistic with regard to
what is the human saying.
So I'm goal-oriented in saying I want to solve the problem,
I want to acquire the knowledge necessary.
But I also have to be opportunistic and responsive
to what the human is saying.
So I think that it's not clear
that we could just train on the body of data to do this.
But we could bootstrap it.
In other words, we can be creative
and we could say, what do we think?
What do we think the structure of a good dialogue is
that does this well?
And we can start to create that.
If we can create that more programmatically,
at least to get this process started,
and I can create a tool
that now engages humans effectively,
I could start generating data,
I could start the human learning process
and I can update my machine.
But I could also start the automatic learning process
as well, but I have to understand
what features to even learn over.
So I have to bootstrap the process a little bit first.
And that's a creative design task
that I could then use as input
into a more automatic learning task.
So some creativity in bootstrapping.
What elements of a conversation do you think
you would like to see?
So one of the benchmarks for me is humor, right?
That seems to be one of the hardest.
And to me, the biggest contrast is from Watson.
So one of the greatest comedy sketches of all time, right?
Is the SNL celebrity Jeopardy with Alex Trebek
and Sean Connery and Bert Reynolds and so on
with Sean Connery commentating
on Alex Trebek's mother a lot.
So, and I think all of them are in the negative point.
That's why.
So they're clearly all losing
in terms of the game of Jeopardy,
but they're winning in terms of comedy.
So what do you think about humor
in this whole interaction,
in the dialogue that's productive?
Or even just whatever, what humor represents to me is,
the same idea that you're saying about framework
because humor only exists within a particular human framework.
So what do you think about humor?
What do you think about things like humor
that connect to the kind of creativity
you mentioned that's needed?
I think there's a couple of things going on there.
So I sort of feel like,
and I might be too optimistic this way,
but I think that there are,
we did a little bit about with puns in Jeopardy.
We literally sat down and said,
well, you know, how do puns work?
And you know, it's like word play
and you could formalize these things.
So I think there's a lot aspects of humor
that you could formalize.
You could also learn humor.
You could just say, what do people laugh at?
And if you have enough, again,
if you have enough data to represent that phenomenon,
you might be able to, you know,
weigh the features and figure out, you know,
what humans find funny and what they don't find funny.
You might, the machine might not be able to explain
why the human body, unless we sit back
and think about that more formally.
I think, again, I think you do a combination of both.
And I'm always a big proponent of that.
I think, you know, robust architectures
and approaches are always a little bit combination
of us reflecting and being creative
about how things are structured and how to formalize them
and then taking advantage of large data
and doing learning and figuring out
how to combine these two approaches.
I think there's another aspect to humor, though,
which goes to the idea that I feel like I can relate
to the person telling the story.
Yeah.
To the person telling the story.
And I think that's an interesting theme
in the whole AI theme, which is,
do I feel differently when I know it's a robot?
And when I know, when I imagine
that the robot is not conscious the way I'm conscious,
when I imagine the robot does not actually
have the experiences that I experience,
do I find it funny or do, because it's not as related,
I don't imagine that the person's relating it
to it the way I relate to it.
I think this also, you see this in the arts
and in entertainment where, like,
sometimes you have savants who are remarkable at a thing,
whether it's sculpture, it's music or whatever,
but the people who get the most attention
are the people who can evoke a similar emotional response
who can get you to, right, about the way they are.
In other words, who can basically make the connection
from the artifact, from the music
or the painting of the sculpture to the emotion
and get you to share that emotion with them.
And then, and that's when it becomes compelling.
So they're communicating at a whole different level.
They're just not communicating the artifact,
they're communicating the emotional response to the artifact.
And then you feel like, oh, wow, I can relate to that person,
I could connect to that, I could connect to that person.
So I think humor has that aspect as well.
So the idea that you can connect to that person,
person being the critical thing,
but we're also able to anthropomorphize objects
pretty, robots and AI systems pretty well.
So we're almost looking to make them human.
So maybe from your experience with Watson,
maybe you can comment on, did you consider that as part,
well, obviously the problem of Jeopardy
doesn't require anthropomorphization, but nevertheless.
Well, there was some interest in doing that,
and that's another thing I didn't want to do,
because I didn't want to distract
from the actual scientific task.
But you're absolutely right.
I mean, humans do anthropomorphize,
and without necessarily a lot of work,
I mean, you just put some eyes
in a couple of eyebrow movements,
and you're getting humans to react emotionally,
and I think you can do that.
So I didn't mean to suggest that,
that that connection cannot be mimicked.
I think that connection can be mimicked
and can get you to, can produce that emotional response.
I just wonder though, if you're told what's really going on,
if you know that the machine is not conscious,
not having the same richness of emotional reactions
and understanding that it doesn't really share
the understanding, but essentially just moving
its eyebrow or drooping its eyes
or making them big or whatever it's doing,
just getting the emotional response,
will you still feel it?
Interesting, I think you probably would for a while,
and then when it becomes more important
that there's a deeper shared understanding,
that it may run flat, but I don't know, I'm-
I'm pretty confident that the majority of the world,
even if you tell them how it works-
It won't matter.
Well, it will not matter,
especially if the machine herself says
that she is conscious.
That's very possible.
So you, the scientist that made the machine is saying
that this is how the algorithm works,
everybody will just assume you're lying
and that there's a conscious being there.
So you're deep into the science fiction genre now,
but yeah, I-
I don't think it's actually psychology.
I think it's not science fiction, I think it's reality.
And I think it's a really powerful one
that we'll have to be exploring in the next few decades.
I agree.
It's a very interesting element of intelligence.
So what do you think,
we've talked about social constructs of intelligence
and frameworks in the way humans kind of interpret information.
What do you think is a good test of intelligence
in your view?
So there's the Alan Turing with the Turing test.
Watson accomplished something very impressive with Jeopardy.
What do you think is a test
that would impress the heck out of you?
That you saw that a computer could do?
They would say, this is crossing a kind of threshold.
That's, that gives me pause in a good way.
My expectations for AI are generally high.
What does high look like, by the way?
So not the threshold, test is a threshold.
What do you think is the destination?
What do you think is the ceiling?
I think machines will, in many measures,
will be better than us, will become more effective,
in other words, better predictors about a lot of things
than ultimately we can do.
I think where they're gonna struggle
is what we've talked about before,
which is relating to communicating with
and understanding humans in deeper ways.
And so I think that's a key point.
Like we can create the super parrot.
What I mean by the super parrot is,
given enough data, machine can mimic your emotional response,
can even generate language that will sound smart
and what someone else might say under similar circumstances.
Like I would just pause on that.
Like that's the super parrot, right?
So given similar circumstances,
moves its faces in similar ways,
changes its tone of voice in similar ways,
produces strings of language that would similar
that a human might say,
not necessarily being able to produce
a logical interpretation or understanding
that would ultimately satisfy a critical interrogation
or a critical understanding.
I think you just described me in a nutshell.
So I think philosophically speaking,
you could argue that that's all we're doing
as human beings to worse.
So I was gonna say, it's very possible,
humans do behave that way too.
And so upon deeper probing and deeper interrogation,
you may find out that there isn't a shared understanding
because I think humans do both.
Like humans are statistical language model machines.
And they are capable reasoners, they're both.
And you don't know which is going on, right?
So, and I think it's an interesting problem
we talked earlier about like where we are
in our social and political landscape.
Can you distinguish someone who can string words together
and sound like they know what they're talking about
from someone who actually does?
Can you do that without dialogue,
without interrogative or probing dialogue?
So it's interesting because humans are really good
in their own mind justifying or explaining what they hear
because they project their understanding onto yours.
So you could say you could put together a string of words
and someone will sit there and interpret it
in a way that's extremely bias to the way they want to interpret
if they want to assume in your own words
or interpret it, they want to assume you're an idiot
and they'll interpret it one way,
they will assume you're a genius
and they'll interpret it another way
that suits their needs.
So this is tricky business.
So I think to answer your question,
as AI gets better and better mimic
and we create the super parrots, we're challenged
just as we are with, we're challenged with humans.
Do you really know what you're talking about?
Do you have a meaningful interpretation,
a powerful framework that you could reason over
and justify your answers, justify your predictions
and your beliefs, why you think they make sense?
Can you convince me what the implications are?
So can you reason intelligently and make me believe
that the implications of your prediction and so forth?
So what happens is it becomes reflective.
My standard for judging your intelligence
depends a lot on mine.
But you're saying that there should be a large group
of people with a certain standard of intelligence
that would be convinced by this particular AI system,
then there would pass.
There should be, but I think depending on the content,
one of the problems we have there is that
if that large community of people are not judging it
with regard to a rigorous standard
of objective logic and reason, you still have a problem.
Like masses of people can be persuaded.
The millennials, yeah.
To turn their brains off.
Right, okay.
Sorry.
By the way, I have nothing against the one of you.
No, I don't know, I'm just...
So you're a part of one of the great benchmarks,
challenges of AI history.
What do you think about AlphaZero,
open AI5, AlphaStar accomplishments
on video games recently?
Which are also, I think, at least in the case of Go
with AlphaGo and AlphaZero playing Go
was a monumental accomplishment as well.
What are your thoughts about that challenge?
I think it was a giant landmark for AI.
I think it was phenomenal.
I mean, as one of those other things,
nobody thought like solving Go was gonna be easy,
particularly because it's hard for humans,
hard for humans to learn, hard for humans to excel at.
And so it was another measure, a measure of intelligence.
It's very cool.
I mean, it's very interesting what they did.
I mean, and I loved how they solved the data problem,
which again, they bootstrapped it
and got the machine to play itself,
to generate enough data to learn from.
I think that was brilliant.
I think that was great.
And of course, the result speaks for itself.
I think it makes us think about,
again, okay, what's intelligence?
What aspects of intelligence are important?
Can the Go machine help me make me a better Go player?
Is it an alien intelligence?
Am I even capable of, like again,
if we put in very simple terms, it found the function.
It found the Go function.
Can I even comprehend the Go function?
Can I talk about the Go function?
Can I conceptualize the Go function like whatever it might be?
So one of the interesting ideas of that system
is it plays against itself, right?
But there's no human in the loop there.
So like you're saying,
it could have by itself created an alien intelligence.
Toward a goal, imagine you're sentencing,
you're a judge and you're sentencing people,
or you're setting policy,
or you're making medical decisions.
And you can't explain.
You can't get anybody to understand
what you're doing or why.
So it's an interesting dilemma for the applications of AI.
Do we hold AI to this accountability that says,
humans have to be able when you take responsibility
for the decision.
In other words, can you explain why you would do the thing?
Will you get up and speak to other humans
and convince them that this was a smart decision?
Is the AI enabling you to do that?
Can you get behind the logic that was made there?
Do you think, sorry to linger on this point
because it's a fascinating one.
It's a great goal for AI.
Do you think it's achievable in many cases?
Or okay, there's two possible worlds
that we have in the future.
One is where AI systems do like medical diagnosis
or things like that or drive a car
without ever explaining to you why it fails when it does.
That's one possible world and we're okay with it.
Or the other where we are not okay with it
and we really hold back the technology
from getting too good before it gets able to explain.
Which of those worlds are more likely, do you think,
and which are concerning to you or not?
I think the reality is it's gonna be a mix.
I'm not sure I have a problem with that.
I mean, I think there are tasks that perfectly fine
with machines show a certain level of performance
and that level of performance is already better
than humans.
So for example, I don't know that I take driverless cars.
If driverless cars learn how to be more effective drivers
than humans but can't explain what they're doing,
but bottom line statistically speaking,
they're 10 times safer than humans.
I don't know that I care.
I think when we have these edge cases,
when something bad happens and we wanna decide
who's liable for that thing and who made that mistake
and what do we do about that?
And I think those edge cases are interesting cases.
And now do we go to designers of the AI and the AI says,
I don't know, that's what it learned to do.
And it says, well, you didn't train it properly.
You were negligent in the training data
that you gave that machine.
Like how do we drive down the real level?
So I think those are, I think those are interesting questions.
So the optimization problem there, sorry,
is to create an ass system
that's able to explain the lawyers away.
There you go, there you go.
I think that, I think it's gonna be interesting.
I mean, I think this is where technology
and social discourse are gonna get like deeply intertwined
in how we start thinking about problems,
decisions and problems like that.
I think in other cases, it becomes more obvious
where it's like, like, why did you decide
to give that person a longer sentence
or to deny them parole?
Again, policy decisions or why did you pick that treatment?
Like that treatment ended up killing that guy.
Like why was that a reasonable choice to make?
And people are gonna demand explanations.
Now there's a reality though here.
And then the reality is that it's not,
I'm not sure humans are making reasonable choices
when they do these things.
They are using statistical hunches, biases,
or even systematically using statistical averages
to make calls.
I mean, this is what happened.
My dad, if you saw the talk I gave about that,
but you know, I mean, they decided
that my father was brain dead.
He had went into cardiac arrest
and it took a long time for the ambulance to get there
and he wasn't not resuscitated right away and so forth.
And they came and they told me he was brain dead
and why was he brain dead?
Because essentially they gave me
a purely statistical argument under these conditions
with these four features, 98% chance he's brain dead.
And I said, but can you just tell me not inductively
but deductively go there and tell me his brain's not functioning
is the way for you to do that.
And the protocol in response was,
no, this is how we make this decision.
I said, this is an adequate for me.
I understand the statistics
and I don't know how, you know,
there's a 2% chance he's told a lot of like,
I just don't know the specifics.
I need the specifics of this case.
And I want the deductive logical argument
about why you actually know he's brain dead.
So I wouldn't sign that do not resuscitate.
And I don't know, it was like,
they went through lots of procedures, big long story,
but the bottom was a fascinating story, by the way,
but how I reasoned
and how the doctors reasoned through this whole process.
But I don't know, somewhere around 24 hours later,
or something, he was sitting up in bed
with zero brain damage.
I mean, what lessons do you draw from that story,
that experience?
That the data that's being used
to make statistical inferences
doesn't adequately reflect the phenomenon.
So in other words, you're getting shit wrong,
sorry, you're getting stuff wrong
because your model is not robust enough
and you might be better off
not using statistical inference
and statistical averages in certain cases
when you know the model is insufficient
and that you should be reasoning
about the specific case more logically and more deductibly.
And hold yourself responsible,
hold yourself accountable to doing that.
And perhaps AI has a role to say the exact thing
where you just said,
which is perhaps this is a case you should think for yourself.
You should reason deductively.
Well, so it's hard because it's hard to know that.
You'd have to go back
and you'd have to have enough data to essentially say,
and this goes back to how do we,
this goes back to the case of how do we decide
whether the AI is good enough to do a particular task?
And regardless of whether or not it produces an explanation.
So, and what standard do we hold, right, for that?
So, if you look at,
you look more broadly, for example,
as my father as a medical case,
the medical system ultimately helped him a lot
throughout his life.
Without it, he probably would have died much sooner.
So, overall, it's sort of worked for him
in sort of a net kind of way.
Actually, I don't know that that's fair.
But it may be not in that particular case, but overall,
the medical system overall does more good than bad.
Yeah, the medical system overall
was doing more good than bad.
Now, there's another argument that suggests
that there wasn't a case, but for the sake of argument,
let's say like that's, let's say a net positive.
And I think if you have to sit there and take that into consideration,
now you look at a particular use case,
like for example, making this decision,
have you done enough studies to know
how good that prediction really is?
And have you done enough studies to compare it?
To say, well, what if we dug in and a more direct,
let's get the evidence, let's do the deductive thing
and not use statistics here?
How often would that have done better?
So you have to do the studies to know how good the AI actually is.
And it's complicated because it depends
how fast you have to make the decision.
So if you have to make the decision super fast,
do you have no choice?
Right.
If you have more time, right?
But if you're ready to pull the plug,
and this is a lot of the argument that I had with a doctor,
I said, what's he gonna do if you do it?
What's gonna happen to him in that room?
If you do it my way?
Well, he's gonna die anyway.
So let's do it my way then.
I mean, it raises questions for our society
to struggle with as the case with your father,
but also when things like race and gender
start coming into play,
when judgments are made based on things
that are complicated in our society,
at least in discourse.
And it starts, you know, I think I'm safe to say
that most of the violent crime is committed by males.
So if you discriminate based, you know,
with the male versus female saying that
if it's a male, more likely to commit the crime.
So this is one of my very positive and optimistic views
of why the study of artificial intelligence,
the process of thinking and reasoning,
logically and statistically, and how to combine them
is so important for the discourse today
because it's causing, regardless of what state AI devices are
or not, it's causing this dialogue to happen.
This is one of the most important dialogues
that in my view, the human species can have right now,
which is how to think well, how to reason well,
how to understand our own cognitive biases
and what to do about them.
That has got to be one of the most important things
we as a species can be doing, honestly.
We have created an incredibly complex society.
We've created amazing abilities to amplify noise
faster than we can amplify signal.
We are challenged.
We are deeply, deeply challenged.
We have, you know, big segments of the population
getting hit with enormous amounts of information.
Do they know how to do critical thinking?
Do they know how to objectively reason?
Do they understand what they are doing,
nevermind what their AI is doing?
This is such an important dialogue to be having.
And, you know, we are fundamentally,
our thinking can be and easily becomes fundamentally biased.
And there are statistics and we shouldn't blind us.
We shouldn't discard statistical inference,
but we should understand the nature of statistical inference.
As a society, as, you know,
we decide to reject statistical inference
to favor understanding and deciding on the individual.
Yes.
We consciously make that choice.
So even if the statistics said, even if the statistics said,
males are more likely to have, you know,
to be violent criminals,
we still take each person as an individual
and we treat them based on the logic
and the knowledge of that situation.
We purposefully and intentionally
reject the statistical inference.
We do that at a respect for the individual.
For the individual, yeah.
And that requires reasoning and thinking.
Looking forward, what grand challenges
would you like to see in the future?
Because the Jeopardy Challenge, you know,
captivated the world, AlphaGo, AlphaZero,
captivated the world,
D-Balloo certainly beating Kasparov,
Gary's bitterness aside and captivated the world.
What do you think, do you have ideas
for next grand challenges for future challenges of that?
You know, look, I mean, I think there are lots
of really great ideas for grand challenges.
I'm particularly focused on one right now,
which is, you know, can you demonstrate
that they understand, that they could read and understand,
that they can acquire these frameworks
and communicate, you know, reason and communicate with humans.
So it is kind of like the Turing test,
but it's a little bit more demanding than the Turing test.
It's not enough to convince me that you might be human
because you can parrot a conversation.
I think, you know, the standard is a little bit higher.
Is, for example, can you, you know, the standard is higher.
And I think one of the challenges
of devising this grand challenge is that we're not sure
what intelligence is.
We're not sure how to determine whether or not
two people actually understand each other
and in what depth they understand it.
They, you know, to what depth they understand each other.
So the challenge becomes something along the lines of,
can you satisfy me that we have a shared understanding?
So if I were to probe and probe and you probe me,
can machines really act like thought partners
where they can satisfy me,
that we have a shared, our understanding is shared enough
that we can collaborate and produce the answers together
and that, you know, they can help me explain
and justify those answers.
So maybe here's an idea.
So we'll have AI system run for president
and convince- That's too easy.
I'm sorry, go ahead.
You have to convince the voters that they should vote.
So like, I guess, what does winning look like?
Again, that's why I think this is such a challenge
because we go back to the emotional persuasion.
We go back to, you know,
now we're checking off an aspect of human cognition
that is in many ways weak or flawed, right?
We're so easily manipulated.
Our minds are drawn for often the wrong reasons, right?
Not the reasons that ultimately mattered us,
but the reasons that can easily persuade us.
I think we can be persuaded to believe one thing or another
for reasons that ultimately don't serve us well
in the long-term.
And a good benchmark should not play with those elements
of emotional manipulation.
I don't think so.
And I think that's where we have to set
the higher standard for ourselves of what, you know,
what does it mean?
This goes back to rationality
and it goes back to objective thinking
and can you produce, can you acquire information
and produce reasoned arguments
and to those reasons, arguments pass a certain amount
of muster and is it,
and can you acquire new knowledge?
You know, can you, for example,
can you reason?
I have acquired new knowledge.
Can you identify where it's consistent or contradictory
with other things you've learned?
And can you explain that to me
and get me to understand that?
So I think another way to think about it perhaps
is can a machine teach you?
Can it help you?
Oh, that's a really nice, nice, nice way to put it.
Can it help you understand?
Can it help you understand something
that you didn't really understand before?
Where it's taking you through?
So you're not, you know, again, it's almost like,
can it teach you?
Can it help you learn?
And in an arbitrary space,
so it can open those domain space.
So can you tell the machine, and again,
this borrows from some science fictions,
but can you go off and learn about this topic
that I'd like to understand better
and then work with me to help me understand it?
That's quite brilliant.
Well, the machine that passes that kind of test,
do you think it would need to have self-awareness
or even consciousness?
What do you think about consciousness
and the importance of it,
maybe in relation to having a body,
having a presence, an entity?
Do you think that's important?
You know, people used to ask me if Watson was conscious,
and I used to think, and I used to think,
I used to think conscious of what, exactly.
I mean, I think, you know,
maybe it depends what it is that you're conscious of.
I mean, so, you know, did it, if you, you know,
it's certainly easy for it to answer questions about,
it would be trivial to program it,
so to answer questions about whether or not
it was playing jeopardy.
I mean, it could certainly answer questions
that would imply that it was aware of things.
Exactly, what does it mean to be aware
and what does it mean to consciousness?
And it's sort of interesting.
I mean, I think that we differ from one another
based on what we're conscious of.
But wait, wait, yes, for sure.
There's degrees of consciousness in there, so it-
Well, and there's just areas.
Like, it's not just degrees.
What do you, what do you, what are you aware of?
Like, what are you not aware of?
But nevertheless, there's a very subjective element
to our experience.
Let me even not talk about consciousness.
Let me talk about another, to me,
really interesting topic of mortality,
fear or mortality.
Watson, as far as I could tell,
did not have a fear of death.
Certainly not.
Most humans do.
Wasn't conscious of death?
It wasn't that.
So there's an element of finiteness to our existence
that I think, like we, like you mentioned, survival
that adds to the whole thing.
That, I mean, consciousness is tied up with that,
that we are a thing, it's a subjective thing that ends.
And that seems to add a color and flavor
to our motivations in a way that seems to be
fundamentally important for intelligence,
or at least the kind of human intelligence.
Well, I think for generating goals.
Again, I think you could have,
you could have an intelligence capability
and a capability to learn, a capability to predict.
But I think without, I mean, again, you get a fear,
but essentially without the goal to survive.
So you think you can just encode that
without having to really?
I mean, you can create a robot now,
and you could say, you know, plug it in
and say, protect your power source, you know,
and give it some capabilities and it'll sit there
and operate to try to protect its power source and survive.
I mean, so I don't know that that's,
philosophically, a hard thing to demonstrate.
It sounds like a fairly easy thing to demonstrate
that you can give it that goal.
Will it come up with that goal by itself?
I think you have to program that goal in.
Well, there's something,
because I think as we touched on,
intelligence is kind of like a social construct.
The fact that a robot will be protecting its power source
would add depth and grounding to its intelligence
in terms of us being able to respect that.
I mean, ultimately, it boils down to us
acknowledging that it's intelligent.
And the fact that it can die,
I think is an important part of that.
The interesting thing to reflect on
is how trivial that would be.
And I don't think if you knew how trivial that was,
you would associate that with being intelligence.
I mean, I literally put in a statement of code
that says, you know, you have the following actions
you can take, you give it a bunch of actions,
like maybe you mount a laser gun on it,
or you give it the ability to scream
and a screech or whatever.
And, you know, and you say, you know,
if you see your power source threatened
and you could program that in,
and you know, you're gonna take these actions to protect it.
You know, you could teach it,
train it on a bunch of things.
So, and now you can look at that and you can say,
well, you know, that's intelligence
because it's protecting its power source, maybe.
But that's again, this human bias that says,
the thing I identify my intelligence
in my conscious so fundamentally with the desire
or at least the behaviors associated
with the desire to survive,
that if I see another thing doing that,
I'm going to assume it's intelligence.
What timeline, year, will society have a,
something that would,
that you would be comfortable calling
an artificial journal intelligence system?
Well, what's your intuition?
Nobody can predict the future,
certainly not the next few months or 20 years away,
but what's your intuition?
How far away are we?
I know, it's hard to make these predictions.
I mean, I would be, you know, I would be guessing
and there's so many different variables,
including just how much we want to invest in it
and how important it, you know,
and how important we think it is.
What kind of investment are we willing to make in it?
What kind of talent we end up bringing to the table?
All, you know, the incentive structure, all these things.
So I think it is possible to do this sort of thing.
I think it's, I think trying to sort of
ignore many of the variables and things like that.
Is it a 10 year thing?
Is it 23?
Probably closer to a 20 year thing, I guess.
But not several hundred years.
No, I don't think it's several hundred years.
I don't think it's several hundred years,
but again, so much depends on how committed we are
to investing and incentivizing this type of work.
And it's sort of interesting,
like I don't think it's obvious how incentivized we are.
I think from a task perspective, you know,
if we see business opportunities
to take this technique or that technique to solve that problem,
I think that's the main driver for many of these things.
From a general intelligence thing,
it's kind of an interesting question.
Are we really motivated to do that?
And like we just struggled ourselves right now
to even define what it is.
So it's hard to incentivize when we don't even know
what it is we're incentivized to create.
And if you said mimic a human intelligence,
I just think there are so many challenges
with the significance and meaning of that
that there's not a clear directive.
There's no clear directive to do precisely that thing.
So assistance in a larger and larger number of tasks.
So being able to,
a system that's particularly able to operate my microwave
and making a grilled cheese sandwich.
I don't even know how to make one of those.
And then the same system would be doing the vacuum cleaning.
And then the same system would be teaching
my kids that I don't have math.
I think that when you get into a general intelligence
for learning physical tasks.
And again, I want to go back to your body question
because I think your body question was interesting,
but you want to go back to learning abilities
to physical tasks.
You might have, we might get,
I imagine in that timeframe,
we will get better and better at learning these kinds of tasks,
whether it's mowing your lawn or driving a car
or whatever it is.
I think we will get better and better at that
where it's learning how to make predictions
over large bodies of data.
I think we're going to continue
to get better and better at that.
And machines will outpace humans
and a variety of those things.
The underlying mechanisms for doing that may be the same,
meaning that maybe these are deep nets,
there's infrastructure to train them,
reusable components to get them to different classes of tasks,
and we get better and better
at building these kinds of machines.
You could see, argue that the general learning infrastructure
in there is a form of a general type of intelligence.
I think what starts getting harder
is this notion of can we effectively communicate
and understand and build that shared understanding
because of the layers of interpretation
that are required to do that,
and the need for the machine to be engaged with humans
at that level in a continuous basis.
So how do you get the machine in the game?
How do you get the machine in the intellectual game?
Yeah, and to solve AGI,
you probably have to solve that problem.
You have to get the machine.
So it's a little bit of a bootstrapping thing.
Can we get the machine engaged in the intellectual,
I can call it a game,
but in the intellectual dialogue with the humans,
are the humans sufficiently in intellectual dialogue
with each other to generate enough data in this context?
And how do you bootstrap that?
Because every one of those conversations,
every one of those conversations,
those intelligent interactions,
require so much prior knowledge
that it's a challenge to bootstrap it.
So the question is, and how committed?
So I think that's possible,
but when I go back to, are we incentivized to do that?
I know we're incentivized to do the former.
Are we incentivized to do the latter significantly enough?
Do people understand what the latter really is well enough?
Part of the elemental cognition mission
is to try to articulate that better and better
through demonstrations
and through trying to craft these grand challenges
and get people to say,
look, this is a class of intelligence, this is a class of AI,
do we want this?
What is the potential of this?
What's the business potential?
What's the society or potential to that?
And to build up that incentive system around that.
Yeah, I think if people don't understand yet,
I think they will.
I think there's a huge business potential here.
So it's exciting that you're working on it.
I kind of skipped over it,
but I'm a huge fan of physical presence of things.
Do you think Watson had a body?
Do you think having a body adds to the interactive element
between an AI system and a human
or just in general to intelligence?
So I think going back to that shared understanding bit,
humans are very connected to their bodies.
I mean, one of the reasons,
one of the challenges in getting an AI
to kind of be a compatible human intelligence
is that our physical bodies are generating a lot of features
that make up the input.
So in other words,
where our bodies are the tool we use to affect output,
but they also generate a lot of input for our brains.
So we generate emotion, we generate all these feelings,
we generate all these signals that machines don't have.
So machines don't have this as the input data.
And they don't have the feedback that says,
okay, I've gotten this, I've gotten this emotion
or I've gotten this idea, I now want to process it
and then it then affects me as a physical being
and then I can play that out.
In other words, I could realize the implications of that
because the implications again on my body complex,
I then process that and the implications again
are internal features are generated.
I learn from them, they have an effect
on my mind body complex.
So it's interesting when we think,
do we want a human intelligence?
Well, if we want a human compatible intelligence,
probably the best thing to do is to embed it
in a human body.
Just to clarify and both concepts are beautiful
is a humanoid robot.
So a robot that looked like humans is one
or did you mean actually sort of what Elon Musk
was working with Neuralink,
really embedding intelligence systems
to ride along human bodies?
No, I mean, riding along is different.
I meant like if you want to create an intelligence
that is human compatible,
meaning that it can learn and develop
a shared understanding of the world around it,
you have to give it a lot of the same substrate.
Part of that substrate is the idea
that it generates these kinds of internal features,
like sort of emotional stuff.
It has similar senses.
It has to do a lot of the same things
with those same senses, right?
So I think if you want that,
again, I don't know that you want that.
Like that's not my specific goal.
I think that's a fascinating scientific goal.
I think it has all kinds of other implications.
That's sort of not the goal.
Like I want to create,
I think of it as I create
intellectual thought borders for humans.
So that kind of intelligence.
I know there are other companies
that are creating physical thought partner.
It's the physical partners for humans.
But that's kind of not where I'm at.
But the important point is that
a big part of what we process
is that physical experience of the world around us.
On the point of thought partners,
what role does an emotional connection
or forgive me, love have to do?
Have to play in that thought partnership?
Is that something you're interested in?
Put another way, sort of having a deep connection
beyond intellectual?
With the AI?
Yeah, with the AI between human and AI.
Is that something that gets in the way
of the rational discourse?
Is that something that's useful?
I worry about biases, obviously.
So in other words, if you develop an emotional relationship
with a machine, all of a sudden you start
or more likely to believe what it's saying,
even if it doesn't make any sense.
So I worry about that.
But at the same time, I think the opportunity
to use machines to provide human companionship
is actually not crazy.
Intellectual and social companionship
is not crazy idea.
Do you have concerns, as a few people do,
Elon Musk, Sam Harris,
about long-term existential threats of AI
and perhaps short-term threats of AI?
We talked about bias,
we talked about different misuses,
but do you have concerns about thought partners,
systems that are able to help us make decisions
together with humans,
somehow having a significant negative impact
on society in the long term?
I think there aren't things to worry about.
I think giving machines too much leverage is a problem.
And what I mean by leverage is too much control
over things that can hurt us,
whether it's socially, psychologically,
intellectually, or physically.
And if you give the machines too much control,
I think that's a concern.
You forget about the AI,
just when you give them too much control,
human-bed actors can hack them and produce havoc.
So that's a problem.
And you imagine hackers taking over the driverless
car network and creating all kinds of havoc.
But you could also imagine,
given the ease at which humans could be persuaded
one way or the other,
and now we have algorithms that can easily take control
over that and amplify noise
and move people one direction or another.
I mean, humans do that to other humans all the time.
And we have marketing campaigns,
we have political campaigns
that take advantage of our emotions or our fears.
And this is done all the time.
But with machines, machines are like giant megaphones, right?
We can amplify this in orders of magnitude
and fine-tune its control so we can tailor the message.
We can now very rapidly and efficiently
tailor the message to the audience,
taking advantage of their biases
and amplifying them and using them
to persuade them in one direction or another
in ways that are not fair, not logical,
not objective, not meaningful.
And humans, machines empower that.
So that's what I mean by leverage.
Like, it's not new, but wow, it's powerful
because machines can do it more effectively,
more quickly, and we see that already going on
in social media and other places.
That's scary.
And that's why I go back to saying
one of the most important public dialogues
we could be having is about the nature of intelligence
and the nature of inference and logic
and reason and rationality
and us understanding our own biases,
us understanding our own cognitive biases
and how they work and then how machines work
and how do we use them to complement it basically
so that in the end we have a stronger overall system.
That's just incredibly important.
I don't think most people understand that.
So like telling your kids or telling your students,
this goes back to the cognition.
Here's how your brain works.
Here's how easy it is to trick your brain, right?
There are fundamental cognitive, but you should appreciate
the different types of thinking and how they work
and what you're prone to and what do you prefer?
And under what conditions does this make sense
versus that makes sense?
And then say, here's what AI can do.
Here's how it can make this worse
and here's how it can make this better.
And that's where the AI has a role
is to reveal that trade-off.
So if you imagine a system that is able to
beyond any definition of the Turing test, the benchmark,
really an AGI system as a thought partner
that you one day will create,
what question, what topic of discussion,
if you get to pick one, would you have with that system?
What would you ask and you get to find out
the truth together?
So you threw me a little bit with finding the truth
at the end, but because the truth is all another topic,
but the, I think the beauty of it,
I think what excites me is the beauty of it is
if I really have that system, I don't have to pick.
So in other words, I can, you know,
I can go to it and say, this is what I care about today.
And that's what we mean by like this general capability.
Go out, read this stuff in the next three milliseconds.
And I want to talk to you about it.
I want to draw analogies.
I want to understand how this affects
the decision or that decision.
What if this were true?
What if that were true?
What knowledge should I be aware of
that could impact my decision?
Here's what I'm thinking is the main implication.
Can you find, can you prove that out?
Can you give me the evidence that supports that?
Can you give me evidence that supports this other thing?
Boy, would that be incredible.
Would that be just incredible?
Just a long discourse.
Just to be part of whether it's a medical diagnosis
or whether it's, you know, the various treatment options
or whether it's a legal case
or whether it's a social problem
that people are discussing.
Like be part of the dialogue,
one that holds itself and us accountable
to reasons and objective dialogue.
You know, I get goosebumps talking about it, right?
I mean, it's like, this is what I want.
So when you create it, please come back on the podcast
and we can have a discussion together
and make it even longer.
This is a record for the longest conversation
and there's an honor.
It was a pleasure, David.
Thank you so much for talking.
Thanks so much, a lot of fun.