Kind: captions
Language: en
following is a conversation with David
Ferrucci 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 of 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
adjoining it to have a lot of wisdom
earned two 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 and iTunes
support it on patreon or simply connect
with me on Twitter Alex Friedman spelled
Fri D M a.m. and now here's my
conversation with David Ferrucci
your undergrad was in biology with a
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 and I
think the thing that got me into
computer science and 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 do we do so if we can describe a
procedure or describe a process even if
that process where the intelligence
process itself then what would be the
difference
so from philosophical standpoint I'm not
trying to convince that there are 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 you know what
we can what we can experience from an
intellectual and physical perspective
I'm not sure there is clearly there
implement there are different
implementations but if you were to say
as a biological information processing
system fundamentally more capable than
one we might be able to build out of
silicon or or some other substrate I
don't 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 that something
that for the most part in the important
aspects echoes are the same kind of
characteristics yeah that's interesting
I mean I so you know your question
presupposes that there's this goal to
recreate you know what we perceive is
biological intelligence I'm not I'm not
sure that's the I'm not sure that that's
how I would state the goal I mean I
think that studying 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 on sending the brain
from that perspective has a there's a
clear clear goal there from the
perspective of saying I want to I want
to 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 it what do we want out of
an intelligence how do we want to
communicate with that intelligence how
do we want to behave how do we want it
to perform now of course it's it's
somewhat of an interesting argument
because I'm sitting here as a human with
a biological brain and I'm critiquing
this trends and weaknesses of human
intelligence and saying that we have the
capacity just the capacity to step back
and say gee what what is intelligence is
what do we really want out of it and
that even in and of itself suggests that
human intelligence is something quite
amiable that it could you know it can it
can it can introspect that it could
introspect that way and the flaws you
mentioned the flaws the human self yeah
but I think I think that flaws that
humans wholeness house is extremely
prejudicial and bias and the way it
draws many inferences do you think those
are sorry to interrupt you think those
are features or are those bugs do you
think the the prejudice the
forgetfulness the fear what other flaws
list them all what love maybe that's a
flaw you think those are all things that
can be get gotten getting in the way of
intelligence or the essential components
of and
well again if you go back and you define
intelligence as being able to sort of
accuracy 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 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 whether they're right or
wrong from some you know objective
reasoning perspective you're gonna favor
them because that's 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 and and survive and make
fast decisions and I think those create
problems when you want to think more
deeply and make more objective and
reasons that 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 in it more naturally
right because that's the thing you kind
of need to not be eaten by the Predators
in the world for example but I mean
better than we've 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
you know and and and teaching I think we
are human - certainly is capable of it
but we find more difficult and then
there are other weaknesses if you will
as you mentioned earlier it's just
memory capacity and how many chains of
inference can you actually go through
without like losing your way so just
focus and so the way you think about
intelligence and we're really sort of
floating this philosophical slightly
but I think you're like 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
discussions we'll have with 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 going to happen
next whether it's to you know predict
the answer of a question or to say look
I'm looking at all the market dynamics
and I'm going to tell you what's going
to happen next or you're in a in a room
and somebody walks in and you're going
to predict what they're going to do next
or what they're going to say next doing
that in a highly dynamic environment
full of uncertainty be able to lots of
lockdown the more 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 going to
happen next accurately and consistently
that's a 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 all right thank you one thing
is needed to predict depends what you
mean by understanding IIIi need to be
able to find that function and this is
very much like what function 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 going to sit there and be
able to predict it and if I can predict
you predict it accurately so that I can
get it right more often than not I'm
smart if I 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 so again well
that's interesting about picking our
goal sort of an interesting thing I
think that's where you bring in what do
you pre-programmed to do we talked about
humans and humans a pre-programmed to
survive so sort of their primary you
know driving goal what do they have to
do to do that and that that could be
very complex right so it's not just it's
not just figuring out that you need to
run away from their ferocious tiger but
we survive in 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 sets
of variables complex constraints rules
if you will that we we 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 going to
happen that's a form of intelligence
that doesn't really record that doesn't
really require anything specific other
than ability to find that function and
and predict that right answer it's
certainly a form of intelligence but
then when we when we say well do we
understand each other in other words do
would you perceive me as as intelligent
beyond that ability to predict so now I
can predict but I can't really
articulate how I'm going to 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 follow you can figure out how to do
this yourself if you hadn't if you did
not have for example the right pattern
matching machinery that I did and now we
have 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 or I can see the I can
see the output knowing so so you're
saying let's
to separate the two things one is you
explaining why you were able to predict
the future and 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
a pressing you item intelligent in other
words you may be convinced that I'm
intelligent in some form so high well
because of my ability to predict so I
would imagine that wow wow you're right
all here you're you're right more times
than I am you're doing something
interesting that's a form 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'm a label you
a savant I mean I may say well you're
doing something weird and it's and it's
just not very interesting to me because
you and I can't really communicate and
and so now this is interesting right
because now this is 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 my we start to
understand each other and then my
respect and my my appreciation my
ability to relate to you starts to
change so now you're not an alien
intelligence anymore yours you're our
human intelligence now because you and I
can communicate and so I think when we
look at when we look at 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 in sort of low there are these
alien intelligences and they're not
really worthless so what we're worth we
don't treat them the same way as a
result of that but it's it's hard
because who knows what you know what's
going on so just a quick elaboration on
that the explaining that you're
intelligent the explaining the the
reasoning the one end to 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 you
usually your task is sorry that 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
ultimately that's what a proof is I mean
even a mathematical proof is is that
because ultimately the other
mathematicians have to be convinced by
your proof otherwise in fact they're
been that the measurement success yeah
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 it's but it's it's a real thing
yeah and and that's sort of the point
right is that ultimately on you know
this notion of understanding us
understanding something there's
ultimately a social concept in other
words you I have to convince enough
people that I I did this in a reasonable
way I did this in a way that other
people can understand and and replicate
and that make sense to them so we're
very human Houghton's 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 consider convince others
that our thinking process you know make
sense did you think the general question
of intelligence is then also social
constructs so if we task asked questions
of an artificial intelligence system is
this system intelligent the answer will
ultimately be a socially constructed I
think I think so I so I think you're
making to be a mess I'm saying we can
try to define intelligence in this super
objective way that says here here's this
data I want to 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 stepfather that I think it I
think it is it doesn't mean it's
useful if it could be incredible 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 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 I mean me or the obviously a
community or a judge of people to decide
whether or not whether or not that makes
sense and by the way that happens with
the 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 think you should buy this much
or would have 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 decision
may be and you ask why and the person so
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 gonna put the company on
the line can you explain to me why I
should believe this and that explanation
may have nothing to do with the truth
just them and all them convinced the
wrong yes they'll be wrong she's got to
be convincing but it's ultimately got to
be convinced 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 and if for example you're giving me
an explanation I mean this is a very
important point right you're giving me
an explanation and I'm and I and I and I
have iton I'm not good and then 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 we I think will talk quite a bit
about the the first on a specific
objective metric benchmark performing
well but being able to explain the steps
the reasoning how hard is that probably
that's I think that's very hard I mean I
think that that's um 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 are 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 a bad 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'd
think about the think tanks or the
scientists of 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 for so
weighed them obviously talked about this
like human flaws or weaknesses we can
persuade through persuade then through
emotional means but to but to get them
to understand and connect to and follow
a logical argument is difficult we try
it we do it we do it as scientists we
try to do it as journalists we know we
try to do it as you know even artists in
many forms as writers as teachers we go
to a fairly significant training process
to do that and then we could ask what
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 step back and say well how do
we get a machine - 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
well there's Facebook Google advertising
based companies you know 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 reason a certain decision was
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 and and - how do you think of the
differences in the reasoning aspect and
the emotional manipulation
well they you know so you call it
emotional manipulation but more
objectively is essentially saying you
know thing you know there are certain
features of things that seem to attract
your attention I'm gonna 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
is it 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 the you know
patterns of features that you seem to be
spending your money on and is there
going to give you something with a
similar pattern so I'm going to learn
that function because the objective is
to get you to click on and/or get you to
buy and or whatever it is I don't know I
mean that it is like 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 you know what your
what your goal is
I guess this seems to very useful for
convincing telling us the thing for
convincing humans yeah it's good because
you gives again this goes back to how
does a human you know what is the human
behavior like how does a human you know
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 these
algorithms are saying look that's up to
you
the reason whether you need something or
not that's your job you know you you met
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 so and I and I and I think that
that's it's not good or bad it it's not
reason or not reason the algorithm is
doing what it does which is saying you
seems to be interested in this I'm going
to show you more that stuff and I think
we're seeing it's 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 you know and like and
that's it and I wash my hands from it
and I say that's all you know that's all
what's going on you know there is you
know people are so harsh on AI systems
so one the bar of performance is
extremely high and yet we also asked
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 it
that so that agrees you that's 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 systems not building a
theory that it's consumable and
understandable other humans that could
being explained and justified and and so
on one hand to say oh you know AI is
doing this why isn't doing this other
thing well those other things a lot
harder and it's interesting to think
about why why why it's harder and
because you're interpreting 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 is it where is that
stuff no one's applying the
interpretation so when I 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
a data like what words are being used
or how long the material is more other
very superficial features what colors
are being used in the material like I
don't know why you're clicking on the
stuff you're looking or if it's products
what the price of what the price is or
what the categories or 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 what the stuff you're reading
like why are you reading it what
assumptions are you bringing to the
table are those assumptions sensible is
the miss the material make any sense
does it does it lead you to thoughtful
good conclusions again there's judgment
this interpretation judgment involved in
that process that isn't really happening
in in in the AI today that's harder
right because you have to start getting
at the meaning of this of the of the
stop 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 it's 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 artifact so if I show
you a painting it's a bunch of colors in
a canvas what does it mean to you and it
may mean different things at different
people because of their different
experiences it may mean something even
different to the artist to 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 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 you know 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 when I want to
align our understanding of that I have
to specify a lot more stuff that's
actually not in it not directly in the
artifact now I have to say well how you
were how are you interpreting this image
and that image and what about the colors
and what do they mean to you what's what
perspective are you bringing to the
table
what are your prior experiences with
those artifacts
what are your fundamental assumptions
and values what 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 and it
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 sick of that
is hard and yet humans 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 have any shares know me as a
society that's correct we have this
shared experience and we have similar
brains so we tend to Institute in other
words part of our shared experiences are
shared local experience like we may live
in the same culture we may live in the
same society and therefore we have
similar education 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 it 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 ask well how is
biological and you know computer
information systems fundamentally
different well one is you know one is
come you means come with a lot of
pre-programmed stuff yeah a ton of
program stuff and they were able to
communicate because they have a lot of
it because they share that stuff do you
think that shared knowledge if it can
maybe escape the hardware question how
much is encoded in the hardware just the
shared knowledge in the software the the
history the many centuries of wars and
so on that came to today that shared
knowledge how hard is it to encode and
did 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 to form machine to
program machine to acquire that
knowledge with a similar foundation in
other words in a similar interpretive
interpretive foundation for processing
that knowledge but what do you mean by
that so in other in other words
foundation we view the world in a
particular way and so in other words we
we have i if you will as humans we have
a frame reference for bringing 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 what there's people
there's collections and groups of people
they have goals the goals largely built
around survival and quality of life that
are their fundamental economics around
scarcity of resources and when when
humans come and start interpreting a
situation like that because you've
brought you've grown up like historical
events they start interpreting
situations like that they apply a lot of
this a lot of this this fundamental
framework for interpreting that well who
are the people
what were their goals what
users did they have how much power
influence that 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
in view a computer with that that stuff
that humans like take for granted when
they go and sit down and try to
interpret things and then and then with
that with that foundation they acquire
they start acquiring the details the
specifics in any 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 in interpret events
very differently than other humans
because they're like using a different
different framework you know movie
matrix comes to mind where you know they
decided the humans were really just
batteries and that's how they
interpreted the value of humans as a
source of electrical energy so but um
but I think that you know for the most
part we we have a way of interpreting
the events or do social events around us
because we have to share at framework it
comes from again the fact that we're
we're similar beings that have similar
goals similar emotions and we is we can
make sense out of these these frameworks
make sense to us so how much knowledge
is there do you think so it's you said
it's possible well there's all its
tremendous amount of detailed knowledge
in the world there you know you can
imagine you know effectively infinite
number of unique situations and unique
configurations of these things but the
the knowledge that you need what I refer
to as like the frameworks for you for
interpreting them I don't think I think
that's those are finite you think the
frameworks I'm more important than the
bulk of them now so it's like framing
yeah because the frameworks do is they
give you now the ability to interpret
and reason and to interpret and
reasoning to interpret and reason over
the specific
in ways that other humans would
understand what about the specifics you
know who acquired the specifics by
reading and by talking to other people
and so mostly actually just even if we
can focus on even the beginning the
common-sense stuff the stuff that
doesn't even require reading or
animalistic requires playing around with
the world or something just being able
to sort of manipulate objects drink
water and so on all does 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
you in some of these basic tasks do you
have that sense as well and if so how do
we get all those details are they
written down somewhere idea 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 for acquiring how that works yeah
I think that 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 were you
talking about before where you see
enough examples enough data about
something you start assuming that and
with similar input I'm going to predict
similar outputs you don't 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 a that's a sickness is horribly
explained that but that's such a deep
idea if you let something go like they
do gravity I mean people were letting
things go and counting on them falling
well before they understood gravity but
that seems to be a that's exactly what I
mean is before you take a physics class
or the or study anything about Newton
just the idea that stuff falls to the
ground and they be able to generalize
that other 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 seemed like gift of Allah
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 social political
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 well Watson with
jeopardy did its 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 reason in
the 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 what about time
travel okay convinced not as convinced
yet okay no I said I I think it is I
mean I I took it as solvable I mean I
think that it's alert it's versatile
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 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 our shared in 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 want
to 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 require to have access to
and acquire
learn the frameworks as well and connect
the frameworks to the data I think this
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
the necessary to gravity not unless they
can also acquire those theories as well
and put the experiential knowledge and
connected back to the theoretical
knowledge I think if we think in terms
of these class of architectures that are
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
jeez in terms of encoding architectures
like that do you think systems they were
able to do this will look like and you
know that works or representing if you
look back to the eighties and nineties
of the expert systems so more like
graphs the systems that are based in
logic able to contain a large amount of
knowledge where the challenge was the
automated acquisition of that knowledge
the 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 think is asking a
question they look like neural networks
is a bit of a red herring I mean I think
that they they will they will certainly
do inductive or pattern match 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 other words to find
repeated patterns in order or in order
for it to make good inductive guesses
but then ultimately to try to take those
learnings and and marry them in other
words connect them to frameworks so that
it can then reason over that in terms of
their humans understand so for example
at elemental cognition we do both we
have architectures that 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 saying like
these graphs of logic if you will there
are also neural networks that acquire
certain class of information they then
they they and align them with these
frameworks but there's also a mechanism
to acquire the frameworks themselves yes
so it seems like the idea of framework
requires some kind of collaboration with
humans absolutely so do you think of
that collaboration as well and unless to
be clear let's be clear only for the for
the express purpose that you're
designing you you're designing machine
designing and intelligence that can
ultimately communicate with humans in
terms of frameworks that help them
understand things right so so now to be
really clear you can create you can
independently create an a machine
learning system and an intelligent
intelligence that I might call an
alien's elegans that does a better job
than you with some things but can't
explain the framework to you that
doesn't mean is 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 I am yeah I per might 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 human I want to meet
would acquire and communicate acquire
knowledge from humans and communicate
knowledge to humans they should be using
what you know 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
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 egg it has the richer or longer
memory but that you know it has the more
rigorous reasoning abilities the deeper
reasoning abilities so be it interesting
you know complementary relationship
between the human and the machine do you
think that ultimately needs explained
ability like a machine so if we look we
study for example Tesla autopilot a lot
or humans I don't know if you've driven
the vehicle or are aware of what is it
so you basically
the human and machine are working
together there and the human is
responsible for their own life to
monitor the system and you know the
system fails every few miles and so
there's there's hundreds of there's
millions of those failures a day and so
that's like a moment of interaction DC
yeah that's exactly right that's a
moment of interaction where you know the
the the machine has learned some stuff
it has a failure
somehow the failures 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 permanent
example of sort of a more another
example is where the machine is
literally talking to you and saying look
I'm I'm reading this thing I know I know
that like the next word might be this or
that but I don't really understand why I
have my gas can you help me understand
the framework that supports this and
then can kind of take acquire that take
that and reason about it and reuse it
the next time it's reading to try to
understand something not on not unlike a
human student might do I mean I remember
like when my daughter was the first
great in she was had a reading
assignment about electricity and you
know somewhere in 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 was electricity created
and so my daughter comes to me and says
I mean I could you know created and
produced or kind of synonyms 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 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 expert
that there's a shared understanding of
what they are Sheriff it's the social
network 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 their individual
scope but there's lots and there are new
ones I think they're I think the way I
think about is kind of an a layer I
think that the architectures are being
layered in that there's there's a small
set of primitives that allow you the
foundation to build frameworks and then
there may be you know 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 or
reasoning by analogy where I could 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 goals 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 we're weird is the where 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 and then well that
you never you know yeah well Kratz and
Republicans
well I Neal's nobody wants that
framework well I mean I think
understands it right I mean you'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 of Magna might
not be that different the implications
of different fundamental values or
fundamental assumptions in those
framework frameworks may reach very
different conclusions so from so from a
social perspective that conclusions may
be very different from an intelligence
perspective I you know I just followed
where my assumptions took me yeah the
product the process itself would look
similar but that's a fascinating idea
that frameworks really helped carve how
a statement will be interpreted
I mean having a Democrat and the
Republican framework and read the exact
same statement and the conclusions that
you derive would be totally different
from an ad respective 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 going to lead you here in
other setups as luncheons is gonna leave
you there and to and in fact you know to
help people reason and say oh I see
where I see where our differences lie
yeah you know 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 then just as
well well there's a lot finding on
there's lots of different
interpretations and the you know the the
broader you know the broader to the the
contents the richer it is and so you
know you you and I can have very
different experiences with the same text
obviously and if we're committed to
understanding each other we start 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 I
like I think of a eyes is really
complementing and helping human
intelligence to overcome some of its
biases and its predisposition to be
persuaded by you know buys but more
shallow reasoning in the sense that like
we get over this idea well I you know
you know I'm right because I'm a
Republican or I'm right because I'm
democratic and someone labeled this is
democratic point of view or it has the
following keywords in it and 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
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 it's completely disintegrating
currently I don't know as we learn how
to do it on social medias right so one
of the greatest accomplishments in the
history of AI is Watson competing
against in a game of Jeopardy against
humans and you were a lead in that
accrue at a critical part of that let's
start 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 actually no but
it's not right it's really not it's
really it's really to get a question and
answer but it's what we call a factoid
questions so this notion of like it's it
really relates to some fact that
everything few people would argue
whether the facts are true or not in
fact most people what and jeopardy kind
of counts on the idea that these these
statements have factual answers and 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 nonlinear it's
like it's a little bit witty it's a
little bit playful sometimes it's a it's
a little bit tricky yeah they're asked
and exactly in numerous witty tricky
ways exactly what they're asking is not
obvious it takes it takes an experienced
humans a while to go what is it even
asking right and it's sort of an
interesting realization that you have
was a missus Oh what's the Jeopardy is a
question answering Shou and there's a go
like I know a lot and then you read it
and you're you're still trying to
process the question and the champions
have answered and moved on there's like
there's three questions ahead at the
time you figured out what the question
even met so there's 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 63 yeah and I think the
questions were much more direct it
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 and 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 buzz in you've been going
before you really know if you know the
answer I think well I think a lot of
humans will will assume they'll they'll
look at the look at their process of
very superficially in other words what's
the topic what are some key words and
just say do I know this area or not
before they actually know the answer
then they'll buzz in and then I'll buzz
in and think about it 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 that knows about that
though just assume they know although
jeopardy and I'll just pose it you know
Watson interestingly didn't even come
close to knowing all of Jeopardy right
Watson even at the peak even at that's
been yeah so for example I mean we had
this thing called recall which is like
how many of all the Jeopardy questions
you know how many did could we even find
like find the right answer for like
anywhere like could we come up with if
we look you know we had up a big body of
knowledge some of the order of several
terabytes I mean from from a web scale
was actually very small but from like a
book scales talking about millions in
bucks right so the equivalent millions
of books and cyclopædia is dictionaries
books it's a ton of information and you
know for I think was 80 only 85% was the
answer anywhere to be found hmm so
you're ready down you're ready down at
that level just to get just to get
started right so and so was important to
get a very quick sense of do you think
you know the right answer to this
question so we have to compute that
confidence as quickly as we possibly
could so it's in effect
to answer it and at least you know spend
some time essentially answering it and
then judging the confidence that we you
know that that our answer was right and
in deciding whether or not we were
confident enough to buzz in and that
would depend on what else was going on
in the game it could because it was a
risk so like if you're really in a
situation where I have to take a gas I
have very little to lose
then you'll buzz in with less confidence
so that was the counter for the the
financial standings of the different
competitors cracks yeah how much of the
game was laughs how much time was left
and where were you were in the standings
things like that what how many hundreds
of milliseconds that we're talking about
here do you have a sense of what is we
targets because we yeah was the targeted
so I mean we targeted answering and
under three seconds and buzzing it so
the decision to buzz in and then the
actual answering are those two yes there
were two there were two different things
in fact we had multiple stages whereas
like we would say let's estimate our
confidence which which is sort of a
shallow answering process and then
ultimate and then ultimately decide to
buzz in and then we may take another
second or something it's kind of go in
there and 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 and
around three seconds or less
so you stepped in so there's this
there's these three humans playing a
game and you stepped in with the idea
that IBM Watson would be one of replaced
one of the humans and compete against
two can you tell the story of Watson
taking on this game sure seems
exceptionally difficult yeah so the
story was that um it was or it was
coming up I think the 10-year
anniversary of a big blue an optical
deep blues IBM wanted to do sort of
another kind of really you know fun
challenge public challenge that can
bring attention to IBM research and the
kind of cool stuff that we were doing
I had been working in an AI at IBM for
some time I had a team doing what's
called open domain factoids
question-answering which is you know
we're not gonna tell you what the
questions are we're not even gonna 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 big Pat it was a very
specific passion of mine language
understanding and always always been a
passion of mine one sort of narrow slice
on whether or not you could do anything
was language was this notion of open
domain and meaning I could ask anything
about anything
factoids meaning it essentially had an
answer and and you know being able to do
that accurately and quickly so that was
a research area that might even already
been in and so completely independently
several you know IBM exactly there's
like what are we gonna do what's the
next cool thing to do and Ken Jennings
was on his winning streak this was like
whatever was 2004 I think was on his win
winning streak when someone thought hey
that'd be really cool um if the computer
can play jeopardy and so this was like
in 2004 they were shopping this thing
around and everyone who's telling the
the research execs no way like this is
crazy
and we had some pretty you know senior
people know if you'll understand the
others crazy and he'll come across my
desk and I was like but that's kind of
what what I'm really interested in doing
and but there was such this prevailing
sense of this is nots 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 that the
open domain question-answering stuff but
I was coming off a couple other projects
I had a lot more time to put into this
and I argued that it could be done and I
argue 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 it was
we just totally told the story of 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
is because I did some brief
experimentation I knew a lot about how
we were approaching on open domain
factoids question asked me we have been
doing it for some years I looked at the
Japanese stuff I said this is going to
be hard for a lot of the points that you
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 were 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 we were basically supposed to the
moonshot this is 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 not to do this is
an active research area we've been in
this for years why wouldn't we take this
Grand Challenge and 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 is way hard here's what we've
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 oh
why I thought it was hard but possible
and I showed examples of you know where
it succeeded where it failed why it
failed and sort of a high level
architecture approach for why we should
do it but for the most part that at that
point the execs really were just looking
for someone crazy enough to say yes
because for several years at that point
everyone has said no I'm not willing to
risk my reputation and my career you
know on this thing clearly you did not
have such fears okay I did not say you
died 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
um failed was because of because the
questions were difficult difficult to
interpret like what are you even asking
for right very often like if if the
question was very direct like what city
you know or what you know even then it
could be tricky but but you know what
city or what person was often when it
would name it very clearly you would
know that and and if there was just a
small set of them in other words we're
gonna ask about these five types like
it's gonna 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 tale meaning you know
that it just went on and on and 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 ten types or twenty
types or fifty 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 a an approach toward that and
the approach to look promising and we we
continue to improve our abilities 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
five years so we did in four so I got
lucky
um but one of the things that that
putting that like stake in the ground
was I and I knew how hard the language
of the standard 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 the question refers to in
reason over that to answer these
questions were obviously we're not going
to be doing that at the same time simple
search wasn't good enough to confidently
answer with this you know a single
correct answer first others like
brilliant that's such a great mix of
innovation in practical engineering
three three four eight so you're not
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 gonna
solving the open the main question
answering problem we're using jeopardy
as a driver for that management hard
enough big benchmark exactly and now
we're how do we do it we're just like
whatever like just figure out what works
because I want to be able to go back to
the académica scientific community and
say here's what we tried here's what
work here's what didn't work I don't
want to go in and say oh I only have one
technology hammer and only gonna use
this I'm gonna do whatever it takes I'm
like I'm gonna think out of the box do
whatever it takes one um and I also
Baloo's another thing I believed I
believe that the fundamental NLP
technologies and machine learning
technologies would be 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 gonna need Maxwell's
equations for question-answering and I
said if we if we need some fundamental
formula that breaks new ground and how
we understand language we're screwed
yeah we're not gonna get there from here
like we I am not counting I am that 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 a an
architecture on how to integrate it well
and then see where it breaks and make
the necessary advances we need to make
and sold this thing works
yeah push it hard to see where it breaks
and then patch it up I mean that's how
people change the world and that's the
you know mosque approaches Rockets
SpaceX that's the Henry Ford and so on a
lot and and I happen to be and in this
case I happen to be right but but like
we didn't know right but you kind of
have to put a second or so how you gonna
run the project so yep and backtracking
to search so if you were to do what's
the brute force solution what what would
you search over so you have a question
how would you search the possible space
of answers look web search has 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 problems 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 device if devices
as big as a room fine it's as big as a
room but we want a self-contained advice
contained device you're not going out
the internet you don't have a life
lifeline to anything so it had to kind
of fit in a shoebox if you will or at
least the size of a few refrigerators
whatever it might be
see but also you couldn't just get out
there you couldn't go off Network right
to kind of go so there was that
limitation but then we did it but the
basic thing was go go do what go do a
web search the problem was even when we
went and did a web search I don't
remember exactly the numbers but someone
the order of 65% at a time the answer
would be somewhere you know in the top
10 or 20 documents so first of all
that's not even good enough to play Jack
pretty you know the words even if you
could pull the avian if you could
perfectly pull the answer out of the top
20 documents top 10 documents whatever
was which we didn't know how to do but
even if you could that do that your
you'd be at and you knew it was Ryan
Lizza we've had enough confidence in it
right so you have to pull out the right
answer you have you depth of confidence
it was the right answer and 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 with nine doesn't even put
you in the winner's circle winner's
circle you have to be up over 70 and you
have to do it really quick and you do
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 I
have to pick it I don't know the right
answer I have a bunch of documents
somewhere in there's the right answer
how do i as a machine go out and figure
out which ones 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 and
then 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
defined where the answer is but we saw
we solved that and some definition of
solves given the Jeopardy challenge how
did you do it forever so how did you
take a body of work and a particular
topic and extract the key pieces of
information so what so now forgetting
about the 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 Jeffrey and we ultimately did
find the body of knowledge that did that
I mean it included Wikipedia and a bunch
of other stuff so like encyclopedia type
of stuff I don't know if you use Mary's
different types of semantic resources
unlike wordnet and other types of Mantic
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
statistical see you know statistically
producing sees using those sees for
other searchers 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 we even up and totally
and you know we had a threat of
resources always trying to figure out
what content could we efficiently
include I mean there's a lot of popular
cut like what is the church lady well I
think was one of the end hey yeah what
we ready I guess that's probably an
encyclopedia so it's a pepino is that
but then we would but then we would take
that stuff when 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 you know the
amount of content will grew it by an
order of magnitude but still so again
from a web scale perspective this is
very small amount of content it's very
select we then we then took all that
content so we we pre analyzed the crap
out of it meaning we we we parsed it you
know broke it down into all this
individual words and then we did
semantic static and semantic parses on
it you know had computer algorithms that
annotated it and we in that we indexed
that in a very rich and very fast index
so we have a relatively huge amount of
you know let's say the equivalent of for
the sake of argument two to five million
bucks
we've now analyzed all that blowing up
at size even more because now with all
this metadata and we then we richly
indexed all of that and in by way in a
giant in-memory cache so Watson did not
go to disk so the infrastructure
component there if you just speak to it
how tough it I mean I know mm maybe this
is 2089
you know that that's kind of a long time
ago right
how hard is it to use multiple machines
Olivia how hard is the infrastructure
part of the hardware component we used
IBM we so we used IBM hardware we had
something like I figured exactly but
2,000 to 3,000 cores completely
connected so had a switch were you know
every CPU was connected to every other
scene they were sharing memory in some
kind of way Lauren up close shared
memory right and all this data was pre
analyzed and put into a very fast
indexing structure that was all all all
in 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 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 the big stuff with the meta the the
big knowledgebase of the metadata and
that's indexed to the simple little
witty confusing question right so
therein lies
you know the Watson architects 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 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
and character string or was 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 and 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 produce combate
with passages so this is these are
passive search algorithms they would
come back with passages and so now you
let's say you had a thousand passages
now for each passage you you parallel
eyes again so you went out and you
paralyze those paralyze the search each
search would now come back with a whole
bunch of passages maybe you had a total
of a thousand or five thousand different
passages for each passage now you don't
figure out whether or not there was a
candidate it would call it candidate
answer in there so you had a whole bunch
of other a whole bunch of other
algorithms
that would find candidate answers
possible answers to the question and so
you had candidate answers jet 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 cracked so right and
speed and accuracy for the most part
we're separated we handle that sort of
in separate ways like I focus purely on
accuracy and to an accuracy are we
ultimately getting more questions and
producing more accurate confidences and
they had a whole nother team that was
constantly analyzing the workflow to
find the bottlenecks and then if you're
getting out of both parallel eyes and
drive the algorithm speed but anyway so
so now think of it like you have this
big fan out now right because you have
you had multiple queries now you have
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 can use how
the query was generated you're going to
use the passage itself and you're going
to 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 scorers so if you have a
200 different scores and you never a
thousand candidates now you have two
thousand scores and and so now you got
to figure out you know how do I now rank
these rank these answers based on the
scores that came back and I want to rank
them based on the likelihood that there
are 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 human
being saying that this this answer do
you think you think of if you want to
think of it what you're doing you know
if you want to think about what a human
would be doing human would be looking at
a possible answer they'd be reading the
you know Emily Dixon Dickinson they've
been reading the passage in which that
occurred they'd be looking at the
question
they'd be making a decision of how
likely it is that Emily Dixon Dickinson
given this evidence in this passage is
the right answer to that quad got it
so that that's the annotation task that
Stan Johnson scoring task so but scoring
implies zero to one kind of trite
continuance is not a binary no give it a
score give it a zero yeah exactly so
it's what humans did 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 could be
relative to 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 mean
I'm misunderstanding the the the process
here this is passages where is the
ground truth coming from grass root
there's only there were answers to the
questions so it's end to end
it's end to end so we also I was always
driving and and performance a very
interesting a very interesting you know
engineering approach and ultimately
scientific and researcher personal
always driving in 10 now that's not to
say we wouldn't make hypotheses that
individual component performance was
related in some way to n10 performance
of course we would because people would
have to build individual components but
ultimately to get your component
integrates with the system you had to
show impact on end-to-end performance
question-answering performance as
there's many very smart people work 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 and they would do research on
their component and they would say
things like you know I'm going to
improve this as a candidate generator
I'm going to improve this as a question
score or as a passive scorer I'm going
to proved as or as a parser and I can
improve it by two percent 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 computer metric is
going to 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 okay what
breakthrough would you say like I'm sure
there's a lot of day to day break this
but it was there like a breakthrough
that really helped improve performance
like wait what 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 fouled as 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 and and 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 we
leverage machine learning in a very
important way so while individual
components could be statistically driven
machine learning components some of them
were your wrist ik 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 the passive selection and so forth
but when we you just plug it in and we
had enough training data to say now we
can we can train and figure out how do
we weigh all the scores relative to each
other based on the predicting the
outcome which is right 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 that yeah the machine
learning is doing the fusion and then
it's a human orchestrated ensemble
that's right friend approaches as a
great still impressive they were able to
get it done a few years that 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 looking back at
those days I'm most proud of my um 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 this 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 domain 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 wanted
jeopardy so it was it was a success it
was I worried that the world would not
understand that has 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 this 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 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 they not
a success in terms of solving natural
language understanding there was a
success of being an inspiration to
future challenges absolutely 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 thats that actually came out very
early on in the project also in fact I
had people who wanted to be on the
project who were
early on who has 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 are you
into this at least in the context of
this prior need to build in the context
to this project in NLU and in building
an AI that understands how it needs to
alter that communicate with humans I
very much care yeah so 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 look if I'm gonna get this
done and when it charts 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
going to get there from here in the time
and you know in the timeframe I think
that's a great way to lead the team but
now there's done and then one when you
look back right so analyse what's the
difference sexy right so so I was a
little bit surprised actually to
discover over time as this would come up
from time to time and would reflect on
it that and and talking to Ken Jennings
a little bit and hearing Ken Jennings
talk about it about how he answered
questions that it might have been closer
to the way humans answer questions than
I might have imagined previously because
humans are probably in the game of
Jeopardy at the level of Ken Jennings
probably also cheating their weight into
winning right now one else is shallow
they're doing that fast as possible
they're doing shallow analysis so they
are very quickly analyzing the question
and coming up with some you know key you
know key vectors or cues if you will and
they're taking those cue
they're very quickly going through like
their library of stuff not deeply
reasoning about what's going on and then
sort of like a lots of different like
what we call these these scores which
kind of score that in a very shallow way
and then say oh boom you know that's
what it is and 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 certain
certainly didn't approach it by saying
you know how would you even do this now
in an elemental cognition like the
project I'm leading now we asked those
questions all the time because
ultimately we're trying to do something
that is to make the the the intelligence
in 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 this 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
or 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 anyway jeopardy the systems
on trying to interpret the question and
that's not interpreting the content
that's reasoning and with regard to any
particular framework I mean it's it is
parsing it and like parsing the contents
and using grammatical cues and stuff
like that so if you think of grammar as
a human framework in some sense and as
that but when you get into the richer
semantic frameworks what are people how
do they think what motivates them what
are the events that are occurring and
why are they occurring and what causes
what else to happen and and and when it
where are things in time and space and
it's 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 like free flowing
communication free flowing
log versus question-answering even with
the framework of the interpretation
dialogue yep
do you see free-flowing dialogue as a
fundamentally more difficult than
question answering even with shared so
dialogue is as important in 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 connects it back to these
frameworks reason and explain itself
that system ultimately needs to be able
to talk to humans or I needs to be able
to interact with humans so in some
sentence to dialogue that doesn't mean
that it it that like sometimes people
talk about dialogue and they think you
know how do humans talk how do you
montork talk to each other in a casual
conversation then you could mimic casual
conversations we're not trying to mimic
casual conversations we're really trying
to produce the machine as goal is it 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 smoke
having a small talk conversation with
your friend down the street this is more
about like you would be communicating to
the commuter computer on Star Trek we're
like what do you want to think about
like what do you want to reason about
I'm going to 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 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 we could
ask follow-up questions so it's that
type of dialogue that you want to
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 the 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 part yes but
what I'd like to think it's more similar
to this pocket as in joking I'll ask you
about humor as well actually but what's
the hardest part of that because it
seems we were quite far away as a
community from thats though to be able
to so one is having a shared
understanding as 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 well
which aspects of this whole problem you
just specified all 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 do this you kind
of have to be creative in the following
sense if I were to do this is purely a
machine learning approach and someone
said learn how to have a good flue in
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
elect a day to collect the data because
I don't know how much data is like that
I think okay okay so this one there's a
human but also even if it's out there
say was out there how do you like
alright so I think I think like an
accessible right so I think any like any
problem like this where you don't have
enough data to represent the phenomenon
you want to learn in other words you
want you if you have enough data you
could potentially learn the pattern in
an example like this it's hard to do it
this is the you know Susie sort of a
human sort of thing to do what you
recently came out IBM was the debate or
projects and surest thing right because
now you had you do have these structured
dialogues these debate things where they
did use machine learning techniques to
generate the you know generate these
debates dialogues are a little bit
tougher in my opinion than generating a
a structured argument where you have
lots of other structural arguments like
this you could potentially annotate that
data and you could say this is a good
response 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 what so I'm goal-oriented
and 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 a 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 if we can create
that more programmatic programmatically
at least to get this process started and
I can create a tool that now engages
humans effectively I could start both I
could start generating data I could
start with the human learning process
and I can update my machine but I can
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 this is
some creativity and bootstrapping all
right what elements of 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 if
you end to me the biggest contrast is
from Watson so one of the greatest
sketches of comedy sketches of all time
right is the SNL celebrity jeopardy with
uh with with Alex Trebek and Sean
Connery and Burt Reynolds and so on with
uh with the Sean Connery commentating on
Alex Trebek smile there a lot so and I
think all of them are in the negative
point what'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
human represents to me is it the same
idea that you're saying about a
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 things going on there so I I 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
with this and with puns and in jeopardy
we literally sat down and said well you
know how do puns work and you know it's
like wordplay and you could formalize
these things so I think there's a lot
aspects of humor that you could
formalize you could also learn new Murr
you could just say what do people laugh
at and if you have enough again if you
have enough data to represent the
phenomenon you know 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 my buddy unless we
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 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 how to combine these two
approaches I think there's another
aspect of human to human though which
goes to the idea that I feel like I can
relate to the person telling the story
telling the person telling the story and
I think that's that's a 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
there's a row but is not conscious the
way I'm conscious when they imagine the
robot does not actually have the
experiences that I experience do I find
it you know funny or do because it's not
as related I don't imagine that the
person is relating it to it the way I
relate to it I think this also you see
this in in the arts and in entertainment
where like you know 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't who
can evoke a similar emotional response
who can get you to emote right about the
way they
in other words who can basically make
the connection from the artifact from
the music of the painting of the
sculpture to the 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 their emotional response
to the artifact and then you feel like
oh wow I can relate to that person I can
connect to that I can connect to that
person so I think humor has that 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 there may be 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
int the promotoras ation but
nevertheless well there was some
interest in doing that and I've that's
an that's another thing I didn't want to
do so I didn't want to distract from the
from the actual scientific test nights
so you're absolutely right I mean humans
do anthropomorphize and and without
necessarily a lot of work I mean just
put some eyes in a couple of eyebrow
movements and you're getting humans to
react emotionally and I and I think you
can do that so I didn't mean to suggest
that that that connection can't 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 doesn't really share
the understanding but is essentially
just moving inside brow or drooping its
eyes or making them big or whatever it's
doing that's 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
under depreciate understanding it may
run flat but I don't know I'm pretty I'm
pretty confident that it will
the majority of the world even if you
tell them how no matter well it will not
matter especially if the Machine herself
says that she is cautious that's very
possible so you the scientists 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 shop you're on
right now but yeah I think it's actually
psychology I think it's not science
fiction
I think it's reality I think it's a
really powerful one that will have to be
exploring in the next few decades it's a
very interesting element of intelligence
so what do you think we've talked about
social constructs of intelligences and
and frameworks and 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 say this is
crossing a kind of threshold that's that
gives me pause in a good way
expectations for a are generally high
what does high look like by the way so
not the threshold test as 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 and
then then then ultimately we can do I
think where they're gonna struggle is
what we talked about before which is
relating to communicating with and
understanding humans in deeper ways and
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 a machine can mimic your emotional
response can even generate language that
will sound smart and what someone else
might say under similar circumstances
look how its paws on that like that's a
super parrot right so given similar
circumstances moves its face its faces
in similar ways changes its tone of
voice in similar ways produce the
strings of language that you know 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 guys describe
me in a nutshell
I think I think philosophically speaking
you could argue that that's all we're
doing as human beings to war so I was
gonna say it's very possible you know
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 and and they are capable
reasoner's you know they're they're both
and you don't know which is going on
right so and I think it's I think it's
an interesting problem we talked earlier
about like where we are in our social
and political landscape can you
distinguish some
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
integrity of a programming dialogue so
it's interesting because humans are
really good at in their own mind
justifying or explaining what they hear
because they project their understanding
on onto yours so you could say you could
put together a string of words and and
someone will sit there and interpret in
a way that's extremely biased this is
the way they want to interpret it they
want to assuming you're an idiot and
they'll true put it one way they've all
seen you're a genius and interpreted
another way that suits their needs so
this is tricky business so I think the
answer your question as AI gets better
and better at better and better mimic
you 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
you know can you
so can you reason intelligently and make
me believe that those um 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 should be by I think one of
the depending on the content one of the
problems we have there is that if that
large community of people are not
judgment 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 them turn their
brains off
right okay sorry I have nothing against
the warning I just so you you're a part
of one of the great benchmarks
challenges of AI history what do you
think about alpha zero open AI five
alpha star accomplishments on video
games recently which are also I think at
least in the case of go without fagala
now for zero playing go was a monumental
accomplishment as well what are your
thoughts about that challenge I think it
was a giant lamare I 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 again it's hard for particularly
hard for humans our team is to learn how
for humans to excel at and so it was up
another measure a measure of
intelligence it's very cool I mean it's
very interesting you know what they did
I mean and I loved how they solved like
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 and and of
course the result speaks for itself I
think it makes us think about again it
is okay what's intelligence what aspects
of intelligence are important can the
can the go machine help me make me a
better go player is it an alien
intelligence it was is am I even capable
of like again if we if we put in very
simple terms it found the function we
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 yeah but
there's no human in the loop there so
like you're saying it could have by
itself created an alien intelligence how
torta torta gorrik imagine you're
sentencing you're judging you're
sentencing people or you're setting
policy or you're you know 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 it's it's an
interesting dilemma
for the applications of AI do we hold AI
to this accountability that says you
know humans have to be humans have to be
able to take responsibility you know for
for the decision in other words can you
explain why you would do the thing well
you will use 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 link on this point because it's a
there's a fascinating one that's a great
goal for AI do you think it's achievable
in many cases or do you 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
would drive a car without ever
explaining to you why it fails when it
does that's one possible world then
we're okay with it or the other where we
are not okay with it and we really hold
back the technology from getting to 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 you know I'm not trying 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
it is already better than humans so for
example I don't know that I get tape
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 there you know ten times safer
than humans I I don't know that I care I
think when we we have these edge cases
when something bad happens and we want
to decide who's liable for that thing
and who made that mistake in what we do
about that and I think in those those
educators are interesting cases and now
do we go to designers of the AI and the
I says I don't know if that's what it
learned to do and it says well you
didn't train it properly you know you
you were you were negligent in the
training data that you gave that machine
like how do we drive down and realize oh
so I think those are I think those are
interesting questions so the
optimization problem there sorry
is to create a system that's able to
explain the lawyers away there you go um
I think that uh uh I think it's gonna be
interesting I mean I think this is where
technology and social discourse are
gonna get like deeply intertwined and
how we start thinking about problems
decisions and problems like that I think
in other cases it becomes more obvious
where you know it's I got like why did
you decide to give that person you know
a longer sentence or or to deny them
parole again policy decisions or why did
you pick that treatment like that
treatment up killing that guy like why
was that a reasonable choice to make so
so and people are gonna demand
explanations now there's a reality
though here and 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
Osmonds is what happened my dad if you
saw that target 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 wasn't not
resuscitated right away and so forth and
they came 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 innocent but can
you just tell me not inductively but
deductively go there and tell me his
brain stopped functioning is the way for
you to do that and they and and their
the the protocol in response was no this
is how we make this decision I said this
is adequate for me I understand the
statistics and I don't have you know
there's a two percent chance he's so
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 brained
it so I wouldn't sign that do not
resuscitate and I don't know it was like
they went through lots of procedures as
a 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
that would zero bushido brain damage any
what lessons do you draw from that story
that experience that the data that
they're you that the data that's being
used to make sophistical inferences
doesn't adequately reflect the
phenomenon so in other words you're
getting shit Ramsar you're getting stuff
wrong because you're 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 models insufficient
and that you should be reasoning at
about the specific case more logically
and more deductively and hold yourself
responsible to hold yourself accountable
to doing that and perhaps AI has a role
to say the exact thing we just said
which is perhaps this is a case you
should think for yourself you should
reason deductively so it's hard it's
it's so it's hard because it's hard to
know that you know 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 um and and what standards do we hold
right for that so um you know if you
look at you you look more broadly for
example as my father as a metal kick
medical case the medical system
ultimately helped him a lot throughout
his life without it he probably would
have died much sooner
so overall sort of you know work for him
and sort of a net in that kind of way
actually I don't know that's fair um but
it maybe not in that particular case but
overall like oh the medical system
overall that's more given a system
overall you know was doing more more
good than bad now is another argument
that suggests that wasn't the case but
for the for the sake of argument let's
say like that's let's say a net positive
and I think you have to sit there and
there and take
take that into consideration now you
look at a particular use case like for
example making this this decision have
you done enough studies to know how good
that prediction really is right and how
you have you done enough studies to
compare it to say well what if we what
if we dug in and in a more direct you
know let's get the evidence let's let's
do the deductive thing and not use the
statistics here how often would that
have done better right you just so you
have to do this studies to know how good
the AI actually is and it's complicated
because depends how fast you have to
make decision so if you have to make the
decision superfast 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 was
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 you know
if you do well he's gonna die anyway so
let's do it my way though I mean it
raises questions for our society to
struggle with as was the case with your
father but also when things like race
and gender start coming into play when
when certain when when judgments are
made made based on things that are
complicated in our society at least in
this course and it starts you know I
think I think I'm safe to say that most
of the violent crimes committed by males
so if you discriminate based you know as
a male versus female saying that if it's
a male more likely to commit the crime
so this is one of my my very positive
and optimistic view 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 a
regardless of what what state AI device
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 yeah 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 as as a species can be
doing honestly we are reached we've
created an incredibly complex society
we've created amazing abilities to
amplify noise faster than we can play
amplifies 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 objectively reason do they
understand what they are doing nevermind
with their AI is doing this is such an
important dialogue you know to be having
and and and you know we are
fundamentally are thinking can be and
easily becomes fundamentally bias and
there are statistics and we shouldn't
blind our so we shouldn't discard
statistical inference but we should
understand the nature of such this
conference as us as a society as you
know we decided to reject statistical
inference to favor individual
understanding and and deciding on the
individual yes we we consciously make
that choice so even if the statistics
said even if the Cystic 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
once we do that at a respect for the
individual for the individual yeah and
then that requires reasoning and
cracking looking forward what Grand
Challenges would you like to see in the
future because the the Jeopardy
challenge you know captivated the world
alpha go alpha zero cap day of the world
deep blue 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 oh you know I
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 Kent you know can you
demonstrate that they understand that
they could read and understand that they
can 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 it's not enough to
convince me that you might be human
because you could you know you can
parrot a conversation I think you know
the the this standard is a little bit
higher is for example can you you know
the santa 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 then
what depth they understand it they you
know and what 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 can can can
machines really act like thought
partners where they could satisfy me
that they that we have a share our
understanding is shared enough that we
can collaborate and produce the answers
together and that you know they they can
help me explain and justify those
answers so maybe here's an idea so we'll
have a Isis
run for president and convinced that's
too easy
from sorry oh no you have to convince
the voters that they should vote for it
so they s what I would again again I
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 matter to 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 I think that's where we
have to set the 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 can you under can you
reason oh 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 kind of machine teach you can
the hell
really nice less than that's where to
put it can you understand something that
you didn't really understand before
where's where is you know it's taking it
so you're not you know again it's almost
like can it can it teach you can it help
you learn and 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 fiction's abut
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
what 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 if Watson was conscious and I used
to think and he said he's the conscious
of what exactly I mean I think you know
main cell it depends what it is that
you're conscious I mean like 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 the
answer questions about whether or not it
was playing jeopardy I mean it could
certainly answer questions that will
imply that it was aware of things
exactly what does it mean to be aware
and what does it mean to conscious 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
for sure there's degrees of
consciousness in there so it well in
those areas like it's not just agrees
what do you what do you what are you
aware of like what are you not aware 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 immortality fear or mortality
Watson as far as I could tell did not
have a fear of death certainly not most
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 I mentioned survival that
adds to the whole thing that I mean
consciousness is tied up with that that
we are us 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 take for
generating goals again I think you could
have you could have an intelligence
capability and a capability to learn I
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 million code I mean
can you create a robot now and you could
say you know and plug it in and say
protect your power source you know and
give it some capabilities and we'll sit
there and operate to try to protect this
power source and survive I mean I so I
don't know that that's false awfully a
hard thing to demonstrate it sounds like
a fairly easy thing to demonstrate that
you can give it that goal we'll come up
with that goal by itself as you have to
program that goal in but there's
something because I think as we touched
on intelligence is kind of like a social
construct the the fact that a robot will
be protecting its power source
would would add depth and grounding to
its intelligence in terms of us being
able to respect 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 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 you
mount a laser gun on her or you may do
you the ability to scream a screech or
whatever and you know and you you say
you know if you see your power source
then you could program that in and you
know you're gonna print it you're gonna
take these actions to protect it you
know you teach it checking it on a bunch
of things so and and now you're gonna
look at that and you say well you know
that's intelligence because it's
protecting power source maybe but that's
again at this human bias that says the
thing I had then I identify my
intelligence and 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 intelligent
what timeline year will society have a
something that would that you would be
comfortable calling an artificial
general intelligence system well what's
your intuition nobody can predict the
future
certainly not next few months or twenty
years away but what's your intuition how
far away are we
I the ideas hearts make these
predictions and 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 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 ten-year thing as a 23 it's
probably closer to a 20-year thing I
guess but not as little 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 this type of work and it's sort of
interesting like I don't think it's
obvious
how incentivize we are I think from a
task perspective you know if we see
business opportunities to take this
technique is a technique to solve that
problem I think that's the main driver
for many from any of these things from a
from a general Tosta seems kind of an
interesting question are we really
motivated to do that and 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 the significance and
meaning of 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 when when you get into a
general intelligence for learning
physical tasks and again yeah I want to
go back to your body questions it's on
your body question was interesting but
you want to go back to you know learning
abilities do physical tasks you might
have we might get Majan 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 as if we're going
to continue to get better and better at
that and machines will out you know
outpace humans and and a variety of
those things the underlying mechanisms
for doing that may be the same meaning
that you know maybe these are deep Nats
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 you know can we
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 at a
continuous basis so how do you get in
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 can we get
the machine engaged and
you know in the intellectual calling a
game but in the intellectual dialogue
with the humans are the humans
sufficiently an intellectual dialogue
with each other to generate enough 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 is a challenge to
bootstrap it so that's so as 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 incentivize to do the latter
significantly enough to people
understand what the latter really is
well enough part of the elemental
cognition mission is to try to
articulate that better and better you
know through demonstrations and to
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 do we want this what what is
the potential of this what are the
business what's the business potential
what's the societal potential to that
and so you know and to build up that
incentive system around that yeah I
think if people don't understand yet I
think they will and is a huge business
potential here so it's exciting that
you're working on it you've kind of
skipped over but I'm a huge fan of
physical presence of things do you think
you know Watson head of body do you
think having a body as to the
interactive element between the AI
system and a human or just in general to
intelligence so I think I think going
back to that shared understanding bit
humans are very connected to their
bodies I mean is 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 are the the tool we
use to affect output but they're also
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 missions that have this as
the input data and they don't have the
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
that and then I can it then affects me
as a physical being and then I and I and
I can play that out in other words I
could realize the implications of tax
implications again on my bond mind body
complex I then process that and the
implications again are internal features
are generated I learned 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 embedded in a human body
just to clarify and both concepts
beautiful is humanoid robots so robots
that look like humans is one or did you
mean actually sort of what Hamas was
working with neural link really
embedding intelligence systems that the
ride-alongs human bodies know I was
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 you
know 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 sentences um right so I think
if you want that again I don't know that
you want that like man 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 to go like I want it
I want to create I think of it as I
create intellectual thought martyrs for
humans so that kind of that kind of
intelligence
I know other companies that are creating
physical thought partners the fiscal
partners to figure out for you but
that's kind of not where we're you know
I'm at but but but the the important
point is that a big part of how 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 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 a
between human and ass
is that something that gets in the way
of the the rational discourse is there
something that's useful I worry about
biases you know obviously so in other
words if you develop an emotional
relationship with the machines do all of
a sudden you start are more likely to
believe what it's saying even if it
doesn't make any sense
so I you know I worry about that but at
the same time I think the opportunity to
use machines to provide human
companionship is actually not crazy and
it's again the intellectual and social
companionship is not crazy the idea do
you have concerns as a few people do you
know 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 humans
somehow having a significant negative
impact on society in the long term I
think there aren't things to worry about
I think the giving machines too much
leverage is a problem and what I mean by
leverage is is too much control for
things that can hurt us whether it's
socially psychological intellectually or
physically and if you give them machines
too much control I think that's a
concern you forget about the AI just
when you give them too much control
human bad actors can hack them and
produce havoc so um you know that's a
problem and you imagine hackers taking
over the driverless car Network and you
know creating all kinds of havoc but you
could also imagine given 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 over that 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 it to image of our
our emotions or our fears and this is
done all the time when but with machines
machines are like giant mecha phones
right we can amplify this and orders of
magnitude and can fine-tune its control
so we can tailor the message we can now
very rapidly and efficiently tailor the
message to the audience taking taking
advantage of you know of their biases
and amplifying them and using them to
pursue a them in one direction or
another in ways that are not fair not
logical not objective not meaningful and
humans the machines and power that so 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
more you know more quickly and we see
that already going on and and and social
media not the plays and other places
that's scary and and that's why like I'm
I'm 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 and sit basically so
that in the end we have a stronger
overall system that's just incredibly
important I don't
most people understand that so so like
telling 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 the different types of
thinking and how they work and what
you're prone to and you know and what
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 then that's
where the as a role is to reveal that
then the that trade-off so if you
imagine a system that is able to beyond
any definition of the Turing test of 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
this is a whole nother 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 and say this is where I care
about today and and 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 this
decision or that decision what if this
were true what if that were true what
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 supports this oh there's a boy
that would that be incredible you
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 an
objective dialogue you know I just I get
goosebumps talking about it right so
when when you create it please come back
on the podcast well the discussion
together and make it even longer this is
a record for the longest conversation
now there's an honor it was a pleasure
David thank you so much for thanks so
much a lot of fun
you