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Language: en
what difference between biological
neural networks and artificial neural
networks is most mysterious captivating
and profound for you first of all
there's so much we don't know about
biological neural networks and that's
very mysterious and captivating because
maybe it holds the key to improving our
differential neural networks one of the
things I studied recently something that
we don't know how biological neural
networks do but would be really useful
for artificial ones is the ability to do
credit assignment through very long time
spans there are things that we can in
principle do with artificial neural nets
but it's not very convenient and it's
not biologically plausible and this
mismatch I think this kind of mismatch
may be an interesting thing to study to
a understand better how brains might do
these things because we don't have good
corresponding theories with artificial
neural Nets and B maybe provide new
ideas that we could explore about things
that brain do differently and that we
could incorporate in artificial neural
Nets so let's break created assignment
up a little bit yeah what it's a
beautifully technical term but it could
incorporate so many things so is it more
on the RNN memory side that thinking
like that or is it something about
knowledge building up common sense
knowledge over time or is it more in the
reinforcement learning sense that you're
picking up rewards over time for a
particular to achieve certain kind of
goals so I was thinking more about the
first two meanings whereby we store all
kinds of memories episodic memories in
our brain which we can access later in
order to help us both infer causes of
things that we are observing now
and assign credit to decisions or
interpretations we came up with a while
ago when you know those memories were
stored and then we can change the way we
would have reacted or interpreted things
in the past and now that's credit
assignment used for learning so in which
way do you think artificial neural
networks the current LS TM the current
architectures are not able to capture
the presumably you're thinking of very
long term yes so current recurrent Nets
are doing a fairly good jobs for
sequences with dozens or say hundreds of
time stamps and then it gets harder and
harder and depending on what you have to
remember and so on as you consider
longer durations whereas humans seem to
be able to do credit assignment through
essentially arbitrary times like I could
remember something I did last year and
then now because I see some new evidence
I'm gonna change my mind about the way I
was thinking last year and hopefully not
do the same mistake again I think a big
part of that is probably forgetting
you're only remembering the really
important things it's very efficient
forgetting yes so there's a selection of
what we remember and I think there are
really cool connection to higher-level
cognition here regarding consciousness
deciding and and emotions like sort of
deciding what comes to consciousness and
what gets stored in memory which which
are not trivial either so you've been at
the forefront there all along showing
some of the amazing things that neural
networks deep neural networks can do in
the field of artificial intelligence is
just broadly in all kinds of
applications but we can talk about that
forever but what in your view because
we're thinking towards the future is the
weakest aspect of the way deep neural
networks represent the world what is
that what is in your view is missing so
currently current state-of-the-art
neural nets trained on large quantities
of images or texts have some level of
understanding of you know what explains
those datasets but it's very basic it's
it's very low-level and it's not nearly
as robust and abstract in general as our
understanding okay so that doesn't tell
us how to fix things but I think it
encourages us to think about how we can
maybe train our neural nets differently
so that they would focus for example on
causal explanations something that we
don't do currently with neural net
training also one thing I'll talk about
in my talk this afternoon is instead of
learning separately from images and
videos on one hand and from text on the
other hand we need to do a better job of
jointly learning about language and
about the world to which it refers so
that you know both sides can help each
other we need to have good world models
in our neural nets for them to really
understand sentences which talk about
what's going on in the world and I think
we need language input to help provide
clues about what high-level concepts
like semantic concepts should be
represented at the top levels of these
neural nets in fact there is evidence
that the purely unsupervised learning of
representations doesn't give rise to
high level representations that are as
powerful as the ones we are getting from
supervised learning
and so the the clues we're getting just
with the labels not even sentences is
already very powerful do you think
that's an architecture challenge or is
it a data set challenge neither I'm
tempted to just end it there in your
library of course data sets and
architectures are something you want to
always play with but but I think the
crucial thing is more the training
objectives the training frameworks for
example going from passive observation
of data to more active agents which
learn by intervening in the world the
relationships between causes and effects
the sort of objective functions which
could be important to allow the the
highest level explanations to to to rise
from from the learning which I don't
think we have now the kinds of objective
functions which could be used to reward
exploration the right kind of
exploration so these kinds of questions
are neither in the dataset nor in the
architecture but more in how we learn
under what objectives and so on yeah
that's a afraid you mentioned in several
contexts the idea is sort of the way
children learn they interact with
objects of the world and it seems
fascinating because it's some sense
except with some cases in reinforcement
learning that idea is not part of the
learning process in artificial neural
network so it's almost like do you
envision something like an objective
function saying you know what if you
poke this object in this kind of way
would be really helpful for me to
further
yes further learn right right sort of
almost guiding some aspect of learning
right right so I was talking to Rebecca
Saxe just an hour ago and she was
talking about lots and lots of evidence
for
infants seem to clearly take what
interest them in a directed way and so
they're not passive learners they they
focus their attention on aspects of the
world which are most interesting
surprising in in a non-trivial way that
makes them change their theories of the
world so that's a fascinating view of
the future progress but Anna the more
maybe boring a question do you think
going deeper and large so do you think
just increasing the size of the things
that have been increasing a lot in the
past few years will will also make
significant progress so some of the
representational issues that you
mentioned that is they're kind of
shallow in some sense Oh higher in a
sense of abstraction up straight in a
sense of abstraction they're not getting
some I don't think that having more more
depth in the network in the sense of
instead of a hundred layers we have ten
thousand is going to solve our problem
you don't think so is that obvious to
you yes what is clear to me is that
engineers and companies and labs grad
students will continue to tune
architectures and explore all kinds of
tweaks to make the current state of the
Arts that he ever slightly better but I
don't think that's gonna be nearly
enough I think we need some fairly
drastic changes in the way that we're
considering learning to achieve the goal
that these learners actually understand
in a deep way the environment in which
they are you know observing and acting
but I guess I was trying to ask a
question is more interesting than just
more layers is basically once you figure
out a way to learn through interacting
how many parameters does it take to
store that information so
I think our brain is quite bigger than
most neural networks right right oh I
see what you mean oh I I'm with you
there so I agree that in order to build
neural nets with the kind of broad
knowledge of the world that typical
adult humans have probably the kind of
computing power we have now is going to
be insufficient so well the good news is
there are hardware companies building
neural net chips and so it's gonna get
better however the good news in a way
which is also a bad news is that even
our state-of-the-art deep learning
methods fail to learn models that
understand even very simple environments
like some Grid worlds that we have built
even these fairly simple environments I
mean of course if you train them with
enough examples eventually they get it
but it's just like instead of what
instead of what humans might need just
dozens of examples these things will
need millions right for very very very
simple tasks and so I think there's an
opportunity for academics who don't have
the kind of computing power that say
Google has to do really important and
exciting research to advance the
state-of-the-art in training frameworks
learning models agent learning in even
simple environments that are synthetic
that seem trivial but yet current
machine learning fails on we've talked
about priors and common-sense knowledge
it seems like we humans take a lot of
knowledge for granted so what what's
your view of these priors of forming
this broad view of the world this
accumulation of information and how we
can teach a neural networks or learning
systems to pick that knowledge up so
knowledge you know for a while the
artificial intelligence what's maybe in
the 80 there's a time or knowledge
representation knowledge
acquisition expert systems I mean though
the symbolic AI was was a view was an
interesting problem set to solve and it
was kind of put on hold a little bit it
seems like because it doesn't work it
doesn't work that's right but that's
right but the goals of that remain
important yes remain important kind of
how do you think those goals can be
addressed right so first of all I
believe that one reason why the
classical expert systems approach failed
is because a lot of the knowledge we
have so you talked about common sense
intuition there's a lot of knowledge
like this which is not consciously
accessible the lots of decisions we're
taking that we can't really explain even
if sometimes we make up a story and that
knowledge is also necessary for machines
to take good decisions and that
knowledge is hard to codify in expert
systems rule-based systems and you know
Costco EAJA formalism and there are
other issues of course with the old AI
like not really good ways of handling
uncertainty I would say something more
subtle which we understand better now
but I think still isn't enough in the
minds of people there is something
really powerful that comes from
distributed representations the thing
that really makes neural Nets work so
well and it's hard to replicate that
kind of power in a symbolic world the
knowledge in in expert systems and so on
is nicely decomposed into like a bunch
of rules whereas if you think about a
neural net it's the opposite you have
this big blob of parameters which work
intensely together to represent
everything the network knows and it's
not sufficiently factorized and so I
think this is one of the weaknesses of
current neural nets that we have to take
lessons from classically I
in order to bring in another kind of
compositionality which is common in
language for example and in these rules
but that isn't so native to New Ulm Ed's
and on that line of thinking
disentangled representations yes so so
let me connect with disentangled
representations if you might if don't
mind yes exactly so for many years I've
thought and I still believe that it's
really important that we come up with
learning algorithms either unsupervised
or supervised but or enforcement
whatever that build representations in
which the important factors hopefully
causal factors are nicely separated and
easy to pick up from the representation
so that's the idea of disentangle
representations it says transform the
data into a space where everything
becomes easy we can maybe just learn
with linear models about the things we
care about and and I still think this is
important but I think this is missing
out on a very important ingredient which
classically AI systems can remind us of
so let's say we have these design
technologies invation
you still need to learn about the the
relationships between the variables
those high-level semantic variables
they're not going to be independent I
mean this is like too much of an
assumption they're gonna have some
interesting relationships that allow to
predict things in the future to explain
what happened in the past the kind of
knowledge about those relationships in a
classically AI system is encoded in the
rules like a rule is just like a little
piece of knowledge that says oh I have
these two three four variables that are
linked in this interesting way then I
can say something about one or two of
them given a couple of others right in
addition to disentangling the the
elements of the representation which are
like the variables in rule-based system
you also need to disentangle the the
mechanisms that relate those variables
to each other so like the rules so the
rules are neatly separated like each
rule is you know living on its own and
when I change a rule because I'm
learning
it doesn't need to break other rules
whereas current your Mets for example
are very sensitive to what's called
catastrophic forgetting where after I've
learned some things and then I learn new
things they can destroy the old things
that I had learned right if the
knowledge was better factorized and and
separated disentangled then you would
avoid a lot of that now you can't do
this in the sensory domain but a decent
okay like an pixel space but but my idea
is that when you project the data in the
right semantic space it becomes possible
to now represent this extra knowledge
beyond the transformation from input to
representations which is how
representations act on each other and
predict the future and so on in a way
that can be neatly disentangled so now
it's the rules or disentangle from each
other and not just the variables that
are disentangled from each other and you
draw a distinction between semantic
space and pixel like yes there need to
be an architectural difference or well
yeah so there's the sensory space like
pixels which where everything is
untangled the the information like the
variables are completely interdependent
in very complicated ways and also
computation like the it's not just
variables it's also how they are related
to each other is is all intertwined but
but I I'm hypothesizing that in the
right high-level representation space
both the variables and how they relate
to each other can be disentangled and
that will provide a lot of
generalization power generalization
power yes distribution of the test set
he assumed to be the same as a
distribution of the training set right
this is where current machine learning
is too weak it doesn't tell us anything
is not able to tell us anything about
how are you let's say our gonna
generalize to a new distribution and and
you know people may think well but
there's nothing we can say if we don't
know what the new distribution will be
the truth is humans are able to
generalize to new distributions how are
we able to do that so yeah because
something these new distributions even
though they could look very different
from the training solutions they have
things in common so let me give you a
concrete example you read a science
fiction novel the science fiction novel
maybe you know brings you in some other
planet where things look very different
on the surface but it's still the same
laws of physics all right and so you can
read the book and you understand what's
going on
so the distribution is very different
but because you can transport a lot of
the knowledge you had from Earth about
the underlying cause and effect
relationships and physical mechanisms
and all that and maybe even social
interactions you can now make sense of
what is going on on this planet where
like visually for example things are
totally different
taking that analogy further and
distorting it let's enter a sign science
fiction world to say Space Odyssey 2001
with hell yeah or or maybe which is
probably one of my favourite AI movies
and then then - and then there's another
one that a lot of people love that it
may be a little bit outside of the AI
community is ex machina right I don't
know if you've seen it yes yes but what
are your views on that movie alright
does it does are you able to wear things
I like and things I hate so maybe you
could talk about that in the context of
a question I want to ask which is uh
there's quite a large community of
people from different backgrounds often
outside of AI who are concerned about
existential threat of artificial
intelligence right you've seen now this
community develop over time you've seen
you have a perspective so what do you
think is the best way to talk about a a
safety to think about it to have this
course about it within AI community and
outside and grounded in the fact that ex
machina is one of the main sources of
information for the general public about
AI so I think I think you're putting it
right there's a big difference between
the sort of discussion we oughta have
within the AG community and the sort of
discussion that really matter in the
general public
so I think the the picture of terminator
and you know AI lose and killing people
and super intelligence that's gonna
destroy us whatever we try isn't really
so useful for the public discussion
because for the public discussion that
things I believe really matter are the
short-term and mini term very likely
negative impacts of AI on society
whether it's from security like you know
Big Brother scenarios with face
recognition or killer robots or the
impact on the job market or
concentration of power and
discrimination all kinds of social
issues which could actually some of them
could really threaten democracy for
example just to clarify when you said
killer robots you mean autonomous
weapons yes weapon systems yes I do not
terminator that's right
so I think these these short and
medium-term concerns should be important
parts of the public debate now
existential risk for me is a very
unlikely consideration but still worth
academic investigation in the same way
that you could say should we study what
could happen if meteorite you know came
to earth and destroyed it so I think
it's very unlikely that this is gonna
happen and or happen it in a reasonable
future it's it's very the the sort of
scenario of an AI getting loose goes
against my understanding of at least
current machine learning and current
neural nets and so on it's not plausible
to me but of course I don't have a
crystal ball and who knows what a I will
be in fifty years from now so I think it
is worth at scientists study those
problems it's just not a pressing
question as far as I'm concerned so
before continuing down the line a few
questions there but what what do you
like and not like about ex machina as a
movie because I I actually watch it for
the second time and enjoyed it I hated
it the first time and I enjoyed it quite
a bit more the second time when I sort
of learned to accept
certain pieces of it CC is the concept
movie hi what was your experience
wouldn't Laura your thoughts so the
negative is the picture it paints of
science is totally wrong science in
general and AI in particular science is
not happening in some hidden place by
some you know really smart guy one
person one person
this is totally unrealistic this is not
how it happens
even a team of people in some isolated
place will not make it
science moved by small steps thanks to
the collaboration and community of a
large number of people interacting and
[Music]
all the scientists who are expert in
their field Canon Oh what is going on
even in the industrial labs its
information flows and leaks and so on
and and and the spirit of it is very
different from the way science is
painted in this movie yeah let me let me
ask on that on that point it's been the
case to this point yeah that kind of
even if the research happens inside
Google or Facebook inside companies it
still kind of comes out like yes come on
absolutely think that will always be the
case so there I is is it possible to
bottle ideas to the point where there's
a set of breakthrough the go completely
undiscovered by the general research
community do you think that's even
possible it's possible but it's unlikely
unlikely it's not how it is done now
it's not how I can foresee it in in the
foreseeable future but of course I don't
have a crystal ball and so who knows
this is science fiction after all but
but usually the ominous that the lights
went off during during that discussion
so the problem again there's a you know
one thing is the movie and you could
imagine all kinds of science fiction the
problem wouldn't for me may be similar
to the question about existential risk
is that this kind of movie pain
such a wrong picture of what is actual
you know the actual science and how it's
going on that that it can have
unfortunate effects on people's
understanding of current science and and
so that's kind of sad is it an important
principle in research which is diversity
so in other words research is
exploration resources explosion in the
space of ideas and different people will
focus on different directions and this
is not just good it's essential so I'm
totally fine with people exploring
directions that are contrary to mine or
look orthogonal to mine it's I I am more
than fine I think it's important I and
my friends don't claim we have universal
truth about what well especially about
what will happen in the future now that
being said we have our intuitions and
then we act accordingly according to
where we think we can be most useful and
where society has the most gain or to
lose we should have those debates and
and and and not end up in a society
where there's only one voice and one way
of thinking in research money is spread
out so disagreement is a sign of good
research good science so yes the idea of
bias in in the human sense of bias yeah
how do you think about instilling in
machine learning something that's
aligned with human values in terms of
bias we and intuitively human beings
have a concept of what bias means of
what fundamental respect for other human
beings means but how do we instill that
into machine learning systems do you
think so I think there are short-term
things that are already happening and
then there are long-term things that we
need to do and the short term there are
techniques that have been proposed and I
think will continue to be improved and
maybe alternatives will come up to take
datasets in which we know there is bias
we can measure it pretty much any data
set where humans are you know being
observed taking decisions will have some
sort of bias discrimination against
particular groups and so on and we can
use machine learning techniques to try
to build predictors classifiers that are
going to be less biased we can do it for
example using adversarial methods to
make our systems less sensitive to these
variables we should not be sensitive to
so these are clear well-defined ways of
trying to address the problem maybe they
have weaknesses and you know more
research is needed and so on but I think
in fact they are sufficiently mature
that governments should start regulating
companies where it matters say like
insurance companies so that they use
those techniques because those
techniques will produce the bias but at
a costs for example maybe their
predictions will be less accurate and so
companies will not do it until you force
them all right so this is short term
long term I'm really interested in
thinking of how we can instill moral
values into computers obviously this is
not something we'll achieve in the next
five or ten years how can we you know
there's already work in detecting
emotions for example in images and
sounds and texts and also studying how
different agents interacting in
different ways may correspond to
patterns of say injustice which could
trigger anger so these are things we can
do in in the medium term and eventually
train computers to model for example how
humans react emotionally I would say the
simplest thing is unfair situations
which trigger anger this is one of the
most basic emotions that we share with
other animals I think it's quite
feasible within the next few years so we
can build systems that can
take these kind of things to the extent
unfortunately that they understand
enough about the world around us which
is a long time away but maybe we can
initially do this in virtual
environments so you can imagine like a
video game we're agents interact in in
some ways and then some situations
trigger an emotion I think we could
train machines to detect those
situations and predict that the
particular emotion you know will likely
be felt if a human was playing one of
the characters you have shown excitement
and done a lot of excellent work with
supervised learning but on a superbug
you know there's been a lot of success
on the supervised learning yes yes and
one of the things I'm really passionate
about is how humans and robots work
together and in the context of
supervised learning that means the
process of annotation do you think about
the problem of annotation of put in a
more interesting way is humans teaching
machines yes is there yes I think it's
an important subject reducing it to
annotation may be useful for somebody
building a system tomorrow but
longer-term the process of teaching I
think is something that deserves a lot
more attention from the machine learning
community so there are people have
coined the term machine teaching so what
are good strategies for teaching a
learning agent and can we design train a
system that gonna be is gonna be a good
teacher so so in my group we have a
project called the baby I or baby I game
where there is a game or scenario where
there's a learning agent and a teaching
agent presumably the teaching agent
would eventually be a human but we're
not there yet and the the role of the
teacher is to use its knowledge of the
environment which it can acquire using
whatever way brute force to help the
learner learn as quickly as possible so
the learner is going to try to learn by
it
of maybe be using some exploration and
whatever but the teacher can choose can
can can have an influence on the
interaction with the learner so as to
guide the learner maybe teach it the
things that the learner has most trouble
with or just at the boundary between
what it knows and doesn't know and so on
so this is there's a tradition of these
kind of ideas from other fields and like
tutorial systems for example and they I
and and of course people in the
humanities have been thinking about
these questions but I think it's time
that machine learning people look at
this because in the future we'll have
more and more human machine interaction
with a human in the loop and I think
understanding how to make this work
better all the problems around that are
very interesting and not sufficiently
addressed you've done a lot of work with
language to what aspect of the
traditionally formulated Turing test a
test of natural language understanding a
generation in your eyes is the most
difficult of conversation but in your
eyes is the hardest part of conversation
to solve for machines so I would say
it's everything having to do with the
non linguistic knowledge which
implicitly you need in order to make
sense of sentences things like the
Winograd schemas so these sentences that
are semantically ambiguous in other
words you need to understand enough
about the world in order to really
interpret probably those sentences I
think these are interesting challenges
for our machine learning because they
point in the direction of building
systems that both understand how the
world works and it's causal
relationships in the world and associate
that knowledge with how to express it in
language either for reading or writing
you speak French yes it's my mother
tongue it's one of the Romance languages
do you think passing the Turing test and
all the underlying challenges we just
mentioned depend on language do you
think it might be easier in front
that is in English now is independent of
language mmm
I think it's independent of language I I
would like to build systems that can use
the same principles the same learning
mechanisms to learn from human agents
whatever their language well certainly
us humans can talk more beautifully and
smoothly in poetry some Russian
originally I know poetry and Russian is
maybe easier to convey complex ideas
than it is in English but maybe I'm
showing my bias and some people could
say that about front half French but of
course the goal ultimately is our human
brain is able to utilize any kind of
those languages to use them as tools to
convey meaning you know of course there
are differences between languages and
maybe some are slightly better at some
things but in the grand scheme of things
where we're trying to understand how the
brain works and language and so on
I think these differences are a minut so
you've lived perhaps through an AI
winter of sorts yes how did you stay
warm and continue and you're resurfacing
stay warm with friends and with friends
okay so it's important to have friends
and what have you learned from the
experience listen to your inner voice
don't you know be trying to just please
the crowds and the fashion and if you
have a strong intuition about something
that is not contradicted by actual
evidence go for it I mean it could be
contradicted by people but not your own
instinct of based on everything you know
of course of course you have to adapt
your beliefs when your experiments
contradict those beliefs but but you
have to stick to your beliefs otherwise
it's it's it's what allowed me to go
through those years it's what allowed me
to
persist in directions that you know took
time whatever all the people think took
time to mature and you bring fruits so
history of AI is marked with these of
course it's mark with technical
breakthroughs but it's also marked with
these seminal events that capture the
imagination of the community most recent
I would say alphago beating the world
champion human go player was one of
those moments what do you think the next
such moment might be okay surface first
of all I think that these so-called
seminal events are overrated as I said
science really moves by small steps now
what happens is you make one more small
step and it's like the the drop that you
know allows to that fills the bucket and
and and then you have drastic
consequences because now you're able to
do something you were not able to do
before or now say the cost of building
some device or solving a problem becomes
cheaper than what existed and you have a
new market that opens up right so so
especially in the world of Commerce and
applications the impact of a small
scientific progress could be huge but in
the science itself I think it's very
very gradual and where these steps being
taken now so there's supervised right so
if I look at one trend that I like in in
in my community so for example in at me
lie in my Institute what are the two
hottest topics Gans and rain for
spurning even though in the montreal in
particular like reinforcement learning
was something pretty much absent just
two or three years ago so it is really a
big interest from students and there's a
big interest from people like me
so I would say this is something where
are we gonna see more progress even
though it hasn't yet provided much in
terms of actual industrial fallout like
even though there's alphago there's no
like Google is not making money on this
right now but I think over the long term
this is really really important for many
reasons so in other words agent I would
say reinforcement learning baby more
generally agent learning because it
doesn't have to be with rewards it could
be in all kinds of ways that an agent is
learning about its environment now
reinforced learning you're excited about
do you think do you think Gans could
provide something yes some moment in in
a well Gans or other generative models I
believe will be crucial ingredients in
building agents that can understand the
world a lot of the successes in
reinforcement learning in the past has
been with policy gradient where you
you'll just learn a policy you don't
actually learn a model of the world but
there are lots of issues with that and
we don't know how to do model-based our
rel right now but I think this is where
we have to go in order to build models
that can generalize faster and better
like to new distributions that capture
to some extent at least the underlying
causal mechanisms in in the world last
question what made you fall in love with
artificial intelligence if you look back
what was the first moment in your life
when he's when you were fascinated by
either the human mind or the artificial
mind you know when I wasn't at the
lesson I was reading a lot and then I I
started reading science fiction there
you go but I got that's that's it that
that's that's where I got hooked and
then and then you know I had one of the
first personal computers and I got
hooked in programming and so it just you
know start with fiction and then make it
a reality that's right
Yoshio thank you so much for talking to
my pleasure
you