SDS PODCAST EPISODE 257: AI: HOW FAR WE€¦ · books on the topic of artificial intelligence, and...
Transcript of SDS PODCAST EPISODE 257: AI: HOW FAR WE€¦ · books on the topic of artificial intelligence, and...
Kirill Eremenko: This is episode number 257 with AI researcher,
Melanie Mitchell.
Kirill Eremenko: Welcome to the SuperDataScience podcast. My name
is Kirill Eremenko, Data Science Coach and Lifestyle
Entrepreneur. And each week we bring you inspiring
people and ideas to help you build your successful
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Kirill Eremenko: Welcome back to the SuperDataScience podcasts
ladies and gentlemen, super excited to have you back
here on the show today. And the guest for today is
Melanie Mitchell, who is a professor at Portland State
University and author of six and soon to be seven
books on the topic of artificial intelligence, and online
course creator, and one of the leading researchers in
the field of AI. And what you should expect from
today's episode is a very chilled, laid back, relaxed
conversation about AI complexity and supporting
topics.
Kirill Eremenko: So we're going to go into a few philosophical areas and
what you'll hear about is complexity, what it is and
how it works, and how it can be seen in different areas
of life from ant colonies, to the human brain, to the
Internet itself. We'll talk about common sense, meta-
cognition, explainable AI, what it is and what the
trade-off is with efficiency of artificial intelligence. We'll
talk a bit about DARPA and military applications of
artificial intelligence and you'll also hear Melanie's
ideas and thoughts on the future of AI, which break
down into two areas which you'll find out in this
podcast.
Kirill Eremenko: So quite a philosophical conversation coming up, and
before we dive straight into it, I'd like to do a shout out
to our fan of the week who is Joseph and who said,
“This series is truly informative. I have just started to
take the first steps in data science and this podcast
not only helps to learn the basics, but keeps us
informed on the latest trends in this field.” Thank you
very much, Joseph. I'm sure you're going to enjoy this
particular episode. And for those of you who haven't
yet left a review, you can head on over to iTunes or to
your favorite podcast App, and leave your comments
there. I'd love to read them and get to know what you
have to say.
Kirill Eremenko: On that note, let's dive straight into it, and without
further ado, I bring to you Melanie Mitchell, a leading
researcher in the field of artificial intelligence.
Kirill Eremenko: Welcome back to the SuperDataScience podcast ladies
and gentlemen, super excited to have you on the show
today because with me, I have Melanie Mitchell calling
in from Portland. Melanie, how are you doing today?
Melanie Mitchell: I'm doing great. How are you?
Kirill Eremenko: I'm well, thank you very much. Super pumped to have
you on the show. I'm very, actually, just as we were
talking before, very excited to talk about all these
topics about your books, about your courses, about
the work that you do, complexity, artificial intelligence,
and all these other areas. Probably to get us started,
can you tell our listeners please, who is Melanie
Mitchell and what is it that you do?
Melanie Mitchell: Right. So, I do research in artificial intelligence and
machine learning, and complex systems. I'm a
professor at Portland State University in Oregon and
I'm also external faculty at the Santa Fe Institute in
New Mexico. And I work on both research and
education, and writing. So, I do a lot of writing and I
have several books on these various topics.
Kirill Eremenko: To be more precise, Melanie has six books and one
more coming out later this year in September, I think
you mentioned. Congratulations. It's so exciting.
Melanie Mitchell: Yes. Thank you. I'm excited about it.
Kirill Eremenko: And in fact, one of your books; Complexity: A Guided
Tour. Won the 2010 Phi Beta Kappa Science Book
Award and was named by Amazon as one of the 10
best books of 2009. Is that right?
Melanie Mitchell: Yes. One of the 10 best science books.
Kirill Eremenko: Best science. Yes, best science book of 2009. Tell us a
bit about that book; Complexity: A Guided Tour. Let's
start with complexity. What is complexity?
Melanie Mitchell: So, complexity is a very broad area that deals with
what are called complex systems, which are systems
that you can say they're more than the sum of their
parts. So think of the brain, for example, which
consists of hundreds of billions of neurons, each doing
some relatively simple operations. But together,
somehow emerging out of that giant system is what we
call intelligence and oceans, and cognition, and all of
that. And so the question of complexity and there's
other complex systems like the economy, the immune
system, insect colonies, people are looking at what are
the commonalities among all of these systems? What
can we say about complexity in general across lots of
different disciplines?
Melanie Mitchell: So my book is an overview for a general audience
about what complex system is, what has been done in
the field, what are the big questions and why is it all
important?
Kirill Eremenko: Interesting. So, what would you say is like one golden
nugget that you can share from your book with us
today?
Melanie Mitchell: So one of the things I talk about is the science of
networks. This is a very general area in which people
look at how networks, that is huge collections of
entities that are linked together in some way. You can
think of a computer network or the brain with neurons
being linked together, or a social network. How are
these networks, structured and is there anything in
common that makes networks in nature and maybe in
technology also work the way that they do? What
makes it resilient?
Melanie Mitchell: And it turns out that there's in the last maybe 30
years, there's been a lot of discoveries about
commonalities and universal laws regarding these
networks. And it's just fascinating that something like
the Internet has some properties in common with the
brain and it has properties in common with
economics. And the question is why? How did these
things come about and how are they resilient? How are
they vulnerable? Yes, and so on.
Kirill Eremenko: Interesting. So it's almost like a template for an entity
that is applied across different areas we see, whether
it's internet and colonies of human brain, but there's
something in common across. And so, by discovering
features in one area, we might be able to see them and
apply them or leverage them in other areas of life.
Melanie Mitchell: Exactly. Right. Yes.
Kirill Eremenko: Very interesting. Somebody recommended me this
book, but I haven't read it myself. Just wanted to get
your opinion on this, have you read "The Square and
the Tower: Networks and Power, from the Freemasons
to Facebook"?
Melanie Mitchell: No, I haven't read that but it sounds fascinating.
Kirill Eremenko: Yes, it would be pretty cool for both of us to read and
talk about it. It sounds like you'd be the perfect person
to discuss it. But anyway, let's get back to your book.
So you have this Complexity: A Guided Tour, and
what's the book that's coming out in September. You
mentioned that that might be the most relevant one for
our audience.
Melanie Mitchell: Yes. That's called Artificial Intelligence; A Guide for
Thinking Humans. And it's a broad overview of modern
day AI, through how do some of the most prominent
systems that we all use or we hear about, how do they
work. What can they actually do versus what are they
claimed to do in the media? How far are we now from
human level AI and what even does human level AI
mean? So the book is really, it combines both
philosophical discussion with actual, getting into the
details of how deep learning works and how programs
like Alpha Go, which a recent program that beat one of
the world Go champions. How does all that work and
just how intelligent are these systems really? So it's
really meant to be an accessible exploration of modern
day AI and some of the big questions surrounding it.
Kirill Eremenko: Also, now, I know the book's not out yet, but is there
anything you can share with us to give us a teaser or a
taster for what to expect inside the book?
Melanie Mitchell: Yes. So one of the things I talk about is the idea of
narrow versus general AI. Do you know what we have?
We've seen a real revolution, you might say an AI over
the last 20 years, where systems, including deep
neural networks, have become incredibly good at
certain tasks like speech recognition, object
recognition in images, playing games like Go and
chess, and so on. But these are all pretty narrow
areas, like Alpha Go is the best chess player in the … I
mean, sorry, the best Go player in the world, but it
can't do anything else, it can't play any other game,
even. It even can't play any slight variation on Go.
Kirill Eremenko: Let alone cooking breakfast or something like that.
Melanie Mitchell: Right. And the question is what would it take to get a
system that would be more general like humans are?
And I think humans often don't even know all the
things that they actually are good at that computers
are actually very bad at. Because things come to us so
easily, like for instance, just having general common
sense, being able to describe what we see in an image,
being able to take something that we learned, like
playing checkers and transfer that to some very
similar games. How does that all work and why can't
current machines do that? That's one of the big things
I talk about in the book.
Kirill Eremenko: Interesting. So when I was learning about artificial
intelligence, what I've found about neural networks
interesting, is that they're designed in a way to mimic
the human brain. But at the same time, they're much
more, as I understand, they're much simpler, much
more basic than even the neurons that we have in our
brains. Would you agree with that and do you have
any additional comments on that?
Melanie Mitchell: Yes, I agree with that. They're inspired by the brain,
and in fact, now they're called neural networks after
all, but there's a lot of important differences. One big
difference is that most of the most successful neural
networks are what they call feed forward. Meaning that
the input goes in one end and it moves through layers
of the neural network in one direction up to the
output, but there's no feedback. Whereas, in the brain,
especially say in the visual system, there's 10 times as
many feedback connections as there are feed forward
connections.
Kirill Eremenko: Interesting.
Melanie Mitchell: When you look out at as some kind of visual scene, not
only is the light coming into your eyes and going…
being processed up through the layers of your brain,
but expectations and knowledge, and emotion, and all
of that is also feeding back to affect receipt. And that's
something that's almost entirely lacking in today's
neural networks. That seems to be incredibly
important.
Kirill Eremenko: Oh, well, how about backpropagation?
Melanie Mitchell: Backpropagation is not the same thing because
backpropagation is a learning method where you look
at the error that a network made on some example
that it was given and then change the weights to make
the output more correct. But that's a one step at a
time learning method. But what I'm talking about is
just not when the network's learning, but when it's
actually doing something like identifying an image as
somebody walking a dog, right?
Melanie Mitchell: See, look out and you see somebody walking a dog.
Okay. You recognize the objects in the image and you
know something about these concepts and the whole,
process of recognition, aside from learning, aside from
backpropagation, that involves a lot of feedback in
humans of things that we already know, things that
we expect to happen, we can make predictions about
what's going to happen next. And that helps us in
making sense of what we see. So perception itself in
humans in the brain is a very dynamic process,
whereas in neural networks, it's a very static process.
Kirill Eremenko: Interesting. Interesting. So sounds like even though
metrics are inspired by the human body, they're quite
away from what the human brain is capable of.
Melanie Mitchell: Yes, that's right. And I think everyone in the field
would quickly acknowledge that's true and say there's
a lot more to be done in the field to make neural
networks more brain-like and there's obviously a lot of
research towards that goal.
Kirill Eremenko: Got you. There's about a hundred billion neurons in
the human brain. How many neurons do we get up to
in neural networks these days?
Melanie Mitchell: Well, I guess you have to have some caveats there. So
there's maybe a hundred billion neurons, but there's
also trillions of connections between them. There's also
other cells in the brain besides neurons that maybe
have a lot of functionality. And the brain also has a lot
of, not only electrical like neurons firing, but also
chemical communication. So it's quite a bit more
complicated than any neural network. I don't know
how many neurons are in the largest neural network
today, but it's almost like comparing apples and
oranges.
Melanie Mitchell: And people sometimes say like, “Oh, in the exponential
growth of hardware, we're going to be able to match
the computational properties of the human brain in 10
years.” But I think that's missing a lot about the
complexity of the brain and how it's wired up, and how
it operates, how its dynamics work. A similar thing
happened with I think the human genome. People
thought that once the genome was sequenced, we'd
understand quite a bit about how living systems work.
But it turns out that the complexity wasn't in the
number of genes, just like the complexity in the brain
isn't the number of neurons, but it's really the inner
connections among them. So there you go with a
network, example of a network.
Kirill Eremenko: Interesting. I get, another network. Yes. Also, sounds
like even if we increase the sizes of neural networks,
there's other considerations that might be necessary in
order to achieve general artificial intelligence one day.
Melanie Mitchell: Yes, absolutely. I don't think there's any controversy
about that, at least in the field. It's not just a matter of
adding more and more neurons and more and more
layers, but there's some other fundamental aspects of
how the brain works, how learning works and so on
that we're really missing in today's neural networks.
Kirill Eremenko: Well, I guess that's good news, especially for
researchers because that's where we get new
inventions coming up all the time. Though by the likes
of the general adversarial networks or the recent
publications by Geoffrey Hinton, or the full world
model, things like that, where people are
experimenting with different approaches that are not
the standard, just grow your neural network size. And
on that note, I wanted to switch to your research a
little bit. Tell us a bit about what is it that you do at
your research? First of all, how big is your research
lab then what do you guys focus on?
Melanie Mitchell: So, I have about six PhD students working with me
and a number of masters students, and some
undergrads. And what we're working on is we're
working on right now vision, how is it that a machine
might be able to make sense of visual input in an
image and not only how to, for instance, recognize all
the objects in an image, but also to have a system that
could make sense of all the relationships among the
objects. For instance, I mentioned the idea of an image
of a person walking a dog. Today's neural networks
can do a good job of recognizing objects in the image.
They could recognize that there's a person, there's a
dog, it might be a leash, it might be in a park with
some trees and so on.
Melanie Mitchell: But it would be… it's often hard for a neural network
to recognize those were the relationships that we
would recognize that yes, the person is actually
walking the dog and they're walking, and they're going
in the same direction and that they're kind of
connected to each other. And in general, this idea of
being able to recognize more complex visual concepts
is difficult. So my work is on integrating deep neural
networks with representations of knowledge. So prior
knowledge that a person might have about concepts
and being able to recognize these more complex visual
concepts in an image or video we're also looking at. So
it's integrating neural network approaches with more
old fashioned AI, more symbolic approaches.
Kirill Eremenko: Okay, interesting. What pops to mind here is that
sometimes as humans, we make… like definitely we're
better at recognizing dogs and people in parks, and
predicting where they're going. But sometimes as
humans we make mistakes in recognition. For
instance, like if you're looking straight and then with
your side vision you see a shadow, sometimes you
might think it's an animal or it's a threat to you, or
something like that. Or there's lots of these visual
illusions where you're looking in the center of an image
and it looks like the image is moving but it's actually
not moving.
Kirill Eremenko: So in that sense, AI might actually be better than us.
So do you think that's a problem in our brain or is
that something that we can leverage in research to
understand better how the brain works? Why does it
make these mistakes?
Melanie Mitchell: Oh, that's a great question. Yes, so humans definitely
make errors. We're susceptible to visual illusions. We
see faces everywhere, even when there are no faces
actually there. We [inaudible 00:24:21] what people
call cognitive biases.
Kirill Eremenko: May I just add one more thing before… while we're on
this, sorry to interrupt. I just remembered, I noticed
this one really peculiar mistake or something that I
was [inaudible 00:24:37]. If you try to look at a
human's face while they're talking upside down, like I
don't recognize people. Like a family video's playing
and I'm lying upside on in the couch, I don't recognize
myself, my brothers, my parents completely. As soon
as I go beyond the 90 degree tilt, it's completely
different people. That blows my mind. Is that just were
not designed to look at people upside down? Is that
why?
Melanie Mitchell: That's interesting. Yes, I think that there's something
very specific about faces in our brains, we are really
attuned to recognizing faces, to looking for faces
because we're such a social species. And so, I think
when you're looking at someone upside down, you're
trying to make sense of their upside down face as a
right side up face, and it doesn't quite make sense. So
I think other objects, we don't have that problem so
much. Faces are just this weird thing that we have. We
have some specific brain hardware specifically for
recognizing faces.
Melanie Mitchell: But what I was going to say about cognitive biases, the
mistakes that we make. So some people have proposed
that AI systems will be better because they won't make
the same mistakes we do. They won't have the same
biases. And that's true in one sense, but in the other
sense, it's not totally clear to me that you can have
general intelligence without having these biases.
Kirill Eremenko: Interesting.
Melanie Mitchell: Yes. And I know I can't prove it, but people talk about
super intelligence, machines that are smarter than
humans in every way and don't have the same biases,
aren't influenced by emotions the way we are. And
therefore, and can read a billion books in an hour and
all of that. I have a suspicion that that's not possible
and that we can't have it both ways. We can't have
general intelligence without some of the biases that we
ourselves have.
Melanie Mitchell: So, I think that's something that's going to be… people
were going to be examining over the next many
decades of trying to understand human intelligence
and trying to get AI. I think that there's going to be a
trade-off between general intelligence and being able to
be unbiased in this sense. So, that's just in relation.
Kirill Eremenko: Very interesting. So have you noticed any of these or
any kind of biases popup in your research so far?
Melanie Mitchell: Oh, that's a good question. There's absolutely art in
some sense, our systems aren't smart enough to have
the same kind of biases that people do but our
systems, they… one of the things that they have is
they have expectations. So because they have some
prior knowledge, so sometimes if my system sees a
person and a dog, they look for a leash, the person
holding a leash sometimes hallucinate it. So that's
sometimes a problem.
Kirill Eremenko: Okay. Got you. Interesting. So tell us a bit about the
world of research. What is they'd like to do research
versus doing applied artificial intelligence in business,
in industries. What are some of the commonalities or
significant differences you would say?
Melanie Mitchell: Yes, it's very different I would say. And usually when
you're having applied, doing some kind of applied
work, you have a very well formulated problem. You
have a data set, you want to cluster it or find certain
communities, say, in a set of data like certain people
who have very similar tastes or something. And you
take some method that you've already had experience
with like clustering and you apply it to this data. You
try and interpret the results.
Melanie Mitchell: So in research it's more like you have to come up with
the question itself and there might not even be any
method that exists that addresses your question. And
your results might end up being completely wrong.
Your hypotheses might be completely wrong and so at
the end of the day, but in research, that's the normal
state of affairs, is that you're wrong. Whereas, I think
in applied research, if you could get a bad result,
that's actually a bad thing.
Melanie Mitchell: So, different people, I've had students who came from
business and just found it, wanted to do something
that they felt was more creative. And I've also had
students that really don't like the open ended nature
of research. They want to do something that has a
more obvious immediate impact and that has a right
answer. So, I think it's not as black and white as I'm
putting it because there's obviously a continuing of
activities that people do between research and
applications. But, I think my constant state of affairs,
it's being wrong and that's what research is, that's
what you are in research and that's okay. That's part
of it.
Kirill Eremenko: Yeah. Got you. It already takes one time to be right to
make a technological revolution, and it's okay if you
had a hundred times wrong before then. Interesting.
And so do you think, do you have application in mind
when you're doing your research or is it research for
the sake of advancing science and then we'll see how
we applied when we get there?
Melanie Mitchell: I think there's both. I mean, I got into the field because
I was really interested in what intelligence is, which is
a broad question. And so, I wanted to understand
intelligence and one way to understand it just by
trying to create it. I can imagine applications for some
of my work, there's all kinds of applications for visual
understanding. And in fact, some of my students have
gone on and worked for companies and applied some
of these ideas. But application, building a system that
other people can actually use for real problems is a
huge undertaking in itself. Even if all the ideas are
worked out, just building a production system is a
huge job that I haven't myself done. So I've been
focusing more on basic research. Yeah.
Kirill Eremenko: Got you. Got you. Okay. All right. And I wanted to talk
a little bit about your piece in the New York Times. So
far, listeners in November last year and New York
Times published a piece by Melanie called Artificial
Intelligence Hits the Barrier of Meaning, and the
subtitle is; Machine Learning Algorithms Don't Yet
Understand Things The Way Humans Do With
Sometimes Disastrous Consequences. Could you give
us a quick overview of what this piece is about and
what prompted you to write it?
Melanie Mitchell: So this piece is… it was in response to a lot of the
media coverage on AI that we've seen. We see
headlines such as Machines Are Now Better Than
Humans At Object Recognition or Machines Have
Surpassed Humans At Reading Comprehension, seen
that kind of thing. And these different views of
machines are better than humans at playing the
world's most difficult game, Go. And so, these are all in
some… you could argue that these are true because
the machines have surpassed humans on some
particular dataset, benchmark data set.
Melanie Mitchell: But it's not really true in general because the
machines, they can do things like translate from one
language to another. We've seen translation programs
say or recognize speech really well, but they don't
understand in the sense of human understanding
what… they don't understand their inputs are their
outputs. And the reason why it can have bad
consequences is that it turns out that this makes the
machines fairly fragile or some people call it brutal,
meaning that they do really well as long as they have
the kind of data they've been trained on. But if the
data changes just a little bit, they can completely fail.
Melanie Mitchell: And also, the people have shown that they're now very
vulnerable to hacking. I don't know if your listeners
have seen these what's called adversarial examples. A
hacker could change an input say to a speech
recognition program just a little bit in a very targeted
way, change the audio signal. And it would not sound
any different to a human, but the machine would
interpret it as something completely different and
possibly something that you might not want the
machine to interpret it as.
Kirill Eremenko: Or that example with the stop sign and a few stickers
on it can make the machine see it's like a 60
kilometers per hour speed limit.
Melanie Mitchell: Exactly. It turns out that these systems are very
vulnerable in vision and language, in playing games
even, face recognition. And so, if we start having broad
applications of these systems that have this kind of
fragility, and I argue in the piece of the fragility is
precisely because they don't understand in the sense
that we understand these concepts, it can have
dangerous consequences. And we've already seen that
in face recognition, for example, where systems can be
fooled pretty easily. And now certain organizations are
using face recognition as a critical security method.
Kirill Eremenko: Like iPhones right now.
Melanie Mitchell: iPhones and I think some police forces are using face
recognition as a way to catch fugitives or spot
criminals, whatever. But it's not very robust because
the system doesn't have the same kind of
understanding of the world that we have. And so, that
was the point of the piece. It's a cautionary note on all,
the AI Revolution, it's real. AI has progressed a huge
amount, but it's still quite fragile in a sense because it
hasn't progressed enough in some sense.
Kirill Eremenko: Interesting. On that whole notion of people being able
to fool AI, I was listening to a talk by Ben Taylor
recently on YouTube and I think it was in that talk
that I heard the notion that as long as we have people
who are in combating criminals who are trying to
create algorithms that are smarter than what hackers
and other people with malicious intent are trying to
create, we're always going to do… it's always like a
double sided coin. You have people creating these
protective algorithms, but that means the knowledge
about how they work and about where they're going is
out there.
Kirill Eremenko: And not to say that those same people are going to go
out and be malicious, but it's the knowledge is out
there and it's potentially accessible. And that means
that somebody can always be a step ahead anyway.
And so, as long as we protect ourselves from hacks
and all these malicious events, the more we do that,
the more stronger and sophisticated the hacks and
malicious events are going to become anyway.
Melanie Mitchell: Yes, I think that's probably right. But then there's a
biological analogy in that, all living things are attacked
by other living things. There's biological arms races all
over the place. So, we humans have these very
complex immune systems that protect us from most of
the things that attack us, but not everything, of
course. No, they're not perfect. But the state of AI right
now is that it's ridiculously easy to attack these
systems. And even without attacks, they're quite
fragile. Even if they're not being attacked, they run
into some situation that they haven't been trained on
that they have a problem.
Melanie Mitchell: And sometimes we'd like to get AI to a part of general
intelligence I think is being more robust to attacks of
any kind. And acts are never going to go away, but
living intelligence system seemed to be more robust
than our current AI systems to being fooled or being
attacked in these ways. And so, we'd like to just
increase the amount of robustness.
Kirill Eremenko: Interesting. And so, do you think that how can we and
on the other hand, can we even make machines
understand meaning better?
Melanie Mitchell: Yes. That's an open question. So one of the big things
that people talk about nowadays is common sense,
that's become a buzzword in AI. People say one of the
problems with AI is it doesn't have common sense and
common sense can mean many things. But one of the
things that people mean is that we humans have vast
knowledge about the way the world works and that
knowledge is used in our perception of things that
occurred in our lives. We know that if you drop
something that's made of glass onto a tile floor, it's
going to shatter. We learn all kinds of things like that,
people call intuitive physics or also intuitive
psychology, how people are going to react.
Melanie Mitchell: I know that if I drop a piece of glass onto a floor and it
shatters, people will be startled. And we learn all that,
some of it is innate, probably, some of it is learned
when you're very, very young. And how do you get
machines to have this general understanding of the
world? And there's a lot of funding now. DARPA, for
instance, which is one of the biggest funders in the
U.S. of AI has a big program called Machine Common
Sense where their goal is to get machines to have the
common sense of an 18 month old baby. And that's
seen as a grand challenge now. And that's part of the
effort to making machines understand the meaning of
the situations they encounter.
Kirill Eremenko: DARPA is part of the military, is that right?
Melanie Mitchell: That's right. The Defense Advanced Research Projects
Agency.
Kirill Eremenko: Interesting. So what are your thoughts on
government's investing more and more funds into
defense in the space of artificial intelligence? And has
that got any dangerous consequences in your mind?
Melanie Mitchell: Yes. I mean, at least in the United States, the Defense
Department has always been the biggest funder of AI.
And DARPA has been one of the biggest funders in the
defense world and in fact, they have set up their grand
challenges for AI that have really pushed the field
forward. So, it was their grand challenge for
autonomous driving that's really pushed the whole
field of self-driving cars. Their grand challenge on
speech recognition that really pushed to the advances
in speech recognition. So they've done a lot of great
things for the field. They really pushed it.
Melanie Mitchell: On the other hand, I'm quite worried about military
applications of AI, especially autonomous weapons
that would presumably make decisions about who to
kill or what thing to bomb, and without any human
input. That's something that I think the military would
like to have but I think it's very dangerous for the
same reasons that I talked about in my New York
Times Op-Ed. That these systems, they don't have the
same understanding that we have, and I think that
presents a lot of danger.
Kirill Eremenko: So like what's an example of that? What's an example
where a system doesn't have the same understanding
as we have? Even though like we've programmed it,
we've created it and we are quite sure that is going to
do as we've told it to do, as we've pre-programmed it.
Do you have any examples where that could back and
backfire?
Melanie Mitchell: Well, one of the problems is that we… I mean, what
you said, we pre-programmed it, but the way that
systems, the most successful AI systems work today is
that they learned from data. We don't program them.
They learn from huge amounts of data. And we don't
understand how they make their decisions because
the system consists of some deep neural network with
millions of weights and it doesn't explain itself. So it
can't explain to us why it made the decision it made.
Melanie Mitchell: Just like the example you gave with the stickers on the
stop sign, why did the system think that that was a
speed limit sign instead of a stop sign? Well, it can't
really explain why and people are still trying to figure
out how these adversarial examples fool these
networks, they don't totally understand it. And so, we
have these systems that work… seem to work really
well on the data that we test them on, but we don't
understand how they work or and we also can't predict
where they're going to fail.
Melanie Mitchell: That's another question in the whole field of AI to have
more explainable AI systems that can explain their
reasoning, and that's very difficult. That's something
we had in the old days of AI when you had expert
systems and they could explain because they used
human programmed rules, but they didn't work very
well. And so, now we have these systems that work
much better but they're much less explainable.
Kirill Eremenko: So this is like a trade-off, right? Like if we want them
to be explainable, we're risking of stifling AI growth.
Not as stifling, but, there's always going to be, due to
the nature of competitive markets, there's always going
to be countries or companies that are developing not
explainable AI and they're going to get ahead. Is that
about right? Like at the moment, is it a trade-off
between explain ability and efficiency?
Melanie Mitchell: Yes, I think that it can be. And there's also a
philosophical question of what does explainability
actually mean? Like, so for instance, the European
Union now has some laws about-
Kirill Eremenko: GDPR, we all love GDPR.
Melanie Mitchell: Yes, it has some laws. And one of the things in that is
the right to an explanation, I think it's called. Or like a
computer system tells me that I can't have a loan that
I applied for. I have a right to an explanation, but what
does that even mean? What's an explanation? You
know? So, if I tell you all of the weight values and my
neural network, is that an explanation? Well, not
really because a human can't understand anything
about it. But it's not clear what constitutes an
explanation. So, think that's what kind of a
philosophical issue that [crosstalk 00:47:43]-
Kirill Eremenko: What would you say expandability is? You are one of
the leading researchers in this field. If anybody, you
should have the answer.
Melanie Mitchell: I think it explainability as we know, depends on, it's
kind of a social construct. I'm going to explain
something to you. I have to have some theory of mind
of you. I have to have some model of what your prior
knowledge is, what level of explanation you're looking
for… And it's really a social thing, I think explanation,
and that's something that we don't really have with
machines, is that whole social component. The
machines don't have any intuition about the people
that they're dealing with, or how to explain…
explanation is very context dependent, let's say.
Kirill Eremenko: Interesting. So, basically we need another AI to explain
AI to humans. I mean explain that.
Melanie Mitchell: That mean something that people are working on, is
what people call metacognition, which is cognition
about cognition. So, understanding your own cognition
enough to explain it to someone.
Kirill Eremenko: Interesting. How far ahead are we on that front?
Melanie Mitchell: Not very far. And people aren't always good at this
either.
Kirill Eremenko: Yeah. Oh, that's so true.
Melanie Mitchell: People will give explanations that really have nothing
to do with the real reason.
Kirill Eremenko: Yeah. I've heard that and I've probably done that many
times myself.
Melanie Mitchell: You don't even know that you're doing that, but people
will… It's been shown many times in psychology
experiments that people rationalize a way things that
they did after the fact. And don't even consciously
know why they did a thing.
Kirill Eremenko: Melanie, I also had this, a recent revelation and I
wanted to run this by you. With humans, I always
thought that like our brain is the main source of all of
the thoughts and actions and so on. And then like,
and then that goes down the body and the rest of the
body is just mostly for executing and surviving and
keeping, keeping the brain running. And there's this
kind of like a show, kind of like a cartoons being
around for a while. It's called Futurama. Have you,
have you seen Futurama?
Melanie Mitchell: Yeah, I've seen it.
Kirill Eremenko: Yeah. So, they have this one character, I think it's, the
preserved Richard Nixon from back in the day, but just
his head, then they put him on the robot and then he
moves around and like, and can think and so on, and
kind of like, that's pretty cool. But what I learned
recently is that a lot of our emotional state is actually
directly connected. There are nerves that go straight
from the core of our brain to or go to the core of our
brain straight from our intestines from stomach and
other smaller, larger intestine and so on. So, basically
your gut flora affects directly how you are feeling and
what mood you're in.
Kirill Eremenko: So, it's actually a much more complex structure than
just the brain itself. And with that in mind, will
machines ever like, even if we were able to recreate the
brain, there's so many other aspects to human
emotion and cognition understanding, meaning, will
machines ever be able to understand this or once we
do create them and give them that capacity to see
meaning, they will just never be able to relate to the
same way that we do to events and objects and things
that they see and hear and experience?
Melanie Mitchell: Yeah. I don't know the answer. I think it's a good
question. There's a branch of AI, it's called embodied
cognition. Which the hypothesis is that it's ridiculous
to think of this idea of a disembodied brain, which is
what most AI systems are, without having a body-
Kirill Eremenko: Thank you for putting into scientific terms, what I
tried to just describe.
Melanie Mitchell: Yeah. I mean it's completely valid. I think there's a lot
to it that we don't realize. People, we see the brain as
being this central processing unit and everything else
has kind of peripheral, but it's really not correct,
because biology figures out more and more about the
complex systems. That is the body, we're going to see
that there's so much more to thinking than just
neurons firing in the brain. I think you're absolutely
right.
Kirill Eremenko: Interesting. So would you say that the… I'm just
curious to get your stance on the whole issue. Some,
researchers and scientists say that AI, general AI, as
soon as it gets here it will be a massive help to us and
save lives and help us invent things and propel the
humanity forward. And others say that one's general
AI, gets here, it will completely not understand
humans and think that we are a plague on this planet
and wipe us out. What, what, what are your thoughts
on these two?
Melanie Mitchell: I think we're very far from understanding what general
intelligence is. So, it's really hard to say what general
AI would do or wouldn't do, or be like. I think we
underestimate the complexity of intelligence, our own
intelligence, which is why we think that a lot of people
think that general AI is imminent. I don't know the
answer cause I don't really think we understand
enough about intelligence to say what would happen. I
think there are dangers that we should be aware of.
Melanie Mitchell: But one of the things I quoted in my Op-Ed was, Pedro
Domingos, who's an AI researcher from University of
Washington. He had a book where he said, “The real
danger… ” I can't remember the quote exactly, but it's
like, people say that AI is going to get super intelligent
and take over the world, but the real problem is it's
actually too stupid and it's already taken over the
world. We trust too much in AI that's not smart
enough. Rather than being faced with the danger of
too smart AI.
Kirill Eremenko: Interesting. Very interesting quotes. When you think
about it, the technology that we use is already the
extension of our lives. Like we look at our mobile
phones like 150 times per day.
Melanie Mitchell: Yeah.
Kirill Eremenko: It's harder to imagine walking outside the house
without your mobile phone. It's ridiculous.
Melanie Mitchell: Yeah.
Kirill Eremenko: Very interesting world we live in. Melanie, on that note,
I actually had just one more question for you. From,
from the perspective that you have and from what
you're seeing at the like at the forefront of artificial
intelligence. Are there any, or is there any
recommendation you can give to all listeners who are
data scientists, aspiring data scientist or business
managers and owners? What's to look into what to be
prepared for in the future of AI in the coming one, two,
three, maybe five years at most?
Melanie Mitchell: There's a couple of things. One is that, the whole
connection between AI and cyber security is getting,
more and more strong that, that AI, as it gets more
capable and more broadly deployed, becomes more
vulnerable to attacks. And that's something that
people are just beginning to grapple with. And some of
the cyber security people have been talking about this
for many years, but I think people in sort of the real
world of AI applications are just beginning to grapple
with the security implications.
Melanie Mitchell: Another thing is that, that I think there's going to be
the next set of advances is probably going to be
around what's people call unsupervised learning. You
know, AI today, it's mostly done by having the system
be trained on millions of examples. And, the examples
have to be labeled by human, as to what their category
is. And that's not very sustainable, because in a lot of
cases hard to get a lot of examples like that. So, we
have to get systems that learn from data, but without
the data being carefully labeled by humans. And that's
as [inaudible 00:57:19] called unsupervised learning,
the quote dark matter of AI.
Kirill Eremenko: That's a beautiful quote.
Melanie Mitchell: Yeah, it has to happen. We have to figure out how to
successfully train systems in an unsupervised way.
But right now no one really knows how to do that very
well. So I think that's actually going to be an area
where there's, we'll, we'll see a lot of progress soon.
Kirill Eremenko: Got you. Thank you. So, cyber security and
unsupervised learning for those listening. Also
Melanie, well on that note, we've slowly approach to
end. Thank you so much for coming on the show. And,
before I do let you go, please let us know, what's the
best ways for all listeners to get in touch and follow
your work?
Melanie Mitchell: So, they can go to my, my webpage. I don't know if you
have a, you can put that on-
Kirill Eremenko: Yes. We'll put that in the show notes. Of course.
Melanie Mitchell: Yeah. And that has my contact information and all of
my papers and everything, so that's probably the best
way to follow them. Follow my work.
Kirill Eremenko: Awesome. Okay. You also have Twitter? I believe.
Melanie Mitchell: I have Twitter. That's right.
Kirill Eremenko: Okay. LinkedIn as well?
Melanie Mitchell: And LinkedIn. Yeah.
Kirill Eremenko: And you mentioned before the start of the podcast, you
have a course in the Santa Fe Institute, about
complexity and it's free. Tell us a bit more about that.
That's, that's a course that anybody can take?
Melanie Mitchell: Yeah. The Santa Fe institute has, an online
educational platform called Complexity Explorer.
Maybe you'll put that in the course notes.
Complexityexplorer.org.
Kirill Eremenko: One word?
Melanie Mitchell: You can put that in the show notes. One word,
complexityexplorer, and then .org. The site has many
courses and tutorials related to complex systems. My
course, I have an online course, they're called
introduction to complexity, which is, has no
prerequisites, anyone can take it. And it's a pretty fun,
easy way to get an introduction and an overview of
complex systems. It's kind of based on my complexity
book and I'm hoping to do one of those on AI as well.
But that's, that's for the future.
Kirill Eremenko: Awesome, fantastic. Of course, guys, look all it's for
the book, the new one. The one, the one that we
mentioned today was… We mentioned two books,
right? So, one is the existing book, Complexity: A
Guided Tour. And the new one, Artificial Intelligence: A
Guide for Thinking Humans, that's coming out in
September.
Melanie Mitchell: Yeah.
Kirill Eremenko: Okay. On that note, Melanie, thank you so much for
being on the podcast. I really appreciate your time and
you sharing your knowledge of [inaudible 01:00:13].
Melanie Mitchell: Oh, it's been great. Thank you so much for having me.
Kirill Eremenko: So, there you have it ladies and gentlemen, that was
Melanie Mitchell, professor at Portland State
University and one of the leading researchers in this
space of AI. What a podcast and how many different
resources that were mentioned today. First of all, my
favorite part of the podcast was probably the whole
notion of complexity and never understood it as clearly
before, but indeed it looks like there are lots of
commonalities between different systems around the
world, starting from ant hills to the human brain to
the internet and many more. And it's very interesting
to learn more about that.
Kirill Eremenko: And speaking about learning as Melanie mentioned,
you can get free access to her course on complexity if
you head on over to complexityexplore.org all one
word. Plus of course I make sure to check out
Melanie's books. She's got six of them and the seventh
one is coming out this September, in 2019 and that
one is going to be about artificial intelligence.
Kirill Eremenko: As usual you can get all of the links and the materials
that we mentioned. I know it might be hard to keep
track of all of them in your mind right now, but don't
worry, you can just head on over to
superdatascience.com/257 that's,
superdatascience.com/257, where you will find all of
the links and materials mentioned on the show,
including all URLs to Melanie’s LinkedIn and Twitter
where you can follow her. Her course and her books
and plus you'll get the transcript for this episode if
you'd like to check it out.
Kirill Eremenko: On that note, thank you so much for being here today.
I hope you enjoyed this chat. Don't forget to leave a
review on iTunes or wherever else you're listening to
this podcast, and I look forward to seeing you back
here next time. Until then, happy analyzing.