Ever since the onset of computing, humans
have been trying to define and then
simulate human intelligence, and by virtue
of doing so we've come up with this war
chest of algorithms and techniques that
mimic various facets of human
intelligence (and sometimes biological
and natural intelligence) and this war
chest of algorithms is effectively
called AI.
Typically you pose the problem
to the algorithm and the algorithm comes
up with a solution or model for the
world, and so we don't have a very exact
definition...but that also is the case for
intelligence itself.
I think it's going to percolate through
various different industries and its
applications, especially in sensing the
world and modeling the world is going to
get more and more pervasive in certain
autonomous situations where the
frequency of decision-making is high,
AI can be used - and increasingly we'll
see applications of that, but you know,
it's difficult to come up with an
application area where AI does not have
a role to play, either in augmenting, you
know, humans, or in even replacing them.
So
I think as our techniques become more
and more powerful, we're able to use them
in more and more places, and there's a
lot of interest as well and pull from
different industries in different
domains and that will also help spread
the use of AI techniques.
For me, the story was, you know, I like to
figure things out, so after I felt like I
figured out how computers work then I
got interested in figuring out how the
brain works, and once I felt like I kind
of had the essentials of that figured
out, then I was interested in how
nature can produce something as complex
and powerful as the human brain or life
itself - and inevitably you look at things
like evolutionary computation as the
underlying mechanism for improving and
adapting and that's where - that's how - I
ended up in that field.
Evolution can be used in blackbox
optimization where you don't really have
to have a very good model or definition
for what this solution looks like, and
you might not have a very good feel for
what the search space of possible
solutions looks like, either, and so
evolution does a very efficient job of
finding acceptable solutions in very
large search spaces.
It also allows for
optimization against multiple objectives:
you're not just solving for one
objective, you can actually have a
combination of objectives, and by virtue
of doing so it gives us a sense of what
is possible; it gives you a spectrum of
good solutions, so for all of those
reasons evolution is a great method for
tackling complex problems in today's
world.
Not only do we have evolutionary
algorithms that scale very well, but we
also now have the means and compute
capacity to run them at very large scale,
so we can tackle problems that are quite
complex in this manner and problems that
are difficult to - difficult for us to
design solutions for.
So for that reason
we initially were quite interested in
this approach for trading in the stock
market, and as we came up with good
solutions there we realized that the
approach is generalizable to other
complex problems as well, so we looked at
other areas - and, namely, digital marketing.
We've also, in research, we've applied it
to a number of other areas, such as cyber
agriculture and healthcare and such.
Digital marketing includes a decision
loop that is all contained and automated,
and there's a lot of data that can be
collected that can help direct that
decision making from the moment where
you engage a user until you take them
through that journey to whatever you're
trying to convert them on, you know,
you can - that journey is
digitized and can be measured.
For
example, at the simplest level, you know,
if the end user gets to the button
you want them to click and whether or
not they click on that, that's a measure
that you have, and you're talking
about large audiences - a population of
users following that journey, whether or
not they actually react to an ad or
whether or not they end up buying
something in an e-commerce site - and so
you have, you can capture that
variability in an adaptive manner.
In
other words, as the world changes and
as the preferences of your users change,
if you have an AI system that can adapt
to that, then you can, you know, maximize
the conversions in a digital journey -
digital marketing journey - so for a
number of reasons it is interesting, you
know, as opposed to, for example, you look
at, you know, healthcare, and how, you know,
in biology, things are not as
deterministic; it's difficult to try
different things out, there's
implications to that: you're dealing with
human livelihood.
And, you know, you might
not have as large a population to
draw from, to learn what your search
space of solutions look like, so
just contrasting that to
other domains, digital marketing seemed
like a very natural next area for us to
focus on, And if you think of where we
started - which is in trading - it's very
similar again in trading, you kind of
have many examples and
there's a lot of data and historical
data that's collected, and the fitness, if
you like, is built in: if you do well, you
get, you know, you make money, if not,
you don't.
And it's kind of multi
objective: you want to do that
consistently, so that consistency is
another objective that you have in there,
but the entire decision loop is
contained in the problem, and it can be
measured, and you can try things out and
it calls for an adaptive solution
because, you know, the world in trading or
in digital marketing six months ago or a
year ago is probably a very different
world than where it is today.
So you want
to be able to keep up with the
underlying models and laws governing how
people react to digital marketing or how
markets operate.
The one that was the most surprising was
when we applied evolution in a blackbox
optimization setting for cyber
agriculture, where the problem was coming
up with recipes for growing basil in a
completely controlled environment.
And it -
the system - ended up discovering things
that we already knew, which is the larger
the plant, the less tasty - and you can
measure taste through measuring
volatiles in the leaf - but also it ended
up discovering something we didn't know,
which is that basil does not really need
to sleep, and if you shine light on it
24/7 it kind of thrives, so that was
something that was unexpected.
And the
evolutionary system discovered that.
I mean, deep learning is deep learning,
evolutionary computation is evolutionary
computation, but in its power to adapt
and its power to scale it's quite unique.
And so - and the fact that it's a
generalized solution - so you can actually
apply it to many, many different problem
domains, so it has characteristics that
are analogous to deep learning.
I don't
think they're exclusive, and often
actually in our application areas we use
them together.
For example, we evolve deep learning
architectures to come up with better
deep learning solutions, but I think in
that statement what is trying to be
conveyed is the fact that the same level
of attention that is afforded to deep
learning, I think, is justified to be paid
to evolutionary computation.
To both
being techniques that were invented, you
know, 30, 40 years ago, and showed promise
in the lab but were not scaled - did not
scale that well - and today they do scale,
so in that regard, yeah, I do believe that
the time has come for the world to pay
attention to evolutionary computation.
Yeah, we do need to make evolutionary
computation more accessible, and, you know,
educate people as to how to use it, you
know, put some of our platforms out in
the cloud and show how easily you can
actually use them to tackle, you know, a
spectrum of different problems.
So I
think we've seen the power of
evolutionary computation.
I think there's a next step there,
which is to kind of get it in the hands
of developers and data scientists and
show areas where it does quite well and
allow them to use it.
So that's one area.
Evolutionary computation itself has many
different incarnations many different
facets: there are, there's a lot of
research that's going on - it's still a
fraction of the research that's going on
in data science, so we do need to get
more funding into research of
evolutionary computation.
The talent, you
know, if you think there's a shortage of
talent along around deep learning in the
world, you know, there's a very small
fraction of that available talent for
evolutionary computation.
So that's more
on the practical side; on the science
side of things there are still problems
in, for example, genetic programming, and
and you know, generalization of
evolutionary computation baselines,
coming up with good benchmarks.
The field
needs to evolve and mature and hopefully
with the attention that it's starting to
receive increasingly that we're going to
get there.
Yeah, evolution can be deceptively simple,
but there are a lot of hyper parameters,
just like deep learning, that you can - if
you don't set properly, you might not get
the results that you're looking for.
At
its essence it's very simple to describe -
same thing, actually, for deep learning in
neural networks, as their essence is
simple to describe but the science and
technology has come a very long way, and
so the state-of-the-art and best
practices do take a while.
So the first
thing I would ask is to be patient with
it, and hopefully as they realize the
power of it, it will give them the
patience to learn and master it to
the point where they can actually get
the benefit.
To democratize AI we have to make it
simple to use.
It's starting to happen a
little bit in certain areas; if you want
to do image recognition, or object
detection, or speech recognition, and so
forth, there are libraries now and
there's off-the-shelf models that
you can use.
I think in areas such as
natural language and conversational
systems increasingly there's available
resources that, again, off the shelf you
can use and plug into your system; you
can create a chatbot pretty quickly.
I
think that there will always be a need
for some level of data science that goes
into tackling more complex problems in
order to pull from the various different
techniques.
I mean we talked about
evolutionary computation, but there are
techniques that are quite relevant that
are not as popular or don't have as sexy
a name (like deep learning) but things,
like, you know, random forests and
Bayesian methods and so forth, that are
very well suited for certain problem
areas.
So, you know, that's where a data
scientist comes in: to actually look at
that available warchest and pick the
ones that apply for a certain problem
area.
So I think that, just as 30 years
ago,
you know, people talked about
computerizing different enterprises, and
there was a computer department, and
people came in and looked at processes
and started computerizing things - today
that's kind of laughable, you know, we are
all computer scientists and we are all
computer users by those standards and
everything is more or less computerized.
In, at least, our fields, I think the same
is gradually gonna happen with AI, and we
won't even call it AI anymore - at that
point we will know
what kind of AI to use for what kind of
problem, and we'll just apply it.
Should
be as easy as that.