Welcome to Episode #227 of CxOTalk.
I’m Michael Krigsman.
I’m an industry analyst and the host of
CxOTalk.
I want to thank LiveStream for supporting
us with just really amazingly great video
infrastructure and streaming.
Today, we have … Oh!
And I forgot to say that if you go to LiveStream.com/cxotalk,
they will give you a discount.
So, do that.
Livestream.com/cxotalk.
What an amazing show we are going to have
today!
When we talk about the future of computing,
there’s a risk that it’s going to sound
like a pretentious topic, but today, with
the two guests we have, it’s actually a
realistic and very fascinating topic to discuss.
We’re going to be speaking today with Anthony
Scriffignano, who is the Chief Data Scientist
of Dun & Bradstreet.
Anthony has been a guest here a number of
times in the past.
Anthony, how are you?
How are you doing?
I’m doing very well, Michael.
Thank you very much.
Well, thank you for being here again on CxOTalk.
And, we’ll also be speaking with Stephen
Wolfram, who is truly one of the fathers of
modern computer science.
And, I don’t think that’s an exaggeration.
Stephen Wolfram, thank you!
This is your first time here.
Thank you for being with us!
Thank you!
So, just to jump in very quickly with some
brief background introductions, I’ll ask
Anthony just to tell us who you are and what
you do.
So, very, very quickly: I’m the Chief Data
Scientist at Dun & Bradstreet, and in my role,
I’m responsible for looking at technologies
and capabilities that are sort of on the edge
of computer science – hence, our interest
in the topic today – not necessarily things
that are common practice, and sometimes not
even things that have words to describe them.
And then also, I work with governments around
the world as they develop legislation around
things like data privacy and data localization,
and so forth, to share best practices and
make sure that we're doing the right thing
for the community at large, and the business
community.
Fantastic!
And, Stephen Wolfram, please tell us a little
bit about yourself.
In a way, you need no introduction, but I
think an introduction is always good.
Well, I’ve kind of alternated between doing
basic science and developing technology for
the last many decades.
I run Wolfram Research, a company I started
30 years ago, and we’ve mainly done three
things: Make a product called “Mathematica”
that gets widely used in research and development
and education; about 90% of US universities
now have licenses for it.
We make a thing called Wolfram Alpha, which
is a system that answers questions, and provides,
for example, the knowledge system for things
like Siri; and most recently, we’ve been
sort of owing all that technology into a thing
we call “Wolfram Language,” which is kind
of a new generation computer language that
has the main objective of sort of building
in a lot of knowledge right into the language
so that it provides the highest level possible
platform from which people can build things.
And the exciting thing for us, in most recent
times, is kind of the deployment of Wolfram
Language not just in our traditional research
and development, and consumer spaces, but
also very much in the enterprise space, and
for software development purposes, and so
on.
Okay.
So, thank you so much.
To begin, I think if we’re going to talk
about the future of computing and as Stephen
and Anthony were saying just before we came
live, it’s a very big topic to talk about
in 45 minutes.
Maybe, we can begin with artificial intelligence,
because it’s such a popular topic today.
And, Anthony, how about you take a stab initially
to define some terms so we have common ground.
When you think about AI, how do you define
it and think about it?
Well, first, I would say that there isn’t
one commonly-accepted definition.
Second, I would say that there’s often very
little intelligence in artificial intelligence.
There are different types of technologies
and capabilities.
Many of them share the fact that there’s
some type of goal that they’re trying to
reach using different methodologies.
Some of them are what we call "regressive
methods," things like machine learning where
they look backward at data that has pre-existed
– the incidence of the application and trying
to project that forward into what happened.
Some of the non-regressive methods are what
we call "neuromorphic methods," these are
methods that are designed to mimic how we
think the brain works.
And then, more recently, we've seen things
like cognitive computing, which are methods
that work alongside an intelligent user to
help that intelligent user to reach that goal
in a more efficient way and learning from
that behavior watching that user.
The other thing I would say about artificial
intelligence is that there is a lot of technology
that underpins it that people normally lump
in; for example, Natural Language Processing.
Some would argue that there are elements of
artificial intelligence in that, and I would
agree with them, but that is not an inclusive
definition.
So overall, some sort of goal is usually not
one that the machine will modify.
Be very afraid when they start doing that.
And then, either a regressive method or a
non-regressive method, and then either working
alongside a use, which is sometimes called
“heuristics,” or working separately and
giving the user the answer.
That’s my shortest possible definition that
I’m comfortable giving in this form.
Stephen Wolfram, how can we make this understandable
to the average person who’s not a computer
scientist, but who wants to understand what
all of this actually means and the implications
of it?
Well I think, artificial intelligence, as
now discussed, and I’ve watched its evolution
over the course of nearly 40 years now, it’s
really an extension of a long-running story
of technology now, which is “How do we take
things which we humans have known how to do,
and make machines do them for us?”
And typically, the pattern is, in the end,
we humans define the goals that we’re trying
to achieve, and then we want to automate as
much as possible getting those goals done.
And over the course of the last few decades,
there have been all kinds of things where
people have said, “Gosh!
When machines can do this or that particular
thing, then we’ll know that we’ve really
achieved artificial intelligence.”
It’s always a bit disappointing because,
in the end, when one thinks it’s sort of
something that’s truly a special human thing,
in the end when it gets done by machines,
it’s just code underneath.
So, a great example of that is what we did
with Wolfram Alpha, where we're able to answer
all sorts of general questions about the world;
you know, "What was last year's revenue of
IBM," or let's take the GDP of such-and-such
a country and compare it with the GDP of some
other country, or figure out if your uncle's,
uncle's, uncle's son, what relation to you
is that?
These kinds of natural language questions
that one asks and then being able to answer
those questions on the basis of knowledge
that our civilzation has accumulated, that
was one of the kinds of characteristic, "When
you can do this, you've got AI," kinds of
directions.
And then, when we brought out Wolfram Alpha
eight years ago, it was like, "Okay, we can
now do a pretty good job of this," and if
we look at how it was done, part of it is
we're kind of leveraging all of the knowledge
that our civilization has accumulated and
turned it into something that a computer can
deal with.
Making it compute-able.
That's a large part of it.
There were some other things that did come
more from a sort of basic science point of
view of being able to understand natural language.
It wasn't obvious it was going to be possible
to understand the typical random things that
people ask their phones and so on.
that had been a problem people had been working
on for a long time, turned out - I hadn't
really realized this – but it turned out
the key extra ingredient that one needed to
do good natural language understanding was
not just being able to pick apart pieces of
English or whatever language one's dealing
with, but also having a lot of actual knowledge
about the world, because that's what allows
one to determine if somebody says "Springfield,"
for example.
You have to realize, "Well, which Springfield
are they probably talking about?"
Well if you know about all the different Springfields
and you know what their populations are, how
popular they are on Wikipedia, where they
are relative to the person asking the question,
then you can figure out what they're talking
about.
But without that underlying knowledge, you
can't really do that.
Maybe, yeah, if I could just jump in on what
you just said, Steven, because there are some
really interesting things in there that I
would love to unpack.
The first thing is, you know, last year’s
revenue at IBM.
At Dun & Bradstreet, we obviously look at
things like that and it’s easy enough to
answer for a public company that files 10-Q’s
and 10-K’s, and when you start asking about
Bob’s Hat Company, or you start asking about
a company in China and you maybe want to ask
the question in English, and the answer to
the data is in Chinese, it starts to get complex
for reasons that transcend Natural Language
Processing.
We have laws in different countries about
what data can cross borders.
Those laws are constantly changing.
There are privacy laws; there are considerations
about data sovereignty, where the data lives,
and so forth.
And so, it’s no small feat that you’re
talking about.
You’re making sound easy, but the orchestration
of curating all of that data in different
places, and then orchestrating an answer in,
I’ll use the phrase “real-time” in quotes,
because there’s no such thing, but orchestrating
the answer in an amount of time that we’re
comfortable with, is no small feat.
None of us would believe that the truth is
out there on the internet all the time, and
yet sometimes we behave that way.
And so, just adjudicating the truth is another
challenge in there.
It’s sort of as you unpack this thing, you
get more and more surprises, and it becomes
a more curiouser and curiouser world.
And so, part of it is making it look more
intelligent, and part of it is giving it an
intelligent, empirical answer that you can
scale and reproduce and learn from.
Those all kind of boil together in what you’re
talking about.
You know, maybe one thing I might discuss
is the question … In the last few years,
one of the big excitements has been the whole
kind of deep learning/neural networks business.
And maybe we should talk a little bit about
how that compares with other things that really
are the mainline artificial intelligence that
people like Anthony and myself make use of
all the time in the systems that we build.
So, what’s happened; the story of neural
networks, which are kind of idealized models
of how brains might work, that story starts
in the 1940’s.
And the models that we’re using today are
pretty much models that were invented in the
1940’s.
For example, as part of sort of basic science
that I did, I worked on these things back
in the early 1980’s, and I tried to make
neural nets that would do interesting things.
It completely failed.
And, then over the course of time, it’s
just a few years ago, it finally got to the
point where it was possible to have powerful
enough computers, large enough training sets
that all the things that we’ve been trying
to do for years actually started to work.
And I think that the thing that's worth understanding
about a neural-net-type story for computing
things is it's all a question of did a programmer
visibly write the code, or did the code somehow
automatically get produced?
And actually, as a result of some of the basic
science that I've done, I got very interested
in the question of, "If you want to find a
program to do something, how do you do it?"
Do you have to get a human to write the program,
and could you just go and search in a very
huge space of possible programs, and just
discover a program out there in this computational
universe of possible programs.
And actually, one of the things that made
Wolfram Alpha possible was a bunch of developments
of being able to search the space of possible
programs to pull in surprising things that
no human will probably have ever have come
up with from this computational universe.
But there’s sort of a version of that that’s
happened recently with neural networks and
so on, where it’s possible now to kind of
give a large training set.
So, for example, a couple of years ago, we
built an image identification system where
you can show it, you can find it on the web,
imageidentify.com.
You show it an object, one of about ten thousand
kinds of objects, and it will tell you, "Yes,
that's a teacup," or "That's an elephant,"
or whatever else.
How is that done?
About 30 million training images, and about
maybe a quadrillion GPU operations to actually
do the training, but basically one is showing
examples to the system.
And what it's doing is basically building
a model on how to make distinctions between
things so that in the end, it will decide
"This is an elephant" and "This is a teacup."
But what’s notable about that is when you
say, “Well what’s the code that does that?”
It isn’t something where you can identify,
“Yes, the code works this-or-that way,”
it’s something where the code has emerged
from looking at all of these examples, it’s
not something where you can say “A human
built all those little pieces, and this is
exactly how it works.”
I think that’s one of the things that’s
sort of a recent feature of the current wave
of artificial intelligence enthusiasm are
these cases where, essentially, there’s
functionality that got produced that no human
was involved step-by-step in making it happen.
And of a way of making that feel a little
bit real.
If I asked you to name five red things, you
could rattle off a clown’s nose, and a stop
sign, and a kid’s ball, and a post-it note,
and an apple, and you’ve never connected
the stop sign and the clown’s nose before,
and now, all of a sudden you’ve done it.
You don’t know how you did it; you know
that the answer is right, and you’re comfortable
with that answer.
You’ve effectively written a synaptic program
in your brain.
Your brain doesn’t understand what it, itself
just did; and that’s a lot of what it’s
like to use this type of technology that you’ll
get to an answer.
And one of the big criticisms is it’s difficult
to understand the provenance of that decision.
If you have to defend that decision say, in
court, or you have to make sure that decision
didn’t include any inappropriate bias like
race or gender, and you have to make sure
that decision is consistent with other decisions
you’ve made, it’s difficult to do that
with this type of technology.
Some of the newer modalities are trying to
address that, effectively having the technology
take notes while it’s forming its own neural
network paths.
But then, you get into issues with performance
and really understanding the answers.
So, I would very much appreciate the journey
that you’re talking about, and I watched
at least part of that journey happen over
time.
I’m still rooting for the day when just
Iike I could ask you as a person, “Well,
what were you thinking about when you did
that?
What was your thought process?”
I’d like to be able to do a better job with
this neuromorphic technology of asking it
that same question.
I think it’s still a shortcoming.
So, you know, one of the things I think is
interesting about that, it's one of the places
where we, as humans, potentially have a dramatic
shortcoming.
Because, when you train up one of these neural
networks to, for example, do physiologic recognition,
what's happening is that it's effectively
as the neural network runs.
It's effectively asking itself a bunch of
questions: "Is this a very vertical-looking
kind of thing?
Is this a very green looking kind of thing?
Is this a…"
It's making a bunch of distinctions.
It's identifying distinctions about things
in the world.
Well, we, as humans, will sometimes have words
for those distinctions.
We'll say, "It's green," or "It's blue," or
whatever else.
"It's round," or "It's square."
Those kinds of things.
It's effectively looking at the world and
figuring out what are the best distinctions
to make to be able to distinguish things in
the world?
And it's coming up with potentially millions
of those distinctions.
We, as humans, we have about five thousand
words for picturable nouns, for example, in
English.
We have a very limited number of these kinds
of distinctions that we make as part of our
human language.
What's happening inside these kinds of AI
systems is that they are learning distinctions
for which there could be words.
They just aren't words in our human languages.
These are a kind of post-linguistic emergent
concepts that exist in systems that we’re
building and over time, some of those concepts
could turn into words that we, as humans,
understand.
But, one of the difficulties is that sort
of what’s going on in the inner life of
the kind of modern AI is something somewhat
beyond what I think humans are going to be
able to wrap their brains around.
We have three categories that we think about,
and I think it’s a cousin of what you’re
talking about.
So we make a distinction between things, and
behaviors, and relationships.
And, so as an example, some of the things
that we’re looking for are abstract, like
“malfeasance”.
We use the word “malfeasance” as opposed
to the word “fraud” because when someone
lies to us, they are doing it in anticipation
of some future gain, so at the time, legally,
that they lie to us, it’s questionable whether
or not it’s fraud, but it’s often a precursor
to fraud.
Now, if we just used standard regressive methods
and modeled the way fraudsters have behaved
in the past, and we know that the best fraudsters
will change their behavior when we know they’re
being watched, then we’ll be modeling how
the best fraudsters are no longer behaving,
which is exactly the opposite of what we want
to do.
So, if we tried to use some sort of learning
technology; I’ll just broaden the term from
AI to some sort of learning technology; one
of the things we want to learn is sort of
the opposite of the space that we’re observing:
what’s not happening, or how is what is
happening changing in quality and character
over time so that we can understand when a
new behavior has emerged, and then use some
sort of discrimination to determine whether
it’s more or less likely to be more malfeasant,
and then hand it off to a human agent to do
that last, really hard part of the work.
So, artificial intelligence in our world is
sometimes reducing the complexity of a problem
that’s very hard to see, and making the
work of those human brains much more valuable
by letting them work on the really hard stuff
and not waste their time on the more obvious
things.
So, really tricky dance.
I have a question for either of you.
Right now, there is so much hype around AI.
Is that hype justified and if the implications
are so profound for society, governments,
businesses, and so forth, how do we get what
the pathway is from here to there; to that
point of, shall we say, the “flowering”
or AI, machine intelligence, and all of its
various forms so it becomes worthy of that
hype?
I hate to say this, and I'll probably get,
you know, attacked for saying this by somebody.
Just like anything else, AI is a tool.
And, just like any other tool, you should
always understand the problem before you pick
up the tool.
So, I think that AI can become very important
when you think about autonomous self-driving
vehicles, […] drones, […] anything where
we might have something thinking for us because
we're not there; I think it's pretty important
that we get better at that.
But, I think we should also be careful when
we anthropomorphize.
Learning isn't really learning, it's curation,
organization, and attaching of that should
be … [and] so forth.
If we really want to be careful about the
terminology, I think AI can be very important
in our future.
The question is whether it’s very important
because it becomes our worst nightmare and
because we forgot to think about these things,
or whether it really improves our human condition.
And honestly, the jury’s out right now.
I can see bad guys doing bad things just with
this amazing technology just as well as I
can see good guys solving real problems.
I think that we’re really at a cusp.
So, I mean, the way I see it, what is happening
with kind of AI is a continuous line from
this really important idea of computation.
I mean, back before the 1930’s and so on,
people imagined that if you wanted to have
machines that did several different things,
you would need several different machines.
Then, there was this kind of notion that arose
in the 1930s of universal computers, the idea
that you could have a single machine that
could just be programmed to do all these different
kinds of things.
That idea, which initially was kind of an
abstract, kind of mathematical idea – [a]
logical kind of idea – that’s the idea
that led to the modern computer revolution,
and so on.
That idea is the only way through getting
locked out.
What we are seeing is kind of the computationalization
of everything.
One of the things I like to say about different
fields of human endeavor is to pick a field
X from sort of archeology to zoology.
They're either is, now, or soon will be a
field called computational X, and that will
be the future of that field.
Some of those fields already exist: computational
biology, computational linguistics, others
are just emerging.
What's happening is there is this kind of
way of thinking about things in computational
terms that really is extremely powerful.
It's kind of, I think the defining idea of
the century, is this idea of thinking about
things in computational terms.
Now, once you are thinking about things in
computational terms, you get to automate a
lot of stuff that you couldn't automate before.
We happen to be kind of in the middle of a
moment when a particular kind of automation
that's made possible by neural nets, and so
on, is in rapid growth.
So, I've probably seen, in my career, I've
probably seen a dozen or two fields that have
gone into this kind of hypergrowth period.
Typically, what happens in fields of human
endeavor, whether it's areas of physics, or
whether it's biology or lots of other kinds
of areas is there will be long periods – decades,
maybe even a century – of fairly slow growth.
And then, some methodological advance will
occur typically, and then there’s this period
of hypergrowth where there’s a new result
every two weeks.
Now, I happen to be lucky enough when I was
a kid, basically, to get involved with particle
physics at a moment in the late 1970s when
it was in the hypergrowth phase, and where
important new results … You know, every
week or two – New Paper with Important New
Thing.
That lasted for about five years.
Since the late 1970s, particle physics has
been in a pretty flat state.
We are, right now, kind of in the middle of
the hypergrowth phase for machine learning
and neural network-type techniques.
There’s a lot of low-hanging fruit to be
picked.
You know, we’ve been having a great time
because we’ve been using this Wolfram language
system that we’ve been building for thirty
years now, and kind of integrating what’s
new with the possibilities of neural networks
with the kind of large-scale language that
we’ve developed, and we’ve now got this
nice, symbolic way to develop a high-level
development mechanism for neural networks
we’ve just released a few weeks ago.
But, it’s always exciting to see in these
periods of hypergrowth for a field.
And what we’re seeing right now for neural
networks is a lot of low-hanging fruit being
picked.
There’s a lot of things where people have
said for years, “Oh, computers will never
be able to do that!”
For example, physiologic recognition was one
of those things.
And then suddenly, it’s like, “Yes, computers
can now do it!”
What we’ve seen over the last couple of
years is with physiologic recognition: basically,
the Big Thing happened a couple of years ago.
You know, we got to the point where we can
basically recognize that more-or-less human
level of performance, a whole bunch of different
kinds of objects.
What happens from here on out, with that kind
of particular problem, is kind of “slow-growth,
hard work.”
But, there are a whole bunch of these things.
We’re probably about halfway through, I
estimate, under the hypergrowth period for
this.
Yeah, I was at a conference last week where
someone was talking about the autonomous self-driving
vehicles, and the importance, obviously, of
object recognition and having an algorithm
drive a car.
And, I won't mention the neighborhood, but
the neighborhood near the organization that
is developing the cars, apparently, the local
youths have found an amusing pastime of making
homemade stop signs and holding them up in
front of the autonomous self-driving vehicles
and make them stop in the middle of traffic.
A human being wouldn't be confused by a kid
holding up a fake stop sign, but an algorithm
designed to recognize a stop sign, until it
realizes it's being tricked that way because
someone told it, will stop every time.
So, I think that I totally agree with what
you’re saying, Stephen, that there’s this
sort of constant evolution and we are certainly
in an explosion of evolution.
I think we’re also, from the way I stand,
sometimes in this rush to get to the market,
and these sort of unintended consequences
don’t get thought about; sometimes, they’re
funny like the stop sign; sometimes, they’re
not so funny, and people find a way to take
down half the internet with a denial of service
attack on security cameras because people
don’t update their software.
I think that people could do a better job
of thinking about unintended consequence,
and I worry a little bit about rush to market.
This has nothing to do with you; I don’t
think you’ve ever rushed to market; but
I think that people who make some of these
things are in a hurry to make the next really
clever thermostat or the next really clever
car, and there are really clever people out
there just waiting for them to do that.
So, you know, one thing I might comment that
I think both of us are somewhat involved in
is the whole question about how do you put
knowledge into compute-able form?
How do you go from what's out there in the
world, to something where knowledge is organized;
where you know systematically that companies
and their characteristics, things like this,
are about chemicals and their characteristics,
all these kinds of things.
And one of the questions is always, "Can you
just go to the web, forage the web, have a
machine intelligence figure out what the right
answer is," and that's been a long story.
And, the basic answer is you can get 85% of
the way there.
You know, it's pretty easy to get; to use
automated methods, to forage Wikipedia and
find out 85% of the correct facts.
The problem is, you don’t know which 15%
are completely wrong.
And, to know that is really something where
one needs a process of curation that is a
mixture of really good automation, with human
activity.
And I think both of us in our rather different
ways have been deeply involved in this curation
process.
It’s something which is not widely understood
in the industry at large.
It’s something where people say, “We’ve
got this AI!
Now we can just automatically ingest knowledge
and get it organized.”
My observation has been, as I say, you’ve
got a certain fraction of the way there, but
in the end, us humans aren’t useless after
all, and you actually need to inject that
sort of moment of human expertise into the
thing.
Can I…
It's such a difficult thing for companies
to understand because it's kind of like, companies
tend to be either "We're a tech company, or
a people company," so-to-speak.
And the tech companies just sort of say, "We'll
just attach the magic AI and it's just going
to work," and the people companies are like,
"Oh, we don't know about this technology stuff,
we'll just have people all the way."
Curation is an interesting thing that is a
complicated management problem, where you
kind of have to use automation, inject human
judgment when it's appropriate, figure out
how to move the process of judgment through
the organization in the right way to actually
be sure that you're getting the right answer.
I would add about that 15% that often, it's
that 15% that ends in that really apocryphal
tale of how someone just failed miserably.
There's some really important significance.
The reason that 15% became sublimated and
wasn't so easily discoverable is exactly the
reason why there was something really valuable
in it.
You think about, to use an example of knowing
all there is to know about companies; I happen
to be in that be in that business, right?
It's not that hard.
We all fail to go on the internet to do research
and find out about a company.
How do you know what you're looking at is
real?
How do you know it's current?
All true information is no simultaneously
true.
All information that you can get to in real-time
wasn't created a minute ago.
You know, being able to curate, to understand,
to put like with like, to triangulate, to
test for veracity, to have some experience;
things get new and things change; when the
environment changes, to understand how it’s
changing.
These are the critical moments where I think
there’s still hope for the need for our
human brains, and I think you’re not going
to program ourselves out of business here.
I think that we’re going to get to solve
bigger and better problems.
If I look at progressive decomposition; taking
those really big problems that are not solved
yet and breaking them down into smaller and
smaller problems that are still not solved,
I think there's great hope for being able
to focus on the more important parts of those
problems with our human brains.
And, these technologies will help get everything
else out of the way, if we let it.
So what are the implications of all of this
for society?
The way that you are talking about it is in,
let’s say, mechanistic, computer science
terms as opposed to the way the software industry
as a whole, in its marketing, talks about
AI; which is in magical results terms…
Yeah.
I think it’s fair to say, and I won’t
speak for both of us, but I’ll speak first
for both of us and then I’ll see if Stephen
agrees with me.
I think we would both say that it’s very
important to have technologies.
It’s very important to advance those technologies.
But, there’s never going to be a substitute
for understanding the problem, for humans
to continue to advance the art.
The machines can help convince the art.
But for the foreseeable future, I think we
still get to conduct the orchestra.
I think that the main question is, what can
be automated in the world?
And, the fundamental thing to realize is that
what can’t be automated is what you’re
trying to do.
That is the definition of the goal.
There is no abstract sort of ultimate, automatic
goal.
The goal is something that’s defined by
us humans on the basis of our history, and
culture, and characteristics, and so on.
The real picture of how we interact with technology
and nowadays with AI, is we, as humans, define
the goals.
We say what should happen and what we want
to achieve, and then it’s a question of,
“Can we make automated systems that do the
best possible job of achieving those things
in the best possible way?”
So, one of the big issues, then, is how do
you tell the machines what you want them to
achieve?
So, in some cases, it’s very straightforward.
But when it gets to be sort of a bigger picture
of … Well, one of the things I’ve been
interested in, in recent times, is kind of
how do we communicate with AIs?
You know, one thing we can do is just say
something with natural language.
That's good when it comes to short things
like asking a knowledge-seeking question or
telling some device to do one particular thing.
That works pretty well with simple, natural
language.
When it gets more complicated, natural language
doesn’t work very well.
We have kind of a model of that right now
when we look at things like legal contracts.
Legal contracts are trying to define what
should happen.
They start by being written in natural language,
but it turns out we need to invent legalese
because we need to take natural language and
make it a bit more precise.
Well, I think there's an end point of that
direction, which is to have a kind of code,
a kind of computer language, which could say
the kinds of things we would want to say in
a legal contract; but can do so in a precise
fashion.
And with our Wolfram Language System, this
is very much the direction that we’ve been
going in.
I mean, what we’ve mostly been dealing with
is things like; we can talk about cities;
we can talk about distances between places
on the Earth; we can talk about all sorts
of things about genes and biology, those kinds
of things.
We can – I like to use the example – “I
want a piece of chocolate.”
We can talk about a piece of chocolate; we
know a great deal of deal of detail about
different brands of chocolate and their nutrition
content, and so on.
The “I want” part we can’t yet talk
about, but we’re working towards being able
to have a precise language for talking about
those kinds of things, and I see that as being
an important intermediary between the way
we think about things and the way that we
can have machines do things.
There’s an interesting nuance in the corners
of this problem that we’re each working.
So, in my world, the problem that we have
is the parts of language that we’re interested
in mostly, are talking about proper nouns.
And proper nouns tend not to be in the dictionary,
and they tend also to have meaning that is
very context-specific.
So, if I say, “Apple announced the new iPhone,
whatever, 19,” I know that a fruit doesn’t
announce anything because a fruit is an inanimate
object.
So, I’m immediately down the path that this
is more likely to be a proper noun, not only
because it’s capitalized, but because it’s
the operator of a transitive verb like that.
So, what our problem is, is not so much to
understand what happened, but to understand
whether this thing that happened over here,
and this thing that happened over here, are
likely to be talking about the same entity,
and whether or not that entity is likely to
be a business.
So, we don’t have to get down to the meaning
of “What did they really do?”, we just
have to know that this is something that Apple
did, and that apple that did it is more likely
to be the computer company than the fruit.
That gets notoriously difficult when we start
crossing language barriers, because the way
you talk about Apple, in Chinese, for example,
happens to be the word for “apple.”
The way you talk about Dun & Bradstreet, for
example, there’s no word for “Dun” or
“Bradstreet,” so you have to use some
other words that either sound like it and
mean something not offensive, etc.
So that understanding, it’s called “semantic
disambiguation” of what are you likely to
be talking about here; not necessarily “What
does it mean?”, but what is it likely to
be, is the tricky part for us.
In your world, you actually didn't know what
the Apple is, and you need to know if it's
a Macintosh apple or a delicious apple – much
more difficult problem on the meaning part,
I would guess; much less of a problem in terms
of a business side of, "Is it an acquisition
or is it some sort of an LLC," you know, what
kind of businessy thing happened here?
[…] And these are good examples of different
types of problems you have in AI.
In one case, I want to know what it means,
and in another case, I want to know that these
two pieces of information are talking about
the same entity and I don’t necessarily
care what it means.
One of the things that we look at is, we actually
look at confounding characteristics in language.
So, we look at sarcasm and neologism.
You talked about the legal documents and the
fact that if I talk about a “lean piece
of meat,” or “leaning into something,”
or a “lien” in a legal document; I know
those are spelled differently, but those are
three different things.
The difference is there and it’s in context.
When we start making up words, when we start
using Twitter handles, when we start talking
about tweeting things, when we start using
sarcasm; when I say, “This is a great company
if you don’t mind destroying the environment,”
we have to figure out that there’s an independent
and a dependent clause there, and they have
opposite sentiment, and destroying the environment
is bad because the environment is good and
destroying it is bad.
Tricky problem.
A different tricky problem.
I think we’re going to separate schools
together on this, and that’s exactly the
nature of AI.
I think one of the challenges is that the
capabilities that computation provides and
that AI, which is sort of a thing that sits
on top of computation provides; there are
all sorts of impressive things that can be
done.
The issue is how do we direct them for human
purposes?
You know, one of the things I’ve been interested
in for a long time and we’re done for a
long time is this business of algorithm discovery
in the computational universe.
If you look out there in the space of all
possible programs, there are programs that
do all sorts of remarkable things.
The issue is can we really mine that space
of possible programs for ones that are useful
to us?
It’s very much analogous to what happens
in physical technology.
It’s like, “Okay, there’s an iron mine
somewhere.
There’s a tantalum mine somewhere.”
You know, we find the material like, let’s
say, tantalum, and we say, or gadolinium:
is this useful for anything?
And the answer is, “Yes.
It’s discovered it’s useful to make magnets
out of.”
There’s this similar kind of issue in the
computation universe of possible programs.
There are a lot of them that do things which
look interesting, but can they be “mined”
for human purposes?
So this, again, puts the pressure on, “Okay,
so what do we actually want to do?”
Computation and now AI provide amazing capabilities.
The issue is what do we want to do with them,
and how do we make something where both we,
as humans, can sort of understand what we’re
asking for, and where the machine can understand
what we’re asking for?
So, for example, in my life, I’ve spent
a huge amount of time developing very high-level
computer languages that let one express things
in a way that are the highest possible level
of expression for humans or what they want
to do that can also be understood by machines.
I think this is … As we look towards the
smart contract world of telling our machines
in general terms, “What do you want to achieve?”,
we need a language to do that.
I think the ultimate smart contract that us
humans have to think about is the whole constitution
of what do we want to AIs to do?
We would like to say, “Okay, AIs, we’re
going to make AIs in charge of all kinds of
things in the world.
We’re going to have all kinds of systems
be done automatically.”
We want to give some overall guidelines.
You call them “ethical guidelines” for
how the AIs should behave with respect to
us.
“Okay, AIs, be nice to us.”
How do we express that?
How do we define what that means?
How do we specify the constitution of the
AIs?
I’ve been interested in this problem of
what is the language that we can use to write
the constitution for the AIs.
And the next question is, “What should the
constitution actually say?”
Yeah.
You’re mentioning, Anthony, the whole question
of looking backward; the regressive approach
to things.
You know, one of the things you might say
is, “Okay, AIs.
Just do things like us humans do.”
So, Anthony Scriffignano, this question that
Stephen just raised on the ethical dimensions:
is it simply a matter of, “Do what we would
want.
Be nice to us.”
Your company, Dun & Bradstreet deals with
financial matters, so dive into the ethical
implications.
And, I think this relates back to an earlier
part of the discussion that [you] brought
up regarding the unintended consequences.
Yes, so I want to first just respond to the
somewhat rhetorical ending of what Stephen
said because I loved it.
If we actually asked our algorithms to behave
as we have, you might be surprised at how
badly they behave, because we correct our
memory and make our prior accomplishments
greater than they were.
We tend to sublimate our prior failures.
We tend to amplify in our minds how successful
certain things were, and I like to say that
we should make new mistakes, right?
So if I just write algorithms that behave
the way I have behaved in the past, then that
means warts and all.
That means foibles.
And so, hopefully, I have learned in my life
and I don’t even behave the way I have behaved
in the past.
So, what I probably best would want them to
do is behave the way I would like to behave
in the future, and that’s a whole …
So you want AIs to be an idealized version
of humanity?
I don't know.
Maybe.
Maybe I do.
But, there is this problem.
There are a couple problems with this.
One is that we don't all want the same thing.
We don't define success the same way.
We don't define winning the same way.
We don't define being nice the same way.
Sometimes being nice to someone is tough love.
Sometimes, you have to do something that isn't
nice to get a quote-on-quote "better outcome."
And then there's this whole question of sub-optimality
that sometimes, what’s better for me isn’t
necessarily better for everyone else or better
for you.
And so, we get into the whole “best alternative
to a negotiated agreement” kind of argument
of, “What’s better for the common good,
and is what’s better for the common good
what we should do […]?”
So, these are all really philosophical questions.
But, it’s so easy to go down that road.
You can imagine how hard it would be to automate
this, and to do this in AI.
When Stephen was talking, I was also thinking
about language.
So, I do a lot of work in computational linguistics
across languages, and within the sphere of
two or more languages, something that you
think is pretty obvious to say can’t easily
be transformed or translated into a language
maybe doesn’t have a word for a “yes”
or a “no,” or a language that doesn’t
have a subjunctive.
In a legal context, there’s a very vast
distinction between “You should” and “You
must.”
It’s critical in English.
If you say, “You should do to the following
things,” or “You must do the following
things,” those are … There are multiple
words for “should” and “must” in Chinese
and some of them are used synonymously.
So, you have to understand the context to
understand if it’s a “should” or a “must”
and sometimes native speakers disagree as
to the interpretation of that.
Think about what that does to a contract.
Now, try to imagine talking to a machine that
set its goals at a level of ambiguity that
we were talking about with this ethics, and
we can’t even get “should” and “must”
right.
That’s a very big problem.
And, learning is important.
You know, I think you're making the case for
what I just spent thirty years trying to do,
which is we need an actual, precise language
for expressing what we want.
When we look at sort of the future of legal
contracts, and so on, today, legal contracts
are written in English, in legalese, whatever
else.
And, you know, people say, "Well, it's important.
There's a little bit of wiggle-room in the
language, and so on."
But, in many cases, it would actually be a
lot better if everybody knew this is exactly
what the contract means.
So, so long as we can express those concepts
in a precise language, it's much better if
both parties can just agree, "This is what
we meant."
And some part of the contract will be written
in something which is almost like logic, some
part of it might be written using some machine
learning classifier.
It might say, "We both agree that it's a Grade
A orange if this machine learning classifier
that we both agreed on looks at it and says
it's a Grade A orange."
And that might be a good way to write the
contract.
And of course, a big advantage of having a
contract that's written in compute-able form
is that, then, you can just say, "Okay, computer.
Figure out if the contract was satisfied or
set things up so that it is satisfied."
And I think it becomes … We're able to kind
of automate a lot of the process of doing
these things.
I think that's a really interesting … "If
both parties agree this is what we really
meant."
It's written in code, not in legalese, then
we have quite an interesting thing going on
and a place where we can get a lot of transactions
to happen in a much more efficient way because
machines can interact with each other using
automatic contracts rather than humans having
to interpret this-or-that thing going in.
I completely … First of all, and I mean
this with a complete absence of sarcasm, I
look forward to your success.
I think that this is something for that 85%
of contracts that are pretty straightforward,
“Here’s what you’re going to do, here's
what I'm going to do, here's how we'll measure
the results, what happens if there's a breach
of the contract," all the elements of a contract,
most of the time, are pretty straightforward;
certainly in commercial terms; if you look
at ECC, things like that.
There are commercial codes that codify these
things.
Great.
When we have to make a contract to describe
what we're going to do for something in the
future that has never been done before; and
maybe doing it with things that we don't have
exact terminology.
Now, it gets tricky.
I think a good argument would be, "Great!
Let's let the jurists and the legal minds
... Let's let everybody focus on that part
of the hard stuff and not worry about the
contract of whether you shipped me the apples
I ordered […]”
I hate to cut you off.
We’re just out of time, and I would ask
each of you to spend a moment and share your
advice to businesspeople, policymakers in
the government, and technologists regarding
AI.
And again, I apologize, Stephen, for cutting
you off.
We’re just out of time.
Well, I guess … Let me say something first,
perhaps.
The most important thing, I think is this
kind of “think computationally.”
Use this kind of computational paradigm that
we are slowly beginning to understand and
think about those things in those terms.
Define things in those terms.
Once you’ve defined things in those terms,
then it’s sort of a much easier problem
to say, “Okay, AIs.
Now we’ve defined what we want in computational
terms.
Now help us achieve this, so to speak.”
That’s my main point.
Think in computational terms.
Get your thoughts organized in a way that
you could imagine explaining them to a computer,
so to speak.
That, I think is the important direction.
So, I always give my advice in threes, and
one of my three is being almost verbatim what
you just said.
I thought it first…just kidding.
The first thing I would say is, be humble.
I think that there is always a difference
between the theoretical, science fiction;
what's possible and what's real.
Make an honest assessment of how much of what
you're trying to do – especially if it's
a commercial application or law about the
future.
Being realistic about what's possible so that
you constrain this to a space that you can
understand.
The second one is what I think you were saying,
Stephen, which is to be as clear as you possibly
can by about your goals.
And if that means you have to invent a whole
new language to do it, then have at it!
But, we’ve got to get this ambiguity and
squishiness out of this to really get better
at it, and I think we’re making great strides
in that regard.
And then, the third thing I would say is to
learn; to make new mistakes; to make sure
that we’re constantly observing our behavior
with this amazing technology and make sure
we’re solving incrementally more important
problems and more difficult problems; that
we’re not rushing to market and making the
same mistakes over again with different names.
Okay.
What an amazing discussion we’ve had.
We have been talking with Stephen Wolfram,
who is truly one of the fathers of modern
computer science and the founder of Wolfram
Research and other companies.
And, Anthony Scriffignano, who is an amazing
thinker, is the Chief Data Scientist at Dun
& Bradstreet.
Gentlemen, thank you so much!
Stephen, thank you for being here, and I hope
you'll come back and do it another time.
And, Anthony, thank you for being here and
you're scheduled to come back.
So, I know you'll be back to do it another
time!
And, my hope is that we can get the two of
you back together again to continue this conversation
because as you both pointed out right at the
start, 45 minutes is not enough time.
And, everybody, thank you for watching!
Come back next week.
We will have another great show.
And, subscribe to our YouTube channel.
Click the YouTube button on your screen and
subscribe.
Thanks, everybody.
Take care.
Bye-bye!
