To Build Truly Intelligent Machines, Teach Them Cause
and Effect
https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/?utm_source=Quanta+Magazine&utm_campaign=1267952a81-RSS_Daily_Computer_Science&utm_medium=email&utm_term=0_f0cb61321c-1267952a81-389846569&mc_cid=1267952a81&mc_eid=61275b7d81
Judea Pearl, a pioneering
figure in artificial intelligence, argues that AI has been stuck in a
decades-long rut. His prescription for progress? Teach machines to understand
the question why.
[[Note the critique of the limitations of current AI - DG.]]
May 15,
2018
Artificial
intelligence owes a lot of its smarts to Judea Pearl. In the 1980s he led
efforts that allowed machines to reason probabilistically. Now he’s one of the
field’s sharpest critics. In his latest book, “The Book of Why: The New Science of Cause
and Effect,” he argues that artificial intelligence has been
handicapped by an incomplete understanding of what intelligence really is.
Three decades ago, a
prime challenge in artificial intelligence research was to program machines to
associate a potential cause to a set of observable conditions. Pearl figured
out how to do that using a scheme called Bayesian networks. Bayesian networks made
it practical for machines to say that, given a patient who returned from Africa
with a fever and body aches, the most likely explanation was malaria. In
2011 Pearl won the Turing Award,
computer science’s highest honor, in large part for this work.
But as Pearl sees it, the
field of AI got mired in probabilistic associations. These days, headlines tout
the latest breakthroughs in machine learning and neural networks. We read about
computers that can master ancient games and drive cars. Pearl
is underwhelmed. As he sees it, the state of the art in artificial intelligence
today is merely a souped-up version of what machines could already do a
generation ago: find hidden regularities in a large set of data. “All the
impressive achievements of deep learning amount to just curve fitting,” he said
recently.
In his new book, Pearl,
now 81, elaborates a vision for how truly intelligent machines would think. The
key, he argues, is to replace reasoning by association with causal reasoning.
Instead of the mere ability to correlate fever and malaria, machines need the capacity
to reason that malaria causes fever. Once this kind of causal framework is in
place, it becomes possible for machines to ask counterfactual questions — to
inquire how the causal relationships would change given some kind of
intervention — which Pearl views as the cornerstone of scientific thought.
Pearl also proposes a formal language in which to make this kind of thinking
possible — a 21st-century version of the Bayesian framework that allowed
machines to think probabilistically.
Pearl expects that causal
reasoning could provide machines with human-level intelligence. They’d be able
to communicate with humans more effectively and even, he explains, achieve
status as moral entities with a capacity for free will — and for evil. Quanta
Magazine sat down with Pearl at a recent conference in San Diego and
later held a follow-up interview with him by phone. An edited and condensed
version of those conversations follows.
Why is
your new book called “The Book of Why”?
It means to be a summary
of the work I’ve been doing the past 25 years about cause and effect, what it
means in one’s life, its applications, and how we go about coming up with
answers to questions that are inherently causal. Oddly, those questions have
been abandoned by science. So I’m here to make up for the neglect of science.
That’s
a dramatic thing to say, that science has abandoned cause and effect. Isn’t
that exactly what all of science is about?
Of course, but you cannot
see this noble aspiration in scientific equations. The language of algebra is
symmetric: If X tells us about Y,then Y tells
us about X. I’m talking about deterministic relationships. There’s
no way to write in mathematics a simple fact — for example, that the upcoming
storm causes the barometer to go down, and not the other way around.
Mathematics has not
developed the asymmetric language required to capture our understanding that
if X causes Y that does not mean that Ycauses X.
It sounds like a terrible thing to say against science, I know. If I were to
say it to my mother, she’d slap me.
But science is more
forgiving: Seeing that we lack a calculus for asymmetrical relations, science
encourages us to create one. And this is where mathematics comes in. It turned
out to be a great thrill for me to see that a simple calculus of causation
solves problems that the greatest statisticians of our time deemed to be
ill-defined or unsolvable. And all this with the ease and fun of finding a proof
in high-school geometry.
You
made your name in AI a few decades ago by teaching machines how to reason
probabilistically. Explain what was going on in AI at the time.
The problems that emerged
in the early 1980s were of a predictive or diagnostic nature. A doctor looks at
a bunch of symptoms from a patient and wants to come up with the probability
that the patient has malaria or some other disease. We wanted automatic
systems, expert systems, to be able to replace the professional — whether a
doctor, or an explorer for minerals, or some other kind of paid expert. So at
that point I came up with the idea of doing it probabilistically.
Unfortunately, standard
probability calculations required exponential space and exponential time. I
came up with a scheme called Bayesian networks that required polynomial time
and was also quite transparent.
Yet in
your new book you describe yourself as an apostate in the AI community today.
In what sense?
In the sense that as soon
as we developed tools that enabled machines to reason with uncertainty, I left
the arena to pursue a more challenging task: reasoning with cause and effect.
Many of my AI colleagues are still occupied with uncertainty. There are circles
of research that continue to work on diagnosis without worrying about the
causal aspects of the problem. All they want is to predict well and to diagnose
well.
I can give you an
example. All the machine-learning work that we see today is conducted in
diagnostic mode — say, labeling objects as “cat” or “tiger.” They don’t care
about intervention; they just want to recognize an object and to predict how
it’s going to evolve in time.
I felt an apostate when I
developed powerful tools for prediction and diagnosis knowing already that this
is merely the tip of human intelligence. If we want machines to reason about
interventions (“What if we ban cigarettes?”) and introspection (“What if I had
finished high school?”), we must invoke causal models. Associations are not
enough — and this is a mathematical fact, not opinion.
People
are excited about the possibilities for AI. You’re not?
As much as I look into
what’s being done with deep learning, I see they’re all stuck there on the
level of associations. Curve fitting. That sounds like sacrilege, to say that
all the impressive achievements of deep learning amount to just fitting a curve
to data. From the point of view of the mathematical hierarchy, no matter how
skillfully you manipulate the data and what you read into the data when you
manipulate it, it’s still a curve-fitting exercise, albeit complex and
nontrivial.
The way
you talk about curve fitting, it sounds like you’re not very impressed with
machine learning.
No, I’m very impressed,
because we did not expect that so many problems could be solved by pure curve
fitting. It turns out they can. But I’m asking about the future — what next?
Can you have a robot scientist that would plan an experiment and find new answers
to pending scientific questions? That’s the next step. We also want to conduct
some communication with a machine that is meaningful, and meaningful means
matching our intuition. If you deprive the robot of your intuition about cause
and effect, you’re never going to communicate meaningfully. Robots could not
say “I should have done better,” as you and I do. And we thus lose an important
channel of communication.
What
are the prospects for having machines that share our intuition about cause and
effect?
We have to equip machines
with a model of the environment. If a machine does not have a model of reality,
you cannot expect the machine to behave intelligently in that reality. The
first step, one that will take place in maybe 10 years, is that conceptual
models of reality will be programmed by humans.
The next step will be
that machines will postulate such models on their own and will verify and
refine them based on empirical evidence. That is what happened to science; we
started with a geocentric model, with circles and epicycles, and ended up with
a heliocentric model with its ellipses.
Robots, too, will
communicate with each other and will translate this hypothetical world, this
wild world, of metaphorical models.
When
you share these ideas with people working in AI today, how do they react?
AI is currently split.
First, there are those who are intoxicated by the success of machine learning
and deep learning and neural nets. They don’t understand what I’m talking
about. They want to continue to fit curves. But when you talk to people who
have done any work in AI outside statistical learning, they get it immediately.
I have read several papers written in the past two months about the limitations
of machine learning.
Are you
suggesting there’s a trend developing away from machine learning?
Not a trend, but a
serious soul-searching effort that involves asking: Where are we going? What’s
the next step?
That
was the last thing I wanted to ask you.
I’m glad you didn’t ask
me about free will.
In that
case, what do you think about free will?
We’re going to have
robots with free will, absolutely. We have to understand how to program them
and what we gain out of it. For some reason, evolution has found this sensation
of free will to be computationally desirable.
In what
way?
You have the sensation of
free will; evolution has equipped us with this sensation. Evidently, it serves
some computational function.
Will it
be obvious when robots have free will?
I think the first
evidence will be if robots start communicating with each other
counterfactually, like “You should have done better.” If a team of robots
playing soccer starts to communicate in this language, then we’ll know that
they have a sensation of free will. “You should have passed me the ball — I was
waiting for you and you didn’t!” “You should have” means you could have
controlled whatever urges made you do what you did, and you didn’t. So the
Now
that you’ve brought up free will, I guess I should ask you about the capacity
for evil, which we generally think of as being contingent upon an ability to
make choices. What is evil?
It’s the belief that your
greed or grievance supersedes all standard norms of society. For example, a
person has something akin to a software module that says “You are hungry,
therefore you have permission to act to satisfy your greed or grievance.” But
you have other software modules that instruct you to follow the standard laws
of society. One of them is called compassion. When you elevate your grievance
above those universal norms of society, that’s evil.
So how
will we know when AI is capable of committing evil?
When it is obvious for us
that there are software components that the robot ignores, consistently
ignores. When it appears that the robot follows the advice of some software
components and not others, when the robot ignores the advice of other
components that are maintaining norms of behavior that have been programmed
into them or are expected to be there on the basis of past learning. And the
robot stops following them.
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