Kind: captions
Language: en
- The following is a
conversion with Judea Pearl,
professor at UCLA and a
winner of the Turing Award,
that's generally recognized as
the Nobel Prize of computing.
He's one of the seminal figures
in the field of artificial intelligence,
computer science, and statistics.
He has developed and championed
probabilistic approaches
to AI, including Bayesian networks,
and profound ideas in
causality in general.
These ideas are important not just to AI,
but to our understanding
and practice of science.
But in the field of AI,
the idea of causality,
cause and effect, to many,
lie at the core of what
is currently missing
and what must be developed
in order to build truly
intelligent systems.
For this reason, and many others,
his work is worth returning to often.
I recommend his most recent
book called "Book of Why"
that presents key ideas
from a lifetime of work
in a way that is accessible
to the general public.
This is the "Artificial
Intelligence Podcast."
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And now, here's my
conversation with Judea Pearl.
You mentioned in an interview
that science is not a collection of facts,
but a constant human struggle
with the mysteries of nature.
What was the first mystery
that you can recall that hooked you,
that captivated your curiosity?
- Oh, the first mystery.
That's a good one.
Yeah, I remember that.
- [Lex] What was it?
- I had a fever for three days
when I learned about Descartes
and a little geometry,
and I found out that you
can do all the construction
in geometry using algebra.
And I couldn't get over it.
I simply couldn't get out of bed.
(chuckles)
- What kinda world does
analytic geometry unlock?
- Well, it connects algebra
with geometry, okay?
So, Descartes has the idea
that geometrical construction
and geometrical theorems and assumptions
can be articulated in
the language of algebra.
Which means that all the proofs
that we did in high school
in trying to prove that
the three bisectors meet
at one point, and that the (chuckles)
All this can be proven by
shuffling around notation.
That was a traumatic experience.
- (chuckles) Traumatic experience.
- [Judea] For me, it was, it
was, I'm telling you, right?
- So it's the connection
between the different
mathematical disciplines,
that they all
- They're not even two
different languages.
- Languages.
- Yeah.
- Which mathematic
discipline is most beautiful?
Is geometry it for you?
- Both are beautiful.
They have almost the same power.
- But there's a visual
element to geometry.
- The visual element,
it's more transparent.
But once you get over to algebra then
linear equations is a straight line.
This translation is easily absorbed.
To pass a tangent to a circle, you know,
you have the basic theorems,
and you can do it with algebra.
But the transition from
one to another was really,
I thought that Descartes was
the greatest mathematician
of all times.
- So, if you think of
engineering and mathematics
as a spectrum--
- [Judea] Yes.
- You have walked casually
along this spectrum
throughout your life.
You know, a little bit
of engineering and then
you've done a little bit of
mathematics here and there.
- A little bit.
We get a very solid
background in mathematics
because our teachers were geniuses.
Our teachers came from
Germany in the 1930s
running away from Hitler.
They left their careers
in Heidelberg and Berlin,
and came to teach high school in Israel.
And we were the beneficiary
of that experiment.
When they taught us math, a good way.
- What's a good way to teach math?
- [Judea] Theorologically.
- The people.
- The people behind the theorems, yeah.
Their cousins, and their nieces,
(chuckles) and their faces,
and how they jumped from the bathtub
when they screamed, "Eureka"
and ran naked in town. (laughs)
- So you were almost educated
as a historian of math.
- No, we just got a
glimpse of that history,
together with the theorem,
so every exercise in math
was connected with a person,
and the time of the person,
the period.
- [Lex] The period also
mathematically speaking.
- Mathematically speaking,
yes, not a paradox.
- Then in university, you had
gone on to do engineering.
- Yeah. I got a BS in
Engineering at Technion.
And then I moved here
for graduate school work,
and I did the engineering in
addition to physics in Rutgers.
And it combined very
nicely with my thesis,
which I did in Elsevier
Laboratories in superconductivity.
- And then somehow thought to switch
to almost computer science
software, even, not switched,
but longed to become to get
into software engineering
a little bit, almost in programming,
if you can call it that in the 70s.
There's all these disciplines.
- Yeah.
- If you were to pick a favorite,
in terms of engineering and mathematics,
which path do you think has more beauty?
Which path has more power?
- It's hard to choose, no?
I enjoy doing physics.
I even have a vortex named with my name.
So, I have investment
in immortality. (laughs)
- So, what is a vortex?
- Vortex is in superconductivity.
- In the superconductivity.
- You have terminal
current swirling around,
one way or the other, going
to have us throw one or zero,
for computer that was
we worked on in the 1960
in Elsevier,
and I discovered a few nice
phenomena with the vortices.
You push current and they move.
- [Lex] So there's a Pearl vortex.
- A Pearl vortex, why, you can google it.
(both laugh)
I didn't know about it,
but the physicist picked up
on my thesis, on my PhD thesis,
and it became popular when
thin film superconductors
became important, for high
temperature superconductors.
So, they call it "Pearl
vortex" without my knowledge.
(laughs)
I discovered it only about 15 years ago.
- You have footprints
in all of the sciences,
so let's talk about the
universe for a little bit.
Is the universe, at the lowest level,
deterministic or stochastic,
in your amateur philosophy view?
Put another way, does God play dice?
- We know it is stochastic, right?
- [Lex] Today. Today we
think it is stochastic.
- Yes, we think
because we have the Heisenberg
uncertainty principle
and we have some
experiments to confirm that.
- All we have is
experiments to confirm it.
We don't understand why.
- [Judea] Why is already--
- You wrote a book about why. (laughs)
- Yeah, it's a puzzle.
It's a puzzle that you have
the dice-flipping machine,
or God, and the result of the flipping,
propagated with a speed faster
than the speed of light.
(laughs) We can't explain it, okay?
But, it only governs
microscopic phenomena.
- So you don't think of
quantum mechanics as useful
for understanding the nature of reality?
- [Judea] No, it's diversionary.
- So, in your thinking, the world might
as well be deterministic?
- The world is deterministic,
and as far as a new one
firing is concerned,
it is deterministic to
first approximation.
- What about free will?
- Free will is also a nice exercise.
Free will is an illusion,
that we AI people are going to solve.
- So, what do you think, once we solve it,
that solution will look like?
Once we put it in the page.
- The solution will look like,
first of all it will look like a machine.
A machine that acts as
though it has free will.
It communicates with other machines
as though they have free will,
and you wouldn't be able
to tell the difference
between a machine that does and a machine
that doesn't have free will, eh?
- So it propagates the
illusion of free will
amongst the other machines.
- And faking it is having it, okay?
That's what Turing test is all about.
Faking intelligence is intelligence,
because it's not easy to fake.
It's very hard to fake,
and you can only fake if you have it.
- (laughs) That's such
a beautiful statement.
(laughs) You can't fake it
if you don't have it, yup.
So, let's begin at the
beginning, with the probability,
both philosophically and mathematically,
what does it mean to say
the probability of something
happening is 50%?
What is probability?
- It's a degree of
uncertainty that an agent has
about the world.
- You're still expressing some knowledge
in that statement.
- Of course.
If the probability is 90%,
it's absolutely different kind
of knowledge than if it is 10%.
- But it's still not
solid knowledge, it's--
- It is solid knowledge, by.
If you tell me that 90% assurance
smoking will give you
lung cancer in five years,
versus 10%, it's a piece
of useful knowledge.
- So this statistical
view of the universe,
why is it useful?
So we're swimming in complete uncertainty.
Most of everything around you--
- It allows you to predict things
with a certain probability,
and computing those
probabilities are very useful.
That's the whole idea of prediction.
And you need prediction
to be able to survive.
If you cannot predict
the future then you just,
crossing the street would
be extremely fearful.
- And so you've done a
lot of work in causation,
so let's think about correlation.
- I started with probability.
- You started with probability.
You've invented the Bayesian networks.
- [Judea] Yeah.
- And so, we'll dance back and forth
between these levels of uncertainty,
but what is correlation?
So, probability is something
happening, is something,
but then there's a bunch
of things happening,
and sometimes they happen
together sometimes not.
They're independent or not,
so how do you think about
correlation of things?
- Correlation occurs when
two things vary together
over a very long time, is
one way of measuring it.
Or, when you have a bunch of variables
that they all vary cohesively,
then we have a correlation here,
and usually when we
think about correlation,
we really think causation.
Things cannot be correlation
unless there is a reason
for them to vary together.
Why should they vary together?
If they don't see each other,
why should they vary together?
- So underlying it somewhere is causation.
- Yes.
Hidden in our intuition there
is a notion of causation,
because we cannot grasp any
other logic except causation.
- And how does conditional
probability differ
from causation?
So, what is conditional probability?
- Conditional probability
is how things vary
when one of them stays the same.
Now, staying the same
means that I have chosen
to look only at those
incidents where the guy
has the same value as the previous one.
It's my choice, as an experimenter,
so things that are not correlated before
could become correlated.
Like for instance, if I have two coins
which are uncorrelated,
and I choose only those
flippings experiments
in which a bell rings,
and the bell rings when
at least one of them is a tail, okay,
then suddenly I see correlation
between the two coins,
because I only looked at the
cases where the bell rang.
You see, it is my design.
It is my ignorance essentially,
with my audacity to
ignore certain incidents,
I suddenly create a correlation
where it doesn't exist physically.
- Right.
So, you just outlined one of the flaws
of observing the world and
trying to infer something
from the math about the world
from looking at the correlation.
- I don't look at it as a flaw.
The world works like that.
The flaws come if you try
to impose causal logic
on correlation.
It doesn't work too well.
- I mean, but that's exactly what we do.
That has been the majority
of science, is you--
- No, the majority of naive science.
Statisticians know it.
Statisticians know that if you condition
on a third variable,
then you can destroy
or create correlations
among two other variables.
They know it.
It's (speaks foreign language).
There's nothing surprises them.
That's why they all dismiss
the systems paradox, look
"Ah, we know it!"
They don't know anything
about it. (laughs)
- Well, there's disciplines
like psychology,
where all the variables
are hard to account for,
and so, oftentimes there is a leap
between correlation to causation.
- What do you mean, a leap?
Who is trying to get
causation from correlation?
There's no one.
- [Lex] You're not proving causation,
but you're sort of discussing it,
implying, sort of hypothesizing
without ability to--
- Which discipline you have in mind?
I'll tell you if they are obsolete.
(Lex laughs)
Or if they are outdated, or
they're about to get outdated.
- Yes, yes.
- [Judea] Oh, yeah, tell me
which ones you have in mind.
- Well, psychology, you know--
- [Judea] Psychology, what, SEM?
- No, no, I was thinking of
applied psychology, studying,
for example, we work with human behavior
in semi-autonomous
vehicles, how people behave.
And you have to conduct these studies
of people driving cars.
- Everything starts with the question:
What is the research question?
- What is the research question?
The research question:
do people fall asleep when
the car is driving itself?
- Do they fall asleep,
or do they tend to fall
asleep more frequently
- [Lex] More frequently
- than the car not driving itself.
- [Lex] Not driving itself.
That's a good question, okay.
- You put people in the car,
because it's real world.
You can't conduct an experiment
where you control everything.
- [Judea] Why can't you con--
- You could.
- [Judea] Turn the
automatic module on and off.
- Because there's aspects
to it that's unethical,
because it's testing on public roads.
The drivers themselves have to
make that choice themselves,
and so they regulate that.
So, you just observe when
they drive it autonomously,
and when they don't.
- But maybe they turn it
off when they're very tired.
- [Lex] Yeah, that kind of thing.
But you don't know those variables.
- Okay, so you have now
uncontrolled experiment,
- [Lex] Uncontrolled experiment.
- When we correct observation of study,
and when we form the correlation detected,
we have to infer causal relationship,
whether it was the automatic piece
that cause them to fall asleep, or,
so that is an issue that
is about 120 years old.
- [Lex] (laughs) Yeah.
- Oh, I should only go
100 years old, okay?
- [Lex] (chuckles) Who's counting?
- Oh, maybe, no, actually I
should say it's 2,000 years old,
because we have this experiment by Daniel,
about the Babylonian king,
that wanted the exiled people from Israel,
that were taken in exile to
Babylon to serve the king.
He wanted to serve them
king's food, which was meat,
and Daniel as a good Jew
couldn't eat non-Kosher food,
so he asked them to eat vegetarian food.
But the king's overseers said, "I'm sorry,
"but if the king sees that
your performance falls
"below that of other kids,
now, he's going to kill me."
Daniel said, "Let's make an experiment.
"Let's take four of us
from Jerusalem, okay?
"Give us vegetarian food.
"Let's take the other guys
to eat the king's food,
"and about a week's time,
we'll test our performance."
And you know the answer,
because he did the experiment,
and they were so much
better than the others,
that the kings nominated them
to super positions, (laughs)
in his case, so it was a first experiment.
So that there was a very simple,
it's also the same research questions.
We want to know if vegetarian food
assists or obstructs your mental ability.
So, the question is a very old one.
Even Democritus, if I could
discover one cause of things,
I would rather discuss one
cause than be King of Persia.
The task of discovering
causes was in the mind
of ancient people from
many, many years ago.
But, the mathematics of doing that
was only developed in the 1920s.
So, science has left us orphaned.
Science has not provided
us with the mathematics
to capture the idea of x causes
y and y does not cause x.
Because all the question of physics
are symmetrical, algebraic.
The equality sign goes both ways.
- Okay, let's look at machine learning.
Machine learning today, if you
look at deep neural networks,
you can think of it as kind
of conditional probability
estimators.
- [Judea] Conditional probability.
Correct.
Beautiful.
Well, did you say that?
- [Lex] What?
- Conditional probability estimators.
None of the machine learning
people clobbered you?
(laughs)
Attacked you?
- Most people, and this is
why today's conversation
I think is interesting is,
most people would agree with you.
There's certain aspects that
are just effective today,
but we're going to hit a wall,
and there's a lot of ideas,
I think you're very right,
that we're going to have to
return to, about causality.
Let's try to explore it.
- Okay.
- Let's even take a step back.
You invented Bayesian networks,
that look awfully a lot
like they express something
like causation, but they
don't, not necessarily.
So, how do we turn Bayesian networks
into expressing causation?
How do we build causal networks?
A causes B, B causes C.
How do we start to infer
that kind of thing?
- We start by asking ourselves question:
what are the factors that
would determine the value of x?
X could be blood pressure, death, hunger.
- But these are hypotheses
that we propose--
- Hypotheses, everything
which has to do with causality
comes from a theory.
The difference is only how
you interrogate the theory
that you have in your mind.
- So it still needs the
human expert to propose--
- Right.
They need the human expert
to specify the initial model.
Initial model could be very qualitative.
Just who listens to whom?
By whom listens I mean one
variable listens to the other.
So, I say okay, the tide
is listening to the moon,
and not to the rooster
crow, okay, and so forth.
This is our understanding of
the world in which we live,
scientific understanding of reality.
We have to start there,
because if we don't know how
to handle cause and effect relationship,
when we do have a model, and
we certainly do not know how
to handle it when we don't have a model,
so that starts first.
An AI slogan is presentation
first, discovery second.
But, if I give you all the
information that you need,
can you do anything useful with it?
That is the first, representation.
How do you represent it?
I give you all the knowledge in the world.
How do you represent it?
When you represent it, I ask you,
can you infer x or y or z?
Can you answer certain queries?
Is it complex?
Is it polynomial?
All the computer science exercises, we do,
once you give me a
representation for my knowledge.
Then you can ask me, now that I understand
how to represent things,
how do I discover them?
It's a secondary thing.
- I should echo the statement
that mathematics in much of
the machine learning world
has not considered
causation, that A causes B.
Just in anything.
That seems like a non-obvious thing
that you think we would
have really acknowledged it,
but we haven't.
So we have to put that on the table.
Knowledge,
How hard is it to create a
knowledge from which to work?
- In certain area, it's easy,
because we have only four
or five major variables.
An epidemiologist or an
economist can put them down.
The minimum wage,
unemployment, policy xyz,
and start collecting data,
and quantify the parameters
that were left unquantified,
with initial knowledge.
That's the routine work that you find
in experimental psychology,
in economics, everywhere.
In health science, that's a routine thing.
But I should emphasize, you should start
with the research question.
What do you want to estimate?
Once you have that, you
have to have a language
of expressing what you want to estimate.
You think it's easy?
No.
- So we can talk about
two things, I think.
One is how the science of
causation is very useful
for answering certain questions,
and then the other is how do
we create intelligent systems
that need to reason with causation?
So if my research question
is how do I pick up
this water bottle from the table?
All the knowledge that is
required to be able to do that,
how do we construct that knowledge base?
Do we return back to the problem
that we didn't solve in the
80s with expert systems?
Do we have to solve that problem,
of automated construction of knowledge?
You're talking about the
task of eliciting knowledge
from an expert.
- Task of eliciting
knowledge from an expert,
or self discovery of more knowledge,
more and more knowledge.
So, automating the building of knowledge
as much as possible.
- It's a different game,
in the causal domain,
because essentially it is the same thing.
You have to start with some knowledge,
and you're trying to enrich it.
But you don't enrich it
by asking for more rules.
You enrich it by asking for the data.
To look at the data, and quantifying,
and ask queries that you
couldn't answer when you started.
You couldn't because the
question is quite complex,
and it's not within the
capability of ordinary cognition,
of ordinary person, ordinary
expert even, to answer.
- So what kind of questions
do you think we can start
to answer?
- Even a simple, I suppose, yeah. (laughs)
I start with easy one.
- [Lex] Let's do it.
- Okay, what's the effect
of a drug on recovery?
Was it the aspirin that caused
my headache to be cured,
or was it the television program,
or the good news I received?
This is already, see,
it's a difficult question
because it's: find the cause from effect.
The easy one is find effect from cause.
- That's right.
So first you construct a model saying
that this an important research question.
This is an important question.
Then you--
- I didn't construct a model yet.
I just said it's important question.
- Important question.
- And the first exercise is,
express it mathematically.
What do you want to prove?
Like, if I tell you
what will be the effect
of taking this drug?
Okay, you have to say that in mathematics.
How do you say that?
- Yes.
- [Judea] Can you write down the question.
Not the answer.
I want to find the effect
of a drug on my headache.
- Right.
- [Judea] Write it down, write it down.
That's where the do-calculus
comes in. (laughs)
- [Judea] Yes.
The do-operator, the do-operator.
- Do-operator, yeah.
Which is nice.
It's the difference between
association and intervention.
Very beautifully sort of constructed.
- Yeah, so we have a do-operator.
So, the do-calculus connected--
and the do-operator itself,
connects the operation of doing
to something that we can see.
- Right.
So as opposed to the purely observing,
you're making the choice
to change a variable--
- That's what it expresses.
And then, the way that we interpret it,
the mechanism by which we take your query,
and we translate it into
something that we can work with,
is by giving it semantics,
saying that you have a model of the world,
and you cut off all the
incoming arrows into x,
and you're looking now in the
modified, mutilated model,
you ask for the probability of y.
That is interpretation of doing x,
because by doing things,
you've liberated them
from all influences that
acted upon them earlier,
and you subject them to the
tyranny of your muscles.
- So you (chuckles) you
remove all the questions
about causality by doing them.
- So there is one level of questions.
Answer questions about what
will happen if you do things.
If you do, if you drink the coffee,
or if you take the aspirin.
- [Judea] Right.
- So how do we get the
doing data? (laughs)
- Hah. Now the question is,
if you cannot run experiments,
right, then we have to rely
on observation and study.
- So first we could, sorry to interrupt,
we could run an experiment,
where we do something,
where we drink the coffee,
and the do-operator allows you
to sort of be systematic
about expressing that.
- To imagine how the
experiment will look like
even though we cannot
physically and technologically
conduct it.
I'll give you an example.
What is the effect of blood
pressure on mortality?
I cannot go down into your vein
and change your blood pressure.
But I can ask the question,
which means I can have
a model of your body.
I can imagine how the
blood pressure change
will affect your mortality.
How?
I go into the model, and
I conduct this surgery,
about the blood pressure,
even though physically I cannot do it.
- Let me ask the quantum
mechanics question.
Does the doing change the observation?
Meaning, the surgery of
changing the blood pressure--
- No, the surgery is very delicate.
- [Lex] It's very delicate.
Infinitely delicate. (laughs)
- Incisive and delicate,
which means, do-x means
I'm going to touch only x.
- [Lex] Only x.
- Directly into x.
So, that means that I change only things
which depend on x, by
virtue of x changing.
But I don't depend things
which are not depend on x.
Like, I wouldn't change
your sex, or your age.
I just change your blood pressure, okay?
- So, in the case of
blood pressure, it may be
difficult or impossible to
construct such an experiment.
- No, but physically, yes.
But hypothetically no.
- [Lex] Hypothetically no.
- If we had a model, that
is what the model is for.
So, you conduct surgeries on the models.
You take it apart, put it back.
That's the idea for model.
It's the idea of thinking
counterfactually, imagining,
and that idea of creativity.
- So by constructing
that model you can start
to infer if the blood
pressure leads to mortality,
which increases or decreases, whi--
- I construct a model.
I still cannot answer it.
I have to see if I have enough
information in the model
that would allow me to find
out the effects of intervention
from an uninterventional
study, from a hands-off study.
- [Lex] So what's needed--
- We need to have assumptions
about who affects whom.
If the graph has a certain property,
the answer is
"yes, you can get it from
observational study."
If the graph is too mushy bushy bushy,
the answer is, "no, you cannot."
Then you need to find
either different kind
of observation that
you haven't considered,
or one experiment.
- So, basically, that puts
a lot of pressure on you
to encode wisdom into that graph.
- Correct.
But you don't have to encode
more than what you know.
God forbid.
The economists are doing that.
They call identifying assumptions.
They put assumptions,
even they don't prevail
in the world, they put assumptions
so they can identify things.
- Yes, beautifully put.
But, the problem is you don't
know what you don't know.
- You know what you don't know,
because if you don't know,
you say it's possible
that x affect the traffic tomorrow.
It's possible.
You put down an arrow
which says it's possible.
Every arrow in the graph
says it's possible.
- [Lex] So there's not a
significant cost to adding arrows,
- The more arrow you add--
- [Lex] The better.
- The less likely you
are to identify things
from purely observational data.
So if the whole world is bushy,
and everybody effect everybody else,
the answer is-- you can
answer it ahead of time.
I cannot answer my query
from observational data.
I have to go to experiments.
- So, you talk about machine
learning as essentially
learning by association, or
reasoning by association,
and this do-calculus is
allowing for intervention.
I like that word.
You also talk about counterfactuals.
- Yeah.
- And trying to sort of
understand the difference
between counterfactuals and intervention,
first of all, what is counterfactuals,
and why are they useful?
Why are they especially useful
as opposed to just reasoning
what effect actions have?
- Well, counterfactual
contains what we know
will equal explanations.
- Can you give an
example of what kind of--
- If I tell you that acting
one way affects something else,
I didn't explain anything yet.
But if I ask you, was it the
aspirin that cure my headache,
I'm asking for explanation:
what cure my headache?
And putting a finger on
aspirin, provide explanation.
It was the aspirin that was responsible
for your headache going away.
If you didn't take the aspirin,
you will still have a headache.
- So by saying, "If I didn't take aspirin,
"I would have a headache,"
you're thereby saying,
"The aspirin is the thing
"that removed the headache."
- Yes, but you have to have
another point of information.
I took the aspirin, and
my headache is gone.
It's very important information.
Now we're reasoning backward, and I say,
"Was it the aspirin?"
- Yeah.
By considering what would have happened
if everything is the same,
but I didn't take aspirin.
- That's right.
So we know that things
took place, you know?
Joe killed Schmo.
And Schmo would be alive
had Joe not used his gun.
Okay, so that is the counterfactual.
It had a confliction.
It had a conflict here, or clash
between observed fact
-- he did shoot, okay --
and the hypothetical predicate,
which says, had he not shot.
You have a clash, a logical clash,
that cannot exist together.
That's counterfactual,
and that is the source of our explanation
of the idea of responsibility,
regret, and free will.
- Yes, it certainly seems,
that's the highest level
of reasoning, right?
Counterfactual.
- [Judea] Yes, and physicists
do it all the time.
- Who does it all the time?
- [Judea] Physicists.
- Physicists.
- In every equation of physics,
you have Hooke's law,
and you put one kilogram on the spring,
and the spring is one meter,
and you say, "Had this
weight been two kilograms,
"the spring would have
been twice as long."
It's not a problem for
physicists to say that.
Instead with mathematics, it
is in the form of an equation,
equating the weight,
proportionality constant,
and the length of the spring.
We don't have the assymetry
in the equation of physics,
although every physicist
thinks counterfactually.
Ask high school kids, had the
weight been three kilograms,
what would be the length of the spring?
They can answer it immediately,
because they do the counterfactual
processing in their mind,
and then they put it into
equation, algebraic equation,
and they solve it.
But a robot cannot do that.
- How do you make a robot
learn these relationships?
- Why use the word "learn?"
Suppose you tell him, can you do it?
Before you go learning,
you have to ask yourself,
suppose I give all the information.
Can the robot perform a task
that I ask him to perform?
Can he reason and say,
"No, it wasn't the aspirin.
"It was the good news we
received on the phone."
- Right, because, well,
unless the robot had a model,
a causal model of the world.
- [Judea] Right, right.
- I'm sorry I have to linger on this--
- [Judea] But now we have to
linger, and we have to say,
"How do we do it?"
- How do we build it?
- [Judea] Yes.
- How do we build a causal model
without a team of human
experts running around--
- No, why did you go
to learning right away?
You are too much involved with learning.
- Because I like babies.
Babies learn fast, and
I'm trying to figure out
how they do it.
- Good.
That's another question:
How do the babies come out
with the counterfactual
model of the world?
And babies do that.
They know how to play in the crib.
They know which balls hits another one,
and they learn it by playful manipulation
of the world.
Their simple world involves
all these toys and balls
and chimes (laughs)
but if you think about
it, it's a complex world.
- We take for granted how complicated--
- And the kids do it by
playful manipulation,
plus parent guidance,
peer wisdom, and heresay.
They meet each other, and they say,
"You shouldn't have
taken my toy." (laughs)
- Right,
and these multiple sources of information,
they're able to integrate.
So, the challenge is
about how to integrate,
how to form these causal relationships
from different sources of data.
- [Judea] Correct.
- So, how much causal
information is required
to be able to play in the
crib with different objects?
- I don't know.
I haven't experimented
with the crib. (chuckles)
- [Lex] Okay, not a crib--
- I know, it's a very interesting--
- Manipulating physical
objects on this very,
opening the pages of
a book, all the tasks,
physical manipulation
tasks, do you have a sense?
Because my sense is the world
is extremely complicated.
- Extremely complicated.
I agree and I don't
know how to organize it,
because I've been spoiled by easy problems
such as cancer and death, okay? (laughs)
- [Lex] First we have to start trying to--
- No, but it's easy, easy in the sense
that you have only 20 variables,
and they are just variables.
They are not mechanics, okay?
It's easy.
You just put them on the graph
and they speak to you. (laughs)
- [Lex] And you're providing a methodology
for letting them speak.
- I'm working only in the abstract.
The abstract is knowledge
in, knowledge out,
data in between.
- Now, can we take a
leap to trying to learn,
when it's not 20 variables
but 20 million variables,
trying to learn causation in this world.
Not learn, but somehow construct models.
I mean, it seems like you would only have
to be able to learn,
because constructing it
manually would be too difficult.
Do you have ideas of--
- I think it's a matter
of combining simple models
from many, many sources,
from many, many disciplines.
And many metaphors.
Metaphors are the basis
of human intelligence.
- Yeah, so how do you
think about a metaphor
in terms of its use in human intelligence?
- Metaphors is an expert system.
It's mapping problem with
which you are not familiar,
to a problem with which you are familiar.
Like I give you a great example.
The Greek believed that
the sky is an opaque sheer.
It's not really infinite
space; it's an opaque sheer,
and the stars are holes
poked in the sheer,
through which you see the eternal light.
It was a metaphor, why?
Because they understand how
you poke holes in sheers.
They were not familiar
with infinite space.
And we are walking on a shell of a turtle,
and if you get too close to the edge,
you're going to fall down
to Hades, or wherever, yeah.
That's a metaphor.
It's not true.
But these kind of metaphor
enabled Eratosthenes
to measure the radius of the Earth,
because he said, "Come on.
"If we are walking on a turtle shell,
"then the ray of light
coming to this place
"will be different angle
than coming to this place.
"I know the distance.
"I'll measure the two angles,
"and then I have the radius
of the shell of the turtle."
And he did.
And his measurement was very close
to the measurements we have today.
It was, what, 6,700
kilometers, was the Earth?
That's something that would not occur
to a Babylonian astronomer,
even though the Babylonian experiments
were the machine learning
people of the time.
They fit curves, and they
could predict the eclipse
of the moon much more
accurately than the Greek,
because they fit curves.
That's a different metaphor,
something that you're familiar with,
a game, a turtle shell.
What does it mean, if you are familiar?
Familiar means that answers
to certain questions
are explicit.
You don't have to derive them.
- And they were made explicit
because somewhere in the
past you've constructed
a model of that--
- You're familiar with,
so the child is familiar
with billiard balls.
So the child could predict that
if you let loose of one ball,
the other one will bounce off.
You attain that by familiarity.
Familiarity is answering questions,
and you store the answer explicitly.
You don't have to derive it.
So this is idea for metaphor.
All our life, all our intelligence,
is built around metaphors,
mapping from the
unfamiliar to the familiar,
but the marriage between
the two is a tough thing,
which we haven't yet been
able to algorithmatize.
- So, you think of that
process of using metaphor
to leap from one place to another.
We can call it reasoning.
Is it a kind of reasoning?
- [Judea] It is a reasoning
by metaphor, but--
- Reasoning by metaphor.
Do you think of that as learning?
So, learning is a
popular terminology today
in a narrow sense.
- [Judea] It is, it is definitely.
- So you may not-- you're right.
- It's one of the most important learning,
taking something which
theoretically is derivable,
and store it in accessible format.
I'll give you an example: chess, okay?
Finding the winning starting
move in chess is hard.
But there is an answer.
Either there is a winning move
for white, or there isn't,
or it is a draw.
So, the answer to that is available
through the rule of the game.
But we don't know the answer.
So what does a chess master
have that we don't have?
He has stored explicitly an evaluation
of certain complex pattern of the board.
We don't have it,
ordinary people, like me.
I don't know about you.
I'm not a chess master.
So for me I have to derive
things that for him is explicit.
He has seen it before, or he
has seen the pattern before,
or similar patterns before,
and he generalizes, and says,
"Don't move; it's a dangerous move."
- It's just that, not
in the game of chess,
but in the game of billiard balls
we humans are able to initially
derive very effectively
and then reason by
metaphor very effectively,
and we make it look so easy,
and it makes one wonder
how hard is it to build it in a machine?
In your sense, (laughs)
how far away are we
to be able to construct--
- I don't know.
I'm not a futurist.
All I can tell you is that we
are making tremendous progress
in the causal reasoning domain.
Something that I even dare
to call it a revolution,
the causal revolution,
because what we have achieved
in the past three decades
is something that dwarf
everything that was derived
in the entire history.
- So there's an excitement
about current machine
learning methodologies,
and there's really important
good work you're doing
in causal inference.
Where do these worlds collide,
and what does that look like?
- First they gotta work
without collisions. (laughs)
It's got to work in harmony.
- [Lex] Harmony.
- The human is going to
jumpstart the exercise
by providing qualitative,
noncommitting models
of how the universe works,
how reality, the domain
of discourse, works.
The machine is going to
take over from that point
of view, and derive whatever the calculus
says can be derived,
namely, quantitative
answer to our questions.
These are complex questions.
I'll give you some examples
of complex questions,
that boggle your mind
if you think about it.
You take the results of
studies in diverse population,
under diverse conditions,
and you infer the cause
effect of a new population
which doesn't even resemble
any of the ones studied.
You do that by do-calculus.
You do that by generalizing
from one study to another.
See, what's common there too?
What is different?
Let's ignore the differences
and pull out the commonality.
And you do it over maybe 100
hospitals around the world.
From that, you can get
really mileage from big data.
It's not only that you have many samples;
you have many sources of data.
- So that's a really
powerful thing, I think,
especially for medical applications.
Cure cancer, right?
That's how, from data,
you can cure cancer.
So we're talking about causation,
which is the temporal
relationships between things.
- Not only temporal.
It was structural and temporal.
Temporal precedence by itself
cannot replace causation.
- Is temporal precedence the
arrow of time in physics?
- [Judea] Yeah, it's important, necessary.
- It's important.
- [Judea] Yes.
- Is it?
- Yes, I've never seen a
cause propagate backwards.
- But if we use the word cause,
but there's relationships
that are timeless.
I suppose that's still
forward an arrow of time.
But, are there relationships,
logical relationships,
that fit into the structure?
- [Judea] Sure. All do-calculus
is logical relationships.
- That doesn't require a temporal.
It has just the condition
that you're not traveling back in time.
- [Judea] Yes, correct.
- So it's really a generalization,
a powerful generalization, of what--
- [Judea] Of boolean logic.
- Yeah, boolean logic.
- [Judea] Yes.
- That is sort of simply
put, and allows us
to reason about the order
of events, the source--
- Not about, between.
But not deriving the order of events.
We are given cause effect relationships.
They ought to be obeying the
time precedence relationship.
We are given that,
and now that we ask questions
about other causal relationships,
that could be derived
from the initial ones,
but were not given to us explicitly.
Like the case of the
firing squad I gave you
in the first chapter and I ask,
"What if rifleman A declined to shoot?
Would the prisoner still be dead?
To decline to shoot, it means
that he disobeyed orders.
The rule of the games were that
he is an obedient marksman.
That's how you start.
That's the initial order,
but now you ask question
about breaking the rules.
What if he decided not
to pull the trigger,
because became a pacifist?
You and I can answer that.
The other rifleman would have
hit and killed him, okay?
I want a machine to do that.
Is it so hard to ask a machine to do that?
It's such a simple task.
But they have to have a calculus for that.
- Yes, yeah.
But the curiosity, the
natural curiosity for me, is
that yes, you're absolutely
correct and important,
and it's hard to believe
that we haven't done this
seriously, extensively,
already a long time ago.
So, this is really important work,
but I also want to know,
maybe you can philosophize
about how hard is it to learn.
- Look, let's assume learning.
We want learning, okay?
- We want to learn.
- So what do we do?
We put a learning machine
that watches execution trials
in many countries, in many
(laughs) locations, okay?
All the machine can learn
is to see shot or not shot.
Dead, not dead.
A court issued an order or
didn't, okay, just the fact.
For the fact, you don't
know who listens to whom.
You don't know that the condemned person
listens to the bullets,
that the bullets are listening
to the captain, okay?
All we hear is one command,
two shots, dead, okay?
A triple of variables:
yes, no, yes, no, okay.
From that you can learn
who listens to whom?
And you can answer the question? No.
- Definitively, no.
But don't you think you
can start proposing ideas
for humans to review?
You want machine to learn it,
all right, you want a robot.
So robot is watching trials
like that, 200 trials,
and then he has to answer the question,
what if rifleman A
refrained from shooting.
- [Lex] Yeah. So how do we do that?
- (laughs) That's exactly my point.
If looking at the facts
don't give you the strings
behind the facts--
- Absolutely,
but so you think of machine learning,
as it's currently defined,
as only something that looks
at the facts and tries to--
- [Judea] Right now they
only look at the facts.
- Yeah, so is there a way
to modify, in your sense--
- [Judea] Yeah, playful manipulation
- Playful manipulation.
Doing the interventionist kind of things.
- But it could be at random.
For instance, the
rifleman is sick that day,
or he just vomits, or whatever.
So, we can observe this unexpected event,
which introduced noise.
The noise still have
to be random to be able
to relate it to randomized experiments,
and then you have observational studies,
from which to infer the
strings behind the facts.
It's doable to a certain extent.
But now that we're
expert in what you can do
once you have a model,
we can reason back and say
what kind of data you need
to build a model.
- Got it.
So, I know you're not a futurist,
but are you excited?
Have you, when you look back at your life,
longed for the idea of creating
a human level intelligence--
- Well, yeah, I'm driven by that.
All my life I'm driven
just by one thing. (laughs)
But I go slowly.
I go from what I know, to
the next step incrementally.
- So, without imagining what
the end goal looks like,
do you imagine--
- The end goal is going to be a machine
that can answer sophisticated questions:
counterfactuals, regret,
compassion, responsibility,
and free will.
- So what is a good test?
Is a Turing test a reasonable test?
- A Turing test of free
will doesn't exist yet.
There's not--
- [Lex] How would you
test free will? That's a--
- So far we know only one
thing, merely (laughs)
if robots can communicate,
with reward and punishment
among themselves,
and hitting each other on the wrists,
and say "You shouldn't have done that."
Playing better soccer
because they can do that.
- [Lex] What do you mean,
because they can do that?
- Because they can
communicate among themselves.
- [Lex] Because of the communication,
they can do the soccer.
- Because they communicate like
us, rewards and punishment,
yes, you didn't pass
the ball the right time,
and so forth;
therefore you're going to sit
on the bench for the next two,
if they start communicating like that,
the question is, will
they play better soccer?
As opposed to what?
As opposed to what they do now?
Without this ability to reason
about reward and punishment.
Responsibility.
- And counterfactuals.
- So far, I can only
think about communication.
- Communication, and not
necessarily in natural language,
but just communication.
- Just communication,
and that's important to have
a quick and effective means
of communicating knowledge.
If the coach tells you you
should have passed the ball,
ping, he conveys so much knowledge to you
as opposed to what?
Go down and change your software, right.
That's the alternative.
But the coach doesn't know your software.
So how can a coach tell you
you should have passed the ball?
But, our language is very effective:
you should have passed the ball.
You know your software.
You tweak the right module, okay,
and next time you don't do it.
- Now that's for playing soccer,
where the rules are well defined.
- No, no, no, they're not well defined.
When you should pass the ball--
- Is not well defined.
- No, it's very noisy.
Yes, you have to do it
under pressure (laughs)
- It's art.
But in terms of aligning values
between computers and humans,
do you think this cause
and effect type of thinking
is important to align the
values, morals, ethics
under which machines make decisions.
Is the cause effect where
the two can come together?
- Cause effect is necessary component
to build an ethical machine,
because the machine has to empathize,
to understand what's good for you,
to build a model of you, as a recipient.
We should be very much--
What is compassion?
The imagine that you
suffer pain as much as me.
- [Lex] As much as me.
- I do have already a
model of myself, right?
So it's very easy for
me to map you to mine.
I don't have to rebuild a model.
It's much easier to say,
"Ah, you're like me."
Okay, therefore, I will
not hit you, okay? (laughs)
- And the machine has to imagine,
has to try to fake to be human.
Essentially so you can imagine
that you're like me, right?
- Whoa, whoa, whoa, who is me?
That's further; that's consciousness.
They have a model of yourself.
Where do you get this model?
You look at yourself as if you
are part of the environment.
If you build a model of
yourself versus the environment,
then you can say, "I need
to have a model of myself.
"I have abilities; I have
desires, and so forth," okay?
I have a blueprint of myself, though,
not a full detail, though,
because I cannot get
the whole thing problem,
but I have a blueprint.
So at that level of a
blueprint, I can modify things.
I can look at myself
in the mirror and say,
"Hmm, if I tweak this model,
"I'm going to perform differently."
That is what we mean
by free will. (laughs)
- And consciousness.
What do you think is consciousness?
Is it simply self awareness,
including yourself
into the model of the world?
- That's right.
Some people tell me no, this
is only part of consciousness,
and then they start telling
what they really mean
by consciousness, and I lose them.
For me, consciousness
is having a blueprint
of your software.
- Do you have concerns
about the future of AI,
all the different trajectories
of all the research?
- [Judea] Yes.
- Where's your hope
where the movement heads?
Where are your concerns?
- I'm concerned,
because I know we are
building a new species
that has the capability of exceeding us,
exceeding our capabilities,
and can breed itself and take
over the world, absolutely.
It's a new species; it is uncontrolled.
We don't know the degree
to which we control it.
We don't even understand what it means,
to be able to control this new species.
So, I'm concerned.
I don't have anything to add to that
because it's such a
gray area, that unknown.
It never happened in history.
The only time it happened in history,
was evolution with the human being.
- [Lex] Right.
- And it was very
successful, was it? (laughs)
Some people say it was a great success.
- For us, it was, but a
few people along the way,
yeah, a few creatures along
the way would not agree.
So, just because it's such a gray area,
there's nothing else to say.
- [Judea] We have a sample of one.
- Sample of one.
- [Judea] It's us.
- Some people would look
at you, and say, yeah
but we were looking to
you to help us make sure
that sample two works out okay.
- Correct.
Actually we have more
than a sample of one.
We have theories.
And that's good; we don't
need to be statisticians.
So, sample of one doesn't
mean poverty of knowledge.
It's not.
Sample of one plus theory,
conjecture or theory,
of what could happen, that we do have.
But I really feel helpless in
contributing to this argument,
because I know so little,
and my imagination is limited,
and I know how much I don't know,
but I'm concerned.
- You were born and raised in Israel.
- [Judea] Born and raised in Israel, yes.
- And later served in the
Israel military defense forces.
- In the Israel Defense Force.
- What did you learn from that experience?
- From that experience? (laughs)
- [Lex] There's a
kibbutz in there as well.
- Yes, because I was in a NAHAL,
which is a combination
of agricultural work
and military service.
I was an idealist.
I wanted to be a member of the kibbutz
throughout my life,
and to live a communal life,
and so I prepared myself for that.
Slowly, slowly I wanted
a greater challenge.
- So, that's a far world away, both in t--
But I learned from that, what a kidada.
It was a miracle
It was a miracle that
I served in the 1950s.
I don't know how we survived.
The country was under austerity.
It tripled its population
from 600,000 to 1.8 million
when I finished college.
No one went hungry.
Austerity, yes.
When you wanted to make
an omelet in a restaurant,
you had to bring your own egg.
And the imprisoned people
from bringing the food
from the farming area, from
the villages, to the city.
But no one went hungry,
and I always add to that:
higher education did not suffer
any budget cuts.
They still invested in me, in
my wife, in our generation.
To get the best education that they could.
So I'm really grateful for the progenity,
and I'm trying to pay back now.
It's a miracle that we
survived the war of 1948.
They were so close to a second genocide.
It was all planned. (laughs)
But we survived it by a miracle,
and then the second miracle
that not many people talk about,
the next phase, how no one went hungry,
and the country managed
to triple its population.
You know what it means
to triple population?
Imagine United States going
from, what, 350 million
to (laugh) unbelievable.
- This is a really
tense part of the world.
It's a complicated part of the world,
Israel and all around.
Religion is at the core
of that complexity,
or one of the components--
Religion is a strong motivating course
for many, many people
in the Middle East, yes.
- In your view, looking back,
is religion good for society?
- That's a good question
for robotics, you know?
- [Lex] There's echoes of that question.
- Should we equip robot
with religious beliefs?
Suppose we find out, or we agree,
that religion is a good thing,
it will keep you in line.
Should we give the robot
the metaphor of a god?
As a metaphor, the robot
will get it without us, also.
Why? Because a robot
will reason by metaphor.
And what is the most primitive
metaphor a child grows with?
Mother smile, father teaching,
father image and mother image, that's God.
So, whether you want it or not, (laughs)
the robot will, assuming
the robot is going
to have a mother and a father.
It may only have program, though,
which doesn't supply
warmth and discipline.
Well, discipline it does.
So, the robot will have
a model of the trainer.
And everything that happens in the world,
cosmology and so on, is going to be mapped
into the programmer. (laughs)
That's God.
- The thing that represents
the origin for everything
for that robot.
- [Judea] It's the most
primitive relationship.
- So it's going to
arrive there by metaphor.
And so the question is
if overall that metaphor
has served us well, as humans.
- I really don't know.
I think it did,
but as long as you keep in
mind it is only a metaphor.
(laughs)
- So, if you think we can,
can we talk about your son?
- [Judea] Yes, yes.
- Can you tell his story?
- [Judea] His story, well--
- Daniel.
- His story is known.
He was abducted in Pakistan,
by al-Quaeda driven sect,
and under various pretenses.
I don't even pay attention
to what the pretense was.
Originally they wanted to
have United States deliver
some promised airplanes, I--
It was all made up, you know,
all these demands were bogus.
I don't know, really,
but eventually he was executed,
in front of a camera.
- At the core of that
is hate and intolerance.
- At the core, yes, absolutely, yes.
We don't really appreciate
the depth of the hate
with which billions of
peoples are educated.
We don't understand it.
I just listened recently
to what they teach you
in Mogadishu. (laughs)
When the war does stop,
and the tap,
we knew exactly who did it.
The Jews.
- [Lex] The Jews.
We didn't know how,
but we knew who did it.
We don't appreciate what it means to us.
The depth is unbelievable.
- Do you think all of
us are capable of evil,
and the education, the indoctrination,
is really what creates evil?
- Absolutely we are capable of evil.
If you are indoctrinated
sufficiently long,
and in depth,
we are capable of ISIS,
we are capable of Nazism,
yes, we are.
But the question is whether
we, after we have gone
through some Western education,
and we learn that everything
is really relative,
that there is no absolute God.
He's only a belief in God.
Whether we are capable,
now, of being transformed,
under certain circumstances,
to become brutal.
- [Lex] Yeah.
- That is a qu-- I'm worried about it,
because some people say yes,
given the right circumstances,
given the bad economical crisis.
You are capable of doing it,
too, and that worries me.
I want to believe that I'm not capable.
- Seven years after Daniel's death,
you wrote an article at
the Wall Street Journal
titled "Daniel Pearl and
the Normalization of Evil."
- [Judea] Yes.
- What was your message back then,
and how did it change
today, over the years?
- I lost.
- [Lex] What was the message?
- The message was that we
are not treating terrorism
as a taboo.
We are treating it as a bargaining
device that is accepted.
People have grievance, and
they go and bomb restaurants.
It's normal.
Look, you're even not
surprised when I tell you that.
Twenty years ago you say,
"What? For grievance you go
"and blow a restaurant?"
Today it's become normalized.
The banalisation of evil.
And we have created that to
ourselves, by normalizing it,
by making it part of political life.
It's a political debate.
Every terrorist yesterday
becomes a freedom fighter today
and tomorrow is become a terrorist again.
It's switchable.
- [Lex] And so, we should call
out evil when there's evil.
- If we don't want to be part of it.
- [Lex] Become it.
- Yeah, if we want to
separate good from evil,
that's one of the first things, that,
in the Garden of Eden, remember?
The first thing that God tells them was
"Hey, you want some knowledge?
"Here is the tree of good and evil."
- So this evil touched
your life personally.
Does your heart have anger,
sadness, or is it hope?
- Look, I see some beautiful
people coming from Pakistan.
I see beautiful people everywhere.
But I see horrible propagation
of evil in this country, too.
It shows you how populistic
slogans can catch the mind
of the best intellectuals.
- Today is Father's Day.
- [Judea] I didn't know that.
- Yeah, what's a fond
memory you have of Daniel?
- Oh, many good memories remains.
He was my mentor.
He had a sense of balance
that I didn't have. (laughs)
- [Lex] Yeah.
- He saw the beauty in every person.
He was not as emotional as I am,
more looking things in perspective.
He really liked every person.
He really grew up with the idea
that a foreigner is a
reason for curiosity,
not for fear.
This one time we went in Berkeley,
and a homeless came out
from some dark alley
and said, "Hey man, can you spare a dime?"
(Judea gasps) I retreated
back, you know, two feet back,
and Danny just hugged him
and say "Here's a dime.
"Enjoy yourself. Maybe you
want some money to take a bus
"or whatever."
Where did he get it?
Not from me.
(both laugh)
- Do you have advice for young minds today
dreaming about creating,
as you have dreamt,
creating intelligent systems?
What is the best way to arrive
at new break-through ideas
and carry them through
the fire of criticism
and past conventional ideas?
- Ask your questions.
Really, your questions are never dumb.
And solve them your own way. (laughs)
And don't take "no" for an answer.
If they're really dumb,
you'll find out quickly,
by trial and error, to see
that they're not leading any place.
But follow them, and try to
understand things your way.
That is my advice.
I don't know if it's going to help anyone.
- [Lex] No, that's brilliantly put.
- There's a lot of inertia
in science, in academia.
It is slowing down science.
- Yeah, those two words, "your way,"
that's a powerful thing.
It's against inertia, potentially.
- [Judea] Against your professor.
(Lex laughs)
- I wrote "The Book of Why" in order
to democratize common sense.
- [Lex] Yeah. (laughs)
- In order to instill
rebellious spirits in students,
so they wouldn't wait until the
professor gets things right.
(both laugh)
- [Lex] So you wrote the
manifesto of the rebellion
against the professor. (laughs)
- [Judea] Against the professor, yes.
- So looking back at
your life of research,
what ideas do you hope ripple
through the next many decades?
What do you hope your legacy will be?
I already have a tombstone carved.
(both laugh)
- Oh, boy.
- The fundamental law of counterfactuals.
That's what it-- it's a simple equation.
Put a counterfactual in
terms of a model surgery.
That's it, because everything
follows from there.
If you get that, all the rest.
I can die in peace,
and my student can derive all my knowledge
by mathematical means.
- The rest follows.
Thank you so much for talking today.
I really appreciate it.
- My thank you for being so
attentive and instigating.
(both laugh)
- We did it.
- [Lex] The coffee helped.
Thanks for listening to this
conversation with Judea Pearl.
And thank you to our
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And now, let me leave you
with some words of wisdom
from Judea Pearl.
You cannot answer a question
that you cannot ask,
and you cannot ask a question
that you have no words for.
Thank you for listening, and
hope to see you next time.