He's co-founded four startups, two of which

have gone public-- serial entrepreneur, widely

respected as a technical innovator,

and a bestselling author.

In the interest of time though, I'm

going to abridge his long list of many accomplishments

and talk about a few things that I think will especially

interest you before he talks about things that especially

interest him.

So one especially interesting thing that he's done-- he

was the co-founder, alongside his friends Kevin Doren

and Robert Carr, of the GO Corporation in 1987.

They were pioneers in the work of tablet and stylus-based

computing, the precursors to the Apple Newton, the Palm Pilot,

and later smartphones and tablets of today.

If you chronicled, actually, his time there,

they have a very interesting book called "Startup:

The Silicon Valley Adventure."

Some of you may have heard of this.

A fun little fact.

Omid Kordestani-- some of you may know him

as our former chief business officer, started

at Google in 1999-- got his start, actually, at Go Corp.

Jerry may talk about that.

I don't know.

It's possible.

Here he is today again to talk about artificial intelligence

and the changing world of work automation.

So we are here for "Humans Need Not Apply."

Give a warm welcome to Jerry Kaplan, everyone.

[APPLAUSE]

JERRY KAPLAN: Thanks, how's the mic?

Oh, that's good.

All right, well, a mentor of mine used to say never

give a talk the first time.

I want you to know I've put together

a special talk for you guys.

This is the first I'm giving it.

We'll see what happens.

I should leave some time.

I have lots of weird anecdotes about Google

that I will be happy to tell when I'm not on the camera,

as long as I'm not being recorded.

OK, so now for something completely different, as

they used to say on "Monty Python."

The common wisdom about artificial intelligence

is that we're building increasingly intelligent

machines that are ultimately going

to surpass human capabilities and steal our jobs

and maybe even escape human control

and take over the world.

So I'm going to present the case today

that that narrative is both misguided and

counterproductive-- that a more appropriate way to frame

this, which is really better supported

by actual historical and current events,

is that AI is simply a natural extension

of long-standing efforts to automate tasks that date back

at least to the start of the Industrial Revolution.

And then I want to talk about the consequences

if you think about it in that particular way.

But let me ask about the audience-- how many of you

are engineers?

OK, how many of you are not engineers?

Two.

How many people haven't raised their hand yet?

Nobody.

OK.

And that's called closure, right?

OK, and how many of you are doing anything

even vaguely related to AI?

Oh, not that many, OK.

Cool.

At least you won't admit it by the time I'm done with my talk,

I think.

OK, so let me start with a little bit of a history lesson.

I'm teaching Impact of Artificial Intelligence

at Stanford.

And much to my shock, the students

who studied artificial intelligence

don't know much about its history.

So here's a kind of irreverent view.

I'm going to start with an unorthodox history of AI.

Now, here's a news flash for you.

Science does not proceed scientifically.

So it's like the making of legislation and sausage.

Perhaps this is better done outside of the public view.

More than you might want to believe,

progress is often due to the clash of egos

and ideas and institutions.

You guys work in an institution.

I'm sure you see that occasionally.

And artificial intelligence is no exception,

so let me start right at the beginning.

Dartmouth College, 1956.

A group of scientists-- they got together

for an extended working session.

How many of you who John McCarthy is?

Oh, man, OK.

He's a mathematician who was then employed at Dartmouth.

Now, he hosted this meeting along with-- raise your hand

if you know these guys-- Marvin Minsky.

Oh, more than John, OK.

He was then at Harvard.

Claude Shannon?

That's good.

You guys should know who Shannon is.

He was at Bell Laboratories.

And Nathaniel Rochester?

Probably no hands.

One hand.

Are you his son?

Sorry?

AUDIENCE: [INAUDIBLE].

JERRY KAPLAN: OK, there you go.

He was at IBM.

Now, here's what these guys had to say,

or John McCarthy had to say.

He called his proposal "A Proposal

for the Dartmouth Summer Research Project

on Artificial Intelligence."

Now, this was the first known use of the term artificial

intelligence.

But what's not commonly known is why did John McCarthy choose

that particular name?

He explained this later-- much later,

actually-- his motivation.

He said, "As for myself, one of the reasons

for inventing the term artificial intelligence

was to escape the association with cybernetics.

Its concentration on analog feedback seemed misguided,

and I wished to avoid having either

to accept Norbert Wiener as a guru

or having to argue with him."

Now, Norbert Wiener, as you may know,

was a highly respected-- Norbert Wiener?

Anybody?

Oh, my god.

OK.

Cybernetics.

Cybernetics.

Good, you've heard the term at least.

He was a highly respected senior mathematician

and a philosopher at MIT.

Now, while he was that, McCarthy, this guy,

was just a junior professor at Dartmouth.

So he didn't want to have to go up against the powers that be.

So to understand the original intention of the founding

fathers of AI, it's worth reading

some of the actual text of this conference proposal.

I think it's on the screen.

"The study is to proceed on the basis of the conjecture

that every aspect of learning or any other feature

of intelligence can in principle be so precisely described

that a machine can be made to simulate it.

An attempt will be made to find out

how to make machines use language,

form abstractions and concepts, solve

kinds of problems now reserved for humans,

and improve themselves."

It's 1950-- what was it, 1956?

"We think that a significant advance

could be made in one or more these problems if a carefully

selected group of scientists work on it together

for a summer."

Now, that's a pretty dubious agenda for a summer break.

Now, many of the Dartmouth conference participants

had their own view about how to best approach

artificial intelligence.

But John McCarthy's specialty was mathematical logic.

In particular, he believed that logical inference

was the key to, using his words, simulated intelligence.

That's what he thought AI was.

Now, his approach, skipping ahead quite a ways,

but his approach eventually became

known as what's called the physical symbol systems

hypothesis.

Anybody here have heard of that?

One.

Good man, OK, you can take over for the rest of the talk.

Now, that was the dominant paradigm

in the field of artificial intelligence for the first 30

years or so after the Dartmouth Conference.

Now, here's John McCarthy.

I'm old enough to have known John McCarthy when

I was a postdoc at Stanford, where he founded the Stanford

Artificial Intelligence Lab.

Now, John was definitely a brilliant scientist.

He invented the programming language Lisp.

Good.

And he invented the concept of time sharing.

Not too many people know that.

But he definitely had the mad professor thing going.

Let's see if this works.

Almost.

I'm using somebody else's computer.

You know, he had the wild eyes and the hair.

The guy on the right, as you may know,

is Professor Emmett Brown, who invented the-- what is it?

The flux capacitor time machine.

How many people know the flux capacitor?

OK, good.

I'm just checking to make sure this talk works.

But I'm confident that John McCarthy, having met him,

never really expected that his clever name emerging

field is going to turn out to be one

of the great accidental marketing coups of all time.

So it's not only inspired generations of researchers,

including myself, but it spawned a virtual industry

of science fiction and Hollywood blockbusters and media

attention and pontificating pundits, also including myself.

How do you name the field something less arousing,

like logical programming, or symbolic systems?

I doubt very many of us would have ever heard

of the field today.

The field would have just motored

along automating various tasks while we marvelled

at the cleverness not of what the creations were,

but of the engineers.

I'm getting a little bit ahead of my story.

In any case, McCarthy's hypothesis

that logic was the basis of human intelligence is, at best,

questionable.

Today, in fact, most AI researchers

have abandoned this approach and believe

it was just plain wrong.

The symbolic system approach has been almost entirely abandoned

in favor of generally what's now referred

to as machine learning.

How many people here are doing machine learning?

Good.

OK, or you certainly know about it.

But rejecting that old approach is throwing the baby out

with the bathwater.

Some truly important advances in computing

came out of symbolic systems, including

things like heuristic search algorithms, logical problem

solvers, game players, reasoning systems.

These were all the old approach.

And many of the results of all that work

are in wide practical use today.

For example, formulating driving directions-- I got

lost coming here.

I didn't know the difference between the express lane

and the regular lane.

I thought I was in the other one.

Take this exit.

No exit.

Laying out factories and warehouses,

proving that complex computer chips actually

meet their specifications-- this all uses early AI techniques.

And I'm sure that there are many more of these to come.

Now, did I mention machine learning?

It's certainly the focus of most current research,

and in some circles, at least where I am,

it's considered a serious candidate

for the real basis of human intelligence.

Now, my personal opinion is that while it's

a very powerful technology, and it's

going to have a very significant practical impact,

it's very unlikely to be the computational equivalent

of the human mind.