Good afternoon.
Welcome to the future forum,
a series of discussions where we
are exploring trends that
are changing the future.
This series is presented by the Sloan
Fellows from the Stanford MSX program.
My name is Ravi.
I'm an engineer by training,
with over ten years of experience.
I've been fortunate to design and
develop products for
some of the leading high tech
companies here in the US.
Currently, as a Sloan Fellow,
I'm privileged to spend
a year in Silicon Valley and
at the Stanford Graduate School
of Business participating in
the evolution of technology and
learning from some of
the brightest minds in business.
The MSX Program is a full time
on-campus one-year management
degree specifically designed for
accomplished and
experienced professionals
from around the world.
My classmates on average have
over 13 years of experience,
come from over 40 different industries,
and have been leaders in driving change.
Today I had the honor of
introducing professor Andrew Ng.
Andrew is one of the leading thinkers in
artificial intelligence with research
focusing on deep learning.
He has taught machine learning for
over 100,000 students through
his online course at Coursera.
He founded and
led the Google Brain project,
which developed massive scale,
deep learning algorithms.
He's currently the VP and
chief scientist of Baidu,
the co-chairman and
co-founder of Coursera, and last but
not least, an adjunct professor
right here at Stanford University.
Please join me, and the 2017 Sloan Fellows
in welcoming Professor Andrew Ng.
>> Thank you.
>> [APPLAUSE]
>> Thank you, and thank you, Ravi.
So what I want to do today
is talk to you about AI.
So as Ravi mentioned,
right now I lead a large AI team
at Baidu, about 1300 scientists and
engineers and so on.
So I've been fortunate to see a lot of
AI applications, a lot of research in AI
as well as a lot of users in AI in many
industries and many different products.
So as I was preparing for
this presentation,
I asked myself what I thought
would be most useful to you.
And what I thought I'd
talk about is four things.
I want to share with you what I
think are the major trends in AI.
Because I guess the title of this
talk was AI is the New Electricity.
Just as electricity transformed
industry after industry 100 years ago,
I think AI will now do the same.
So I share with you some of these
exciting AI trends that I and
many of my friends are seeing.
I want to discuss with you some
of the impact of AI on business.
Whether, I guess, to the GSPC and
to the Sloan Fellows, whether
you go on to start your own company after
you leave Stanford, or whether you join
a large enterprise, I think that there's a
good chance that AI will affect your work.
So I'll share with you some
of the trends for that.
And then talk a little bit about
the process of working with AI.
This is some kind of practical advice for
how to think about,
not just how it affects businesses, but
how AI affects specifically products and
how to go about growing those products.
And then finally, I think for the sign
up of this event, there was a space for
some of you to ask some questions and
quite a lot of you asked questions
about the societal impact of AIs.
I'll talk a little bit about that as well,
all right?
So the title of this talk is projected,
no, I guess not, all right.
I think on the website the title was
listed as the AI is the New Electricity.
So it's an analogy that we've been
making over half a year or something.
About 100 years ago, we started to
electrify the United States, right,
develop electric power.
And that transformed transportation.
It transformed manufacturing, using
electric power instead of steam power.
It transformed agriculture, right.
I think refrigeration was a really,
a transformed healthcare and so
on and so on.
And I think that AI is now positioned to
have an equally large
transformation on many industries.
The IT industry, which I work in,
[COUGH] is already transformed by AI.
So today at Baidu, Web search,
advertising, all powered by AI.
The way we decide whether or not to
approve a consumer loan, really that's AI.
When someone orders takeout through
the Baidu on-demand food delivery service,
AI helps us with the logistics.
They route the driver to your door,
helps us estimate to tell you how long
we think it'll take to get to your door.
So it's really up and down.
Both the major services, many other
products in the IT industry are now
powered by AI,
just literally possible by AI.
But we're starting to see this
transformation of AI technology
in other industries as well.
So I think FinTech is well on its way
to being totally transformed by AI.
We're seeing the beginnings of
this in other industries as well.
I think logistics is part way
through its transformation.
I think healthcare is just
at the very beginnings, but
there's huge opportunities there.
Everyone talks about self-driving cars.
I think that will come as well, a little
bit, that will take a little bit of time
to land, but
that's another huge transformation.
But I think that we live in a world where
just as electricity transformed almost
everything almost 100 years ago,
today I actually have a hard time thinking
of an industry that I don't think
AI will transform in the next
several years, right?
And maybe throughout this presentation,
maybe at the end of doing Q and
A, if you can think of an industry
that AI won't transform, okay,
like a major industry, not a minor one.
Raise your hand and let me know.
I can just tell you now,
my best answer to that.
So I once, [COUGH] when my friends and I,
sometimes my friends and
I actually challenge each other
to name an industry that we don't
think would be transformed by AI.
My personal best example is hairdressing,
right, cutting hair.
>> [LAUGH]
>> I don't know how to build a robot
to replace my hairdresser.
Although I once said this
same statement on stage.
And one of my friends, who is a robotics
professor, was in the audience.
And so my friend stood up, and
she pointed at my head, and she said,
Andrew, for most people's hairstyles,
I would agree you can't build a robot.
But for your hairstyle, Andrew, I can-
>> [LAUGH]
>> All right.
So despite all this hype about AI,
what is AI doing?
What can AI really do?
It's driving tremendous economic value,
easily billions.
At least tens of billions,
maybe hundreds of billions of
dollars worth of market cap.
>> But what exactly is AI doing?
It turns out that almost all this
ridiculously huge amounts of value of AI,
at least today, and the future may be
different, but at least today almost all
this massive economic value of AI is
driven by one type of AI, by one idea.
And This technical term is that
it's called Supervised Learning.
And what that means is
using AI to figure out
a relatively simple A to B mapping,
or A to B response.
Relatively simple A to B or
input those response mappings.
So, for example, given a piece of email,
if I input that,
and I ask you to tell me
if this is spam or not.
So, given an email, output 0 or 1 to tell
me if this is spam or not, yes or no?
This is an example of a problem where
you have an input A, you can email, and
you want a system to give your response B,
0 or 1.
And this today is done
with Supervised Learning.
Or, given an image.
Tell me what is the object
in this image and
maybe of a thousand objects or
10,000 objects.
Just try to recognize it.
So you input a picture and
output a number from say,
one to 1000 that tells
you what object this is.
This, AI can do.
Some more interesting examples.
When you're given an audio clip,
maybe you want to output the transcript.
So this is speech recognition, right.
Input an audio clip and output
detects transcript of what was said,
so that's speech recognition.
And the way that a lot of AI is built
today is by having a piece of software
learn, I'll say exactly in a second
what I mean by the word learn,
what it means for a computer to learn,
but a lot of the value of AI
today is having a machine learn
these input to response mappings.
Given a piece of English text,
I'll put the French translation, or
I talked about going from audio to text or
maybe you want to go from text,
and have a machine read out the text
in a very natural-sounding voice.
So, it turns out, that the idea
of supervised learning, is that,
when you have a lot of data,
of both A and B both.
Today, a lot of the time, we have
very good techniques for automating,
for automatically learning
a way to map from A to B.
For example, If you have a giant database
of emails, as well as annotations of what
is spam and what isn't spam, you could
probably learn a pretty good spam filter.
Or I guess I've done a lot of
work on speech recognition.
If you have, let's say,
50,000 hours of audio, and if you
have the transcript of all 50,000 hours of
audio, then you could do a pretty good job
of having a machine figure out what is
the mapping between audio and text.
So, the reason I want to go into
this level of detail is because
despite all the hype and
excitement about AI,
it's still extremely limited today,
relative to what human intelligence is.
And clearly you and I,
every one of us can do way more than
figure out input to response mappings.
But this is driving incredible
amounts of economic value, today.
Just one example.
Given some information about an ad,
and about a user, can
you tell me whether you
usually click on this ad?
Leading Internet companies have a ton of
data about this, because of showing people
some number of ads that we sold
whether they clicked on it or not.
So we have incredibly good models for
predicting whether a given user
will click on a particular ad.
And by showing users the most relevant
ads this is actually good for
users because you see more relevant ads
and this is incredibly lucrative for
many of the online internet
advertising companies, right.
This is certainly one of the most
lucrative applications we have today,
possibly the most lucrative, I don't know.
Now, at Baidu,
you have worth of a lot of
product managers.
And one question that I got from a lot
of product managers is, you're trying to
design a product and you want to know,
how can you fit AI in some bigger product?
So, do you want to use this for
spam filter?
Do you want to use this to
maybe tag your friends' faces?
Or do you want to use this, where do you
want to build speech recognition in your
app, but can AI do other things as well.
Where can you fit AI into, you know,
a bigger product or a bigger application.
So, some of the product managers I was
working with were struggling to understand
what can AI do and what can't AI do.
So I'm curious.
How many of you know what a product
manager is or what a product manager does?
Okay good, like half of you.
Is that right?
Okay, cool.
I asked the same question at
an academic AI conference and
I think only about one fifth of
the hands went up, which is interesting.
Just to summarize when we in the workflow,
a lot of tech companies,
it's the product manager's
responsibility to work with users,
look at data, to figure out what
is a product that users desire.
To design the features and sometimes also
the marketing and the pricing, as well.
But let me just say design the features
and figure out what the product is
supposed to do, for example,
should you have a light button or not?
Do you try to have a speech
recognition feature or not?
So it's really to design the product.
If you give the product spec to
engineering which is responsible for
building it, right, that's a common
division of labor in technology companies
between product managers and engineers.
So product managers,
when I was working with them,
was trying to understand what can AI do?
So there's this rule of thumb that
I gave many product managers,
which is that anything that
a typical human can do.
With, at most, one second of thought.
Right, we can probably now or
soon, automate with AI.
And this is an imperfect rule.
There are false positives and
false negatives with these heuristics so
this rule is imperfect but
we found this rule to be quite helpful.
So today, actually at Baidu, there
are some product managers running around
looking for tasks that they could
do in less than a second and
thinking about how to automate that.
>> [LAUGH]
>> I have to say, before we came up with
this rule, they were given
a different rule by someone else.
And before I gave this heuristic,
someone else told them product managers,
assume AI can do anything.
>> [LAUGH]
>> And
that actually turned out to be useful.
Some progress was made
with that heuristic, but
I think this one was a bit better.
A lot of these
things on the left you could do
with less than a second of thought.
So one of the patterns we see is that
there are a lot of things that AI can do,
but AI progress
tends to be fastest if you're trying
to do something that a human can do.
For example,
build a self-driving car, right?
Humans can drive pretty well, so
AI is making actually pretty
decent progress on that.
Or diagnose medical images.
If a human radiologists can
read an image The odds of AI
being able to do that in the next
several years is actually pretty good.
There are some examples of
tasks that humans cannot do.
For example, I don't think,
well, very few humans can predict how
the stock market will change, right?
Possibly no human can.
And so this much harder to
get an AI to do that as well.
And there a few reasons for that.
First is that if a human can do it,
then first,
you're at least guaranteed
that it's feasible, right?
Even if a human can't do it,
like predict the stock market,
maybe it's just impossible, I don't know.
A second reason is that
if a human can do it,
you could usually get data out of humans.
So we have doctors that are pretty
good at reading radiological images.
And so if A is an image and
B is a diagnosis,
then you can get these doctors
to give you a lot of data,
give you a lot of examples of both A and
B, right?
So things that humans can do,
can usually pay people, hire people or
something, and get them to provide
a lot of data most of the time.
And then finally, If a human can do it,
you could use human insight
to drive a lot of progress.
So if a AI makes a mistake diagnosing
a certain radiology image,
like an x-ray scan, like an x-ray image,
then AI makes a mistake.
Then if a human can diagnose this type of
disease, you can usually talk to the human
and get some insights about why they
think this patient has lung cancer or
whatever and try to code into an AI.
So one of the patterns you see across
the AI industry is that progress
tends to be faster when we try to
automate tasks that humans can do.
And there are definitely many exceptions,
but I see so
many dozens of AI projects and
I'm trying to summarize trends I see.
They're all not 100% true,
but 80 or 90% true.
So for a lot of projects, you find it
if the horizontal axis is time and
this is human performance,
In terms of how accurately you
can diagnose x-ray scans or
how accurately can classify spam email or
whatever.
You find that over time the AI
will tend to make rapid progress
until you get up to
human level performance.
And if you ever surpass it, very often
your progress slows down
because of these reasons.
And so this is great,
because this gives AI a lot of
space to automate a lot of things.
The downside to this is
the jobs implication, right.
If we're especially good at doing
whatever humans can do, then I think AI
software will be in direct competition
with a lot of people for a lot of jobs.
I would say probably already a little
bit now, but even more so in the future.
And I'll say a little
about that later as well.
The fact that we're just very good at
automating things people can do and
we're actually less good at doing
things people also can't do.
That actually makes the competition
between AI and people for jobs laborious.
So all right,
let me come back to the AI trends.
And one of these I'm going to delve
a little bit deeper into the AI trends is,
I bet some of you will be asked by your
friends afterward, what's going on in AI?
And I hope to give you some answers that
let you speak intelligently as well,
to others about AI.
It turns out one of the ideas
about AI have been around for
many years, frankly, several decades.
But it's only in the last several years,
maybe the last five years,
that AI has really taken off.
So why is this?
When I'm asked this question,
why is AI only now taking off?
There's one picture that I always draw.
So I'm going to draw that picture for
you now.
Which is [COUGH] that,
if on the horizontal axis,
I plot the amount of data,
[COUGH] And on the vertical axis,
I plot the performance of our AI system.
It turns out that several years ago,
maybe ten years ago,
we were using earlier generations of AI
software, earlier generations of most
common machine learning algorithms,
to learn these A to B mappings.
And for the earlier generations of, so
this is an earlier machine learning.
Sorry, let me call this traditional
machine learning algorithms, all right.
It turns out that for
the earlier generations of machine
learning algorithms, even as we
fed it more data, its performance
did not keep on getting better.
It was as if beyond a certain point,
it just didn't know what to do with all
the additional data you are now giving it.
And here by data, I mean the amount of A,
comma B data, right?
With both the input A as well as
the target B that you want to output.
And what happened over last several
years is because of MOS law and
also GP use,
maybe especially in GPU computing,
we finally have been able to build
machine learning pieces of software
that are big enough to absorb these
huge data sizes that we have.
So what we saw was that,
if you feed your data into a small,
Neural network, we'll say a little bit
later what a neural network is, but
an example of machine learning technology.
If you've heard the term deep learning,
which is working really well but
also a bit overhyped.
Neural network and
deep learning are roughly synonyms.
Then with a small neural network,
the performance looks like that.
If you build a slightly larger neural net,
The performance looks like that.
And there's only, if you have the
computational power to build a very large,
Neural net that your performance
kind of keeps on going up, right?
Sorry, I think this line should
be strictly above the others,
something like that, right?
And so
what this means is that in today's world,
to get the best possible performance, in
order to get up here, you need two things.
First, you need a ton of data, right?
And second, you need the ability to
build a very large neural network.
And large is relative, but because of this
I think the leading edge of AI research,
the leading edge of neural net research
is today shifting to supercomputers,
or HPCs, or high performance computers or
super computers.
So in fact today, the leading AI teams
tend to have this old structure where you
have an AI team and you have some of
the machine learning researchers, right?
Abbreviates to ML.
And you have HPC, or
high performance computing or
super computing researchers
are working together to build a giant,
to build the big ion, to build
the really giant computers that you need
in order to hit the levels
of today's performance.
I'm seeing more and more themes that
kind of have an old structure like this.
And the old structure is organized like
this because, frankly, one of the things
we do at Baidu, for example,
it requires such specialized expertise in
machine learning and such specialized
expertise in HPC that there's no one human
on this planet that knows both subjects
to the levels of expertise needed.
Correctly right?
So, let's see.
So let me go even further into,
[INAUDIBLE] in the questions that some of
you asked on the website signing up for
this event, some of you asked about
what evil AI killer was taking over
humanity and so on-
>> [LAUGH]
>> People do worry about that.
So to kind of address that, I actually
want to get just slightly technical and
tell you what is a neural network, right?
So a neural network loosely
inspired by the human brain, right?
And so a neural network is a little
bit like a human brain, all right.
So that analogy I just made is so
easy for people like me, right,
to make to the media, that this
analogy tends to make people think
we're building artificial brains,
just like the human brain.
The reality is that today,
frankly, we have almost no idea
how the human brain works.
So we have even less idea of how to
build a computer that works just like
the human brain.
And even though we like to say, neural
net works a little bit like the brain,
they are so different that I think we've
gone past the point where that analogy is
still that useful, right?
It's just that maybe, we don't have
a better analogy right now to explain it.
But so then, let me actually tell
you what a neural network is, and
I think you'll be surprised
at how simple it is, right.
So let me show you an example of
the simplest machine learning problem,
which is, let's say you have a data set
where you want to predict the price of
a house, right?
So you have the data set where the
horizontal axis is the size of the house,
and the vertical axis is the price
of the house, square feet, dollars.
So you have some data set like this,
Right?
And so well, what do you do?
You fit a straight line to this, right?
So this can be represented
by a simple neural network,
where you input the size, And
you output the price, okay?
And so just this straight line
function is represented via a neuron,
which I'm going to draw in pictures
as a little circle like that, okay.
And, if you want a really fancy neuron,
maybe it's not just fitting in a straight
line, maybe it's I don't know,
at this smart you realizes that price
should never be negative or something,
but the first approximation, let's just
say is, cutting a straight line, right?
Maybe you don't want it to be negative or
something, [SOUND].
Now, so, this is maybe the simplest
possible in your network,
one input,
one output with a single neuron.
So what is in neural network?
Well, it's just to take
a bunch of these things,
where you take a bunch of these things,
and stringing them together.
So instead of predicting the price
of house just based on the size,
maybe you think that the price of a house
actually depends on several things,
which is, first, there's the size, and
then there's the number of bedrooms.
And depending on the square footage and
the number of bedrooms,
this tells you what family size this
can comfortably support, right.
Can this support a family of two,
a family of four,
a family of six, whatever, right,
and then, well, what else?
Based on the zip codes of the house,
as well as the average
wealth of the neighborhood,
maybe this tells you
about the school-to-school quality, right.
So, with two little neurons,
one that tells us a family size,
a house can support one that tells us his
group quality and maybe the zip code also
tells us, how walk without is this, right?
And maybe if I'll buy a house maybe
ultimately I care about my family size and
support, is this a walkable region,
was the school quality.
So let's say this things and
string them into another neuron.
Another linear function or
something like it that then
[SOUND] outputs the price, okay?
So this is in your neural network and one
of the magics of a neural network is that,
I gave this example, as if when
we're building this neural network,
we have to figure out that family size,
walkability and school quality
are the three most important things that
determine the price of a house, right.
As I drew this neural network talked
about those three concepts part
of the magic of the new network is that
when you are training one of these
things you don't need to figure out
what are the important factors,
all you need to do is give it the input
A [SOUND], and it responds B [SOUND] and
it figures out by itself what all
of these intermediate things that
really matter for
predicting the price of a house.
And part of the magic is when you have
a ton of data, when you have enough data,
A and B, it can figure out an awful
lot of things by itself, all right?
I've taught machine learning for
a long time,
I was a full-time faculty at Stanford for
over a decade,
now I'm still adjunct
faculty in the CS department.
But whenever I teach people the
mathematical details of a neural network,
often I get from the students like almost
a slight sense of disappointment [LAUGH].
Like is this really this simple,
[LAUGH] you gotta be fooling me, but
then you implement it and it actually
works when you feed it a lot of data.
Because all the complexity,
all the smarts of the neural network
comes from us giving it tons of data.
Maybe tens of thousands or hundreds or
thousands or more of houses and
their prices, and only a little bit
of it comes from the software, so
the software, well known trivia.
Software is really not that easy, right.
The software is a piece of
network that only kind of knows.
The data is a passive,
larger source of information for
the smarts of the neural network,
then the software that we have to write,
[SOUND], so, and let's see, yeah.
One of the implications of this is [SOUND]
when you think about building businesses,
we think about building
products of businesses,
what is the scarce weasels, right?
If you want to build a defensible
business that deeply incorporates AI,
what are the moats?
Or how do you build
a defensible business in AI?
Today, we're fortunate
that the AI community,
the AI research community is quite open.
So almost all,
maybe all of the leading groups,
tend to publish our results
quite freely and openly.
[SOUND] And if you read our papers at
Baidu, we don't hold anything back.
If you read our state of the art speech
recognition paper, our state of the art
face recognition paper,
we really try to share all the details.
And we're not trying to hide any details,
right.
And many leading,
researchers in AI do that, so
it's difficult to keep
algorithms secret anyway.
So how do you build
a defensible business using AI?
I think today,
there are two scarce resources.
One is data, it's actually very difficult
to acquire huge amounts of data, right,
A come a B.
Maybe to give you an example, one of
the projects, well a couple examples,
speech recognition,
I mention just now we've been training on.
50,000 hours of data.
This year, we expect to train
about 100,000 hours of data.
That's over 10 years of audio data, right?
So literally, if I pull my laptop and
start playing audio to you to go through
all the data our system listens to,
we'll still be here listening until
the year 2027, I guess, right?
So this is massive amounts of data
that is very expensive to obtain.
Or take face recognition.
We've done work on face recognition.
So to say some numbers,
the most popular academic computer vision
benchmark slash competition has
researchers work on about 1
million images, right, and
the very largest academic
papers in computer vision publish papers
on maybe 15 million images, right,
of the kind of recognizing objects
from pictures or whatever.
At Baidu, to train our really leading
edge, possibly best in the world,
but I can't prove that, definitely very,
very good face recognition system.
We train it on 200 million images, right,
so this scale of data is
very difficult to obtain.
And I would say that, honestly,
if I were leading a small team of five or
ten people, I would have no idea, frankly,
how to replicate this scale of data and
build a system like we're able
to in a large company like I do,
with access to just
massive scale data sets.
And in fact, at large companies,
sometimes we'll launch products, not for
the revenue, but for the data, right?
We actually do that quite often.
Often I get asked, can you give me a few
examples, and the answer, unfortunately,
is usually no, actually.
But I frequently launch products where
my motivation is not revenue but
is actually data, and we monetize
the data through a different product.
So I would say that today in the world
of AI, the two scarcest resources are,
I would say the most scarce
resource today is actually talent
because AI needs to be customized for
your business context.
You can't just download an open source
package and apply it to your problem.
You need to figure out where does
the spam filter fit in your business or
where does speech recognition
fit in your business.
And what context, where can you fit
in this AI machine learning thing?
And so this is why there is a talent
war for AI because every company,
to explore your data, you need
that AI talent that can come in to
customize the AI, figure out what is A and
what is B, where to get the data,
how to tune the algorithm to work for
your business context.
I'd say maybe that's
a scarce resource today.
And then second is data is proving
to be a defensible barrier for
a lot of AI-powered businesses.
So there's this concept
of a virtuous circle
[COUGH] of AI that we see in
a lot of products as well.
Which is, [COUGH] you might
build a product, [COUGH] right?
For example, we built a speech recognition
system to enable a voice search,
right, which we did at Baidu.
Because the US search companies have
done that, too, some of the US, anyway.
The speech recognition system,
whatever, some product,
because it's a great product,
we get a lot of users, right?
The users using the product
naturally generates data, right, and
then the data through ML feeds into our
product to make the product even better.
And so this becomes a positive feedback.
That often means that the biggest and
the most successful products, the most
successful products, the most successful,
the best product often has the most users.
Having the most users usually
means you get the most data, and
with modern ML,
having the most data sometimes, usually,
often means you can do the best AI,
that's machine learning.
And therefore have an even better product,
and
this results in a positive
feedback loop into your product.
And so when we launch new products,
we often explicitly plan out how
to drive this cycle as well.
And I'm seeing pretty sophisticated
strategies in terms of deciding how to
roll out the product, sometimes by
geography, sometimes market segment,
in order to drive this cycle,
in order drive the cycle, right?
Now this concept wasn't around for a long
time, but this is really a much stronger
positive feedback loop just recently,
because of the following reasons.
Is traditional AI algorithms
work like that, so
there was kind of beyond a certain point,
you didn't need more data, right?
This is data performance.
So I feel like ten years
ago data was valuable, but
it created less of a defensive barrier
because beyond a certain threshold,
the data, it just didn't really matter.
But now the AI works like that, the data
is becoming even more important for
creating defensible barriers for
AI kind of businesses.
Let's see, all right.
Strike that question then.
Several of you asked me about,
actually Robbie was kind enough
to take the audience questions
from the sign-up form and
summarize them into major categories.
So he summarized the questions into
your major heading categories, right?
So one of them was AI society impact.
One was your practical questions for AI.
One of the headings that
Robbie wrote was scared.
As in, will AI take over the human race or
kill humans or whatever?
So I feel like there is this,
so this is a circle of AI.
There is a, I'm not sure what to call it,
I'm going to call it the non-virtuous-
>> [LAUGH]
>> Circle of hype.
>> [LAUGH]
>> When preparing for
this talk, I actually went to a thesaurus
to look up antonyms, opposites,
of the word virtuous, and vile came up.
But I thought, [LAUGH], vile circle of
height was a bit too provocative, I know.
But I feel like that we are,
unfortunately, there is this evil AI hype.
AI take over the world instead of humans,
whatever.
Unfortunately, some of that evil AI hype,
right, fears of AI,
is driving funding, because what if
AI could wipe out the human race?
Then sometimes we have the individuals,
or sometimes government organizations or
whatever.
They now think, well,
let's fund some research, and
the funding goes to anti-evil AI.
>> [LAUGH]
>> And
the results of this work drives more hype,
right, and I think this is actually a very
unhealthy cycle that a small part
of AI communities are getting into.
And I'll be honest.
Unfortunately, I see a small group of
people, it's a small group, with a clear
financial incentive to drive the hype,
because the hype drives funding to them.
So I'm actually very
unhappy about this hype.
And I'm unhappy about it for
a couple of reasons.
First I think that there is no clear path
to how AI can become sentient, right?
Part of me, I hope that there will be a
technological breakthrough that enables AI
to become sentient, but
I just don't see it happening.
It might be that that breakthrough
might happen in decades.
It might happen in hundreds of years.
Maybe it'll happen thousands
of years from I don't know.
I really don't know.
The timing of technology breakthroughs
is very hard to predict.
I once made this analogy that worrying
about evil AI killer robots today
is a little bit like worrying about
overpopulation on the planet Mars, right?
>> [LAUGH]
>> And
I do hope that someday
we'll colonize Mars and
maybe someday Mars will be overpopulated.
And some will ask me Andrew
there are all these young,
innocent children dying of pollution on
Mars, how can you not care about them?
And my answer is I haven't
land to the planet yet, so
I don't know how to work
productively on that problem.
>> [LAUGH]
>> So, maybe the dilemma.
If you ask me,
do I support doing research on x, right?
Do I support research
on almost any subjects?
I usually want to say yes, of course.
I research on anti evil
AI on a positive thing.
But I do see that there's a massive
misallocation of these sources.
I think if there were two
people in United States,
maybe ten people in United States where
I can go and to anti evil A.I. is fine.
The ten people working on over
population of Mars is actually fine,
form a committee, write some papers.
>> [LAUGH]
>> But
I do think that there is much too much
investment in this right now, right?
So yeah, so sleep easy.
And maybe the other thing, quite a lot
of you asked about the societal impact,
which what I found is varying.
The other thing I worry about is
this evil AI hype being used to
whitewash a much more serious issue,
which is job displacement, right?
So frankly, I know a lot of leaders
in machine learning, right?
And I talk to them about their project.
And there's so many jobs that are squarely
in the cross hairs of my friends'
projects, and the people doing those jobs,
frankly, they just don't know, right?
And so, in Silicon Valley,
we're being responsible for
creating tremendous wealth,
but part of me feels like we
need to be responsible as well for
owning up to the problems we cause and
I think job displacement is
the next big one, thank you.
Thank you.
And I'm going to say just a little
bit more about that at the end.
And then we shouldn't whitewash
this issue by pretending
that there's some other futuristic fear,
to fearmonger about and
try to solve that by
ignoring the real problem.
We'll see.
So the last thing I want to talk about is,
AI product management.
So AI is evolving rapidly super exciting,
they're just opportunities left and right,
but I want to share with you some of
the challenges I see as well, right?
Already some of the things we're
working on that I end up bleeding
as well I feel like our own
thinking is not yet mature.
But that you run into if you try
to incorporate AI into business.
So AI Product Managements.
So maybe many of you know what a PM is,
but let me just draw for
you a Venn diagram.
That's my simple model of how PMs and
engineers should work together, right?
So let's say this is the set of
all things that users will love.
Right, so the set of all possible things,
all the possible products
that users will love.
And this is a set of all
things that are feasible.
Right, meaning that today's technology or
technology now or the near future
enables us to build this, right?
So for example I would love
a teleportation device, but I don't think
that's technological feasible, so
teleportation device will be here, but
we'll all love one, but
I don't think it's feasible.
There are a lot of things that
are feasible but then no one wants it.
But will throw a lot of those as Slick and
Dally as well.
And I think the secret is to try to
find something in the middle, right?
And so, roughly, I think of the PMs job as
figuring out what is this set on the left.
And research engineering's job as
figuring out what's in this right side.
And then the two kind of work
together to built something
that's actually in the intersection,
right?
Now, one of the challenges is
that AI is such a new thing that
the work flows and processors that
we're used to in tech companies,
they're not quite working for AI tools.
So, maybe for example, in Slick and
Dally we have pretty well established
processors, product managers and
engineers and engineer to do their work.
For example, for a lot of apps the product
manager will draw a wire frame, right?
Where, so for example, actually for
the search app, right?
The PM might decide well put a logo there,
put a Search bar there,
put a microphone there, put a camera
there, and then put a news feed here,
and then actually, well we actually moved
our microphone button down here and
we'll have a social button.
This button, this button.
So a product manager would draw
this on a piece of paper or
on the cat thing, and
an engineer would look at this
drawing that the product manager drew, and
they would write a piece of software and
this is actually a rough for
the Baidu search, yeah?
The search button in terms of news here,
right?
It will open like a, it combines
the search as well as a social newsfeed.
Not very social, a newsfeed, both in one.
But, so this works for
if you pull open your app or
you build a lot of apps like a news app or
a social feeds app or whatever,
this type of working together works
with established process of doing this.
But how about an AI app?
You can't wire frame a self-driving car
that runs by wire frame from a self
driving car or if you want to
build a speech recognition system.
The PM draws this button, but
I don't know how good, how accurate,
is my speech recognition
system need to be.
So while the processes are not- So
what if this wire frame was a way for
the PM and the engineer to communicate.
We are in still frankly trying to figure
out what are good ways for a PM and
an engineer to communicate a shared
vision of what a product should be.
Is that make sense?
So PM does a lot of work, goes out,
figures out what's important to users and
they have in their head some idea
what this product should be.
But how do they communicate
that to the engineer?
All right.
And so, as a complete example of that.
[COUGH] Let's say that
you're trying to build
a be recognition system, I do know how
to work on speech recognition, right so.
My team and I, they all work on speech
recognition so we talk about that a lot.
If you're trying to build a speech
recognition system, say to enable voice
search there a lot of ways improve
the speech recognition system.
Maybe you want it to work better
even in noisy environments, right?
But a noisy environment, it could mean
car environment, or it could mean a cafe
environment, people talking versus
a car noise, a highway pursuit.
Or maybe you really need it to work
on low bandwidth audio, right?
Maybe sometimes users are just in
a bad cell phone coverage setting, so
you need it to work better
on low bandwidth audio.
Or maybe you need it to work
better on accented speech, right?
I guess US has a lot of accents.
China also has a lot of accents.
What does accented speech mean?
Does it mean a European accent,
or Asian accent?
European does it mean British,
or Scottish?
You know what does accent really mean?, or
maybe you really care about
something else, right?
So, one of the practices we've come up
with, is that one of the good ways for
a PM to communicate with an engineer,
is through data, and what I mean is for
many of my projects we ask the PM to be
responsible for coming up with a data set.
For example, give me,
let me say 10,000 audio clips
[NOISE] that really shows me what
your really care about, right?
So and so, if the PM,
comes up with ten a thousand or
ten thousand examples, of a people of
recordings of a speech, and give us data,
to the engineer, and just the engineer
has a clear target to the info.
So, found that having a PM responsible for
collecting really a test set
is one of the most effective processes for
letting the PM specify what they really
care about, and so if all 10,000 audio
clips have a lot of car noise, this is
a clear way to communicate to the engineer
that you really care about car noise.
If it's a mix of these different things,
then it communicates to an engineer
how exactly,
what mix of these different phenomena.
The PM wants you to optimize for, right?
I have to say, this is one of those
things that's obvious in hindsight, but
that surprisingly few AI teams do this.
One of the bad practices I've seen
is when the PM gives an engineer
10,000 audio clips, but they actually care
about a totally different 10,000 ones.
That happens surprisingly often in
multiple companies, right?, and
then I feel like where still in
the process of advancing the bleeding
edge of these workflow processes,
so how to think about new products.
So, here's another example.
Some law work on conversational agents,
right?, so, they're conversational agent.
I might Say to the AI may you
please order takeout for me?, and
then the AI says well what restaurant
do you want to order from?
And you'd say I feel like a hamburger.
So you'd go back and
forth like a conversation or
a chat bot to help you order food or
whatever.
So again if you were to draw a wire frame,
the wire frame would be while you
say this, the chat box says this,
you say this chat box says this, but
this is not a good spec for the AI right?
The wireframe is the easy part,
the visual design, you can do that, but
how intelligent is this
really supposed to be?
So the process that we developed
by doing this, we asked the PM and
the engineer to sit down together and
write out 50 conversations that the chat
box is meant to have with you, right?,
so for example, if you sit down and
write the following.
Let's say the user, U for user, says,
Please pack for restaurant [SOUND] right,
for my anniversary next Monday.
I'm abbreviating this
just to write faster.
Please book a restaurant for
my anniversary.
The PM then says, well in this case,
[SOUND] I want the AI to say,
okay, and do you want flowers?, right?
Do you want me to order flowers?
[SOUND].
[SOUND], What we found is that this then
creates a conversation between the PM and
the engineer where the engineer asks a PM,
wait, do you want me to suggest
an appropriate gift for
all circumstances and all possible.
I would suggest some other I don't know
what to buy for Christmas, I guess, or
is it only for anniversaries
you want to buy flowers?, and
I don't have just buy any other gift and
offer anything other than anniversaries,
right?, [LAUGH]
>> Then we found then the process of
writing out 50 columns between consulate
agents and engineer PMs seen down and
work through this conversations,
that those are good process
to enable the PM to specify what
they think is the set on the left,
of what the use of the and for
the engineer to tell the PM what
the engineer thinks is feasible given
today's chat box technology, right?
And so this is actually a process that
we're using in multiple products,
so I think that AI Technology is
advancing rapidly and there's so
many shiny things in AI.
The things you see the most in PR
are often the shiniest technology but
the shiniest technology is often
not the most useful, right?
But I think that's we're still missing
a lot of the downstream parts of
the value chain of how to take the shiny
AI technology that we find out in
research papers and how to think about,
how did the product or business, and,
we're definitely, it definitely feels
you know, software engineering today has
established processes like code review and
you know agile development.
Some of you know what those are, right?,
but these was established processes for
writing Kahoot, I think we're still in the
early phases of trying to figure out how
on earth to organize the work of AI and
the work of AI product.
And this is actually a very exciting time
to enter this field., [COUGH], Let's see.
[SOUND], All right,I want this time for
questions so,
all right, more quick I want to
share with you some specific
examples of some time opportunities
that AI, these are things
that are coming in the very near future,
[SOUND], Let's see.
I think I mentioned, [COUGH],
well, I mentioned Fintech,
I'm going to talk about that, in the near
term future, I think speech recognition,
We'll take off, it's just in
the last year or two that speech
recognition reached the level of accuracy,
was becoming incredibly useful.
So about four,
five months ago, there was a Stanford
University led study done by James Landay,
led by James Landay, who is a professor of
Computer Science, together with us, I do,
and the University of Washington, and
showed that speech input on this cellphone
is 3x faster, using speech recognition
than typing on the cell phone, right?
So, speech recognition has passed
the accuracy threshold where
you actually are much faster and much more
efficient using speech recognition than
typing on the cell phone keyboard, and
that's true for English and Chinese, but
I think, and at Baidu over the past year,
we saw 100% year on year growth on
the user speech recognition
across all of our properties.
So I think we're beyond the knee of
the curve where speech recognition
will take off rapidly, and
so, I guess in the U.S., there are
multiple companies doing small speakers.
Baidu has a different vision moves,
but, I think that is a device you can
come on with your voice in your home
also take off rapidly, so whenever
an operating system that would release the
hardware makers and they know that, right?
What else?
Computer vision Is
coming little bit later.
You know, I see something sink off
faster in China than the US, so,
because all of us living in
the US are familiar US once,
I might mean to a little bit even
sharing things I see from China.
One thing that sinking off very
rapidly is Face Recognition, [SOUND],
so I think because China is a mobile
first society right?, and all of us,
most of us in U.S. first on the laptop or
a desktop, then we got our smartphone.
Lot people in China really
just have a smartphone or
first get a smartphone then a laptop or
a desktop Or laptop I guess,
I'm not sure who buys this house anyone,
but because of that in China a lot of
people, let's see, you can apply for
an educational loan on
your cellphone in China.
And just based on buttons,
just based on using your cellphone,
we will send you a lot of money,
right, for your education.
So because of these very material,
financial transactions are happening over
your cellphone, before we send you a lot
of money we would really like to verify
that you are who you say you are,
right, before we send it to someone
that claims to be youm but isn't you.
So, this in turn has driven a lot
of pressure for progress and
face recognition, and so
face recognition on mobile devices as
a means of biometric identity
verification is taking off in China.
And then we've also done things like,
today in Baidu headquarters,
instead of, do I have it, no, I don't.
Right, instead of having a swipe an ID
card to get inside the office building,
today I do buy and take water, I can just
woke up and there's a face recognition
just to recognize my face, and
I just walk right through.
Just yesterday or
the day before, I posted a video on my
personal YouTube channel demoing this.
You can look that up later if you want.
But we now have face recognition systems
that are good enough that we trust it with
pretty security protocol applications,
right, if you look just like me,
you can actually get inside
my office at IT and Gibson.
>> [LAUGH]
>> So we really trust our face recognition
system, so it's pretty easy.
So let's see, and I think both of these
have been obvious to us for some time,
so our capital investment and
investments have been massive.
These are well beyond the point where
a small group could be competitive with us
unless there's some unexpected
technological breakthrough.
I'll mention some things
a little further out.
I'm personally very bullish about
the impact of AI on healthcare.
I've spend quite a bit
of time on this myself.
And I think, well,
the obvious one that a lot of people
talk about is medical imaging.
I do find it challenging.
Yeah, I do think that a lot of
radiologists that are graduating today,
will be impacted by AI, definitely,
sometime in the course of their careers.
If you're planning for
a 40-year career in radiology,
I would say that's not a good plan.
>> [LAUGH]
>> But beyond radiology,
I think that many other verticals,
some of which we're working on, but
there's a huge opportunity there.
And anyway, and on and on and on,
right?, and I think Fintech is there.
I hope education will get there, but
I think education has other things
to solve before reading these issues
impact by AI, but I really think
that AI will be an incredibly
impactful in many different verticals.
So let's see.
And what I talked about today was kind
of AI technology today, right, so
really supervised learning, and
I will say that the transformation
of all of these industries, there's
already a relatively clear road map for
how to transform multiple industries
using just supervised learning.
There are researchers working
on even other forms of AI,
you might hear one say
unsupervised learning or
reinforcement learning or transfer
learning, there are other forms of
AI as well that may be don't need as
much data or maybe has other advantages.
Most of those are in the research phase,
most of them are used in very relatively
small ways, than not what's
driving economic value today, but
may of us hope that there will
a breakthrough in this other areas and
if that comes to pass, then that will
unlock additional ways of value.
So, let's see the few that AI has
had several winters before, right?
I think the field over height
went some of the high went down.
So we think they were maybe
two winters an AI, right, but
many disciplines undergo a few winters,
winter and then eternal spring,
and I actually think that AI has pass
into the phase of eternal spring.
I think one of the questions
someone asked,
when will AI no longer be the top
technology or something, right,
and I feel like if you look at slick and
technology, right?
I think when the eternal spring of silicon
technology, or maybe some other metal,
some other material will surpass it,
but the concept of a transistor and
computational circuits, that seems like
it's going to be with the human race for
a long time.
And I think we have reached that point for
AI where AI, new networks,
deep learning, I think it will
be with us for a long time.
Completely conscious of yourself, but they
could be a very long time, because it's
trading so much value already and
because there is this clear road map for
transforming seven industries even with
the ideas we have, but hopefully there
will be even more breakthroughs and
even more of these technologies.
All right, very last topic,
you know the jobs issue,
I think that's, to the extent that
we're causing these problems,
we should, the job displacement issue,
I think we should own up to it.
Just as AI displaces jobs, similar to
the earlier ways of job displacement,
I think that AI will create new jobs as
well, maybe even ones we can't imagine.
So that's why I actually seen
development for a long time.
I think one of the biggest challenges
of education is motivation, right?
As in is really good for you to take these
courses and study, but it's actually
really difficult for an individual
to find the time, and the space, and
the energy to do the learning that
gives them these long term benefits.
So when the, after
the automation replaced a lot of
agriculture the United States built
its current educational system,
your K-12 and university.
It was a lot of work to build
the world's current educational system.
With AI displacing a lot of jobs I'm
confident that there will be new jobs but
I think also we need a new
educational system to help
people whose jobs are displaced reskill
themselves to take on the new jobs.
So one of the things that
some governments, well,
one of the things that we should move
toward is a model of basic income but
not universal basic income where,
your paid to quote do nothing,
but I think government should give people
a safety net, but pay the unemployed to
study, right, to provide the structure
to help the unemployed to study so as to
increase the odds of gaining the skills
needed to re-enter the workforce and
contribute back to the tax base that is
paying for all this on a basic income.
So I think we need a new, new deal in
order to evolve society towards this
new world where there are new jobs,
but job displacements
are also happening faster than before, and
they have been saying more about that.
Finally, really, final, final thing.
I know that often hearing the GSB,
many of you have fantastic
product business, or
social change ideas,
one of the things I hope to do
is try to connect,
frankly connect GSB and CS.
I think that GSB and CS are really
complimentary sense of expertise, but for
various complicated
reasons that we get into,
the two communities don't
seem very connected.
So-
>> [LAUGH]
>> Yeah, I'm in the process of organizing
some events that I hope will
bring together some CS, some GSB,
maybe also some VC,
some capital investments
to those of you interested in exploring
new opportunities that AI creates.
So if you want to be informed of that,
sign up for this mailing list
at bit.ly /gsb-ai.
There are some things being organized.
They're already underway, but actually
instead of taking a picture of this,
if you just go and sign up for
this on your cellphone, right now.
>> [LAUGH]
>> Yes.
[LAUGH] You can do this
while I'm taking questions.
And some of these things
are already underway, but
when they're ready to be announced, I'll
announce it to the mailing list there, so
that you can come in and be connected to
some of these other pieces at the campus.
So with that, I'm happy to take questions,
but let me say thank you all very much.
>> [APPLAUSE]
>> Thanks so much, Andrew.
>> Thank you.
>> It's a great talk,
and a lot of us, I know,
want to be engaged in product development
and product management in the field of AI.
And you've given us
a lot of good frameworks
to think about these conversations.
And the mailing list is right there,
in case you wanted to note down.
So Andrew has gracefully accepted to
fill some questions until about 5:30.
So if you have any questions there
are going to be some Sloane fellows that
are going to be moving around the room,
so please attract their attention.
But I can kick off with a question.
I really wanted to ask this question,
because it reminded me of my TSBSA,
which is what scares you about AI and why?
But I guess you already answered part of
that, so maybe you can touch on that.
And another question,
which I felt was interesting was,
what is the role of known technical
leaders in development of AI?
Who's in charge of the ethical
decisions being made in directing AI?
>> All right, someone scarcely
ever has any job displacement.
I think that, honestly, part of me,
I really honest with you guys, right?
Part of me wonders with
the recent presidential election,
part of me really wonders if
many of us in Silicon Valley,
have we really failed a large faction of
America, and it's being really honest.
I'm not saying I agree with everything
happening with politics right now, but
part of me actually wonders if we create
a tremendous wealth, but also frankly,
if we left a lot of people behind.
And I think it is past time for
us to own up to it, and
also take responsibility
of addressing that.
Let's see, what was the other question?
>> It was about-
>> Ethical.
And I think in terms of ethical issues,
there are some things,
but I think that I think jobs are so
important,
I'm just tempted not to
talk about anything else.
But I think that AI is really powerful,
and can do all sorts of things.
And we see lots of,
I think there's some small issues.
Such as, AI is sometimes bias, right?
For example,
if you do a web search, right?
We want to make sure
that if you search for
a certain ethnic group, you don't get
lost results that says well, this is,
check out their criminal record or
something like that, right?
We don't want AI to exhibit bias.
Or that AI thinks you're male versus
female, we don't want to show you
very different types of information
that they confirm is gender stereotypes.
So I think there's some
cultural bias issues.
I think that openness,
AI community is very open today.
I think we must fight to make
sure what to keep it open.
I think the number one
by far is actually jobs.
Maybe take some questions?
How does the microphone work?
>> Hi, Catherine Shen here,
I'm a set 16, graduated last year.
And thanks for the talk.
I had a question around, you mentioned the
defensibility of AI as the three things,
so access the data, talent scarcity and
positive feedback loop.
And one in three, so accessing data and
positive feedback loop seems to really
benefit large companies or companies
that already have the AI technology.
And so I'm wondering at what point
is it going to be really tough for
startups to, well, become a AI startup.
And secondly, for investors,
at what kind of scale
do those investments need to make for
a startup to be successful?
>> Sure, yeah, and just to clarify,
I think the scarce resources are data and
talent.
And then a positive feedback
loop is a strategy or
a tactic to drive the data, right?
So I think that for the problems I
talked about, like speech recognition,
face recognition is going to be it'd be
really difficult for a small company to
acquire enough data to tailor or
whatever the computer effectively.
Unless there's an unexpected technological
breakthrough that's small groups do
stuff that can't be done
with today's technology.
But I think there's lots
of small verticals.
So for example, take medical imaging.
There are some medical
diseases where there are so
few cases around the world that
if you have 1,000 images or
something, that might be almost all
the data that exist in the world.
So that's one.
There are just some verticals
that there isn't that much data.
But I think the other things it that
there's so many opportunities in AI today.
Honestly, my team is regularly write full
fledged business plans, do the market
research, size of the market, figure out
the economics and all of that is good.
With a full fledged business platform and
a new vertical, and
we decide let's not do it.
Because we just don't have enough
talent to go after all the big options.
So we decide, let's not do it,
because there's something even
bigger we want to to do, right?
So I think today, we're fortunate
to have so many opportunities.
That there are plenty of opportunities
that the large companies are,
frankly, not pursuing because
today's world has more opportunities
than talented AI researchers.
>> Question over there.
>> Hi.
Hi, Andrew, what do you think of the use
of AI in the creation, sorry, over here,
in the creation of inventions?
So it's something that's usually
the reserve of what's the human mind,
the use of AI to create inventions,
even patentable inventions.
>> Yeah, I am seeing very early phases.
You know,
creativity is a very funny thing, right?
So can I compose music,
it's so subjective.
I feel like even with a 20 year old
technology, automated music composition by
computers, a lot of us thought that the
automatic composition sounded horrible.
But there were some people that love it,
like the 20 year old technology.
So I don't know.
We're seeing a lot of cool work with
AI doing special effects on images,
synthesizing, make this picture,
if it was painted by a certain painter.
I don't know, it feels like a small,
but very interesting area right now.
But making complex inventions,
like inventing a totally new,
very complicated system with many pieces
I think that's beyond what I
will see a clear path to today.
>> Couple of questions here.
>> So-
>> Yeah, go ahead.
>> So as you drew the other [INAUDIBLE]
when you talked about data versus
performance, and you said [INAUDIBLE]
>> [INAUDIBLE]
>> Yeah, so could people hear, or
should I repeat for the mic?
>> Repeat.
>> Sure, so
scalability draws a lot of problems in AI.
But if Moore's Law is coming to an end,
how does that affect
the scalability of AI?
It turns out that, let's see, so
I think that as I've seen the road maps
of multiple of high performance
computing hardware type companies.
And whereas,
most offer single process are doesn't
seem to be working very well anymore.
I have seen specific and I think credible
roadmaps of microprocessing companies that
show that for the types of computations
we need for deeper, for neural networks,
I am confident that it will keep on
scaling for the next several years.
And so this is same day processing,
single instruction multiple data.
It turns out it's much easier to
paralyze than a lot of the workload.
Your word processor's actually
much harder to paralyze and
your network is actually
much easier to paralyze.
So I feel there is still a lot of
headroom for faster computation.
I will say that when I look
across a mix of problems,
many of the problems, AI problems,
are bottlenecked by data.
But many of the problems are also just
bottlenecked by computational speed.
There are some problems where
our ability to acquire data
exceeds our ability to process
that data inexpensively.
So further progresses in HPC,
which I think there is a roadmap for,
should open up more of that value.
>> There's a question right behind.
>> Is this on?
Hello.
Hi, Andrew, my name is Erica Lee.
I'm a startup founder
working machine learning.
So two questions, you mentioned that
algorithms aren't like the special
sauce to being successful in AI.
What do you recommend for people, though,
building and working on AI about IP
protection or best ways to get around
that to still build a valuable product?
And then two, you mentioned
the relationship between the PM and
an engineer about the cycle of data and
how to communicate.
That's for building a product, though.
What about people doing some R&D research
on reinforcement on supervised learning?
Is there a certain lifecycle
of strategy would go for
research breakthroughs or
to improve the research processes?
>> Yeah, maybe, sure, right.
Boy, all right, so I think, yeah,
IP protection is one of those things
that we give advice on and I get in
trouble with lawyers or something.
>> [LAUGH]
>> Honestly,
I don't have a strong opinion.
I see a lot of companies file for
some patents, but how much you can rely on
them for defensibility is an open
question, check your lawyer.
I actually don't have
a strong opinion that.
We do tend to think strategically
about data as a defensible barrier,
though, we rely on data.
In terms of, you said processes for
R&D, right?
The research academic committee
tends to favor novelty.
Anything novel and shiny,
you can get a paper published.
I would say that, maybe if you want
to train up a team of engineers,
I've supervised PhD students
at Stanford for a long time.
I feel like if you want to be
a deep learning researcher, and
if you go to published papers,
the formula I usually give people is this.
Read a lot of papers.
Go beyond reading papers but
go and replicate existing
research papers yourself.
This is one thing that is
underappreciated, actually.
Even pull back a little bit from
trying too hard to invent a new thing.
I spend a lot of time
replicating published results.
I found that to be very good training
process for new researchers.
And then the human brain
is this marvelous thing.
It works every time.
I've never seen it fail.
But if you read enough papers and
really study them and understand them and
replicate enough results,
pretty soon you have your own ideas for
pushing forward the state of the art.
I've mentored enough PhD students to
ascertain with high confidence that this
is a very reliable process.
And then go submit your paper and
get it published.
>> Over there.
>> Thank you.
So I'm a mechanical
engineering student aspiring to
be a roboticist when I graduate.
I was wondering what
are the best opportunities for
mechanical engineers to go into
as it relates to AI and robotics.
Would you know that?
>> Yeah, so I've seen a lot of ME people
take up very successful careers in AI.
>> Actually some of my PhD students,
actually one of my PhD students was
an ME PhD student, and he transferred to
the CSPH Department and he did very well.
So I think that robotics has
many opportunities in specific.
Well, you're a Stanford student, right?
>> Yes.
>> Cool.
I would say, take some CS-AI classes and
try to work with the AI faculty.
I do think that there are a lot of
opportunities to build interesting
robots in specific verticals.
So I think precision agriculture
is a very interesting vertical.
Right, so
there are now multiple startups using AI.
Actually, for example,
some of my friends are running Blue River,
which is using computer vision to look
at specific plants, specifically,
heads of cabbages, and kill off.
We'll have AI decide which heads of
cabbage to kill and which to let live so
as to maximize crop yield, right?
So there's one application where AI is
letting you make, well, this is life and
death decisions, but this is life and
death of heads of cabbage, not of humans.
>> [LAUGH]
>> But
it is letting you make one at a time life
and death decisions by heads of cabbage.
But I think that precision
agriculture is one vertical.
I don't know, yeah, I think actually it's
interesting work on surgical robotics as
well, but that has a bigger
kind of FDA process approval.
So that's a longer cycle.
But I'm seeing less of a, actually one
of the things taking off in China,
the love of companionship robots,
more social companionship robots that
are being built in southern China.
It's not really taken off in the US yet,
but
there suprisingly many of
these things in China.
>> Thank you.
>> Right there?
>> Hi, I'm Phil.
I'm cofounder of Eurobaby, it's a Palo
Alto based startup that helps parents
to understand the developmental needs of
their child and pair with baby products.
I'd love to hear your take
on pairing AI with humans.
If you think it's usually for most
applications the faster way to focus on
an AI only approach right away or actually
have a hybrid solution of AI and humans.
It's, for example, in self-driving cars or
chatbots and some.
>> Yeah,
I don't have a general rule for that.
It's so case by case, I guess.
A lot of speech recognition work is
about making humans more efficient in
terms of how you communicate with or
through a cell phone, for example.
And then for self-driving cars, we know
that if a car is driving and it wants you
to take over, you need maybe 10, 15,
maybe even longer seconds to take over.
So it's incredibly difficult to bench
the attention from the distracted human
back to take over a car.
So that's why I think level four autonomy
will be safer than trying to have
a human take over at a moment's notice
when the car doesn't know what to do.
So that might be one case where
this mix between full and
partial automation is challenging
from a user interface point of view.
So I don't have a general rule for that.
>> There's some questions on the top.
Let's go a different direction.
>> Okay, so
when you talk about opportunities for AI,
you mentioned the online education.
I just wanted to know more about this.
You mentioned that the motivation
problem is one of the problems for
online education.
But do you think this is the biggest
challenge that online education is facing
that AI could probably solve?
Or do you think there are some other
challenges for online education?
On motivation, I mean,
people don't want to spend enough
time to finish the whole course.
>> Yeah, so I think that's actually,
AI is helping of education and
people talked about personalized
tutors for a long time.
And today Coursera uses AI to give you
customized course recommendations and
there's AI for also grading.
So I would say it's definitely
helping at the margins, but
I would say that education still has a big
digital transformation to go through,
maybe even without that
much involvement of AI.
Maybe one pattern that is true for
a lot of industries is first comes
the data and then comes the AI.
So healthcare needs this pattern.
Over the past year, thanks to,
well, partially, Obamacare, right,
there's a huge movement
in the United States,
a movement in other countries, too,
towards electronic health records.
EHR, so the rise of EHR and
the fact that your X-ray scanners all
went from film to digital x-rays.
So that wave of digitization has
now created a lot of data that AI
can eat to create more value.
I would say that a lot of education
still feels like it's first undergoing
the digital transformation.
And while AI can certainly help, I think
there's still a lot of work to do for
just a digital transformation.
>> I think there's just one
more question on the top.
>> Yeah, if we could talk a little bit
about how Baidu is using AI for managing
your own cloud data centers, primarily
idea operations management use cases.
>> Sure, so I guess, boy,
let's see, I'll give one example.
We talked about this.
Several years ago, almost three years ago,
we did a project showing that
we can detect hardware failures,
especially hardware failures
a day ahead of time using AI.
And so this allows us to do preemptive
maintenance, a hot swap of hard disks.
Copying the data off even before it fails,
thus reducing constant easing reliability.
We've also been working to
reduce power consumption
of the data centers,
something low balancing uses AI.
I can't point to one big thing,
but I feel like many places,
AI has had an impact on optimizing various
aspects of data center performance.
>> We'd like to stay for longer but we
have to leave the room for the next event,
so will probably be the last question.
Hey, how are you here?
I actually studied at both CS and
the GSB before.
So my question is you actually mentioned
that that's the sweet spot for
AI progress.
If human can process for
less than a second,
then that would be a good problem set for
AI to solve.
Can you comment on the other way,
on the other side of spectrum?
In your experience,
a problem would take a lot more seconds or
a long time for humans to process,
yet after careful modeling or
careful planning,
you are able to solve the problem by AI.
Can you give some examples on that?
>> Yeah, so
there are things that AI can do that
humans can't do in less than a second.
So for example, I think Amazon today does
a way better job recommending books to me
than even my wife does, right?
And the reason is Amazon has a much
more intimate knowledge of what
books I browse and
what books I read than even my wife does.
Advertising, honestly, leading Internet
companies have seen so much data about
what ads people click on and don't click
on, could remarkably be good at that task.
So there are some problems where a machine
can consume way more data than any
human can and
model the patterns and predictions.
So this is something that AI surpasses
human performance because it consumes so
much data, right, like Amazon knowing my
book preferences better than my wife.
Let me finish.
And then the other thing of tasks that
take a human more than one second to do,
a lot of the work of designing
AI into the workflow
is piecing many small AI pieces
together into a much bigger system.
So for example, to build a self-driving
car, we use AI to look at
a camera image, radar, LIDAR,
whatever, the sensor data.
Let me just say,
a picture of another car and
supervised learning estimates
the position of the other car.
Supervised learning,
estimates the position of the pedestrians.
But these are just two small pieces, well,
two important pieces, of the overall AI.
Then there's a separate piece
that tries to estimate, well,
where is this car going to
be in five seconds?
Where's this pedestrian going?
There's another piece that plans, well,
given that all of these objects are moving
in this way, how do I plan my car so
that I don't hit anything?
And then after that, there's then
how do I turn the steering wheel?
Do I turn the steering wheel five degrees
or seven degrees to follow this path?
So often a complicated AI system
has many small pieces, involve
the ingenuity is figuring out where to
take this superpower, supervised learning,
and put it into this much bigger system
that creates something very valuable.
>> Probably take one more question behind.
I'm Mahidhar, I'm a solutions
architect in a company called OTP.
My question was, you mentioned about
jobs and wealth distribution as well.
Since it's a management forum, I wanted
to ask, what sort of role do you see for
product managers when interacting with for
example sociologists or
legal profession,
based on examples to give you.
Building a car, self driving car,
if there's a collision which is about
to happen where the developer or
the AI has to take into consideration
the person driving the car.
Or the pedestrian who it's about to hit.
That's a legal question.
So but
there'll be a lot of questions like these.
What do you see the role of management
interacting with different function areas?
>> Yeah, so the most famous example of a
variation of what you said is then called
the trolley problem, is a philosophy
that cause ethical dilemma.
Where I guess I think your car is,
the classical version,
you have a trolley running on rails.
And the trolley is about to hit and
kill five people.
And you have the option of yanking on
the lever to divert the trolley to
kill one person.
So the ethical dilemma is
do you yank on the lever or
not, because if you do nothing,
five people die.
If you do something, one person dies,
but you killed that person.
So are you going to kill someone,
right, versus not doing anything?
So it turns out that the trolley problem
wasn't important, even for trolleys.
Right, when we built trolleys and
whatever,
several hundred years in
the history of trolleys,
I don't know that anyone actually had to
decide whether or not to yank the lever.
It's just not an important problem
outside the philosophy classes.
>> [LAUGH]
>> And I think that when the self-driving
car teams are not debating this,
philosophers are debating this.
Frankly, if you're ever
facing a trolley problem,
chances are you made a mistake long ago.
You should.
>> [LAUGH]
>> Now, when
was the last time you faced a trolley
problem, right, when driving your car?
I expect a self-driving car to face
it about as often as you have driving
your car, right,
which is probably pretty much never.
So I think right now the problem
with self-driving cars is there's
a big white truck parked across the road.
Your options are slam the truck and
kill the driver or brake.
And we don't always make
the right decision for that.
So I would solve that first-
>> [LAUGH]
>> Before solving the trolley problem.
>> [LAUGH]
>> That's, I think,
a good point to end this great talk.
Thanks a lot.
>> Thank you.
>> [APPLAUSE]
