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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]
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