- For our next session we have Eric Schmidt.
He's the executive chairman of Alphabet
which owns among other companies,
Google and the AI research company Deep Mind
and he's also chair of the defense innovation
advisory board, so has a unique perspective
on artificial intelligence in the national security space.
Please welcome Mr. Schmidt.
(clapping)
- It's an honor to follow Andrew.
- So thanks for joining us.
I wanted to start by asking you
from where you sit looking at your companies
and thinking about the United States as a whole
how big is Artificial Intelligence?
How important is this to be a leader in this space?
- Well if you're in business that has a lot of data
^and in an information business of some kind
^then it's more than big.
^It's essential because it will become
^your competitive advantage and I think it's true
^that pretty much all of the knowledge system
^kind of companies will become AI companies
^in that sense.
^A lot of people believe it will be more than that
^but that I know from the standpoint of Alphabet
^because it touches everything we are doing.
^When I was CEO we had Mobile First,
the new CEO announced AI First as our core
strategy.
That's how serious it is.
- What do you see as the key drivers of competitive
advantage?
What are the things that allow a company to
be ahead of rivals?
Is it capital?
Is it data?
What are those key components?
- It's sort of both.
The first observation is this stuff is still
essentially magic.
The scientists that are working on it
cannot for example explain certain failure modes.
We don't really exactly understand how the learning
occurs.
There's evidence for example that the face recognition
and the image recognition is using slightly different
parameters than what we do as humans.
That's an example.
So it's a slightly different animal if you will.
So at the moment I think it's in the province
of very, very highly skilled mathematicians,
those sorts of people and we're in a situation
where those sorts kinds of people graduating
out of Carnegie Mellon and others are
in the highest demand I've ever seen with huge
multi-million dollar packages in their 20's coming out.
That's how valuable these people are in the marketplace.
The other thing we're seeing is that these
algorithms at least today require a great deal of
training data.
When I say a great deal I mean like millions
of entries in the matrices.
Billions of pieces of data.
So the classic example is people will say,
"Why can't you figure out terrorism?"
Well the good news is that terrorism is very rare.
So it's much harder to apply AI to that problem.
Where's trying to understand traffic right as an
example, something that occurs every day
is far, far easier because you have so much
training data.
Another reason to think that AI works so well
in Biology is that as much as we would like
to find differences among ourselves
we are all the same biologically,
and the fact that we have so many examples of us
that we can measure means enormous gains in our
healthcare globally for everybody.
- So in your role as Chairman of the Defense Innovation
Advisory Board what is your assessment
of how the Defense Department is doing in terms
of looking at Artificial Intelligence
and incorporating this.
- So a couple of years ago I became chairman
of an external advisory board called the
Defense Innovation Board and the idea what to try
to get the military up to speed with things
that were going outside the military.
We made a set of ten recommendations,
which are public and you can read them
and I would encourage you to do so.
We obviously worked pretty hard on them.
They cover a lot of ground, looking at this audience
^you're pretty familiar with.
^The nature of innovation in the military, the kind of,
^what are the problems and so
^for the third offset, work is here etc.
^There are all sorts of examples of all of this.
^One of the most important points we made is
^if the military is not leading in AI,
^that we recommend for example the creation of the equivalent
of an AI institute and I can give you a positive
and a negative example.
So at peace time anyway and also war time,
what do our men and women do mostly?
They mostly watch things.
Computer vision is a solved problem compared
to human vision.
Roughly speaking, people's ability to watch
continuous scenes with no change is not 100 percent.
There are studies which show the error rates
are one third out of a hundred, one third kind of
error rates in various tests.
Whereas computers can watch a scene
which is monotonous for a very, very long time
and then they will alert you for a change.
That seems like the simplest possible thing
and yet we have this whole tradition
of the military standing watch and so forth
and so on as if that's a good use of human beings.
So the core narrative about AI is not that AI
will be like us, that AI is different than us.
That the best uses of AI will be human and AI
collaboration of one kind or another.
The vision one is a classic example.
It's also true of self driving cars.
It turns out the military has started,
it's interesting it started in the intelligence group,
a group called project Maven, which they are
talking about, which actually combines a lot
of these very clever under a General Shanahan.
He's very clever.
- I guess how do you see those initiatives going?
I mean do you see the DOD making progress
in adopting that?
- You worked at OSD.
Looking at this audience I don't need to tell you
that the military is very large, very cumbersome.
The military as a general statement doesn't build
things, it uses contractors.
So the problem is that the military,
the contractors builds what the military
asks for because they are contractors
and the military has not been asking for AI systems.
It takes five to 10, to 20, to 30 years to go
from the spec to the delivery.
Am I summarizing the history of 40 years of procurement
or did I get it wrong?
I could make it worse if you want in my report.
So the core problem is how do you get the leaders
who want passionately to get stuff done,
to be able to deliver these solutions quickly.
My own view is that we have write AI
and machine learning more technically correctly
in every procurement.
The military contractor base is largely good
at hardware, not very good at software.
I don't need to tell you that you use it every day.
Software systems aren't interconnected.
They don't use cloud computing.
There's a long list.
These are covered by our recommendations.
So Maven is interesting to give you an example
because it's an intelligence application
which is good because it isn't life safety.
You know in other words it can make a mistake
when it makes a recommendation and they train it
on open source data which I think is most interesting.
So there's another good example of the military
coming up with a new strategy that uses open source data
to make their systems smarter.
One of the tricks with AI is you need large amounts
of training data and there's always more open source data
than secret data.
- One of the things that's different in this space right now
than say earlier major military innovations
from 30 years ago.
Things like stealth, precision guided weapons,
is these are coming from outside the defense sector right?
How does the Defense Department begin to import
these technologies?
You talk about the human capital component as well.
How does the DOD compete for talent and then draw
in companies into this space?
- Well in the first place, there's lots of money
in the DOD.
^The programs are large.
^They're multi-year.
^They have these large procurements.
^Much of the software work is not of the best quality.
^There is plenty of room here to bring in some
^of the kind of people that I'm talking about.
^The military has mechanisms to bill software contractors
^so they don't have to pay military salaries
^to some of these people which are way too low
^for the market.
^So I think the mechanisms, the specific
mechanisms to get access I think it's really around
strategy and will.
My overall feeling is that if you start
with vision which is something that everyone is doing,
everybody is watching, everybody is learning,
everyone can understand that.
So why are we taking people exquisitely trained
men and women of our military and having them watch
something boring all day?
I know that sounds repetitive but that's literally
what they do
for hours and hours and hours.
The other activity which I find very disturbing,
is called,
basically it's called Turn and Type.
Sometimes they have two different screens.
This system uses one dataset.
This other system uses another set and again
they have soldiers taking the data from one screen
and typing it into another because their systems
aren't interoperable.
Again these are straightforward problems.
I know we want to talk about the stuff that
Andrew talked about, incredibly interesting stuff
but a lot of the gains here are making
the existing folks just that much more effective.
It's all right in front of them.
- When you talk about that vision are you seeing
the senior department leaders that you interact with,
that they are starting to get it.
They are starting to say,
"This is a key area to be ahead on."
- They are.
Under Ash Carter and now under Mattis,
everybody kind of gets this.
This problem is the,
everyone can understand something but they cannot
collectively act is sort of the core governance problem.
So you have to come up with ways for them to be able
to get the resources and so forth.
Now if we were in a huge war with a major adversary
I'm sure the rules would be different
but right now the planning procedures and so forth
in my view take too long.
That's why in our recommendations we talked about
embracing a culture of innovation, trying things
failing quick, all the standard Silicon Valley kinds
of things people talk about.
It will help if the military moves to cloud computing
for much of it's infrastructure.
The DOD has just announced a major cloud computing
initiative which they are very serious about.
I'm told compared to other initiatives, the DOD is moving
very, very quickly compared to many other things it's doing.
In my view it's not fast enough, but I say that
at Google too so it's okay.
- I want to turn to China for a second.
Some of the top AI companies in the world
are in China.
What is your sense of how the United States
is relative to China in terms of the talent and
innovation of Artificial Intelligence.
- I don't know if you all saw, but a month ago
China actually announced it's AI strategy,
and I actually read it.
It's pretty simple.
By 2020 they will have caught up.
By 2025 they will be better than us,
and by 2030 they will dominate the industries of AI.
Just stop for a sec.
That's the government that said that.
Weren't we the ones that were in charge of AI
dominance here in our country?
Weren't we the ones who invented this stuff?
Weren't we the ones that were going to go
exploit the benefits of all this technology
for betterment and American exceptionalism
in our own arrogant view?
Trust me, these Chinese people are good.
We were in, in April we brought our AlphaGo game
which is a remarkable reinforcement learning activity
and brought it to Shanghai, Wuzhen technically
and had a series of demonstration games against
the very top Go player in the world and we won
every game and he was a brilliant player.
He invented new moves.
We invented new moves.
Historic for a Go player and then we retired the game.
The game that was played learned how to play Go
in seven days from zero.
That's how smart these systems have become
and beat the top human in a game that's two thousand
five hundred years old.
To me the more interesting thing about going there
was to see that all of the top computer science people
from China had shown up.
My conclusion was they are going to use this technology.
They are going to use for both commercial as well
as military objectives with all sorts of implications
over that period.
But we know what they are doing.
They have announced their strategy okay.
So you're crazy to treat them as somehow second class
citizens.
They have, another example, we do tests globally.
There are these things called Top Coder Contests,
where you have a coding program, literally
for programmers and Asians as a general statement
and Chinese in particular tend to win many
of the top spots.
So if you have any kind of prejudice
or concern that somehow their system
and their educational system is not going to produce
the kind of people that I'm talking about,
you're wrong.
- So what steps should the US be taking to remain
competitive.
Presumably that's not where we'd like to be in 2030.
- Well for one thing we don't have a national
strategy to just send that.
So if you believe that this is as important
as I suspect all of us do.
I certainly believe that we need to get out
our act together as a country.
So, give you another example, look at the history
of Sputnik and the highway system.
What happened was the Russians, everyone knew
the Russians were going to do this
and then the Sputnik movement happened
and then NASA happened.
Eisenhower and the interstate highway system in the 50's.
This is that moment.
This is the moment where the government collectively
with private industry needs to say these technologies
are important.
By the way, whether they really needed the interstate
highway system to move all the missiles around
the country benefited by the interstate highway system
enormously.
Just think about it.
So whether it's from a position of fear
where people are afraid of something, or whether
it's a position of leadership, I don't really care
how we get there.
I do think a national focus on investing,
so the specifics are pretty straightforward.
Investing in research.
American is the country that leads in these areas.
There's every reason to think we can continue
that leadership.
It's also how we will differentiate our businesses
globally.
Software is the leading, the leading place
where software is done in the world is done
in America for many reasons including cultural
and so forth, there's all sorts of reasons
to do that.
I'll give you another one.
^Let's talk about immigration.
^Shockingly some of the very best people
^are in countries that we won't let in to America.
^Would you rather have them building AI somewhere
^else or would you rather have them building here?
^I'll give you a specific example.
^Iran produces some of the smartest and top computer
^scientists in the world.
^I want them here.
^To be clear, I want them working for Alphabet and Google.
^(laughing)
^I'm very, very clear on this.
^It's crazy not to let these people in.
^So I can go on.
- Good, well let's open up some questions
from the audience.
We got a great selection of folks here
from across government and the private sector
and academia, working on AI.
So let's here some questions.
We got folks in the back with some microphones.
- We have a question over here.
We have one here.
- Someone will be coming to you with a mic.
- Hi, thanks a lot for your talk.
I loved most, of course I'm going to pick on the one
piece that worried me.
You were talking about replacing large numbers of
military people with an AI vision system.
Could that be a single point of failure?
Could that be something that could be hacked
or manipulated.
whereas thousands of soldiers would presumably
whistle blow if anything weird was going on.
- So there are many, many issues around the design
of these systems that you want to get right.
So the first thing to do is, the military's position is
again I agree with is that a human should be in the loop
in anything involving kinetic activity.
That seems obvious to me.
So you want to start where the areas where you're
making the soldiers more efficient.
So yes of course, if you build a tool that is then
hacked by the Russians or the Chinese or whatever
scenario you have, that's worse than the tool,
not having the tool.
So make one that's not hackable.
I think these are all straightforward engineering
problems and they need to be done right.
In the same sense that the ships need not to sink
and the airplanes need to fly and so forth
and so on.
These are known problems.
The reason I picked vision, is vision is easily
understood and we understand its failure models.
It's also something where people can say,
let me check that.
Let me check another source.
Do I agree that a missile was launched?
The computer says a missile was launched,
let me check over at this other site.
Let me see if everything agrees,
before I do anything.
Maybe it's a false positive.
We understand how to build reliable systems using
that kind of error rates.
- Okay, Dr. Moore.
(indistinct speech)
(laughing)
- You forget for a decade.
- I think that right now, the technology that we build
to monitor machine learning systems,
you probably know the code words internally,
the things that we use to make sure that at
Halloween our machine learning systems didn't go crazy
because they were so overwhelmed with queries
for candy.
That was very much ahead of what I see happening
in academia
or in the Defense Department at the moment.
I'm very interested in putting in a large program
to help the rest of society catch up.
We being Google, I don't want to sound boastful,
we are really good at this and in fact about
half of our machine learning teams were writing
the systems which are watching, monitoring,
constantly testing, and diagnosing machine learning.
Do you think we should invest in that?
Do you think we should maybe actually start to
open some of the commercial techniques
to the rest of the community?
- I do.
I also think that the way this will really work,
if you go back to what Andrew's point about it's
very easy to mash up a powerful system today.
^You precisely cannot do that in the military.
^There are a gazillion laws that prevent you from doing
^"mashups" and having fun all sorts of again reasons.
^So, I'm very much in favor of partnerships between
^military and universities, whether there's simply
a building next to a university or the military
helping fund research in the university in an
unclassified, open source kind of way.
So that the military can get the benefit of these kinds
of systems.
I think that's good for the country,
good for the universities, good for the research,
as long as it's done in the open and not classified.
- If you actually just wait until the mic comes,
because then we can pick you up on the live stream.
Awesome, thanks.
- Eric, thank you very much for those views.
Very interesting.
My principal question is around how you see the China/US
competition, a few observations on that is that
China is in a position to spend orders of magnitude
greater capital in both creating the demand
for these AI products and directly building the
AI products.
With the One Belt One Road they are also integrating
and partnering up with many other countries
that produce great data that they will have access to.
In fact there are some statistics that indicate
that because of the immigration climate
in the United States, Chinese students and other
smart students that would have chosen the US as their
number one destination are now not doing so
in measurable ways.
One can go on and one, but at the same time
in the US we've seen this budget proposal which
actually the most recent Trump budget proposal
which actually cuts down science and technology funding.
There's a whole bunch of things also that sort of get
to the heart of what we're trying to do with education
by making all of these changes.
It's certainly not to promote more science.
Certainly there's nothing about AI.
So all of these decisions, they have a lifetime.
When you make a decision like this and you commit
to a decision like this, you don't get to redo it
three months and six months down the road.
It has a consequence once you go down that path.
So given that these decisions now have a window
of consequence how do you rate the probability of
in five years the US remaining a dominate AI player
compared to China?
- I'm assuming that our lead will continue
for the next five years and that China will catch up
extremely quickly.
So in five years, we'll be kind of at the same level,
possibly.
It's hard to see how China would have passed us in that
period although their rate of improvement is so impressively
good.
It feels as an American that we're fighting
this particular conflict over AI with one hand behind
our back, which is support by your point.
What I'd rather do is not adopt Chinese policies,
but rather sort of more like us.
Do more of the things that we are particularly good at.
Funding basic research as you pointed out,
the Trump budget does reduce that.
It's the wrong direction.
Let's find other ways to do it.
There is good news.
^This explosion in opportunity is being funded
^by the private sector.
^So for the next five years or so the number one
^majors in the leading universities are computer science.
^It's extraordinary.
Princeton.
^Number one major used to be Economics.
^Now it's Computer Science, Economics is second.
^My professor friends have told me that the quality
^of the students that they are getting are the
^tops students that are coming into the university
^including compared to the Physics students,
^the Math students, the other traditional people
^we would compete with for the smartest students.
^So there's reasons to believe that this next
generation of young scientist and inventors
are even better than the people, the old people
that are talking about this now, relative to that.
These are 19, 20, 21 year olds.
So I'm very optimistic about that.
I'm concerned that the country does not frame this problem
correctly.
I used immigrations research funding as two examples.
There's plenty of others.
- Yes.
- Question over here.
Can we get the mics to the next questioner?
Let's speed this up.
Can we get a mic over to this gentleman over here?
- Jonathan Goldberg, really appreciate the talk as well.
We've talked a lot about technology.
We've talked a lot about software,
but this is really a people problem.
I'm really interested in your perspective
about the behavioral change and the culture
that embraces this.
How do we do that with organizations that are
a hundred years old, our government even more so
to enable that behavioral change that culture
of Artificial Intelligence?
Thank you.
- So, I never want to use nuclear as a good example
but if you think about the nuclear emergency
in the 40's and 50's ultimately the government
created a set of institutions, other labs
around the country that were run by physicists
because the physics of nuclear is very difficult.
These are highly competent organizations full
of incredibly smart people working on these very, very
difficult science problems.
So we do have a model there,
but it was understood that it was a specialized
skill.
Part of the reason our recommendations said we created
the equivalent of an AI institute is it's not like normal
stuff.
That's an example.
I think that what will happen is that every major
public organization will have as its supplier
some of the people I'm describing, whether they
are separate software companies or they work for them
because it's too fundamental.
Give you another example.
If you run a normal big business in America
a team of 10 or 20 of these people can probably
increase your profits by five percent.
Just by pattern matching,
because these algorithms are very, very at looking
and discovering patterns that you don't see.
My favorite example is we have these large data
centers.
We care a great deal about them.
We have the best data center engineers in the world,
according to the data center engineers.
So we take our group in London called Deep Mind
and we ask them to use the reinforcement learning
tools to look at simple energy efficiency
of our data centers.
Data centers are defined by how much electricity
they use, moving power around cooling,
pretty boring stuff but you think it's more busy
on Monday than on Sunday.
That kind of thing.
They've tuned it as best they can,
and they are really good.
In two weeks the system came up with a 15 percent
improvement over the top engineers in the world.
Now what's a 15 percent improvement in energy efficiency
worth to a large distribution company in the
oil and gas business, or in the transportation business.
Of any kind.
It's enormous.
Those are all of their profits.
That's how powerful this technology is.
- Yes, I thank you.
Jeff Alvin, State Department.
The tech sector traditionally is wary of
too much regulation with the federal government
over site and intervention.
So on this particular aspect of the intersection
of Artificial Intelligence and national security.
What's your sense of the willingness of Alphabet
or Facebook, or Amazon, or Microsoft to work
proactively with the federal government
especially when it comes to trying to set
limits or constraints on the types of technologies
you develop or the export of technologies
to certain countries.
- So, why would you want to put limits on the
technology I just described?
- To prevent an AI arms race, or to prevent
proliferation of weaponized AI.
- Okay, so there's a lot of presumptions in that
question.
- That's why we're here.
- Understood.
So the first question is do you think there's
going to be an AI arms race?
- Yes.
- You do.
- Well you just said China will be ahead of us by 2020.
Doesn't that concern you?
- It does. It doesn't necessarily mean it's an arms
race. Depends on how it's done.
- Well you said China's interested in using AI
for weapons for military purposes.
- I said for commercial as well as defense purposes.
- So it doesn't concern you at all?
- It actually does, but the reason I'm provoking you
is that it's so easy to go to the movie.
The movie of the robot.
- I'm not talking about the Terminator, but as you
mentioned the example of nuclear weapons in the 40's.
We might be at that stage in the next few years
when it comes to AI.
- Hopefully we are not going to be.
So go through some of the arms race questions.
So the first question is cyber security.
One of the things that's different between cyber security
and nuclear is we have the possibility of defending
ourselves against it.
So before we get focused on how terrible things are
maybe we should fix our cyber systems to become
more resilient.
Starting with upgrading all those Windows 95
systems that the government uses.
Last time I checked we're still trying to get
to Windows 10.
Windows 95 has been thoroughly penetrated by the Chinese
and every undergraduate at every university.
So you think your network is secure,
trust me it's not.
So there's a set of things we can do there
that are well beyond before an arms race.
I can go on.
I think the general answer on regulation is
we worry about premature regulation.
So there's plenty of things to worry about
but I don't know what you'd regulate now.
So under that doctrine, you would say "Well
if you invent a new algorithm you have to give it to
the government".
Well at the moment you give it to the government
it would be leaked.
Because their systems are so insecure.
So again I would be very, very careful about
simple answers to these questions.
I would prefer to be more like us.
^Which is to invest in our core, especially
^younger talent coming out of universities
^and allow that we will lead through technological
^innovation and so forth and stay ahead.
^That would be my goal.
- Do you see certain components of this technology
that we might want to protect and not allow
others not proliferate, not export to others?
- It's unclear to me that that would work
very well.
Because the industry is moving so quickly,
the understanding of the underlying math and so forth
is so broad now and there isn't a physical material
you can constrain.
It's a reasonable debate to have
but an example is that if my own view
that if anybody is going to invent a safe
form of general intelligence it will be the
Deep Mind subsidiary of Alphabet which is in London.
Why is it in London?
Because there's a brilliant collection of people
that came out of Cambridge and they've been adding
people and so forth.
By the way, their complaint is they want to make
sure the Europeans will be able to move into
Britain because there's plenty of incredibly smart Germans
and French people that worry that in Brexit they
may not be able to move.
Same argument but in a different context.
- Question over here.
- Eric, thank you for sharing your view for us.
Your peer, Elon Musk at Silicon Valley, apparently
had a more pessimistic view on AI
and he partially warned that AI is going to be a
disaster to human beings, to society.
So I have a very simple question maybe in a broad
context.
So if you look at Artificial Intelligence today
we call it AI, but actually what it really does
is taking what you shared with us, AlphaGo,
learn this new game in seven days.
So that's not really a regular intelligence.
It's creating a supreme intelligence
and so in the near future what's going to happen
is were going to have imagine a country to country
confrontation or could be good guys versus bad guys
confrontation.
So essentially what's going to happen is
this supreme intelligence against supreme intelligence.
I know it's a very kind of question to answer
to predict what's going to happen
but a simple question I have for you is
take Deep Mind organization.
You guys have this AlphaGo.
Do you have another AlphaGo within the same
organization and you are trying to compete
the AI to the supreme intelligence.
What's going to happen?
- So I understood your question and I'm afraid I'm gonna
have to cast it into the movie department, because
we're not today building super intelligences.
^We are building systems that are very good
^at imprecise pattern matching.
^These are not super intelligences in the term
^that you are describing, but there are people
^who think that we will eventually be able to do that
^but certainly not in the next few years.
^We're some time away and it's absolutely true
^that AlphaGo is a tremendous achievement but it was
^done against a system of absolutely clear rules
^which is why games are so susceptible for it.
It remains an open question how much farther it will go.
So I'm not as worried about the scenario you described.
You can imagine a movie where there's these super
intelligences and they have a big battle
and ultimately they have a combination.
I don't think Elon is right at all.
I think that AI is the inverse of what he said.
I think it will make people enormously smarter,
enormously more productive, live longer, have
better lives, this is my personal view and most
people I work with agree.
- Question over here.
- Richard Hartman from the University of Cincinnati.
Couldn't agree more with everything that you've said.
I want to take you back up into that strategic
space in which we have an absence of strategy.
So when I think strategically I see a distribution
of AI cyber power.
Obviously we're talking about China, talking about
the United States, I wonder if I could draw you out
and talk about Alphabet as an actor in this
space.
So we've been caught up in this partnership model
for a long time and that has very interesting
implications where it implies a certain type of
relationship, but when we think strategically
and we think about Alphabet as an actor in this space
it suggests to me a much better way of thinking about
this as an alignment model.
That Alphabet has interests in this space
which may or may not the same as China's,
may not be the same as the United States
and how do you align those to achieve strategic ends
that from the United States perspective would advance
national security.
Does that make sense?
That we should start to think about your company
and others in that space as actors with interests
that are going to influence and instruct the area that
we have to seek security and power in?
- I'm sure we will be a contributor but it's important
to remember that we operate under US law
and US regulations.
^The three companies that are really leading
^in this area are ourselves, Facebook, and a Chinese
^company.
^There are others coming.
^I think there will be a set of companies.
^It will amass enough people and data and so forth
^to do interesting things in their businesses
^with others joining, but I think ultimately
these issues will be resolved in a higher court
if you will.
I will tell you that our contribution is that
we are trying to move the research ahead quickly.
We released a library called TensorFlow which
is one of the mashup systems that people use
which has been remarkably popular.
- Question over here?
Yes, sir go ahead.
- Thank you Eric for the very interesting feedback.
My name is Jeff Clune.
I'm a professor of Artificial Intelligence and also helping
to create Uber's Artificial Intelligence lab which in many
respects is following up on the model that Google
created and I want to talk about that model
really quickly.
One of the things I find really commendable and
remarkable about what Google did years ago
is it started paying people to come and just do research
within the company walls for pay.
Which I imagine is a tremendous expense.
You have all of Jeff Dean's group in Google Brain, Deep Mind
and all the other researchers and so I wanted to
hear a little bit of your thoughts in terms of what
you recommend to other CEOs and government officials,
especially in the military in terms of is it worth
the expense to hire people to come and just do research
and not focus on products in your company.
I know you mentioned the great example by
Mustafa's group of RL being applied to your own
power factories.
So to what extent have you been justified in this amazing
investment and pure researchers that are just publishing
and kind of doing the typical academic thing but on
Google's payroll.
- So in our case as an AI First company,
you would want to be the inventors of some of the
future of it so it makes perfect sense.
I've worked in research labs for my whole career.
I worked at Xerox PARC 40 years ago, so I can't imagine
a tech company without being able to fund either
directly or through universities the basic research
in the field that they take most advantage.
I make the same argument of the military.
It just makes no sense to me that the military
would avoid having either researchers itself
or researchers that it funds, collaborations
with universities partly so they can get an internal
heads up. It's just so much a better feeling when you
can make a local phone call or the equivalent
to somebody who is on the payroll if you will
or in your tribe, trying to understand what's
really going on.
So think about, let's use the China threat
in the military context.
So lets say the military is standing there doing
a China analysis and I'm sure that they do this,
Wouldn't it be nice if they had some folks that they trusted
that they could call.
They could really talk about where the Chinese research is,
where the American research is, and where the differences
may emerge.
- We have time for one more question so Greg you've
got the last question.
- Dr. Schmidt thanks very much for being with us today.
I'm curious what you think the correct relationship
is between the large Silicon Valley technology companies
and the Department of Defense and the US Intelligence
community.
Please correct me if I'm wrong but my understanding
is the terms of acquisitions for Deep Mind actually
restricted the level of collaboration between
Deep Mind and the US national security community
and so I'm curios one whether or not those restrictions
are real and two what is the proper relationship
between technology companies and US AI researchers
and the national security community?
Thank you.
- I don't remember the specifics on the term
but there's a general concern in the tech community
of somehow the military industrial conflicts
using their stuff to kill people incorrectly if you will.
^I don't know how to say it any other way. It comes from
^essentially related to the history of the Vietnam war
^and the founding of the tech industry.
^So in reality, I and the other people that I'm part
^of the board with are trying to at least to allow
^for communications to occur.
^As to whether I for example am conflicted in the sense
^that I can't suggest Alphabet things inside the military
^nor would I ever do that.
^So in practice I don't have as big a concern in this
^because the militaries procurement processes and the
way it operates are so different from the way the tech
community operates.
My guess is what will happen is that there will be companies
founded that are more in alignment with the mission
and values of the military that will use this technology
to help keep the country safe etc.
A very good example is group DIUX which is headed
by a guy named Raj Shah and DIUX by statute has
the ability to invest in small tech companies
for things which are helpful to the military's mission.
So they are for example investing in drone companies
that are doing interesting things, sensor companies
that are doing interesting things.
That is a great idea and there should be lots more
of that.
That's the more likely scenario.
- Thank you Eric, we're about out of time.
- Thank you all and thank you all
for caring so much about this.
(clapping)