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Language: en
all the cost is in the equipment to do
it and the trend on equipment is once
you figure out how to build the
equipment the trend of cost is zero Ilan
said first you figure out what configure
machine you want the atoms in and then
how to put them there right yeah cuz
well what here's the you know his his
great insight is people are how
constraint I have this thing I know how
it works and then little tweaks to that
will generate something as opposed to
what do I actually want and then figure
out how to build it it's a very
different mindset and almost nobody has
it obviously well let me ask on that
topic you were one of the key early
people in the development of autopilot
at least in the hardware side Elon Musk
believes that autopilot and vehicle
autonomy if you just look at that
problem
can follow this kind of exponential
improvement in terms of the ha the how
question that we're talking about
there's no reason why I can't what are
your thoughts on this particular space
of vehicle autonomy and you're a part of
it and Elon Musk's and Tesla's vision
well the computer you need to build was
straightforward and you could argue well
it doesn't need to be 2 times faster or
5 times or 10 times but that's just a
matter of time or price in the short run
so that's that's not a big deal you
don't have to be especially smart to
drive a car so it's not like a super
hard problem I mean the big problem with
safety is attention which computers are
really good at not skills well let me
push back on one you see everything you
said it's correct but we as humans tend
to tend to take for granted how how
incredible our vision system is so you
can drive a car of a 2050 vision and you
can train a neural network to extract a
distance of any object in the shape of
any surface from a video and data but
that really simple not simple I look
that's a simple data problem it's not
it's not simple it's
because it's not just detecting object
it's understanding the scene and it's
being able to do it in a way that
doesn't make errors so the beautiful
thing about the human vision system and
the entire brain around the whole thing
is we were able to fill in the gaps it's
not just about perfectly detecting cars
its inferring the occluded cars it's
trying to it's it's understanding the I
think it's mostly a bigger problem you
so you think what data you know with
compute with improvement of computation
with improvement and collection there is
a you know when you're driving a car and
somebody cuts you off your brain has
theories about why they did it
you know they're a bad person they're
distracted they're dumb you know he can
listen to yourself right so you know if
you think that narrative is important to
be able to successfully drive a car then
current autopilot systems can't do it
but if cars are ballistic things with
tracks and probabilistic changes of
speed and direction and roads are fixed
and given by the way they don't change
dynamically right you can map the world
really thoroughly you can place every
object really thoroughly right you can
calculate trajectories of things really
thoroughly right but everything you said
about really thoroughly has a different
degree of difficulty so you could say at
some point computer autonomous systems
we way better it's things that humans
are allows yet like it'll be better at
abstention they'll always remember there
was a pothole in the road that humans
keep forgetting about they'll remember
that this set of roads houses weirdo
lines on it the computers figured out
once and especially if they get updates
so if so many changes a given like look
Taketa robots and stuff somebody said is
to maximize to Gibbons okay right so
though having a robot pick up this
bottle cap is way easier to put a red
dot on the top because then you have to
figure out you know and if you want to
do a certain thing with it you know
maximize two Givens is the thing and
autonomous systems are happily
maximizing the Givens
like humans when you drive someplace new
you remember it because you're
processing it the whole time after the
50th time you drove to work you get to
work you don't know how you got there
right you're on autopilot right
autonomous cars were always on autopilot
but the cars have no theories about why
they got cut off or why they're in
traffic so they'll never stop paying
attention right so I tend to believe you
do have to have theories mental models
of other people especially pedestrians
cyclists but also other cars so
everything you said is like is actually
essential to driving driving is a lot
more complicated than people realize I
think sort of to push back slightly but
cut into traffic right yeah you can't
just wait for a gap you have to be
somewhat aggressive you'll be surprised
how simple the calculation for that is I
may be on that particular point but
there's a that it it may be a sure to
push back I would be surprised you know
what yeah I'll just say where I stand I
would be very surprised but I think it's
you might be surprised how complicated
it is that I'd say that I tell people's
like progress disappoints in the short
run the surprises in the long run it's
very possible yeah I suspect in 10 years
it'll be just like taken for granted
yeah but probably right now look like
it's gonna be a $50 solution that nobody
cares about
it's like GPS is like Wow GPS is we have
satellites in space that tell you where
your location is it was a really big
deal now everything that's a GPS I mean
yeah it's true but I do think that
systems that involve human behavior are
more complicated than we give them
credit for so we can do incredible
things with technology that don't
involve humans but when you look humans
are less complicated than people you
know frequently absque ribe maybe I
sound awful right out of large numbers
of patterns and just keep doing it over
and over but I can't trust you because
you're a human that's something
something a human would say but I might
lose my hope was on the point you've
made is even if no matter who's right
there I'm hoping that there's a lot of
things that humans aren't good at that
machines are definitely good at
said attention and things like that will
they'll be so much better that the
overall picture of safety in autonomy
will be obviously cars will be safer
even if they're not as good I'm a big
believer in safety I mean there are
already the current safety systems like
cruise control that doesn't let you run
into people and lane-keeping there are
so many features that you just look at
the pareto of accidents and knocking off
like 80 percent of them you know super
doable just a wing guard on the
autopilot team and the efforts there the
it seems to be that there's a very
intense scrutiny by the media and the
public in terms of safety the pressure
the bar but before autonomous vehicles
what are your sort of as a person
they're working on the hardware and
trying to build a system that builds a
safe vehicle and so on what was your
sense about that pressure is it unfair
is it expected of new technology it
seems reasonable I was interested I
talked to both American and European
regulators and I was worried that the
regulations would write into the rules
technology solutions like modern brake
systems imply hydraulic brakes so if
you'll read the regulations to meet the
letter of the law for brakes it sort of
has to be hydraulic right and the
regulator said they're they're
interested in the use cases like a
head-on crash an offset crash don't hit
pedestrians don't run into people don't
leave the road don't run a red light or
a stop light they were very much into
the scenarios and you know and they had
they had all the data about which
scenarios injured or killed to most
people and for the most part those
conversations were like what's the right
thing to do to take the next step now
Elon is very interested also in the the
benefits of autonomous driving or
freeing people's time and attention as
well as safety
and I think that's also an interesting
thing but you know building an
autonomous system so they're safe and
safer and people seemed since the goals
to be tannic seifer's and people having
the bar to be safer than people and
scrutinizing accidents seems
philosophically you know correct so I
think that's a good thing what R is is
different than the things you've worked
at Intel AMD apple with autopilot chip
design and hardware design what are
interesting or challenging aspects of
building this specialized kind of
competing system in the automotive space
I mean there's two tricks to building
like an automotive computer one is to
software our team the machine learning
team is developing algorithms that are
changing fast
so as you're building the accelerator
you have this you know worry or
intuition that the algorithms will
change enough that the accelerator will
be the wrong one right and there's the
generic thing which is if you build a
really good general-purpose computer say
it's performance is one and then GPU
guys will deliver about five extra
performance for the same amount of
silicon because instead of discovering
parallelism you're given parallelism and
then special accelerators get another
two to five X on top of a GPU because
you say I know the math is always 8-bit
integers into 32-bit accumulators and
the operations are the subset of
mathematical possibilities so although
you know AI accelerators have a claimed
performance benefit over GPUs because in
the narrow math space you're nailing the
algorithm now you still try to make it
programmable but the AI field is
changing really fast so there's a you
know there's a little creative tension
era of I want the acceleration afforded
by specialization without being over
specialized so that the new algorithm is
so much more effective that you'd have
been better off on a GPU so there is
attention there
to build a good computer for an
application like automotive there's all
kinds of sensor inputs and safety
processors and a bunch of stuff so one
of loans goal is to make it super
affordable so every car gets an
autopilot computer so some of the recent
startups you look at and they have a
server in the trunk because they're
saying I'm gonna build this autopilot
computer replaces the driver so their
cost budgets ten or twenty thousand
dollars and ian's constraint was I'm
gonna put one every in every car whether
people buy autonomous driving or not so
the cost constraint had in mind was
great right and to hit that you had to
think about the system design that's
complicated it's it's fun you know it's
like it's like it's craftsmen's work
like a violin maker right you could say
Stradivarius this is incredible thing
the musicians are incredible but the guy
making the violin you know picked wood
and sanded it and then he cut it you
know and he glued it and you know and he
waited for the right day so that when
you put the finish on it didn't you know
do something dumb that's craftsmen's
work right you may be a genius craftsman
because you have the best techniques and
you discover a new one but most
engineering is craftsmen's work and
humans really like to do that you know
smart humans oh no everybody all humans
I know I used to I dug ditches when I
was in college I got really good at it
satisfying yeah so digging ditches is
also cross mill work yeah of course Joe
so there's an expression called complex
mastery behavior so when you're learning
something that's fun because you're
learning something when you do something
it's wrote and simple it's not that
satisfying but if the steps that you
have to do are complicated and you're
good Adam it's satisfying to do them and
then if you're intrigued by it all as
you're doing them you sometimes learn
new things that you can raise your game
but craftsmen's work is good in
engineers like engineering is
complicated enough that you have to
learn a lot of skills and then a lot of
what you do is then craftsmen's work
which is fun autonomous driving building
a very a resource-constrained computer
so computer has to be cheap enough that
put in every single car that's
essentially boils down
ooh craftsmen's work it's saying genius
no there's thoughtful decisions and
problems to solve and trade-offs to make
do you need 10 Cameron ports or 8 you
know it's your building for the current
car the next one you know how do you do
the safety stuff you know there's
there's a whole bunch of details but
it's fun but it's not like I'm building
a new type and they're all networked
which has a new mathematics and a new
computer at work do you know that that's
like there's a there's more invention
than that but the rejection to practice
once you pick the architecture you look
inside and what do you see adders and
multipliers and memories and you know
the basics so computers was always just
this weird set of abstraction layers of
ideas in thinking that reduction to
practice is transistors and wires and
you know pretty basic stuff and that's
an interesting phenomena by the way that
like factory work like lots of people
think factory work is Road assembly
stuff I've been on the assembly line
like the people work that really like it
it's a really great job it's really
complicated putting cars together is
hard right and in the cars moving and
the parts are moving and sometimes the
parts are damaged and you have to
coordinate putting all the stuff
together and people are good at it
they're good at it and I remember one
day I went to work and the line was shut
down for some reason and then some of
the guys sitting around were really
bummed because they they had reorganized
a bunch of stuff and they were gonna hit
a new record for the number of cars
built that day and they were all gung ho
to do it and these were big tough
buggers yeah you know but what they did
was complicated and you couldn't do it
yeah and I mean well after a while you
could but you'd have to work your way up
cuz you know like putting a bright
what's called the brights to the trim on
a car on a moving assembly line where it
has to be attached 25 places in a minute
and a half is unbelievably complicated
and and and human beings can do it's
really good
I think that's harder than driving a car
by the way putting together work at
working on the factory to smart people
can disagree yeah I think
driving a car well we'll get to the
factory something and then we'll see
you're not for us humans driving a car
is easy I'm saying building a machine
that drives the car is not easy okay
okay driving a car is easy for humans
because we've been evolving for billions
of years drive cars yeah no juice the
pail if the cars are super cool
no now you join the rest of the internet
and mocking me okay yeah yeah I'm trig
by your you know your anthropology yeah
we have to go dig into that there's some
inaccuracies there yes okay but in
general what have you learned in terms
of thinking about passion craftsmanship
tension chaos you know the whole mess of
it or what have you learned have taken
away from your time working with Elon
Musk working at Tesla which is known to
be a place of chaos innovation
craftsmanship and I really like the way
he thought like you think you have an
understanding about what first
principles of something is and then you
talk to you alone about it and you you
didn't scratch the surface you know he
has a deep belief that no matter what
you do is a local maximum right and I
had a friend he invented a better
electric motor and it was like a lot
better than what we were using and one
day he came by he said you know I'm a
little disappointed because you know
this is really great and you didn't seem
that impressed and I said you know and
the super intelligent aliens come are
they gonna be looking for you like where
is he the guy who built the motor yeah
probably not you know like like the but
doing interesting work that's both
innovative and let's say craftsmen's
work on the current thing it's really
satisfying it's good and and that's cool
and then Elon was good taking everything
apart like what's the deep first
principle Oh know what's really know
what's really you know you know you know
that
you know ability to look at it without
assumptions and and how constraints is
super wild you know we build rocket ship
and usually what's a car you know
everything and that's super fun and he's
into it too
like when they first landed to SpaceX
Rockets at Tesla we had a video
projector in the big room and like five
hundred people came down and when they
landed everybody cheered and some people
cried it was so cool all right but how
did you do that
well no super hard and then people say
well it's chaotic really to get out of
all your assumptions you think that's
not going to be unbelievably painful mmm
there's Elon tough yeah probably
the people look back on it and say boy
I'm really happy I had that experience
to go take apart that many layers of
assumptions
sometimes super fun sometimes painful so
it could be emotionally and
intellectually painful that whole
process just stripping away assumptions
yeah I imagine 99 percent of your
thought process is protecting your self
conception and 98% of that's wrong yeah
now you got that math right how do you
think you're feeling when you get back
into that one bit that's useful and now
you're open and you have the ability to
do something different I don't know if I
got the math right it might be ninety
nine point nine but in 850 imagining it
the 50% is hard enough
yeah now for a long time I've suspected
you could get better like you can think
better you can think more clearly you
can take things apart and there's lots
of examples of that people who do that
so any line is an example of that
Pariwar an example says you know if I am
I'm fun to talk to him certainly I've
learned a lot of stuff right well here's
the other thing is like I talked like
like I read books and people think oh
you read books well no I brought a
couple of books awake for 55 years
well maybe 50 cuz I didn't read learned
reading taught us H or something and and
it turns out when people write books
they often take 20 years of their life
where they passionately did something
reduce it to to 200 pages that's kind of
fun
and then the goal you go online and you
can find out who wrote the best books
and who like you know that's kind of
Alda so there's this wild selection
process and then you can read it and for
the most part to understand it and then
you can go apply it like I went to one
company I thought I haven't managed much
before so I read 20 management books and
I started talking to him basically
compared to all the VP's running around
I'd run night read 19 more management
books than anybody else was it even that
hard
yeah and half the stuff worked like
first time it wasn't even rocket science
but at the core of that is questioning
the assumptions okay sort of entering
the thinking first principles thinking
sort of looking at the reality of the
situation and using it using that
knowledge applying that knowledge yes so
I would say my brain has this idea that
you can question first assumptions and
but I can go days at a time and forget
that and you have to kind of like circle
back data observation because it is
because ecology
well it's hard to keep it front and
center because you know you're you
operate on so many levels all the time
and you know getting this done takes
priority or you know being happy takes
priority or you know screwing around
takes priority like like like how you go
through life is complicated yeah and
then you remember oh yeah I could really
I think first principles oh that's
that's tiring you know what you do for
awhile that's kind of cool so just as
the last question in your sense from the
big picture from the first principles do
you think you kind of answered already
but do you think autonomous driving is
something we can solve on a timeline of
years so 1 2 3 5 10 years as opposed to
a century
yeah definitely just to linger on it a
little longer where's the confidence
coming from is it the fundamentals of
the problem the fundamentals of building
the hardware and the software as a
computational problem understanding
ballistics rolls topography it seems
pretty solvable I mean and you can see
this you know like like speech
recognition for a long time people are
doing you know frequency and domain
analysis and and all kinds of stuff and
that didn't work for at all right and
then they did deep learning about it and
I worked great and it took multiple
iterations and you know time is driving
his way past the frequency analysis
point you know use radar don't run into
things and the data gather it's going up
in the computation showing up and the
algorithm understanding is going up and
there's a whole bunch of problems
getting solved like that the data side
is really powerful but I disagree with
both you and you and I'll tell you and
once again as I did before that that
when you add human beings into the
picture the it's no longer a ballistics
problem it's something more complicated
but I could be very well proven cars are
hardly damped in terms are ready to
change like the steering and the
steering systems really slow compared to
a computer the acceleration of the
acceleration is really slow yeah on a
certain time scale on a ballistics time
scale but human behavior I don't know it
yeah I shouldn't see beings are really
slow to it weirdly we operate you know
half a second behind reality nobody
really understands that one either it's
pretty funny yeah yeah so now I will be
with very well could be surprised and I
think with the rate of improvement in
all aspects on both the computing the
software and the hardware there's gonna
be pleasant surprises all over the place
you