- Tesla has a unique
advantage when it comes
to making self-driving cars a reality,
but it might not be what you think.
Stay tuned.
(light music)
- The person who has the biggest data set
is ultimately going to win.
- Now I hear a lot of
people in the media talk
about self-driving cars
like they're already here.
Like, I can go get in one right now.
They throw around terms
like artificial intelligence
or A.I., machine learning,
deep learning, computer vision,
and all kinds of other things,
and I'm not sure they really know
what they're talking about.
I've been in tech for almost 20 years,
and I'm not sure I know either.
I wanted to learn more about these,
so I called up friend, fellow
online author, data geek,
and also doctor of computer vision.
He literally has a PhD in
this, Doctor Satya Mallick.
And thought I would ask him
to explain it to me a little bit.
- So A.I. is, you can think about A.I.
as conceptually the biggest circle.
It has everything inside it.
So the field started roughly in the 1960s,
where the idea was that can you make
computers do things that humans do.
So, earlier before that,
people were thinking
of computers as advanced calculators.
You punch in some numbers,
it does something.
Now, the idea was to replicate
human intelligence using computers.
Can a computer think like a human?
And for that, it needs to work
on things that it has not seen before.
That was the main difference.
In case of regular
programming, you know the data,
you know what the data is,
you know what the output
should be, and it just applies.
It's a forward problem, in some sense.
In case of A.I. you're
solving the inwards problem.
So, I'm looking at you,
I don't know who you are.
Face recognition.
It is the inwards problem, right?
In computer graphics also,
it's a forward problem.
So you want to generate the
scene from a structure, right?
In computer vision it
is the inwards problem.
So, A.I. was this idea,
how can you replicate human
intelligence using a computer?
And at that time, it was not clear
what should you do, right?
Do you come up with a set of rules,
or do you collect a lot of data?
And the thing that has won out is that
you cannot come up with enough
rules to model everything.
There are just too many rules.
So, what they came up with is what if we
come up with a data
driven approach, right?
So that you're also taking over
the expert from when it was rule based.
An expert had to say that this
is the right rule to apply.
But when it is a data driven approach,
there is no expert required.
You just collect the data and let
the machine learn from the data.
- [Interviewer] Mm-hmm.
- And nobody's telling the
machine this is important.
There is some of that.
But not explicitly cooking up the rules.
So machine learning is when
you try to learn from the data.
When the machine learns from the data.
So, that's a smaller set, right?
A.I. was the bigger set where nobody knew.
It is silent on how you solve the problem.
It just had to replicate
human intelligence
to solve like humans, but it
was silent on how you do it.
Machine learning is you do it using data.
So, it's saying data is the way to go.
And in computer vision is again
a subset of machine learning
or they're overlapping in some sense,
where you're saying that images
concerns visual data, right?
So there's speech and other
things, but computer vision
only deals with visual images and videos.
And in computer vision, it's
not a perfect overlap like
it's not a subset of machine learning.
There is overlapping area,
because when you build panoramas
using pictures, that's
also computer vision.
So anything you want to
do either extract data
from images or combine data in images
in interesting ways,
they're all computer vision.
Yeah, so that brings us to deep learning.
Now, deep learning is essentially--
So, in machine learning
there are many different ways
of solving the problem,
and one of the ways
is using neural networks.
And the way to think about neural networks
is think about it as a black box.
Inside the neural network,
there are various layers,
and each layer tries to understand
a level of abstraction of the image.
So now, there are more--
Whenever you have more than one layer,
it is called a deep neural network.
Because it is more than one.
But these days you can
have hundreds of layers.
So the state of the art, and
that's what deep learning is,
it's a neural network
with hundreds of layers.
- Alright, that makes
sense, so you have A.I.,
which is the field, and then within that,
you have machine learning
and computer vision,
the part where we teach
a computer how to see,
is kind of a subset of that, but also has
some other components, so it's more like
an overlapping circle thing.
Kind of like how there's beer and then
there's ales and lagers and then
there's IPAs and stouts
and those kind of things.
So it's just a hierarchy here of terms,
but they do seem to be
thrown around quite a bit.
So the goal is to teach
a computer how to see,
to give it vision, but how
does that work exactly?
Here's Satya again.
- So, 2012, in 2010 actually
the story begins in 2010.
Researchers at Stanford, they released,
and in collaboration with Google,
they released this huge
data set called ImageNet.
Now, ImageNet was, it had more than
a million images at that time,
now it's way more than that.
And 1,000 different classes,
so cats, dogs, each of them is a class.
Cats, dogs, table, chair, et cetera.
And they had 1,000 different classes
and more than a million different images.
Now, when this data set was
released, because there are
1,000 different classes, you
can imagine that accuracy would
be if you take a random guess
is one over 1,000, right?
That is the accuracy.
So, they made the
problem slightly simpler,
you just need to get it in the top five.
You have five guesses, if you are
in the top five, you're good.
So, that level of accuracy in 2010
when the competition was
released, it was 72%, I believe.
So, 72% accuracy, pretty good.
I mean, impressive, because
at that time it was mind blowing.
In 2011, there was an
incremental progress.
So from 72%, I believe, we went to 74%.
And that's how you expect
technology to progress;
One, two percent every
year, and in 10 years
you are 80, 90%, whatever.
But in 2012, the second entry was
75% or something like that, which is
in line with what we had seen.
The first entry was 85%. 85%!
It's like humiliating to others!
It's like Usain Bolt
looking over his shoulders
to see who's coming behind,
or how far they are.
- Alright, things are
becoming a bit more clear,
and, as you guessed it,
the key to this whole thing
is having enough data
to teach the computer
what it's actually looking at.
One of the toughest parts, however,
is telling the computer
what it's actually seeing,
so that way it understands
how to learn from that.
Now Satya has an idea how Tesla here
may have a unique advantage.
- In self-driving cars, the data
is automatically labeled in some cases.
Not always there is automatic labeling.
For example, you're
driving, and the car is--
the cameras are monitoring;
you press the break hard.
It tells you there was something wrong,
and you combine this data--
When you're driving,
the steering wheel data
tells you that there's a curvature.
And, so, you can combine all this data,
and it can be very, very useful.
- So by recording what
the car is actually seeing
while it drives, and then recording
the person's reactions to that,
Tesla is in really good shape when it
comes to getting enough data to
truly make a self-driving car.
But Satya thinks there's something else
that gives them an edge here.
- Okay now coming back
to Google and Tesla,
in the case of Tesla, they are taking
one step at a time, so they are saying
we may not go to completely, like,
hands-free mode, but we will go in steps.
First it will assistive;
you can take off your hands
on highways and stuff like that,
and I believe that's a very nice approach.
That's probably the right approach
for any car company like Tesla.
- Alright, so that's it, right?
Tesla has the data, they obviously have
a great team of engineers, but data
is the key ingredient here, of course.
Why isn't it here?
Why can't I go out and get in my car
and just tell it where to go?
Why do I have to be burdened by
using my hands to steer the car?
It doesn't make any sense!
Well, as you may have
guessed, humans, well...
We get in the way.
- So, I think Victor Hugo or somebody
said that nothing is as powerful
as an idea whose time has come.
(laughs)
So it's very difficult,
very, very difficult,
to stop this juggernaut, right?
It is going to happen.
The risks could be...
It's very difficult to imagine
something that would completely stop it.
It could be the legal problems,
because it's only 99.99%,
and that last .001%,
it takes us much longer than expected.
Instead of it taking five years,
it takes 20, 30, 40 years.
So it's still, then it
doesn't become legal,
because you know about that risk,
which is greater than human risk.
It doesn't need to be even
greater than human risk.
Even if it is doing better than humans
on an average, that's not enough.
Because in one case you
can blame the human.
In the other case you
cannot blame the human,
and that's an ethical
issue, in some sense.
- Like I said, humans
getting in the way again.
And to our future robot overlords:
remember, I was on your
side from the beginning.
So what's next?
What will the future look like?
Will we even needs roads?
- Roads? Where we're
going, we don't need roads.
- For example, when a vast
majority of cars are driverless,
or lets say 100% of the
cars are driverless,
do you need traffic signals?
Because they can be
communicating with each other,
and going in fleets instead
of a single vehicle.
They could all be moving together,
and even if it's partially driverless,
so half the people are driverless
and half the people are
not, then if you know
that the car in front
of you is driverless,
these guys can communicate.
The light turns green;
they all move together
instead of, you know, right
now there is a cascade.
- Wow!
I think this is going to
be an exciting future,
and I hope you guys are
excited about it, too.
The whole fleet thing,
not having stoplights,
potentially not having parking
lots, all this is great.
But until then I guess I'm gonna have
to rough it with autopilot.
So what do you think?
Are self-driving cars gonna be a boon
for our economy and our society?
Is it gonna save millions of lives,
or is this just another thing
that is kinda science fiction?
I'm really curious.
Leave me a comment down below,
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