This is lecture 1 of course 6.S094: Deep Learning for Self-Driving Cars (2018 version). This class is free and open to everyone. It is an introduction to the practice of deep learning through the applied theme of building a self-driving car.

OUTLINE:
0:00 - Introduction
8:14 - Self-Driving Cars
14:20 - Deep Learning

INFO:
Slides: http://bit.ly/2HlyFHI
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Playlist: https://goo.gl/SLCb1y

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LINKS:
Playlist: https://goo.gl/SLCb1y
Lecture 1: Deep Learning - https://youtu.be/-6INDaLcuJY
Lecture 2: Self-Driving Cars - https://youtu.be/_OCjqIgxwHw
Lecture 3: Deep Reinforcement Learning - https://youtu.be/MQ6pP65o7OM
Lecture 4: Computer Vision - https://youtu.be/CLOAswsxudo
Lecture 5: Deep Learning for Human Sensing - https://youtu.be/Z2GfE8pLyxc

Guest talk: Sacha Arnoud, Waymo - https://youtu.be/LSX3qdy0dFg
Guest talk: Emilio Frazolli, nuTonomy - https://youtu.be/dWSbItd0HEA
Guest talk: Sterling Anderson, Aurora - https://youtu.be/HKBhP9JISF0

2017:
Guest talk: Sertac Karaman, MIT - https://youtu.be/0fLSf3NO0-s
Guest talk: Chris Gerdes, Stanford - https://youtu.be/LDprUza7yT4

Thank you everyone for braving the cold and the snow to be here.

This is 6S094, Deep Learning for Self-Driving Cars.

And it's a course where we cover the topic of deep learning.

Which is a set of techniques that have taken a leap in the last decade For our understanding of what artificial intelligence systems are capable of doing.

And self-driving cars, which is systems that can take these techniques and integrate them in a meaningful, profound way into our daily lives in a way that transforms society.

So that's why both of these topics are extremely important and extremely exciting.

My name is Lex Friedman and I'm joined by an amazing team of engineers in Jack Terwilliger, Julia Kindlesberger, Dan Brown, Michael Glazer, Lee Ding, Spencer Dodd, and Benedict Jenick, among many others.

We build autonomous vehicles here at MIT.

Not just ones that perceive and move about the environment But ones that interact, communicate and earn the trust And understanding of human beings inside the car The drivers and the passengers And the human beings outside the car The pedestrians and other drivers and cyclists.

The website for this course, selfdrivingcars.mit.edu If you have questions, email deepcars.mit.edu slackdeep-mit For registered MIT students, You have to register on the website And by midnight, Friday, January 19th, Build a neural network and submit it to the competition that achieves the speed of 65 miles per hour on the new Deep Traffic 2.0.

It's much harder and much more interesting than last year's for those of you who participated.

There's guest speakers that come from Waymo, Google, Tesla.

And those are starting new autonomous vehicle startups In Voyage, Neutronomy and Aurora.

For those of you who brave the snow and continue to do so, towards the end of the class there will be free shirts.

Yes, I said free and shirts in the same sentence.

Okay, first the deep traffic competition.

There's a lot of updates and we'll cover those on Wednesday.

It's a deep reinforcement learning competition.

Not only can you control 1 car within your network, you can control up to 10.

This is multi-agent deep reinforcement learning.

Dynamic driving scene segmentation competition.

Where you're given the raw video, The kinematics of the vehicle, so the movement of the vehicle.

For the training set you're given ground truth labels, Pixel level labels, scene segmentation and optical flow.

And with those pieces of data, You're tasked to try to perform better Than the state-of-the-art in image-based segmentation.

Why is this critical and fascinating in an open research problem?

Because robots that act in this world, in the physical space, Not only must interpret, use these deep learning methods To interpret the spatial visual characteristics of a scene, They must also interpret, understand and track the temporal dynamics of the scene.

This competition is about temporal propagation of information, not just scene segmentation.

And finally, Deep Crash, Where we use deep reinforcement learning To slam cars thousands of times Here at MIT at the gym.

You're given data on a thousand runs Where a car knowing nothing is using a monocular camera as a single input driving over 30 miles an hour through a scene it has very little control through very little capability to localize itself it must act very quickly in that scene you're given a thousand runs to learn anything.

This competition will result in 4 submissions That we evaluate everyone's in simulation.

But the top 4 submissions, We put head to head at the gym And until there is a winner declared We keep slamming cars At 30 miles an hour Deep crash And also on the website is from last year And on GitHub there's DeepTesla Which is using the large-scale naturalistic driving data set We have to train a neural network to do end-to-end steering That takes in monocular video from the forward roadway and produces steering commands that steering commands for the car.

Lectures, today we'll talk about deep learning.

Tomorrow we'll talk about autonomous vehicles.

Driving scene understanding, so segmentation.

On Friday, we have Sasha Arnou, the director of engineering at Waymo.

Waymo is 1 of the companies that's truly taking huge strides in fully autonomous vehicles.

They're taking the fully L4, L5 autonomous vehicle approach and it's fascinating to learn.

He's also the head of perception for them.

To learn from him, what kind of problems they're facing and what kind of approach they're taking on.

We have Emilio Frizzoli, who 1 of last year's speakers, Sertac Karaman, said Emilio is the smartest person he knows.

So Emilio Frizzoli is the CTO of Neutonomy, an autonomous vehicle company that was just acquired by Delphi for a large sum of money and they're doing a lot of incredible work in Singapore and here in Boston.

Next Wednesday, we are going to talk about the topic of our research and my personal fascination is deep learning for driver state sensing, understanding the human, perceiving everything about the human being inside the car and outside the car.

1 talk I'm really excited about is Oliver Cameron on Thursday.

He is now the CEO of autonomous vehicle startup Voyage.

He was previously the director of the self-driving car program for Udacity.

He will talk about how to start a self-driving car company.

For those, he said that MIT folks, entrepreneurs, if you wanna start 1 yourself, he'll tell you exactly how.

And then Sterling Anderson, who was the Director previously of Tesla Autopilot team And now is a co-founder of Aurora.

The self-driving car startup that I mentioned That has now partnered with NVIDIA and many others.

This class is about applying data-driven learning methods To the problem of autonomous vehicles.

Why self-driving cars are fascinating And an interesting problem space.

Quite possibly, in my opinion, This is the first wide-reaching and profound integration Of personal robots in society.

Wide-reaching because there's 1 billion cars on the road.

Even a fraction of that Will change the face of transportation And how we move about this world.

Profound, and this is an important point, That's not always understood, Is there's an intimate connection between a human and a vehicle when there's a direct transfer of control.

It's a direct transfer of control that takes that his or her life into the hands of an artificial intelligence system.

You can Google first time with Tesla autopilot on YouTube And watch people perform that transfer of control.

There's something magical about a human and a robot working together That will transform what artificial intelligence is in the 21st century And this particular autonomous system AI system, self-driving cars Is on the scale and the profound, the life critical nature of it is profound in a way that it will truly test the capabilities of AI.

There is a personal connection that will argue throughout these lectures That we cannot escape considering the human being That autonomous vehicle must not only perceive And control its movement through the environment It must also perceive everything about the human driver and the passenger And interact, communicate and build trust with that driver Because, in my view as I will argue throughout this course, An autonomous vehicle is more of a personal robot than it is a perfect perception control system.

Because perfect perception and control through this world full of humans is extremely difficult and could be 2, 3, 4 decades away.

Full autonomy, Autonomous vehicles are going to be flawed.

And we have to design systems that are effectively caught, That effectively transfer control to human beings When they can't handle the situation And that transfer of control Is a fascinating opportunity for AI Because the obstacle avoidance, perception of obstacles and obstacle avoidance is the easy problem.

It's the safe problem, going 30 miles an hour, navigating through streets of Boston is easy.

It's when you have to get to work and you're late Or you're sick of the person in front of you That you want to go in the opposing lane and speed up That's human nature and we can't escape it Our artificial intelligence systems Can't escape human nature They must work with it What's shown here Is 1 of the algorithms we'll talk about next week For cognitive load Where we take the raw 3D convolutional neural networks Take in the eye region the blinking and the pupil movement to determine the cognitive load of the driver.

We'll see how we can detect everything about the driver, where they're looking, emotion, cognitive load, body pose estimation, Drowsiness.

The movement towards full autonomy is so difficult, I would argue that it almost requires human level intelligence.

That the, as I said, 234 decade out journey For artificial intelligence researchers to achieve full autonomy Will require achieving, solving some of the problems Fundamental problems of creating intelligence And that's something we'll discuss in much more depth In a broader view in 2 weeks For the Artificial General Intelligence course Where we have Andrej Karpathy from Tesla, Ray Kurzweil, Mark Ryberg From Boston Dynamics Who asked for the dimensions of this room Because he's bringing robots.

So that is why I argue the human-centered artificial intelligence approach.

In every algorithmic design, considers the human.

For autonomous vehicle on the left, The perception, seen understanding, and the control problem, As we'll explore through the competitions, And the assignments of this course, Can handle 90 and increasing percent of the cases But it's the

of the cases as we get better and better That we have to, we're not able to handle through these methods And that's where the human, perceiving the human is really important This is the video from last year of Arc de Triomphe Thank you, I didn't know it last year, I know now.

That's 1 of millions of cases Where human to human interaction Is the dominant driver, Not the basic perception Control problem.

Because deep learning is a set of methods That do well from a lot of data.

And to solve these problems, where human life is at stake, we have to be able to have techniques that learn from data, learn from real-world data.

This is the fundamental reality of artificial intelligence Systems that operate in the real world.

Whether that's on the left for the perception, the control side, Or on the right for the human, The perception and the communication interaction And collaboration with the human, And the human-robot interaction.

It's a set of techniques If you allow me the definition of intelligence Being the ability to accomplish complex goals Then I would argue the definition of understanding Maybe reasoning is the ability to turn complex information Into simple, useful, actionable information And that is what deep learning does.

Deep learning is representation learning, or feature learning if you will.

It's able to take raw information, raw complicated information, that's hard to do anything with, And construct hierarchical representations of that information To be able to do something interesting with it.

It is the branch of artificial intelligence Which is Most capable and focused on this task.

Forming representations from data, Whether it's supervised or unsupervised, Whether it's with the help of humans or not, It's able to construct, Structure, find structure in the data, Such that you can, extract simple, useful, actionable information.

On the left, from Ian Goodfellow's book, is the basic example of image classification.

The input of the image on the bottom with the raw pixels And as we go up the stack, as we go up the layers Higher and higher order representations are formed From edges to contours, to corners, to object parts and then finally the full object semantic classification Of what's in the image.

A favorite example for me, Is 1 from 4 centuries ago.

And representing that place in the universe, whether it's relative to earth or relative to the sun.

On the left is our current belief, on the right is the 1 that is held widely 4 centuries ago.

Representation matters because what's on the right Is much more complicated than what's on the left.

You can think of in a simple case here, When the task is to draw a line that separates green triangles and blue circles.

In the Cartesian coordinate space on the left, The task is much more Difficult, impossible to do well.

On the right, it's trivial in polar coordinates.

This transformation is exactly what we need to learn.

So you can take the same task of having to draw a line that separates the blue curve and the red curve on the left.

If we draw a straight line, it's going to be a high, there's no way to do it with 0 error, with 100% accuracy.

But what we can do with deep learning, With a single hidden layer network, done here, Is form the topology, The mapping of the space in such a way in the middle, That allows for a straight line to be drawn That separates the blue curve and the red curve.

The learning of the function in the middle Is what we're able to achieve with deep learning.

It's taking raw complicated information and making it simple, actionable, useful.

And the point is that this kind of ability to learn from raw sensor information means that we can do a lot more with a lot more data.

So deep learning gets better with more data.

And that's important for real-world applications.

This is us driving with 2 perception control systems.

1 is a Tesla vehicle with the autopilot version 1 system that's using a monocular camera to perceive the external environment and produce control decisions.

And our own neural network running on a Jetson TX2 That's taking in the same With a monocular camera and producing control decisions And the 2 systems argue And when they disagree They raise up a Flag to say that this is an edge case That needs human intervention.

There is covering such edge cases Using machine learning is the main problem Of artificial intelligence when applied to the real world.

Inspired very loosely, and I'll discuss about the key difference between our own brains and Artificial brains.

Because there's a lot of insights in that difference.

But inspired loosely by biological neural networks.

Here is a simulation of a thalamocortical brain network.

The full human brain is a lot more than that.

100 billion neurons, 1,000 trillion synapses.

There's inspirational music with this 1 that I didn't realize was here.

The human neural network is a hundred billion neurons, right?

1 of the state-of-the-art neural networks is ResNet-152, Which has 60 million synapses.

That's a difference of about a 7 order of magnitude difference.

The human brains have 10 million times more synapses Than artificial neural networks.

Plus or minus 1 order of magnitude, depending on the network.

So what's the difference between a biological neuron And an artificial neuron?

The topology of the human brain have no layers.

Very little structure in our human brain, In terms of how neurons are connected.

They're connected often to 10,000 plus other neurons.

The number of synapses from individual neurons That are input into the neuron is huge.

They're asynchronous, the human brain works asynchronously.

Artificial neural networks work synchronously.

The learning algorithm for artificial neural networks, The only 1, the best 1, is back propagation.

And we don't know how human brains learn.

Processing speed, this is 1 of the only benefits we have with artificial neural networks Is artificial neurons are faster But they're also extremely power inefficient And there is a division into stages of training and testing With neural networks.

With biological neural networks, As you're sitting Here today, they're always learning.

The only profound similarity, The inspiring 1, The captivating 1, Is that both are distributed computation at scale.

There is an emergent aspect to neural networks, Where the basic element of computation, A neuron, is simple, is extremely simple.

But when connected together, Beautiful, Amazing, powerful approximators can be formed.

A neural network is built up with these computational units Where the inputs, there's a set of edges with weights on them.

The weights are multiplied by this input signal.

A bias is added with a nonlinear function That determines whether the network gets activated or not Well, the neuron gets activated or not Visualized here And these Neurons can be combined in a number of ways.

They can form a feed-forward neural network, Or they can feed back into itself, To form, to have state memory, In recurrent neural networks.

The ones on the left, Are the ones that are most successful, For most applications in computer vision.

The ones on the right, Are very popular and specific, When temporal dynamics or dynamics time series, of any kind are used.

In fact, the ones in the right are much closer To the way our human brains are, Than the ones on the left.

But that's why they're really hard to train.

1 beautiful aspect of this emergent power From multiple neurons being connected together Is the universal property That with a single hidden layer These networks can learn any function Learn to approximate any function Which is an important property to be aware of.

Because the limits here are not in the power of the networks.

The limits is in the methods by which we construct them and train them.

What kinds of machine learning, deep learning are there?

Memorizers, the approaches that essentially memorize patterns in the data And approaches that we can loosely say are beginning to reason to generalize over the data with minimal human input.

On top, on the left are the quote-unquote teachers, is how much human input in blue is needed to make the method successful.

For supervised learning, which is what most of deep learning successes come from, Where most of the data is annotated by human beings.

Most of the data that's part of the training Needs to be annotated by human beings.

With some additional successes coming from augmentation methods that extend that, extend the data based on which these networks are trained.

And the semi-supervised reinforcement learning and unsupervised methods that we'll talk about later in the course.

That's where the near-term successes we hope are.

And with the unsupervised learning approaches, that's where the true excitement about the possibilities of artificial intelligence lie.

Being able to make sense of our world with minimal input from humans.

So we can think of 2 kinds of deep learning impact spaces.

Is taking a problem, formalizing it, Collecting enough data on it, And being able to solve a particular case That provides value.

Of particular interest here is a network That estimates apartment costs in the Boston area.

So you could take the number of bedrooms, The square feet in the neighborhood, And provide as output the estimated cost.

On the right Is the actual data of apartment cost.

We're actually standing in a area that has over $3,000 for a studio apartment.

And then there's general purpose intelligence.

Or something that feels like approaching general purpose intelligence.

Which is reinforcement and unsupervised learning.

Here with Andrej Karpathy's Pong to Pixels.

A system that takes in 80 by 80 pixel image and with no other information is able to beat, is able to win at this game.

No information except a sequence of images, raw sensory information, The same way, the same kind of information That human beings take in From the visual, audio, touch, sensory data The very low-level data And be able to learn to win In this very simplistic, In this very artificially constructed world but nevertheless, a world where no feature learning is performed.

Only raw sensory information is used to win.

We'll talk about that on Wednesday, with deeper enforcement learning.

So, but for now, we'll focus on supervised learning.

Where there is input data, there is a network we're trying to train, a learning system, And there's a correct output that's labeled by human beings.

That's the general training process for a neural network.

Input data, labels, and the training of that network, that model.

So that in the testing stage, A new input data that has never seen before It's tasked with producing guesses And is evaluated based on that For autonomous vehicles that means being released Either in simulation or in the real world to operate And how they learn, how neural networks learn, is given the forward pass of taking the input data, whether it's from the training stage, in the training stage, the taking the input data producing a prediction, And then given that there's ground truth in the training stage, we can have a measure of error based on a loss function that then punishes the synapses, the connections, the parameters that were Involved with making that wrong prediction.

And it back propagates the error through those weights.

We'll discuss that in a little bit more detail in a bit here.

Really, you can think of input as being anything.

It can be a number, a vector of numbers, A sequence of numbers, a sequence, a vector of numbers.

Anything you can think of, From images to video, to audio, to text, Can be represented in this way.

And the output can the same, Be a single number, or it can be images, video, text, audio 1 to 1 mapping on the bottom 1 to many, many to 1, many to many And many to many with different starting points for the data.

Deep learning, the same as neural networks.

It's really Deep neural networks, large neural networks.

It's a subset of machine learning that has been Extremely successful in the past decade.

Multi-layer perceptron, deep neural network, Recurrent neural network, long short-term memory network, LSTM, Convolutional neural network, and deep belief networks.

All of these will come up through the slides.

And there is specific operations, layers within these networks Of convolution, pooling, activation, and back propagation.

This concept that we'll discuss in this class.

Activation functions, there's a lot of variants.

On the left is the activation function, the left column.

On the x-axis is the input, on the Y-axis is the output.

The sigmoid function, the output, If the font is too small, the output is not centered at 0.

For the tanh function, It's centered at 0, but it still suffers from vanishing gradients.

Vanishing gradients is when the value, the input is low or high.

The output of the network, as you see in the Right column there, the derivative of the function is very low.

For ReLU, It's also not zero-centered, But it does not suffer from vanishing gradients.

Backpropagation is the process of learning.

It's the way we take, go from error, compute as the loss function, on the bottom right of the slide, taking the actual output of the network with the forward pass, subtracting it from the ground truth, squaring, dividing by 2, and using that loss function then back propagate through to construct a gradient to back propagate the error to the weights that were responsible for making either a correct or an incorrect decision.

So the sub-tasks of that, there's a forward pass, there's a backward pass, and a fraction of the weight's gradient subtracted from the weight.

That process is modular, so it's local to each individual neuron, which is why it's extremely, we're able to distribute it across multiple, across the GPU, parallelized across the GPU.

So, learning for neural network, These computational units are extremely simple.

They're extremely simple to then correct when they make an error, When they're part of a larger network that makes an error.

And all that boils down to is essentially an optimization problem.

Where the objective utility function is the loss function And the goal is to minimize it And we have to update the parameters, the weights and the synapses And the biases to decrease that loss function And that loss function is highly nonlinear.

Depending on the activation functions, Different properties, different issues arise.

Where the derivative is exactly 0, For inputs less than 0.

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MIT 6.S094: Deep Learning