This is a guest talk for course 15.S14: Global Business of Artificial Intelligence and Robotics (GBAIR) taught in Spring 2017.

Slides on slideshare: https://www.slideshare.net/lexfridman/mit-sloan-intro-to-machine-learning-in-360vr

Slides (PDF): https://www.dropbox.com/s/26co4m36ew6952d/gbair-lex-lecture-combined.pdf?dl=1

If you're interested in more related course lectures, check out the course I'm teaching on Deep Learning for Self-Driving Cars:

Course website: http://selfdrivingcars.mit.edu
Playlist: https://goo.gl/SLCb1y

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Resolution is not great but we wanted to try something different.

So if you're on a desktop or laptop you can pan around with your mouse or if you're on a phone or tablet you should be able to just move your device to look around.

Of course it's best viewed with a VR headset.

The video that follows is a guest lecture on machine learning that I gave at MIT Sloan course on the business of artificial intelligence.

The lecture is non-technical and intended to build intuition about these ideas amongst the business students in the audience.

The room was a half circle, so we thought, why not film the lecture in 360?

We recorded a screencast of the slides and pasted it into the video so that the slides are more crisp.

Let me know what you think and remember it's an experiment.

So this course is talking about the broad context, the impact of artificial intelligence, the global, there's global, which is the global impact of artificial intelligence, There's the business, which is when you have to take these fun research ideas that I'll talk about today, a lot of them are cool on toy examples, when you bring them to reality you face real challenges, which is what I would like to really highlight today.

That's the business part, when you want to make real impact, when you're going to make these technologies a reality.

So I'll talk about how amazing the technology is for a nerd like me, but I'll also talk about how, when you take that into the real world, what are the challenges you face?

So machine learning, which is the technology at the core of artificial intelligence, we'll talk about the promise, the excitement that I feel about it, the limitations, We'll bring it down a little bit.

What are the real capabilities of technology?

We're for the first time, really, as a civilization, exploring the meaning of intelligence.

It is, if you pause for a second and just think, you know, maybe many of you want to make money out of this technology, many of you want to save lives, help people, but also on the philosophical level, we get to explore what makes us human.

So while I'll talk about the low-level technologies, also think about the incredible opportunity here.

We get to almost psychoanalyze ourselves by trying to build versions of ourselves in the machine.

How powerful is machine learning that lies at the core of artificial intelligence?

Is it simply a helpful tool, a special purpose tool to help you solve simple problems?

If you're, which is what it currently is.

Currently, machine learning, artificial intelligence, is a way, if you can formally define the problem, you can formally define the tools you're working with, you can formally define the utility function, what you want to achieve with those tools.

As long as you can define those things, we can come up with algorithms that can solve them, as long as you have the right kind of data, which is what I'll talk about.

And the question is, into the future, Can we break past this very narrow definition of what machine learning can give us, which is solve specific problems, to something bigger?

To where we approach the general intelligence that we exhibit as human beings.

When we're born, we know nothing, and we learn quickly from very little data.

We don't know what are the limitations of technology.

The first 2 is what's really, the first is what's achieved success today, supervised learning.

What I'm showing here on the left of the slide is the teachers, is The data that is fed to the system, and on the right is the students, which is the system itself for machine learning.

Whenever everybody talks about machine learning today, for the most part, they're referring to supervised learning, Which means every single piece of data that is used to train the model is seen by human eyes and those human eyes with an accompanying brain label that data in a way that makes it useful to the machine.

This is critical because that's 1, the blue box, the human, is really costly.

So whenever every single piece of data that needs to be, that's used to train the machine needs to be seen by a human, you need to pay for that human.

And second, you're limited to just the time.

There's the amount of data necessary to label what it means to exist in this world is humongous.

Augmented supervised learning is when you get a machine to really help you a little bit.

There's a few tricks there, but it's still only tricks.

It's still the human is at the core of it.

And the promise of future research that we're pursuing, that I'm pursuing, and perhaps in the applications if we get to discuss, or some of the speakers here get to discuss, they're pursuing in semi-supervised and reinforcement learning, where the human starts to play a smaller and smaller role in how much they get to annotate, they have to annotate the data.

And the dream of the sort of wizards of the dark arts of deep learning are all excited about unsupervised learning.

It has very few actual successes in application in the real world today, but it is the idea that you can build a machine that doesn't require a human teacher, a human being to teach you anything, fills us artificial intelligence researchers with excitement.

The learning system in the middle, there's a training stage where you teach it something.

All you need is some data, input data, and you need to teach it the correct output for that input data.

So you have to have a lot of pairs of input data and correct output.

There'll be a theme of cats throughout this presentation.

So if you want to teach a system the difference between a cat and a dog, you need a lot of images of cats, and you need to tell it that this is a cat.

This bounding box here in the image is a cat.

You have to give it a lot of images of dogs and tell it, okay, in these pictures there are dogs.

And then there's a spelling mistake on the second stage, is the testing stage when you actually give it new input data, it's never seen before.

And you hope that it has given, for cat versus dog, enough data to guess, is this new image that I've never seen before a cat or a dog?

Now 1 of the open questions you want to keep in mind is, what in this world can we not model in this way?

I offer to you that there's nothing you can't model in this way.

So let's think about what, in terms of machine learning, can be, so let's start small.

First on the bottom of the slide, left, is one-to-one mapping, where the input is an image of a cat, and the output is a label that says cat or dog.

You can also do 1 to many where the input is an image of a cat and the output is a story about that cat, a captioning of the image.

You can, first of all, you can do it the other way.

Many-to-one mapping, where you give it a story about a cat and it generates an image.

We translate a sentence from 1 language to another.

Input data, provide it with correct output, and then let it go into the wild where it runs on input data it hasn't seen before to provide guesses.

Whatever you can convert into 1 of the following 4 things.

Numbers, vector of numbers, so a bunch of numbers, a sequence of numbers where the temporal dynamics matters, so like audio, video, where the sequence, the ordering matters, or a sequence of vector numbers.

If you can convert it into numbers, And I propose to you that there's nothing you can't convert into numbers.

If you can convert it to numbers, you can have a system learn to do it.

Generate numbers, vectors of numbers, sequence of numbers, or sequence of vectors and numbers.

First is there any questions at this point?

We have a lot of fun slides to get through, but I'll pause every once in a while to make sure We're on the same page here.

So what kind of input are we talking about?

Images, so faces, or medical applications for looking at scans of different parts of the body to determine if they're, to diagnose any kind of medical conditions.

Text, so conversations, your texts, article, blog posts for sentiment analysis.

Question and answering, so you ask it a question where the output you hope answers.

Sounds or voice recognition, any kind of, anything you can tell from audio.

Time series data, so financial data, stock market, you can use it to predict anything you want about the stock market, including whether to buy or sell.

If you're curious, it doesn't work quite well as a machine learning application.

Physical world, so cars or any kind of object, any kind of robot that exists in this world.

So location of where I am, location of where other things are, the actions of others, that could be all input.

Classification is saying is this a cat or a dog?

Regression is saying to what degree I turn the steering wheel?

Sequence, it's generating audio, generating video, generating stories, captioning, text, images, generating anything you could think of as numbers.

And at the core of it is a bunch of data agnostic Machine learning algorithms.

There's traditional ones, nearest neighbors, naive Bayes support vector machines.

A lot of them are limited and I'll describe how.

There's nothing special and new about neural networks.

And I'll describe exactly the very subtle thing that is powerful, that's always been there all along.

And certain things have now been able to unlock that power about neural networks, but it's still just the flavor of a machine learning algorithm.

And the inspiration for neural networks, as Jonathan showed last time, is our human brain.

It's perhaps why the media, perhaps why the hype is captivated by the idea of neural networks is because you immediately jump to this feeling like because there's this mysterious structure to them that scientists don't understand.

Artificial neural networks I'm referring to and the biological ones.

We don't understand them, and the similarity captivates our minds and we think, well, this approach is perhaps as limited as our, as limitless as our own human mind.

In fact, the artificial neuron, their artificial neural networks are much simpler computational units.

At The core of everything is this neuron.

This is a computational unit that does 2 very simple operations.

On the left side, it takes a set of numbers as inputs.

It applies weights to those inputs, sums them together, applies a little bias, and provides an output somewhere between 0 and

So you can think of it as a computational entity that gets excited when it sees certain inputs and gets totally turned off when it gets other kinds of inputs.

weights, it gets really excited when it sees pictures of cats.

So, that's the job of this neuron, is to detect cats.

Now what, the way you build an artificial neural network, The way you release the power that I'll talk about in the following slides about the applications and what can be achieved is just stacking a bunch of these together.

This is an extremely simple computational unit.

So you need to sort of pause whenever we talk about the following slides and think that there's a few slides that I'll show that say neural networks are amazing.

I want you to think back to this slide that everything is built on top of these really simple addition operations with a simple nonlinear function applied at the end.

We stack them together in a feed-forward way, so there's a bunch of layers.

And when people talk about deep neural networks, it means there's a bunch of those layers.

And then there's recurrent neural networks that are also a special flavor that's able to have memory.

So as opposed to just pushing input into output directly, it's also able to do stuff on the inside in a loop where it remembers things.

This is useful for natural language processing, for audio processing, whenever the sequence is not, the length of the sequence is not defined.

Okay, slide number 1 in terms of neural networks are amazing.

But also I want you to use your imagination.

There's a universality to neural networks.

Means that the simple computational unit on the left is an input, on the right is the output of this network.

With just a single hidden layer, it's called a hidden layer because it sits there in the middle of the input and the output layers.

A single hidden layer with some number of nodes can represent any function.

That means anything you want to build in this world.

Everyone in this room can be represented with a neural network with a single hidden layer.

So the power, and this is just 1 hidden layer, the power of these things is limitless.

The problem of course is how do you find the network, so how do you build a network that is as clever as many of the people in this room.

But The fact that you can build such a network is incredible.

And the way you train a network, so it's born as a blank slate, some random weights assigned to the edges.

Again, a network is represented, the numbers at the core, the parameters at the core of this network are the numbers on each of those arrows, each of those edges.

And the way you teach it something, unfortunately, currently, as I said, in a supervised learning mechanism, you have to give it pairs of input and output.

You have to give it pictures of cats and labels on those pictures saying that they're cats.

And the basic fundamental operation of learning is when you compute the measure of an error and you back propagate it to the network.

What I mean, everything is easier with cats.

And so the input here is a cat and the neural network we trained, it's just guessing, it doesn't know.

So we have to, this is the measure of error, yes.

And you have to back propagate that error.

You have to reward the network for doing a good job.

And all you do, what I mean by reward, there's weights on each of those edges, and so the node, the individual neurons that were responsible, back to that cat neuron, that cat neuron needs to be rewarded for seeing the cat.

So you just increase the weights on the neurons that were associated with producing the correct answer.

Now you give it a picture of a dog, and the neural network says cat.

Well that's an incorrect answer, so no, there's a high error, it needs to be back propagated through the network, so the weights that are responsible with classifying this picture as a cat need to be punished, They need to be decreased.

And you just repeat this process over and over.

This is what we do as kids when we're first learning.

For the most part, we're also supervised learning machines in the sense that we have our parents and we have the environment, the world, that teaches us about what's correct and what's incorrect.

And we back propagate this error and reward through our brain to learn.

The problem is, as human beings, we don't need too many examples, and I'll talk about some of the drawbacks of these approaches.

You fall off your bike once or twice and you learn how to ride the bike.

Unfortunately neural networks need tens of thousands of times when they fall off the bike in order to learn how to not do it.

And 1 key thing I didn't mention here is when we refer to input data, it's, when we refer to input data, we usually refer to sensory data, raw data.

We have to represent that data in some clever way, in some deeply clever way, where we can reason about it, whether it's in our brains or in the neural network.

And a very simple example here to illustrate why representation of data matters.

So the way you represent the data can make the discrimination of 1 class from another, a cat versus dog, either incredibly difficult or incredibly simple.

Here is a visualization of the same kind of data in Cartesian coordinates and polar coordinates.

On the right, you can just draw a simple line to separate the 2.

What you want is a system that's able to learn the polar coordinate representation versus the Cartesian representation automatically.

And this is where deep learning has stepped in and revealed the incredible power of this approach.

Which, deep learning is the smallest circle there.

It's a type of representational learning.

Machine learning is the bigger, second to the biggest.

So this class is about the biggest circle, AI.

Includes robotics, includes all the fun things that are built on learning.

And I'll discuss while machine learning, I think will close this entire circle into 1.

But for now, AI is the biggest circle, then a subset of that is machine learning, and a smaller subset of that is representation learning.

So deep learning is not only able to say, given a few examples of cats and dogs, to discriminate between a cat and a dog.

It's able to represent what it means to be a cat.

It's, so it's able to automatically determine what are the fundamental units at the low level and the high level.

What it means to represent a cat, from the whiskers to the high level shape of the head to the fuzziness and the deformable aspects of the cat.

Not a cat expert, but I hear These are the features of a cat, versus that are essential to discriminate between a cat and a dog.

Learning those features as opposed to having to have experts, this is the drawback of systems that Jonathan talked about from the 80s and 90s where you had to bring in experts for any specific domain that you try to solve, you had to have them encode that information.

Deep learning, this is simply the only big difference between deep learning and other methods is that it learns the representation for you.

Nobody has to step in and help it figure out what that cats have whiskers and dogs don't.

The fact that it can learn these features, these whisker features, is as opposed to having 5 or 10 or a hundred or 500 features that are encoded by brilliant engineers with PhDs, it can find hundreds of thousands, millions of features automatically.

So stuff that can't be put into words or described, in fact, it's 1 of the limitations of neural networks is they find so many fundamental things about what it means to be a cat that you can't visualize what it really knows.

The critical thing here is because it's able to automatically learn those hundreds of millions of features, it's able to utilize data.

It doesn't start, the diminishing returns don't hit until, well, we don't know when they hit.

The point is with the classical machine learning algorithms, you start hitting a wall when you have tens of thousands of images of cats.

With deep learning, you get better and better with more data.

Neural networks are an amazing slide too.

Here's a game, a simple arcade game where there's 2 paddles, they're bouncing a ball back and forth.

You can figure out an artificial intelligence agent that can play this game.

It can, not even that well, just kind of, it kind of learns to do alright and eventually win.

With deep learning, as opposed to encoding the position of the paddles, the position of the ball, having an expert in this game, there's many, come in and encode the physics of this game.

The input to the neural network is the raw pixels of the game.

Pixels are images that are built up of pixels.

So there's this array of numbers that represent each image, and then you give it several tens of thousands of images that represent a game.

So you have this stack of pixels and stack of images that represent a game.

And the only thing you know, this giant stack of numbers, the only thing you know is at the end you won or lost.

So based on that, you have to figure out how to play the game.

You know nothing about colors or balls or paddles or winning or anything.

That it even works and it works, it wins.

It's amazing because that's exactly what we do as human beings.

So I need you to pause and think about this.

We'll talk about special intelligence and the usefulness, and okay, there's cool tricks here and there that we can do to get you an edge on your high-frequency trading system.

General intelligence is the same intelligence we use as babies when we're born.

What we get is an input, sensory input, of image sensory input.

Right now, all of us, most of us are seeing, hearing, feeling with touch, And that's the only input we get.

And with that input we have to learn something.

A trivial example, but 1 of the first examples where this is truly working.

Sorry to linger on this, but it's a fundamental fact.

The fact that we have systems that now outperform human beings in these simple arcade games is incredible.

That previous slide is why I think machine learning is limitless in the future.

Again, the representation of the data matters.

And if you want to have impact, we currently can only tackle the small problems.

We can classify, given the entire image of a leopard, of a boat, of a mite, with pretty good accuracy of what's in that image.

We can find exactly where in that image each individual object is.

The learning system in the middle, a neural network, as long as you give it a set of numbers as input, and the correct set of labels as output, it learns to do that for data it hasn't seen in the past.

Let me pause a second and maybe if you have any questions, does anyone have any questions about the techniques of neural networks?

So, that's a great question and in a couple of slides I'll get to it exactly.

So the data representation, I'll elaborate in a little bit, but loosely, the data representation is for a neural network is in the weights of each of those arrows that connect the neurons.

So I'll show to really clarify that example of what that means.

The Cartesian versus polar coordinates is just a very simple visualization of the concept.

But you want to be able to represent the data in an arbitrary way, where there's no limits to the representation.

It can be highly nonlinear, highly complex.

See all Lex Fridman transcripts on Youtube

MIT Sloan: Intro to Machine Learning (in 360/VR)