Introductory lecture on Human-Centered Artificial Intelligence (MIT 6.S093) I gave on February 1, 2019. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.

INFO:
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Slides: http://bit.ly/2IDMd0U 
Transcript: http://bit.ly/2IDMkcQ
Playlist: http://bit.ly/deep-learning-playlist

OUTLINE:
0:00 - Introduction to human-centered AI
5:17 - Deep Learning with human out of the loop
6:11 - Deep Learning with human in the loop
8:55 - Integrating the human into training process and real-world operation
11:53 - Five areas of research
15:38 - Machine teaching
19:27 - Reward engineering
22:35 - Question about representative government as a recommender system
24:27 - Human sensing
27:06 - Human-robot interaction experience
30:28 - AI safety and ethics
33:10 - Deep learning for understanding the human
34:06 - Face recognition
45:20 - Activity recognition
51:16 - Body pose estimation
57:24 - AI Safety
1:02:35 - Human-centered autonomy
1:04:33 - Symbiosis with learning-based AI systems
1:05:42 - Interdisciplinary research

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Welcome to Human-Centered Artificial Intelligence.

The last couple of decades in the developments of deep learning have been exciting in the problems that we've been able to automate, in the problems that we've been able to crack with learning-based methods.

1 of the things underlying this lecture and the following lectures is the idea that with purely the learning-based approach that we have been using, there's certain aspects that are fundamental to our reality that we're going to hit a wall on, that we have to integrate, incorporate the human being deeply into the learning-based systems in order to make the systems learn well and operate in the real world.

The underlying first prediction under the idea of human-centered AI in this century is that the learning based approaches that have been successful over the past 2 decades, like deep learning, machine learning approaches that learn from data, are going to continue to become better and dominate the real-world applications.

So as opposed to fine-tuned, optimization-based models that do not learn from data, more and more we're going to see learning based methods dominate real-world applications.

That's the underlying prediction that we're working with.

Now, if that's the case, the corollary of that, if learning-based methods is the solution to many of these real-world problems, is the way we get smarter AI systems is by improving the machine learning and the machine teaching.

Machine learning is the thing that we've been talking about quite a bit, that's the deep learning, that's the algorithm, the optimization of neural network parameters where you learn from data.

That's the current focus of the community, current focus in the research, and the thing that's behind the success of much of the developments in deep learning.

It's optimizing not the models, not the algorithms, but optimizing how you select the data based on which the algorithms learn.

Just like when you yourself are learning as a student or as a child how to operate in this world, the world and the parents and the teachers around you are informing you with very sparse information but providing the kind of information that is most useful for your learning process.

The selection of data based on which to learn, I believe is the critical direction of research where we have to solve in order to create truly intelligent systems and ones that are able to work in the real world and I'll explain why in ways that I'm referring to.

The implications of learning-based systems.

So when you have a learning system, a system that learns from data, neural networks, machine learning, learns from data.

The fundamental reality of that is the model is trying to generalize across the entirety of the reality which we'll have to be tasked with operating based on a very small subset of samples from that reality.

And that generalization means that you it's there's always going to be a degree of uncertainty.

There's always going to be a degree of uncertainty.

There's always going to be a degree of incomplete information.

And so no matter how much we want to, these systems will not be provably safe.

So we can't put anything concrete down to how guaranteed to be safe in some specific way unless it's extremely constrained.

Therefore we need human supervision of these systems.

The systems will not be provably fair from an ethics perspective, from a discrimination perspective, from all degrees of fairness.

Therefore, we need human supervision of these systems.

At any step of the pipeline in which they made the decisions AI systems will not be perfectly explainable to the satisfaction of us as human supervisors.

So there again, human supervision constantly will be required.

And the solution to this is a whole set of techniques, whole set of ideas, they're kind of, they were putting under the flag of human-centered artificial intelligence, human-centered AI.

And the core ideas there is that we need to integrate the human being deeply into the annotation process and deeply into the human supervision of the real world operation of the system.

So both in the training phase and the testing phase, the execution, the operation of the system.

So this is what deep learning looks like with the human out of the loop.

The human contributes to a learning model by helping annotate some data, and that data is then used to train a model that hopefully generalizes in the real world and that model makes decisions.

And deep learning is really exciting because it's able to, in a greater and greater degree of autonomy, able to form high-level representations of the raw data in a way that it's actually able to do quite well on certain kinds of tasks that were before very difficult.

But fundamentally the human is out of the loop, both of the training and the operation.

First you build the data set, annotate the data set, and then the systems run away with it.

They train on the data and the real world operation does not involve the human, except as the recipient of the service the system provides.

Now the human in the loop version of that, the human centered version of that, means that annotation and operation of the system is both aided by human beings in a deep way.

So we can look at a human experts, so individuals and crowd intelligence, the wisdom of the crowd and the wisdom of the individual.

At the training phase, the first part of that is the objective annotation.

We need to significantly improve objective annotation, meaning annotation where the human intelligence is sufficient to be able to look at a sample and annotate it.

This is what we think about as an ImageNet and all the basic computer vision tasks where a single human is enough to do a pretty damn good job of determining what's in a particular sample.

Things that are difficult for humans to determine as a singular sample of a human being as a crowd, we kind of converge in these difficult questions.

These are questions at a low level of emotion, these things that are a little bit fuzzy, that require multiple people to annotate, and at the high level are ethical questions of decisions that AI system is tasked to making or we're tasked to making that nobody really knows the right answer to.

And as a crowd, we kind of converge in the right answer.

That's where the crowd intelligence comes in on the data annotation step.

Now in the operation, once you train the model, the supervision again of the system based, and I'll give examples of this more concretely, on the wisdom of the individual is, for example, operating an autonomous vehicle, the supervision of that autonomous vehicle, a single driver, is tasked with supervising the decisions of that AI system.

That's a critical step for a learning-based system that's not guaranteed to be safe, that's not guaranteed to be explainable, And the subjective side of that, where the crowd intelligence is required, where a single person is not able to make it, these are again ethical questions about the operation of autonomous systems.

The supervision of autonomous vehicles, the supervision of systems in the medical diagnosis, in medicine in general, and this is AI operating in the real world, making ethical decisions that are fundamentally difficult decisions for humans to make, and that's where the crowd intelligence needs to come in.

And so we have to transform the machine learning problem by integrating the human being.

First up top in the training process on the left that's the usual machine learning formulation of a human being doing brute force annotation of some kind of data set cats and dogs and ImageNet, segmentation data set in cityscapes, video action recognition in the YouTube data set.

Given the data set, humans put in a lot of expensive labor to annotate what's going on in that data and then the machine learns.

The flip side of that, the machine teaching side, the human centered side of that, is the machine instead, the learning model, the learning algorithm, talking about mostly neural networks here, is tasked with providing, selecting the subset, the small, sparse subsets of the data that are most useful for the human to annotate.

So instead of the human doing the brute force task first of the annotation, the machine queries the human.

This is the field called machine teaching.

The machine queries the human with questions.

And therefore, the task is, and this is wide open research field, the task is to minimize in several orders of magnitude the amount of data that needs to be annotated.

In the real world operations side, the integration of the human looks like this.

On the left, the machine, now trained with a learning model, makes decisions, and the human living in this world receives the service provided by the machine.

Whether that's medical diagnosis, whether that's an autonomous vehicle, whether that's a system that determines whether you get a loan or not, so on.

With the human-centered version of that, the machine makes a decision, but it's able to provide a degree of uncertainty.

It's 1 of the big requirements, to be able to specify a degree of uncertainty of that decision such that when uncertainty is below a certain threshold, human supervision is sought.

And again, in that decision, whether that's a costly decision financially or a costly decision in terms of human life, human supervision is sought.

And the service is received by the human, by the very same humans that are providing the supervision, or another set of humans.

But ultimately, the decision is over-sought by human beings.

This is what I believe is going to be the defining mode of operation for AI systems in the 21st century, is we won't be able to, as much as we'd like to, escape, to create perfect AI systems that escape the need to work together with human beings at every step.

There is 5 areas of research, grand challenges here that define human-centered AI.

I'll focus on a few today and focus on 1 very much so and even with that degree of high pruning we have a hundred 20 slides so I'll skip around But on the human centered AI during the learning phase, there is the methods, the research arm of machine teaching.

How do we select, how do we improve supervised learning as opposed to needing 10,000, 100,000, a million examples?

How do we reduce that where the algorithm queries only the essential elements and able to learn effectively from very little information, from very little samples, just like we do when we're students, when we learn fundamental aspects of math, language, and so on, we just need a few examples, but those examples are critical to our understanding.

And the second part of that is the reward engineering, that during a learning process, injecting the human being into the definition of the loss function, of what's good, what's bad.

Systems that have to operate in the real world have to understand what our society deems as good and bad.

And we're not always good at injecting that at the very beginning.

There has to be a continuous process of adjusting the rewards, of reward re-engineering by humans so that we can encode human values into the learning process.

Now on the second part on the human centered AI during real world operation when the system is actually trained, there is the interactive element of robots and humans working together.

That means the part I'll focus on quite a bit today because there's been quite a lot of development and progress on the deep learning side is human sensing, is algorithms that understand the human being.

Algorithms that from taking raw information whether it's video, audio, text, begin to get a context, a measure of the state of the human being in the short term and the long term over time, the temporal understanding and the instantaneous understanding.

So once you understand the human, the perception problem, you have to interact with them and interact in such a way that is continuous, collaborative, and a rich, meaningful experience.

We're in the very early days of creating anything like rich meaningful experiences with AI systems, especially learning based AI systems.

And the safety, in the real world operation, safety, ethics, unrolling the results of the engineered rewards that were in place during the learning process, now come to fruition.

And we need to make sure that the train model does not result in things that are highly detrimental, catastrophic to our safety, or highly detrimental to what we deem as good and bad in society, of discrimination, of ethical considerations, and all those kinds of things.

The gray area, the line we all walk as a society in the crowd intelligence.

We have to provide bounds on AI systems, and there's an entire group of work.

I'll mention what we're doing in that area.

So first, on the machine teaching side and the efficient supervised learning, I'd like to sort of do 1 slide on each of these to kind of give you an idea.

Near term and do 2 things for each area that we will elaborate in future lectures on and some of it I'll elaborate today.

First, the near term directions of research, the things that are within our reach now, and a sort of thought experiment, a grand challenge, that if we can do it, that'll be damn impressive.

That will be a definition of real progress in this area.

So near-term directions of research for machine teaching for improved supervised learning, integrating the human into the annotation process, is instead of annotating brute force, is annotate by asking the human questions.

So we have to transform the way we do annotations where the process of annotation is not defining the data set and then you go through the entire data set.

It's a machine teaching system that queries the user for questions to annotate.

And on the algorithm side, active learning, these are all sort of areas of work where we can be more clever about the way we use data, or select data on which to train.

So active learning is actively selecting during the training process which part of the data to train on and annotate.

Data augmentation is taking things that have been supervised by a human and expanding them, modifying the data, warping the data in interesting ways such that it expands, it multiplies the human effort that was injected into helping understand what's in the data.

The one-shot learning, zero-shot learning, are all in transfer learning, are all in that category.

And self-play is in the reinforcement learning area where the system constructs a model of the world and goes along alone in a room and plays with that model to try to figure out the different constraints of the model, how do you achieve good things there.

An example grand challenge here that would define serious progress in the field is if we take ImageNet or COCO, the ImageNet challenge or COCO object detection challenge and training only on a totally different kind of data be able to achieve state-of-the-art results.

So training only on Wikipedia with the text and images that are there on Wikipedia be able to perform object detection on the state of the art benchmark of COCO.

The COCO is a data set of different objects with rich annotation of the localization of the objects.

That I believe is exactly the kind of thing that all the problems in the transfer learning and efficient data annotation machine teaching have to be solved to achieve that.

Another way to another challenge you can think of if we can even just simplify it more is achieve 3% .3% error on MNIST that's the handwritten recognition task that everybody always provides as an example.

So achieve a very good accuracy, state of the art accuracy, by training only on a single example of a digit as opposed to training on thousands training on 1 example.

That's something that most of us humans can do given 1 example of a new language you haven't seen before for each character, after studying them for a little bit, be able to now classify future characters at high accuracy.

The second part of the learning process where the human needs to be injected and the near term directions of research there is the reward engineering and the tuning of those, continuous tuning of those rewards by a human being.

So OpenAI is doing quite a bit of work here.

And it's really my favorite example of this.

On the left, human is controlling a boat that's finishing a race.

On the right is a RL agent, reinforcement learning agent that's controlling a boat that's trying to not finish a race, trying to maximize the reward defined prior to by initially by a human being.

And what it finds is that you can get much more reward by collecting green turbos that appear as opposed to finishing the race.

It realizes that finishing the race actually gets in the way of maximizing reward, and So that's the unintended consequences of a reward function that was specified previously, and most human supervisors of this result would be able to adjust through the, re-engineer the reward function to be able to get the robot to the AI system here to finish the race.

And that kind of continuous monitoring monitoring of the performance of the system during the training process is a near term direction of research that's a few deep mind, open AI and ourselves are taking on.

Example Grand Challenge is allowing AI system to operate in a context where there's a lot of fuzziness for us humans.

There's a lot of uncertainty, there's a lot of gray area, there's a lot of challenging aspects in terms of what is right and what is wrong that we're continuing to improve on.

Example I provide here is 1 of the least popular things in the world is the US Congress.

So replacing US Congress, it's a body of representatives of the people of the United States and they make bills based on the belief of the people.

That sounds a lot like what Netflix does in recommending what movie you should watch next in representing what people love to watch.

So it makes perfect sense that an AI system should be able to take on this challenge.

And that would, I see that as a grand challenge, is replacing some of the fundamental representation of large crowds of people that make ethical decisions replaced by a human-centered AI system.

Okay, in real-world operation, the first thing we have to do before we have a robot and a human work together, the first thing is the robot has to perceive the human, question?

The question was do you want to, so there's currently there's Congress, do you want to change the way Congress works, make it better, or do you wanna just take the system that currently is and automate it?

So the idea is take the system as it currently is supposed to be and automate that.

So an AI system can provide a lot more transparency of the inputs.

The idea of Congress is supposed, the only inputs is supposed to be the people and the beliefs of the people.

And there's also, you know, but that, and there's rich information there.

So for example, I mean, the input, there's a, you know, for me, not saying anything about politics, but there's certain issues I care a lot about and certain issues I don't care much about.

And then there's certain issues that I know a lot about and certain issues I know very little about.

And those don't actually intersect that well.

I'm very opinionated about things I don't know anything about.

So being able to put that representation of me into a system that would take a lot of our entire nation together and be able to make bills that represent the people.

Now the challenge there it can't be just the training set and then the system now operates.

No, there has to be that human center element where we're constantly supervising, just like we're in theory supposed to be supervising our congressmen and congresswomen.

Human sensing, the first part, in order to have an AI system that works with a human being, the AI system has to perceive, understand the state of the human being at the very simplest level and the more complex temporal contextual over time level.

So the near term directions of research is purely the perception problem, the deep or deep learning shines of taking data, whether that comes in the visual, audio, text and so on, and being able to classify the physical, mental, social state, social context of the person.

Be able to, everything, this is what I'll cover a little bit of today, everything from face detection, face recognition, emotion recognition, natural language processing, body pose estimation, those same recommender systems, speech recognition, all of those conversions of raw data that capture something about the human being into actually meaningful, actionable information.

The grand challenge there is emotion recognition.

You know, there's been a lot of companies and ideas that we've somehow cracked emotion recognition, that we are able to determine the mood of a person.

But really that's, for those who were here last year with Lisa Feldman Barrett, if you're sort of very honest and you study emotional intelligence and emotion and the expression of emotion, it's a fascinating area and we're not even close to being able to build perception systems that detect emotion.

What we're more so doing is detecting very simple facial expressions that correspond to our storybook versions of emotion, smiling, crying, like frowning in a caricatured way.

So if you build a system that has a high accuracy of doing real emotion recognition, you can think of it as stated here, an AI system that classifies, binary classification problem, 95% accuracy of whether you want to be left alone or not.

And being able to do that after collecting data for 30 days.

That I see is a really clean formulation of exactly the kind of human understanding we need to be able to build in our learning models, and we're very far away from that.

Especially the long temporal aspect of that, of being able to integrate data over a long period of time.

Then the second part of human-robot interaction in the real world operation is the experience.

This is where we're now just beginning to consider that interactive experience of how do we have a rich, fulfilling experience.

We have autonomous vehicles, for example, semi-autonomous vehicles, whether that's Tesla, Volvo, Super Cruise with the Cadillac.

There's a bunch of systems that have now greater and greater degrees of automation in the car and we get to have the human interact with that AI system and trying to figure out how do we have a rich, fulfilling experience.

In the, currently the Volvo system, that experience is more limited.

It's more kind of traditional driving situation.

In the Tesla, you have a much bigger display about what's going on.

In the Super Cruise, there's a camera looking at your eyes.

In the Cadillac Super Cruise system, There's a camera looking at your eyes in the Cadillac super cruise system.

There's a camera looking at your eyes determining if you're awake or not, paying attention or not.

And there's like an experience there that we're trying to create.

And in the Tesla case, the miles are racking up.

There's now over a billion miles driven in the Tesla.

And the same in the fully autonomous side with Waymo, they've now reached 10 plus million miles driven autonomously.

And there's a lot of people experimenting with this, but that's the that collaborative interaction of going back and forth, of being able to for the AI system to express the degree of uncertainty as about the environment, the AI system being able to express when it needs help and not, be able to communicate what are its limitations and capabilities and so on.

Trade off control, be able to seek human supervision.

There's a dance there that's really, that takes into consideration everything from the neurobiological research to psychology to the deep to deep learning into the pure robotics HR or HRI human robotic interaction aspects.

1 grand challenge would be, Tesla's driven 1000000000 miles now under autopilot, under the semi-autonomous mode.

The grand challenge here is when we start getting to the kind of mileage that we see in the United States every year.

You start getting into the hundreds of billions of miles driven semi-autonomously.

We get to see teenagers, 16, 17, 18, using these systems for the first time.

We get to see older folks, folks who don't necessarily drive or use any kind of AI in their lives, get to use these systems.

And of course the old Turing test, now reimagined by Alexa, with the Alexa Prize challenge of Social Bot is creating natural language is such a beautiful thing to explore human-robot interaction with is both on the audio side and just the text side is passing the Turing test.

That's a true grand challenge in a real way where you wanna have a conversation with a robot for prolonged periods of times, maybe more than even some of your other friends.

And On the other side of friends is the risk, the catastrophic risk that's potential when you have an AI system that's learning from data.

The near-term directions of research is purely the human supervision of AI decisions in terms of safety and ethics.

There's a lot of systems like with cars or medical diagnosis and so on where there's some life critical safety critical aspect that we want to be able to supervise the safety of that and there's ethical decisions in terms of who gets alone or not, who gets a certain criminal penalty or not, any degree to which AI systems are incorporated into that, you have to consider ethical questions.

And even just the crude, the low-level perception systems, like face recognition, you want to make sure that your face recognition systems are not discriminating based on color or gender or age and so on, you want to make sure that at that basic fundamental level of ethics, the systems are trained in a way they maintain our human values, or the better angels of our nature, the better sides of our values, some of the brighter aspects of our values.

And the other thing is, in terms of just maintaining values, that's the normal, that's looking at the mean of the distribution.

But we also want to control the outliers from the AI system not to do anything catastrophic.

So the unintended consequences, when something happens that you didn't anticipate, you want to be able to put boundaries on that.

And the grand challenge there, really it all boils down to the ability of an AI system to say that it's uncertain about something.

And that measure of uncertainty has to be good.

It has to be able to make a prediction always accompanied with uncertainty.

Even on things he hasn't seen before, That's the real challenge.

To be able to be trained on cats and dogs and then seeing a giraffe and saying, I'm not sure what that is.

Because right now, I would probably confidently say it's a dog, depending on the giraffe.

But we want to be able to have an extremely high accuracy in the ability of AI systems to determine their own uncertainty, to know what they don't know.

From that comes the ability to stop under things that it's uncertain about, catastrophic events.

The first aspect of real world operation is understanding the human.

1 of the places where deep learning has really shined is the perception problem.

And it all begins at the ability to look at raw data and convert that into meaningful information.

That's really the understanding the human comes in.

Not the kind of understanding that when you're in a relationship with somebody, when you're friends with somebody, over a long period of time, you gain an understanding of their quirks, limitations, capabilities, so on.

But the first step is to just to be able to, when you see them, recognize who they are, what's on their mind, what's their, the body language, the what are they saying with their mouth, all those basic raw perception tasks, that's where deep learning really shines.

I'd like to cover the state of the art in those various perception tasks.

Now there's a full slide presentation with this and I'm skipping around.

The full slide presentation has the following structure for each of these topics.

It has the motivation, description, the excitement, the worry, the future impact is the first part and then there's 5 papers.

1 defining the quote-unquote old-school seminal work that opened the field, then the early progress in the field.

Paper 3 is the recent breakthrough, often associated with deep learning.

And paper 5 is the thing that defines the future direction, a possible set of things that define the future direction, and then the open problems in the field and where the future research is very much needed.

That's kind of the structure of every topic I'll cover here as quickly as possible.

It's the first thing, you know, the face contains so much rich information about the state of the human being.

So understanding the human being really starts at the face.

And detecting the face is the first step, detecting the body, and then that there's a head on top of that body, that's the first step.

And then there is the task of face recognition, been an exceptionally active area of research because it has a lot of applications and through that research we're able to now study study a lot of aspects how we perform perception on the face.

So recognition purely stated is the recognizing the identity of a human face.

Now, recognition means there's a database of identities.

And you're trying to determine which of them it is, which of the 7000000000 it is, or whatever the database is.

The face verification problem is something that your phone uses when you unlock it with your face, is it saying is it you or not?

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MIT 6.S093: Introduction to Human-Centered Artificial Intelligence (AI)