[User] Please transcribe this text

[Assistant] This is the Marv X Paradox that Jonathan brought up, is that the easy problems are hard. The things we think are easy are actually really hard. Uh, this is a state of the art robot on the right playing soccer. And that was a state of the art, uh, human on the left playing soccer. And...


[User] Please transcribe this text

[Assistant] The question was, you know, there's a fundamental difference between the way we train neural networks and the way we train biological neural networks for evolution by discarding through natural selection a bunch of the, the, the, uh, the neural networks that didn't work so well.


[User] Please transcribe this text

[Assistant] That's, so first of all, the process of evolution is, I think, not well understood. Meaning,


[User] Please transcribe this text

[Assistant] Sorry, the role, careful here, uh, haha.


[User] Please transcribe this text

[Assistant] Uh, the role of evolution in the evolution of our cognition. Of our intelligence.


[User] Please transcribe this text

[Assistant] I don't know if that's, so this is an open question. So maybe clarify this point. Is neural networks?


[User] Please transcribe this text

[Assistant] Artificial neural networks are fixed, for the most part, in size. This is exactly right. It's like a single human being that gets to learn. We don't have mechanisms of, uh, of modifying or evolving those neural networks yet.


[User] Please transcribe this text

[Assistant] Although you could think of researchers as doing exactly that. They're, you have grad students working on different neural networks and the ones that don't do a good job don't get promoted and get a good, you know, there is a natural selection there. But, uh, other than that, it's a, it's an open question. It's a fascinating one. Yep.


[User] Please transcribe this text

[Assistant] Stay tuned and keep your head up because the future I believe is really promising.


[User] Please transcribe this text

[Assistant] And the slides will be made available, for sure.


[User] Please transcribe this text

[Assistant] I think a lot of the explorations of what it means to build an intelligent machine has been in sci-fi movies. We're now beginning to actually make it a reality. Uh, this is Space Odyssey to keep with that theme in the previous lecture that we had.


[User] Please transcribe this text

[Assistant] This is, as opposed to the dream-like monolith view, uh, when the astronaut is gazing out into the open sky at the stars, we're going to look at the practice of AI today.


[User] Please transcribe this text

[Assistant] And how we go, if you're familiar with the movie, when this new technology appeared before our eyes, uh, and we're full of excitement, how we transfer that into actual, practical impact on our lives.


[User] Please transcribe this text

[Assistant] To quickly review what we talked about last time, we, I presented the technology and asked the question of whether this technology merely serves a special purpose to answer specific tasks that can be formalized or whether it can be through


[User] Please transcribe this text

[Assistant] Through the process of transferring the knowledge learned on one domain be generalizable to where an intelligent system that's trained in a small domain can be used to achieve, uh, general intelligent tasks like we do as human beings.


[User] Please transcribe this text

[Assistant] The, this is kind of the stack of artificial intelligence, uh, uh, going from all the way up to the top of the environment, the world.


[User] Please transcribe this text

[Assistant] The sensors, sets of data, the intelligent system, the way it perceives this world, then once you have this, you convert the world into some numbers, you're able to extract some representation of that world, and this is where machine learning starts to come into play, and then there's...


[User] Please transcribe this text

[Assistant] The part where I rate, I will raise it again today is can machine learning be doing the following steps too that we can do very well as human beings is the reasoning step.


[User] Please transcribe this text

[Assistant] You know, you can tell the difference between a cat and a dog, but can you now start to reason about what it means to be alive, what it means to be a cat, a living creature, and what it means to be this kind of physical object, or this kind of physical object, and take what's called common sense, things we take for granted, start to construct models of the world through reasoning.


[User] Please transcribe this text

[Assistant] Descartes, I think therefore I am. We want our neural networks to come up with that on their own. And once you do that,


[User] Please transcribe this text

[Assistant] Action. You'll go right back into the world and you start acting in that world. So the question is, can machine learning, can this be learned from data? Or does, do experts need to encode the knowledge of reasoning, the knowledge of actions, the set of actions? That's kind of the question, open questions I raised. It continues throughout the talk today.


[User] Please transcribe this text

[Assistant] So as we start to think about how artificial intelligence, especially machine learning, as it realizes itself through robotics, gets to impact the world, we start thinking about what are the easy problems and what are the hard problems. And it seems to us that


[User] Please transcribe this text

[Assistant] Uh, vision and movement, walking is easy because we've been doing it for millions of years, hundreds of millions of years. And thinking is hard. Reasoning is hard. I propose to you that it's perhaps because we've only been doing it for a short time and so think we're quite special because we're able to think.


[User] Please transcribe this text

[Assistant] So we have to kind of question of what is easy and what is hard. Because when we start to develop some of these systems,


[User] Please transcribe this text

[Assistant] Oh man, you start to realize that all of these problems are equally hard. So the problem of walking that we take for granted, the actuation and the physical


[User] Please transcribe this text

[Assistant] The ability to recognize where you are in the physical space, to sense the world around you, to, uh, deal, deal with the uncertainty of the perception problem. And then, uh, so all of these robots, by the way, this is for the most recent DARPA challenge, which MIT was also part of.


[User] Please transcribe this text

[Assistant] And so, what are these robots doing? They, uh, they don't have any, they only have sparse communication with human beings on the periphery. So most of the stuff they have to do autonomously. Like get inside a car, this is an MIT robot unfortunately.


[User] Please transcribe this text

[Assistant] That's walking. So this kind of raises to you the very real...


[User] Please transcribe this text

[Assistant] Aspect here, you want to build applications that actually work in the real world. And that's the first challenge and opportunity here. The many of the technologies we talked about


[User] Please transcribe this text

[Assistant] When we transfer them from a small data set in the lab to the real world. For the computer vision is perhaps one of the best illustrations of this. Computer vision is the task, as we talked about, of interpreting images. And so when you, there's been a lot of great accomplishments on interpreting images. Cats versus dogs.


[User] Please transcribe this text

[Assistant] Now when you try to create a system


[User] Please transcribe this text

[Assistant] Like the Tesla vehicle that I've often, that we work with and I always talk about.


[User] Please transcribe this text

[Assistant] Is, uh, it's a vision-based robot, right? It has radar for basic obstacle avoidance, but most of the understanding of the world comes from a single monocular camera. Now they've expanded the number of cameras, but for the most time, there have been a hundred thousand vehicles driving on the roads today with a single, essentially a single webcam.


[User] Please transcribe this text

[Assistant] So, when you start to do that, you have to perform all of these extractions of texture, color, optical flow, so the movement through time, temporal dynamics of the images, you have to construct these patterns, construct the understanding of objects and entities and how they interact, and from that you have to act in this world.


[User] Please transcribe this text

[Assistant] And that's all based on this computer vision system. So it's no longer cats versus dogs.


[User] Please transcribe this text

[Assistant] Classification, the wrong detection is the difference between life and death.


[User] Please transcribe this text

[Assistant] So let's look at cats, where things are a little more comfortable. So computer vision, and I would like to illustrate to you why this is such a hard task. We talked about we've been doing it for 500 million years, so we think it's easy.


[User] Please transcribe this text

[Assistant] And all you're getting is the reflection from the different surfaces in here, uh, of light, and there's perception, there's sensors inside your eyes converting that into numbers. It's really very similar to this.


[User] Please transcribe this text

[Assistant] Numbers, in the case of what we use with computers, RGB images, where the individual pixels are numbers from 0 to 255, so 256 possible numbers, and there's just a bunch of them. And that's all we get. We get a collection of numbers where they're spatially connected, ones that are close together are part of the same object, so


[User] Please transcribe this text

[Assistant] Cat pixels are all connected together. That's the only thing we have to help us. But the rest of it is just numbers, intensity numbers. And we have to use those numbers to classify what's in the image.


[User] Please transcribe this text

[Assistant] And if you really think about it, this is a really difficult task. All you get is these numbers. How the heck are you supposed to form a model of the world with which you can


[User] Please transcribe this text

[Assistant] Detect pedestrians with really 99.99999% accuracy. Because these pedestrians, or these cars, or cyclists in the car context, or any kind of applications you're looking at.


[User] Please transcribe this text

[Assistant] Even if, if your job is in the factory floor to detect the, uh, the, uh, the defective gummy bears that are flying past at like a hundred miles an hour, your task is you don't want that bad gummy bear to get by that your product and the, uh, the brand will be damaged. However serious or not serious your application is,


[User] Please transcribe this text

[Assistant] What you have to be, you have to have a computer vision system that deals with


[User] Please transcribe this text

[Assistant] All of these aspects, viewpoint variation, scale variation, no matter the size of the object is still the same object. No matter the viewpoint from which area you look at that object is still the same object.


[User] Please transcribe this text

[Assistant] The lighting that moves, we have lighting consistently here because we're indoors, but when you're outdoors, or you're moving, the scene is moving, the lighting, the complexity of the lighting variations is incredible.


[User] Please transcribe this text

[Assistant] From the illumination to just the movement of the different, uh, objects in the scene.


[User] Please transcribe this text

[Assistant] I will argue that the pure perception task is too hard.


[User] Please transcribe this text

[Assistant] That you come to the table as human beings with all this huge amount of knowledge.


[User] Please transcribe this text

[Assistant] That you're not actually interpreting all the complex lighting variations that you're seeing. You actually know enough about the world, enough about your commute home, enough about the way


[User] Please transcribe this text

[Assistant] Uh, the kinds of things you would see in this world about Boston, about the way pedestrians move, the certain light of day, you bring all that to the table that makes the perception task doable.


[User] Please transcribe this text

[Assistant] And that's one of the big missing pieces in the technology. As I'll talk about, that's the open problem of machine learning. It's how to bring all that knowledge, first of all build that knowledge, and then bring that knowledge to the table. As opposed to starting from scratch every time.


[User] Please transcribe this text

[Assistant] Cats, I promise cats. Okay, so the, to me occlusion for most of the computer vision community, this is one of the biggest challenges and it really highlights


[User] Please transcribe this text

[Assistant] How far we are from being able to reason about this world. Occlusions are when, what an occlusion is, is when the objects you're trying to detect something about, classify the object, detect the object, the object is blocked partially by another object in front of them.


[User] Please transcribe this text

[Assistant] Uh, this is something that you think is trivial perhaps. You don't even really think about it because we reason in a three-dimensional way. But the occlusion aspect is, is, makes, uh, makes perception incredibly difficult.


[User] Please transcribe this text

[Assistant] So we have to design, think about this. So this image is converted into numbers. And we, for the task of detecting, is there a cat in this image? Yes or no? You have to be able to reason about this image with that object in the scene. Most of us are able to very easily detect that there's a cat in this image.


[User] Please transcribe this text

[Assistant] We're able to detect that there is a cat in this image. Now think about this. There's a single eye and there's an ear.


[User] Please transcribe this text

[Assistant] So you have to think about what is it part of our brain that allows us to understand, to, to suppose that with some high degree of accuracy that there's a cat here in this picture. I mean the degree of occlusion here is immense.


[User] Please transcribe this text

[Assistant] We watched this for hours. And so let me give you another, this is kind of a paper that's often cited, or a set of papers.


[User] Please transcribe this text

[Assistant] To illustrate how difficult computer vision is, how thin the line that we're walking with all of these impressive results that we've been able to show recently in the machine learning community. In this case,


[User] Please transcribe this text

[Assistant] Uh, network trained on ImageNet, so basically on detecting cats versus dogs or different categories in inside images. If you're, you can find, uh, an arbitrary number of images that look like noise up in the top row where the algorithm


[User] Please transcribe this text

[Assistant] used to classify those images in ImageNet of cat versus dog is able to confidently say with 99.6% accuracy or above that it's seeing a robin or a cheetah or an armadillo or a panda in that noise.


[User] Please transcribe this text

[Assistant] So it's confidently saying, given this noise, that that's obviously a Robin. So you have to realize that the kind of...


[User] Please transcribe this text

[Assistant] This is patterns. The kind of processes it's using to understand what's contained in the image is purely a collection of patterns that it has been able to extract from other images that has been human, annotated by humans.


[User] Please transcribe this text

[Assistant] And that perhaps is very limiting.


[User] Please transcribe this text

[Assistant] To trying to create a system that's able to operate in the real world.


[User] Please transcribe this text

[Assistant] This is a very, sort of, uh, this is a very clean illustration of that concept. In the same, you can confidently predict in those images below


[User] Please transcribe this text

[Assistant] Where there are strong patterns, it's not even noise, uh, strong patterns that have nothing to do with the entities being detected. Again, confidently, that same algorithm is able to see a penguin, a starfish, a baseball, and a guitar, in that noise.


[User] Please transcribe this text

[Assistant] Uh, a, uh, image and I can distort it with some very little amount of noise and if that


[User] Please transcribe this text

[Assistant] If that noise is applied to the image, I can completely change the confident prediction about what's in that image. So to explain what's being shown. So on the left, in the column in the left, and again here.


[User] Please transcribe this text

[Assistant] Accurately, confidently, that there is a dog in that image. But if we apply just a little bit of noise to that image, to produce that image, imperceptible to our human eyes, the difference between those two, the same algorithm is saying that there's confidently an ostrich in that image.


[User] Please transcribe this text

[Assistant] So another thing to really think about that noise can have such a significant impact on the prediction of these algorithms. This is really, really quite honestly out of all the things I'll say today and I'm aware of, one of the biggest challenges of


[User] Please transcribe this text

[Assistant] So say a car company has to produce a vehicle and it has sensors in that vehicle, how do you know that that, those sensors will not start generating slight noise due to interference of various kinds? And because of that noise, instead of seeing a pedestrian, it will see nothing. Or the opposite, it will see pedestrians everywhere.


[User] Please transcribe this text

[Assistant] Uh, so of course the most dangerous is when it will not see an object and collide with it, in the case of cars.


[User] Please transcribe this text

[Assistant] There's also spoofing, which a lot of people, as always with security, people are really concerned about.


[User] Please transcribe this text

[Assistant] And perhaps people here are really concerned about this issue. I think this is a really important issue, but because you can apply noise and convince the system that you're seeing an ostrich when there's in fact no ostrich, you can do the same thing in a, uh,


[User] Please transcribe this text

[Assistant] In an attacking way. So you can attack the sensors of a car and make it believe, like with lidar spoofing, so spoof lidar or radar or ultrasonic sensors to believe that you're seeing pedestrians when they're not there and the opposite. To hide pedestrians, make pedestrians invisible to the sensor when they're in fact there.


[User] Please transcribe this text

[Assistant] So whenever you have indulgent systems operating in this world,


[User] Please transcribe this text

[Assistant] They become susceptible to the fact that everything, so much of the work is done in software and based on sensors. So at any point in the chain if there's a failure


[User] Please transcribe this text

[Assistant] You have to be able to detect that failure, and right now we have no mechanisms for automatically detecting that failure. So, on the data side, so one challenge is, that we're constantly dealing with, is that we


[User] Please transcribe this text

[Assistant] The algorithms in machine learning algorithms that we're using are need labeled data.


[User] Please transcribe this text

[Assistant] And we have very little label data. Label data, again, is when you have pairs of input data and the ground truth, the true label annotation class that that image belongs to, or concept.


[User] Please transcribe this text

[Assistant] And it doesn't have to be an image, it can be any source of data. It's a really costly process to do.


[User] Please transcribe this text

[Assistant] Rely, every breakthrough we've had so far relies on that, uh, label data. And, because of its cost, we don't have much of it.


[User] Please transcribe this text

[Assistant] So all the problems that come from data can either be solved by having a lot more of this data, which, uh, I believe is, uh, most people believe is too challenging. It's too challenging to have human beings annotate huge amounts of data. Or we have to develop algorithms that are able to do something with the unlabeled data.


[User] Please transcribe this text

[Assistant] It's the unsupervised, semi-supervised, sparsely supervised reinforcement learning. As we talked about last time, I'll mention again here.


[User] Please transcribe this text

[Assistant] So one way you, uh, understand, uh, something about data when you don't have labels is you reason about it. All you're given is a few facts when you're a baby.


[User] Please transcribe this text

[Assistant] Your parents give you a few facts and you go into this world with those facts and you grow your knowledge graph, your knowledge base, your understanding of the world from those few facts. We don't have a good method of doing that in automated, unrestricted.


[User] Please transcribe this text

[Assistant] The inefficiency of our learners, the machine learning algorithms I've talked about, neural networks, need a lot of examples of every single concept that they're given in order to learn anything about them.


[User] Please transcribe this text

[Assistant] Thousands, tens of thousands of cats are needed to understand what the spatial patterns at every level, uh, the representation of a cat, the visual representation of a cat.


[User] Please transcribe this text

[Assistant] We don't, we can't do anything with a single example. There's a few approaches, but nothing quite


[User] Please transcribe this text

[Assistant] So leveraging, this is something being called human computation. That term has fallen out of favor a little bit. One of my big passions is human computation is using something about our behavior, something about what we do in this world online or in the real world to annotate data automatically.


[User] Please transcribe this text

[Assistant] So, for example, as you drive, which is what we do, everybody has to drive, and we can collect data about you driving in order to train self-driving vehicles to, uh, to, to drive. And that's a free annotation.


[User] Please transcribe this text

[Assistant] So here are the annotated datasets we have. The supervised learning datasets. There's many, but these are some of the more famous ones. From the toy datasets of MNIST, to the large, broad, arbitrary categories of images datasets, which is what ImageNet is.


[User] Please transcribe this text

[Assistant] And, uh, there's in healthcare, there's in audio, there's in video, there's, uh,


[User] Please transcribe this text

[Assistant] You know, there's a huge number of data sets now, but each one of them is usually on a scale of hundreds of thousands, millions, tens of millions, not billions or trillions.


[User] Please transcribe this text

[Assistant] Which is what we need to create systems that operate in the real world. And again, these are the kinds of machine learning algorithms we have. There's five listed here.


[User] Please transcribe this text

[Assistant] What is the input to the system that requires to train it?


[User] Please transcribe this text

[Assistant] From the supervised learning at the very top is where we have all of our successes. And everything else is where the promise lies. The semi-supervised, the reinforcement, or the fully unsupervised learning where the input from the human is very minimal. And another way to think about this


[User] Please transcribe this text

[Assistant] So every, whenever you think about machine learning today, whenever somebody talks about machine learning, what they're talking about is systems that memorize, that memorize patterns.


[User] Please transcribe this text

[Assistant] And so, this is one of the big criticisms of the current machine learning approaches, where all they're doing is, you're providing, they're only as good as the human annotated data that they're provided. We don't have mechanisms for actually understanding


[User] Please transcribe this text

[Assistant] You can pause and think about this. In order to create an intelligent system, it shouldn't just memorize. It should understand the representations inside that data in order to operate in that world.


[User] Please transcribe this text

[Assistant] And that's the open question, one of them. And one of the challenges and opportunities for machine learning researchers today is to extend machine learning from memorization to understanding. This is that duck.


[User] Please transcribe this text

[Assistant] If you get information from the perception systems that it looks like a duck, from the audio processing that it quacks like a duck, and then from video classification that it, the activity recognition that it swims like a duck, the reasoning step is how to connect those facts to then say that it is in fact a duck.


[User] Please transcribe this text

[Assistant] Okay, so that's on the algorithm side and the data side.


[User] Please transcribe this text

[Assistant] Now this is one of the reasons compute, computational power, computational hardware, that is at the core of the success of machine learning. Uh, so our algorithms have been the same since the 60s, since the 80s, 90s, depending on how you're counting. The big breakthroughs came in compute.


[User] Please transcribe this text

[Assistant] So there's Moore's law. Uh, most of you know the way our, the CPU side of our computers works for a single CPU is that it's


[User] Please transcribe this text

[Assistant] For the most part, uh, executing a single action at a time in a sequence. So sequential.


[User] Please transcribe this text

[Assistant] Very different from our brain, which is a massively parallelized system.


[User] Please transcribe this text

[Assistant] So because it's sequential, the clock speed matters, because that's how fast essentially those instructions are able to be executed. And so we're, we're leveling off physics.


[User] Please transcribe this text

[Assistant] It's stopping us from continuing Moore's Law. Intel and AMD are aggressively pushing this Moore's Law forward.


[User] Please transcribe this text

[Assistant] But, and there's some promise that it will actually continue for another 10 or 15 years.


[User] Please transcribe this text

[Assistant] Then there's another form of parallelism, massive parallelism. It's the GPU. This is essential for neural networks.


[User] Please transcribe this text

[Assistant] Uh, this is essential to the success, recent success of neural networks is the ability to utilize these.


[User] Please transcribe this text

[Assistant] Inherently parallel, uh, architectures of graphics processing units, GPUs. The same thing used for video games. This is the, uh, this is the reason Nvidia stock is, uh, uh, doing extremely well.


[User] Please transcribe this text

[Assistant] Is, is GPUs. So it's parallelism of basic computational processes that make machine learning work on a GPU.


[User] Please transcribe this text

[Assistant] One of the limitations of GPUs, one of the challenges is in bringing them to, in scaling and bringing them into real world applications is power usage, is power consumption. And so there is a lot of specialized


[User] Please transcribe this text

[Assistant] Chips, uh, specialized just from the neural network architectures coming out from Google.


[User] Please transcribe this text

[Assistant] It's unclear how far this goes. So this is sort of the direction of trying to design an electronic brain so it has the efficiency. Our human brain is exceptionally efficient at running the neural networks in our heads.


[User] Please transcribe this text

[Assistant] For several reasons, one, of course, as I'm sure we'll talk about throughout this class, is about, uh, the thing in our smartphones, battery usage. And this is, uh, the big one, community, I think.


[User] Please transcribe this text

[Assistant] I think it could be attributed to the big breakthroughs in machine learning recently, uh, in the last decade is the, you know, compute is important, algorithm development is important.


[User] Please transcribe this text

[Assistant] But it's the community of nerds. Global. This is global artificial intelligence. And I will show in several ways why global is essential here.


[User] Please transcribe this text

[Assistant] Hundreds of thousands, millions of programmers, uh, mechanical engineers.


[User] Please transcribe this text

[Assistant] Building robots, building intelligent systems, building machine learning algorithms.


[User] Please transcribe this text

[Assistant] The exciting nature of the growth of the community perhaps is the key for the future to unlock in the power of machine learning.


[User] Please transcribe this text

[Assistant] So this is just one example, github is a repository for code, and this is showing on the y-axis at the bottom is 2008 when github first opened, going up to 2012.


[User] Please transcribe this text

[Assistant] Quick, near-exponential growth of the number of users participating and the number of repositories. So these are standalone, unique projects that are being hosted on GitHub.


[User] Please transcribe this text

[Assistant] So this is one example I'll show you about, uh, this competition that we're recently running and then I'll challenge people here to participate in this competition if you dare. Uh, so this is a, uh, chance for you to build a neural network in your browser so you can do this on your phone, uh, later tonight of course.


[User] Please transcribe this text

[Assistant] Uh, uh, on your phone, you can specify various parameters of the neural network, specify different numbers of layers and the depth, the depth of the network, the number of neurons in the network, the type of layers. It's pretty self-explanatory, it's super easy.


[User] Please transcribe this text

[Assistant] Uh, in terms of just, uh, tweaking little things. And remember, machine learning to a large part is an art at this point. It's, uh, more perhaps than even, you know, more than a well understood theoretically bounded science, which is one of the challenges, but it's also an opportunity.


[User] Please transcribe this text

[Assistant] Deep traffic is a chance. So we've all been stuck in traffic. There you go. Americans spend 8 billion hours stuck in traffic every year. That's our pitch for this competition.


[User] Please transcribe this text

[Assistant] So deep neural network can help. And so you have a neural network that drives that little car with an MIT logo, red one, uh, on this highway and tries to weave in and out of traffic to get to his destination.


[User] Please transcribe this text

[Assistant] And, uh, trying to achieve a speed of 80 miles an hour, which is the speed limit, which is the physical speed limit of the car. Of course, the actual speed limit of the road is 65 miles an hour, but we don't care about that. We just want to get to work as quickly as possible or home.


[User] Please transcribe this text

[Assistant] The basic structure of this game is, and I wanna explain this game a little bit, and then tell you how incredibly popular it's gotten, and how incredibly


[User] Please transcribe this text

[Assistant] Powerful the networks that people built from all over the world. The community that's built of this over a single month is incredible. And this happens for thousands of projects out there. Now, another challenge and opportunity. Okay, so you may have seen this, this is kind of ethics.


[User] Please transcribe this text

[Assistant] Uh, most engineers, most, I personally don't like, I love the, uh, I love philosophy, uh, but this kind of construction of ethics that's often presented here.


[User] Please transcribe this text

[Assistant] So what is this question? You know, when you have a car and you have a bunch of pedestrians, do you hit the larger group of pedestrians or the smaller group of pedestrians?


[User] Please transcribe this text

[Assistant] Uh, avoid the group of pedestrians, but put yourself into danger. These kinds of ethical questions of an intelligent system. It's a very interesting question.


[User] Please transcribe this text

[Assistant] Uh, it's, uh, it's one that we can debate and there's really no good answer, quite honestly. But it's a problem that both humans and machines struggle with.


[User] Please transcribe this text

[Assistant] And so it's not interesting on the engineering side. We're interested with problems that we can solve on the engineering side. So the kind of problem that I'm obsessed with and very interested in is the real world problem of controlling a vehicle through this space.


[User] Please transcribe this text

[Assistant] So this, uh, it happens in a few seconds here. So this is a Manhattan-New York intersection, right?


[User] Please transcribe this text

[Assistant] Uh, this is pedestrians walking perfectly legally. I think they have a green light. Of course, there's a lot of jaywalking too as well. Uh, well this car just flies, it's not part of the point, but yes, exactly, there's an ambulance. And so, there's another car that starts making a left turn in a little bit.


[User] Please transcribe this text

[Assistant] May have missed it, hopefully not. So yeaah, and then there's another car after that too, that just illustrates when you design an algorithm that's supposed to move through the space.


[User] Please transcribe this text

[Assistant] Like watch this car. The aggression it shows. Now this isn't a trivial example, for those that try to build robots, this is, this is the real question, is how do you design a system


[User] Please transcribe this text

[Assistant] That's able, so you have to think, you have to put reward functions, objective functions, utility functions under which it performs the planning. So a car like that has


[User] Please transcribe this text

[Assistant] Several thousand candidate trajectories you can take to that intersection. You can take a trajectory where it speeds up to 60 miles an hour and doesn't stop and just swerves and hits everything. Okay, that's a bad trajectory, right? Then there's a trajectory which most companies take.


[User] Please transcribe this text

[Assistant] Which is, most of Google's self-driving car and every company that's concerned about PR is whenever there's any kind of obstacle, any kind of risk that's at all reasonable, that you can maybe even touch an obstacle, then you're not going to take that trajectory. So what that means is you're going to navigate through this intersection at 10 miles an hour,


[User] Please transcribe this text

[Assistant] And let people abuse you by walking in front of you because they know you're not going to stop. And so in the middle there is


[User] Please transcribe this text

[Assistant] Hundreds, thousands of trajectories that are ethically questionable in the sense that you're putting other human beings at risk in order to safely and successfully navigate the intersection.


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[Assistant] And the design of those objective functions is the kind of question you have to ask for intelligent systems, uh, for cars. There's no grandma and a few children you have to choose who gets to die. Very difficult problems of course.


[User] Please transcribe this text

[Assistant] The problem of what I'm very interested in is streets of Boston, streets of New York, is how to gently nudge yourself through a crowd of pedestrians in the way we all actually do when we drive in New York.


[User] Please transcribe this text

[Assistant] In order to be able to safely navigate these environments. And these questions come up in healthcare, these questions come up in factory, in robots, in armed, in uh, humanoid robots that operate with other human beings.


[User] Please transcribe this text

[Assistant] And that's one of the big challenges. Another sort of fun illustration.


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[Assistant] Uh, that's folks at OpenAI use often to illustrate, well let me just pause for a second, the gamified version of this. There's a game called Coast Runners and you're racing against other boats along this track and your job is, there's your score here at the bottom left.


[User] Please transcribe this text

[Assistant] Uh, number of laps, your time, and you're trying to get to the destination as quickly as possible while also collecting funky little things like, uh, these green...


[User] Please transcribe this text

[Assistant] These green little things along the way. Okay, so what they've done is the build intent system, the one, the general purpose one that we talked about last time, that learns, oops, that learns how to navigate successfully through the space. So you're trying to maximize the reward.


[User] Please transcribe this text

[Assistant] And what this boat learns to do is instead of finishing the race, uh, it learns to find a loop where it can keep going around and around, collecting those green dots, and it learns the fact that they regenerate with time. So it learns to maximize the score by going around and around.


[User] Please transcribe this text

[Assistant] Now these are the kinds of things, this is the big challenge of reward functions, of designing systems, of designing what you want your system to achieve, is not only is it difficult to, the ethical questions are difficult, but just avoiding the pitfalls of local optima.


[User] Please transcribe this text

[Assistant] Figuring out something really good that happens in the short term, the greedy, what is it, those psychology experiments where the kid eats the marshmallow.


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[Assistant] And can't wait for, you know, can't delay gratification. This kind of, the idea of delayed gratification in the case of designing intelligent systems is a huge actual serious problem. And this is a good illustration of that.


[User] Please transcribe this text

[Assistant] Uh, we flew through a few concepts here, uh, is there any, is there any questions, uhm, about some of the compute and the algorithm side we talked about today?


[User] Please transcribe this text

[Assistant] So the question was, yea, you, you highlighted some of the limitations of, uh, machine, computer vision algorithms, machine learning algorithms, but you haven't highlighted some of the limitations of human beings. And if you put those in a column and you compare those,


[User] Please transcribe this text

[Assistant] Is, uh, are machines doing better overall, or is there any kind of way to compare those? I mean, there is actually interesting work on ImageNet, so ImageNet is this categorization, uh, task of where you have to classify images, and you can ask the question, when I present you images of cats and dogs, where are machines better than humans, and when are they not?


[User] Please transcribe this text

[Assistant] So you can compare when machines do better, what are the fail points, and what are the fail points for humans. And there's a lot of interesting visual perception questions there. But I think overall,


[User] Please transcribe this text

[Assistant] It's certainly true that machines fail differently than human beings, but in order to make an artificial intelligence system that's usable and could make you a lot of money, uh, and people would want to use, it has to be better for that particular task in every single way.


[User] Please transcribe this text

[Assistant] Uhm, in order, in order for you to want to use a system, it has to be, it has to be superior to human performance, and usually far superior to human performance. So, it's, on the philosophical level, it's an interesting thing to compare, what are we good at, what are not, but if you're using, uhm,


[User] Please transcribe this text

[Assistant] Amazon Echo, your voice recognition, uh, or any kind of natural language, chatbots, or a car. You're not going to be, well, this car is not so good with pedestrians, but I appreciate the fact that it can stay in the lane.


[User] Please transcribe this text

[Assistant] Unfortunately, you have a very high standard for every single thing that you're good at and it has to be superior to that. I, I think. Maybe, maybe that's unfair to the robots.


[User] Please transcribe this text

[Assistant] I'm more of the nerd that makes the technology happen, uh, but it's certainly on the self-driving car aspect, uh, policy's probably the biggest challenge. And I don't think there's good answers there.


[User] Please transcribe this text

[Assistant] Some of those ethical questions that come up, it feels like, so we work a lot with Tesla.


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[Assistant] So I'm driving a Tesla around every day. We're playing around with it and studying human behavior inside Teslas. And it seems like there's so much hunger amongst the media to jump on something. And it feels like a very shaky PR terrain. A very shaky policy terrain we're all walking because we have no idea how


[User] Please transcribe this text

[Assistant] How we coexist with intelligence systems and so, and then of course the government is nervous because how do we regulate this shaky terrain and everybody's nervous and excited. So, I'm not sure there's nobody.


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[Assistant] Last night, when I was so close to finding out the truth,


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[Assistant] I've got one shot to do this test and if I don't pass then my whole law school journey is over.


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[Assistant] How's everything going with Kanye? He's like, I haven't signed the divorce papers. And he's like, well I don't want a divorce so I'm not gonna sign them. I'm very sure now.


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[Assistant] I love it. I love you being up in my space. Scott Budnick is a good friend of mine and he's really dedicated his life to prison reform. He is the first person that brought me to a prison.


[User] Please transcribe this text

[Assistant] And once I realized that I can make a difference, it was really kind of hard to stop.


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[Assistant] I really invest in the people that I work with, especially on death row cases.


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[Assistant] I worked on a case for a guy named Brandon Bernard.


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[Assistant] First and foremost, Brandon, I want you to know that I really believe in you and I'll do anything that I can to help.


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[Assistant] I facetimed him, I've spoken to him, I mean the guy's like, legitimate, the sweetest guy ever.


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[Assistant] Let's do it. Let's do it. But sometimes, you know, we're able to stop it and sometimes we're not.


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[Assistant] And the fact that he was calming me down from crying was really, like, broke my heart.


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[Assistant] And he was executed. We weren't able to stop it. I mean, but this is why I do what I do.


[User] Please transcribe this text

[Assistant] Because the system is so broken. I think my goal is just sharing the process so that people can see what our system is really like and how inhumane the death penalty really is. I have to fight harder to make sure that this doesn't happen to someone else.


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[Assistant] I have not been able to stop thinking about Julius Jones.


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[Assistant] You know, he's about to be executed in three days. The parole board is twice, has like recommended he get life with the possibility of parole.


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[Assistant] Julius Jones has been on death row for over 20 years. A lot of people believe him to be an innocent man that's on death row.


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[Assistant] Clemency is where the governor or the president can reduce someone's sentence or even grant them a pardon.


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[Assistant] I think what got to me is he was hanging out with his friends. There's a lot of evidence that his co-defendant planted evidence and then pled guilty and got a lesser charge.


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[Assistant] I always try to think of a case and think about what would I do if this was my son.


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[Assistant] That just stuck out to me and I really wanted to help him and his family.


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[Assistant] And then Scott Budnick and I decided to partner up to really fight for Julius.


[User] Please transcribe this text

[Assistant] Kanye is here to pick up the kids. I just woke up and the mornings that he takes them to school is kind of relieving for me because I get to do a little bit of work.


[User] Please transcribe this text

[Assistant] And we now trade off on who takes the kids to school in the morning.


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[Assistant] We are at the ARC offices and it is the Anti-Recidivism Coalition.


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[Assistant] Scott Bundick has dedicated his money and his time and his resources to starting ARC, which basically is a program where when people get out of prison, they can come to him for a job, for a place to stay.


[User] Please transcribe this text

[Assistant] So Scott and I have been working on Julius' case for over two years. Julius' execution is coming up and we have to do everything we can to stop it. Before we really dig into a case, Scott and I make like a vision board.


[User] Please transcribe this text

[Assistant] I think that would be really helpful. I can get everyone on this like group chat that I'm on, on it, which is like 35 different celebrities and people that I think are really influential.


[User] Please transcribe this text

[Assistant] His execution is just a few days away. The pardons and parole board has recommended clemency for Julius to the governor twice, which is really rare. The governor can accept that and grant him clemency or the governor can do nothing and his execution will still continue. This is a life or death situation.


[User] Please transcribe this text

[Assistant] What Julius obviously would like is his sentence commuted and his sentence changed to time served because he's been in for 20 years.


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[Assistant] Kanye is here to pick up the kids, to take them to school. He loves to do that.


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[Assistant] So of course in his very Kanye way, he comes in a full fire truck.


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[Assistant] No matter what we're going through, I always want my kids to be around their dad as much as possible and just have their mornings with dad and get dropped off at school.


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[Assistant] I'm at my office and I'm so anxious because Julius is supposed to be executed today at 4pm and I'm just waiting for the governor's ruling.


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[Assistant] Just different scenarios and just being a friend and just to kind of get his mind off of how serious everything is around him.


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[Assistant] Today just feels really heavy. My phone is blowing up from so many different people and we're just waiting. I mean there's demonstrations happening. But every minute that goes by literally feels like an hour.


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[Assistant] Julius Jones will not be executed but will get a life sentence without the possibility of parole.


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[Assistant] And as much of a win as this is, I know it's heartbreaking for Julius because imagine an innocent man stuck in prison for life without the possibility of parole.


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[Assistant] Yeaah, and he, you know, now it's just a different fight.


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[Assistant] And what, I mean, now he can, you know, his mom hasn't even hugged him. He's never even touched his children before.


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[Assistant] So the fact that he can now, like, have a whole different experience, I just thought of that, like, that's a whole different situation.


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[Assistant] The death row in Oklahoma, there's no contact visits.


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[Assistant] Julius has not even been able to hug his mother in over 20 years. So the fact that he's going to be able to be reunited with his family and physically touch them must be such an amazing feeling for a mother.


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[Assistant] Yep, so true, so true. Okay, thank you for calling.


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[Assistant] I think people don't understand that if something happens to one person, it really affects an entire family.


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[Assistant] Head spaces to fight a different fight. But it's not over, it's not, it's not gonna be over until he comes home.


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[Assistant] To blend our families, like it's all I could ever want.


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[Assistant] Are you excited for tomorrow?


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[Assistant] They want to come with us in the car. I said they can sit in the back seat. My assistant texts me that the results are on my phone and I just have to log in to the California State Bar website. I'm just gonna open them and see if I pass the bar. Sit back there. I know, you guys, we're not driving anywhere. Just sit in there.


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[Assistant] I go in the car because if I'm upset I don't wanna have everyone stare.


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[Assistant] You are only allowed to take the baby bar a certain amount of times. This was the last time I could ever take it, so if I don't...


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[Assistant] I just feel like I didn't. Okay, password attempt fail. Why isn't the password working?


[User] Please transcribe this text

[Assistant] Welcome. Guys, it's loading. This is the slowest thing I've ever... I think my heart is racing so fast.


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[Assistant] I literally start crying because I just know how much time and effort I put into this.


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[Assistant] I'm so happy North is here to see this because my kids know that I've taken time away from them.


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[Assistant] P-A-S-S. Can you read that? P-A-S-S. I passed.


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[Assistant] Everyone told me this was the impossible way and I would never pass this test and I did. We're winners today!


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[Assistant] Thing I'm gonna post and it says there is a defibrillator by the whatever. If he has a heart attack, you must revive him.


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[Assistant] Yeah, it's crazy. That's what's in the handbook.


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[Assistant] Do it for Julius, do it for his family, do it for our country because, uh, this, this cannot happen in a civilized nation.


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[Assistant] So far, I have been able to quote from the reports which I have forwarded during those early days to Sherlock Holmes.


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[Assistant] Now, however, I have arrived at a point in my narrative where I am compelled to abandon this method, and to trust once more to my recollections, aided by the diary which I kept at the time.


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[Assistant] A few extracts from the latter will carry me on to those scenes which are indelibly fixed in every detail upon my memory.


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[Assistant] I proceed then from the morning which followed our abortive chase of the convict and our other strange experiences upon the moor. October 16—A dull and foggy day with a drizzle of rain.


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[Assistant] The house is banked in with rolling clouds, which rise now and then to show the dreary curves of the moor with thin silver veins upon the sides of the hills, and the distant boulders gleaming where the light strikes upon their wet faces.


[User] Please transcribe this text

[Assistant] It is melancholy outside and in. The baronet is in a black reaction after the excitements of the night. I am conscious myself of a weight at my heart and a feeling of impending danger, ever-present danger, which is the more terrible because I am unable to define it. And have I not cause for such a feeling?


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[Assistant] Consider the long sequence of incidents which have all pointed to some sinister influence which is at work around us.


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[Assistant] There is the death of the last occupant of the hall, fulfilling so exactly the conditions of the family legend, and there are the repeated reports from peasants of the appearance of a strange creature upon the moor.


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[Assistant] Twice I have with my own ears heard the sound which resembled the distant baying of a hound.


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[Assistant] It is incredible, impossible, as it should really be outside the ordinary laws of nature.


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[Assistant] A spectral hound, which leaves material footmarks and fills the air with its howling, is surely not to be thought of.


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[Assistant] Stapleton may fall in with such a superstition, and Mortimer also, but if I have one quality upon earth, it is common sense, and nothing will persuade me to believe in such a thing.


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[Assistant] To do so would be to descend to the level of these poor peasants, who are not content with a mere fiend dog, but must needs describe him with hell-fire shooting from his mouth and eyes.


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[Assistant] Holmes would not listen to such fancies, and I am his agent.


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[Assistant] But facts are facts, and I have twice heard this crying upon the moor.


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[Assistant] Suppose that there were really some huge hound loose upon it. That would go far to explain everything,


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[Assistant] But where could such a hound lie concealed? Where did it get its food?


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[Assistant] Where did it come from? How was it that no one saw it by day?


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[Assistant] It must be confessed that the natural explanation offers almost as many difficulties as the other.


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[Assistant] And always, apart from the hound, there is the fact of the human agency in London, the man in the cab, and the letter which warned Sir Henry against the moor. This at least was real.


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[Assistant] But it might have been the work for protecting friend as easily as of an enemy. Where is that friend or enemy now? Has he remained in London? Or has he followed us down here? Could he—could he be the stranger whom I saw upon the tour?


[User] Please transcribe this text

[Assistant] It is true that I have had only the one glance at him, and yet there are some things to which I am ready to swear.


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[Assistant] He is no one whom I have seen down here, and I have now met all the neighbours.


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[Assistant] The figure was far taller than that of Stapleton, far thinner than that of Franklin. Barrymore it might possibly have been, but we had left him behind us, and I am certain that he could not have followed us. A stranger then is still dogging us, just as a stranger dogged us in London. We have never shaken him off.


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[Assistant] If I could lay my hands upon that man, then at last we might find ourselves at the end of all our difficulties.


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[Assistant] To this one purpose I must now devote all my energies. My first impulse was to tell Sir Henry all my plans.


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[Assistant] My second and wisest one is to play my own game and speak as little as possible to anyone.


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[Assistant] He is silent and distray. His nerves have been strangely shaken by that sound upon the moor.


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[Assistant] Barrymore asked Leave to speak with Sir Henry, and they were closeted in his study some little time.


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[Assistant] Sitting in the billiard room I more than once heard the sound of voices raised, and I had a pretty good idea what the point was which was under discussion.


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[Assistant] After a time the baronet opened his door and called for me.


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[Assistant] Barrymore considers that he has a grievance," he said.


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[Assistant] He thinks that it was unfair on our part to hunt his brother-in-law down when he of his own free will had told us a secret.


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[Assistant] The butler was standing very pale, but very collected before us.


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[Assistant] I may have spoken too warmly, sir," said he, and if I have I am sure that I beg your pardon. At the same time I was very much surprised when I heard you two gentlemen come back this morning and learn that you had been chasing Selden.


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[Assistant] Poor fellow has enough to fight against without my putting more upon his track.


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[Assistant] If you told us of your own free will, well, it would have been a different thing," said the baronet.


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[Assistant] You only told us, or rather your wife only told us when it was forced from you, and you could not help yourself.


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[Assistant] I didn't think you would have taken advantage of it, Sir Henry. Indeed, I didn't.


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[Assistant] The man is a public danger. There are lonely houses scattered over the moor, and he is a fellow who would stick at nothing.


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[Assistant] Look at Mr Stapleton's house, for example, with no one but himself to defend it.


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[Assistant] There's no safety for anyone until he's under lock and key.


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[Assistant] You break into no house, sir. I give you my solemn word upon it. But you'll never trouble anyone in this country again. Are you sure you, sir, Henry?


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[Assistant] For God's sake, sir, are you big of you not to let the police know that he's still on the moor?


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[Assistant] They've given up the chase there, and he can lie quiet until the ship is ready for him. You can't tell on him without getting my wife and me into trouble. I beg you, sir, to say nothing to the police." What do you say, Watson?" I shrugged my shoulders.


[User] Please transcribe this text

[Assistant] If he were safely out of the country it would relieve the tax-payer of a burden.


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[Assistant] But how about the chance of his holding someone up before he goes?" He would not do anything so mad, sir. We have provided him with all that he can want. To commit a crime would be to show where he was hiding." That is true," said Sir Henry.


[User] Please transcribe this text

[Assistant] God bless you sir, and thank you for my art. It would have killed my poor wife had he been taken again.


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[Assistant] I guess we are aiding and abetting a felony, Watson, but after what we've heard I don't feel as if I could give the man up, so there's an end to it. All right, Barrymore, you can go.


[User] Please transcribe this text

[Assistant] That I should like to do the best I can for you in return.


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[Assistant] I know something, Sir Henry, and perhaps I should have said it before, but it was long after the inquest that I found it out. I'd never breathed a word about it yet to mortal man. It's about poor Sir Charles's death. The baronet and I were both upon our feet. Do you know how he died? No, sir, I don't know that. What, then? I know why he was at the gate at the hour. It was to meet a woman.


[User] Please transcribe this text

[Assistant] I can't give you the name, sir, but I can give you the initials. Our initials were LL. How do you know this, Barrymore?


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[Assistant] Well, Sir Henry, your uncle had a letter that morning.


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[Assistant] He had usually a great many letters, but he was a public man, and well known for his kind heart, so that everyone who was in trouble was glad to turn to him.


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[Assistant] But that morning, as it chanced, there was only this one letter, so I took the more notice of it.


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[Assistant] And never would have done that had it not been for my wife.


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[Assistant] Only a few weeks ago she was cleaning out Sir Charles's study.


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[Assistant] It had never been touched since his death, and she found the ashes of a burned letter in the back of the grate. The greater part of it was charred to pieces, but one little slip.


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[Assistant] The end of a page hung together, and the writing could still be read, though it was grey on a black background.


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[Assistant] It seemed to us to be a postscript at the end of the letter, and it said,


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[Assistant] Please, please, as you are a gentleman, burn this letter and be at the gate by ten o'clock.


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[Assistant] Beneath it were signed the initials L L. Have you got that slip? No, sir. It crumbled all to bits after we moved it.


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[Assistant] Had Sir Charles received any other letters in the same writing?


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[Assistant] Well sir, he took no particular notice of his letters. I should not have noticed this one, only it happened to come alone. And you have no idea who L. L. is?


[User] Please transcribe this text

[Assistant] No, sir, no more than you have, but I expect if we could lay our hands upon that lady we should know more about Sir Charles's death.


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[Assistant] I cannot understand, Barrymore, how you came to conceal this important information. Well,


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[Assistant] It was immediately after that our own trouble came to us. And then again, sir, we were both of us very fond of Sir Charles, as we well might be considering all he has done for us. To rake this up couldn't help our poor master, and it's well to go carefully when there's a lady in the case—even the best of us. You thought it might injure his reputation? Well, sir, I thought no good could come of it.


[User] Please transcribe this text

[Assistant] But now you've been kind to us, and I feel as if it would be treating you unfairly, not to tell you all that I know about the matter. Very good, Barrymore, you can go." When the butler had left us, Sir Henry turned to me. Well, Watson, what do you think of this new light? It seems to leave the darkness rather blacker than before. So I think.


[User] Please transcribe this text

[Assistant] But if we can only trace LL, it should clear up the whole business. We've gained that much.


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[Assistant] We know that there's someone who has the facts. If we can only find her, what do you think we should do?


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[Assistant] Let Holmes know all about it at once. It'll give him the clue for which he's been seeking.


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[Assistant] I am much mistaken if it does not bring him down.


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[Assistant] I went at once to my room and drew up my report of the morning's conversation for Holmes.


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[Assistant] It was evident to me that he had been very busy of late, for the notes which I had from Baker Street were few and short, with no comments upon the information which I had supplied, and hardly any reference to my mission.


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[Assistant] No doubt his blackmailing case is absorbing all his faculties.


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[Assistant] And yet this new factor must surely arrest his attention and renew his interest. I wish that he were here.


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[Assistant] All day today the rain poured down, rustling on the ivy and dripping from the eaves. I thought of the convict out upon the bleak, cold, shelterless moor. Poor devil! Whatever his crimes, he suffered something to atone for them.


[User] Please transcribe this text

[Assistant] And then I thought of that other one, the face in the cab, the figure against the moon.


[User] Please transcribe this text

[Assistant] Was he also out in that deluge, the unseen watcher, the man of darkness?


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[Assistant] In the evening I put on my waterproof and I walked far upon the sodden moor, full of dark imaginings, the rain beating upon my face and the wind whistling about my ears.


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[Assistant] God help those who wander into the great mire now, for even the firm uplands are becoming a morass. I found the black tour upon which I had seen the solitary watcher.


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[Assistant] And from its craggy summit I looked out myself across the melancholy downs.


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[Assistant] Rain squalls drifted across their russet face, and the heavy slate-coloured clouds hung low over the landscape, trailing in grey wreaths down the sides of the fantastic hills.


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[Assistant] In the distant hollow on the left, half hidden by the mist, the two thin towers of Baskerville Hall rose above the trees.


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[Assistant] They were the only signs of human life which I could see, save only those prehistoric huts which lay thickly upon the slopes of the hills.


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[Assistant] Nowhere was there any trace of that lonely man whom I had seen on the same spot two nights before.


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[Assistant] As I walked back I was overtaken by Dr. Mortimer, driving in his dog-cart over a rough moorland track which led from the outlying farmhouse of Falmyre.


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[Assistant] He has been very attentive to us, and hardly a day has passed that he has not called at the hall to see how we are getting on. He insisted upon my climbing into his dog-cart, and he gave me a lift homeward.


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[Assistant] I found him much troubled over the disappearance of his little spaniel.


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[Assistant] It had wandered onto the moor, and had never come back.


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[Assistant] I gave him such consolation as I might, but I thought of the pony on the Grimpen Mire, and I do not fancy that he will see his little dog again.


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[Assistant] By the way, Mortimer," said I, as we jolted along the rough road,


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[Assistant] I suppose there are a few people living within driving distance of this whom you do not know. Hardly any, I think.


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[Assistant] There are a few gypsies and labouring folk for whom I can't answer, but among the farmers or gentry there is no one whose initials are those. Wait a bit, though," he added after a pause.


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[Assistant] She married an artist named Lyons, who came sketching on the moor. He proved to be a black card and deserted her. The fault from what I hear may not have been entirely on one side. Her father refused to have anything to do with her because she had married without his consent, and perhaps for one or two other reasons as well.


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[Assistant] So between the old sinner and the young one, the girl has had a pretty bad time. How does she live? I fancy old Franklin allows her a pittance.


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[Assistant] But it cannot be more, for his own affairs are considerably involved.


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[Assistant] Whatever she may have deserved, one could not allow her to go hopelessly to the bad.


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[Assistant] Her story got about, and several of the people here did something to enable her to earn an honest living.


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[Assistant] Stapleton did for one, and Sir Charles for another.


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[Assistant] But I managed to satisfy his curiosity without telling him too much.


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[Assistant] For there is no reason why we should take anyone into our confidence.


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[Assistant] Tomorrow morning I shall find my way to Coombe Tracy.


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[Assistant] And if I can see this, Mrs Laura Lyons, of equivocal reputation, a long step will have been made towards clearing one incident in this chain of mysteries.


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[Assistant] I am certainly developing the wisdom of the serpent, for when Mortimer pressed his questions to an inconvenient extent,


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[Assistant] I asked him casually to what type Franklin's skull belonged.


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[Assistant] And so heard nothing but craniology for the rest of our drive. I have not lived for years with Sherlock Holmes for nothing.


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[Assistant] I have only one other incident to record upon this tempestuous and melancholy day.


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[Assistant] This was my conversation with Barrymore, just now, which gives me one more strong card which I can play in due time. Mortimer had stayed to dinner.


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[Assistant] And he and the baronet played a cart afterwards.


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[Assistant] The butler brought me my coffee into the library, and I took the chance to ask him a few questions.


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[Assistant] Has this precious relation of yours departed, or is he still lurking out yonder?


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[Assistant] I don't know, sir. I hope to heaven that he's gone, for he's brought nothing but trouble here. I've not heard of him since I left our food for him last, and that was three days ago. Did you see him, then?


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[Assistant] No sir, but the food was gone when next I went that way. Then he was certainly there.


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[Assistant] So you would think, sir. Unless it was the other man who took it.


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[Assistant] I sat with my coffee-cup half-way to my lips and stared at Barrymore. You know that there is another man, then? Yes, sir. There is another man upon the moor. Have you seen him? No, sir. How do you know of him, then?


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[Assistant] Selden told me of him, sir. A week ago or more. He's in Eiding, too. But he's not a convict, as far as I can make out. I don't like it, Dr. Watson. I tell you straight, sir, that I don't like it." He spoke with a sudden passion of earnestness.


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[Assistant] Now listen to me, Barrymore. I have no interest in this matter but that of your master. I have come here with no object except to help him. Tell me frankly, what is it that you don't like?


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[Assistant] Barrymore hesitated for a moment as if he regretted his outburst, or found it difficult to express his own feelings in words.


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[Assistant] It's all these goings on, sir," he cried at last, waving his hand towards the rain-lashed window which faced the moor.


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[Assistant] There's foul play somewhere, and there's black villainy brewing. To that I'll swear.


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[Assistant] Very glad I should be, sir, to see Sir Henry on his way back to London again. But what is it that alarms you?


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[Assistant] Look at Sir Charles's death. That was bad enough. For all that the coroner said, look at the noises on the moor at night. There's not a man who would cross it after sundown if he was paid for it.


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[Assistant] Look at this stranger, riding out yonder, and watching and waiting. What's he waiting for? What does it mean?


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[Assistant] It means no good to anyone of the name of Baskerville, and very glad I shall be to quit of it all in the day that Sir Henry's new servants are ready to take over the hall. But about this stranger, said I, can you tell me anything about him?


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[Assistant] What did Selden say? Did he find out where he hid, or what he was doing?


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[Assistant] He saw him once or twice, but he's a deep one and gives nothing away.


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[Assistant] At first he thought that he was the police, but soon he found that he had some lay of his own.


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[Assistant] A kind of gentleman he was, as far as he could see, but what he was doing he could not make out. And where did he say that he lived? Among the old houses on the hillside, the stone huts where the old folk used to live. But how about his food?


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[Assistant] Selden found out that he has got a lad who works for him, and brings all he needs. I dare say he goes to Coombe Tracy for what he wants. Very good, Barrymore. We may talk further of this some other time.


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[Assistant] When the butler had gone I walked over to the black window, and I looked through a blurred pane at the driving clouds and at the tossing outline of the windswept trees. It is a wild night indoors, and what must it be in stone hut upon the moor?


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[Assistant] What passion of hatred can it be, which leads a man to lurk in such a place at such a time?


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[Assistant] And what deep and earnest purpose can he have which calls for such a trial?


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[Assistant] There in that hut upon the moor seems to lie the very centre of that problem which has vexed me so sorely.


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[Assistant] I knew Jameson was wrong about you. You're a little weird, but you got a good heart.


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[Assistant] Mom used to make those. Haven't had any in a long time. I'll check it out. Thanks, spider guy.


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[Assistant] Today I wanna, I wanna cover three things, I wanna talk about the course details, uh, I wanna talk about what is microeconomics, and then I'll talk, start our, start, uh, the substance of the course by talking about supply and demand.


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[Assistant] Angle to it, I sort of do economic policy as government policy is my thing.


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[Assistant] So I, I think it's what makes economics exciting and it sort of offers a, I think an interesting angle to understand.


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[Assistant] Why we're learning what we're learning, I think sometimes in an intro class it's sort of hard to understand why the heck you're doing things.


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[Assistant] However, that's just sort of a slight flavor if you're really more interested in this. I teach a whole course called 1441. I'm not teaching it this year, but it'll be taught by a visitor in the spring, uh, Kristen Butcher from Wellesley, and I'll be teaching it next year that dives much more into these policy issues.


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[Assistant] So I'm gonna sort of use government policy as sort of an organizing theme, but it won't be sort of the dominant theme of the class.


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[Assistant] Finally, three points about my teaching style. Uhm, I don't write everything in the board. We're not in high school anymore. You're actually responsible for what I say, not what I write. Uh, partly that's because my handwriting is brutal, uhm, as you can tell already. Uhm,


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[Assistant] Uh, so what that means is please, please do not be afraid to ask me what the hell I just wrote on the board. Okay, there's no shame in that.


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[Assistant] Don't just lean to your neighbor and say, what the hell is right on the board? Like, ask me, because if you can't read it, I'm sure someone else can't read it. So feel free to ask.


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[Assistant] And in general, please feel free to engage with questions in this class. The other point of my teaching style is I talk way too fast. And the longer I go, there's a mathematical function, which is the longer I go without interruption, the faster I speak. Until I just spin off. Okay? So basically, please ask questions if anything's not clear or you just want to


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[Assistant] Ask questions about some related tangent or whatever. Please feel free to do so. You might think, how would that work in a class this big?


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[Assistant] There's always way too few questions. Even a class is big. So never be afraid that like it'll slow me down or whatever. Ask a question. We have plenty of time on the class. And you'll be doing your classmates a favor because it'll slow me down.


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[Assistant] Uhm, finally, last point, I have this terrible tendency to use the term guys in a gender neutral way. So, this class I like to see looks like it's a fairly healthy representation of both males and females. When I say guys, I don't mean men, I mean people. I mean people. So don't take it, women don't take it personally. Guys means economic agents, it means people, it doesn't mean men. Just the way, just a bad tendency, drives my wife crazy.


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[Assistant] I've decided better to just, uh, apologize up front than try to, try to fix it throughout, which is impossible.


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[Assistant] That's about, about right. That's why I did my high school online class. That's the answer I wanted to hear. Uhm, uh, okay. So, uh, tell your friends still in high school, or take an extra lecon if your high school lecon teacher isn't great. Tell them to go on edX and take the class, uh, and, and h- help out your friends still in high school.


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[Assistant] Okay, so uhm, what is microeconomics? Microeconomics is a study of how individuals and firms make decisions in a world of scarcity.


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[Assistant] Scarcity is what drives microeconomics. Okay?


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[Assistant] Basically, what microeconomics is, is a series of constrained optimization exercises.


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[Assistant] Where economic agents, be they firms or individuals, try to make themselves as well off as possible given their constraints. Yeah.


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[Assistant] Uh, I will, but not as much as I should. Uh, essentially we have another course in the department called 1413, Behavioral Economics, which gets in that much more. I will sprinkle it throughout, but, uh, but not as much as I actually believe in it.


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[Assistant] Uh, in other words, the way we think about economics is it's best to sort of get the basics down before you start worrying about the deviations. Finds better to climb the tree before you start going out in the branches.


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[Assistant] Okay, so basically, uhm, what this course is then about is it's about trade-offs.


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[Assistant] It's about how do you, given that you're constrained, how do you trade off things to make yourself as well off as possible? And behind this notion of trade-offs is gonna be


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[Assistant] I'll say about a hundred times this is the most important thing in the course, so just ignore that. But this is one of the most important things, I'll say one of the most important things in the course, is the notion of opportunity cost.


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[Assistant] Opportunity cost is a very important concept that, uh, we teach. So the first concept we teach, uhm, which is that every action or every inaction


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[Assistant] Has a cost in that you could have been doing something else instead. Okay. So if you buy a shirt, you could have bought pants. If you sit at home and watch TV, you could have been out working.


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[Assistant] Everything you do has the next best alternative you could have done instead. And that is called the opportunity cost. Okay? And that's a critical concept in economics.


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[Assistant] And that is why in some sense we are called, referred to casually as the dismal science.


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[Assistant] Economics are referred to as the dismal science. First of all, in slatted we're considered a science. Uh, but we're called the dismal science because our whole point is that nothing is free. There's always a trade-off. There's always an opportunity cost. Anything you do, you could be doing something else instead.


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[Assistant] Okay, and your constrained optimization means you're gonna have to pass up one thing, uh, to do another.


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[Assistant] Now some may call it dismal, but as a former MIT undergraduate, I call it fun. And this is why I think MIT is the perfect place to be teaching economics.


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[Assistant] Because MIT engineering is all about constrained optimization. That's what engineering is.


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[Assistant] And economics is just the engine, it's just the principles you learn in engineering.


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[Assistant] Uh, applied in different contexts. So, there's, if we think about the 2007 contest, that still exists with the robots?


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[Assistant] Okay, the 2007 contest is a contest where you're given a limited set of materials and you have to build a robot that does some tasks like pushing ping pong balls off a table or something like that. Okay, that's just constrained optimization. It's got nothing to do with economics, but it's constrained optimization.


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[Assistant] So just think of, uhm, just think of, uh, microeconomics as like engineering, but actually interesting.


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[Assistant] Okay, so think of microeconomics as engineering, but instead of building something to push a ping pong ball off tables.


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[Assistant] You actually build people's lives and businesses and understand the decisions that drive our economy.


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[Assistant] Okay, so the same principles that you could think of for your engineering classes, but applied to people's lives. And that's why in fact modern economics was born in this room, either this room or 26-100. Okay? By Paul Samuelson in the 1940s and 50s, who wrote the fundamental textbook that gave birth to modern economics.


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[Assistant] Because he was here and applied the kind of engineering principles of MIT to actually develop the field of modern economics. What we'll learn today was developed at MIT, so it's a great place to be learning it. Now, with that as background, any questions about that? About what is microeconomics?


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[Assistant] Okay, with that as background, let's turn to our first model we'll talk about this semester, which is the supply and demand model.


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[Assistant] Supply and demand. Now, the way we're gonna proceed in this course is going to drive you crazy.


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[Assistant] Because we're going to proceed by teaching very, as the very first question pointed out, by teaching very simplified models.


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[Assistant] Okay, we're gonna essentially, what is a model? A model is technically a description between any two or more economic variables or any two or more variables.


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[Assistant] Okay, but unlike the models you use in all your other classes,


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[Assistant] These aren't laws, by and large. They're models. So we don't have a relationship between energy and mass, which we can write down. It's a law and we're done. We have models which are never 100% true, but always pretty true. Pretty being somewhere between 10 and 95% true. Okay?


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[Assistant] So basically, the idea is to make a trade-off, we want to write down in our models a set of simplifying assumptions.


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[Assistant] On the one hand, we'd like a model that captures as well as possible the phenomenon in the real world, like equals mc squared. But we want to do so in the most tractable possible way,


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[Assistant] So that we can teach it from first principles and not, you know, don't need an hour to teach every single, uh, every single insight we have. Okay?


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[Assistant] So basically, in economics, we tend to resolve that by erring on the side of tractability.


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[Assistant] That is why I can teach you the entire field of microeconomics. Which is really sort of, macro is kind of a fun application, micro is really economics. Okay, I can teach you the entire field of microeconomics in a semester. Because we're going to make a whole huge set of simplifying assumptions to make things tractable.


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[Assistant] But, the key thing is that you will be amazed at what these models will be able to do.


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[Assistant] With a fairly simple set of models, we'll be able to offer insights and explain a whole huge variety of phenomena. Never perfectly, but always pretty well. Generally pretty well.


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[Assistant] Okay, and so that is essentially the trade-off we're gonna try to do, uh, uh, this semester. So the line I like is the statistician George Box said that all models are wrong, but some are useful.


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[Assistant] Now obviously it doesn't apply to models, models in the hard sciences, but in the social sciences that's true. And basically I'm gonna write down a set of models like that.


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[Assistant] Now with every model I write down, I'm gonna try, my goal is to have you understand it at three levels. The first and most important level is the intuitive level.


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[Assistant] Okay, the level which you sort of understand, I call it passing the mom test. You can go home and explain it to your mom at Thanksgiving, or at the end of the semester. Okay, no offense to dads, just call it the mom test.


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[Assistant] Okay, so basically that's the intuitive level. You really understand it the way that you can explain it. The second is graphical.


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[Assistant] We were going to do, most of our models here were developed in a graphical framework. Okay, using x, y graphs.


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[Assistant] That we're really, in economics, we think delivers a lot of shorthand power. And the third is mathematical.


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[Assistant] Okay, the mathematical is probably the least important, but it's the easiest to test you on.


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[Assistant] So, we're gonna need to know things mathematically, uh, as well. Okay? Uhm,


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[Assistant] So let's start by considering the supply and demand model by using the famous example brought up by Adam Smith.


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[Assistant] Adam Smith is sort of considered the father of economics. If Paul Sam is the father of modern economics, Adam Smith is the father of all economics. His 1776 book, The Wealth of Nations,


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[Assistant] Did an incredible job of actually laying out the entire core of the economics field.


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[Assistant] Uh, no math, just words, but kind of, he just nailed it.


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[Assistant] And uhm, he started, one of his most famous examples was the water diamond paradox.


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[Assistant] Okay, he said, think about water and diamonds. Let's start with water. Nothing is more important for life than water. It's the building block of all of life. Okay, even when we look for life on other planets, we always start by looking for water. Now think of diamonds.


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[Assistant] One of the more frivolous things you can buy, certainly irrelevant to, uh, leading a successful or happier, productive life or any life. Okay?


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[Assistant] For most of us, waters free and diamonds are super expensive. Okay? How can this be? Adam Smith asked.


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[Assistant] Well, the answer he posed is that what I first described was just demand. That is, we demand lots of water. We demand fewer diamonds. But we have to match that with the concept of supply.


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[Assistant] Okay, and the supply of water is almost infinite.


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[Assistant] While the supply of diamonds, maybe not naturally, maybe it's through, uh, decisions of various businesses, but it's somewhat limited. Okay?


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[Assistant] So basically, uhm, what, what he developed is what we call the supply and demand scissors. That you can't just think of


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[Assistant] Supply or demand and isolation, you have to put them together if you want to explain the real world phenomenon we see, like the fact that water is cheap and, uh, diamonds are expensive.


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[Assistant] So let's just talk, let's talk about an example. So there's one graph that's hanging out in the back. Okay, uhm, which is let's talk about the market for roses. Okay, uhm, so in the market for roses, we have


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[Assistant] A demand curve and a supply curve. So what we have here, this is the kind of XY graph we're going to look at all throughout the semester.


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[Assistant] Now, what I'm gonna do here, I'm just giving you an overview, we're gonna, we are going over the next six, like five or six lectures, dive into where this domain curve comes from. Okay, we'll go to first principles and build it back up.


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[Assistant] But for now, we need to know about demand curve, is it simply represents the relationship.


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[Assistant] Between the price of a good and how much people want it. Therefore, we assume it is downward sloping at higher prices.


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[Assistant] People want less of the good, and we'll just, we'll derive where that comes from shortly, starting next lecture. But for now, I think it's pretty intuitive that if the price of roses is higher, people want fewer of them. And that's why it's downward sloping. Okay? That basically as the price of roses goes up, people want fewer roses. The yellow curve is the supply curve.


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[Assistant] Now after we've derived the demand curve, we'll then go and spend about 12 lectures deriving the supply curve. That's a bit harder. But once again, we'll start from first principles and build it up.


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[Assistant] For now, you just need to know is that's how much firms are willing to supply given the price. So basically, as the price goes up, firms want to produce more roses. Okay? The higher price means you make more money, so you want to produce more of them.


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[Assistant] This is a slightly less intuitive than demand, but we'll derive and explain how it can be. But for now, just go with the basic intuition that if you're making something and you can sell it in the market for a higher price, you're going to want to make more of it. Okay? And that leads to the upward sloping supply curve. Where the points meet is the market equilibrium. Where supply and demand meets is the market equilibrium. Okay? And that is the point where both consumers and producers


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[Assistant] Because they are ha- on their demand curve is the point 600.


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[Assistant] Is the point three dollars and six hundred roses. That is they are willing to buy six hundred roses at three dollars.


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[Assistant] Okay, producers are happy because on their supply curve is the same point. They are willing to supply 600 rows at $3. That is the one point where consumers are happy and producers are happy. Therefore, it's the equilibrium. Highly nontechnical. But that's the basic intuition. Okay?


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[Assistant] The point at which they're both willing to make that transaction, the point at which they're both satisfied with that transaction, is the equilibrium. Which in this case is $3 per rose and 600 roses. Now this raises lots of questions. Where do the curves come from? How does equilibrium get achieved?


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[Assistant] Uhm, why the heck do we give roses? These are a bunch of questions. We will, uhm, we will all, we will come to all these questions over the next set of lectures.


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[Assistant] But the basic thing is to understand this intuition of Adam Smith's supply and demand model. Okay? Questions about that?


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[Assistant] This model also raises another important distinction that will focus on the semester and is easy to get mixed up. So I want you to, if you're ever unclear, I want you to ask me about it. And that's the distinction between positive


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[Assistant] Analyses. Positive versus normative. Positive analysis is the study of the way things are.


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[Assistant] While normative analysis is the study the way things should be. Okay, positive analysis is the study the way things are.


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[Assistant] While normative analysis is to study the way things should be.


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[Assistant] Let me give you a great example, which is, uh, eBay auctions. Okay.


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[Assistant] Auctions are a terrific example. They're like the textbook example of the competitive market. You can see it in your head. Demand comes from a bunch of people going on and bidding. People who want it more, bid more. So you actually get a demand curve. The higher the price, the fewer people you're getting to bid.


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[Assistant] Supply is how much, how much, how many units of it are for sale on eBay. You bid until those two meet.


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[Assistant] One example of an eBay auction that got a lot of attention a number of years ago, early in the days of eBay, was someone offered their kidney for auction. They said, look, I got two kidneys, you only need one to live. There are people out there who need a kidney. I'm putting my kidney on eBay for auction. Uhm,


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[Assistant] And what happened, bidding went nuts. It started at 25,000. It climbed to 5 million.


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[Assistant] Before the auction was shut down, eBay decided they wouldn't allow you to sell your body on eBay. Bottled parts on eBay. Okay? So, this raises two questions. The first is the positive question. Why did the price go so high? So what's the answer to that? What's the answer to the positive question?


[User] Please transcribe this text

[Assistant] Low supply, high demand. Demand is incredibly high because I die without it. Supply is low because like not a lot of us are willing to sell their kidneys on eBay. Okay. So low supply, high demand led to a high price. Adam Smith at work. That's the positive analysis. Okay.


[User] Please transcribe this text

[Assistant] But this raises the norm, but then there's the normative question, which is should you be allowed to sell your kidneys on eBay?


[User] Please transcribe this text

[Assistant] That's the normative question. The positive question is what happens if you do. The normative question is should you? Now, the standard economics answer to start would be of course you should. We're in a world where thousands of people die every year because there's a waiting list for a kidney transplant. Okay?


[User] Please transcribe this text

[Assistant] And these are people who would happily pay a lot of money to stay alive, I presume. Okay? Meanwhile, there's hundreds of millions of people walking around with two kidneys who only need one. And many of these people are poor.


[User] Please transcribe this text

[Assistant] And might be happy to take the risk that their other, that one kidney will be fine as it is for most everyone for most of their life. And return for having a life changing payment from a stranger.