Good afternoon. I'm Carol Christ, I'm the Chancellor at UC Berkeley. It's my privilege and pleasure to welcome you to the second of this year's 110th annual Martin Meyerson Faculty Research Lectures. For more than a century, Berkeley's Academic Senate has singled out distinguished members of our faculty whose research has changed the trajectory of their disciplines and of our understanding. These lectures shine a light on an essential part of our mission, creating new knowledge. The curiosity and creativity that fuel the quest to learn and understand are at the heart of our commitment to making the world a better place through what we discover, what we teach, and the public service we provide. This year's lectures represent the continuation of a treasured tradition that has recurred annually with one exception. In the wake of World War One and the influenza pandemic, the faculty research lectures were suspended in 1919. In 2020, when virtual events were in vogue and Zoom kept us together, the lectures went on. Being selected to deliver a faculty research lecture is rightfully seen as a high honor. To stand out among peers who exemplify academic excellence is no small thing. For students, members of the campus community and the public, this is a wonderful way to experience scholarly research of the highest caliber. Join me in welcoming the past recipients who are with us today. Professors, please stand when I read your name and let's hold our applause till everyone is recognized. And if I don't have your name but you came in a little bit after my little list was made, please stand also. John Clark, Marvin Cohen, Bill Dietrich, Catherine Gallagher, Martin Jay, Victoria Khan, Thomas LeCurre, Anthony Long, David Rolay, Barbara Romanowicz, Nancy Sheper Hughes, and our first lecturer this year, Francesca Rockberg. The two individuals chosen by our academic senate to give this year's faculty research lectures are Francesca Rockberg, who spoke in February, and Jitendra Malik, who's presenting this afternoon. Today's lecture is also one of several taking place this spring that demonstrate the depth and impact of Berkeley's faculty work on the broad and evolving topic of artificial intelligence, a field of study and practice that's changing the world before our very eyes. For a complete schedule of these related talks and a few stories about our work in AI, you can visit Berkeley.edu. I should also note that today's lecture is being live streamed and will be available on YouTube from the faculty research lectures website and from the Berkeley AI website. Now I'm pleased to introduce today's speaker. Jitendra Malik is the Arthur J. Chick Professor in the Department of Electrical Engineering and Computer Sciences. He's also a faculty member in bioengineering and is a member of the Berkeley AI Research Cognitive Science and Vision Science Groups. Jitendra's research group has worked on many different topics in computer vision, human visual perception, robotics, machine learning, and artificial intelligence. Several well-known concepts and algorithms arose in this research such as anisotropic diffusion, normalized cuts, high dynamic range imaging, shape contexts, and RCNN. Over his 37 years at UC Berkeley, he's mentored more than 70 PhD students and postdoctoral fellows. Jitendra's honors include the 2013 Distinguished Researcher in Computer Vision Award, the 2014 KS Food Prize from the International Association of Pattern Recognition, the 2016 Allen Newell Award, the 2018 Award for Research Excellence in AI, and the 2019 Computer Society Computer Pioneer Award. He's a member of the National Academy of Engineering, the National Academy of Sciences, as well as a fellow of the American Academy of Arts and Sciences. Please join me in welcoming Professor Jitendra Malak, who will speak to us about the Sensory Motor Road to Artificial Intelligence. Thank you, Chancellor, for this very generous introduction and thank you to all my colleagues who have made time to come here. It's my pleasure to talk on this very, very hot topic today, but I'm going to talk about natural intelligence first, because we can't talk about artificial intelligence without knowing something about the natural variety. So we could talk about intelligence as having started about 550 million years ago in the Cambrian era when we had our first multicellular animals that could move about. So these were the first animals that could move and that gave them an advantage because they could find food in different places. But if you want to move and find food in different places, you need to perceive, you need to know where to go to, which means that you need to have some kind of a vision system or a perception system. And that's why we have this slogan, which is from Gibson. We see in order to move and we move in order to see.  So the predator has to become extra efficient in vision or move faster and so on and so forth. And this is through most of the history of evolution, those have been the most important components of the brain of an animal, this ability to move and this ability to perceive. If we come closer to the modern era, so let's say the hominid line, when we have separated off from our other primate, sort of hominid, fellow primates, in the last five million years, you have this evolution of bipedalism, which frees the hand for toolmaking and tool use, the opposable thumb. And there's this interesting quote from Anoxagaris, a Greek philosopher, which is, it is because of his being armed with hand that man is the most intelligent animal. It was really the development of the brain followed the development of the hand. And I use that as a metaphor for manipulation, toolmaking and all the rest. And then when we come to the most recent era, the last 50,000 years or so, is when we have modern humans coming out of Africa and language, abstract thinking, symbolic behavior, all that we now call the common person in the street thinks of as intelligence. These are, if you want to think of the last 24 hours as the history of intelligence, then in the last three minutes, the last three minutes of that is essentially all this language, symbolic behavior, which we are so proud to call amongst us as a sign of intelligence. So now let's turn to artificial intelligence. And I cannot not mention the large language models, chat GPT, GPT 3, 4, 5 and 17 to come in the future. And these are remarkable. And we are going to have lectures in this series, I think on April 19, we have one of the key creators of chat GPT, who happens to be a Berkeley CSPHD, John Schulman, who's going to talk. So what I want to say is, this result is a table of results on various standard tests. So for example, the uniform bar exam, the current version of GPT does it the 90% and 11. Okay. There are results for SAT math, GREs and so on and so forth. I'm sure our chancellor will be pleased to note that English language and composition still is at level two. So there is hope for our colleagues in literature and composition yet. Just a few things. Often right now we have of course that kind of technology in computing that has enabled us to train these really large models, the kind of data on the web, like a trillion tokens which were used. But there are ideas which go back to the 1950s, the great Shannon, Claude Shannon, for people who are from electrical engineering and applied math backgrounds, and this linguist, Firth, who said, you shall know a word by the company it keeps. And that's the core idea of how we train this. We delete a word and we try to predict it from its model. Now, of course, Firth got forgotten and Chomsky became the ruler of the roost. And Chomsky was against, was a nativist. But in my view, Chomsky was wrong. And now we have proved that Chomsky is wrong. I don't want to pick up a fight with my linguist friends just yet, maybe at the end. We see in these systems emergent syntax and linguistic competence, they are like an associative memory of the web. Anyway, this is great success, okay? And contrast it with a great failure. Not yet a failure, but certainly not yet delivered success. We don't have self-driving cars. I mean, this is the thing that everybody in the street was expecting. And this is an old idea. I mean, this guy, Dick Mance, he had cars driving on the Autobahn in the 1980s. And then 90s and in fact, we had work at Berkeley in the 90s with self-driving cars. And of course, there's a lot of hype and the great Elon Musk has told us in, I think, 2019, he said, by the middle of next year, we will have over a million Tesla cars on the road with full self-driving hardware. Okay, whether that was 2019, then 2020, and we are 2022, and we still don't have a million cars with self-driving cars on the road. Okay, so now this is something that like a high school kid of age 16, we give them 20 hours of training and we think they can learn to drive. And there is the bar exam, which we think is the result of years of training. And we are at the 90% level in AI. So there seems to be something fundamentally wrong here or puzzling here. And I can make it worse. Here's a table I found of kitchen verbs, you know, various activities like stirring and slicing onions with a knife and mixing things and so on. These are things that a 12 year old can do. Okay, and no robot today can. Okay. And this is, I think, a very important thing for people, everybody to realize that in artificial intelligence, we suffer from what's known as Morovaix Paradox. Morovaix Paradox and Morovaix quoted it, said it in the 1980s, that it was sort of known earlier. It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers and difficult or impossible to give them the skills of a one year old when it comes to perception and mobility. And Steve Pinker, slightly later, said it very pithily. The main lesson of 35 years of AI research is that the hard problems are easy, and that the easy problems are hard. Okay, what everybody in the person in the street thinks of as easy is actually hard. What a child has managed to accomplish by age two. What? However, we think of as hard. The result of years of education is actually not so hard. Okay, or we have made progress on it. I won't go on to Pinker's quote, where he sort of makes our sessions about which jobs are in danger, but the good news I'll state is he's gonna he claims that the gardeners receptionist and cooks are secure in their jobs for decades to come. Okay, so that's good. But anyway, my job is to try to challenge even their jobs, right? And this comes down to that's why the title of my talk is sensory motor intelligence, which is all that stuff which an evolutionary time was early, right, perception movement, things like that. How do we make progress on those problems? Where is obviously these these later problems of advanced culture are not so hard. So there's a question as to why and this is debatable. I think Hans Morrowick gave a reason which I don't think is fully right. But let me state his intuition, which is that we'll have more difficulty in engineering to reverse engineer skills that are the result of hundreds of millions of years of evolution. So perception action was early in the process. And that's hard. We don't have anything which has the intelligence of a cat today. I think a better argument, at least from today, when we most of our advanced systems are based on machine learning, is that we lack the kind of digitized data on the web for training machine learning models. So this like the the the the text on the web, right? Every book has been digitized. Wikipedia exists. There are all these blogs. So as a result, there is there are a trillion words on the web. That's the kind of knowledge which the systems like chat, GPT and GPT four can exploit. This is not typically knowledge that a child acquires by the by age of five, you start reading books when you after you've gone to school. The challenge is the experiences that a child has had before age five. And those experiences are very personal and embodied. And they do not exist on the web, at least not yet. Okay. And and and I think that's part of the of the challenge. It's not that if they existed on the web, we would know how to exploit them. But certainly they don't exist on the web. So in in the rest of this talk, I'm going to talk a little bit about our attempts on that challenge, the challenge of trying to build a two year old, not not sort of somebody who can pass the law exam at 90% time. So and this is going to be a I'll show you work on our in robotics, mainly about legged locomotion and vision and things like that. And this is our robot. Can you hear? Can you maybe raise the volume a little bit? Anyway, this is on the Berkeley Marina, this robot is struggling, but it manages to walk. Here's another example. Okay, notice the variety in terrain, right? The rocky terrain versus this leafy terrain, down slopes, etc, etc. And more. So, so this is the challenge. So the challenge is being able to do stuff to walk around in this variety of terrain. Okay, and here's I think. This is the robustness of our motor control systems that they can manage deal with this variety. And I'm going to start by situating this work in some major intellectual traditions. So there's an intellectual tradition of pattern recognition, which is what is behind most of the successes of machine learning. We we have developed techniques by where we give lots and lots of examples and say, these are examples of dogs. We cannot define the picture of a dog sort of axiomatically like a mathematician might, but we can give lots of examples of dogs or lots of examples of cats. And then we train these, these statistical machine learning systems, which use those examples to induce what the pattern is. And generalization is the central problem. Not all dogs look the same, not all cats look the same, and we should be able to do that. In motor control, we have an additional challenge, which is that we need robustness to disturbances. This is sort of a central aspect of control. You you knock the system a little bit, it should still work correctly. And then there is adaptation. And I want to emphasize that I'm using two different terms here. Robustness is typically to deal with noise. Adaptation means that they are very significantly different terrains and your system must work well under all of these conditions. And this connects to, I will not have too many equations in case my colleagues get scared. The basic idea here, go back to the development of control theory from around the 1960s, both in the in the US and in the Soviet Union, there were parallel developments. And these were amazingly successful. These these intellectual results from that era, they led, you know, John F. Kennedy made this statement by the end of the decade, we'll have man on the moon. Well, the people who delivered were obviously the aerospace people, but also the control theory people, because we needed to have these aircraft, these spacecraft move in orbits, which were controlled and accurate. So, so that is an old tradition. And that's what we'll draw draw on the basic. Okay, if you want to know what that equation stands for, X is something like state like what physicists would call, you know, positions and velocities and so forth. And X dot is how the state changes. The additional thing here is you, which is about control, how we externally put inputs into the system. So for a rocket, it might be firing thrusters and so forth. And and we choose to apply the right input you so as to have a desired result. And I think again, going back to that sixties tradition is the concept of adaptive control. So this this matrix A here, this refers to characteristics of the system. And those characteristics change. So in my example, that robot has to walk on different kinds of terrain, right? And those create very different conditions for it to walk. And the old examples were about aircraft, which in the course of a flight, a lot of the fuel will get used up. So therefore, the mass will be lower and you need to do the right things here. And this is again a time honored problem. But I think it's a problem which today with the tools of deep learning neural networks, we can revisit and we can make substantial progress on. And what we have done, I think falls into that family. And we'll see the applications of that to robotics. And I'll show you a few in my talk. And the key work that I'll talk about in a bit more detail is what we call rapid motor adaptation for legged robots. Okay, how do we do this? Again, I want to give you a flavor of this line of work. It's basically it's reinforcement learning to train these control laws, which means that you need to tell the robot has to learn how to move each of its joints. And a traditional control theorist would write down the equations, write down the math, derive the equations and that's how they would do it. We will learn it. And the way you learn it is basically by trial and error. It's called reinforcement learning. But that's just a fancy name for trial and error. And we need zillions of trials. And by the way, this is also true for human babies. Karen Adolf, who is a researcher at NYU, she has done a lot of experiments showing that babies actually do a lot of trial and error and fall many times. It's not that they just succeed in walking in like once in one go. So our robot has to do a lot of trial and error. Real hardware gets damaged, so you do it in simulation. And what do we do? We basically don't build in anything. We must, we just have these objectives that we set. You must try to walk without falling. You try to have a desired velocity. You try to use minimum energy. Something reasonable like that. And so the jargon here is we have this physical simulator. Policy, policy refers to what's called the controller, the brain of the robot, if you will. And it gets as an input, the state. The state here means what are all my joint angles? And A here refers to actions, which means what action did it perform last time? So what was my previous action and where are, what are all my joint angles? And now the policy gets that as input. It's like keeping track of what's your current state and your history. And then the action you command is, okay, how should I change all my joint angles? Okay. And this is going to be learned by trial and error. Now it turns out that doing this is hard because of all the variability in these different environments that we might have. That, the policy that will work for walking on hard ground is not the same policy which will work for sand or slippery surfaces or upstairs or downstairs and, and so on. So what do we do? So what we can do is to say put in some, in the simulator we have prior knowledge of that. We know what the mass is. We know the friction. We can, we can choose lots of variability in the condition. So we, we must exercise the robot to walk in all these different conditions in simulation. And we can, okay, and then that gets encoded into this environmental factor encoder, which you can think of as another neural network. And it captures that knowledge in the form of this variable Z. Z sub t, which is maybe eight dimensional. So this Z sub t is going to encode things like am I on flat ground? Am I on sloping ground? Am I on slippery ground? Things like that. And it's all done implicitly. We are not choosing a meaning for those variables. It's all done as part of this giant learning problem. And you do this for lots and lots of trials and at some point your robot manages to learn how to walk. Okay, so these we call extrinsics. And reward is, what is important for walking? What's important for walking is that you move, but you don't fall and you use minimal energy. I mean for, if you think about a biological setting, animals use a lot of their energy in hunting for food or evading, being hunted and so on and so forth. So this is important. Okay, so we can train this and in fact we trained it and this is our robot and it's able to walk and it's in simulation. It's just that this robot, if we try to take it to the real world, okay, I have a little problem. And the little problem is that this environmental factors are not known. Okay, so these are not available. So now I have a problem because I cheated in my simulator, the simulator which you can think of as like a video game simulator, right? It's capturing the physics and in that I train in different conditions and I've got it, right? Well, in the real world, how do I know which condition that I am, am I part of? So I'm stuck. So here is where the aha moment comes in. There is learning of the policy. How does the robot move? And then we can add a layer of meta learning. Okay, what is meta learning here? Meta learning here is observe your own behavior and from that deduce something about the conditions you are in. So the intuition is something like this. If I'm walking on, if I was doing the same set of movements on a sandy beach, what would happen is I would put my foot down, lift it up with the same force, but it wouldn't come up. It will come up only partially. So the same actions that I apply in different conditions result in different outputs and I can become aware of that and that is the signal to me that I am in different conditions. So the same actions have different consequences and those can be like my readout of what conditions I am. Note, by the way, that the robot that I've shown so far is actually blind. So all it has are its tactile senses, its proprioceptive sense and so on. So that's the key idea. This so-called adaptation module and I'm skipping details for obvious reasons. What it does is that it is like it's like that meta reasoning thing. It's operating at one level above. It has access to the past history. So maybe the last 0.2 seconds, maybe the last one second and in that one second I commanded the actions. So I know what I did. Biologists call this the efference copy. I have the states which I know because I have sensors in my body which keep track of all the joint angles and from that history I can estimate this z which is this extrinsic which you can think of as a proxy to the environment that I am in and that's it and it turns out so the discrepancy between the expected movement and actual measured movement and we continuously estimate these online and okay and then here is a little the next observation which is that in fact this process itself can be trained. So we have two levels of learning. Learning the basic policy and this meta policy and the meta policy also gets trained in simulation because in simulation I can vary the conditions and then I can train this estimator which estimates which condition I am in and yeah that's this part and then okay so now hopefully not use too much of jargon here and now we are ready to go and we have basically the same policy which runs in all of these conditions. So our slogan is one policy to walk them all okay and why it works is because it is not the same policy to walk them all that is one style of work that's the there's a what's called robust a robust policy might be one which where you use exactly the same everywhere no what we want is something which is adaptive it should do different things I should walk differently on concrete versus walking on sand okay and that's and that I can do if I can estimate what conditions I am in and basically we have the machinery for that so these are these are just some kinds of results okay so let me give you another example to illustrate how this is happening by analysis of adaptation module so soon you are going to see Ashish Kumar who is the student was the lead author in this work and this was work done in the COVID era so I told him you can take the robot home and then basically he was sleeping with the robot for two years and great research resulted as a okay and now let's see what he does so he's going to pour some olive oil on this mattress okay okay a waste of good olive oil no doubt and then you'll see the robot soon okay okay and if you notice he's got little plastic shoes on the socks on the robot and now and now the robot is going to walk okay and let's see this in slow mo okay so the robot has a default policy for walking on hard ground and it's getting into trouble on the slippery surface where the friction is much too low so what should happen what but it recovers so what's actually happening underneath the hood is that this meta thing this adaptation module is taking over the adaptation module is figuring out oh something must be wrong so now we see this and what you see in the charts the top rows correspond to the foot placement and then the the middle is the the force applied the torque applied at the knee but these these two curves correspond to two of the dimensions of this eight dimensional vector of state so notice so when it recovers it has what it has what's happened in the first phase is that it's it's it's got the wrong estimates of the physical conditions so it's not doing the right thing as soon as it's got the right estimate of which environmental condition it is in it which is happening sort of towards the right of that plot so think of the two bottom curves then after that it's got the right z and then the policy is different and then it does it works so so here's a different example of a modification which is that we throw a weight on this robot and this poor guy has to struggle okay and and now you'll see it struggling but adaptation and then it recovers and it does it fairly quickly like point two seconds so when we fall we do not fall instantaneously there's a physical process and it takes like half a second one second and in that time if you have estimated the conditions you're good and here are some examples where the top is our policy the the bottom shows what happened without that adaptation okay so the society for prevention of cruelty to robots has prevented me from doing more experiments like this okay now i'll show you some more stuff i think i've got through if you the anyway the meat the core idea i have now done and now i'm going to give you many corollaries of this so one is that this is the advantage of a learning framework is that many things emerge i mean the emergence is the the big thing in learning so here what we're going to do is we'll just give the robot the assignment go at point five meters per second go at one meter per second go at one point five meters per second everything else is the same it's exactly the same machinery and here's what we discover so what you see in these plots are the footfalls so the solid parts so it's right foot right front left front right rear left rear and the solid part means that the foot is on the ground so when it's asked to walk at point three seven five meters per second you see a gate which is like this slow walk if you ask it to walk at point nine meters per second okay then what you get is a trot so in for horses you have all these gates trot gallop canter etc and these were not programmed in these just emerge and then if you have set a really high speed and now of course you notice that most of the time the all the full there are times when all four feet are above the ground so what's an explanation for this okay so this is what happens from learning but as scientists we want to know more and it turns out that people had already thought of it in biomechanics and the explanation is energy energy matters and you want to be efficient energetically so for example for humans at low speeds energetically walking is efficient and at high speeds running is efficient for horses you have multiple gates and these were studies done by you know taking horses and putting a bag around their nose and measuring oxygen and so on and there are particular phases in which this efficiency occurs and exactly the same is true for our quadruped and then here are some fun examples of basically we now are just remotely telling it to go at different speeds and its gate changes smoothly and I think this is just meant to you okay and now let me say by the way everything here was done blind okay and I'm a I've spent 35 years of my life studying vision but we wanted to be but I our theory was blind people can walk so certainly blind robots should be able to walk then why do you need vision well you need vision in these kinds of conditions right you you need to walk on stepping stones on stairs and so on and this is some work that I'll show you and okay I'll not go too much technical the that that there's a traditional technique for this which is to build maps by combining information from multiple views it turns out that view is is suffers from the effect of noise because every time you estimate the camera it's noisy so the resulting map is way too noisy and what we did was we we developed direct policies so direct here means that you have the visual data and you try to develop control so it's like you're trying to make it into a reflex rather than a very conscious process of build a map plan your footsteps and so on and and I'm going fast but the philosophy is very similar to what we did earlier in simulation you can train it with privileged information so you can give the robot access to what the terrain is and it ignore all the symbols please it learns a policy for moving in this terrain and now what you do is to deploy it what we do is we put a camera we put a camera on the head of the robot and this camera gets you this kind of an image and with that image what you try to do is to try to guess what the environmental parameters are so it's now going to be more about terrain geometry whereas previously it was more about terrain friction hardness and the like but roughly speaking think of it as being the same philosophy and then we can implement this and uh okay blah blah okay uh okay let's let's see some demo okay so this is the robot it has a camera on its head it has no advanced knowledge of anything okay it does not know the terrain at once okay so different examples so we have to improvise some obstacles okay so these are stairs okay these are fairly tall stairs okay this you might recognize this is the rose garden in berkley and we were convinced that this would not work will it okay anyway you get the idea so so this is a remarkable success because this is not something which typically any other system could do effectively so this paper won the best prize at the robotics conference in last December okay i i will see if this video works so this is work with a colleague in mechanical engineering let me see if this works okay so this is a biped so bipeds are harder quadrupeds are more stable and uh and uh okay i think i'll i'll uh i think i will do let's see if i'll move it i'll speed the video a bit okay okay so someday this robot will carry loads for you okay uh okay uh this general idea is applicable not just for locomotion but also for manipulation so i'll show you an example where we have a hand and again this hand is trying to twirl different objects and the interesting point here is that there are all these different objects of different sizes shapes weights friction and so on and it succeeds and it turns out it's the same basic philosophy what we call rapid motor adaptation which is you you perform some action you see the consequences of the action and you use this to at the meta level figure out what conditions you are in okay and uh okay i'll go i will skip this okay let me i wanted to make a philosophical point here okay uh how to think about intelligence so i have the last five minutes so i want to get to that this is a beautiful quote from alan turing who's considered like the founding figure of computer science and he said this in this 1950 article which proposes the turing test and everybody reads that article but never goes to section six which is where this occurs instead of trying to produce a program to simulate the adult mind why not rather try to produce one which simulates the child's if this were then subjected to an appropriate course of education one would obtain the adult brain and during was 1950 but since then we know a lot more our colleagues in psychology and neuroscience and child development have told us a lot about how children learn so children's learning uh my colleague Alison Gopnik has this phrase the scientist in the crib the child is doing these experiments putting things into her mouth has multiple senses touch uh touch vision hearing etc and then they are cross coupled uh and then that child eventually uh in that first year has become essentially a sensory motor genius and then in the second and then at some point starts to acquire language and then of course goes to school and then of course then the the the the stage the kind of learning which is embodied in gpt 3 and gpt 4 takes off but it takes off on a base of this more elementary sensory motor learning and my belief is and this is for now an ideological belief that this is the pathway for in artificial intelligence as well that we need to copy that and uh this is uh article from linda smith and michael gaser or two psychologists that these are aspects of how children learn multimodal sensing touch audition vision incremental we build on our past knowledge physical it's not the brain in a vat it's not the mind in a vat it is interacting with the world embodied uh explore social we learn from others and then of course we do like use language so i wish to fight against the tyranny of linguistic imperialism okay and uh in the last three two minutes i'll talk about a project uh with antonio locations here and she shared myself and this one we tried to take this idea of sort of copy the idea of childhood learning which is cross model and we said here's a robot which has got a camera but it's going to learn in the real world how to use its vision system so it starts out blind so it's struggling okay and then the vision system with a vision system of course you can climb stairs and you'll see that but how do we train the vision system and we wanted it to learn in the wild so here was our intuition if you think of a robot in the stairs it's in its proprioception its senses its joint angles can let it compute what's the depth of the its left leg and right leg and so on it has that geometry from its joint angles from its internal state so can we use it for training without uh okay so the idea was the proprioception predicts the depth of every leg and the vision system gets an image and what we ask the vision system to do is to predict what the depth will be one point five seconds later so so and that was the idea that you just shift what signal it will know one point five seconds later and use that to do this advanced prediction and so we have this robot which is learning day by day so the first day it's clumsy the second day it goes up further and then on the right you see the success rate and then finally on the third day you will see that it actually will it make it all the way it makes it all the way and we can do experiments like we can mess up its vision system there's a ability that humans have that we are calibrating all the time so Antonio is going to rotate the camera and that's going to make it uh it's so it's messed it up it's like taking your eyes and you know switching them moving them 30 degrees but because it is always learning it's always adapting it's going to initially it will struggle I hope not I don't hope but it will okay and then it recovers because it's learning from its own experiences and and then this is a little let me play this one so this is before adaptation and then this is after adaptation and adaptation is just a few minutes so this is called the prism test in in vision science and humans can do it in like 10 minutes well so can a robot so I hope I've taken you on this little journey of what some of the some work into some what I think is potentially interesting work in sensory motor learning and I want to conclude with acknowledging some people acknowledgement first I want to thank all my past and present PhD student this work is really due to them I merely a spokesperson on the stage and of course all my past and present postdoctoral fellows thank you thank you very much and then I want to say something about my colleagues particularly in the ECS department with whom I've spent 37 wonderful years starting with a lab I started as a 25 year old assistant professor and I think Berkeley was the best place for starting assistant professor it probably still is and I'm very thankful for the opportunity that was given to me I'm thankful to my colleagues in the rest of the university from whom I learned so much at vision science cognitive science neuroscience I learned all about the brain perception and action from them I want to thank my currently my colleagues in bear with whom I have such fun time and the wonderful Angie who keeps the place running and finally of course my wife Isha and my son Kaby for so much patience and support thank you very much we we have a robot which you can which we which we're we're gonna skip or outside he'll have he'll show it outside so after the talk people can check it out yeah but I'm happy to answer questions yes can you please use the mic yes I'm wondering whether there are any limitations thank you for the great talk thank you are there any limitations between the time that you need to learn a task and that the time that you actually want or or need to perform it to to be efficient you see what I mean because there's two times yes right so so I distinguish between two kinds of learning so in our we do this learning in simulation which takes a very very long time but since it's done in simulation it's totally safe and we can take a very huge complexity it can have a huge complexity then the learning which you do and and so there are no limits there we can train forever then there are limits then what there is the learning which you do in the real world that has to be very rapid so the initial adaptation that I showed that was on the order of a 0.5 second or 0.3 second that kind of thing if it is longer than that you'll fall right but we are adapting all the time if I sprain my ankle I can walk and that's adaptation then there was the second so that's the time constant that I need it needs to be fast but not instantaneous and then I talked about this recalibration that we have which is of our entire sensory motor system it happens when we wear glasses it happens when we grow and things like that that you have a bit more leeway but it's in our system we showed it could be done in a few minutes which is roughly comparable to what humans can do for this effect it's called the prism adaptation so I don't have a generic answer for all things but I'm just showing you for these systems what it is it's important the timing is important adaptation has to be quick enough otherwise it won't work question yeah yeah thank you for your talk the title of your talk kind of implies that there are multiple roads to artificial intelligence what would be the second road well the the obvious road that people are taking right now is the is the road from linguistic data from all the words on the web that's how you train GPT-3 and chart GPT and I am saying that captures a certain kind of knowledge but it is an incomplete story when we want to talk about intelligence as natural intelligence goes as an engineer you can totally use that incomplete system in some way but I mean I'm still striving after human level intelligence in all its facets and for someone like me that is not enough I want that intelligence which is embodied and grounded in the real world which must start from that that experience yeah I don't know who's yeah okay I think then there's a question here in the front yeah yeah yeah there's someone there and then maybe you'll get yeah yeah thank you thank you very much for a great talk you started your talk with with autonomous vehicles and I think recently in use was a situation where autonomous vehicles in San Francisco was in an intersection and there was another car like doing donuts on the intersection and the problem was that the autonomous car hadn't seen the situation before so there is the one old data in the simulation and the car wasn't able to deal with the situation right and my question is how to deal with safety critical situations because in such situations for example the robot has no rights to you know to engage in this situation and the human can reason about the situation based on other knowledge yeah so the question was really about situations which are not seen in training so this comes down to the emergence issue what abilities emerge so humans certainly have I mean I'm driving on a on the road and there's this truck and some junk falls from the back if it's newspapers I will drive over if it's rocks I'll switch to a different lane right because we know something about the properties of rocks versus newspaper now that is connected to our vast wider knowledge so the the article of ideological belief among AI researchers is that with enough knowledge and that then these abilities will emerge it remains to be proved certainly the more narrowly trained systems that we have today don't have it but that's one answer the second answer is that we may want to explicitly have what's called system one and system two so this is terminology from Daniel Kahneman system one is more reflexive system two is is this deliberative logical conscious system okay by the way there's our dog walking okay it can do fancier things than then walk on the ground but at least it is it can walk on the ground but a colleague here had a question I think he had a question over there yeah you were using the infant infant learning for this adaptability of the of your AI but it takes say a very say you know a very simple animal perhaps it's not a simple animal like an ant the ant can walk and do all that ants need to do immediately from a hatching so I'm wondering yeah yeah sorry his question is a is a fundamental and deep one which is that there are certainly species which don't need all this learning period okay so this is Alison Gopnik has an article on this so this is the thing about precociality so and what you get is a very hardwired system which is not so adaptive but you but you can work on day zero and human babies don't have that which gives us much greater adaptability so what the price you pay for that is that there's a period of vulnerability for a period of 10 years the human child cannot survive by himself or herself right that's the price you pay but as a result you get a system which is much more adapted to the to the world and somehow evolution their different species have made these different trade-offs on this dimension what training we do in simulation I like to think of it as being somewhat akin to the process of evolutionary search I mean as a species we have we have certain knowledge which is encoded in our genome and that has come from some 550 million years search process so I want to distinguish between the the learning that occurs over that time versus the learning that occurs in the life of an individual I mean the world keeps changing right and that that is what we get from this thank you thank you


I see you My sir We're just trying to get a picture Got three more All right, we're just gonna tell him unfortunately we can't do anything about until we get cleared by hours But we have to wait to verify and just thank them for the Three individuals about a hundred meters out keep an eye on them over see time of day time on deck Turn around All right, so what's her restoration? 90% 98 or 50 She's moaning Fifty megs or actually do a hundred megs. How much do you weigh? How much do you weigh? One fifteen.


I like to shoot because it's a passion and something I love to do. My father was in the Air Force. He taught me a lot about shooting. He was the original one that showed me the basics in shooting. When I was ten years old, my grandfather took me hunting and I killed my first deer. It just became a passion from there. I've shot one Marine Corps competition on the civilian side. I've probably shot about fifteen and I have no plans to stop them. I plan to do it for as long as I'm able. It's really something that comes natural to me and competition is always fun, you know. Compare yourself against others, see how well you do, and not to mention the community in the shooting competition. Marine Corps marksmanship, they really have an excellent program. They teach you the basics in shooting and then they go in from there and show you the rest. So I'm able to take the basics that they have shown me about how to walk and pull the trigger, how to do a slow, safe, please, proper cheek weld, and how to focus on your sights. But the Marine Corps has definitely opened my eyes to the rest of the shooting world and how to make the little fixes that I needed that I wouldn't have known how to apply on my own because it comes down to the basics and the Marine Corps does a very good job at teaching the basics. If you don't have a good foundation, you're not going to do anything.


What is going on guys welcome back to the channel critical overload here so talk about several different topics in this video here again today We'll be talking about Jeepers Creepers 4 hellraiser Halloween ends and scream 6 now just to kick it off with Jeepers Creepers 4 Jeepers Creepers 4 Jeepers Creepers reborn. It's coming back to theaters tomorrow and This is according to what scream media put out in their post on Twitter on Facebook on the official Jeepers Creepers reborn account They stated that this is happening due to demand now who who is checking for this again? Who who who is clamoring to see this again and again? I know somebody out there is but as a vast majority who? The movie just happened to come out and it just happened to make over two million dollars worldwide when you did little to no marketing Because what's so confusing is the way that they want this trilogy to start off with the Jeepers Creepers reborn They wanted to be a trilogy starter and its release is just an absolute mess For instance, the director doesn't even know if this new release tomorrow is wide or limited And what I mean by that is one of you shout out to you again as You should sent me this image of Timo having his conversation sharing that re-release news He's and then when asked is this the is the theatrical re-release wide or limited like before He said I wish I knew I just got this note from the distributor that that it's now out in theaters But they didn't tell me how wide and big this he's clueless guys. He's clueless Clearly clueless. We know that the course case is still ongoing The film again has made over two million dollars so far in total and the soonest I'd expect they knew Jeepers Creepers movie aka Jeepers Creepers 5 would be 2025 or 24 If Miria's allegations are true then nothing should stop them from finding another company to co-produce a fifth film with Get it into the hands of another distributor besides screen media and get back on track while still settling that court Case and I'll have some videos coming back coming out soon to pick this movie apart on the things that don't make sense But the earliest I would expect a Jeepers Creepers 5 at this point would be 2024 to go and to go along with The 20th anniversary or 23rd anniversary or the actual 23 year later mark for the original Jeepers Creepers And that original cycle ending how we know that was the year Jeepers Creepers 2 was set in when it went off in 2003 So just a jump in a Hellraiser Jamie Clayton in a new interview to explain this new take on Hellraiser pinhead And we got some first reactions as well about the movie recently So Jamie Clayton said this about her iteration or her version of the Hell Priest this I would say This film's version of the priest is now a woman which takes the burden off of the audience of trying to compare the performance to Doug Bradley's iconic portrayal of this character lovingly known by the fan's pinhead. There's a sensuality to her There's a grace. There's a beauty and a hunger and a desire So I like the way she's describing the character and again, I can't wait to see Hellraiser when it debuts on Hulu I'm hoping I get to see it early next week But the film again had a secret screening last night at Fantastic Fest and it has gotten good reactions for the most part praising Brutner's direction Clayton's performance and the film being a worthy addition to the franchise if you type in hashtag Hellraiser on Twitter several reactions should pop up They're calling it like a breath of fresh air saying the series is back on track So, you know the standard things you usually get after you've had a series of abysmal Straight-to-video movies that I think Hellraiser did have I think most of those sequels that I've seen were straight-to-video I was very young when I saw them. I will also admit. I haven't seen all of them I think I haven't seen the most recent two But it's nice to hear to this this franchise is apparently back on track with the upcoming Hellraiser film from David Bruckner who I had I had a lot of faith in considering most of his work that I've seen so far has been a hit I haven't seen many misses from David Bruckner. So jumping into Halloween ends Halloween ends Had a new feature that came out the final battle feature it with Jamie Lee Curtis recounting the difference It's between Laurie Strode in the 2018 movie and Halloween ends She points out that in the 2018 movie was more about Laurie who was prepared for over 40 years While this one is more of her trying to move on and then Michael catches her off guard Which is of course very apparent considering that she's working on a memoir and she was Clearly I would say going off of the theatrical ending for kills and the extended cut ending She was ready to go after Michael So she managed to I guess let that go move on with her life Probably realize that a lot of what she did in her youth with Karen and her youth too Was probably will contribute it to her putting her in this situation that got her killed So she was trying to forgive herself from that But then of course Michael returns catching her off guard the most interesting thing in the feature It are not Jamie's words to me But the shots of the film throughout she calls Halloween ends crazy intense and I'm sure that shocks no one Of course, it will be intense for all of us Laurie Strode fans Anyway, who are realizing that we won't get to see her ever again at least not portrayed by her originator of Jamie Lee Curtis Halloween is I think it's supposed to have its premiere at Beyond Fest Next week or sometimes so early reactions should be here soon from that screening I do believe it's time to also accept that Corey Cunningham is going to be your copycat killer Everything is just pointing to that the only thing that comes down to now is let's just see how it's executed I will say this I am confident that something about this will just be stronger than what we got from Friday the 13 part 5 That's just me but jumping into scream 6 Scream 6 had had some potential new character names revealed from our user clips comprehend Again, if you believe them or you don't that's up to you Kevin Williamson also talked about Ghostface's longevity When speaking to Collider Williamson stated they that there was a point I guess where he didn't think this year's could go on and on But now he does think it that it can he's been he said he's been proven wrong repeatedly since the last one was such a pleasure I also think one of the wonderful things about scream is that there's a different killer in every film You know, there's Jason bore. He's Michael or Freddy. They don't go away and Ghostface changes with each movie So we have an opportunity to change the motive change the story and we can always twist and turn it Since the original screen was always meant to be a game changer, which it was yes The series can indeed go on but it shouldn't just because it can that's my thoughts on that And I know many of you will probably agree with that sentiment as well Many of you still think that the franchise should have ended with three Clips revealed two character names of Carly who's very similar to Tatum as far as her attitude and fear fearlessness goals She enjoys partying and pushes Tara to let loose of it I'm gonna assume that Carly is Liana Liberato's character Then he also revealed a character of detective Perillo A old school New York style cop leans into that New York accent He said this is not Dermart Moroni's character because we know Dermart Moroni is also playing a cop Says his daughter gets caught up in this upcoming Ghostface free and that's what gets him involved again I believe Carly is Liana Liberato So we'll see what happens with Scream 6 because I think marketing will amp up in October We'll get our first teaser maybe along with Scream to rerelease and you know We'll have opportunities to go crazy or whatever else comes maybe some new character posters or new feature It's from the cast let me know just think of all this down in the comment section below if you haven't already of course make She subscribe turn on post notifications so that you never miss a video in the description I will have links to all my social media accounts I'm on Facebook Twitter and Instagram you can message me there of course to let me know if there are any movies news or Reviews you allow me to cover in the future and with all that in mind guys. I will see you in the next video


Good afternoon and thank you for joining us. My name is Alex White, I'm Director General here at the Institute of International and European Affairs. And it's great to have you with us for what I know is going to be a most interesting and stimulating webinar this afternoon because we're delighted to be joined by Professor Penny Goldberg, who is a professor of economics and professor of management at Yale University and former chief economist of the World Bank Group has been generous enough to speak to us this afternoon and addressing the topic globalization. According to our guest speaker the recent retreat from globalization has been triggered by a perception that increased competition from global trade is not fair. That leads to increased inequality within countries. So, I suppose the question is to ask whether this phenomenon is a small hiccup in the overall wave of globalization historically in the hope the sweep of history as aware, or are we at the beginning of a new era of deed globalization, which we hear a lot about. And in her address, Professor Goldberg will argue that the answer to that question depends on the policy choices that we make. And actually in her latest book, the unequal effects of globalization. Professor Goldberg calls for alternative policy approaches or at least that we would explore alternative policy approaches, including place based policies, whilst at the same time, continuing to sustain international cooperation. So just about the arrangements for the next hour or so Penny will speak to us for about 20 minutes or so give or take, and then we'll go to a Q&A in our normal fashion. We'll take that discussion by using the Q&A function. And you can send your questions into us and please do send them in throughout the session as they occur to you rather than necessarily waiting until at the end of the presentation but once that's the presentation is finished, we will as I say, go then to those questions. And you can also if you're interested, you can participate in the discussion on Twitter or should say on X, using the handle at IEA. The presentation and Q&A are both on the record this afternoon. Penelope or Penny Kujani Goldberg is the Elihu Professor of Economics and an affiliate of the Economic Growth Center at Yale University, which is what you can see behind her in the shower. You will be able to see a backdrop in a moment or so in the shot. From November 2018 to March 2020, she was the chief economist of the World Bank Group. Professor Goldberg was president of the Econometric Society in 2021 and has previously served as vice president of the American Economic Association. From 2011 to 17 she was editor in chief of the American Economic Review. She's a member of the National Academy of Sciences and the American Academy of Arts and Sciences recipient of the Guggenheim Memorial Foundation and Sloan Research Fellowships and recipient of the Bodhisattva Prize in Social Sciences. I promise you I could go on. There are many pages of achievements and distinctions that our guest speaker has but I think what we'll do is we'll go to Penny, we'll go to the presentation. So Professor Goldberg, the floor is yours. Thank you for being with us. Thank you very much for this very generous introduction and most importantly for the opportunity to talk about a topic that's very important to me in this forum. I have prepared a few slides that I'm going to share with you in a moment and as Alex said, I will try to give my presentation brief talk for about 20, 25 minutes and then leave time for questions. So let me share my screen. So I hope you can see it. Yep. So my talk will focus on what I will call the unequal effects of globalization. And I will draw heavily, you know, in the spirit of self promotion, I will draw heavily on a book on a monograph I recently completed on precisely this topic. So I will try to make two main points. The first is that when we talk about inequality in the relationship to globalization, we are talking about an extremely complex phenomenon with a lot of nuance. And the answers are going to depend on what dimension of inequality we focus on. This is important because if we don't identify the problem correctly, we cannot come up with the right remedies. The second point is going to be that I will argue that the increase in a particular dimension of inequality, namely geographical spatial inequality within advanced countries. What I mean by that is the gap between communities areas that are very wealthy and areas that are less so that is perhaps the most important reason behind the recent backlash against globalization. So let me start with a very brief overview of the evidence when it comes to inequality. So I assume some of you in the audience have taken international economics and you may have heard of the so-called Heckscher-Olin model of trade. This is the trade course, the workhorse model of international trade. And the big contribution of that model was that it focused on the distributional conflict generated by trade, not just on the aggregate effects that we tend to think are beneficial for the economy as a whole, but on distributional tensions. And in this traditional model, the conflict is a class conflict. So it's a conflict between capital and labor. In more modern versions of this work, it's presented as a conflict between skilled labor and unskilled labor. But it's always been about factors of production. And this framework provided a very natural framework for thinking about inequality in the 80s and the 90s, so in the previous century. Why? Because this was a period where we had very fast growth in trade and globalization in general. And at the same time, we had a rise in inequality in advanced economies. So it was very natural to connect the two. That said, there has been a large amount of research in the 80s and 90s. I contributed among others to that research. And we all fail to find an empirical connection between trade and inequality and the measure that meant the dimension of inequality. I have in mind here is the so called skill premium. So this is the difference between the wage of a skilled worker and an unskilled worker. And in this entire literature, skill means education, especially college education in advanced countries. So the gap between educated workers and less educated workers kept increasing in advanced economies. It was very natural to connect that to imports from low wage countries, but despite all our efforts, we could not find any evidence. There is also an additional casual observation that supports this conclusion that it was not driven by trade. And this is the fact that we also observe this increase in inequality in low wage countries in poor countries and their trade theory would predict that the effects should go in the opposite direction. Now, there are many caveats associated with this argument that they have written about this, but overall, the consensus among academics and as you probably know, economists disagree about almost everything. It's very rare for academics to reach consensus or anything, but the consensus in the 90s was that trade may have played a role, but it was a very minor role compared to other developments, most importantly technology. So this consensus changed in the 2000s, in the early 2000s, and the question is why? So one hypothesis is that it's because China entered the world trade and what you see in this graph is the growth of world Chinese exports, so the China's exports to the world between 1980 and 2021. And what you can see starting in 2000 is exports that rising very fast and a very large fraction of these exports goes into the United States and other advanced countries. There are many reasons for this growth, but for now, let's take that as a given it is what it is. This is one hypothesis that it's all because of China that had been trade with low wage countries before but these countries were relatively small relative to the United States or to great Britain. China is big, it matters because of its size. That said, there's an alternative hypothesis and this hypothesis is that the reason people started finding effects on inequality in the 2000s is because they changed their focus. So instead of focusing on the skill premium, the skill premium was they had done in the 80s and the 90s, they started focusing on a different dimension of inequality, namely spatial inequality. So inequality between rich and poor areas. So in the United States, the comparison was between areas in which there was a lot of industrial production that was displaced eventually by Chinese imports and communities that were not affected by imports. In Great Britain, there has been this long standing divide between Northern England and London, the London area and the south. So these regional inequalities became the focus of attention, at least in academic work and there are many studies that documented again and again, that opening up to trade in this dimension of inequality, not just in the United States, but also in developing countries that were not affected by Chinese exports or for example, in India or Brazil. In the US that has been also work on NAFTA that documented the same effect again that had nothing to do with China and how to do with Mexico. And but again, this studies documented this increase in regional inequality. Again, important to emphasize this study saying nothing about the aggregate effects, no one questions the fact that trade had many beneficial effects on on on people as consumers especially. But all these effects pertain to this regional dimension that became first order starting the year 2000. This brings up the issue that that I want to focus on that was my first point in the opening slide, namely that when we talk about inequality, we need to be specific about what exactly we mean by inequality. And here is a schematic that helps organize our thoughts. So what about inequality as being global in nature by global I mean what's the inequality that you get if you pull the entire population of the world, independent of where people live. And within country inequality so the inequality you get if you condition on the country let's say Ireland or the United States or Greece and so on. So in a country you can distinguish between how trade affects people as consumers and people as workers. So let me start by emphasizing a trade off or at least what is perceived as a trade off. People studying to in the year 2000 have increasingly perceived the trade off between the reduction of global inequality and the reduction of within country inequality. I think there is consensus by now that global inequality.  This type of inequality has been reduced dramatically after World War Two. And I think there's also consensus, even among skeptics that globalization and particularly the integration of China and East Asia into the world trading system played a very important role in this development. However, there is this as I mentioned there is this increasing increasingly strong sentiment that all this has happened at the expense of within country inequality in advanced. So let me show you a few graphs that will illustrate these points. So this graph shows you how world exports as percent percentage of GDP have evolved over a long time horizon so between 1827 and 2019. And you can see some developments that you certainly know about so during World War One and World War Two war trade collapses after World War Two. So after after 1945 we have all these trade agreements and all the efforts to make countries more interconnected partly as a means as a way of ensuring that we maintain peace and we've achieved that in Europe. But so trade starts going up after 1945 and it keeps rising it keeps rising through the 50s and the 60s, but then comes the year 1989. So here, and then you can see this very fast rise of trade as percent of GDP. So this is the period this is the beginning of the period that has been termed the era of hyper globalization that's when trade was incredibly fast. And you can see from the graph this is historically unprecedented. There has been a lot of trade in previous times, but what happens after 89 is truly unprecedented. And that goes on until approximately until actually 2008 2009 that's when the financial crisis happens during the financial crisis trade collapses. But then the year after the after the after the main financial crisis after 2009 trade comes back again it bounces back. But then then it never it never gains its previous momentum so it keeps growing, but at a slower pace. And at that point there is a lot of debate whether we have the beginning of the globalization. In my opinion at that time we can just talk about slow realization if you want or whatever term you want to use. There is no evidence that we have a retreat from globalization in just a trade is growing more slowly than before. However, what I want to emphasize is that during this period, again that that's 89 to 2009 approximately, we also have an amazing decline in global poverty and you can see that in this graph. This graph shows you the poverty rates the extreme poverty rates as defined by the World Bank between 1990 and 2020 these are the real data and then it's projected these are the rest of projections by the World Bank as to what is going to happen for the near future. And you can see that in the black the black area represents East Asia and Pacific essentially extreme poverty is eliminated. This is mainly the story of China here and some other countries. Also India to a large extent but not as much as China. Even America in Europe again over these almost eliminated South Asia, I'm sorry South Asia India is in South Asia so again you can see the decline in poverty you see that clearly, but it's not as pronounced as what you see in the black area which is East Asia. And then you see the area at the top this is Africa this is where the problem persists we still have extreme poverty extremely high extreme poverty rates in Africa. Now, do this does this graph prove that it was globalization that was behind this decline in extreme poverty, not necessarily, but the correlations are strongly suggestive, except, especially if you take into account the fact that Africa is the continent that's least connected to the rest of the world and there we don't see any advances, East Asia with the entry of China in the world trading system is the continent that experienced the biggest gains. Another way to show that is a graph that actually came from the World Bank originally by work of Bronco Milanovic and Chris of Luckner. And what this graph shows this is a so called growth incidence curve. So it shows again for this period of hyper globalization. It shows what the income growth rate was in percentage rates that's the, I'm sorry that's the vertical axis against the percentile of the global income distribution so what you can see is that the people that were at the left part of the global income distribution so these are the people who are globally poor, they experienced very fast growth around 40% 50% the middle around, you know the 50th 60th percentile they experienced the fastest growth around 70 80%. So they are these are actually the people. If you take the world as a whole, who are really poor and growth in this in this segment of the population is extremely fast. It's much faster than it is at the 80 or 90th percentile to be fair to the literature with this curve has been more recently updated by a team of researchers that include PKT and Saez and Zachman so these are economists were well known for having focus on the top 1% and having used tax data as opposed to survey data to document increases in inequality and what they show there is a graph that shows a little bit different. The graph shows that a lot of the game has been at the very top of the distribution so the top 1% capture a large part about 27% of the total growth. So this is true, but at the same time you see here that the people at the very low end of the distribution so that the truly poor the globally poor also grew very fast about 120%. So you still have very fast growth among the very poor. However, what this figure also shows is if you look in the middle, the middle is in relative terms squeezed and this is where the middle class of the US and Western Europe find themselves. So this is, again, in absolute terms, everyone is doing better but in relative terms, the people in the middle are doing worse. These are the graphs like that that led to the perception that there is a trade off between global and within country inequality. Now coming to the within country inequality, we can look at people as workers, or as consumers. I already mentioned that if you look at people as workers, there is very little, there is no evidence that the wage differential that reflects skill as reacted to trade liberalization. However, a special inequality became much more salient feature of inequality and let me show you, let me make the point showing just one graph on Brazil. Brazil is convenient because it offers the right data and the right trade policy experiment for looking at this issue. In other countries, it's less clear, less clear card that includes the work on on the US and Britain, but they have been studies making the same points in this context. What happens in Brazil is you have a major liberalization in the early 90s. And what this graph shows is what happens to them to the employment, the formal employment in those areas that were hit harder by this liberalization relative to the areas that were less exposed to the trade liberalization. So it focuses on relative effects between areas that were a lot exposed to imports, various areas that were exposed less. And what you can see is that you have a prolonged decline in earnings, formal earnings, but it's not a short-lived phenomenon, we're not talking about one or two years, it lasts almost 20 years until it eventually stabilizes. If you look at earnings in again formal earnings, you get very much the same picture. This is the pre-liberalization period. There is a positive trend, so earnings are going up. Then comes the liberalization. Wages take a hit and then they keep declining. It's not again, it's not a short-term phenomenon. The wages keep going down for 20 years and eventually everything stabilizes. And the reason it stabilizes in Brazil is because there is a very large informal sector. This is a different story in developing countries and eventually all these workers get absorbed by the informal sector. So this is a very somber message that this paper conveys, namely that we're not talking here about short-term displacement effects. There are true displacement effects that trade liberalization brings about. And again, you don't see these effects if you look at the skilled premium, but if you look at inequality across regions, then they become apparent. So I mentioned the effects on employment and earnings, but people also found effects on education, on child labor in developing countries, on crime, on mental health. So it seems those areas that experience a negative demand shock because of imports are confronted with a long-run problem. And here I have a very brief overview of other dimensions of inequality that people have connected to this increase in imports. And in all these cases, this inference is based on comparisons, on spatial comparisons between areas exposed to imports and areas that are not exposed to imports. So it seems what happens is imports are a negative demand shock and some areas, some regions end up in a bad equilibrium. Now, Alex mentioned at the beginning that in the introduction that one of the conclusions of my monograph is that perhaps given all these we should consider place-based policies. And in fact, this is something that I do believe in and I would not have said that a few years ago. Place-based policies cover a very bad name in economics for a very good reason. Many of them have proven to be completely inefficient and captured by lobbies. As I said, traditional policies to compensate losers from liberalizations, from globalizations have not worked. And so given that and given the importance of spatial inequality in recent results, it seems to me it's worth reconsidering previous results. That doesn't mean that we should unconditionally embrace place-based policy, but at least we should realize there may be a need for it and consider how we can make it most effective. I talked a lot about people as workers, so let me also briefly talk about people as consumers. This is all of us. And the trade models again tell us that what trade does is it leads to lower prices, higher quality of goods, more variety. So what do the data tell us? The work on this aspect, surprisingly perhaps, is more limited than the work on workers. And there are two questions you can ask in this context. The first is do prices respond to trade barriers? This is a question that's particularly relevant these days when we are confronted with high inflation. And second, if prices do respond to trade barriers, is it the case that the people who benefit the most from price declines are the poor relative to the rich? Because if that's the case, then trade also affects inequality through prices. So to make a long story short, what people have found is that prices do respond to trade barriers. There is a lot of evidence from the recent trade war between the US and China that shows these effects. Although the effect may be not always one for one, the effects on the distribution are more mixed. And that said, in my opinion, price changes are more likely to affect low income households simply because low income households consume more. High income households tend to save more. So given that consumption is more important to low income households, any developments that lead to lower prices of consumption are going to have these proportional effects on these low income households. So but at any rate, the price effects and that's a well known problem in trade are less silent than employment on wage effects. So even if some households or if all households benefited from trade, all of us benefited in the form of lower prices in the form of more variety. The perception that some groups of the population were heard by trade is dominant. And the special effects, as I mentioned before, the fact that some communities are in decline, these effects have been large and persistent. So let me conclude by coming to the new policy environment. So we find ourselves in an era of, I would say the globalization, if you are uncomfortable with this term, I think it's safe to say we have a major backlash against globalization. And when we think about this backlash, I would roughly chart three phases in this, in this backlash. The first is between 2016 and 2020. So that starts with Brexit in your world with the tariffs that the US imposed on China in my world, then the retaliation of China. And what drives these developments is a sense that first we have unfair competition between countries or an increasing sense that China is advancing at the expense of the US and the West. But at the same time, the sentiment that I outlined before that there is a trade off between reductions in global inequality and within country equality. We have regional inequalities, we have labor market disruptions, and all this leads to a public sentiment that's supportive of a new wave of trade protection, Brexit, Trump tariffs. That said, what I have found in my work and others can found to that trade remain robust through these developments, the public sentiment is becoming increasingly negative towards trade. But if you look at the trade data, you don't see, you don't see any changes. And phase two is during the coffee time so mainly 2020 and 2022. And then we are presented with new arguments against trade, this have nothing to do with inequality, then it's all about resilience. In my opinion, and I have written about this extensively elsewhere and we can talk about that later trade put incredibly resilient during the time, despite all the talk about disruptions. There is certainly disruptions. There is no question about that. But let's remember what the magnitude of the shock was the world economy shut down on and off for two years. And despite this shutting off. We managed to actually get through this. We didn't start in Moscow, in almost any country. Most economies bounce back. So I don't think any of these would have been achieved without international trade. And in fact, as Pfizer pointed out, even the manufacturing of the vaccine of the mRNA vaccine required ingredients that came from 19 different countries. So it would not have been possible without the cooperation and among many different countries and trade between these countries. So despite the incredible backlash that we all experienced during that time, we'll say that actually trade contributed to resilience during that time. And then phase three starts, in my opinion, around 2000, February 2022 with the invasion of Ukraine. And then we have new arguments against trade based on national security. So resilience is again a first order concern, but now it's not resilience against natural shocks or health shocks is resilience to geopolitical risks. And that sets off new developments that support the coupling from Russia in Europe and the coupling from China in the US. So I think it's fair to say that we are in a new era now we see the reversal of the case of liberalizations and multilateralism in the US and in the UK. So these are two of the champions of free trade. There is still trade agreements going on in other parts of the world, mainly Africa, but the significance of the US and the UK cannot be ignored. These are the two countries that have always pushed for free trade. We've seen in the last two years we've seen profound changes in US trade policy, and I will leave the sanctions against Russia aside because Russia is an aggressor. There's a lot of war going on, but many of the policies that the US undertook have to do with China. I listed here some of them and all this has prompted Edward Luce of the Financial Times to declare about a year ago superpower that's the US declared war on a great power that's the China, and nobody noticed. So the fact is we don't talk much about it, but the fact is we are in a very different era, there is not just less cooperation on the trade front, there is a three economic war going on between the US and China. And I would like to conclude with a following thought. It's very easy to blame all this on the invasion of Ukraine and geopolitical risk. One needs to wonder if we would be where we are right now, if the public had not been gradually prepared for this backlash against globalization. So there is a difference I think between what the catalyst was for the recent developments and what the two causes are behind what's happening. I expect that economic historians are going to be debating these issues for many years to come, but in my opinion the rising inequality in the previous decades has played a crucial role. Because it prepared the public and many policymakers for receiving the protectionism that we witnessed in the last few years. We saw many movements in that direction, both in the US and in Europe we saw the rise of extremist views, we saw the anti globalization sentiment becoming stronger over time, and none of the things that happened in 2016 or prior to 2022 had a big impact on trade, but what they did is little by little there were stepping stones little by little, they prepared the ground for what happened in 2022 and 2023. So in my opinion inequality was not perhaps not the only cause but one of the main causes behind what's going on today. And with this I will stop here thank you very much for your attention and for your time, and I will be very happy to take questions.


You were on that team with John Wall. I know how close you guys were. You knew each other before even Kentucky. Tell me this, is it true he pranked you in high school and told you that he was going to Duke and they just hung up? We thought about teaming up as youngsters and he played a nice joke on me right the day before. John was a super hype player coming out. So deservedly so. I don't want to say that in a disrespectful way. But deservedly so. So he was one of the last guys to announce. So, you know, leading up, you know, I thought we were on the same page.   I'm like, I called the Duke, I called him like 10 times. Like, no answer, no answer.   It's like not thought out at all.


It seems like you found a little something at the plate today. What was different for you in the batter's box? I just made a little changes with my right hand swing.  And I wasn't able to get to that inside pitches and so I try to stay a little bit taller. And I was able to see the ball a lot better and it resulted in three hits today. Yesterday you went into Aaron Boone's office and decided to not play in the game. Can you just kind of give your perspective on how you were affected by the situation in Minnesota and why you made that trip into Aaron Boone's office?  You know, it was tough, you know, to read that somebody just got murdered.  So, you know, it's extremely tough, you know, in the situations like that happen. Brandon Cutty, please honor you. Aaron, could you tell us how the support you received from the clubhouse, how your teammates reacted? I mean, it was incredible.    You know, we'll take care of this game for you. And, you know, it's nice to have that. Aaron Boone, you have the next question.  I don't feel like playing tonight and then ultimately what led to you going into his office? Well, you know, I was going back and forth nearly on the drive over to the field. And then by the time I got to the field, you know, I just knew it wasn't, it wasn't right for me to play. So I kind of went in there and must have been like four o'clock or so. I don't really remember, but, you know, pretty early, you know, before BP to do that. Thank you, Aaron. So please, Aaron, was there any thought to not playing tonight also? Or when did you decide that tonight would be okay? Um, you know, I just, I just felt like yesterday wasn't right. And today was, you know, a day that I wanted to play. I wanted to go out. I wanted to compete, you know, and competing is what gets my mind off of it.  Take one or two more. Pete Caldera, go ahead. Aaron, it looked like before the game you were speaking with some of the Blue Jays players. I imagine you engage your own teammates also in discussion. Was it helpful to talk through this or was it something that you just needed to process on your own?  I didn't really start talking about it until, you know, once I got, you know, right around game time.  So it's definitely, you know, nice to have a support system that can, you know, help you. Kristi Acker, please unmute. Hey, Aaron, how did you find out about the incident and just, can you just tell us what your initial reaction was or how emotional it was for you? You know, just kind of waking up in the morning and just scrolling through Twitter is kind of kind of how it happened. And then obviously you see the game with the Red Sox in Minnesota postponed, you know, and, you know, it's kind of initially how I got all that information. That's pretty much in the morning when I woke up.


I think to begin with with CVs going to the career service that the university is a really good place to start. They can advise you on best practice what a good CV looks like and they'll also be able to give you a template so you can begin to fill in the different sections rather than starting from scratch with a blank page which can be pretty daunting. So the best way to prepare your CV is to decide on a format, make sure it's structured, keep it concise, you don't need to go over two pages and remember that you're applying for a graduate role so keep your education and your skills at the top of your CV. To prepare for a CV I would start with research, I would look at the company that you are applying for, the role that you're interested in, this allows you to figure out whether the company is actually right for you and whether the role itself is also something that you are truly interested in doing.  I would recommend trying to write your CV from somebody else's perspective talking about yourself. If you're uncomfortable talking about what you did or saying I am this person and I have this experience try and be as objective as possible and talk about yourself as another person and you can always change the pronouns later. Your CV should be approximately two pages long, if it's too short you might be lacking detail, if it's too long it kind of makes us beg the question is all of this relevant? It's also very overwhelming when at Mainframe we receive thousands of CVs a year so we want to be able to look at your CV and kind of get a snapshot of you without having to go two in depth and read between the lines really. At IWA you have to appreciate that we have over two and a half thousand applications per year so we like to see about two pages worth of information on your CV so being succinct but then also providing the detail and context that we need to really understand who you are. So it's a really key thing to get your CV the right length. Somewhere between one to two pages is kind of ideal. If you're trying to squeeze it all into one it might look a bit cluttered and really hard to read but two is kind of a good size especially at the graduate level. We don't expect CVs from students to be any longer than two pages. I've definitely seen some longer ones and I think it's important to keep it really clear, concise and with all the relevant information so no more than two pages, maximum being about three. At the beginning of a student CV I really like to see a short personal statement just to capture in a snapshot who you are, your experiences from each of the different areas of your life and then also finally what you're looking for in a role and in a company. So yeah that's an important part to me. So a personal statement career objective I think with anything it really does come down to you but it's something you need to keep up to date and make sure that it still aligns with what you're looking to do with your career. You're looking for aligns with what the company you're applying to can give you. If you're applying to be an engineer at an accounting firm and that's in your objective you're probably not going to get far along the process. So we don't ask that you include a personal statement or career objective on your CV. This is kind of a case by case and it's more whether you feel that that's relevant on your CV. So for example if you know exactly where you want to be in three to five years time and you've got a clear vision of how to get there then it's really good to put that on. Also sometimes a personal summary can be good to get a feel for that person's personality but again it's not a prerequisite for us. I think your personal statement should rather be included in your cover letter. Your cover letter and your CV are two separate documents. Your CV should be focusing on the facts. So again your work experiences and educational history. No definitely not. We don't ask for a specific skills section on a CV and the reason is we would rather see how you apply those skills so be that through your application questions or how you represent that through a cover letter and we would rather see that incorporated rather than kind of a list of bullet points of skills. I would suggest a technical skills section so if you are studying a degree in computer science or information systems it's really important to list those skills so whether it's the type of languages you've studied coding systems databases definitely but please don't include a personal attributes or competency based list rather try and incorporate those competencies in the body of your work experience. So for example if you think you're a problem solver tell us about how you solved the problem at work what impact it might have had on the business and your role. Yes so it's important to remember that an interviewer sometimes will only review your CV in about five to seven seconds so you want to keep your CV very structured absolutely if you've got technical skills list them separately from your soft skills. I think a separate skills section of Done Well can help you stand out from the crowd Deloitte get about 3,000 graduate applications during their graduate period so you need to be able to stand out but make sure there's skills that do make you stand out so if you have a special skill in a technology something that's different to just your run in the mill attention to detail and communication I think bullet point sort of three or four of your top skills are good to kind of draw the eye of the recruiter and go okay this person's got something we'd like and we want to pursue further. Each CV is different but if you have skills that are relevant to the role definitely put it in there so for example if you had a forklift licence and very good numerical skills I would definitely put that on your CV for us it would highlight that you might be a good candidate for our logistics division but make sure that they are backed up with the examples otherwise it just kind of looks like you're putting it on there for the sake of putting it on there. In terms of the layout of the CV I prefer to see things in chronological order or in different sections according to your voluntary experience your education and your work experience a skills section isn't necessarily important to me I like to see them woven into the different areas of your life that you're talking about however if you're more comfortable in breaking that out separately then that's up to you you have creative control on this one. So in terms of listing your referees if it's someone that's potentially known to the business and would be somebody that the business would respect in that field then absolutely because that could be the reason you get through to an interview otherwise there's no requirement to do so Deloitte wouldn't contact your references until you'd actually been offered the role and at that time because you need to make sure your referees are happy to be a referee for you so we'd ask you to check with them and then we'd make contact. It's definitely not necessary to list your referees on a CV in KPMG's process we would contact you later on down the line if we do need those details. You don't need to list your referees just say referees available upon request. My top three tips with CVs would be to begin with the career service there's no points struggling on your own when they're the experts so go and get some advice from them go and get a template from them and start there. Secondly I would advise that you include hard facts in your CV that focus on the results of what you've achieved say for example if you have bullet points under each of your work experiences or voluntary experiences I want you to list what you did how you did it and why you did it so the why is the result and that's the part that students often miss out we're looking for the impact that you personally delivered so we can imagine the impact that you're going to be able to deliver in a role with us Finally I think you should be quite quantitative in the results that you give so really bring to life the story that you're trying to tell so talk to me about the percentage cost savings that you delivered or how much money you raised for your voluntary charity work we like to see stories with detail and that will set you apart from other students we see two and a half thousand during our campaign so go into the detail and give us facts and figures that we can use to really remember you and understand you with so three of the things for a successful CV so one thing for me personally is I think it's really important when you are applying for a graduate to put your education as further up on the front of your CV as you can because it's really clear for a recruiter then to see who you are what you're studying, things like that I would definitely also recommend putting as much detail on there about work experience, voluntary, extracurriculars, clubs, societies anything you've been involved in all detail is good so there's no detail that will be detrimental to your CV We like to see your hobbies and your interests so this kind of gives us a little bit more understanding about who you are and what you like to get up to outside of work or what you like to get outside of university I would definitely get somebody to look over your CV so proof reading is really really critical grammar and spelling is a critical part of CV writing as well as you could look at making your CV format PDF so once you are sending in your application you might find that the format changes once you send it through to someone else's computer so PDF format is quite good so the most important thing when you are creating your CV is to keep the format neat and tidy keep the information concise and factual one of those things and I come across it quite often is when individuals say that they are very detail orientated or have a strong eye for detail but yet I come across numerous spelling and grammar mistakes so please ensure you use spell check and proof read your CV before you are sending it out I think other things to consider when you are doing a CV and Christian there actually often get asked is whether to include a photo I personally recommend not to include a photo I think it's unnecessary for your skills if you do decide to use a photo make sure that it's appropriate to the company that you are applying for that it's not too casual and that you're smartly dressed within it a photo should be about a headshot of something that you would look like if you were to turn up for an interview however it's a lot easier not to have a photo and everyone's got linked in these days so that's a good shortcut I think something else to consider with your CV and is very important is spell checking if you need to get someone to read over your CV and double or triple check that everything is in line especially if you're going to describe yourself as an accurate person don't misspell the word accurate it's never a good look and if you've been working it's always really helpful to see if that's been part-time or full-time work or if it was over the holidays sometimes it looks like you've been holding a full-time job down while you've been studying two or three different degrees and it's really confusing One of the things that I think can trip people up on the CV is having a photo once again it's your decision whether you do so but if you're going to have one don't make it a selfie in front of the bathroom mirror make it a professional shot the reason I don't personally think photos are good on CVs is that whoever's reviewing it without even thinking about it might have already judged you before they've even had a chance to look at what you can bring to the role this sense of unconscious bias so my recommendation is no but once again it's your CV so you decide whether you want to do that or not another tip for your CV and this sounds really silly but just make sure your contact details are really easy to spot we're going through the recruiter needs to get through things pretty quickly so they need to be able to contact you so make sure those are really clear on your CV First and foremost, please proofread it if you say in your skills section that you've got great attention to detail but then we come down and we see spelling mistakes or punctuation errors it kind of doesn't really match up so you could get your family and friends to proofread it for you if you didn't feel confident in that formatting is really important so if we see a CV that's well formatted it will immediately put us into a more positive frame of mind because it's easy to read, it's clear and well structured and thirdly, put in your personality because we do get so many CVs across our desks every day if you can put your personality in there it will help you stand out from everyone else so for example we had one candidate put on her CV that she plays kayak polo and immediately we were like what is that we never heard of it before so we were immediately intrigued and wanted to get her in to see what it was how much involvement she had with it and why she went there in the first place