Hi everyone, Joe for jaspyscasebreaks.com coming at you with a quick two-box break of 2020 Panini Immaculate Football two-box picker team number six Anything special down in the description? No, just describing all the awesome stuff that's in these two-box breaks big. Thanks to All of these lovely people here for getting into the action on a Friday. Thanks for spending a bit of your Friday and your new year with us We appreciate that All right now remember the squiggly marks that I made on the boxes so we know they're all from the same case Pack a top over here That's right. Jack. You got last spot mojo. That's the star next to his name in case you were wondering 60% of the time last spot mojo hits 100% of the time to use this blank card here to Slow play some of the hits a little bit. We've got Mike Evans 20 out of 25 Go under the buccaneers that'll be for Peter past and present Dick buccas and Khalil Mack nice 44 out of 49 for the Bears That'll be for Joe H and the Bears. That's pretty cool. Thanks. I think I actually grabbed two cards right here Here's two out of five Roger Stabak Three color patch for Randy finally a cowboy for Randy and a train whistle as well Randy Last two box mojo indeed. There you go, right? All aboard the big head express There is a thin card right here that your 2018 Walter Payton man of the year award winner Chris Long on card autograph for the Eagles That last spot mojo right there folks. Is that your first train whistle, Randy? Congrats. We got cam acres 24 out of 99 rookie eye black Jersey and autograph silver ink auto Going to Kurt and the Rams and the last one here is who JK Dobbins So we got two players cam acres and JK Dobbins two players still in the playoffs With some good performances their stock could rise up pretty nicely 54 out of 99 for the JK Dobbins Joshua B with the purple birds next box We've got a redemption on the bottom here Any guesses on the redemption for bragging rights got one out of ten Two-color dual relic James Morgan for the Jets. That'll be for Tyler S Tyler Brenner guessing chase young. That's a good guess You got Phillip Rivers 44 out of 60 We'll see him in action this weekend Joshua B Colts Next up. We've got Kishan Vaughn nice rookie eye black jersey and autograph Buccaneers colors pop against that black background there 30 out of 99 Kishan Vaughn Peterang with Tampa Bay Robert guessing Joe Burrow must be a CD Lam says Randy Justin Jefferson Travis is hoping Jack also guessing chase young We've got a cool two out of four nameplate nobility Denzel Mims tire spalding Jets And out of fives and under he get the train whistle all aboard the big-head express That actually turned out to be pretty good All right one more than the redemption.  There you go So the Travis is hitting check that Tyler spalding the T T names are hitting Tyler and then Travis There you go Tyler with the train whistle Travis with the old-school stealer right here. All right, and the redemption Immaculate signature patches rookie gold D D Andre Swift I Thought Tyler Brenner had got that Debo Samuel guess for a second there. No, it's Detroit Patrick Davis with the Lions And there you go ladies and gentlemen a nice two box break of 2020 immaculate football pick your team at number six We've got another two boxes in the store right now and that's from a fresh case So go check that out on jazpiece case breaks calm. I'm Joe. I'll break some more with you next time. Bye. Bye


I'm the comic weekly man the jolly comic weekly man and I'm here to read the funnies to you happy boys and honey yes boys and girls it's comic weekly time and here I come right into your house to bring a little fun and happiness right out of the pages of Puck the comic weekly straight into your living room your friend the comic weekly man the jolly comic weekly man hello a little miss honey how are you today I'm just fine I'm just fine too thank you my turn for what you're trying to read in the funny so I'll tell you a joke by telling you a joke very well what is it well this old lady walk into this hotel see yes and this old lady walks up this clerk see yes and this old lady says to the clerk very very good every Saturday night my bath it's a very good joke Puck the comic weekly very well I'll read that in just a moment but before I do let's listen to this nice man now here we go with Puck the comic weekly and on the first page under bringing up father Beatle Bailey very well my lady toot me a toot and tweet me a tweedle squeeze out music for Bailey the beetle at the army camp where Beatle is stationed we're in the kitchen the captain is telling the cook a cookie I got a tip the general is coming to eat dinner with us and expect our mess hall thanks I'll cook a special receipt and five minutes later the cook is carrying a kettle of stew to the stove this will be a work of art first the little dash of salt then in the door comes Beatle Bailey hey here's the salt you want a cookie Beatle trips over a pig and he drops the sack of salt into the stew look what you're done when he's only salt cookie and only five pounds the cook sets down his kettle picks up a box of soap chips last picture top row throws it at Beatle the soap hits Beatle bounces off him into the stew look what you're dead first picture bottom row the captain and the general walk in watch it captain sorry sir this visit is a nice prize general the captain leaves the general over to the table sorry sir cookie give the general a dish of your stew but captain come come cookie let's see what you're feeding the man if you'd desist general I do desist insist the cook ladles out the dish full of stew and sets it in front of the general the general takes a taste wonderful I must have this recipe whereupon the captain turns to the cook write it up for him but captain write it every step just as you did it every step yes cookie every step I want my wife to try it tonight last picture the generals at home in his kitchen and is reading the recipe to his wife who is following the directions after you trip and spill the salt he'll drop in a box of soap chips yes dear soap chips and that's an order that's some recipe why the poor wife her knees will be all covered with bruises yes you'll have to do her cooking with pills on her knees well Beatle Bailey is funny so anything that we learn from Beatle Bailey has to be funny well now let's turn over the page and go past little iodine and Prince valiant and turn over page 3 and here on page 4 is Walt Disney's the sword in the road oh yes and I'm anxious to see what's going to happen because you remember this is in the early days of England and child's band and has rescued the princess Mary who's the woman he loves from that mean Duke of Buckingham who's the man yes but the Duke of Buckingham has pursued them and just as Charles Brandon and his friends Sir Edwin Caskadan had put Mary in a boat to row her out to a ship the Duke galloped up on his horse and rode into the water when attacked Charles Brandon and Charles Brandon is in the water way up to his waist and the Duke and hit him in the back with his sword I wonder what'll happen will Charles Brandon be killed let's read now and find out here we go with the sword in the road it's Mary Mary England when knighthood was in flower music to be witch our story our Brandon falls face forward into the water quickly Buckingham dismounts to finish Brandon off but Brandon is on his feet in a second sword in hand he faces the Duke Brandon parries the Duke's thrust and tries to work his way out of the water to shore where he'll have sure of footing last picture top rule he makes a sudden lunge and knocks the dagger out of Buckingham's left hand and then first picture bottom row the two men are on shore Brandon lunges at the Duke steadily he drives the Duke back against the rocks with the true skill of a born swordsman he presses the Duke harder and harder and then a quick thrust and his sword pierces Buckingham's right shoulder last picture Brandon stands over the wounded Duke and looks at him in contempt I cannot be so merciful as to finish you King Henry shall have that pleasure yes he certainly knows how to handle that sort yes just like that maybe we'll find that out next week but now let's look across the page there's Roy Rogers yes he met his friend cube root who is quite an inventor that's right TNT an explosive so powerful that even a small amount of it can blow a building apart that's right and just as cube root was telling Roy about the explosive a masked man stepped into the door gun in hand and told cube to hand the explosive over to him well let's read now and find out here we go with Roy Rogers King of the Cowboys I yep I owe now here we go with Roy and trigger a yep I owe and stands directly under a metal pulley gun in hand I want some of that silent explosive that cube root invented quick but sir QT and T is my most valuable discovery I've only a small quantity of it made up I said I'm in a hurry now move yes sir yes sir Roy holding his hands up sees that his left hand is beside the rope that holds the pulley up to the ceiling come on move hurry it up quietly Roy loosens the rope I said hurry last picture top row of the pulley drops from the ceiling under the crooks head a second later Roy has the outlaws arms pinned behind you this gracious Roy how very clever of you first picture bottom row cube rips the handkerchief up the man's face was spaskum why Roy he purchased some of my catatonic last week that's when I told him about my noiseless explosive are you a faker six of my steers died from taking that stuff and I am to get even now our basket miss mistaken Roy my tonic is harmless and I'll prove it by drinking some hey now don't be foolish cube stop but it's too late cube is smaller the tonic great sky drank it suddenly cubes face turns white and he slowly sags to the floor a cube you Roy goes to help cube Bascom picks up the keg of explosive and dashes out the door last picture Roy whips out his guns and whirls around don't shoot you hit the explosive we'll be blown to bits yes and now Bascom is getting away with the TNT I wonder if Roy will let him get away with well maybe we'll find out about that next week but now let's turn over the page and see who's there yes and Wiley was there he met a strange hermit who seemed to be friend flash from the hermit's place flash could see a deserted city below the hermit warned him that there would be danger there but flash said he was going down to investigate anyway but as soon as flash left the hermit looked into the skies and said here well let's read now and find out here we go with flash Gordon a riga diga doon doon saskam attach let's set music for heroic plans flash approaches the strange city here's the mystery city quiet enough fact is no one in sight he moves toward the gate when suddenly what's that he sees no one there he calls I'm here on a peaceful mission you can come into the open flash advances last picture top row through the gate stops an amazement hey what's this walls coming up out of the ground first picture bottom row he spins around and makes a dash for the open gate and then this looks like last picture he turns to retreat but now escape is blocked completely a fourth wall then a ceiling slide into place and flash stands there helpless I'm trapped yes and he seemed to be so helpful to flash and now he's turned around and warned some mysterious person and look what's happened we'll find out about that next week but now it's time to pick up the second section of the comic weekly well I'll read that in just a moment but first here's that nice man again with something interesting to say now here we go again with putt the comic weekly and on the first page of the second section Dagwit and Blondie very well my lady I'm a food I'm a thumbs-up zombie conjure me music for Dagwit and Blondie down at the office Dagwood is busy working the office boy comes in hey Mr. Munster the boss just hung up a suggestion box on the hall and a few minutes later the whole office staff is standing around the suggestion box and Mr. Ditters makes a sweet little speech now I want everybody to feel free to offer any suggestions that might be helpful to this office everybody is working away at their jobs again and then the door to Dagwood's office open and out comes back with a slip of paper in his hands straight to the suggestion box he goes he drops a slip of paper into the suggestion box with a big smile on his face I'll be the very first to use it half hour later Dagwood is busy working at his desk suddenly a voice roars through his desk speaker come into my office Dagwood pops out of his chair and goes into Mr. Ditters office Mr. Ditters you sent for me yes Dagwood I did is this your suggestion that the office force be given two full summer months vacation like the school kids yes I'm Mr. Ditters that's mine last picture second row dithers leave for Dagwood first picture third row Dagwood tries to get up from the floor but Mr. Ditters knocks him down with a typewriter again Dagwood tries to get up and did this breaks the water cooler over him last picture third row only a gentle suggestion first picture bottom row dithers door opens and out sails Dagwood now Dagwood although I don't agree with your first suggestion I want you to feel free to offer others the door to Dagwood's office opens Dagwood is busy one eye is black his face is booed there are bumps on his head his shirt is torn to pieces he's aching all over he staggers to the suggestion box and drops in a letter then staggers back to his office closes the door and is seen no more ten minutes later Mr. Ditters is at the suggestion box he sees the note therein takes it out reads it suggestion is this suggesting that the suggestion box be removed yeah I'm afraid so looks like poor Dagwood's in for another storm so do I but I wouldn't guarantee he doesn't for a day good yes they certainly do well now let's turn over the page and go past Nuffie Smith and the lone ranger on page three turn over page three and here on page four of the second section is Dick's adventures and he's gone to the White House in Washington with his friend Samuel Morris yes that's right Samuel Morris has been commissioned to paint the president's portrait but Mr. Morris didn't want to do it because he didn't believe he could paint well enough but they told him they should go ahead and so he did I wonder if it turns out to be a good portrait well let's read now and find out here we go with Dick's adventures say the magic words with me let's have music for adventurous dick weeks go by and finally Morris finishes the portrait of president Monroe the time comes when it is shown to everyone last picture top row Morris unveils it everyone in Washington hails it as a masterpiece but one old hangry man looks at it and says I disagree this is no masterpiece first picture second row the old man speaks up I'm general John Trumbull sir you know my portraits of Washington Hamilton the Duke of Wellington John Jay and many others I have a right to tell you young man that you'd make a better cobbler than an artist quietly Morris not but Dick sees the black despair in his eyes suddenly Morris lunges for his picture he's not stop Mr. Morris hey what are you going to do I'm going to destroy it I'll smash it to pieces but you can't sir it's beautiful he now stops us up you have painted it let it stand first picture bottom row the prompt action of dick and John Trumbull saves the picture from destruction the others leave the room more stands quietly he says to dick artist long dick my pictures are no good I'll destroy them but I'll keep right on painting for the rest of my life but wait a minute Mr. Morris wait a minute you've got to invent the telegraph you've got to invent the last picture dick sits up in his bed he sees is in his own room in the world today and he says oh gosh I guess I was dreaming president it wasn't nice of him to say was no good I agree with you and he certainly shouldn't have said it in front of all those people I'm glad that they stop Mr. Morris from destroying it Mr. Morris has to invent the telegraph I'll maybe we'll find out more about that next week and I'll look below Dick's adventures there's Rusty Riley and I'm anxious to read that because you remember there was a theater coming near the milestone farm that's right and one of the actors in the company stole a very expensive string of pearls from a wealthy girl who was acting in the show and when he stole the pearls he was dressed up in Rusty's coat and cap and so some of the people have told the detective that Rusty was the thief and after interviewing Rusty the detective feels sure that Rusty is innocent and he's gone to the home of the wealthy girl and she didn't want to have Rusty arrested but her mother does I wonder what will happen let's read now and find out here we go with Rusty Riley gallop and run till the road is dusty give us music for his horse and Rusty at Tweety's home the girl is saying to her mother mother think what you're doing having that boy Rusty arrested why it could ruin his whole life stuff and nonsense Tweety he's just a young gangster I never approved of Quentin Miles taking him from the orphanage the detective stands up half in hand I have to admit the circumstance of evidence is pretty strong against him ma'am but well I'll have another talk with him meanwhile at the milestone farm in the barn Rusty walks into the tack room and Tex looks up and says hey what's wrong Rusty you look as worried as a new bright bacon biscuits oh golly Tex I'm in a terrible lot of trouble I think I'm gonna be arrested last picture top row Tex stands up and grins and arrested what did you do rob a bank or hold up a mail train it's no joke Tex the police think I stole a pearl necklace from the lady at the theater first picture Rusty tells Tex the whole story you see Tex Mr. Baker the detective he says that an electrician in a stagehand saw me outside in his castle's dressing room just after four o'clock but gee I was on my way home with dawn at that time only I can't prove it now now now don't let it get you down partner the truth is a mighty powerful thing to heaven your side I reckon I'll move you over to that theater and ask a question or two I wish you take this costume back Tex I won't be using it now and and please bring back my jacket and my cap and pants and a short time later at the playhouse Tex is finishing a conversation with Mr. Fidgley the director of the company oh yes Mr. Purdy I saw Rusty start from home of the horse about 3 30 of course he could have come back well thanks very much Mr. Fidgley I'll get his things you say they're in Mr. Grant's dressing room yes right down there that door there thanks very kindly sir Tex goes into the dressing room he sees Rusty's jacket hanging up on the wall well I'll be a lock beer jack rabbit a coat hanger you know all the time I've been at milestone form I never seen Rusty put that jacket he isn't on a coat hanger last picture in a tavern in the town two men are talking it's shorty who has just handed the pearls to the gangster this morning squashed me with Nick these pearls are worth at least three or four grand in a legit sale shorty but these here oyster eggs is hot we got a deal with a fence so am I because already Texas discovered an interesting clue that Rusty never hangs his jacket on a hanger well that's something we'll have to wait until next week to find out but now look across the page there's Donald Duck yes we will read your favorite favorite right now here we go with Donald Duck say the magic words with me squeegee jump squeegee jump squeegee let's have music to fit a clock it's early in the morning and the alarm clock goes off and a half hour later Donald goes in to wake up the boys he looks at the bed and sees Huey Louie and the dog and Huey he reaches over pulls out the dog drops him on the floor scrum pooch I told you boys a dozen times don't let that howl sleep with you when I could Donald he likes a nice bed half hour later Donald is heading for the shopping district last picture top row oh little guy soft on it first picture bottom row Donald is standing in front of a pet shop he sees a beautiful dog bed in the window and a price tag which reads deluxe dog bed only $30 and 15 minutes later Donald pulls up in front of his house again he lifts out the deluxe dog bed after no kids and dogs you can solve any problem and five minutes later he's showing the deluxe dog bed to the boys well how do you like it boys cool absolutely cool and that night last picture just before Donald goes to bed he stops in the boys room to see if they sound asleep and there in the dog's bed are Dewey Louie and Huey and in the boys bed lies the dog dreaming of dog biscuits and Donald goes well he solved this problem but it didn't work out the way he thought this one turned out backwards so do I well now that's all the time I have but before I go here's that nice fellow with some more interesting information well honey and all your boys and girls I've got to go now okay that's a date and a date with all your boys and girls be sure to meet me with our little friend miss honey next week when I read pop the comic weekly for I'm the comic weekly man the jolly comic weekly man I'll be back to read the funnies to you happy boys and honey don't forget boys and girls see you all next week your friend the comic weekly man the jolly comic weekly man


Well, Reverend Jackson, thank you so much for the opportunity to talk to you. I want to begin by looking at your historic run for office when you laid claim to the Democratic nomination. How did that pave the way for a Barack Obama presidency? It wouldn't be that presumptuous, but suffice it to say, at some point someone starts to run, just to run. And if I had not run it, Lockhart would have set people up to disappoint them. So I ran. I learned what is a surrogate. I learned the campaign in an hour and a half and not just in the southern states. We were told, you shouldn't go to Iowa, Iowa is too white, only to get it defined. You can't be valid if you don't take it all on. So by going to Iowa, we found that the family farmer and who had lost their farm to the corporate farmer and the black industry, the record, lost their job, the corporations going abroad had more income than they realized. That was an economic class issue. Class issue. So we began to hook up the family farmer and the unemployed urban worker. And our coalition was born, so we got double digits in Iowa. That was a big deal. We actually beat Gore and Gephardt in Iowa. That was a big deal. But I mean, the whites could hear our voices beyond limits of race. That's significant. You mentioned that because, you know, just around where the newspaper headlines. And people to this day refer to that era as one of the watershed moments in American politics in democratic politics. Did you feel that you were on the threshold of a major change? And when you look back, do you feel it was a great move for you to have made that kind of a bold run and to really democratize the process? I didn't know how big the moment was in the moment because I was running as an organizer. In the political season of primaries, the candidates determined the agenda and the press. We couldn't get our civil rights issues raised. We were talking about urban policy and free mandala and gender equality. We couldn't be heard by running for the presidency. And so by 1988, that was an appreciation of how we brought to the conversation.   You know, we understand. I said, I'm anxious to be part of foreign policy to come to the conversation. I said, what do you know about foreign policy? I said, we can't be on the foreign policy. Slavery was the foreign policy. I said, oops. The point of the matter, we were trying to expand consciousness. Looking back, how crucial was it for students to lead activism back in the 50s and the 60s? Students basically came south and led the drive to illegal apartheid and illegal segregation.  And I found that the people are vested in keeping us apart because they exploit our apartness. So one generation fought to end segregation as a matter of law. Another generation fought for the right to vote. So now fighting to reduce student loan debt, student loan debt, credit card debt, it cost too much to go to school. Students with the best minds can even apply to attend. The University of Missouri became almost the poster child around the country for diversity in higher ed. How important is it? What do you think it's going to be about the Christ of America? The difference on the football field, not in the classrooms, not in the faculty, not in tenured professors. And the football team said they would not play football unless it was a dress. It was in the film of that economic engine and PR or magnet called football. That's what captured the nation's attention. And those young men made a statement to the nation. Bernie Sanders made it clear on the campaign trail to let us know, Senator Bernie Sanders, that he supported Iran in the 80s for president. And he built lightly his populism on your campaign of the 84 and 88 in challenging the status quo and so forth. He brought a lot of people around this movement, the Sanders movement, around democratic politics. What happens to that movement now moving forward as they demand political accountability? Those who in that movement must be long distance runners. If they let their inspiration evaporate, a trend in the vapor now, it was just a fad. They must not allow themselves, not to vote feverishly, to fulfill the mission. I would think that Dr. King would find us to enjoy in this moment. He would urge us in the classrooms watching this taping. You're the classmate with whom you're not comfortable. I mean, a roommate.  Identify your own death in the can of your own religious group or your athletic partners. But join the university, the universitas, the university community. If you come to the university mission four years later, you've learned that lesson. You can cope with a challenging world. If you've just learned how to survive in your silo, you live beneath your privilege. So learn to live, share, and grow together. Thank you, Reverend. Right. The Reverend Jesse Jackson, founder and president of the Rainbow Push Coalition, America's premier civil rights leader, and Ben Kalei Thompson.


Good morning. Welcome to Purdue Engineering Distinguished Lecture Series. I'm Wayne Chen, Associate Dean for Research and Innovation in the College of Engineering. This series is established in 2018. Since then, we have been inviting world-renowned faculty and professionals to Purdue Engineering to encourage thought-provoking discussions and ideas with our faculty and students regarding the grand challenges and opportunities in their fields. So we've been doing this once every few months, and it's a distinguished series. Traditionally, Dean is going to introduce our distinguished speaker. So it is my pleasure to introduce our new Dean, Dr. Arvind Rahman, who is the new John Aderson Dean of Purdue University College of Engineering. He's also the Robert Adams Professor of Mechanical Engineering. Dean Rahman. Hi. Good morning, everyone. Everyone who is in this great atrium and all those online. This lecture is going to be broadcast live, so welcome to all of you. It's really an honor and a privilege to introduce our speaker today. He is one of the true giants in computer vision. Professor Jitendra Malik is the Arthur J. Chick Professor at EECS and UC Berkeley. He also has a part-time appointment at Metta. His research has spanned many areas, computer vision, AI, machine learning, robotics, human visual perception, and many more. But he and his research group have been known for tremendous impact in the community, right? They have been known for an isotropic, the Perona-Mulloch diffusion algorithms. Not often that you get algorithms named after yourself. Normalized cuts, image segmentation, shape context, and object detection algorithms, high dynamic range imaging, and many more, right? These have had a tremendous impact in the field. He's been recognized for the impact in many different ways. He was a 2013 IEEE Distinguished Research Researcher in Computer Vision. In 2014, and this is something we at Purdue are very proud of, that he was the winner of the King Tseng Fu Prize from IAPR, the International Association for Pattern Recognition. I don't know if many of you know the history, but Professor King Tseng Fu was a Gauss Distinguished Professor here in Electrical Computer Engineering, and he established IAPR. So that's a great connection. Dr. Mulloch, if you have a chance, we'd love to show you the mural in the Fu Conference Room dedicated to Professor King Tseng Fu, so you're welcome to that as well. In 2016, he won the ACMAAI Allen Newell Award, and in 2018, the IJCAI Award for Artificial Intelligence. In 2019, this was followed by the IEEE Computer Society's Pioneer Award. Dr. Mulloch is a member of the National Academy of Engineering, as well as the National Academy of Science, and he is, of course, a AAAA, Arts and Sciences Academy member as well. Please join me in welcoming Professor Mulloch. Thank you. Thank you very much for this generous introduction. It's my pleasure to be here. And Professor King Tseng Fu's name was mentioned. He is one of the pioneers of our field. So the field of pattern recognition, there was for a long time this sort of tussle between pattern recognition and symbolic approaches to AI. And of course, the final answer is going to be a bit of both, but what we are currently seeing is actually the victory of the pattern recognition school of AI. I mean, a lot of the machine learning techniques and neural network techniques in some sense go back to the pattern recognition tradition, which Professor Fu pioneered. So it's a great pleasure for me to be here and at Purdue and speaking at this event. So I'm going to talk today about robots that learn and adapt, and it's a high-level talk for a general audience. So I want to start by talking about what is natural intelligence before we talk about artificial intelligence. And the way we can think about natural intelligence is through evolution, the lens of evolution. So something like 550 million years ago is when you had the first multicellular animals that could move, and moving gave them an advantage because they could find food in different places. But if you could go to food in different places, you need to know where to go, and for that you need perception, something like a vision system. So the earliest animals which had perception, they had a combination of this ability to move and this ability to see. And there's a psychologist Gibson who has this statement, we see in order to move and we move in order to see. So this is the most central aspect of intelligence, I would argue, this combination of movement and perception. Moving down the evolutionary tree, you have hominids, the early humans when they will be branched off from other primates. And then you have the development of bipedalism. So you walk on two legs, so now your hands are free for tool use and so on. And the development of tools gave us the ability to modify our environment in a more significant way. And the brain developed in response to exploit that. So there is this quote from a Greek philosopher, it is because of his being armed with hands that man is the most intelligent animal. The development of the hand preceded the development of increased brain capacity. And then of course we can go on to modern humans coming out of Africa maybe about 60,000 years ago and somewhere earlier than that, maybe a million years ago, maybe half a million who knows, we have the development of language and so on. So these things like language and symbolic behavior is relatively recent in the evolutionary history of us. From this 500 million years, if you think of one million years, if you think of the whole history of intelligence as 24 hours, then this is the last two, three minutes. Now of course that is what we care about a lot today because everybody is talking about chat GPT and I think we will have this discussion at greater length in the afternoon and the panel. But it's incredible. I think what this can do, it has captured the popular imagination and it do well at LSATs and all these various kinds of tests which the general person in the street thinks of as associated with intelligence. On the other hand, I want to talk about what we can't do or we have not been able to do. Self-driving cars, it's a similar thing, a similar capability which is in the popular imagination and the first self-driving cars were in the 1980s. This person whose photo is there is Dickman's. Dickman's had demonstrated cars driving on the Autobahn in Germany in the 1980s and solving the control problem of staying in the lane and so on. And we still don't have self-driving cars. Elon Musk told us in 2018, he promised that by the middle of next year we will have a million Teslas all self-driven. Not quite there, right? Okay. And then I will make it even more mundane and driving a 16-year-old kid in 20 hours of driving experience can do this. Whereas you're talking about becoming a lawyer which supposedly takes years of training and we can solve one problem but we can't solve the other problem. And I'll pick something even more mundane what a 12-year-old can do, right? In a kitchen with some implements, we can do all these things. So this is a collection of verbs of, you know, if you want to make an omelet, you need to be able to stir, you need to be able to chop, you need to be able to slice, things like this. And we can, a kid of 12 can do this. No robot today can do this. Okay. So this is what is known as a Morovaix paradox in our business. And Hans Morovaix said this in 1988 but I think it was actually folk wisdom. Lots of people knew it. He articulated it and so he deserves credit for this. Which is that 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 one-year-olds when it comes to perception and mobility. And Steve Pinker has a nice slogan which is the main lesson of 35 years of AI research is that the hard problems are easy and that the easy problems are hard. And then it goes on to say that the gardeners, receptionists and cooks are securing their jobs for years to come. Okay. So the question is why? I think this is an interesting question and I think, again, I think we'll get to this in the panel this afternoon. So I don't want to, you know, think of this as anticipation that we will discuss it more. Which is that, why is this so? And one argument can be, I think Morovaix original argument was that it's more difficult to reverse engineer skills which are older in the evolutionary process and have taken much more time to optimize and perfect. And I think another argument could be in terms of data, but I don't want to go into it because this is the afternoon panel. But so my focus is I really want to build the two-year-old or potentially the home robot. I think it would be wonderful if all of us could have a robot in the home which could assist with various chores or more seriously. I mean a very large fraction of the population is elderly and we need them taken care of and a robot in the home could take care of help out in many ways. So this is really a societal need, I would argue. And it's what I feel that I want to work on. In the next 10 years, if we can have a home robot, I'll be happy. Just like we had home computers which in the 1980s took off.  It took off because there were applications like spreadsheets and video games and word processing which made using a computer worthwhile and it became cheap enough and useful enough. Well, when will that happen for robots? So that's the application and now let's get into it. Okay, so I'm going to start with some examples. So this is a robot that we have and it's walking in this terrain, okay. And it's walking autonomously. I'm not controlling it with something. And here is... So this robot is a fairly cheap robot. It's manufactured by a Chinese company called Unitary and they can sell it to you for $10,000 or less. I think the versions for much less now. So it's in the range. This is the kind of price point. When it's a few thousand dollars, you can imagine a machine like that in every home. Okay, but robots have to walk in these different situations. Very importantly, this robot is blind. It has no vision right now. I'm going to add vision later, but right now it's blind, but it has to walk in all these different kinds of terrains, right, which it doesn't know in advance. And importantly, it's going to use the same controller. I'm not allowed to say, oh, swap in controller A, controller B, controller C, controller F, 7.3. No, it's the same controller which has to work in all these conditions. Okay, more examples. So I think that is the problem of robotics. We need to be able to work in a wide variety of conditions. In the walking case, this is about walking in different terrain. Okay, it has to work. If you're talking about manipulating an object, if I have to slice a tomato, I should be able to slice different kinds of tomatoes with different kinds of knives. So I want to connect this with the field of pattern recognition versus control theory as a big grand idea. Pattern recognition, as I said, is a field pioneered by Professor Fu. And in pattern recognition, what we regard as a central issue is generalization. So what is a cat? What is a chair? What is a dog? Visually, right? We realize that mathematically, we cannot come up with necessary and sufficient conditions for defining a chair or the appearance of a chair. We do it by examples, and then we train some machine learning system, some classifier to do this. So generalization over aspects like pose, lighting, and so on are critical. If you think about it in the context of action, in the context of control, what do we need? We need robustness. There's a standard concern of control theory. The standard concern of control theory is that you're trying to do something, like maybe maintain the temperature of the house at whatever, 75 degrees Fahrenheit, and there'll be some perturbation, maybe some window is open and cold air comes in. So if you do something to compensate for the disturbance. So that's the central aspect of control theory. But I would argue that there's an equally important aspect, which is adaptation. So adaptation to different terrains, and I'm going to go into that in more detail. So I will not have too many equations in this slide, but this one equation I have to have because it defines the problem of control theory, which is control developed around 1960. This is very important. I think we give a lot of credit to aerospace people, and they should get credit. I mean, after all, we are in the Armstrong building, so how can I not give credit to aerospace people? But also you should give credit to the control theorists, because if there wasn't control theory, you would not be able to have accurate orbits and not be able to... I mean, John F. Kennedy could say we will send man to the moon and bring him back safely, but the safely part depends on having accurate orbits, and that's control theory, right? And 1960 is like a very crucial date here because the period just before and after, you had all these brilliant pieces of work. I mean, the U.S. with people like Bellman and the Soviet Union with people like Pontreagan, Kalman in the U.S., and then less appreciated in the control theory world, but as a pre-genitor of a lot of machine learning, Arthur Samuel's work on reinforcement learning. This is also around 1960. And these are all related because reinforcement learning is just trial and error way of solving the control problem, which is an optimal control problem. So in this equation x dot equals AX plus BU, what we have is X is the state, okay, so that from a physics setting, it would correspond to, say, positions and velocities of particles, or in the case of my robot, it might be positions and joint angles and their velocities. U is the controller, so I can apply some extra input. And A and B here are matrices of linear formulation, but you can imagine a nonlinear version of this setup. Okay. Okay, so this is a very, I'm using very classic terminology here, circa 1960. Look at that matrix A. So that matrix A is what captures the system dynamics. How the system changes from one time instant to the next, right? And this is Newtonian mechanics, for example. But I want to use this matrix A to capture the effects of different kinds of terrain and so on. So what that tells me is that this matrix A is actually not a constant. And it can change very, very significantly. Now that is the problem that I'm going to focus on, and that's what I'm going to call adaptive control. It's related to the classical idea of adaptive control that electrical engineers have studied and they studied it going back to the 1960s. But I'm going to do it using the tools of deep learning, machine learning, neural networks and so on. So basically, if you want to understand philosophically where all of my work is going to be a classic problem approached with tools of neural networks, and I'm bringing these two together. Okay. So that's the setup. And it's essentially that this matrix A is not a constant but it varies and I have to try to do the right thing for different kinds of choices of system dynamics. Okay, so that sets up the problem formulation. And I'm going to talk about this walking robot as a running example but the techniques that we have developed will apply in other cases and I'll show you a few other examples. So we call this rapid motor adaptation. But think of it mentally as, okay, that matrix A is changing around and I have to adapt to it. And that's what we are going to do for legged robots. Okay. So how do we do this? So we train we... So the problem of we start out by solving the problem of legged locomotion. And again, this is simulation technology has just got better and better over time. So of course control theorists have always tried to model the system and write down the equations. But after that the equations can get kind of tricky. And in particular, when you talk about legged locomotion contact is made and broken. And therefore the differential equations that you must use when you have contact with one legged versus the air. I mean, these change a lot and then you get into hybrid system. I mean, the whole thing is it requires a lot of mental gymnastics to make that work. On the other hand, the physics is actually not straightforward. So putting it into a simulator is actually quite easy. And the simulators work faster just because of computers are getting faster. Right? So simulators work. So what we are going to do is to train this robot to walk in simulation and using the machinery of reinforcement learning which if you don't know it's a very simple mental model. It's trial and error. The robot is going to try to walk. I'm going to give some commands to all its joints and so on. And then I'll see what happens if it walks good plus one, if it falls bad minus one. And over time it tries to have that kind of behavior which gets it more plus ones rather than more minus ones. And you can put this in a mathematical way. But that's it. I mean, there are some details but there are no pre-programmed gates. We don't start out by saying that it's a walk or a gallop or a trot or whatever. I mean, traditionally in walking controllers you input that. It turns out that this is not necessary. All you need to do is to have some kind of cost function. What is desired? What is desired is that you walk in a way without falling and you use minimum energy. Very simple. And now you just do trial and error. And it's okay. And then you will try to find some controller which is optimal in some sense. So that's it. So that's the basic idea. So in simulation we have these parameters like mass friction terrain height. And you can vary all of these. So we actually have some kind of a fractal terrain generator. And the robot has to walk in fractal terrain. So fractal terrain prepares it for small steps, large steps, lots of variation.  So in a simulator you can create lots and lots of conditions. And we know all those variables because it's a simulator. I have access to all of that. And the robot has to walk. And this box, this base policy this red guy this is the controller. The job of the controller is to issue commands. So the commands here are indicated by A. And the commands here are what are desired values for the joint angles and velocities. And you give those commands to a low level PD controller which is actually managing the motor currents and so on. Okay. And so what is the, any controller needs to get some input. So the input here is X of T which is the state which is all the joint angles and so on. A which is the actions which in control theory you would use the symbol U. So reinforcement learning and control theory do the same thing but they use different symbols. Okay. One uses X, one uses S, one uses U, one uses A. And I have tried to keep both sides happy by using X which control theory is like and A which reinforcement learning people like. Okay. So A is the action at the previous stage. Okay. But here is one idea which is that we know that the same policy is not going to work in all conditions. When I'm walking on hard ground versus walking on a down slope versus walking in sand, I need to do, I need to issue slightly different commands to all my motors. So how do I capture that? You make your policy have an extra argument Z. And this Z captures some aspect of the terrain. Okay. And Z is going to be a fairly low dimensional thing. Maybe five, maybe eight dimensions and so on. So these five or eight dimensions I think we concretely used eight dimensions. These eight dimensions capture the variability in the terrain. And so what the policy gets are two arguments. The usual things for any controller, the state and the previous action and this variable Z. Okay. So if you go back to my previous equation, X dot equals AX plus BU where that A is the system dynamics, well that A changes depending on Z and now you're supplying that Z. And Z in this case is known. Right? Why is it known? Because it's a simulator. I choose all those conditions. I choose the mass. I choose the terrain height, et cetera, et cetera. So what we have is but we can have lots of variables but actually those variables all don't matter. And you can do something equivalent to a PCA, a principal component analysis to reduce the dimensionality. And that is captured by this red box called the environmental factor encoder which compresses it down to some eight numbers. And in this case it's not PCA but it's the nonlinear version of that which is you have some neural network encoder with a few layers but it's compressing it down. And we don't have to specify exactly what eight dimensions should be. This training process is end to end and it will figure out a way to compress the relevant physical variables down. Okay. So we call this extrinsics. Okay. And what is the robot trying to do? It's trying to walk and there's a reward function or if you want you can call it cost function. So again there's a reinforcement learning and control theorists do the opposite. Reinforcement learning people maximize reward control theory people try to minimize the cost but it's just a minus sign. So the reward function it tries to minimize the energy consumption. Okay. So we do this and then our robot can walk and this is the result of the robot walking in a simulator. And now we're going to take this and make it work in the real world. Okay. And so it seems straightforward, right? Okay. And I'm going to take it, make it work. But the problem is that I can't, I don't no longer have this Z because the Z in a simulator was known because I knew the mass, I knew the terrain height, etc. And this environmental factor encoder just took this privileged information and compressed it. But in the real world I don't know it. So what am I going to do? So basically that's what I have to figure out some way of estimating. So that's my problem. And what's the solution? So the solution it comes turns out to be let me give you the intuition and then we'll do the mathematical aspect a little bit later. The intuition is so imagine this is a blind robot by the way. So if I'm walking on this surface which is very hard versus when I'm walking on a beach when I walk on a beach what happens and I apply the exactly the same commands. So on a beach when I put my foot down and then I lift it up it doesn't lift up as much because it sinks in and it doesn't lift up. So even if I am blind just by my applying exactly the same commands to my legs and feet I get different behavior which reveals to me that I am in different conditions. So that's the insight. So the insight here is that the sequence of past states and actions if I look at my last one second of history and I commanded certain actions and I achieved certain states that is kind of a diagnostic for what kind of terrain I am walking in. So this is what we call this thing called the adaptation module. So I am saying this adaptation module if we give it the input of the past history it may be able to take that history and figure out what's the Z what are the latent variables corresponding to the terrain. And so that's the idea. Now I have just postulated this magic module which will estimate the Z. Okay, how do I estimate this? So that's the problem. So then let me reveal how that can be solved. So the way this so we are we do a phase one and phase two phase one is training in simulation phase two what we do is also in simulation and now what we do is to say that I am going to train this adaptation module but in simulation I actually know what the Z is. So in simulation I had access to the ground truth values of the terrain and from that I had an estimator for Z. And now I tell my adaptation module you have access to the past history and you predict the same Z. So this is a regression problem which is a most straightforward supervised learning problem. So this adaptation module is trying to mimic what you would produce if you had access to the ground truth variables. And okay, so that's how we can train the adaptation module. So you see yet another neural network which is trained. And now and now we have a system which can work autonomously because we have trained this adaptation module and it's going to use the past history to work. And now we have this slogan is one policy to walk them all. So it's exactly the same controller it varies in different so it can walk in all these conditions. I'll give you an example to explain this adaptation module idea. Okay, so here is so these experiments were done by the way during Covid times so my student I told him take the robot home and so he's doing these experiments at his home and he's got this mattress and he's going to this is his name is Ashish, he's going to pour some olive oil okay and there's a plastic sheet and then he has got this robot and he's put some plastic socks on the robot and now the robot is going to try to walk. And notice what happens it's about to slip and then it recovers and you can see it is slow-mo. So what should happen in theory? What should happen in theory is that it's always recording this history of observations and what we wanted to do is that in very quickly in like half a second to estimate the new Z and this is basically what you will see in these plots. So the top four rows they show the footfall pattern so which feet is on the ground so it's right rear front left things like that the four legs and then the the two curves at the bottom there's a red curve and a blue curve and they are two of the eight dimensions of this variable of this latent variable Z and what you see is that as it will walk and it will stumble the estimate of those Z1 and Z5 change. So it stumbles but now it's got a correct Z after recovered and then its policy adapts to that. So the secret is you adapt but you adapt quickly like in half a second and so that saves you from falling if it took you five seconds to adapt you will have fallen. You can't do it in 0.01 seconds because you don't have data but that's it. It's like we all recover from stumbles. So this is an example so this is a payload throw and slow mo and then the same behavior that it estimates so this algorithm is called RMA rapid motor adaptation and it enables this estimation and then this change again default adaptation recovered kit. More examples I didn't have adaptation. Okay, so I think I've now covered the main technical insight in the work and now I'll just show you lots and lots of ways in which this makes various problems easy to solve. So there's one problem which is that we want to walk at different speeds okay and this is what we did we did not have to pre-program anything we said to the robot walk at 0.375 meters per second and then this is what it did okay this is the gate that emerged and then we said so what you see at the bottom are the four feet and then we said walk at a speed of 0.9 meters per second but notice that these are quite different gates but we did not program them we just said to the robot walk at a speed okay and it turns out that there's an explanation for this which people in biomechanics have done the hypothesis is that an animal always tries to move at efficient to minimize energy because if you're a tiger or a deer it's for both of you it's important that you use your energy more efficiently and at slow speeds for humans this is also true for humans at slow speeds running is efficient at high speeds running is efficient for horses you have these gallop trot canter etc and people have these curves which show when they are efficient and we get exactly the same behavior with a robot and the beauty is that this just emerges you didn't have to do anything and you just said okay and now using RMA these can be just it works in different settings okay so this is blind robot and we showed that it could go at any speed different gates emerge all that was nice and then I've spent most of my career in vision and then here I'm working on blind robot so there's some disconnect here so the question is do we need vision to walk clearly blind people can walk so therefore we have to start there but there are settings where vision is helpful if you have to go across a river and stepping stones then you probably want you probably want setting so this is a setting where we developed the role of vision again I'm going to go fast here but a traditional approach would be that you build up a terrain by combining data from multiple views and that turns out to be very noisy and a much better strategy is that you go directly from vision to control you don't have to first build a map of the terrain for robotics people there's this approach called SLAM by which you take multiple views and you build a map and I'm saying it's not necessary you want to go from vision to control and the philosophy I showed earlier works that in a simulator you can have privileged information you know the terrain the robot knows in fact in advance what the terrain coming up will be because it's a simulator and so you start with that okay so you strain it to work with that kind of knowledge that it will never have in the real world but then what you do is that with a camera with an imagine that you have some kind of camera on the head of the robot which sees this RGBD depth this egocentric depth and then you try to use that to approximate what this privileged information would be and again we capture it in a small number of dimensions so we had previously this variable Z now you have a variable gamma and so it's the same philosophy but now for estimating the terrain you're reducing it to a small number of variables and let's see how it works and now you can deploy it in the real world and I'll show you some demos here it does not know the terrain there is no nothing, everything is on board on board sensing, on board compute so then we have done some experiments on bipedal robots but okay wait there's something here where my display has got thank you okay I think I have okay so you got the flavor but I'll show you some other examples of the same idea so this is here we have a robot doing a manipulation task and these are different kinds of objects of different size and different shape and basically the same idea works so many people design controllers but they'll design it for a specific object in a specific shape here what is happening is exactly okay so this shows what's the various conditions so we have very light objects like a shuttle cock and very heavy objects and it all works because of the same philosophy so we first train in simulation where we allow for a lot of variation and then we have this variable Z which captures some parameters of the condition which may be in this case they correspond to different size and shape of the object and then we can deploy it in the real world I want to conclude with some general remarks about how we should think about learning broadly and I like to think of it with children's development as an example so children and this is an idea actually it goes back to Turing Turing in his paper the famous 1950 paper said that instead of trying to build an adult brain we should try to build a child's brain and then subject it to a program of education or learning and children have this very rich process of learning they are constantly experimenting with the world they are looking at they take an object, put it in the mouth they have the touch signal they have the vision signal they have auditory signal they are proceeding stages and psychologists have characterized this actually professor Linda Smith who is at Indiana University be multimodal, incremental, physical explore and then finally use language and these ideas are equally relevant for us in computer vision and robotics and I'll conclude with this last example which is learning visual locomotion with cross-modal supervision so we decided can we train a robot to learn to walk in the real world and it has only an RGB camera it does not have RGBD and what we did was we said it's going to start out blind so this is how a blind robot walks and do you see what behavior it discovered it's very similar to a blind person using a stick to poke at obstacles now the problem that I pose to you is how do you make use of vision so now I put a camera so it's got a camera and with a camera obviously it walks better but I want to train in the real world how to use the camera and it turns out there's a simple idea which you can use imagine think of this robot in the steps so you can the robot can at this point calculate the depth of every leg because it knows all its joint angles and so on so using proprioception its own joint angles and so on it knows the height of the ground underneath each of its four feet that's easy for it to calculate with its internal knowledge so what we do is with vision so proprioception tells me all the depth when I get there and vision is giving me images so what I do is I tell my vision system predict 1.5 seconds in advance what will be the depth when I get there okay so it's a self supervised training system I have both the training signals I'm going to train my vision system with what my proprioception will sense 1.5 seconds later and that's it and that gives you a totally self supervised system and the beauty is that with this we got we were able to train a system which we learned from day to day and every day its performance went up and you can now do funny things like you can mess with a camera in real time we took this camera and what Antonio is going to do is to modify he's going to rotate the camera this is if you do this in a robotics lab your colleagues will hate you because you have messed up the result of calibration for okay and what will happen is that initially the robot will stumble okay but it's collecting data it's collecting visual data and it's collecting proprioception data and in due course of time it will just start to so let me show you this this is what happens before adaptation and this is what happens after one minute of training so I believe that this is the future that we should learn we should have robots which are constantly adapting and we have a lot to learn from the strategies used in biology thank you very much Professor Malik for a very thought provoking and fascinating lecture now we're open to questions from audience louder please hi professor thank you for the talk I actually have two questions so typically when we do neural networks we have to train it using back propagation right now with our reinforced learning model we don't have such an expression to tell oh if I change this particular weight I know exactly how the weight changes so how do we actually train it it's just pure trial error by are we just yes so you have other kinds of algorithms so it is harder then with supervised learning so here you have to use algorithms which are called policy gradient and what they are trying to do is that there will be some trials which are successful trials and some trials which are not so successful where success means more reward versus less reward and you try to make the network which predicts an action give more of the actions which lead to good rewards and this is called a policy gradient so the gradient is on the policy and there are optimization algorithms called PPO and TRPO which enables you to solve that problem it does take longer I see so the way we compute the gradient is to evaluate it in the real world. Thank you for the fantastic talk so I have a question about providing guarantees for stability or robustness in the theoretical sense so I think the generalization capabilities of learning based approaches are beyond what model based approaches can achieve for local motion control but do you think providing theoretical guarantees about stability is important if it is important then what are your thoughts about approaching that problem. So the question is about theoretical guarantees it's a fair question and I think this is actually an important I think of it as a philosophical question which engineers have confronted for years or decades or centuries really often times we have courses in practical engineering which are not yet theoretically well founded so people in Europe were building these cathedrals in 1400 and 1300 without the benefit of Galileo and Newton and once in a while they did fall it's actually not true that they always succeeded but they could build them the other example which more current is steam engines versus the science of thermodynamics. Now after steam engines were actually being used in practice so I think of this somewhat similar sometimes theory is ahead of practice and sometimes theory is behind practice and what we have got right now in the advent of neural networks and deep learning is that practice is running way ahead of theory but it is still desirable to develop that theory and it will come along but I as practitioner don't want to stop practice until I have theory because I want to do all these cool things so in the meanwhile I'm going to have tests which are experimental in nature a bit similar to what doctors do so in medicine they don't understand the system well what do they do they do these control trials there will be a set of patients who get a drug and a similar set of patients who doesn't get a drug and if you see that the drug does well we give it to more people even though we don't necessarily theoretically understand the mechanism of how the drug functions so I am advocating that kind of an experimental approach for now while in parallel people pursue developing the theory yeah over there Thank you professor for the wonderful talk so I had a question regarding the policy the reinforcement learning in particular and how you propose to transfer what it has learned in one environment to another because you train a policy on a particular set of problem a particular set of environment and if you change the environment all together will the same policy work in a different environment okay so the answer is that I don't train in just one environment I train in many different environments which are all in simulation so in simulation I have a lot of variation so it's captured by this variable Z which is the extrinsic I train in situations which are equivalent to walking on flat ground situations which are equivalent to walking in sand and so on so it's trained in a very very wide variety of situations and the goal is that when you are walking in the real world it is kind of like being in one of the situations I had seen in simulation and I need to know which one though and that is through this variable Z which I estimate in the real world that's a spirit so I do need to have a lot of variety in training if there is some set of conditions which are not encountered in training but then I encounter them in the real world and they are not like anything I had seen in training then I'm in trouble so is it just one policy or do you no so that's why the policy has an extra argument which is which I called Z it's like a function if you have a function of one variable F of X and then I can have different functions F of X, G of X, H of X or I could have that same function F but have an extra argument F of X, Y so that extra argument I'm making the function F, G, H, etc that's what I'm doing we have a question from online I'm interested in the self supervised training strategy you mentioned I'm wondering is there a memory like concept related to the self supervised vision system for example after we rotate the camera the robot will learn to walk under the new camera configuration soon but will it forget how to walk under the previous camera configuration and if true how long does this take to happen yes it does forget because what it has got is the new set of the new connection between the proprioception and the vision system and so it forgets the old one but then what I can do is I can fix it back and then it will recover so it's always learning and by the way this is we did this experiment based on what has been done for humans so humans there's an experiment called the prism adaptation experiment so I have my glasses and I put a prism in front of one of my in my eye so what's happening is all the rays are now bent by say 10 degrees so if I try to reach an object I'm going to miss it but if I keep trying to reach it in 10 minutes I'm able to do it well and now if you remove the prism I again try to reach it and I miss it again right and then but after a few minutes I recover so there are these experiments that psychologists have done like you can take your pair of glasses which invert the world so everything is being seen upside down it's horrible initially you can't do it but people have shown that after that you can in fact learn to ride a bike okay so our system is very adaptive but then if you remove those glasses again you are in trouble and you have to go back hello thank you for the beautiful talk and it's very inspiring I have one question about the Z parameter that you learned which is the result of the learning right but as said I have a kind of do you have a correlations it's not a physical parameter Z is actually some of the states that you learn which is affected by that it's not a single parameter but it is I think of it as a compression of like maybe 15 parameters the 15 parameters which capture aspects of the terrain and those are being compressed down to say some 5 which is some kind of mixture and so this can be compared to what in control theory is called systems ID systems identification then they would try to estimate all those 15 parameters here I am saying I am going to estimate only the smaller number but the smaller number is good enough and that compression I actually also learn so do you have a model for given the physical parameter to map it to Z such that you have a we have this black box neural network now I conjecture that it is capturing physical invariance so I'll give an example from say fluid mechanics so in fluid mechanics we have these numbers like Reynolds number Froud number like I forgot in my fluid mechanics there must be people in the audience who remember those things Prandtl number right these kinds of things well those are dimensionless constants which capture certain ratios and those are the important variables and that's why we can do experiments in wind tunnels and then and then in the real world and the wind tunnel will be much smaller scale and so I am where is so I am conjecturing that these these parameters which I learned they correspond to those kinds of dimensionless constants which are the most important for the physics of the system that's the last question I have is the adaptive control I think traditionally it's not really it's not really fast but basically you're doing the adaptive control too right but using the neural network compared to just model base type of thing is there any kind of comparative study of the convergence that your method can know I have no convergence proofs I I mean I think we it's of course you know adaptive control was developed in the 1960s and the computers of 1960s and the computers of today are very different so I can do a lot more I mean I have computers compared to what which are much faster so that's why I can do much more now hi very wonderful talk professor so I have one question regarding the parameter Z so if you have like one policy which learns to control in diverse situations let's say walking on the beach versus walking on hard surface will there come an interference in learning Zs from different diverse conditions and what would be the steps yeah no so the network has enough capacity that in a sense it has kept all those policies in the same network and it's like an indexing thing it's choosing which one to do based on the Z so it's basically like a vector and you just do some search across that vector yeah I mean it's a neural network but I mean you can think of it more metaphorically that you are indexing the policy by Z but it's actually all stored together thank you one online yes another online question how well do you speculate will such RL based systems perform when adapting to dynamically changing environments well that is what our hope is because the environments that I showed were dynamically adapting and the answer is you always need some time and we call our approach rapid motor adaptation and our goal is or the systems that we have shown are on the order of a fraction of a second like half a second and for the system that I'm talking about like walking where you will fall down right I think half a second is good enough to recover there may be settings where half a second is not good enough and so I can't make a general guarantee that this will work that usually physical conditions don't change so drastically so immediately so my claim is that 0.2 0.5 second is good enough and that's what we see in our examples thank you very much time is up I have to cut it here if you have more questions we have a more interactive panel session this afternoon and you're welcome to come back join the panel session for this morning lecture this will conclude here thank you professor again for this fantastic lecture thank you


My name is Roxana Donis-Jew and I'm excited to share with you our recent work on finding a population-specific single nucleotide polymorphism that conferred an increased risk of phenostromboembolism in African Americans. Our paper is published in Molecular Genetics and Genomic Medicine. There are 300,000 to 900,000 cases of deep vein thrombosis and pulmonary embolism in the United States alone each year. What exactly are deep vein thrombosis, also known as DVTs, and pulmonary embolisms, also known as PES? In a deep vein thrombosis, a clot forms in a vein, usually one of the deeper veins of the leg. This can cause significant pain and swelling in the local area. This clot can also become dislodged and travel back to the right side of the heart and then get pumped into the vasculature of the lungs. If the clot is big enough, it can impede blood flow, keeping blood from being oxygenated. A big enough PE can result in death. In individuals of African American descent, the incidence of this disease is 30 to 60% higher than in individuals of European descent. Yet, most of what we know about genetic risk factors for DVTs and PES have been studied in individuals of European descent. So as I mentioned before, we don't know much about genetic risk factors for DVT-PE in African American descent individuals. That's why when we were doing a study looking at genetic risk factors for the dosing of the anticoagulant warfarin, we became very excited when we made a serendipitous discovery. During a quality control step where I was making sure that none of the individuals were related to one another in the study, we found that three individuals had high genetic similarity. This genetic similarity seemed to indicate that they were a mom with two daughters. It's a little bit strange for three people in the same family to all be on warfarin. So I went back to look at what was their indication for being on the drug. I found that they all had a history of multiple DVTs and PES. I went back to our clinical collaborators to say, see if we could find out more about this family. This pedigree represents the history of thromboembolism that we see in the family. The individuals labeled one, two, and three were all exome sequenced for a previous project. We didn't have any access to any other family members, but based on the exome data of three individuals, we were able to find a candidate variant, a mutation in protein S. After we found our candidate mutation in protein S, we were able to get permission to mine the clinical data of these three individuals. The clinical data revealed that all three family members had a deficiency in free protein S. This clinical data supported what we had found genetically. Once we had both genetic and clinical evidence of a protein S mutation, possibly causing hereditary thromboembolism in our family, we had another important question. Does this mutation have an effect in the population? We created a multicenter cohort of 306 African American cases with either DVT or PE and 370 African American controls. Using this data, we found that the protein S mutation was significantly enriched in the DVT-PE group with an odds ratio of 4.6. We found a mutation in protein S, an anti-coagulant protein. Previously, mutations in this protein have led to hereditary deep vein thrombosis and pulmonary embolism in European descent families. In many of those cases, those mutations were private to that family and quite rare. In contrast, the mutation we found has about a 1% frequency in African descent populations. We presented here a population-specific genetic risk factor for DVT-PE in individuals of African descent. We hope there are many future studies looking at additional genetic risk factors for this disease in this understudied population. To find out more about our study, read our paper.


All right, separate traders welcome on back to put a boner week here this Friday March 10th shortly after 2 30 p.m Eastern time welcome on back We got the markets tanking right now. So great to see all this back inside live trading room Ray and Michael Lewis included we had a Brian and Ron before Chris and Chuck we got all this just joining us right now Otherwise inside our live trading room and then all was back on social media over streams alike there Great to have us all so with that said though Let's dig right into it markets have been plunging across the late morning early afternoon Just before look like it was gonna begin to move up Come on. We've we've we've seen this rodeo before right? We've been there done that so it's a lower high Pulling back down You know perhaps there's a chance for this to make like a better squeeze in the final 15 minutes of the close But I would just look to focus more on lower highs and continuation of the trend that you have So, you know right now UV XY ETF moves inversely basically to have the indices move and with that ended up leading to a nice pop early on Actually making a bit of a pullback on the one-minute chart right here But otherwise we'll look to see if this can make a higher low and a move back up as this moves inversely to the market So I'll put this on our main list at least to begin going into the afternoon But of course our focus this not just this afternoon this whole day You know all the bank stocks right between SIVB which is not only still halted they've actually I think shut down the company basically right California regulators ended up actually Shutting down the entire company as a whole so obviously big implications to the stock right there and with that between that SIP ACW which is moving back on up. There you go, Mike. We'll go over all of those here in just a moment I'm just gonna write this down here to start the UV XY ETF Again, should the markets make a higher low? I'm sorry. Should the make it should the markets make a lower high This will make a higher low and a move back up Or if I'm wrong with that then vice versa. We'll see All right, so otherwise actually let's jump to the SIVB. I'm sorry SIVB this thing is still halted like I just said California regulator state regulator ended up shutting down the company as a whole and I think the FDIC took over as well That was basically the headline that came out across the afternoon So obviously we haven't been able to trade this but we have the SI stock Which was you know more of a dud than anything this thing dropped off and we weren't able to short it So you know had a little squeeze in pre-market, but that was that now that leads me to a couple of the other trades I think we had FCR FRC one of those ones out there first Republic Bank ended up making this crazy drop and squeeze back up It looks like it's trying to eat back up right now Mike perhaps with PACW so I ended up just getting stopped out break even this is my second trade on this on the afternoon So I'm looking for a short and I ended up getting some money just before this meeting started on the first half of a position I jumped back in Mike from 1340 I'll bring up book mat for this trade But there's a couple of levels that I want to follow going into the close for this And if this thing tanks right now without me, I will be pretty pissed So let me try and finish up this meeting fast But with that in mind though a big level here 86,000 share print got filled off of 1346 That was at the the time 115 Eastern so you can see it's been holding that pretty nicely Just before though. I was looking for a lower high right from here 1318 it ended up popping above it it pulled back under it And I was looking for a nice pull back here at this stage going into the meeting So at that point I ended up getting stopped out and then from there, right? You know, hey right now going into the close It's still kind of within this range and it's still just extremely tradable in terms of the volatility And for the PACW in particular not the others really as much But this one it does have a good spread order book is kind of thin, but it's tradable It's what you want to see right now Ron said there is a tremendous amount of stocks hitting a 52 week low Well markets dropping off pretty nicely So, you know, we'll go over some shorts alongside the PACW here potentially will put this one in particular on our list Michael B says right below him. So if I so if I CEO buys 180,000 we'll take a look at that if it's moving well, but you know, hey PACB is a short We'll see if this could hold under if not 1346 then otherwise 1318 if it breaks back under and holds under 1318 here, and I hope it doesn't happen Well, I'm yapping here, but if it ends up breaking back below this this can lead to a new pullback nice drop-off going into the close I think we had the WAL stock as well This could be another bank stock to if I'm not mistaken. That was to had called this one out earlier today, too More from the morning session, but I ended up making a lower low afterwards as you could see You know, this one's just much more illiquid much thinner volume out there. You got a one share here one share there It's not even a contract. Those are actual just shares So, you know goes to show that there's like really no big money being traded on this stock So I'm not too particular about this one right now, but at least PACW wrote PACB PACW that one All right, let's go from top to bottom though. Otherwise, let's see what else we can get here going into the close you EDA My dyslexia just trick me up there E UDA there we go. And if you think that I joke around with that, that's not the case. I just All right E UDA ended up making a nice pop earlier this morning But as you could see flattened out made the false break over the highs and then now looking pretty ugly So the stage right now not too not too good for this E UDA UVA UVIX this basically is UVXY just a different VIX ETF So I'm not gonna put them both on our list one of the others fine CZU nice pop on CZU earlier this morning more of a nice trend of anything from the lows Push up towards 275 before pulling back and then broke higher heading into like the early afternoon by one o'clock topped off Just over three. So yeah, I mean nice little short squeeze play This stock is because if you look at the daily chart This has just been the theme of the last like month and a half ultimately But you know all these different short squeezes of late this thing fell from 180 all the way down You know, hey eventually it was gonna make some sort of squeeze You always say don't hold your breath waiting for it over time But ended up making just a fraction of the run that it made for us back in July August last year So again, it's a huge success right now though. It's doing the dump-off part dropping off much like typical short squeezes do This looking good. All right, UNCY. I mean basically all week We've been following this thing and mainly with good reason, you know, maybe one day it was a tease But otherwise from this morning ended up providing a very similar trend off the lows very nice trend You know much like the CZU trade But as you could see now revving back up breaking back above big volume level roughly off 265 266 there You know look to see you kind of retest that a few times coming up I don't know if I'm fully convinced on it running higher now, but uh, definitely off to a good start We'll see if this can keep going Lewis has a good short for me. I'll take a look in just a moment Lewis looking at the chat board as we go through and then phil picking up some money There we go phil one of our long time students here has been here as long as I've been employed with Fausto He's gotten 29 cents on this trade. I think it was an option to call if not mistaken. Maybe maybe not. I can't tell maybe Either way a fantastic catch from Philly says no, okay So he got it always the time he wrote the time filled my apologies He got in a 622 the price 115 eastern the time There we go. Feel excellent. Nice All right, let's keep it let's keep it going see what else we got here a nice trade on the, uh, UNCY though JFBR Look at the PACW go. It's definitely making a nice squeeze. It popped over my level there 1346 It's trying to make the better pop now. It's good. Nice trade JFBR ended up making a nice pop earlier this morning ended up just flattening out though. So Passing there gonna pass probably on a lot of different trades from the gainer side, right Market still hasn't made like a local higher high yet. It's actually building a higher low pretty nicely, but Technically hasn't broken the last local peak from 2 o'clock Each yfm. It's at the highs It's only trading 1.2 million shares right now It's like so slow though, right? I mean you want to have a little activity there if anything So just because it's at the highs I'm not really that particular about it same with RLX. You could say the same thing Trying to go to sleep RLX is the stock for you Labd nice pop another ETF though, you know, we already got we already got the uvxy on there This ended up having more of like a nice late morning run though labd Is this the bearish version or the bullish bearish? Yeah All right, so you're gonna get a bunch of different inverse or bearish ETFs just on the loser side here or on the gainer side right now Vixy vxx two more right there Don't need them tza same thing I'm just trying to weed those out right now as I kind of finish up the gainer side AM AM so this one from earlier this week ended up popping up pretty nicely was a great swing trade Right now though. I'm not really doing too much Again not gonna find a whole lot here folks. I gotta cross my t's dot my eyes I gotta do my job So I know I'm not gonna find a whole lot on the gainer side right now Uh billy, you know Hey in the event where the markets make a really strong dead cap balance going into the close If that were to be the case I know with as much as billy is up right now Maybe that could benefit more from a stronger push Uh, you know, it's not far fetched But definitely trading a low volume per minute compared to what we've normally seen the stock trade especially from this morning You know like earlier this morning the stock was trading on average roughly around 30 to 40 thousand shares a minute Right now it's trading less than nine So past there All right, let's sit from the gainer side.  He got bird It's actually shortable on my platform Uh, a little too close to the ground level though from my liking a little too close to the ground floor, right? Uh, you know, if there's a stock below five dollars, that does not mean that you can't short it I mean if there's a broker that has specific restrictions That's one thing but otherwise trade station or other major brokerages Uh, you know, you're going to be able to short some stocks not all of them So there's certain rules and implications with doing so, but You know, hey the closer it gets to zero. I'm not going to be as interested So, you know, it's actually provided a nice trader earlier, but for right now though, I'm going to pass Uh, the w al passing on this But I guess some of the bank stocks making that squeeze up is I just kind of alluded to on p a c w You know just broke up a little bit here, but really didn't do much afterwards So if you're in from 13 it made for a good trade from that price But otherwise though, let me put a line at that other level. I had referenced that being 13 46 That was the big selling level there So, you know, I'm going to look for a short if you could break back under this one now at this point If you could break under more selling here at 13 46, then there's definitely probable cause to take a short Funny that I just said like probable cause like I'm a police officer, right? All right So we keep going I'm watching a bunch of different cop videos on facebook during my afternoon Tell you know, it's been a very fun day all right Doc you saw and there we go Lewis and right in time. He says doc you sign if it falls below 50 if it falls below 50 and Uh, yeah, I mean if it does it could be a pretty good opportunity But maybe not even today maybe like an early next week monday if it you know gaps lower and then retraces up or you know, just breaks under a come monday morning perhaps but Uh, you know, it's down big and it is at the lows. So I do like that right now. We'll see if this can End up pull back a little bit lower The pacw trade ended up holding off pretty nicely off 13 46 as support for right now move back up there briefly So again, only if it breaks back under that level when I look for that short Otherwise maybe try and take it from the next leg higher. Maybe off of I don't want to say 14 Maybe 15 it might have some room to go up actually perhaps now if it's not going to break back under 13 46 I could see reason at 15 or shortly under. All right. What else we got here though. Otherwise, uh blue cabba It's been a nice run here from the end of last year cheese. I didn't realize that I don't think we really traded that one as much sun run Nice drop off from earlier today But bottomed out a little bit not really down enough for me to look for that dead cat bounce like the others All right, so let me know what you got here folks for me If there's anything else up down left right more than 5% that you'd want me to look at check out Or my thoughts on otherwise, let me know Let's take my email address in the chat board early for all this on social media all of our streams there I always like to post at the end of the streams. I did not yesterday. So Josh at CT trading comm early just any questions that you got from you over the weekend I do have to get back to a couple of us from our trading room at least that it sent me over emails from yesterday So if you're tuning in and you didn't get a response back I want you to know that I know that I gotta do my homework myself get back to you later on tonight at least But I always say keep the emails coming in folks All right, gonna check out benzinga pro before we wrap up right now spy still hasn't broken that local higher high just yet Again pacw already on our list, but Pulling back again be careful. It's all about that 1346 to me Uh, what else do we have just that we haven't checked out yet? Maybe like a gm maybe a Shopify Shopify nice little move off the lows there. Don't get me wrong. Good sideless stock. I've always said good sideless stock Shopify is Cruise lines like ccl and clh just kind of stagnant right now. Those aren't looking as good there Neo obviously not looking too good itself Sober from the gainer side of benzinga pro. Let me Raise my volume criteria on this thing now. Jeez Get some low volume stocks. How about edible ebbl at a crazy little move earlier, but dumping off now Llap Try and but Folks, I think we're scrapping the bottom of the barrel here Right, I don't really see too much out there that looks too thrilling or really for me to Jump in on headfirst, right? So we have four stocks to work. We'll three stocks to work with and one etf We got that uv x y obviously if the markets make You know a pullback right now once more type topped off right now the spy right here You can see on the lower of my five minute chart on the bottom left So just be careful about that All right folks So otherwise for all of us inside the live trading room rich will be on the mic taking you into the clothes from 3 To 4 p.m. I'm going to post my email here as well. Josh at c2 trading calm Any questions feel free to shoot me a quick email or send me over your trade journal And I'll be sure to go over that at least on saturday or sunday coaching recordings I'll have out by tomorrow and then otherwise i'll be in the chat up until 3 30 today folks So let me know if there's any questions via private chat But enjoy the weekend and we'll catch back with everyone otherwise on monday. Take care