Deep Learning State of the Art (2020)

welcome to 2020 and welcome to the deep learning lecture series let's start it off today to take a quick whirlwind tour of all the exciting things that happened in seventeen eighteen and nineteen especially and the amazing things were going to see in this year in 2020 also as part of this series is going to be a few talks from some of the top people in learning in artificial intelligence after today of course start at the broad the celebrations from the Turing award to the limitations and the debates and the exciting growth first and first of course the step back to the quote I've used before I love it I'll keep reusing it AI began not with Alan Turing or McCarthy but with the ancient wish to forge the gods a quote from Pamela McCord Akande machines who think that visualization there is just three percent of the neurons in our brain of the thalamocortical system that magical thing between our ears that allows us all to see and hear and think and reason and hope and dream and fear our eventual mortality all of that is the thing we wish to understand that's the dream of artificial intelligence and recreate recreate versions of it echoes of it in engineering of our intelligence systems that's the dream we should never forget in the details I'll talk the exciting stuff I'll talk about today that's sort of the the reason why this is exciting this mystery that's our mind the modern human brain the modern human as we know them today know and love them today it's just about 300,000 years ago and the Industrial Revolution is about 300 years ago that's point one percent of the development since the early modern human being is when we've seen a lot of the machinery Machin was born not in stories but in actuality is the machine was engineered since the Industrial Revolution and the steam engine and the mechanized factory system and the machining tools that's just point one percent in the history and that's the three hundred years now resume in to the 60 70 years since the the founder the father arguably of artificial intelligence Alan Turing and the dreams you know that there's always been the dance in artificial intelligence between the dreams the mathematical foundations and the and and when the dreams meet the engineering the practice the reality so Alan Turing has spoken many times it by the year 2000 that he would be sure that the Turing test natural language we passed it seems probably he said that once the machine thinking method had started it would not take long to outstrip our feeble powers they would be able to converse with each other to sharpen their wits some stage therefore we should have to expect the machines to take control a little shout out to self play there so that's the dream both the father of the mathematical foundation of artificial intelligence and the father of dreams in artificial intelligence and that dream again in the early days was taking reality the practice met with the perceptron often thought of as a single layer neural network but actually was not as much known as Frank Rosenblatt was also the develop or the multi-layer perceptron and that history zooming through has amazed our civilization to me one of the most inspiring things in this in the world of games first with the great Gary Kasparov losing to IBM D blue in 1997 then Lisa Dahl losing to alphago in 2016 seminal moments and captivating the world through the engineering of actual real-world systems robots on four wheels as we'll talk about today from Weymouth to Tesla to all the autonomous vehicle companies working in the space robots on two legs captivating the world of what actuation what kind of manipulation can be achieved the history of deep learning from 1943 the initial models from neuroscience thinking about neural networks how to model neural networks mathematically to the creation as I said of the single layer and the multi-layer perceptron by Frank Rosenblatt and so 57 and 62 to the ideas of backpropagation and recurrent neural nets in the 70s and 80s - convolutional neural networks and LCL is a bi-directional rnns in the 80s and 90s to the birth of the deep learning term and the new wave the revolution in in 2006 - the image net and Alix net the seminal moment that captivated the possibility the imagination of the AI community of what neural networks can do in the image and natural language space closely following years after to the to the development of the popularization of Gans generative adversarial network so the alpha going alpha zero in 2016 and seven and as we'll talk about language models of transformers in seventeen eighteen and nineteen those has been the last few years have been dominated by the ideas of deep learning in the space of natural language processing ok celebrations this year the Turing award was given for deep learning this is like deep learning has grown up we can finally start giving awards yawn laocoön Geoffrey Hinton yoshua bengio received the Turing award for the conceptual engineering breakthroughs that have made deep neural networks a critical component of computing I would also like to add that perhaps the popularization in the face of skepticism for those a little bit older have known the skepticism the neural networks have received throughout the 90s in the face of that skepticism continuing pushing believing and working in this field and popularizing it through in the face of that skepticism I think is part of the reason these three folks have received the award but of course the community that contributed to deep learning is bigger much bigger than those three many of whom might be here today at MIT broadly in academia in industry looking at the early key figures Walter Pitts and Warren McCulloch as I mentioned for the computational models of the neural nets these ideas of that of thinking that the kind of neural networks biological illness can have on our brain could be modeled mathematically and then the engineering of those models into actual physical and conceptual mathematical systems by Frank Rosenblatt 57 again single layer multi-layer in 1962 you could say Frank Rosenblatt is the father of deep learning the first person to really in 62 mentioned the idea of multiple hidden layers in neural networks as far as I know somebody was correct me but in 1965 shout out to the Soviet Union and Ukraine the person who is considered to be the father of deep learning Alexei even Enco + V G lapa co-author of that work is the first learning algorithms are multi-layer perceptrons multiple hidden layers the work on back propagation on automatic differentiation 1970 1979 convolutional neural networks were first introduced and john hopfield looking at recurrent neural networks what are now called hopfield networks a special kind of attorney all networks okay that's the early birth of deep learning I want to mention this because there's been a kind of contention space now that we can celebrate the incredible consciousness deep learning much like in reinforcement learning and academia credit assignment is a big problem and the embodiment of that almost the point of meme is the the great jurgen schmidhuber I encourage for people who are interested in the amazing contribution of the different people in the deep learning field to read his work on deep learning in neural networks it's an overview of all the various people who have contributed besides young laocoon Geoffrey Hinton and yoshua bengio it's a big beautiful community so full of great ideas and full of great people my hope for this community given the tension as some of you might have seen around this kind of credit assignment problem is that we have more not on this slide but love that can never be enough love in the world but general respect open - and collaboration and credit sharing in the community less derision jealousy and stubbornness and silos academic silos within institutions within disciplines also 2019 was the first time it became cool to highlight the limits of deep learning this is the interesting moment in time several books several papers have come out in the past couple of years highlighting that deep learning is not able to do the kind of the broad spectrum of tasks that we can think of the artificial intelligence isn't being able to do like read common sense reasoning like building knowledge bases and so on rodney brooks said by 2020 the popular press starts having stories that the era of deep learning is over and certainly there has been echoes of that through the press through the Twittersphere and all that kind of world and I'd like to say that a little skepticism a little criticism is really good always for the community but not too much like a little spice in the soup of progress aside from that kind of skepticism the growth of cvpr iclear europe's all these conference submission papers has grown year over year there's been a lot of exciting research some of which I'd like to cover today my hope in this space of deep learning growth celebrations the limitations for 2020 is that there's less both less hype unless anti-hype less tweets on how there's too much hype in AI and more solid research less criticism and more doing but again a little criticism there's a little spice is always good for the recipe hybrid research less contentious counterproductive debates and more open-minded in the interdisciplinary collaboration across neuroscience cognitive science computer science robotics Mathematics Physics across all these disciplines working together and the research topics that I would love to see more contributions to as we will briefly talk about in some domains is reasoning common sense reasoning integrating that into the learning architecture active learning a lifelong learning multimodal multitask learning open domain conversation so expanding the success of natural language to dialog to open domain dialogue and conversation and then applications the two most exciting one of which we'll talk about is medical and autonomous vehicles then algorithmic ethics in all of its forms fairness privacy bias there's been a lot of exciting research there I hope that continues taking responsibility for the flaws in our data and the flaws and our human ethics and then robotics in terms of deep learning application robotics I'd love to see a lot of development continued development deep reinforcement learning application and robotics and robot manipulation by the way there might be a little bit time for questions at the end if you have a really pressing question you can ask it along the way two questions so far thank God okay so first the practical the deep learning and deep IRL frameworks this has really been a year where the frameworks have really matured and converge shores to popular deep learning frameworks that people have used as tensorflow and Pytor attesa float 2.0 and pi torch 1.3 is the most recent version and they've converged towards each other taking the best features removing the weaknesses from each other so that competition has been really fruitful in some sense for the development of the community so on the tensorflow side eager execution so imperative programming the kind of how you would program in python has become the default has been first integrated made easy to use and become the default and i'm the pie tour site or script allowed for now graph representation so do what you're used to be able to do and what used to be the default mode of operation intensive flow allow you to have this intermediate representation that's in graph form the unintentional flow side just the deep caris integration and and promotion is the primary citizen it's the default citizen of the api of the way you would tracker tends to flow allowing complete beginners just anybody outside of machine learning to use tensor flow with just a few lines of code to train and do inference with a model that that's really exciting they cleaned up the API the documentation and so on and of course maturing the the javascript in the browser implementation intensive flow tends to flow light being able to run toothless phone on phones mobile and serving apparently this is something industry cares a lot about of course is being able to efficiently use models in the cloud and pi torch catching up with TPU support and experimental versions of pi torch mobile so being able to ride a smartphone on their side this tense exciting competition oh and I almost forgot to mention we have to say goodbye to our favorite Python - this is the year that support finally in the January 1st 2020 support for Python - and tensor flows and pythor support for python - has ended so goodbye print goodbye cruel world okay on the reinforcement learning front we're kind of in the same space as JavaScript libraries are in there's no clear winners coming out if if you're a beginner in the space the one I recommend is as a fork of open air baselines as stable baselines but there's a lot of exciting ones some of them are really closely built on tensorflow some are built on PI torch of course from Google from facebook from deep mind dopamine TF agents tensile force most of these I've used if you have specific questions I can answer them so stable baselines is the open any baselines for because I said this implements a lot of the basic deep RL algorithms PPO as you see everything good documentation and just allows very simple minimal few lines of code implementation of the basic the matching of the basic algorithms of the open air gym environments that's the one I recommend ok for the framework world my hope for 2020 is framework agnostic research so one of the things that I mentioned is PI torch has really become almost overtaking tensorflow in popularity in the research world what I'd love to see is being able to develop an architecture in tensorflow or developing an PI torch which you currently can and then Trent once you train the model to be able to easily transfer it to to the other from PI to telephone from test flow to PI torch currently takes three four five hours if you know what you're doing in both languages to do that it'd be nice if if there was a very easy way to do that transfer then the maturing of the DRL frameworks I'd love it to see open AI step up deep mind to step up and really take some of these frameworks to maturity that we can all agree on much like opening idea for the environment world has done and continued work that Kerris has started and many other rappers around tensorflow started of greater and greater abstractions allowing machine learning to be used by people outside of the machine learning field I think the the powerful thing about supervised sort of basic vanilla supervised learning is that people in biology and chemistry in neuroscience in in physics in astronomy can can deal with the huge amount of data that they're working with and without needing to learn any of the details of even Python so that that I would love to see greater and greater abstractions which empower scientists outside the field ok natural language processing 2017-2018 was in the transformer was developed and its power was demonstrated most especially by burt achieving a lot of state-of-the-art results on a lot of language benchmarks from synthesis classification to tagging question answering and so on there's hundreds of data sets and benchmarks that emerged most of which Burt has dominated in 2018-2019 was sort of the year that the transformer really exploded in terms of all the different variations again starting from Burt Excel net it's very cool to use Burt in the name of your new derivative transformer Roberto distill Burt from hugging face Salesforce opening eyes GPT - of course Albert and Megatron from Nvidia huge transformer a few tools have emerged so one on hugging face is a company and also a repository that has implemented in both pi torch intensive flow or a lot of these transformer based national language models so that's really exciting so most people here can just use it easily so those are already pre trained models and the other exciting stuff is Sebastian Reuter great researcher in the in the field of natural language processing has put together an LP progress which is all the different benchmarks for all the different natural language tasks tracking who sort of leaderboards of who's winning where okay I'll mention a few models that stand out the work from this year Megatron LM from Nvidia is basically taking I believe the GPT - transformer model and just putting it on steroids right eight point three versus one point five billion parameters and a lot of interesting stuff there as you would expect from Nvidia of course it's always brilliant research but also interesting aspects about how to train in a parallel way model and data parallelism in the training the first breakthrough results in terms of performance the model that replaced Bert as king of transformers is XL net from CMU of Google research they combined the directionality from Bert and the recurrence aspect of transformer excelled the relative position embeddings and the recurrence mechanism of transform excel to taking the bidirectionality and the recurrence combining it to achieve state-of-the-art performance on 20 tasks Albert is a recent addition from Google research and it reduces significantly the amount of parameters versus Bert's by doing a parameter sharing across the layers and it has achieved state-of-the-art results on 12 an LP tasks including the the difficult Stanford question answering benchmark of squad 2 and they provide that provide open source tensorflow implementation including a number of ready to use pre trained language models ok another way for people who are completely new to this field a bunch of apps right with transformer is one of them from hugging face a pop-top that allows you to explore the capabilities to these language models and I think they're quite fascinating from a philosophical point of view and this this has actually been at the core of a lot of the tension of how much do these transformers actually understand basically memorizing the statistics of the language in a self supervised way by reading a lot of text is that really understanding a lot of people say no until it impressed us and then everybody will say it's obvious but right with transformer is a really powerful way to generate text to reveal to you how much these models really learn before this yesterday actually just came up with a bunch of prompts so on the left is a prompt you give it the meaning of life here for example is not what I think it is it's what I do to make it and you can do a lot of prompts with this nature it's very profound and some of them will be just absurd you'll make sense of it statistically but it'll be absurd and reveal that the model really doesn't understand the fundamentals of the prompt is being provided but at the same time it's incredible what kind of text is able to generate the limits deep learning I was just having fun with this at this point still the are still in the process of being figured out very true had to psych this most important person in the history of deep learning is probably Andrew and I have to agree so this model knows what it's doing and I tried to get it to say something nice about me and that's a lot of attempts so this is kind of funny is finally did it did one I said likes Prima's best qualities that he's smart said finally but I said never nothing but ever happens but I think he gets more attention a very every Twitter comment ever and that's very true ok a nice way to sort of reveal through this that the models are not able to do any kind of understanding of language is just to do problems that show understanding of concepts of being able to reason with those concepts common sense reasoning trivia one is doing 2+2 is a 3 5 is a six seven the result of the simple equation 4 + 2 + 3 is like you got it right and then it changed its mind okay 2 minus 2 is 7 so on you can reveal any kind of reasoning you can do with blocks you can ask it about gravity all those kinds of things it shows that it doesn't understand the fundamentals of the concepts that are being reasoned about and I'll mention of work that takes it beyond towards that reasoning world in the next few slides but I should also mentioned will this GPT 2 model if you remember about a year ago there was a lot of thinking about this 1.5 billion parameter model from open AI it is so the thought was it might be so powerful that it would be dangerous and so the idea from opening eyes when you have an AI system that you're about to release that might turn out to be dangerous in this case used probably by Russians fake news for misinformation that kind of death that's the kind of thinking is how do we release it and I think while it turned out that the GPG to model is not quite so dangerous that humans are in fact more dangerous than AI currently the that thought experiment is very interesting they released a report run release strategies in the social impacts of language models that almost didn't get as much intention as I think it should and it was a little bit disappointing to me how little people are worried about this kind of situation there is more of an eye-roll about oh these language models aren't as smart as as as we thought they might be but the reality is once they are it's a very interesting thought experiment of how should the process go of companies and experts communicating with each other during that release the support think thinks through some of those details my takeaway from just reading the reporter from this whole year of that event is that conversation on this topic are difficult because we as the public seem to penalize anybody trying to have that conversation and the model of sharing privately confidentially between ml machine learning organizations and experts is not there there's no incentive or model or history or a culture of sharing okay best paper from ACL the the main conference for languages was on the difficult task of so we talked about language models now there's the task taking it a step further of dialogue multi-domain task oriented dialogue that's sort of like the next challenge for dialogue systems and they've had a few ideas on how to perform dialogues state tracking across domains achieving state of the art performance on multi laws which is a five domain challenging very difficult fide domain human to human dialogue dataset there's a few ideas there I should probably hurry up and start skipping stuff the common sense reasoning which is really interesting is the this one of the open questions for the deep learning community a community in general is how can we have hybrid systems of whether it's symbolic and deep learning or generally common sense reasoning with learning systems and there's been a few papers in this space on my favorite from Salesforce on building a data set where we can start to do question answering and figuring out the concepts that are being explored in the question and answering here the question while eating a hamburger with friends what are people trying to do multiple choice have fun tasty indigestion the idea that needs to be generated there and that's where the language model would come in is that usually a hamburger with friends indicates a good time so you basically take the question generate the common sense concept and from that be able to determine the multiple choice what's being what's happening what's the state of affairs in this particular question okay I'll let surprise again hasn't received nearly enough attention that I think you should have perhaps because there hasn't been major breakthroughs but it's open domain conversations that all of us anybody who owns an Alexa can can participate in as a provider of data but there's been a lot of amazing work from universities across the world on the elect surprised in the last couple of years and there's been a lot of interesting lessons summarized in papers and blog posts a few lessons from Alcoa that I particularly like and this is kind of echoes the work in the IBM Watson who the Jeopardy challenge is that one of the big ones is that machine learning is not an essential tool for effective conversation yet so machine learning is useful for general chitchat when you fail at deep meaningful conversation or actually understanding what the topic or talking about so throwing in chitchat and classification sort of classifying intent finding the entities detecting the sentiment of the sentences that's sort of a an assistive tool but the fundamentals of the conversation are are the following so first you have to break it apart sort of conversation is a you can think of it as a as a long dance and the way you you have fun dancing is you break it up into a set of moves and turns and so on and focus on that sort of live in the moment kind of thing so focus on small parts of the conversation taken at a time then also have a graph sort of conversation is also all about tangents so have a graph of topics and be ready to jump context from one context to the other and back if you look at some of these natural language conversations they publish it's just all over the place in terms of topics you jump back and forth and that's the beauty the humour the wit the fun of conversations you jump jump around from topic to topic and opinions one of the things that natural language systems don't seem to have much is opinions if I learned anything one of the simplest ways to convey intelligence is to be very opinionated about something and confident and that's that's a really interesting concept about constantly and in general there's just a lot of lessons oh and finally of course maximize entertainment not information this is true for Thomas vehicles this is true for natural language conversation is fun should be part of the objective function okay lots of lessons to learn there this is really the lobner prize the Turing test of our generation that's I'm excited to see if there's anybody able to solve the lexer prize again a lexer prize is your task with talking to a bot and the measure of quality is the same as the lobner prize is just measuring how good was that conversation but also the task is to try to continue the conversation for 20 minutes if you try to talk to a bot today like and you have a choice to talk to a bot or go do something else watch Netflix the you last but probably less than 10 seconds you'd be bored the the point is to continue trapping you in the conversation because you're enjoying it so much in the 20 minutes is that's a really nice benchmark for passing the spirit of what the Tory test stood for examples here from the elect surprised than the alko's bought so the difference in two kinds of conversations so alko says have you been in Brazil the user says what is the population of Brazil Alco says it is about 20 million user says well okay this is what happens a lot with like I meant your multi domain conversation is once you jump to a new domain you stay there once you switch context you stay there the reality is you want to jump back and continue jumping around like in the second most more successful conversation have you been in Brazil what is the population of Brazil it is around 20 million anyway I was saying have you been in Brazil so they're jumping back in context that's how conversation goes change you to change in and back quickly there's been a lot of sequins to sequins kind of work using natural language to summarize a lot of applications one of the for me I cleared that I wanted to highlight from Technion that I find particularly interesting is the abstract syntax tree based summarization of code so modeling computer code in this case sadly Java and c-sharp in in trees in syntax trees and then using operating on those trees to then do the summarization in text here an example of a basic power of to function on the bottom right in Java the code to SEC summarization says get power of two that's an exciting possibility of automated documentation of source code I thought it was particularly interesting in the future there's bright ok hopes for 2020 for natural language processing is reasoning common-sense reasoning becomes greater and greater part of the former type language model work that will be seen in the deep learning world extending the context from thousands from hundreds of thousands of words to tens of thousands of words being able to read entire stories and maintain the context which transformers again with excel net transformer Excel is starting to be able to do but we're still far away from that long-term lifelong maintenance of context dialogue open domain dialogue forever since Alan Turing - today is the dream of artificial intelligence being able to pass the Turing test and the dream of sort of natural language model transformers are self supervised learning and the dream of Yann laocoön is - for these kinds of where previously were called unsupervised but he's calling now self supervised learning systems to be able to sort of watch youtube videos and from that start to form representation based on which you can understand the world sort of the the hope for 2020 and beyond is to be able to transfer some of the success of transformers to the world of visual information the world of video for example DRL and self play this has been an exciting year continues to be an exciting time for reinforcement learning in games and robotics so first dota2 an open AI an exceptionally popular competitive game esports game that people compete when millions of dollars with so this is a lot of world-class professional players in so in 2018 open at five this is a team play tried their best at the international and lost and said that we're looking forward to pushing five to the next level which they did in april two thousand eighteen they beat the 2018 world champions in five on five play so the key there was compute eight times more training compute because the the the actual compute was already maxed out the way they achieved the 8x is in time simply training for longer so the current version of open ni 5 is jacob will talk about next Friday has consumed 800 petaflop a second days and experienced about 45,000 years of dota self play over 10 real-time months again behind a lot of the game systems talk about the they use self play so they play against each other this is one of the most exciting concepts in deep learning systems that learn by playing each other and incrementally improving in time so starting from being terrible and getting better and better and better and better and you're always being challenged by a slightly better opponent because the because of the natural process of self play that's a fascinating process the 2019 version the last version of open AI 5 well has a 99.9 win rate versus the 2018 version ok then deep mind also in parallel has been working and using self play to solve some of these multi agent games which is a really difficult space when people have to collaborate as part of the competition it's exceptionally difficult from the reinforcement learning perspective so this is from raw pixels so all the arena capture the flag game quake 3 arena one of the things I love just as a sort of side note about both opening eyes and deep mind and general research and reinforcement learning there will always be one or two paragraphs of philosophy in this case from deep mind billions of people inhabit the planet each with their own individual goals and actions but still capable of coming together through teams organizations and societies in impressive displays of collective intelligence this is a setting we call multi agent learning many individual agents must act independently yet learn to interact and cooperate with other agent this is an immensely difficult problem because with Co adapting agent the world is constantly changing the fact that we seven billion people on earth people in this room in families in villages can collaborate while being for the most part self-interested agents is fascinating one of my hopes actually for 2020 is to explore social behaviors that emerge in reinforcement learning agents and how those are echoed in in real human to humans social systems okay here's some visualizations the agents automatically figure out as you see in other games they figure out the concepts so knowing very little knowing nothing about the rules of the game about the cost of the game about the strategy and the behaviors able to figure it out there's the TC visualizations of the different states importance states and concepts in the game that this figures out and so on skipping ahead automatic discovery of different behaviors this happens in all the different games we talk about from dota to Starcraft 2 to quake the different strategies that it doesn't know about it figures out automatically and the really exciting work in terms of the multi agent RL on the deep mind side was the meeting world-class players and achieving grandmaster level in a game I dunno about which is Starcraft in December 2018 alpha star beat mana one of the world's strongest professional Starcraft players but that was in a very constrained environment and there's a single race I think Protoss and in 2019 alpha star Beach Grand Master level by doing what we humans do so using a camera observing the game and playing as part of against other humans so this is not an artificial side system this is doing exact same process humans will undertake and achieve Grandmaster which is the highest level ok great I encourage you to observe a lot of the interesting on their blog posts and videos of the different strategies that the there are our Allegiance are able to figure out here's a quote from the one of the professional Starcraft players and we see this with alpha zero - and chess is alpha stars and intriguing unorthodox player one with the reflexes and speed of the best pros but strategies and style they're entirely its own the way alpha star was trained with agents competing against each other in a league has resulted in gameplay that's unimaginably unusual it really makes you question how much the stock has diverse possibilities Pro players have really explored that's the really exciting thing about reinforcement learning agent in chess and go in games and hopefully simulated systems in the future that teach us teach experts that think they understand the dynamics of a particular game a particular simulation of new strategies of new behaviors to study that's one of the exciting applications from almost a psychology perspective that I'd love to see reinforcement learning push towards and on the imperfect information game side poker in 2018 CMU no Brown I was able to beat had two head-to-head No Limit Texas Hold'em and now team six player No Limit Texas Hold'em against professional players many of the same results mania the same approaches was self play iterative Monte Carlo and there's a bunch of ideas in terms of the abstractions so there's so many possibilities under the imperfect information that you have to form these bins of abstractions in both the actions bass in order to reduce the action space and the information abstraction space so the probabilities of all the different hands that can possibly have and all the different hands that the betting strategies could possibly represent and sort of you have to do this kind of course planning so there they use self play to generate a course blueprint strategy that in real time they then use Monte Carlo search to adjust as they play again unlike the deep mind open eye approaches very few very minimal compute required and they're able to achieve to beat to beat world-class players again I like this is getting quotes from the professional players after they get beaten so Chris Ferguson famous world's he is a poker player said pluribus that's the name of the agent is a very hard opponent to play against it's really hard to pin him down on any kind of hand he's also very good at making thin value bets on the river he's very good at extracting value out of his good hands sort of making bets without scaring off the opponent Darren Elias said it's major strength is its ability to use mixed strategies that's the same thing that humans try to do it's a matter of execution for humans to do this in a perfectly random way and to do so consistently most people just can't then in the robotic space there's been a lot of applications reinforcement learning one of the most exciting is the manipulation sufficient manipulation to be able to solve the Rubik's Cube again this is learned through reinforcement learning again because self plays in this context is not possible they use automatic domain randomization ADR so they generate progressively more difficult environments for the hand there's a giraffe head there you see there's a lot of perturbations to the system so they mess with it a lot and then a lot of noise injected into the system to be able to teach the hand to manipulate the cube in order to then solve the actual solution of figuring out how to go from this particular face to the solved cube is an obvious problem the the paper in this work is focused on the the much more difficult learning to manipulate the cube it's really exciting again a little philosophy as you would expect from open AI is they have this idea of emergent meta learning this idea that the capacity of the neural network that's learning this manipulation is constrained while the ADR the automatic domain randomization it's progressively making harder and harder environment so the capacity of the environment to be difficult is unconstrained and because of that the there's a an emergent self optimization of the neural network to learn general concepts as opposed to memorize particular manipulations the hope for me in the deep reinforcement learning space I mean for 2020 is the continued application robotics even sort of legged robotics but also robotic manipulation human behavior it's the use of multi agent self plays I've mentioned to explore naturally emerging social behaviors constructing simulations of social behavior and seeing what kind of multi human behavior emerges in soft play context I think that's one of the nice there are always I hope there'll be like a reinforcement learning self play psychology department one day like where you use reinforcement learning to study to reverse-engineer human behavior and study it through that way and again in games I'm not sure with the big challenges that it remained but I would love to see to me at least it's exciting to see learned solution to games to self play instead deep learning I would say there's been a lot of really exciting developments here that deserve their own lecture I'll mention just a few here from MIT in early 2018 but it sparked a lot of interest in 2019 follow-on work is the idea of the lottery ticket hypothesis so this work showed that sub-networks small sub-networks within the larger network are the ones that are doing all the thinking the same results in accuracy can be achieved from a small sub Network from within a neural network and they have a very simple process of arriving at a sub network of randomly initializing in your network that's I guess the lottery ticket train the network controller converges this is an iterative process prune the fraction of the network with low weights reset the waste of the remaining network with the original initialization these same lottery ticket and then train again the pre the pruned on train Network and continue this iteratively continuously to arrive at a network that's much smaller using the same original initializations this is fascinating that within these big networks there's often a much smaller network that can achieve the same kind of accuracy now practically speaking it's unclear with that what are the big takeaways there except the inspiring takeaway that there exist architectures that are much more efficient so there is value in investing time in finding such networks then there is disentangle representations which again to serve its own lecture but here showing a 10 vector representation and the goal is where each part of the vector can learn one particular concept about a data set so the dream of unsupervised learning is you can learn compress representations where every one thing is disentangled and you can learn some fundamental concept about the underlying data that can carry from data saturdays that it is that there's the best disentangle representation there's theoretical work best ICML paper in 2019 showing that that's impossible so disentangled representation is impossible without some without inductive biases and so the suggestion there is that the biases that you use should be made explicit as much as possible the open problem is finding good inductive biases funster provides model selection that work across multiple data set that we're actually interested in a lot more papers but one of the exciting is the double dissent idea that's been extended and to the deep you know network context by open AI to explore the the phenomena that as we increase the number of parameters in a neural network the test error initially decreases increases and just as the model is able to fit the training set undergoes the second descent so decrease increase decrease so there's this critical moment of time when the the training set is just fit perfectly okay and this is the open air shows that it's applicable not just the model size but also the training time and data set time this is more like an open problem of why this is trying to understand this and how to leverage it in optimizing training dynamics and your networks that's a there's a lot of really interesting theoretical questions there so my hope there for the science that deep learning in 2020 is to continue exploring the fundamentals of model selection training dynamics the folks focused on the performance of the training in terms of memory and speed has worked on and the representation characteristics with respect to architecture characteristics so a lot of the fundamental work there in the understanding neural networks two areas that I had hold two sections on and papers which is super exciting my first love is grass so graph neural networks is a really exciting area of deep deep learning graph convolution neural networks as well for solving combinatorial problems and recommendation systems are really useful in any kind of problem that is fundamentally can be modeled as a graph can be then solved or least aided in buying y'all know there's a lot of exciting area there and Bayesian deep learning using Bayesian neural networks that's been for several years and exciting possibility it's very difficult to Train large Bayesian networks but in in the context that you can and it's useful small datasets providing uncertainty measurements in the predictions it's extremely powerful capability of Bayesian Nets a Bayesian your networks and online incremental learning these new levels releases there's a lot of really good papers there it's exciting okay autonomous vehicles oh boy let me try to use this few sentences as possible to describe this section of a few slides it is one of the most exciting areas of applications of AI and learning in the real world today and I think it's the way that artificial intelligence it is the place where artificial intelligence systems touch human beings that don't know anything about artificial intelligence the most hundreds of thousands soon millions of cars will be interacting with human beings robots really so this is a really exciting area and a really difficult problem and there's two approaches one is level two where the human is fundamentally responsible for the supervision of the AI system and level four or at least the dream is where the AI system is responsible for the actions and the human does not need to be a supervisor okay two companies represent each of these approaches that are sort of leading the way way mo in October 2018 ten million miles on road today this year they've done twenty million miles in simulation ten billion miles and a lot I've gotten a chance to visit them out in Arizona they're doing a lot of really exciting work and they're obsessed with testing so the kind of testing they're doing is incredible twenty thousand classes of structured tests of putting the system through all kinds of tests that these years can think through and that appear in the real world and they've initiated testing on road with real consumers without a safety driver which if you don't know that is that means the car is truly responsible there's no human catch the exciting thing is that there is seven hundred thousand eight hundred thousand Tesla autopilot systems that means there's these systems that are human supervised they're using fun a multi-headed neural network multitask neural network to perceive predict and act in this world so that's a really exciting real-world deployment large-scale of neural networks as a fundamentally deep learning system unlike way mo which is deep learning is the icing on the cake for for Tesla deep learning is the cake okay it's at the core of of the perception and the action of the system performs they have to de done over two billion miles estimated and that continues to quickly grow I'll briefly mention which i think is a super exciting idea in all applications of machine learning in the real world which is online so iterative learning active learning Andrey Carpathia was the head of autopilot calls is this the data engine it's this iterative process of having a neural network performing the task discovering the edge cases searching for other edge cases they're similar and then we're training the network annotating the education time we train them and continuously doing this loop this is what every single company that's using machine learning seriously is doing very little publications on this space and active learning but this is the fundamental problem machine learning it's not to create a brilliant neural network is to create a dumb neural network that continuously learns to improve until it's brilliant and that process is specially interesting when you take it outside of single task learning so most papers are written on single task learning you take whatever benchmark here in the case of driving this object detection landmark detection driving buleria trajectory generation right that all those have benchmarks and you can have some separate neural networks for them that's a single task with combining to use a single neural networks that performs all those tests together that's the fascinating challenge where you're reusing parts of the neural network to learn things that are coupled and then to learn things that are completely independent and doing the continuous active learning loop they're inside companies in case the test that way mode in general it's exciting to have people these are actual human beings that are responsible for these particular tasks they've become experts of particular perception tasks expert as a particular planning task and so on and so the job of that expert is both to train the neural network and to discover the edge cases which maximize the improvement of the network that's where the human expertise comes in a lot ok and there's a lot of debate it's an open question about which kind of system will be which kind of approach would be successful a fundamentally learning based approach as is with the level two with the Tesla autopilot system that's learning all the different tasks that I invited in involved with driving and as it gets better and better and better less and less human supervision is required the pro of that approach is the camera based systems have the highest resolution so that it's very amenable to learning but the con is that it requires a lot of data a huge amount of data and when nobody knows how much data yet the other con is human psychology is the driver behavior that the human must continue continue mean remain vigilant on the level for approach that leverages besides cameras and radar and so on also leverages lidar map the pros that it's much more consistent reliable explainable system so the detection the accuracy of the detection the the depth estimation of the detection of different objects is much higher accurate with less data the cons is it's expensive at least for now it's less amenable to learning methods because much fewer data low resolution data and must require at least for now some fallback whether that's the safety driver or teleoperation the open questions for the deep learning level to Tesla autopilot approach is how hard is driving this is actually the open question for most disciplines in artificial intelligence how difficult is driving how many education is driving have can that can we learn to generalize over those edge cases without solving the common sense reasoning problem it's kind of its kind of task without solving the human level artificial intelligence problem and that means perception how hard is perception detection intentional modeling human mental model modeling the trajectory prediction then the action side the game theoretic action side of balancing like I mentioned fun and enjoy ability with the safety of the systems because these are life critical systems and human supervision the vigilance side how good can auto Poli get before visuals decrement significantly and so people fall asleep becomes distracted stop watching movies so on and so on the things that people naturally do the open question is how good can auto pilot get before that becomes a serious problem and if that decrement nullifies the safety benefit of the use of autopilot which is autopilot AI system when the sensors are working well is perfectly vigilant they have AI is always paying attention the open questions for the light are based the level for the way mo approach is when we have maps lidar and geo-fenced routes that are taken how difficult is driving the traditional approach to robotics the from the DARPA challenge to today for most autonomous vehicle companies is to do HTML to use lidar for a really accurate localization together with GPS and then the perception problem becomes the icing on the cake because you already have a really good sense of where you are with obstacles in the scene and the perception is not a safety critical task but a task of understand interpreting the environment further so you have more yeah it's it's naturally by nature already safer but how difficult is nevertheless is that problem if perception is the hard problem then the the lighter based approaches is nice if action is the hard problem then both Tesla and William will have to solve the action problem without the sensors don't matter there it's the the difficult problem the planning the game theoretic the human the modeling of mental models and the intentions of other human beings the pedestrians and the cyclists is the hard problem and then the other side the ten billion miles of simulation the open problem from reinforcement learning deep learning in general is how much can we learn from simulation how much of that knowledge can we transfer to to then with the real world systems my hope in the autonomous vehicle space may I assist a driving space is to see more applied deep learning innovation like I mentioned these are really exciting areas at least to me of active learning multitask learning and lifelong learning online learning iterative learning there's a million terms for it but basically continually learning and then the multitask learning to solve multiple problems over-the-air updates I would love to see in terms of the autonomous vehicle space this is common for this is a prerequisite for online learning if you want a system that continuously improves from data you want to be able to deploy new versions of that system test is one of the only vehicles that I'm aware of in the level two space that's deploying software updates regularly and built an infrastructure to deploy those updates so the updating your networks that to me seems like a prerequisite for our for solving the problem of autonomy in the level 2 space any space is deploy updates and for research purposes public datasets continue there's really a few public datasets of edge cases I'd love to continue seeing that from automotive companies and autonomous vehicle companies and simulators Carla and video draft constellation voyage deep drive there's a bunch of simulators coming out that are allowing people to experiment with perception with planning with reinforcement learning algorithms I'd love to see more of that and less hype of course less hype one of the most overhyped spaces besides sort of AI generally is autonomous vehicles and I'd love to see real balanced nuanced in-depth reporting by journalists and companies on successes and challenges of autonomous driving if we skip any section it would be politics but me may be briefly mentioned somebody said Andrew yang Yang so it's exciting for me to see exciting and funny and awkward to see artificial intelligence discussed in politics so one of the presidential candidates discussing artificial intelligence awkwardly so there's interesting ideas but there's still a lack of understanding of fundamentals artificial intelligence there's a lot of important issues but he's bringing artificial intelligence to the public discourse that's nice to see but is the early days and so as a community that informs me that we need to communicate better about the limitation capabilities of artificial intelligence Automation broadly the American initiative AI initiative was launched this year which is our government's best attempt to provide ideas and regulations about what does the future of artificial intelligence look like in our country again awkward but important to have these early developments early ideas from the from the federal government about what what are the dangers and what are the hopes the the funding and the education required to build a successful infrastructure for artificial intelligence the the fun part there's a lot of tech companies being brought before government it's really interesting in terms of power some of the most powerful people in our world today are the leaders of tech companies and the fundamentals of what the tech companies work on is artificial intelligence systems really recommendation systems advertisement discovery from Twitter to Facebook to YouTube use the recommendation systems and all of them are now fundamentally based on deep learning algorithms so you have these incredibly rich powerful companies they're using deep learning coming before government that's trying to see awkwardly trying to see how can we regulate and it's I think the role of the AG community broadly to inform the public and inform government of how we talk about how we think about these ideas and also I believe it's the the the role of companies to publish more has been very little published on the details of recommendation systems behind Twitter Facebook YouTube Google so all those systems is very little as published perhaps it's understandable why but nevertheless as we consider the ethical implications of these algorithms there needs to be more publication so here's just a harmless example from deep mind talking about the recommendation system behind the Play Store app discovery so there there's a bunch of discussion about the kind of neural net that's being used to propose the candidate generation so this is after you install a few apps the generation of the candidate it shows you ranked the next app that you're likely to enjoy installing and so there they tried lsdm and transformers and then narrowed it down to a more efficient model that's being able to run fast that's a that's a that's an attention model and then there's some again harmless D biasing harmless in terms of topics the the model learns to bias in favor the apps that are shown and then thus installed more often as opposed to the ones you want so there's some waiting to adjust for the biasing towards the apps that are popular to allow the possibility of you installing apps that are less popular so that kind of process and publishing and discussing in public I think is really important and I would love to see more of that so my hope in this in the politic space in the public discourse space for 2020 is less fear of AI and more discourse between government and experts on topics of privacy cybersecurity and so on and then transparency and recommender systems I think the most exciting the most powerful artificial intelligent system space for the next couple of decades is recommendation systems very little talked about it seems like but they're going to have the biggest impact on our society because they affect how the information we see how we learn what we think how we communicate these algorithms are controlling us and we have to really think deeply as engineers of how to speak up and think about their their societal implications not just in terms of bias and so on which are sort of ethical considerations are really important but stuff that's like the elephant in the room that's hidden which is controlling how we think how we see the world the moral system under which we operate quickly to mention and wrapping up with a few minutes of questions if there are any is the deep learning courses this year before the last few years has been a lot of incredible courses on deep learning on reinforcement learning what I would very much recommend for people is the fast day I course I'm Jeremy Howard which uses their wraparound PI torch it's to me the best introduction to deep learning for people who are here or might be listening elsewhere are thinking about learning more about deep learning that's that that is the to me the best course also paid but enjoying everybody loves enjoying is the deep learning AI Coursera course on deep learning is excellent for especially for complete beginner for sort of beginners and then Stanford has two excellent courses on visual recognition so convolutional nets originally taught by andrew karpati and natural language processing excellent courses and of course here at MIT there's a bunch of courses especially on the fundamental the mathematics non linear algebra statistics and I have a few lectures up online that you should never watch then on the reinforcement learning side David silver is one of the greatest people in understanding reinforcement learning from deep mind he has a great course introduction to reinforcement learning spinning up and deeper enforce millennium opening I highly recommend here just for the slides that I'll share online there's been there's a lot of tutorials one of my favorite list of tutorials which is I believe the best way to learn machine learning deep learning natural language processing in general is is just code just just build it yourself build the models oftentimes from scratch here's a list of tutorials with that link over 200 tutorials on topics from deep RL to optimization to backprop l STM's commotion over Kurnool networks everything over 200 of the best machine learning NLP and Python tutorials by Robbie Allen you can google that or you can click the link I love it highly recommend the three books I recommend of course learning book by yoshua bengio and good fellow and erinkoval that's more sort of the fundamental thinking about from philosophy of the specific techniques of the deep learning and the practical grokking deep learning which Andrew Trask will be here Wednesday his book grokken deep learning I think is the best for beginners book on deep learning I love it he implements everything from scratch it's extremely accessible 2019 I think was published maybe 18 but I loved it and then Francois Sholay the best book on Keros in tensorflow and really deep learning as well as as deep learning with python although you shouldn't buy it I think because he is supposed to come up with version 2 which I think will cover tensorflow 2.0 you know being excellent book and when he's here Monday you should torture him and tell him to finish writing he was supposed to finish writing in 2018 ok my journal hopes as I mentioned for 2020 is I love to see common-sense reasoning and to not necessarily enter the world of deep learning but be a part of artificial intelligence and the problems that people tell you tackle as I've been harboring active learning is to me is the most important aspect of real-world application of deep learning there's not enough research there should be way more research I'd love to see active learning lifelong learning that's what we all do as human beings that's what AI systems need to do continually learn from their mistakes over time start out dumb become brilliant over time open domain conversation with the elect surprise I would love to see breakthroughs there Alexa folks thinks were still two or three decades away but that's what everybody says before the breakthrough so I'm excited to see if there's any brilliant grad students that come up with something their applications in autonomous vehicles the medical space algorithmic ethics of course ethics has been a lot of excellent work in fairness privacy and so on robotics and as I said recommendation systems the most important in terms of impact part of artificial intelligence systems I mentioned soup in terms of progress there's been a little bit of tension a little bit of love online in terms of deep learnings I just wanted to say that the kind of criticism and skepticism about the limitations of deep learning are really healthy in moderation Jeff hidden one of the three people to receive the Turing award as as many people know is said that the future depends on some graduate student who is deeply suspicious of everything I have said so that suspicion skepticism is essential but in moderation just a little bit the more important thing is perseverance which is what Geoffrey Hinton and the others have had through the winters of believing in neural nets and an open - for returning to the world of symbolic AI of expert systems of complexity and cellular automata of old ideas in AI and bringing them back and see if there's ideas there and of course you have to have a little bit of crazy nobody ever achieved something brilliant without being a little bit of crazy and the most important thing is a lot of hard work it's not the cool thing these days but hard work is everything I like what JFK said how about us going to the moon us I was born in the Soviet Union see how I conveniently just said us going to the moon is we do these things not because they are easy but because they're hard and I think that artificial intelligence is one of the hardest the most exciting problems there before us so with that like to thank you and see if there's any questions back in the 1980s perilous distributing processing books came out they had most of the stuff in it back then what's your take on the roadblocks the most important roadblocks apart from maybe funding I think fundamentally I mean they're they're well known as limitations is that they're really inefficient at learning and they're not so they're really good at extracting representations from raw data but not good at learning knowledge bases of like accumulating knowledge over time that that's the fundamental limitation I export systems are really good at accumulating knowledge but very bad at doing that and automated the way symbolic AI so I don't know how to overcome a lot of people say there's hybrid approaches I believe more data bigger networks and better selection of data will take us a lot farther hello Lex I'm wondering if you recall what was the initial spark or inspiration that drove you towards work in AI was it when you were pretty young or was it in more recent years so I want to become a psychiatrist I wanted to I thought of it as kind of engineering the human mind by sort of manipulating it I thought that's what I thought of psychiatry is by using words to sort of explore the depths of the mind and be able to adjust it but then I realized that psychiatry can't actually do that and modern psychiatry is more about sort of bioengineering as drugs and so or sort of I thought that the way to really explore the engineering of the mind as the other side is to build it sort of and that's also when C++ really became the cool hot thing so I learned to program at 12 and then never look back hundreds of thousands of lines later just I love program I love building and that's to me is the best way to understand the - to build it speaking of building mind do you personally think that machines will ever be able to think and the second question will they ever be able to feel emotions a hundred percent yes 100 percent they'll be able to think and they'll be able to feel emotions because so those concepts of thought and feeling are human concepts and to me they'll be able to fake it they're therefore they'll be able to do it like I've made I've been playing with Roombas a lot recently Roomba vacuum cleaners and so I've now started having Roombas Scream something like like there's like moaning in pain and they became I feel like they're having emotions so like the faking creates the emotion yeah so that the display of emotion is emotion to me and then the display of thought is thought I guess that's the sort of everything else is everything else is impossible to pin down I'm asking so what about the ethical aspects of it I'm asking because I was born in the Soviet Union as well and one of my favorite recent books is Victor Phil Evans I and it's about AI feel emotions and suffering from it so I don't know if you've read that book what do you think about like AI feeling emotions in that context or in general ethical aspects yeah it's a it's a really difficult question answer yes I believe AI will suffer and it's unethical to torture I act but I believe suffering exists in the eye of the observer sort of like if a tree falls and nobody's around to see it it never suffered it's us humans that see the suffering in the tree in the animal and our fellow humans and sort of in that sense the first time a programmer with a straight face delivers a product that says it's suffering is the first time it becomes unethical to torture AI systems and I can do we can do that today like I already built the Roombas I they won't sell currently but I think the first time a Roomba says please don't hurt me that's when we start to have serious conversations about the ethics and it's it sounds ridiculous I'm glad that's being recorded because it won't be ridiculous in just a few years yeah is the enforcement learning a good candidate for achieving general artificial intelligence at the and are any other are there any other good candidates around so they're to me the answer's no but it can teach us some valuable gaps that can be filled by other methods so I believe that simulation is different than the real world so if you could simulate the real world then DRL so any kind of reinforcement learning with deep representations would be able to achieve something incredible but to me simulation is very different than the real wall so you have to interact in the real world and there you have to be much more efficient with learning and to be more efficient learning you have to have ability to automatically construct common sense like common sense reasoning seems to include like a huge amount of information that's accumulated over time and that feels more like programs than functions I like how like Ilya sutskever talks about deep learning learns functions approximator z' deep RL learns an approximator for policy or whatever but not programs it's not learning a thing that's able to sort of that's essentially what reasoning is a program it's not a function so I think I think no but it'll continue to one inspires and to inform us about what the true gaps are I think the ability to but I'm so human centric but I think the approach of being able to take knowledge and put it together sort of building into more and more complicated pieces of information concepts being able to reason in that way there's there's a lot of methodologies that old-school sort of that's the falls under the ideas of symbolic AI of doing that kind of logic reasoning accumulating knowledge bases that's going to be an essential part of general intelligence but also the essential part of general intelligence is the roomba that says I'm intelligence if you if you don't believe me like a very confident like because right now like Alexa is very nervous like oh what can I do for you but once the Lexus says like you know is is upset that you would like turn her off or treat her like a servant or or say that she's not intelligent that's that that's what intelligence that's emerging because they think he was a pretty dumb and what general we're all like intelligence is a very kind of relative human construct that we've kind of convinced each others of and and once a I systems are also playing that game of creating constructs and that human communication that's going to be important but of course for that you still need to be have pretty good witty conversation and for that you need to do the symbology I think I'm wondering about the autonomous vehicles whether they are responsive to environmental sounds I mean like notice in her cart autonomous vehicle driving erratically won't respond to my beep that's a really interesting question as far as I know no I think way mo hinted that they look at sound a little bit I think they should so there's a lot of stuff that comes from audio that's really interesting the sort of way Moe have said that they use audio for sirens so detecting sirens from far away yeah I think audio is a lot of interesting information like the sound that the car that tires make on different kinds of roads is very interesting we kind of we use that information ourselves too depending on kind of like off-road wet road when it's not raining sounds different than dry Road there's a lot of little subtle information pedestrians yelling and that kind of stuff it's actually very difficult to know how much we get from audio most robotics folks think that audio is useless I'm a little skeptical yeah but nobody's been able to identify why audio might be useful so I have two questions my first is what do you think is the ultimate sort of end point for super machine intelligence like will we sort of be relegated to some obscure part of the earth like we've done some next primates and next intelligent primates and my second question is should there be should we have equal rights for beings made out of silicon versus carbon for example like robots for you know separate rights or say might equal rights with humans yeah so the future of super intelligent I think I have much less work I see very much fewer paths to a AGI systems killing humans than I do for a jazz systems living among us so I think I see exciting exciting or not so exciting but not harmful futures I think it's very difficult to create AI systems that will kill people that aren't like literally weapons of war they're like it'll always be people killing people like the things we should be worried about as other people that's the that's the fun of us so there's a lot of ways you know nuclear weapons there's a lot of existential threats to our society that are fundamentally human at the core and AI will be might be tools of that but there'll be also tools to defend against that I also see AI proliferating as companions I think companionship will be a really interesting like we will more and more live as we already do in the digital world like you have an identity on a Twitter and Instagram especially if it's anonymous or something you have the this identity you've created and that will continue growing more and more especially for people born now that it's kind of this artificial identity that we live much more in the digital space and in that digital space as opposed to physical space is where AI can thrive much more currently you you'll thrive there first and so that will live in the world with a lot of intelligent first assistants but also just intelligent agents and I do believe they should have rights and in this contentious time of people groups fighting for rights I feel really bad saying they should have equal rights but I believe that I've talked to if you read the work of Peter Singer of looking I like my favorite food is steak I love meat but I also feel horrible about the torture of animals and that's that's the same kind of to me the way our society thinks about animals is a very similar way we should be thinking about robots well we will be thinking about robots and I would say about 20 years what one final class yeah will they become our masters no it will not be our masters what I'm really worried about is well who will become our masters are owners of large tech companies who use these tools to to control human beings first unintentionally and then intentionally so we need to make sure that we democratized AI it's the same kind of same kind of thing that we did with government we make sure that we at the heads of tech companies if maybe people in the store will be heads of tech companies one day we have people like George Washington who relinquished power at the finding this country forget forget all the other horrible things he did but the he relinquished power as opposed to Stalin and all the other horrible human beings who have sought instead absolute power which will be the 21st century AI will be the tools of power in the hands of 25 year old nerds it should be very careful about that future so the humans will become our masters not the AI AI will save us so on that no thank you very much [Applause] you