Building Machines That Learn and Think Like People | Two Minute Papers #223

dear fellow scholars this is two minute papers with károly on affair this paper discusses possible roadmaps towards building machines that are endowed with human-like thinking and before we go into that the first question would be is there value in building machines that think like people do they really need to think like people isn't it a bit egotistical to say if they are to become any good at this and this task they have to think like us and the answer is well in some cases yes if you remember deep minds deep queue learning algorithm it was able to play on a superhuman level on 29 out of 49 different atari games for instance it did quite well in breakout but less so in frostbite and by frostbite I mean not the game engine but the Atari game from 1983 where we need to hop from ice floe to ice floe and construct an igloo however we are not meant to jump around orbit rarely we can gather these pieces by jumping on the active ice floes only and these are shown with white color have a look at this plot it shows the score it was able to produce as a function of game experience in ours as you can see the original DQM is doing quite poorly while the extended versions of the technique can reach a relatively high score over time this looks really good until we look at the x-axis because then we see that this takes around 460 two hours in the scores plateau afterwards well compare that to humans that can do at least as well or a bit better after a mere two hours of training so clearly there are cases where there is an argument to be made for the usefulness of human-like AI the paper describes several possible directions that may help us achieve this two of them is understanding intuitive physics and intuitive psychology even young infants understand that objects follow smooth paths and expect liquids to go around barriers we can try to endow an AI with similar knowledge by feeding it with physics simulations and their evolution over time to get an understanding of similar phenomena this could be used to argument already existing neural networks and give them a better understanding of the world around us intuitive psychology is also present in young infants they can tell people from objects or distinguish other social and anti-social agents they can also learn goal based reasoning quite early this means that a human who looks at an experienced player play Frostbite can easily derive the rules of the game in a matter of minutes kind of what we are doing now neural networks also have a limited understanding of compositionality and causality and often perform poorly when describing the content of images that contain previously known objects interacting in novel and seen ways there are several ways of achieving each of these elements described in the paper if we manage to build an AI that is endowed with these properties it may be able to think like humans and through self-improvement may achieve the kind of intelligence that we see in all these science-fiction movies there is lots more in the paper learning to learn approximate models for thinking faster model free reinforcement learning and a nice Q& A section with responses to common questions and criticisms it is a great read and is easy to understand for everyone I encourage you to have a look at the video description for the link to it scientists at google deepmind have also written a commentary article where they largely agree with the premise is described in this paper and add some thoughts about the importance of autonomy in building human-like intelligence both papers are available in the video description and both are great reads so make sure to have a look at them it is really cool that we have plenty of discussions on potential ways to create a more general intelligence that is at least as potent as humans in a variety of different tasks what a time to be alive thanks for watching and for your generous support and I'll see you next time you