 Dear Fellow Scholars, this is two-minute papers with Karojona Ifei. 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 agotistical 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 DeepMind's DeepQ 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 flow to ice flow and construct an igloo. However, we are not meant to jump around arbitrarily, we can gather these pieces by jumping on the active ice flows 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 hours. As you can see, the original DQN 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 462 hours and the scores plateau afterwards. Well, compared that to humans that can do at least as well, or a bit better, after a mere 2 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 augment already existing neural networks and give them a better understanding of the world around us. Active 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 scene 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 it 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 premises 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!