 Dear Fellow Scholars, this is two-minute papers with Kato Ejol and Ifehir. This one is going to be huge, certainly one of my favorites. This work is a combination of several techniques that we have talked about earlier. If you don't know some of these terms, it's perfectly okay. You can remedy this by clicking on the pop-ups or checking the description box, but you'll get the idea even watching only this episode. So first, we have a convolutional neural network. This helps processing images and understanding what is depicted on an image. And a reinforcement learning algorithm. This helps creating strategies or to be more exact, it decides what the next action we make should be, what buttons we push on a joystick. So this technique mixes together these two concepts and we call it deep-queue learning, and it is able to learn to play games the same way as a human would. It is not exposed to any additional information in the code. All it sees is the screen and the current score. When it starts learning to play an old game, Atari Breakout, at first, the algorithm loses all of its lives without any signs of intelligent action. If we wait a bit, it becomes better at playing the game, roughly matching the skill level of an adapt player. But here's the catch. If we wait for longer, we get something absolutely spectacular. It finds out that the best way to win the game is digging a tunnel through the bricks and hit them from behind. I really didn't know this, and this is an incredible moment. I can use my computer, this box next to me that is able to create new knowledge, find out new things I haven't known before. This is completely absurd. Science fiction is not the future, it is already here. It also plays many other games. The percentages show the relation of the gamescores compared to a human player. Half 70% means it's great, and above 100% it's superhuman. As a follow-up work, scientists at DeepMind started experimenting with 3D games, and after a few days of training, it could learn to drive on ideal racing lines and pass others with ease. I've had my driving license for a while now, but I still don't always get the ideal racing lines right. Bravo. I have heard the complaint that this is not really intelligence because it doesn't know the concept of a ball or what it is exactly doing. Edgar Dijkstra once said, the question of whether machines can think is about as relevant as the question of whether submarines can swim. Beyond the fact that rigorously defining intelligence leans more into the domain of philosophy than science, I'd like to add that I am perfectly happy with effective algorithms. We use these techniques to accomplish different tasks, and they are really good problem solvers. In the breakout game, you, as a person, learn the concept of a ball in order to be able to use this knowledge as a machinery to perform better. If this is not the case, whoever knows a lot, but can't use it to achieve anything useful, is not an intelligent being but an encyclopedia. What about the future? There are two major unexplored directions. The algorithm doesn't have long-term memory, and even if it had, it wouldn't be able to generalize its knowledge to other similar tasks. Super exciting directions for future work. Thanks for watching and for your generous support, and I'll see you next time.