 Dear Fellow Scholars, this is Two Minute Papers with Dr. Karo Zsolnai-Fehir. Today, it is possible to teach virtual characters to perform highly dynamic motions like a cartwheel or backflips. And not only that, but we can teach an AI to perform this differently from other characters to do it with style, if you will. But, today we are not looking to be stylish, today we are looking to be efficient. In this paper, researchers placed an AI in a physics simulation and asked it to control a virtual character and gave it one task to jump as high as it can. And when I heard this idea, I was elated and immediately wondered, did it come up with popular techniques that exist in the real world? Well, let's see... Yes! Oh, that is indeed a force-bury flop. This allows the athlete to jump backwards over the bar, thus lowering their center of gravity. Even today, this is the prevalent technique in high-jump competitions. With this technique, the take-off takes place relatively late. The only problem is that the AI didn't clear the bar so far, so can it. Well, this is a learning-based algorithm, so with a little more practice it should improve... Yes! Great work! If we lowered the bar just a tiny bit for this virtual athlete, we can also observe it performing the Western role. With this technique, we take off a little earlier and we don't jump backward, but sideways. If it had nothing else, this would already be a great paper, but we are not nearly done yet. The best is yet to come. This is a simulation, a virtual world, if you will, and here we make all the rules. So, the limit is only our imagination. The authors know that very well and you will see that they indeed have a very vivid imagination. For instance, we can also simulate a jump with a weak take-off flag and see that with this condition, the little AI can only clear a bar that is approximately one foot lower than its previous record. What about another virtual athlete with an inflexible spine? It can jump approximately two feet lower. Here's the difference compared to the original. I am enjoying this a great deal and it's only getting better. Next, what happens if we are injured and have a cast on the take-off knee? What results can we expect? Something like this. We can jump a little more than two feet lower. What about organizing the Olympics on Mars? What would that look like? What would the world record be with the weaker gravity there? Well, hold on to your papers and look. Yes, we could jump three feet higher than on Earth and then......out. Well, miss the phone-maring, but otherwise very impressive. And if we are already there, why limit the simulation to high jumps? Why not try something else? Again, in a simulation, we can do anything. Previously, the task was to jump over the bar, but we can also recreate the simulation to include, instead, jumping through obstacles. To get all of these magical results, the authors propose a step they call Bayesian Diversity Search. This helps systematically creating a reach selection of novel strategies and it does this efficiently. The authors also went the extra mile and included a comparison to motion capture footage performed by a real athlete. But note that the AAS version uses a similar technique and is able to clear a significantly higher bar without ever seeing a hijab move. The method was trained on motion capture footage to get used to human-like movements like walking, running, and kicks, but it has never seen any hijab techniques before. Wow! So, if this can invent hijumping techniques that took decades for humans to invent, I wonder what else could it invent? What do you think? Let me know in the comments below. What a time to be alive! This episode has been supported by Lambda GPU Cloud. If you're looking for inexpensive Cloud GPUs for AI, check out Lambda GPU Cloud. They recently launched Quadro RTX 6000, RTX 8000, and V100 instances, and hold onto your papers because Lambda GPU Cloud can cost less than half of AWS and Asia. Plus, they are the only Cloud service with 48GB RTX 8000. Join researchers at organizations like Apple, MIT, and Caltech in using Lambda Cloud instances, workstations, or servers. Make sure to go to LambdaLabs.com slash papers to sign up for one of their amazing GPU instances today. Our thanks to Lambda for their long-standing support and for helping us make better videos for you. Thanks for watching and for your generous support, and I'll see you next time.