What Happens When AI Robots Design Themselves

Key Takeaways

you probably clicked on this video and thought this is some sort of genetic algorithm used on robots but surprisingly no and actually give me a few minutes and now let this ai research paper have a chance to properly impress you you've probably seen some videos where it says ai learns how to play mario or how to run or mostly anything from code bullet and they often use this technique called evolutionary algorithm or genetic algorithm which imitates how evolutions work and from that to find the best ai that can perform a specific task and notice how i put the quotation marks around the word best well you are about to get some math flashbacks so don't mind if i do evolution sounds like the best solution to a lot of problems but in fact it is hilariously bad and here is the reason so let's say you want to climb the highest mountain but the fog around you is too heavy so you are only able to blindly climb up any hill that goes upwards so after you have reached the top of the hill you would think that you have reached the highest point above the sea level that you could have ever gotten and this is the rookie mindset of evolution or genetic algorithms the truth is behind that fog there are tens of thousands of mountains that are way higher than where you currently are however the rookie mindsets cannot break out of their bubble by descending the mountain and climb up another one because this would mean they would lose their current highest sea level and so it would just keep jumping at the mountain top or try to put stones underneath their feet to get higher sea level instead so which hill you choose to start affects the outcome and this is the key problem of evolution and genetic algorithms it depends too much on chances not to also mention the lack of diversity for the end product so basically we humans are just really damn lucky in the evolution lottery so the difference is that instead of just one person climbing up one hill it uses algorithms that provides as many people as the amount of hills there are i know this sounds absurd but this is exactly what their ai research paper introduced just saying robo grammar does not have any new searching algorithm developed it just used a pretty famous algorithm called monty carlo tree search short for mcts which is a stochastic searching algorithm for decision making and planning most noticeably used and go on top of that robogrammer does a really clever way to find the optimization through their own new search called graph heuristic search which incorporates a kind of deep learning network called the craft neural network to learn the heuristic to improve the mountain choosing process this sounds really straightforward but why has no one else thought of this earlier though mcts was out around 2012 and we only got this in the end of 2020 well here is the thing in the older papers for robot designing the constructed models would suffer from super low computation speed if the robot designs are too complex to put this in the climbing mountain analogy the more complex mountain is shaped the longer we need to evaluate each person's efficiency climbing up the mountain since the climb up would now have some absurd requirements like you have to vertically ascend while one hand juggling and doing derivatives mentally now this is just evaluating one person that is climbing one mountain so for the computer to evaluate every person on every kinds of hill may be impossible to compute in the life span of the universe so ghs solved this computational problem by having the ai train to heuristically choose which mountain would be high and easy to climb reducing the need to calculate every single person climbing the mountain and what's even more impressive is that the general search algorithm usually has one goal in mind that may be how to win the go game but robogrammer has two goals in mind that is the easiest mountain to climb while being the tallest to swap out the analogy it aims to have complex robot designs and be the most efficient traversing through different terrains so when given a terrain to generate a robot design based on the robot parts given this method is able to generate multiple optimal robot designs that have wildly different looks than the other ones and this is something evolution algorithms can't easily achieve in the real world there are creatures like the fishes that are able to swim in a continuous motion or like the squids are able to swim fast instantaneously but both could be considered optimal in its terrain so here we can see the comparison between non-optimal designs non-optimal designs with more input parts and one of the optimal design robots for climbing up the stairs like terrain and when you look closely at the optimized robot you can see it uses a pair of rather odd looking forearms but it can efficiently pull the whole body up the stairs and with the three joints body the legs are able to quickly get up the stairs by twisting the joints and provide a continuous movement to proceed upwards and since there could be multiple optimized robots for a specific terrain so for the flat terrain there could be a huge number of different robot designs here we can see two really different robot designs that are constructed both in a really unique way especially for the second one it looks kind of like an arthropod for a gapped terrain you can see that one of the optimized robots has longer legs to cross through those gaps and has a long body to easily move around the gaps in a rich terrain one of the optimized robots has a more elevated pair of forearms just like what we saw similarly to the robot that climbs up the stairs for low friction terrain like the icy lake surface it promotes robot designs where it can produce the most traction for moving and sliding in between which utilizes the low friction property on the other hand another optimized design uses multiple long legs to gain more directional control across the surface and here comes a funny one to easily navigate through these walls in a straight line this robot design has a really long body which let its center of gravity swing around a corner and continuously move in a forward direction with minimal directional change and with the mix of wool terrain and the ridged terrain you can see a mixed attributes of the long body and elevated forearms to easily go through this mixed terrain my speculation of why these robots look like arthropods a lot are because the input robot parts are still limited so to have results similar to mammals are just kind of impossible since it requires large muscle fibers which is still not available yet and it cannot be easily represented by mechanical parts but from this ai research paper as a starting point to more advanced research paper in the future there are quite a lot of usage popping into my mind that could be pretty interesting first i think this can be a really good way to analyze a lot of the current machineries and evaluate if they are actually the optimal and the most efficient design for its purpose second this can be a really good starting point for ai generated mobs for games like no man's sky by first evaluating how the terrain of the planet is being generated and structured ai robots generation can be used in game to generate unique creatures in relation to its planet terrain so the mobs are basically limitless and unique and in relation to the environment they are living in which can make planetary exploration in a game way more fascinating third the combination with 3d printing technology can be pretty beneficial to actual planetary explorations too in real life we cannot afford time and materials just to test out prototypes when the resources are limited so having the ability to simulate terrains and generate robots that can get across a biome easily with limited parts this sounds pretty handy in this particular situation and if you want to know more about robo grammar i'll link the research paper down in description there are more technical aspects written on its research paper so check it out if you want and this video is sponsored by infinite red infinite consulting handles your mobile web and ai needs if you're looking for someone to build your app visit with the link down in the description thank you guys for watching big shout out to mark finn and many other patreons that support me on patreon follow me on twitter and join my discord if you haven't and i will see you in the next one