Click For Photo: https://techcrunch.com/wp-content/uploads/2019/09/polyworld.jpg?w=764
The latest research from OpenAI put its machine learning agents in a simple game of hide-and-seek, where they pursued an arms race of ingenuity, using objects in unexpected ways to achieve their goal of seeing or being seen. This type of self-taught AI could prove useful in the real world as well.
The study intended to, and successfully did look into the possibility of machine learning agents learning sophisticated, real-world-relevant techniques without any interference of suggestions from the researchers.
Tasks - Objects - Photos - Actions - World
Tasks like identifying objects in photos or inventing plausible human faces are difficult and useful, but they don’t really reflect actions one might take in a real world. They’re highly intellectual, you might say, and as a consequence can be brought to a high level of effectiveness without ever leaving the computer.
Whereas attempting to train an AI to use a robotic arm to grip a cup and put it in a saucer is far more difficult than one might imagine (and has only been accomplished under very specific circumstances); the complexity of the real, physical world make purely intellectual, computer-bound learning of the tasks pretty much impossible.
Time - Tasks - World - Robot - Facing
At the same time, there are in-between tasks that do not necessarily reflect the real world completely, but still can be relevant to it. A simple one might be how to change a robot’s facing when presented with multiple relevant objects or people. You don’t need a thousand physical trials to know it should rotate itself or the camera so it can see both, or switch between them, or whatever.
OpenAI’s hide-and-seek challenge to its baby ML agents was along these lines: A game environment with simple rules (called Polyworld) that nevertheless uses real-world-adjacent physics and inputs. If the AIs can teach themselves to navigate this simplified reality, perhaps they can transfer those skills, with some modification, to...
Wake Up To Breaking News!