The DeepMind team behind AlphaTensor have detailed how they use deep reinforcement learning to gamify the machine learning process.
Machine learning research is progressing at an ever-faster pace. We are likely still decades away from reaching the singularity, but AI has already become the buzzword that every tech company is throwing around. Countless AI models exist, but many rely on similar training techniques to develop and refine their capabilities. Reinforcement learning is a popular method, and the DeepMind team behind AlphaTensor have detailed how they use deep reinforcement learning to gamify the machine learning process.Reinforcement learning broadly describes techniques that use rewards and penalties to guide an AI model through a complex task. A human analogy could be playing any game with a ranking system. Better play (e.g. winning games) is rewarded by moving up the leaderboard while mistakes are met with a drop in rank. Along the way, players will try different tactics and strategies to adapt to what opponents are doing. Of course, some humans may not be bothered to care about a ladder rank, but AI models can be compelled with software.AlphaTensor is an AI model based on AlphaZero which is tasked with discovering algorithms to solve arbitrary matrix multiplication problems. Matrix multiplications are used to describe transformations in space and the matrices represent a mathematical concept called a tensor , the general term for scalars and vectors. Tensor math is at the heart of linear algebra and has applications in various fields from materials science to machine learning itself.
