Google's DeepMind: Optimized algorithms not trained on human code

Today, large language models write effective code because they have trained on human models. However, this training means they are not likely to develop something that humans haven't done previously.

That’s why to optimize well-understood algorithms, it’s best not to base them on human code. The question that surfaces is how do you get an AI to identify a truly new and unique approach? Programmers at DeepMind decided to replicate the approach they used with Chess and Go and transformed code optimization into a game. They engineered algorithms that treated the latency of the code as a score and tried to minimize that score resulting in software that had the ability to write tight, highly efficient code.

They did this through a complex AI system called AlphaDev that consists of several distinct components. Its representation function tracks the overall performance of the code as it's developed, including the general structure of the algorithm and the use of x86 registers and memory.The main advantage of this new system is that its training doesn't have to involve any code examples, as it generates its own code examples and then proceeds to evaluate them.

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