“Search” here means trying a bunch of possibilities and seeing what works. Like how a sudoku solver or pathfinding algorithm does search, not how a search engine does.

But the domain of “AI Research” is broad and imprecise - not simple and discrete like chess game states. What is the type of each point in the search space for AI Research?

Well if we knew how to implement it, then we'd already have it eh?

In chess we know how to describe all possible board states and the transitions (the next moves). We just don’t know which transition is the best to pick, hence it’s a well defined search problem.

With AI Research we don’t even know the shape of the states and transitions, or even if that’s an appropriate way to think about things.

They mean something like the minmax algorithm used in game engines.

Given the example of Pfizer in the article, I would tend to agree with you that ‘search’ in this context means augmenting GPT with RAG of domain specific knowledge.

In this context, RAG isn't what's being discussed. Instead, the reference is to a process similar to monte carlo tree search, such as that used in the AlphaGo algorithm.

Presently, a large language model (LLM) uses the same amount of computing resources for both simple and complex problems, which is seen as a drawback. Imagine if an LLM could adjust its computational effort based on the complexity of the task. During inference, it might then perform a sort of search across the solution space. The "search" mentioned in the article means just that, a method of dynamically managing computational resources at the time of testing, allowing for exploration of the solution space before beginning to "predict the next token."

At OpenAI Noam Brown is working on this, giving AI the ability to "ponder" (or "search"), see his twitter post: https://x.com/polynoamial/status/1676971503261454340