#semantic-scholar — Public Fediverse posts on home.social

I’ve been watching the reactions to Google Scholar Labs with considerable interest. The responses range from enthusiastic embrace to outright rejection. One response particularly intrigued me—someone mentioned they were initially reluctant to try because they’d heard it was “AI-powered” but became more interested when they read my review and realized what that actually meant (they expected it to generate answers to questions when all it did was do better ranking).

Another interesting puzzle was when I noticed some library guides listing Semantic Scholar as “Semantic/Neural Search” when a technical look at their main retrieval method reveals the main search is still largely lexical search.

While one can understand and agree with the listing of Semantic Scholar as “AI powered” due to clear AI features like TLDR, doing the same for Lens.org and OpenAlex is a much harder sell, because they not only just do keyword search but they lack the obvious AI features of Semantic Scholar.

It’s made me realise we might all be talking past each other because we haven’t actually defined what we mean.

“AI-powered search engine” is a handy catch-all term used by vendors, but it actually hides a diverse set of systems and functionality. In this post, I’ll dissect the different ways academic search can be “AI-powered” so you can decide which types actually cross the line for you.

I am going to argue that we often mean at least 4 different things when we call something “AI-powered search”

Level 1: Post-Retrieval AI Features
Level 2: Going beyond Lexical Search with Semantic Search
Level 3: LLMs for Retrieval and/or Relevance Ranking
Level 4: Synthesis and Generation Across Papers
Level 0??: Use of AI to extract, cluster, or organise metadata used for retrieval

Levels here may not be the right framing, as the four different categories are largely orthogonal (except maybe Level 3 is a subset of level 2), but they map to most common academic search products (e.g. Level 4 is usually Deep Research) and higher levels generally reflect higher risk and greater amount of pushback from librarians.

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The Spectrum of AI in Search

Level 1: Post-Retrieval AI Features

Does “AI-powered search engine” mean “AI” that impacts the search process only? Not necessarily. There’s a whole category of AI features that don’t affect the search results you get at all. Things like optional summarisation of individual items (e.g., AI Insights on Ebscohost databases), translation tools, or text-to-speech features.

These are post-retrieval conveniences. You search, you get your results list (however that list was generated), and then you have the option to use AI to help you process what you found. The search itself? Unchanged.

In theory, if you don’t like these features, you can ignore them.

Editor’s Note: Read the rest of the story, at the below link.

Continue/Read Original Article Here: What Do We Actually Mean by “AI-Powered Search”?