Computer science knows a great deal about search and inference. What about exploration? That’s what I’ve been tracking down these last few weeks. That’s what I’ve been noticing as I go over my photos.
In search you’ve got a specific goal. You’re looking through a collection of objects and you have a good characterization of what you’re looking for. But there are different kinds of collections, different sizes, and this affects the procedure you use to conduct the search.
Inference is different. You’re starting with something you know and looking for something else, something that follows from, depends on, what you already know. So you make inferences. Of course, there are various kinds of inference. Analogy is different from (strict) deduction. Etc.
Exploration is a distinctly different mode. I note that it’s one of the basic modes that McCulloch identified in his reticular activation system paper. You aren’t looking for anything in particular. But something might turn up. If and when it does, you need to search “around it” and draw inferences to figure out what it is. This, I think, is where the DMN (default mode network) comes into play.
Computationally, what does exploration look like? That’s the mode we need to deal with unstructured and open-ended situations. That’s a mode that isn’t tested by these benchmarks. Benchmarks are about search and inference.
More later.
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