Could you educate me on what is beam search ? Or link a good ressource
EDIT: https://www.width.ai/post/what-is-beam-search
So the wider the beam, the better the outcome?
Yep, no reasoning, just a marketing term to say "more accurate probabilities"
EDIT: https://www.width.ai/post/what-is-beam-search
So the wider the beam, the better the outcome?
Yep, no reasoning, just a marketing term to say "more accurate probabilities"
Your link seems to do a good job of explaining beam search but it's a classic algorithm in state space exploration so most books on search algorithms and discrete optimization will have a section about it.¹
1: https://books.google.com/books?id=QzGuHnDhvZIC&q=%22beam%20s...
Reasoning "the process of thinking about something in order to make a decision"
Thinking: "the activity of using your mind to consider something"
Mind: "the part of a person that makes it possible for him or her to think, feel emotions, and understand things"
I conclude that "An algorithm that searches for the highest probability answer" is not described by "reasoning"
I also think that the definition of Mind of Cambridge is incomplete and lacks the creativity part along with cognition and emotions. But it's a vastly different topic.
A: Doing it with B.
B: Using C to do it.
C: The part that does B
Without defining, "Your", "Consider", "person", "thinking", "feel", and "understand" it could be anything.
There's more than enough leeway in those undefineds to subjectively choose whatever you want.
I recall reading about a theory of neurology where many thoughts (neural circuits) fired simultaneously and then one would "win" and the others got suppressed. The closest thing I can find right now is Global Workspace Theory.
I looked into it, this "beam search" is nothing but a bit of puffed up nomenclature, not unlike the shock and awe of understanding a language such as Java that introduces synonyms for common terms for no apparent reason, not unlike the intimidating name of "bonferroni multiple test correction" which is just a (1/n) divison operation.
"Beam search" is breadth-first search. Instead of taking all the child nodes at a layer, it takes the top <n> according to some heuristic. But "top n" wasn't enough for whoever cooked up that trivial algorithm, so instead it's "beam width". It probably has more complexities in AI where that particular heuristic becomes more mathematical and complex, as heuristics tend to do.