Re:“Math 2.0” will need to value mathematical progress more ...
by catlifeonmars on Thursday October 08, 2026 @01:39PM
(#50009067)
I think that’s a bad example, as classifiers tend to output a floating point number and you use some threshold/activation function to collapse the classifier into a particular state. In that sense there is nothing qualitatively different when the classifier outputs 0.85 vs 0.87.
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Re:“Math 2.0” will need to value mathematical progress more ...
by bonoboTP on Thursday October 08, 2026 @02:01PM
(#50009475)
LLMs also output a distribution over tokens at the output. I don't see the point you're making.
Re:“Math 2.0” will need to value mathematical progress more ...
by catlifeonmars on Saturday October 10, 2026 @01:43PM
(#50035252)
That’s interesting right? The distribution by itself doesn’t provide the structure required. You need an activation function (idk what the term of architecture for this is in ML) to impose some sort of semantic structure on top of the distribution (eg softmax)