4 ms·
This article is deeply flawed. First of all, it's premise is flawed; secondly, it's supporting evidence is flawed. Example from supporting evidence: "then peo
by lvh 13y ago
This article is deeply flawed. First of all, it's premise is flawed; secondly, it's supporting evidence is flawed.
Example from supporting evidence:
"then people would be naturally great at probability, and we know we suck at probability"
No, this does not follow at all. There is absolutely no reason to assume that a probabilistic algorithm would somehow lead to better results when dealing with probabilities at a much higher level.
Example from premise:
- No argument for why "if real intelligences aren't probabilistic, therefore probabilistic intelligences can't work" is given.
- Just because your models come from statistical learning (such as SVMs or Bayesian methods, as the author names) does not mean your resulting algorithm is probabilistic.
Flagging.
- middayc 13y agoso you think that we use probability (and are good at it) on a low level but that doesn't translate to being sufficient in probability on a higher level. Where is the border between low and high? I am not saying any probabilistic intelligence can't work. Please define any "intelligence" or link to definition. I said I think "we" or organic intelligence doesn't with use of probability. I am also not saying probabilistic models are useless / bad / or not worth exploring further for making better programs or anything, I just don't think human intelligence uses them as a basis.