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I'm not claiming it's literally a linear model, obviously. The point is that they are relatively simple mathematical models (that actually use linear algebra in
by qsort 6y ago
I'm not claiming it's literally a linear model, obviously. The point is that they are relatively simple mathematical models (that actually use linear algebra inside, https://en.wikipedia.org/wiki/Artificial_neuron https://en.wikipedia.org/wiki/Artificial_neuron does the output formula remind you of anything?) different in scope/use but not conceptually from linear regressions.
Attaching (meta)physical meanings ("neurons", "intelligence") to any of that is dubious at best.
- mbeex 6y agoWithout non-linearity you are mathematically provable not able to get solutions for most contemporary problems. Refusing the general AI hype bullshit and over-simplifying the hard foundations are quite different things, IMO.
- qsort 6y agoI don't disagree with this. It was a misuse of terminology on my part, my broader point has absolutely nothing to do with the mathematical details of neural networks.
- mbeex 6y agoReason for the nitpicking is, that for me - as a trained mathematician - and seemingly some other commenters, the gap between linearity and non-linearity is yet inside mathematics quite substantial. This is true also in general. Non-linearity might be not sufficient, but it is certainly necessary for any definition and/or serious discussion of AGI.
- unishark 6y agoJust insert the words "these days" and "mostly", and you're pretty much covered. Also a honorable mention for calculus perhaps. Even decision trees use gradient boosting very commonly.
- ogogmad 6y agoHow is this different from saying that a brain is "just" a bunch of atoms, which individually are quite simple? It's when you put the individually simple pieces together and run an optimiser that you get something clever. The result of that optimiser is a complex and very large array of weights.
- qsort 6y agoThe difference is that we have neither the scientific knowledge nor the computational power to make an exact model of the human brain (nor we are anywhere close to either requirement, AFAIK). In other words, we cannot pinpoint the exact sequence of operations and events that produce a certain behavior, while we absolutely can do that with ML models (you could in principle run machine learning models with pen and paper, obviously). If you could show me a program that emulates my brain to a certain degree of precision (it's unlikely that such a program would be a deterministic one, by the way), I would have no problem accepting that my brain is not meaningfully different from that program.
- malkarouri 6y agoSo you are defining intelligence to be whatever the brain does? That is certainly a philosophical position.
- qsort 6y ago"Intelligence" is a word that, etymologically and semantically, is related to human or human-like capabilities. You wouldn't say that a leaf floating on a lake is swimming, and likewise, claiming that computers are "learning" or "intelligent" is at best a thin analogy and at worst a mischaracterization of the process. What's happening in my brain is something we don't have full scientific knowledge of, but we know it's not x86 machine code. While the two processes may be in many ways similar, conflating the two into this ill-defined concept of "intelligence" is a discussion about semantics more than anything else.
- 6y ago