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>make large and dumb models even larger, throw in more data and it will somehow magically lead to artificial common sense. What exactly do you think a human br
by R0b0t1 6y ago
>make large and dumb models even larger, throw in more data and it will somehow magically lead to artificial common sense.
What exactly do you think a human brain is?
- heavyset_go 6y agoNot an organ that performs backpropagation or gradient descent.
- AQXt 6y agoWhat if I tell you it is? Will that information back-propagate into your brain?
- heavyset_go 6y agoI'd ask you to show me how and where backpropogation takes place in the brain. Then I'd ask how thin you're willing to stretch the "neural net" metaphor to re-apply it to biology.
- R0b0t1 6y agoAdmittedly backpropogation/gradient descent bother me a fair bit but in a human brain what is desireable still has some kind of encoding, the mechanism is just more complicated and more general.
- nradov 6y agoWe don't know exactly what a human brain is. That's kind of the problem.
- bra-ket 6y agoWell we do know something that was learned by brilliant scientists over more than 100 years of neuroscience and cognition research, but apparently it's almost completely ignored by people who aspire to create an 'intelligence'. The idea of neural network in its current form came from someone in 1943 going through actual neuroscience papers and translating them to mathematical model by abstracting away the noise. While brilliant, Mccullough and Pitts work was done in pre-historic times if you consider the amount of information we learned about the brain and cognition since. Saying "We don't know much about human brain" is just being lazy. We know too much about the brain! Brain research has collected so much empirical data and a good amount of good theories. For an example of a good theory at various levels of abstraction see Moser, O'Keefe and Nadel work on place cells and grid cells, Kandel on learning, Edmund Rolls on vision, Olshausen & Field on sparse coding, Kanerva' SDM, Plate' convolutions, Widdows on geometry and meaning, Wickelgren and J. R. Anderson on associative memory, Fukushima' neocognitron, Hofstadter on analogical reasoning, Quillian on semantic memory, Pribram holonomic theory, Valiant' neuroids, Pearl' causality. More of these "bridges" and a meta-bridge is needed if you're serious about AI.
- jugg1es 6y agoYou are mixing up our ability to understand certain components of brain function (definitely respectable!) with our understanding of how the brain confers what we consider human-level intelligence and cognition (virtually none!).
- bra-ket 6y agowell, you've got to start somewhere, and arguably only by understanding something limited, like navigation in innate coordinate space takes us to a higher abstraction like concept spaces, which in turn can take us to higher cognitive abilities. And we do know a whole lot on how navigation works in animal and human brain, for example, and there is an active research on abstract representations in the brain as an extension of spatial navigation "toolbox". reduction is a basic principle of science.