Re:33TB of text data for a 1T-parameter model
by neatze on Sunday April 16, 2023 @04:07AM
(#35588150)
It always seems naive to compare neural structures and functions to artificial networks, dynamics are simply not there, even single neural would require multi layered network (if not mistaken 7 layer network, and very long training time), it is fascinating how brain self-(re)organizes through out life time from small number of examples here and there.
As far my understanding goes to very large degree it is unknown how to model such dynamics, where it would be possible to start with no spiking neurons and evolve effective/stable learning behavior from small number of examples/experiences.
Re:33TB of text data for a 1T-parameter model
by stevenhuang on Monday April 17, 2023 @04:15AM
(#35598040)
For all we know it may not even be necessary to model the higher fidelity aspects of our brains for a form of intelligence to emerge.
There may be many roads to intelligence. The path biology and evolution took may just be one such path.
Re:33TB of text data for a 1T-parameter model
by neatze on Monday April 17, 2023 @07:06AM
(#35599126)
I accept this possibility, but because we don't understand, seems wrong to assume any other path, furthermore, there is some evidence that intelligence/cognition is convergent in neural structure and function, in addition to that most ANN are inspired from studying real neurons.
Re:33TB of text data for a 1T-parameter model
by pmoriarty on Sunday April 16, 2023 @08:04AM
(#35589253)
"Cluster density, cluster location, eagerness to connect, distribution of cell types, etc. are all coded in DNA and contribute to creating "initial weights""
Are they coded in DNA, or do epigenetic factors, the environment cells grow in, adjacent cells, their interaction, etc, play a role here as well?
Obviously newborns need to develop and take in stimulus before they "know" anything, so the initial conditions are not sufficient, but they are obviously necessary to make the limited learning useful.