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Hmm… so are we thinking of the local linear approximations of the connections between the neurons? In ANNs, I guess with RELU things are (almost everywhere) locally linear (by which I mean affine)… And, I would imagine(?) that in C.Elegans, that the neuron activations are far from linear? So, if it isn’t a problem there, then I guess it would be also not a problem when things are piecewise linear? Though… in the case of ANNs, generally they are feedforward, and even when they are recurrent, it is discrete time instead of continuous time? Would the same techniques really carry over?
Good question! Control theory also has a discrete version that people use when appropriate. Also, both neural net of C. elegans and artificial neural net are globally non-linear. But people have shown that a linear approximation can explain most features in a network even as complex as human brain. Although we might need to add non-linear corrections depending on our question.
I'm thinking about functionals rather than local approximations. Think solving some approximate differential equation that is designed to be linear so we can both solve it in a reasonable time without sampling it and also analyze its behavior. Say, finding an optimal function or path along a dynamical system.
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![The Dark Matter of AI [Mechanistic Interpretability]](http://i.ytimg.com/vi/UGO_Ehywuxc/mqdefault.jpg)
![The Dark Matter of AI [Mechanistic Interpretability]](/img/tr.png)
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High Quality work! Thank you so much !
Exceptional work.
Thank you for the kind words!
Hmm… so are we thinking of the local linear approximations of the connections between the neurons?
In ANNs, I guess with RELU things are (almost everywhere) locally linear (by which I mean affine)…
And, I would imagine(?) that in C.Elegans, that the neuron activations are far from linear?
So, if it isn’t a problem there, then I guess it would be also not a problem when things are piecewise linear?
Though… in the case of ANNs, generally they are feedforward, and even when they are recurrent, it is discrete time instead of continuous time?
Would the same techniques really carry over?
Good question! Control theory also has a discrete version that people use when appropriate.
Also, both neural net of C. elegans and artificial neural net are globally non-linear. But people have shown that a linear approximation can explain most features in a network even as complex as human brain. Although we might need to add non-linear corrections depending on our question.
I'm thinking about functionals rather than local approximations. Think solving some approximate differential equation that is designed to be linear so we can both solve it in a reasonable time without sampling it and also analyze its behavior. Say, finding an optimal function or path along a dynamical system.
Wow, nice video!
Thank you! Cheers!
You're doing a fantastic job! I need some advice: I have a SafePal wallet with USDT, and I have the seed phrase. (alarm fetch churn bridge exercise tape speak race clerk couch crater letter). What's the best way to send them to Binance?
Thanks for your comment! I focus on physics and data science, and cryptocurrency transfers aren’t my area of expertise.
@@CompuFlair FYI that is a bot and SafePal is a well known scam that uses both scammers and unsuspecting people to advertise for it.
bruh
thanks bruh
Remove the music. It s annoying.
RHLF is already a “control system “ buddy . Us AI people already did that. 😂
People love attitudes like this. Super helpful, really driving innovation forward. Love your attitude. Cheers. 🍻
Not really the same kind of thing?