Michael Levin

speaker
450 appearances 1 recordings 1 series first heard May 2025 last heard May 2025

Michael Levin’s voice in public audio — every appearance, attributed to the second.

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The biological networks.
Networks are better at this, significantly better at this.
They do more memories, they do more, you know, causal emergence.
So so so c clearly evolution, I think, liked this feature.
But even the random ones do it a little bit.
And so that suggests to me that even before you get into any kind of selection or any kind of evolutionary cycles, just from the math of alone, you get this little free gift that that that that kick starts some of the stuff.
And then of course you can optimize the heck out of it, you know, as you as you do these uh these selection cycles.
But but right at the beginning, just just the the the math that that that governs these kind of networks.
And of course, some of it, you know, Stu Kaufman talked about some of this stuff a long time ago, but it's it's not just the dynamics, it's not just a dynamical system.
It actually has both learning ability and this kind of um cohesive integration that grows as a result of experience.
So I think we can trace, and as you said, you know, both both very sort of very high and very low.
There is this kind of weird cycle where you can take metrics from the kinds of things that that some you know uh neuroscientists.
scientists do with with human patients and look at how this works with very simple systems.
Yeah.
Um, well, I I mean the first thing is I'm I'm completely on board with things like absences being causal functional uh in inputs into into everything that happened.
So so I I am on board with that.
Uh and and I think you know, I think I think Terry will will will say the probably the the you know the most about it.
Um
What I would like to say is that um I I I in in addition to constraints, I I tend to track two other two other things that that are kind of in that in that class.
One is this interesting aspect um that was really highlighted by the by morphological computing, uh by the where, you know, Pfeiffer and and Bondard and and and folks like that, where
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