
A Separate Kind of Intelligence - hos234
https://www.edge.org/conversation/alison_gopnik-a-separate-kind-of-intelligence
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pipingdog
> So, you start out with a system that’s very plastic but not very efficient,
> and that turns into a system that’s very efficient and not very plastic and
> flexible. 

> It’s interesting that that isn’t an architecture that’s typically been used
> in AI. 

No?  That sounds exactly like training a model, then applying the trained
model. 

~~~
ssivark
Not really.  A model (after training) continues to be as plastic as it was
before.  Correspondingly, it can be made to "forget" what it has "learned" and
learn something else. [ At least architecture-wise.  Whether the learned
parameter values reduces plasticity is a non-trivial statement that needs to
be demonstrated]

If contemporary AI/ML models behaved like what Alison Gopnik says, then their
tendency to overfit would be even more of a problem -- you couldn't even
transfer them from simulation domain to reality -- since they would lose all
their plasticity overfitting to simulation! 

Also, this article contains lots of other interesting ideas to think about. 
Highly recommend reading all of it. 

~~~
personjerry
The learning coefficient decreases over time making the model less plastic. 

~~~
vankessel
Only because you have chosen for the learning rate to decrease over time so
the model converges.  You could reset it. 

I would even argue the LR is not relevant to plasticity, it's a meta-variable
for training the model and not to do with the model itself. 

A plastic model would be one that could accomplish a given task after being
trained on it, and then trained on a second task while maintaining the ability
to accomplish the first task. 

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ImaTigger
Elman proposed (and I think built) a model in the mid 1990s (see his book:
Rethinking Innateness) that works in exactly this manner: A "wave of growth"
moves across an initially highly connected "cortical" network, where parts
learn (parcel out), and then become fixated, as other, nearby parts learn.  You
end up with what amounts to the end-result of what would happen if you built a
stack of deep-learned transducers with higher order concepts built in top of
lower order ones. 

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artur_makly
[https://en.wikipedia.org/wiki/Theory_of_multiple_intelligenc...](https://en.wikipedia.org/wiki/Theory_of_multiple_intelligences)

