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> 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 flexib
by pipingdog 7y ago
> 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 7y agoNot 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 7y agoThe learning coefficient decreases over time making the model less plastic.
- vankessel 7y agoOnly 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.
- IAmGraydon 7y agoThat’s exactly what I was thinking as I read this.