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This is the topic for David Spivak's guest lecture which is to happen on March 27th:
Dynamic organizational systems: from deep learning to prediction markets
The abstract is below:
In training artificial neural networks (ANNs), both neurons and arbitrary populations of neurons can be seen to perform the same type of task. Indeed, at any given moment they provide a function A-->B, and given any input from A and loss signal on B, they do two things: provide an updated function A-->B and backpropagate a loss signal on A. Populations of neurons, which we called "Learners", can be put together in series or in parallel, forming a symmetric monoidal category. However, ANNs satisfy an additional property: there is a consistent method by which the functions update and errors backpropagate; namely, they all use gradient descent. The chain rule implies that the composite of gradient descenders is again a gradient descender.
In this talk I will discuss a generalization called "dynamic organizational systems", which includes ANNs, prediction markets, Hebbian learning, and strategic games. It is founded on the category Poly of polynomial functors, which generalizes Lens. I will review the relevant background on Poly and then explain dynamic organizational systems as coherent procedures by which a network of component systems can rewire its network structure in response to the data flowing through it. I'll explain the ANN case, and possibly the prediction market case, time permitting.
This lecture will happen at 5PM UK time, instead of 4PM as we've had it usually for Cats4AI.
The Youtube streaming link for tomorrow's talk is here and Zoom link here (same as usual).
Could you please update the website (https://cats.for.ai/program/) with the recording link? (Also for Jules' talk)
My bad! I've been moving countries and got pretty behind on tasks. I've just added the recording links to the website and will add the slides once we have them