Is It Possible to Optimize Social Relations?

Panelists:

• Yael Niv.
  • @yael_niv
• Konrad Kording
  • @KordingLab.
  • Previous BI episodes:
    • BI 027 Ioana Marinescu & Konrad Kording: Causality in Quasi-Experiments.
    • BI 014 Konrad Kording: Regulators, Mount Up!
• Sam Gershman.
  • @gershbrain.
  • Previous BI episodes:
    • BI 095 Chris Summerfield and Sam Gershman: Neuro for AI?
    • BI 028 Sam Gershman: Free Energy Principle & Human Machines.
• Tim Behrens.
  • @behrenstim.
  • Previous BI episodes:
    • BI 035 Tim Behrens: Abstracting & Generalizing Knowledge, & Human Replay.
    • BI 024 Tim Behrens: Cognitive Maps.

This is the third in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.

The other panels:

• First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
• Second panel, about linear systems, real neurons, and dynamic networks.
• Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
• Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
• Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.