Phil 12/8/17

7:00 – 4:00 ASRC MKT

  • Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
    • Judea Pearl 
    • Current machine learning systems operate, almost exclusively, in a statistical, or model-blind mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal inference.
  • Talk with the first-ever robot politician on Facebook Messenger
  • Reddit network visualizations and a pass at a map (from 2014)
  • Continuing Schooling as a strategy for taxis in a noisy environment here
    • The models also suggest that there is a trade-off in strengthening tendencies to align with neighbours: strong alignment produces tight angular distributions, but increases the time needed to adjust course when the direction of the gradient changes. A reasonable balance seems to be achieved when individuals take roughly the same time to coalesce into a polarized group as they do to orient to the gradient in asocial taxis. (pp 518)
      • There is something about the relationship between explore and exploit in this statement that I really need to think about
  • Continuing white paper. Done with intro, background, and phase 1
  • Thinking about explore/exploit and how it’s not a good narrative. Everyone wants to be an explorer. So I’m trying to think of other framings: Nomad/Farmer?
  • More progress on the white paper. Split out just the LMN work (Phase 1&2). There will be a separate effort for mapping.