This time in LessWrong Tel Aviv, Daniel Armak will review Nick Lane's books on evolutionary biology. The abstract of the talk:
"The largest scale of biology contains many unexplained facts. Many important traits are exclusive to eukaryotes: large cell and genome size, internal complexity, multicellularity, sex and the haploid-diploid cell cycle, mitochondria, phagocytosis, and hundreds more. What is the relation between them? How did they evolve, why do all eukaryotes have them (or once had), and why don't any other cells have any of them? What are some of them even for? I will present a summary of Nick Lane's books, themselves a popular summary of other researchers, that attempts to answer many questions with one big answer."
The event will take place in the Cluster. Entrance is from Totzeret Haaretz street. Ring the doorbell on the brown door.
Facebook event: https://www.facebook.com/events/435804276616336/
Contant number: +972542600919 (Vadim Kosoy)
When I started hearing about the latest wave of results from neural networks, I thought to myself that Eliezer was probably wrong to bet against them. Should MIRI rethink its approach to friendliness?
"Neural networks" vs. "Not neural networks" is a completely wrong way to look at the problem.
For one thing, there are very different algorithms lumped under the title "neural networks". For example Boltzmann machines and feedforward networks are both called "neural networks" but IMO it's more because it's a fashionable name than because of actual similarity in how they work.
More importantly, the really significant distinction is making progress by trail and error vs. making progress by theoretical understanding. The goal of AI safety research should be shifting the balance towards the second option, since the second option is much more likely to yield results that are predictable and satisfy provable guarantees. In this context I believe MIRI correctly identified multiple important problems (logical uncertainty, decision theory, naturalized induction, Vingean reflection). I am mildly skeptical about the attempts to attack these problems using formal logic, but the approaches based on complexity theory and statistical learning theory that I'm pursuing seem completely compatible with various machine learning techniques including ANNs.