This page is a stub. It does not necessarily represent much of what is known on the topic.

Our understanding is that the degree to which neural networks learn concepts that are potentially understandable to humans is an open question.

Details

A very incomplete list of sources on the topic:

• Acquisition of Chess Knowledge in AlphaZero (McGrath et al, 2021)¹
  From the paper: ‘…In this work we provide evidence that human knowledge is acquired by the AlphaZero neural network as it trains on the game of chess. By probing for a broad range of human chess concepts we show when and where these concepts are represented in the AlphaZero network….’
• Zoom in: An Introduction to Circuits (Olah et al, 2020)²
  From the paper: ‘In contrast to the typical picture of neural networks as a black box, we’ve been surprised how approachable the network is on this scale. Not only do neurons seem understandable (even ones that initially seemed inscrutable), but the “circuits” of connections between them seem to be meaningful algorithms corresponding to facts about the world. You can watch a circle detector be assembled from curves. You can see a dog head be assembled from eyes, snout, fur and tongue. You can observe how a car is composed from wheels and windows. You can even find circuits implementing simple logic: cases where the network implements AND, OR or XOR over high-level visual features.’

Notes

Featured image: from Olah, et al., “Zoom In: An Introduction to Circuits”, Distill, 2020., CC-BY 4.0

The AI Vignettes Project is an ongoing effort to write concrete plausible future histories of AI development and its social impacts.

Details

Purposes

We hope to:

• Check that abstract views about the future of AI have plausible concrete instantiations. (Especially, hypothesized extinction scenarios, and proposed safe scenarios.)
• Develop better intuitions about possible scenarios by thinking through them concretely.
• Notice recurring themes in concrete stories, that may be worth thinking about more broadly.
• Fill out the space of plausible feasible scenarios with concrete illustrations, to decrease bias in thinking about the future.
• Keep a collection of AI vignettes for others to use, in the above or other ways.

Methods

Our current intended method is:

1. Write draft vignettes, with no particular systematic method for choosing an unbiased selection of scenarios
2. Request comments on their realism
3. Modify according to comments
4. Repeat 2-3 until realism critiques subside

Work so far

AI Impacts has run two small workshops where participants wrote AI vignettes.

Vignette collection

This is a subset of vignettes arising from this project, or similar.