The Connection Between the Generator and Discriminator Gap in AI Knowledge | 2min snip from AXRP

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22 - Shard Theory with Quintin Pope

AXRP - the AI X-risk Research Podcast

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CHAPTER

The Connection Between the Generator and Discriminator Gap in AI Knowledge

Ais generative knowledge proceeds so much greater than humans because it brought in a bunch of experimental data then like presumably you have. What I'm arguing here is that there's like a connection between the generator versus discriminator gap that you need in order to learn a capability at all and the generator and discriminators gap that is useful for humans to like supervise behaviors. If there's some basis by which the models training data is able to distinguish between good capabilities and bad capabilities then I think that puts you in a much better place to look at the models behaviors, plans and so on and so forth.

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What can we learn about advanced deep learning systems by understanding how humans learn and form values over their lifetimes? Will superhuman AI look like ruthless coherent utility optimization, or more like a mishmash of contextually activated desires? This episode's guest, Quintin Pope, has been thinking about these questions as a leading researcher in the shard theory community. We talk about what shard theory is, what it says about humans and neural networks, and what the implications are for making AI safe.

Patreon: patreon.com/axrpodcast

Ko-fi: ko-fi.com/axrpodcast

Episode art by Hamish Doodles: hamishdoodles.com

Topics we discuss, and timestamps:

- 0:00:42 - Why understand human value formation?

- 0:19:59 - Why not design methods to align to arbitrary values?

- 0:27:22 - Postulates about human brains

- 0:36:20 - Sufficiency of the postulates

- 0:44:55 - Reinforcement learning as conditional sampling

- 0:48:05 - Compatibility with genetically-influenced behaviour

- 1:03:06 - Why deep learning is basically what the brain does

- 1:25:17 - Shard theory

- 1:38:49 - Shard theory vs expected utility optimizers

- 1:54:45 - What shard theory says about human values

- 2:05:47 - Does shard theory mean we're doomed?

- 2:18:54 - Will nice behaviour generalize?

- 2:33:48 - Does alignment generalize farther than capabilities?

- 2:42:03 - Are we at the end of machine learning history?

- 2:53:09 - Shard theory predictions

- 2:59:47 - The shard theory research community

- 3:13:45 - Why do shard theorists not work on replicating human childhoods?

- 3:25:53 - Following shardy research

The transcript: axrp.net/episode/2023/06/15/episode-22-shard-theory-quintin-pope.html

Shard theorist links:

- Quintin's LessWrong profile: lesswrong.com/users/quintin-pope

- Alex Turner's LessWrong profile: lesswrong.com/users/turntrout

- Shard theory Discord: discord.gg/AqYkK7wqAG

- EleutherAI Discord: discord.gg/eleutherai

Research we discuss:

- The Shard Theory Sequence: lesswrong.com/s/nyEFg3AuJpdAozmoX

- Pretraining Language Models with Human Preferences: arxiv.org/abs/2302.08582

- Inner alignment in salt-starved rats: lesswrong.com/posts/wcNEXDHowiWkRxDNv/inner-alignment-in-salt-starved-rats

- Intro to Brain-like AGI Safety Sequence: lesswrong.com/s/HzcM2dkCq7fwXBej8

- Brains and transformers:

- The neural architecture of language: Integrative modeling converges on predictive processing: pnas.org/doi/10.1073/pnas.2105646118

- Brains and algorithms partially converge in natural language processing: nature.com/articles/s42003-022-03036-1

- Evidence of a predictive coding hierarchy in the human brain listening to speech: nature.com/articles/s41562-022-01516-2

- Singular learning theory explainer: Neural networks generalize because of this one weird trick: lesswrong.com/posts/fovfuFdpuEwQzJu2w/neural-networks-generalize-because-of-this-one-weird-trick

- Singular learning theory links: metauni.org/slt/

- Implicit Regularization via Neural Feature Alignment, aka circles in the parameter-function map: arxiv.org/abs/2008.00938

- The shard theory of human values: lesswrong.com/s/nyEFg3AuJpdAozmoX/p/iCfdcxiyr2Kj8m8mT

- Predicting inductive biases of pre-trained networks: openreview.net/forum?id=mNtmhaDkAr

- Understanding and controlling a maze-solving policy network, aka the cheese vector: lesswrong.com/posts/cAC4AXiNC5ig6jQnc/understanding-and-controlling-a-maze-solving-policy-network

- Quintin's Research agenda: Supervising AIs improving AIs: lesswrong.com/posts/7e5tyFnpzGCdfT4mR/research-agenda-supervising-ais-improving-ais

- Steering GPT-2-XL by adding an activation vector: lesswrong.com/posts/5spBue2z2tw4JuDCx/steering-gpt-2-xl-by-adding-an-activation-vector

Links for the addendum on mesa-optimization skepticism:

- Quintin's response to Yudkowsky arguing against AIs being steerable by gradient descent: lesswrong.com/posts/wAczufCpMdaamF9fy/my-objections-to-we-re-all-gonna-die-with-eliezer-yudkowsky#Yudkowsky_argues_against_AIs_being_steerable_by_gradient_descent_

- Quintin on why evolution is not like AI training: lesswrong.com/posts/wAczufCpMdaamF9fy/my-objections-to-we-re-all-gonna-die-with-eliezer-yudkowsky#Edit__Why_evolution_is_not_like_AI_training

- Evolution provides no evidence for the sharp left turn: lesswrong.com/posts/hvz9qjWyv8cLX9JJR/evolution-provides-no-evidence-for-the-sharp-left-turn

- Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets: arxiv.org/abs/1905.10854

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