How to Use Learned Loss Functions to Optimize Internal Cognitive States | 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

How to Use Learned Loss Functions to Optimize Internal Cognitive States

In deep learning with actual language models you can make like a sentiment classifier out of a language model and then use the classification gradient well. So it's possible to use learned classifiers essentially as parts of loss functions over internal cognitive states that is consistent with the toolboxes with the toolbox of deep learning okay. It seems almost like in this story basically at some point you could just be an expected utility optimal thing or very close to one do you think that's right yeah so I used to think this that like the shards would do all the work for me but once I've got like learned cognitive loss functions that are like organizing my thoughts to steer me more in some directions than away from other

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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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