The current alignment plan, and how we might improve it
$\require{mathtools}
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% Delimiters
% (I needed to create my own because the MathJax version of \DeclarePairedDelimiter doesn't have \mathopen{} and that messes up the spacing)
% .. one-part
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%% .. two-part
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%
% Greek
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% the following cause issues with real LaTeX tho :/ maybe consider naming it \fhi instead?
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%
% Miscellaneous
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% .. operators
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% .. functions
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% .. analysis
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% .. sets
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% .... set operations
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% .. over and under
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% .... two-part
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% .. arrows
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% .. operators and relations
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% .. punctuation and spacing
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%
% Levels of closeness
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% .. vanilla versions (is it within a constant?)
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% .. dotted versions (is it equal in the limit?)
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% .. log versions (is it equal up to log?)
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% Logic and bit operations
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% .. named tensors
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% Probability
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% .. operators
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% ... information theory
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% .. other divergences
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% Complexity classes
% .. classical
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% .. probabilistic
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% .. circuits
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% .. resources
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% .. keywords
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% .. functions (small caps sadly doesn't work)
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%
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% Alignment
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%%% TYPESETTING %%%
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% In "text"
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% remove these last two if using real LaTeX
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%
% Fonts
% .. bold
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% .. calligraphic
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% .. typewriter
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Personal summary of Buck Shlegeris’s talk at EA Global Bay Area 2023.
How to think about the impact of different types of alignment projects, based on how they help in a realistic near-term scenario.
The scenario
Magma, a safety-conscious AI company, just got a major breakthrough in AGI, and starts thinking about how to make things things go well. They have a trade-off with how much to use it:
- if they don’t use it at all, a less careful company will create an unaligned AGI and kill everyone;
- if they use it too much, they create risk themselves.
They could use their AI to help out with:
- alignment research,
- enforcing agreements about AI development,
- making it harder for unaligned systems to cause trouble (e.g. hardening, eating low-hanging fruit, detection).
v0 plan
Magma just go ahead and train the thing to be nice and helpful based on human feedback. Two reasons why things could go wrong:
- outer alignment failure: they gave incorrect feedback because the model’s outputs were too complicated to understand;
- inner alignment failure: they gave correct feedback, but when we moved to production, the AI executed a coup.
v1 plan
Magma takes the obvious corresponding precautions, like
- using other AIs to help evaluate the AI proposals (e.g. debate, IDA);
- trying to make the AI think it moved to production and seeing if it executes a coup;
- deployment-time measures, like checks and balances, whistleblowing.
This is not great: coups seems pretty hard, but these techniques are not foolproof (Shlegeris’s P(doom) is 25%). Shlegeris suggests evaluating every alignment proposal in terms of how it incrementally helps makes this plan more hopeful.
Five types of technical research
- Ready to go. Techniques that have been demonstrated to work, and are obviously applicable.
- e.g. critiques, scalable oversight, model psychology
- Worst-case techniques. If it works out, give theoretical guarantees that apply no matter what happens.
- e.g. ARC theory
- Gain of function. Experiment with alignment failure in current systems (+training wheels) to understand the threat better.
- Low-hanging fruit. Cheap tricks where there’s no great story of why it’ll help but they’re so easy that why not try.
- e.g. find the “truth vector” in activation space, most of conventional ML research
- Big if true. Not clear it’s possible, but extremely great if it works.
- e.g. interpretability
(It seems to me like 2 and 5 have a lot of overlap.)
As we get closer to AGI:
- Gain of function and low-hanging fruit will be more realistic.
- Less time for fancy stuff (e.g. worst-case techniques and big-if-true).
- Hopefully, fancy stuff will graduate to “ready to go” in time.