$\require{mathtools}
%
%%% GENERIC MATH %%%
%
% Environments
\newcommand{\al}[1]{\begin{align}#1\end{align}} % need this for \tag{} to work
\renewcommand{\r}{\mathrm}
%
% Greek
\newcommand{\eps}{\epsilon}
\newcommand{\veps}{\varepsilon}
\newcommand{\Om}{\Omega}
\newcommand{\om}{\omega}
\newcommand{\Th}{\Theta}
\let\fi\phi % because it looks like an f
\let\phi\varphi % because it looks like a p
%
% Miscellaneous shortcuts
% .. over and under
\newcommand{\ss}[1]{_{\substack{#1}}}
\newcommand{\ob}{\overbrace}
\newcommand{\ub}{\underbrace}
\newcommand{\ol}{\overline}
\newcommand{\tld}{\widetilde}
\newcommand{\HAT}{\widehat}
\newcommand{\f}{\frac}
\newcommand{\s}[2]{#1 /\mathopen{}#2}
\newcommand{\rt}[1]{ {\sqrt{#1}}}
% .. relations
\newcommand{\sr}{\stackrel}
\newcommand{\sse}{\subseteq}
\newcommand{\ce}{\coloneqq}
\newcommand{\ec}{\eqqcolon}
\newcommand{\ap}{\approx}
\newcommand{\ls}{\lesssim}
\newcommand{\gs}{\gtrsim}
% .. miscer
\newcommand{\q}{\quad}
\newcommand{\qq}{\qquad}
\newcommand{\heart}{\heartsuit}
%
% 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
\newcommand{\p}[1]{\mathopen{}\left( #1 \right)}
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% .... (use phantom to force at least the standard height of double bars)
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%% .. two-part
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% ..... (use phantom to force at least the standard height of double bar)
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%
% Levels of closeness
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\newcommand{#6}{\mathrel{(\sdot{#1})}}
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}
\let\lt\undefined
\let\gt\undefined
% .. vanilla versions (is it within a constant?)
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\newcommand{\gz}{\scirc>}
\newcommand{\gt}{\succ}
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\newcommand{\gttt}{\mathrel{\gt\!\!\gt\!\!\gt}}
% .. dotted versions (is it equal in the limit?)
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\newcommand{\eqqqd}{\sdot\eqqq}
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\newcommand{\gd}{\sdot>}
\newcommand{\gtd}{\sdot\gt}
\newcommand{\gttd}{\sdot\gtt}
\newcommand{\gtttd}{\sdot\gttt}
% .. log versions (is it equal up to log?)
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\newcommand{\eqlog}{\slog\eq}
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%
% Miscellaneous
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% .. operators
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\DeclareMathOperator{\polylog}{polylog}
\DeclareMathOperator{\quasipoly}{quasipoly}
\DeclareMathOperator{\negl}{negl}
\DeclareMathOperator*{\argmin}{arg\,min}
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% .. functions
\DeclareMathOperator{\id}{id}
\DeclareMathOperator{\sign}{sign}
\DeclareMathOperator{\err}{err}
\DeclareMathOperator{\ReLU}{ReLU}
% .. analysis
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\newcommand{\partf}[2]{\f{\part #1}{\part #2}}
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% .. sets of numbers
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%
%%% SPECIALIZED MATH %%%
%
% Logic
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%
% Linear algebra
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\DeclareMathOperator{\dom}{dom}
\DeclareMathOperator{\Img}{Im}
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% ... named tensors
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%
% Probability
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% ... information theory
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\DeclareMathOperator*{\H}{H}
\DeclareMathOperator*{\I}{I}
\DeclareMathOperator*{\D}{D}
%
%%% SPECIALIZED COMPUTER SCIENCE %%%
%
% Complexity classes
% .. classical
\newcommand{\Poly}{\mathsf{P}}
\newcommand{\NP}{\mathsf{NP}}
\newcommand{\PH}{\mathsf{PH}}
\newcommand{\PSPACE}{\mathsf{PSPACE}}
\renewcommand{\L}{\mathsf{L}}
% .. probabilistic
\newcommand{\formost}{\mathsf{Я}}
\newcommand{\RP}{\mathsf{RP}}
\newcommand{\BPP}{\mathsf{BPP}}
\newcommand{\MA}{\mathsf{MA}}
\newcommand{\AM}{\mathsf{AM}}
\newcommand{\IP}{\mathsf{IP}}
\newcommand{\RL}{\mathsf{RL}}
% .. circuits
\newcommand{\NC}{\mathsf{NC}}
\newcommand{\AC}{\mathsf{AC}}
\newcommand{\ACC}{\mathsf{ACC}}
\newcommand{\TC}{\mathsf{TC}}
\newcommand{\Ppoly}{\mathsf{P}/\poly}
\newcommand{\Lpoly}{\mathsf{L}/\poly}
% .. resources
\newcommand{\TIME}{\mathsf{TIME}}
\newcommand{\SPACE}{\mathsf{SPACE}}
\newcommand{\TISP}{\mathsf{TISP}}
\newcommand{\SIZE}{\mathsf{SIZE}}
% .. keywords
\newcommand{\co}{\mathsf{co}}
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%
% Boolean analysis
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\DeclareMathOperator{\fbsens}{\mathrm{fbs}}
\DeclareMathOperator{\Cert}{\mathrm{C}}
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\DeclareMathOperator{\CDT}{\mathrm{CDT}} % canonical
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% .. functions (small caps sadly doesn't work)
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\DeclareMathOperator{\Maj}{\mathrm{Maj}}
\DeclareMathOperator{\HW}{\mathrm{HW}}
\DeclareMathOperator{\Thr}{\mathrm{Thr}}
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\DeclareMathOperator{\RotTribes}{\mathrm{RotTribes}}
\DeclareMathOperator{\CycleRun}{\mathrm{CycleRun}}
\DeclareMathOperator{\SAT}{\mathrm{SAT}}
\DeclareMathOperator{\UniqueSAT}{\mathrm{UniqueSAT}}
%
% Dynamic optimality
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%
% Alignment
\DeclareMathOperator{\Amp}{\mathrm{Amp}}
%
%%% TYPESETTING %%%
%
% In text
\renewcommand{\th}{^{\mathrm{th}}}
\newcommand{\degree}{^\circ}
%
% Fonts
% .. bold
\newcommand{\BA}{\boldsymbol{A}}
\newcommand{\BB}{\boldsymbol{B}}
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% .. calligraphic
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% .. typewriter
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\newcommand{\TX}{\mathtt{X}}
\newcommand{\TY}{\mathtt{Y}}
\newcommand{\TZ}{\mathtt{Z}}$
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.
- 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.
(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.