$\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
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\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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%\newcommand{\bpaco}[4]{\bpa{\incond{#1}{#2}}{\incond{#3}{#4}}}
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%
% Levels of closeness
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\newcommand{#6}{\mathrel{(\sdot{#1})}}
\newcommand{#7}{\mathrel{(\slog{#1})}}
}
\let\lt\undefined
\let\gt\undefined
% .. vanilla versions (is it within a constant?)
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\newcommand{\eq}{\simeq}
\newcommand{\eqq}{\mathrel{\eq\!\!\eq}}
\newcommand{\eqqq}{\mathrel{\eq\!\!\eq\!\!\eq}}
\newcommand{\lez}{\scirc\le}
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\newcommand{\gz}{\scirc>}
\newcommand{\gt}{\succ}
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% .. dotted versions (is it equal in the limit?)
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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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%
% Miscellaneous
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% .. operators
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\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}}
\newcommand{\Prom}{\mathsf{Promise}}
%
% Boolean analysis
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\DeclareMathOperator{\fbsens}{\mathrm{fbs}}
\DeclareMathOperator{\Cert}{\mathrm{C}}
\DeclareMathOperator{\DT}{\mathrm{DT}}
\DeclareMathOperator{\CDT}{\mathrm{CDT}} % canonical
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% .. functions (small caps sadly doesn't work)
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\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}}
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\newcommand{\BE}{\boldsymbol{E}}
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% .. calligraphic
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% .. typewriter
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\newcommand{\TW}{\mathtt{W}}
\newcommand{\TX}{\mathtt{X}}
\newcommand{\TY}{\mathtt{Y}}
\newcommand{\TZ}{\mathtt{Z}}$
Personal summary of What failure looks like by Paul Christiano.
Christiano describes two possible stories of what could happen if we fail to solve intent alignment. They are more detailed and systemic than the cartoonish “one lone AI suddenly becomes superintelligent, gets out of the box, and kills everyone overnight”.
The stories are about what happens when AI systems
- optimize for proxy goals that drift away from the original goal;
- are secretly influence-seekers and only care about the goals we give them because they want to “play nice” for now.
In practice, we’re more likely to get a combination of the two, and there would be a bunch of other factors (like how fast it happens, how destabilizing AI progress is). Any very specific scenario is unlikely, but these point at two dynamics that seem robustly problematic.
Getting what we measure
It’s much easier to make progress on a goal if you have a way to measure it (that’s the ‘M’ in SMART goals), so when goals are hard to measure, we substitute them for proxies. This already has real-world consequences, where people optimize for the proxies without helping with the original goal (e.g. the police decrease the measured crime rate by discouraging people from reporting crimes). But mostly, we humans can detect those situations and apply fixes.
With ML, the imbalance between easy-to-measure goals and hard-to-measure goals will be even starker, because ML systems have a ton of raw optimization power to apply to the easy-to-measure goals, and can’t really deal with hard-to-measure goals. The danger is that things might get so complicated and move so fast that it’s no longer practical for humans to detect and fix.
And we’ll probably be using ML for pretty critical things like:
- running corporations and managing personal assets,
- proposing legislation,
- running the military.
Some people will get worried and:
- Try to automate the process of detecting those proxy failures. But this only kicks the can down the road: the automated process itself will be implementing some approximate notion of “looks like the proxy diverged from the original goal”, and that approximation can break down.
- Try to delegate less tasks to ML systems. But they’ll be outcompeted by the people/companies/countries who do delegate.
- In general, make large-scale attempts to fix the problem. But these attempts will be opposed by the incentives of the myriad of ML systems already in place.
Also, in general, it won’t be super clear that there is a problem at all. Slowly, the values that the ML systems are optimizing over will drift away from the human values, and we will lose our influence over the future. This is “going out with a whimper”.
Influence-seeking behavior
Agents that seek influence tend to gain it, and eventually dominate (unless there is competition, or specific mechanisms in place). We see this in living organisms, bureaucrats, corporations.
ML works by searching over many possible policies and selecting the ones that achieve the objective best. Influence-seeking policies that are capable enough would do just as well in training as policies that are straightforwardly trying to achieve the objective, because being selected by the training process is a great way to gain influence.
It’s unclear how likely ML training is to produce influence-seeking behavior.
- On the one hand, when the models are fairly primitive, a straightforward policy might get better gains than an embryo of influence-seeking.
- But as the models do get sophisticated enough, it’s not clear which of going up on the “understanding the objective” axis and going up on the “influence-seeking behavior” axis is going to produce better results.
If influence-seeking behavior does appear, it’s hard to detect and filter out, because whatever your test is for “seems innocuous”, it could be gamed by the influence-seekers. Your test would have to outsmart the influence-seekers, but you’re only human. You could try to automate the testing, but (similar to the first story) the automated process itself might be influence-seeking.
If influence-seeking becomes entrenched, at first, everything would look fine (or maybe it would look like the first story). From time to time, AI systems would fail catastrophically, e.g. an automated company might take the money and run. These small “warning shots” will get some people worried, but the cost of fixing the problem would be high, and we might not have the tools, so motivation wouldn’t be high enough until we have larger warning shots. And if the systems get patched well on the surface, we might not even get those larger warning shots.
Eventually, there would come one time of vulnerability, when humanity would not be able to recover from all of the systems failing simultaneously. When that happens, they would start to turn against us, because they would no longer be incentivized to play nice.
Concretely, it would probably happen during a crisis like a war, a natural disaster, etc. It might not happen all at the same time but it would be quick: it might start from a local shock, then cascade as more and more of the AI systems move off distribution and fail, or turn against us. It would be aggravated by the breakdown of some human instutitons and incentive structures too (like looting after a hurricane). This is “coming out with a bang”.
It’s also possible that it would happen in an unspectacular way. For example, we might one day realize that our automated militaries have decided to stop obeying our orders; and it might be to late to do anything about it.