These are unanswered questions which we've reviewed and decided are within Stampy's scope. Feel free to answer them if you want to help out. Your answers will be reviewed, stamped, and possibly improved by others so don't worry about them not being perfect :)
If AI takes over the world, how could it create and maintain its hardware, its power supply and everything else that humans currently provide?
This is actually an active area of AI alignment research, called "Impact Measures"! It's not trivial to formalize in a way which won't predictably go wrong (entropy minimization likely leads to an AI which tries really hard to put out all the stars ASAP since they produce so much entropy, for example), but progress is being made. You can read about it on the Alignment Forum tag, or watch Rob's videos Avoiding Negative Side Effects and Avoiding Positive Side Effects
Once an AGI has access to the internet it would be very challenging to meaningfully restrict it from doing things online which it wants to. There are too many options to bypass blocks we may put in place.
It may be possible to design it so that it does not want to do dangerous things in the first place, or perhaps to set up tripwires so that we notice that it’s trying to do a dangerous thing, though that relies on it not noticing or bypassing the tripwire so should not be the only layer of security.
If the AI system was deceptively aligned (i.e. pretending to be nice until it was in control of the situation) or had been in stealth mode while getting things in place for a takeover, quite possibly within hours. We may get more warning with weaker systems, if the AGI does not feel at all threatened by us, or if a complex ecosystem of AI systems is built over time and we gradually lose control.
Paul Christiano writes a story of alignment failure which shows a relatively fast transition.
AI Safety Support offers free calls to advise people interested in a career in AI Safety, so that's a great place to start. We're working on creating a bunch of detailed information for Stampy to use, but in the meantime check out these resources:
- 80,000 Hours AI safety syllabus
- Adam Gleave's Careers in Beneficial AI Research document
- Rohin Shah's FAQ on career advice for AI alignment researchers
- AI Safety Support has lots of other good resources, such as their links page, slack, newsletter, and events calendar.
- Safety-aligned research training programs (under construction).
On the pessimistic end you find people like Eliezer Yudkowsky, who said: "I consider the present gameboard to look incredibly grim, and I don't actually see a way out through hard work alone. We can hope there's a miracle that violates some aspect of my background model, and we can try to prepare for that unknown miracle; preparing for an unknown miracle probably looks like "Trying to die with more dignity on the mainline" (because if you can die with more dignity on the mainline, you are better positioned to take advantage of a miracle if it occurs)."
While at the optimistic end you have people like Ben Garfinkel who put the probability at more like 0.1-1% for AI causing an existential catastrophe in the next century, with most people lying somewhere in the middle.
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Will superintelligence make a large part of humanity unemployable?
Some economists say human wants are infinite, and there will always be new and currently unimaginable kinds of jobs for people to do.
Others say this won't be true if AGI can do _anything_ human minds can do.
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If I know how to use formal proof assistants (e.g. Mathcomp/Ssreflect), could this be useful in helping with well-aligned AGI? Is computer-certified proof something we might want?
Some other notes
- https://github.com/deepmind/cartesian-frames I emailed Scott about doing this in coq before this repo was published and he said "I wouldn't personally find such a software useful but sounds like a valuable exercise for the implementer" or something like this.
- When I mentioned the possibility of rolling some of infrabayesianism in coq to diffractor he wasn't like "omg we really need someone to do that" he was just like "oh that sounds cool" -- I never got around to it, if I would I'd talk to vanessa and diffractor about weakening/particularizing stuff beforehand.
- if you extrapolate a pattern from those two examples, you start to think that agent foundations is the principle area of interest with proof assistants! and again- does the proof assistant exercise advance the research or provide a nutritious exercise to the programmer?
- A sketch of a more prosaic scenario in which proof assistants play a role is "someone proposes isInnerAligned : GradientDescent -> Prop and someone else implements a galaxybrained new type theory/tool in which gradient descent is a primitive (whatever that means)", when I mentioned this scenario to Buck he said "yeah if that happened I'd direct all the engineers at redwood to making that tool easier to use", when I mentioned that scenario to Evan about a year ago he said didn't seem to think it was remotely plausible. probably a nonstarter.
AI alignment encompasses everything related to solving the problem of how to build artificial intelligences such that they share the values of their creators. This appears to be very hard and may be the most important problem humanity will ever face, as whether we succeed might mean the difference between extinction and humanity flourishing until the end of the universe.
A value handshake is a form of trade between superintelligences, when two AI's with incompatible utility functions meet, instead of going to war, since they have superhuman prediction abilities and likely know the outcome before any attack even happens, they can decide to split the universe into chunks with volumes according to their respective military strength or chance of victory, and if their utility functions are compatible, they might even decide to merge into an AI with an utility function that is the weighted average of the two previous ones.
This could happen if multiple AI's are active on earth at the same time, and then maybe if at least one of them is aligned with humans, the resulting value handshake could leave humanity in a pretty okay situation.
See The Hour I First Believed By Scott Alexander for some further thoughts and an introduction to related topics.
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"The real concern" isn't a particularly meaningful concept here. Deep learning has proven to be a very powerful technology, with far reaching implications across a number of aspects of human existence. There are significant benefits to be found if we manage the technology properly, but that management means addressing a broad range of concerns, one of which is the alignment problem.
The basic concern as AI systems become increasingly powerful is that they won’t do what we want them to do – perhaps because they aren’t correctly designed, perhaps because they are deliberately subverted, or perhaps because they do what we tell them to do rather than what we really want them to do (like in the classic stories of genies and wishes.) Many AI systems are programmed to have goals and to attain them as effectively as possible – for example, a trading algorithm has the goal of maximizing profit. Unless carefully designed to act in ways consistent with human values, a highly sophisticated AI trading system might exploit means that even the most ruthless financier would disavow. These are systems that literally have a mind of their own, and maintaining alignment between human interests and their choices and actions will be crucial.
Ajeya Cotra has written an excellent article named Why AI alignment could be hard with modern deep learning on this question.
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