Ah gotcha, yes lets do my $1k against your $10k.
bgold
Karma: 764
Given your rationale I’m onboard for 3 or more consistent physical instances of the lock have been manufactured.
Lets ‘lock’ it in.
@Raemon works for me; and I agree with the other conditions.
This seems mostly good to me, thank you for the proposals (and sorry for my delayed response, this slipped my mind).
OR less than three consistent physical instances have been manufactured. (e.g. a total of three including prototypes or other designs doesn’t count)
Why this condition? It doesn’t seem relevant to the core contention, and if someone prototyped a single lock using a GS AI approach but didn’t figure out how to manufacture it at scale, I’d still consider it to have been an important experiment.
Besides that, I’d agree to the above conditions!
(8) won’t be attempted, or will fail at some combination of design, manufacture, or just-being-pickable. This is a great proposal and a beautifully compact crux for the overall approach.
I agree with you that this feels like a ‘compact crux’ for many parts of the agenda. I’d like to take your bet, let me reflect if there’s any additional operationalizations or conditioning.
However, I believe that the path there is to extend and complement current techniques, including empirical and experimental approaches alongside formal verification—whatever actually works in practice.
FWIW in Towards Guaranteed Safe AI I we endorse this: “Moreover, while we have argued for the need for verifiable quantitative safety guarantees, it is important to note that GS AI may not be the only route to achieving such guarantees. An alternative approach might be to extract interpretable
policies from black-box algorithms via automated mechanistic interpretability… it is ultimately an empirical question whether it is easier to create interpretable world models or interpretable policies in a given domain of operation.”
I agree with this, I’d like to see AI Safety scale with new projects. A few ideas I’ve been mulling:
- A ‘festival week’ bringing entrepreneur types and AI safety types together to cowork from the same place, along with a few talks and lot of mixers.
- running an incubator/accelerator program at the tail end of a funding round, with fiscal sponsorship and some amount of operational support.
- more targeted recruitment for specific projects to advance important parts of a research agenda.It’s often unclear to me whether new projects should actually be new organizations; making it easier to spin up new projects, that can then either join existing orgs or grow into orgs themselves, seems like a promising direction.
First off thank you for writing this, great explanation.
Do you anticipate acceleration risks from developing the formal models through an open, multilateral process? Presumably others could use the models to train and advance the capabilities of their own RL agents. Or is the expectation that regulation would accompany this such that only the consortium could use the world model?
Would the simulations be exclusively for ‘hard science’ domains—ex. chemistry, biology—or would simulations of human behavior, economics, and politics also be needed? My expectation is that it would need the latter, but I imagine simulating hundreds of millions of intelligent agents would dramatically (prohibitively?) increase the complexity and computational costs.
This seems like an important crux to me, because I don’t think greatly slowing AI in the US would require new federal laws. I think many of the actions I listed could be taken by government agencies who over-interpret their existing mandates given the right political and social climate. For instance, the eviction moratorium during COVID, obviously should have required congressional action, but was done by fiat through an over-interpretation of authority by an executive branch agency.
What they do or do not do seems mostly dictated by that socio-political climate, and by the courts, which means less veto points for industry.
I agree that competition with China is a plausible reason regulation won’t happen; that will certainly be one of the arguments advanced by industry and NatSec as to why it should not be throttled. However, I’m not sure, and currently don’t think it will, be stronger than the protectionist impulses,. Possibly it will exacerbate the “centralization” of AI dynamic that I listed in the ‘licensing’ bullet point, where large existing players receive money and de-facto license to operate in certain areas and then avoid others (as memeticimagery points out). So for instance we see more military style research, and GooAmBookSoft tacitly agree to not deploy AI that would replace lawyers.
To your point on big tech’s political influence; they have, in some absolute sense, a lot of political power, but relatively they are much weaker in political influence than peer industries. I think they’ve benefitted a lot from the R-D stalemate in DC; I’m positing that this will go around/through this stalemate, and I don’t think they currently have the softpower to stop that.
hah yes—seeing that great post from johnwentsworth inspired me to review my own thinking on RadVac. Ultimately I placed a lower estimate on RadVac being effective—or at least effective enough to get me to change my quarantine behavior—such that the price wasn’t worth it, but I think I get a rationality demerit for not investing more in the collaborative model building (and collaborative purchasing) part of the process.
I’m sorry I didn’t see this response until now—thank you for the detailed answer!
I’m guessing your concern feels similar to ones you’ve articulated in the past around… “heart”/”grounded” rationality, or a concern about “disabling pieces of the epistemic immune system”.
I’m curious if 8 mo’s later you feel you can better speak to what you see as the crucial misunderstanding?
Out of curiosity what’s one of your more substantive disagreements with Thiel?
I’d be quite interested in reading that guide!
Forecast − 25 mins
I thought it was more likely that in the short run there could be a preference cascade among top AGI researchers, and as others have mentioned due to the operationalization of top AGI researchers might be true already.
If this doesn’t become a majority concern by 2050, I expect it will be because of another AI Winter, and I tried to have my distribution reflect that (a little hamfistedly).
Thanks for posting this. I recently reread the Fountainhead, which I similarly enjoyed and got more out of than did my teenage self—it was like a narrative, emotional portrayal of the ideals in Marc Andreessen’s It’s Time to Build essay.
I interpreted your section on The Conflict as the choice between voice and exit.
The larger scientific question was related to Factored Cognition, and getting a sense of the difficulty of solving problems through this type of “collaborative crowdsourcing”. The hope was running this experiment would lead to insights that could then inform the direction of future experiments, in the way that you might fingertip feel your way around an unknown space to get a handle on where to go next. For example if it turned out to be easy for groups to execute this type of problem solving, we might push ahead with competitions between teams to develop the best strategies for context-free problem solving.
In that regard it didn’t turn out to be particularly informative, because it wasn’t easy for the groups to solve the math problems, and it’s unclear if that’s because of the problems selected, the team compositions, the software, etc. So re: the larger scientific question I don’t think there’s much to conclude.
But personally I felt that by watching relay participants I gained a lot of UX intuitions around what type of software design and strategy design is necessary for factored strategies—what I broadly think of as problem solving strategies that rely upon decomposition—to work. Two that immediately come to mind:
Create software design patterns that allow the user to hide/reveal information in intuitive ways. It was difficult, when thrown into a huge problem doc with little context, to know where to focus. I wanted a way for the previous user to only show me the info I needed. For example, the way workflow-y / Roam Research bullet points allow you to hide unneeded details, and how if you click on a bullet point you’re brought into an entirely new context.
When designing strategies try focusing on the return signature: When coming up with new strategies for solving relay problems, at first it was entirely free form. I as a user would jump in, try pushing the problem as far as I could, and leave haphazard notes in the doc. Over time we developed more complex shorthand and shared strategies for solving a problem. One heuristic I now use when developing strategies for problem solving that use decomposition is to prioritizing thinking about what each sub part of the strategy will return to the top caller. That clarifies the interface, simplifies what the person working on the sub strategy needs to do, and promotes composability.
These ideas are helpful because—I posit—we’re faced with Relay Game like problems all the time. When I work on a project, leave it for a week, and come back, I think I’m engaging in a relay between past Ben, present Ben, and future Ben. Some of these ideas informed my design of templates for collaborative group forecasting.
Thanks, rewrote and tried to clarify. In essence the researchers were testing transmission of “strategies” for using a tool, where an individual was limited in what they could transmit to the next user, akin to this relay experiment.
In fact they found that trying to convey causal theories could undermine the next person’s performance; they speculate that it reduced experimentation prematurely.
… my god…
Cumulative Y2K readiness spending was approximately $100 billion, or about $365 per U.S. resident.
Y2K spending started as early 1995, and appears t peaked in 1998 and 1999 at about $30 billion per year.
https://www.commerce.gov/sites/default/files/migrated/reports/y2k_1.pdf