All of harfe's Comments + Replies

Mid 2027 seems too late to me for such a candidate to start the official campaign.

For the 2020 presidential election, many democratic candidates announced their campaign in early 2019, and Yang already in 2017. Debates happened already in June 2019. As a likely unknown candidate, you probably need a longer run time to accumulate a bit of fame.

Also Musk's regulatory plan is polling well

What plan are you referring to? Is this something AI safety specific?

I wouldn't say so, I don't think his campaign has made UBI advocacy more difficult.

But an AI notkilleveryoneism campaign seems more risky. It could end up making the worries look silly, for example.

Their platform would be whatever version and framing of AI notkilleveryoneism the candidates personally endorse, plus maybe some other smaller things. They should be open that they consider the potential human disempowerment or extinction to be the main problem of our time.

As for the concrete policy proposals, I am not sure. The focus could be on international treaties, or banning or heavy regulation of AI models who were trained with more than a trillion quadrillion (10^27) operations. (not sure I understand the intent behind your question).

A potentially impactful thing: someone competent runs as a candidate for the 2028 election on an AI notkilleveryoneism^[1] platform. Maybe even two people should run, one for the democratic primary, and one in the republican primary. While getting the nomination is rather unlikely, there could be lots of benefits even if you fail to gain the nomination (like other presidential candidates becoming sympathetic to AI notkilleveryoneism, or more popularity of AI notkilleveryoneism in the population, etc.)

On the other hand, attempting a presidential run can easi... (read more)

Some of these are very easy to prove; here's my favorite example. An agent has a fixed utility function and performs Pareto-optimally on that utility function across multiple worlds (so "utility in each world" is the set of objectives). Then there's a normal vector (or family of normal vectors) to the Pareto surface at whatever point the agent achieves. (You should draw a picture at this point in order for this to make sense.) That normal vector's components will all be nonnegative (because Pareto surface), and the vector is defined only up to normalizati

For a provably aligned (or probably aligned) system you need a formal specification of alignment. Do you have something in mind for that? This could be a major difficulty. But maybe you only want to "prove" inner alignment and assume that you already have an outer-alignment-goal-function, in which case defining alignment is probably easier.

insofar as the simplest & best internal logical-induction market traders have strong beliefs on the subject, they may very well be picking up on something metaphysically fundamental. Its simply the simplest explanation consistent with the facts.

Theorem 4.6.2 in logical induction says that the "probability" of independent statements does not converge to $1$ or $0$, but to something in-between. So even if a mathematician says that some independent statement feels true (eg some objects are "really out there"), logical induction will tell him to feel uncert... (read more)

A related comment from lukeprog (who works at OP) was posted on the EA Forum. It includes:

However, at present, it remains the case that most of the individuals in the current field of AI governance and policy (whether we fund them or not) are personally left-of-center and have more left-of-center policy networks. Therefore, we think AI policy work that engages conservative audiences is especially urgent and neglected, and we regularly recommend right-of-center funding opportunities in this category to several funders.

it's for the sake of maximizing long-term expected value.

Kelly betting does not maximize long-term expected value in all situations. For example, if some bets are offered only once (or even a finite amount), then you can get better long-term expected utility by sometimes accepting bets with a potential "0"-Utility outcome.

This is maybe not the central point, but I note that your definition of "alignment" doesn't precisely capture what I understand "alignment" or a good outcome from AI to be:

‘AGI’ continuing to exist

AGI could be very catastrophic even when it stops existing a year later.

eventually

If AGI makes earth uninhabitable in a trillion years, that could be a good outcome nonetheless.

ranges that existing humans could survive under

I don't know whether that covers "humans can survive on mars with a space-suit", but even then, if humans evolve/change to handle... (read more)

it is the case that most algorithms (as a subset in the hyperspace of all possible algorithms) are already in their maximally most simplified form. Even tiny changes to an algorithm could convert it from 'simplifiable' to 'non-simplifiable'.

This seems wrong to me: For any given algorithm you can find many equivalent but non-simplified algorithms with the same behavior, by adding a statement to the algorithm that does not affect the rest of the algorithm (e.g. adding a line such as foobar1234 = 123 in the middle of a python program)). In fact, I would c... (read more)

This is not a formal definition.

Your English sentence has no apparent connection to mathematical objects, which would be necessary for a rigorous and formal definition.

I think you are broadly right.

So we're automatically giving $x_1$ ca. $2^{1000}$ higher probability – even before applying the length penalty $\frac{1}{2^{|p|}}$.

But note that under the Solomonoff prior, you will get another $2^{-2000-|G|}$ penalty for these programs with DEADCODE. So with this consideration, the weight changes from $2^{-1000}$ (for normal $p_1$) to $2^{-1000}(1+2^{-|G|})$ (normal $p_1$ plus $2^{1000}$ DEADCODE versions of $p_1$), which is not a huge change.

For your case of "uniform probability until ${10}^{90}$" I think you are right about exponential decay.

That point is basically already in the post:

large language models can help document and teach endangered languages, providing learning tools for younger generations and facilitating the transmission of knowledge. However, this potential will only be realized if we prioritize the integration of all languages into AI training data.

I have doubts that the claim about "theoretically optimal" apply to this case.

Now, you have not provided a precise notion of optimality, so the below example might not apply if you come up with another notion of optimality or assume that voters collude with each other, or use a certain decision theory, or make other assumptions... Also there are some complications because the optimal strategy for each player depends on the strategy of the other players. A typical choice in these cases is to look at Nash-equilibria.

Consider three charities A,B,C and two pla... (read more)

There is also Project Quine, which is a newer attempt to build a self-replicating 3D printer

This was already referenced here: https://www.lesswrong.com/posts/MW6tivBkwSe9amdCw/ai-existential-risk-probabilities-are-too-unreliable-to

I think it would be better to comment there instead of here.

One thing I find positive about SSI is their intent to not have products before superintelligence (note that I am not arguing here that the whole endeavor is net-positive). Not building intermediate products lessens the impact on race dynamics. I think it would be preferable if all the other AGI labs had a similar policy (funnily, while typing this comment, I got a notification about Claude 3.5 Sonnet... ). The policy not to have any product can also give them cover to focus on safety research that is relevant for superintelligence, instead of doing some s... (read more)

It does not appear paywalled to me. The link that @mesaoptimizer posted is an archive, not the original bloomberg.com article.

I haven't watched it yet, but there is also a recent technical discussion/podcast episode about AIXI and relatedd topics with Marcus Hutter: https://www.youtube.com/watch?v=7TgOwMW_rnk

It suffices to show that the Smith lotteries that the above result establishes are the only lotteries that can be part of maximal lottery-lotteries are also subject to the partition-of-unity condition.

I fail to understand this sentence. Here are some questions about this sentence:

• what are Smith lotteries? Ctrl+f only finds lottery-Smith lottery-lotteries, do you mean these? Or do you mean lotteries that are smith?

• which result do you mean by "above result"?

• What does it mean for a lottery to be part of maximal lottery-lotteries?

• does "also subj

This sounds like https://www.super-linear.org/trumanprize. It seems like it is run by Nonlinear and not FTX.

I think Proposition 1 is false as stated because the resulting functional $f^{+}:M^{\pm }\left(X\right)\oplus R\to R$ is not always continuous (wrt the KR-metric). The function $f:[0,1]\to [0,1]$, $x\mapsto x^{1/3}$ with $X=[0,1]$ should be a counterexample. However, the non-continuous functional $f^{+}$ should still be continuous on the set of sa-measures.

Another thing: the space of measures $M^{\pm }\left(X\right)$ is claimed to be a Banach space with the KR-norm (in the notation section). Afaik this is not true, while the space is a Banach space with the TV-norm, with the KR-metric/norm it should not be complete and is merely... (read more)

I think the "deontological preferences are isomorphic to utility functions" is wrong as presented.

Firts, the formula has issues with dividing by zero and not summing probabilities to one (and re-using variable $x$ as a local variable in the sum). So you probably meant something like $$P\left(x\right)=\frac{e^{u\left(x\right)}}{{\sum }_{y\in X}{e}^{u\left(y\right)}}.$$ Even then, I dont think this describes any isomorphism of deontological preferences to utility functions.

• Utility functions are invariant when multiplied with a positive constant. This is not reflected in the formula.

• utility maximizers usually take the a

Some of the downvotes were probably because of the unironic use of the term TESCREAL. This term mixes a bunch of different things together, which makes your writing less clear.

Sure, I'd be happy to read a draft

This article talks a lot about risks from AI. I wish the author would be more specific what kinds of risks they are thinking about. For example, it is unclear which parts are motivated by extinction risks or not. The same goes for the benefits of open-sourcing these models. (note: I haven't read the reports this article is based on, these might have been more specific)

Thank you for writing this review.

The strategy assumes we'll develop a good set of safety properties that we're demanding proof of.

I think this is very important. From skimming the paper it seems that unfortunately the authors do not discuss it much. I imagine that actually formally specifying safety properties is actually a rather difficult step.

To go with the example of not helping terrorists spread harmful virus: How would you even go about formulating this mathematically? This seems highly non-trivial to me. Do you need to mathematically formulate ... (read more)

A common response is that “evaluation may be easier than generation”. However, this doesn't mean evaluation will be easy in absolute terms, or relative to one’s resources for doing it, or that it will depend on the same resources as generation.

I wonder to what degree this is true for the human-generated alignment ideas that are being submitted LessWrong/Alignment Forum?

For mathematical proofs, evaluation is (imo) usually easier than generation: Often, a well-written proof can be evaluated by reading it once, but often the person who wrote up the proof had to consider different approaches and discard a lot of them.

To what degree does this also hold for alignment research?

The setup violates a fairness condition that has been talked about previously.

From https://arxiv.org/pdf/1710.05060.pdf, section 9:

We grant that it is possible to punish agents for using a specific decision proce- dure, or to design one decision problem that punishes an agent for rational behavior in a different decision problem. In those cases, no decision theory is safe. CDT per- forms worse that FDT in the decision problem where agents are punished for using CDT, but that hardly tells us which theory is better for making decisions. [...]

Yet FDT does

Is the organization who offers the prize supposed to define "alignment" and "AGI" or the person who claims the prize? this is unclear to me from reading your post.

Defining alignment (sufficiently rigorous so that a formal proof of (im)possibility of alignment is conceivable) is a hard thing! Such formal definitions would be very valuable by themselves (without any proofs). Especially if people widely agree that the definitions capture the important aspects of the problem.

I think the conjecture is also false in the case that utility functions map from $O^{\omega }$ to $[0,1]$.

Let us consider the case of $A=\{a_1,a_2\}$ and $O=\{o_1,o_2\}$. We use $U_1\left(o\right)=1-2^{-k}$, where $k$ is the largest integer such that $o$ starts with $o_1^k$ (and $U_1\left(o_1^{\omega }\right)=1$). As for $U_2$, we use $U_2\left(o\right)=1-3^{-k}$, where $k$ is the largest integer such that $o$ starts with $o_1^k$ (and $U_2\left(o_1^{\omega }\right)=1$). Both $U_1$ and $U_2$ are computable, but they are not locally equivalent. Under reasonable assumptions on the Solomonoff prior, the policy $\pi$ that always picks action $a_1$ is the optimal policy for both $U_1$ and $U_2$ (see proo... (read more)

"inclusion map" refers to the map $i_X$, not the coproduct $X\coprod Y$. The map $i_X$ is a coprojection (these are sometimes called "inclusions", see https://ncatlab.org/nlab/show/coproduct).

A simple example in sets: We have two sets $X$, $Y$, and their disjoint union $X\bigsqcup Y$. Then the inclusion map $i_X$ is the map that maps $x$ (as an element of $X$) to $x$ (as an element of $X\bigsqcup Y$).

What is an environmental subagent? An agent on a remote datacenter that the builders of the orginal agent don't know about?

Another thing that is not so clear to me in this description: Does the first agent consider the alignment problem of the environmental subagent? It sounds like the environmental subagents cares about paperclip-shaped molecules, but is this a thing the first agent would be ok with?

This does not sound very encouraging from the perspective of AI Notkilleveryoneism. When the announcement of the foundation model task force talks about safety, I cannot find hints that they mean existential safety. Rather, it seems about safety for commercial purposes.

A lot of the money might go into building a foundation model. At least they should also announce that they will not share weights and details on how to build it, if they are serious about existential safety.

This might create an AI safety race to the top as a solution to the tragedy of the

There is an additional problem where one of the two key principles for their estimates is

Avoid extreme confidence

If this principle leads you to picking probability estimates that have some distance to 1 (eg by picking at most 0.95).

If you build a fully conjunctive model, and you are not that great at extreme probabilities, then you will have a strong bias towards low overall estimates. And you can make your probability estimates even lower by introducing more (conjunctive) factors.

Nitpick: The title the authors picked ("Current and Near-Term AI as a Potential Existential Risk Factor") seems to better represent the content of the article than the title you picked for this LW post ("The Existential Risks of Current and Near-Term AI").

Reading the title I was expecting an argument that extinction could come extremely soon (eg by chaining GPT-4 instances together in some novel and clever way). The authors of the article talk about something very different imo.

From just reading your excerpt (and not the whole paper), it is hard to determine how much alignment washing is going on here.

• what is aligned chain-of-thought? What would unaligned chain-of-thought look like?
• what exactly means alignment in the context of solving math problems?

But maybe these worries can be answered from reading the full paper...

I think overall this is a well-written blogpost. His previous blogpost already indicated that he took the arguments seriously, so this is not too much of a surprise. That previous blogpost was discussed and partially criticized on Lesswrong. As for the current blogpost, I also find it noteworthy that active LW user David Scott Krueger is in the acknowledgements.

This blogpost might even be a good introduction for AI xrisk for some people.

I hope he engages further with the issues. For example, I feel like inner misalignment is still sort of missing from the ... (read more)

I googled "Zeitgeist Addendum" and it does not seem to be a thing that would be useful for AGI risk.

• is a followup movie of a 9/11 conspiracy movie
• has some naive economic ideas (like abolishing money would fix a lot of issues)
• the venus project appears to not be very successful

Do you claim the movie had any great positive impact or presented any new, true, and important ideas?

There is also another linkpost for the same blogpost: https://www.lesswrong.com/posts/EP92JhDm8kqtfATk8/yoshua-bengio-argues-for-tool-ai-and-to-ban-executive-ai

There is also some commentary here: https://www.lesswrong.com/posts/kGrwufqxfsyuaMREy/annotated-reply-to-bengio-s-ai-scientists-safe-and-useful-ai

Overall this is still encouraging. It seems to take serious that

• value alignment is hard
• executive-AI should be banned
• banning executive-AI would be hard
• alignment research and AI safety is worthwhile.

I feel like there are enough shared assumptions that collaboration or dialogue with AI notkilleveryoneists could be very useful.

That said, I wish there were more details about his Scientist AI idea:

• How exactly will the Scientist AI be used?
• Should we expect the Scientist AI to have situational awareness?
• Would the Scientist AI be allowed to write large s