Aprillion (Peter Hozák)
No idea about original reasons, but I can imagine a projected chain of reasoning:
there is a finite number of conjunctive obstacles
if a single person can only think of a subset of obstacles, they will try to solve those obstacles first, making slow(-ish) progress as they discover more obstacles over time
if a group shares their lists, each individual will become aware of more obstacles and will be able to solve more of them at once, potentially making faster progress
humans don’t actually try to maximize their own IGF
Aah, but humans don’t have IGF. Humans have https://en.wikipedia.org/wiki/Inclusive_fitness, while genes have allele frequency https://en.wikipedia.org/wiki/Gene-centered_view_of_evolution ..
Inclusive genetic fitness is a non-standard name for the latter view of biology as communicated by Yudkowsky—as a property of genes, not a property of humans.
The fact that bio-robots created by human genes don’t internally want to maximize the genes’ IGF should be a non-controversial point of view. The human genes successfully make a lot of copies of themselves without any need whatsoever to encode their own goal into the bio-robots.
I don’t understand why anyone would talk about IGF as if genes ought to want for the bio-robots to care about IGF, that cannot possibly be the most optimal thing that genes should “want” to do (if I understand examples from Yudkowsky correctly, he doesn’t believe that either, he uses this as an obvious example that there is nothing about optimization processes that would favor inner alignment) - genes “care” about genetic success, they don’t care about what the bio-robots outght to believe at all 🤷
transformer is only trained explicitly on next token prediction!
I find myself understanding language/multimodal transformer capabilities better when I think about the whole document (up to context length) as a mini-batch for calculating the gradient in transformer (pre-)training, so I imagine it is minimizing the document-global prediction error, it wasn’t trained to optimize for just a single-next token accuracy...
A hundred-dollar note is only worth anything if everyone believes in its worth. If people lose that faith, the value of a currency goes down and inflation goes up.
Ah, the condition for the reality of money is much weaker though—you only have to believe that you will be able to find “someone” who believes they can find someone for whom money will be worth something, no need to involve “everyone” in one’s reasoning.
Inflation is much more complicated of course, but in essence, you only have to believe that other people believe that money is losing value and will buy the same thing for higher price from you to be incentivized to increase prices, you don’t have to believe that you yourself will be able to buy less from your suppliers, increasing the price for higher profits is a totally valid reason for doing so.
This is also a kind of “coordination by common knowledge”, but the parties involved don’t have to share the same “knowledge” per se—consumers might believe “prices are higher because of inflation” while retailers might belive “we can make prices higher because people believe in inflation”...
Not sure myself whether search for coordination by common knowledge incentivizes deceptive alignment “by default” (having an exponentially larger basin) or if some reachable policy can incentivize true aligmnent 🤷
yeah, I got a similar impression that this line of reasoning doesn’t add up...
we interpret other humans as feeling something when we see their reactions
we interpret other eucaryotes as feeling something when we see their reactions 🤷
Some successful 19th century experiments used 0.2°C/minute and 0.002°C/second.
Have you found the actual 19th century paper?
The oldest quote about it that I found is from https://www.abc.net.au/science/articles/2010/12/07/3085614.htm
Or perhaps the story began with E.M. Scripture in 1897, who wrote the book, The New Psychology. He cited earlier German research: "…a live frog can actually be boiled without a movement if the water is heated slowly enough; in one experiment the temperature was raised at the rate of 0.002°C per second, and the frog was found dead at the end of two hours without having moved." Well, the time of two hours works out to a temperature rise of 18°C. And, the numbers don't seem right. First, if the water boiled, that means a final temperature of 100°C. In that case, the frog would have to be put into water at 82°C (18°C lower). Surely, the frog would have died immediately in water at 82°C.
sampled uniformly and independently
🤔 I don’t believe this definition fits the “apple” example—uniform samples from a concept space of “apple or not apple” would NEVER™ contain any positive example (almost everything is “not apple”)… or what assumption am I missing that would make the relative target volume more than ~zero (for high n)?
Bob will observe a highly optimized set of Y, carefully selected by Alice, so the corresponding inputs will be Vastly correlated and interdependent at least for the positive examples (centeroid first, dynamically selected for error-correction later 🤷♀️), not at all selected by Nature, right?
Staying in meta-level, if AGI weren’t going to be created “by the ML field”, would you still believe problems on your list cannot possibly be solved within 6-ish months if companies would throw $1b at each of those problems?
Even if competing groups of humans augmented by AI capabilities existing “soon” were trying to solve those problems with combined tools from inside and outside ML field, the foreseeable optimization pressure is not enough for those foreseeable collective agents to solve those known-known and known-unknown problems that you can imagine?
oh, I didn’t realize there was this event yesterday, I wrote an ai-safety inspired short story independently 😅 if anyone would wish to comment, feel free to leave me a github issue
https://peter.hozak.info/fiction/heat_death/prologue
Now, suppose Carol knows the plan and is watching all this unfold. She wants to make predictions about Bob’s picture, and doesn’t want to remember irrelevant details about Alice’s picture. Then it seems intuitively “natural” for Carol to just remember where all the green lines are (i.e. the message M), since that’s “all and only” the information relevant to Bob’s picture.
(Writing before I read the rest of the article): I believe Carol would “naturally” expect that Alice and Bob share more mutual information than she does with Bob herself (even if they weren’t “old friends”, they both “decided to undertake an art project” while she “wanted to make predictions”), thus she would weight the costs of remembering more than just the green lines against the expected prediction improvement given her time constrains, lost opportunities, … - I imagine she could complete purple lines on her own, and then remember some “diff” about the most surprising differences...
Also, not all of the green lines would be equally important, so a “natural latent” would be some short messages in “tokens of remembering”, not necessarily correspond to the mathematical abstraction encoded by the 2 tokens of English “green lines” ⇒ Carol doesn’t need to be able to draw the green lines from her memory if that memory was optimized to predict purple lines.
If the purpose was to draw the green lines, I would be happy to call that memory “green lines” (and in that, I would assume to share a prior between me and the reader that I would describe as:"to remember green lines" usually means "to remember steps how to draw similar lines on another paper" ... also, similarity could be judged by other humans ... also, not to be confused with a very different concept "to remember an array of pixel coordinates" that can also be compressed into the words "green lines", but I don't expect people will be confused about the context, so I don't have to say it now, just keep in mind if someone squirts their eyes just-so which would provoke me to clarify).
I agree with what you say. My only peeve is that the concept of IGF is presented as a fact from the science of biology, while it’s used as a confused mess of 2 very different concepts.
Both talk about evolution, but inclusive finess is a model of how we used to think about evolution before we knew about genes. If we model biological evolution on the genetic level, we don’t have any need for additional parameters on the individual organism level, natural selection and the other 3 forces in evolution explain the observed phenomena without a need to talk about invididuals on top of genetic explanations.
Thus the concept of IF is only a good metaphor when talking approximately about optimization processes, not when trying to go into details. I am saying that going with the metaphor too far will result in confusing discussions.
TypeError: Comparing different “solutions”.
How do I know that I generated a program that halts?
a) I can prove to myself that my program halts ⇒ the solution consists of both the program and the proof ⇒ the verification problem is a sub-problem of the generation problem.
b) I followed a trusted process that is guaranteed to produce valid solutions ⇒ the solution consists of both the program and the history that generated the proof of the process ⇒ if the history is not shared between the 2 problems, then you redefined “verification problem” to include generation of all of the necessary history, and that seems to me like a particularly useless point of view (for the discussion of P vs NP, not useless in general).
In the latter point of view, you could say:
Predicate: given a set of numbers, is the first the sum of the latter 2?
Generation problem: provide an example true solution: “30 and prime factors of 221”
Verification problem: verify that 30 is the sum of prime factors of 221
WTF does that have to say about P vs NP? ¯\_(ツ)_/¯
“Can’t we test whether the code works without knowing anything about programming?”
Knowing what to test to reliably decrease uncertainty about “whether the code works” includes knowing “a fair bit” about software engineering.
I agree with the distinction that being a programmer is not the only way how to know about programming, many hiring managers are not programmers themselves, they just have to know a fair bit about software engineering.
Oh, I should probably mention that my weakness is that I cannot remember the stuff well while reading out loud (especially when I focus on pronunciation for the benefit of listeners)… My workaround is to make pauses—it seems the stuff is in working memory and my subconscious can process it if I give it a short moment, and then I can think about it consciously too, but if I would read out loud a whole page, I would have trouble even trying to summarize the content.
Similarly a common trick how to remember names is to repeat the name out loud.. that doesn’t seem to improve recall for me very much, I can hear someone’s name a lot of times and repeating it to myself doesn’t seem to help. Perhaps seeing it written while hearing it might be better, but not sure… By far the best method is when I want to write them a message and I have to scroll around until I see their picture, after that I seem to remember names just fine 😹
Yeah, I myself subvocalize absolutely everything and I am still horrified when I sometimes try any “fast” reading techniques—those drain all of the enjoyment our of reading for me, as if instead of characters in a story I would imagine them as p-zombies.
For non-fiction, visual-only reading cuts connections to my previous knowledge (as if the text was a wave function entangled to the rest of the universe and by observing every sentence in isolation, I would collapse it to just “one sentence” without further meaning).
I never move my lips or tongue though, I just do the voices (obviously, not just my voice … imagine reading Dennett without Dennett’s delivery, isn’t that half of the experience gone? how do other people enjoy reading with most of the beauty missing?).
It’s faster then physical speech for me too, usually the same speed as verbal thinking.
Can you help me understand a minor labeling convention that puzzles me? I can see how we can label from the Z1R process as in MSP because we observe 11 to get there, but why is labeled as after observing either 100 or 00, please?
PSA: This is the old page pointing to the 2022 meetup month events, chances are you got here in year 2023 (at the time of writing this comment) while there was a bug on the homepage of lesswrong.com with a map and popup link pointing here...
https://www.lesswrong.com/posts/ynpC7oXhXxGPNuCgH/acx-meetups-everywhere-2023-times-and-places seems to be the right one 🤞
are you thinking about sub-human-level of AGIs? the standard definition of AGI involves it being it better than most humans in most of the tasks humans can do
the first human hackers were not trained on “take over my data center” either, but humans can behave out of distribution and so will the AGI that is better than humans at behaving out of distribution
the argument about AIs that generalize to many tasks but are not “actually dangerous yet” is about speeding up creation of the actually dangerous AGIs, and it’s the speeding up that is dangerous, not that AI Safety researchers believe that those “weak AGIs” created from large LLMs would actually be capable of killing everyone immediatelly on their own
if you believe “weak AGIs” won’t speed creation of “dangerous AGIs”, can you spell out why, please?
if everyone followed the argmax approach I laid out here. Are there any ways they might do something you think is predictably wrong?
While teamwork seems to be assumed in the article, I believe it’s worth spelling out explicitly that argmaxing for a plan with highest marginal impact might mean joining and/or building a team where the team effort will make the most impact, not optimizing for highest individual contribution.
Spending time to explain why a previous research failed might help 100 other groups to learn from our mistake, so it could be more impactful than pursuing the next shiny idea.
We don’t want to optimize for the naive feeling of individual marginal impact, we want to keep in mind the actual goal is to make an Aligned AGI.
I agree with the explicitly presented evidence and reasoning steps, but one implied prior/assumption seems to me so obscenely wrong (compared to my understanding about social reality) that I have to explain myself before making a recommendation. The following statement:
implies a possibility that approximately neg-linear correlation between number of people and time could exist (in multidisciplinary software project management in particular and/or in general for most collective human endeavors). The model of Nate that I have in my mind believes that reasonable readers ought to believe that:
as a prior, it’s reasonable to expect more people will finish a complex task in less time than fewer people would, unless we have explicit reasons to predict otherwise
Brooks’s law is a funny way to describe delayed projects with hindsight, not a powerful predictor based on literally every single software project humankind ever pursued
I am making a claim about the social norm that it’s socially OK to assume other people can believe in linear scalability, not a belief whether other people actually believe that 4x the people will actually finish in 1⁄4 time by default.
Individually, we are well calibrated to throw a TypeError at the cliche counterexamples to the linear scalability assumption like “a pregnant woman delivers one baby in 9 months, how many …”.
And professional managers tend to have an accurate model of applicability of this assumption, individually they all know how to create the kind of work environment that may open the possibility for time improvements (blindly quadrupling the size of your team can bring the project to a halt or even reverse the original objective, more usually it will increase the expected time because you need to lower other risks, and you have to work very hard for a hope of 50% decrease in time—they are paid to believe in the correct model of scalability, even in cases when they are incentivized to say more optimistic professions of belief in public).
Let’s say 1000 people can build a nuclear power plant within some time unit. Literally no one will believe that one person will build it a thousand times slower or that a million people will build it a thousand times faster.
I think it should not be socially acceptable to say things that imply that other people can assume that others might believe in linear scalability for unprecedented large complex software projects. No one should believe that only one person can build Aligned AGI or that a million people can build it thousand times faster than a 1000 people. Einstein and Newton were not working “together”, even if one needed the other to make any progress whatsoever—the nonlinearity of “solving gravity” is so qualitatively obvious, no one would even think about it in terms of doubling team size or halving time. That should be the default, a TypeError law of scalability.
If there is no linear scalability by default, Alignment is not an exception to other scalability laws. Building unaligned AGI, designing faster GPUs, physically constructing server farms, or building web apps … none of those are linearly scalable, it’s always hard management work to make a collective human task faster when adding people to a project.
Why is this a crux for me? I believe the incorrect assumption leads to rationally-wrong emotions in situations like these:
Let me talk to you (the centeroid of my models of various AI researchers, but not any one person in particular). You are a good AI researcher and statistically speaking, you should not expect you to also be an equally good project manager. You understand maths and statistically speaking, you should not expect you to also be equally good at social skills needed to coordinate groups of people. Failling at a lot of initial attempts to coordinate teams should be the default expectation—not one or two attempts and then you will nail it. You should expect to fail more ofthen than the people who are getting the best money in the world for aligning groups of people towards a common goal. If those people who made themselves successful in management initially failed 10 times before they became billionaires, you should expect to fail more times than that.
Recommendation
You can either dilute your time by learning both technical and social / management skills or you can find other experts to help you and delegate the coordination task. You cannot solve Alignment alone, you cannot solve Alignment without learning, and you cannot learn more than one skill at a time.
The surviving worlds look like 1000 independent alignment ideas, each pursued by 100 different small teams. Some of the teams figured out how to share knowledge between some of the other teams and connect one or two ideas and merge teams iff they figure out explicit steps how to shorten time by merging teams.
We don’t need to “stack”, we need to increase the odds of a positive black swan.
Yudkowsky, Christiano, and the person who has the skills to start figuring out the missing piece to unify their ideas are at least 10,000 different people.