titotal

• titotal 23 Oct 2025 11:48 UTC

  69 points

  54

  on: The Doomers Were Right

  Last month, doomers predicted that the Rapture would happen. The doomers were wrong, as they have been all the other dozens of notable times they predicted this.

  Doomers predicted that the Y2K bug would cause massive death and destruction. They were wrong.

  Moving to your social examples, doomers predicted that the legalisation of gay marriage would lead to the legalisation of bestiality. They were wrong.

  You even provide an example yourself where people claim that D&D leads to satanism. This didn’t happen! Having an orc hero is not satanism! satanism has remained a niche religion. The doomers were wrong.

  Cherrypicking examples where doomerist predictions were wrong and examples where they were right is a very poor way to figure out whether a particular doomerist prediction is wrong or right.

  Skeptics are not arguing that no technology or social movement has ever had a negative effect, they are arguing that humans are biased towards apocalyptic thinking and overestimating the threat of new stuff. If you want to rebut this, you have to actually collect some unbiased data to test the claim.

• titotal 24 Jun 2025 9:00 UTC

  13 points

  2

  in reply to: elifland’s comment on: A deep cri­tique of AI 2027’s bad timeline models

  I’m leaving the same comment here and in reply to daniel on my blog.

  First, thank you for engaging in good faith and rewarding deep critique. Hopefully this dialogue will help people understand the disagreements over AI development and modelling better, so they can make their own judgements.

  I think I’ll hold off on replying to most of the points there, and make my judgement after Eli does an in-depth writeup of the new model. However, I did see that there was more argumentation over the superexponential curve, so I’ll try out some more critiques here: not as confident about these, but hopefully it sparks discussion.

  The impressive achievements in LLM capabilities since GPT-2 have been driven by many factors, such as drastically increased compute, drastically increased training data, algorithmic innovations such as chain-of-thought, increases in AI workforce, etc. The extent that each contributes is a matter of debate, which we can save for when you properly write up your new model.

  Now, let’s look for a second at what happens when the curve goes extreme: using median parameters and starting the superexponentional today, the time horizon of AI would improve from one-thousand work-years to ten-thousand work-years In around five weeks. So you release a model, and it scores 80% on 1000 work year tasks, but only like 40% on 10,000 work year tasks (the current ratio of 50% to 80% time horizons is like 4:1 or so). Then five weeks later you release a new model, and now the reliability on the much harder tasks has doubled to 80%.

  Why? What causes the reliability to shoot up in five weeks? The change in the amount of available compute, reference data, or labor force will not be significant in that time, and algorithmic breakthroughs do not come with regularity. It can’t be due to any algorithmic speedups from AI development because that’s in a different part of the model: we’re talking about three weeks of normal AI development, like it’s being done by openAI as it currently stands.. If the AI is only 30x faster than humans, then the time required for the AI to do the thousand year task is 33 years! So where does this come from? Will we have developed the perfect algorithm, such that AI no longer needs retraining?

  I think a mistake could be made in trying to transfer intuition about humans to AI here: perhaps the intuition is “hey, a human who is good enough to do a 1 year task well can probably be trusted to do a 10 year task”.

  However, if a human is trying to reliably do a “100 year” task (a task that would take a team of a hundred about a year to do), this might involve spending several years getting an extra degree in the subject, read a ton of literature, improving their productivity, get mentored by an expert in the subject, etc. While they work on it, they learn new stuff and their actual neurons get rewired.

  But the AI equivalent to this would be getting new algorithms, new data, new computing power, new training. ie, becoming an entirely new model, which would take significantly more than a few weeks to be built. I think there may be some double counting going on between this superexp and the superexp from algo speedups.

A deep cri­tique of AI 2027’s bad timeline models

A widely shared AI pro­duc­tivity pa­per was re­tracted, is pos­si­bly fraudulent

Slop­world 2035: The dan­gers of mediocre AI

Is “su­per­hu­man” AI fore­cast­ing BS? Some ex­per­i­ments on the “539″ bot from the Cen­tre for AI Safety

• titotal 12 Sep 2024 13:23 UTC

  6 points

  6

  in reply to: Jeremy Gillen’s comment on: james oofou’s Shortform

  List of lethalities is not by any means a “one stop shop”. If you don’t agree with Eliezer on 90% of the relevant issues, it’s completely unconvincing. For example, in that article he takes as an assumption that an AGI will be godlike level omnipotent, and that it will default to murderism.

• titotal 12 Sep 2024 9:37 UTC

  5 points

  4

  on: Is this a Pivotal Weak Act? Creat­ing bac­te­ria that de­com­pose metal

  building a bacteria that eats all metals would be world-ending: Most elements on the periodic table are metals. If you engineer a bacteria that eats all metals, it would eat things that are essential for life and kill us all.

  Okay, what about a bacteria that only eats “stereotypical” metals, like steel or iron? I beg you to understand that you can’t just sub in different periodic table elements and expect a bacteria to work the same. There will always be some material that the bacteria wouldn’t work on that computers could still be made with. And even making a bacteria that only works on one material, but is able to spread over the entire planet, is well beyond our abilities.

  I think list of lethalities is nonsense for other reasons, but he is correct in that trying to do a “pivotal act” is a really stupid plan.

• titotal 11 Sep 2024 9:44 UTC

  3 points

  1

  in reply to: Adam Scholl’s comment on: AI fore­cast­ing bots incoming

  Under peer review, this never would have been seen by the public. It would have incentivized CAIS to actually think about the potential flaws in their work before blasting it to the public.

• titotal 10 Sep 2024 11:07 UTC

  28 points

  11

  on: AI fore­cast­ing bots incoming

  I asked the forecasting AI three questions:

  Will iran possess a nuclear weapon before 2030:

  539′s Answer: 35%

  Will iran possess a nuclear weapon before 2040:

  539′s Answer: 30%

  Will Iran posses a nuclear weapon before 2050:

  539′s answer: 30%

  Given that the AI apparently doesn’t understand that things are more likely to happen if given more time, I’m somewhat skeptical that it will perform well in real forecasts.

• titotal 14 Aug 2024 13:08 UTC

  16 points

  13

  on: Rabin’s Paradox

  The actual determinant here is whether or not you enjoy gambling.

  Person A who regularly goes to a casino and bets 100 bucks on roulette for the fun of it will obviously go for bet 1. In addition to the expected 5 buck profit, they get the extra fun of gambling, making it a no-brainer. Similarly, bet 2 is a no brainer.

  Person B who hates gambling and gets super upset when they lose will probably reject bet 1. The expected profit of 5 bucks is outweighed by the emotional cost of doing gambling, a thing they are upset by.

  When it comes to bet 2, person B still hates gambling, but the expected profit is ridiculously high that it exceeds the emotional cost of gambling, so they take the bet.

  Nobody is necessarily being actually irrational here, when you account for non-monetary costs.

Most smart and skil­led peo­ple are out­side of the EA/​ra­tio­nal­ist com­mu­nity: an analysis

• titotal 28 May 2024 15:20 UTC

  7 points

  7

  on: Real Life Sort by Controversial

  I believe this is important because we should epistemically lower our trust in published media from here onwards.

  From here onwards? Most of those tweets that chatgpt generated are not noticeably different from the background noise of political twitter (which is what it was trained on anyway). Also, twitter is not published media so I’m not sure where this statement comes from.

  You should be willing to absorb information from published media with a healthy skepticism based on the source and an awareness of potential bias. This was true before chatgpt, and will still be true in the future.

Mo­ti­va­tion gaps: Why so much EA crit­i­cism is hos­tile and lazy

• titotal 22 Apr 2024 9:04 UTC

  2 points

  0

  in reply to: gilch’s comment on: Any ev­i­dence or rea­son to ex­pect a mul­ti­verse /​ Everett branches?

  No, I don’t believe he did, but I’ll save the critique of that paper for my upcoming “why MWI is flawed” post.

• titotal 10 Apr 2024 16:35 UTC

  6 points

  2

  in reply to: RHollerith’s comment on: Any ev­i­dence or rea­son to ex­pect a mul­ti­verse /​ Everett branches?

  I’m not talking about the implications of the hypothesis, I’m pointing out the hypothesis itself is incomplete. To simplify, if you observe an electron which has a 25% chance of spin up and 75% chance of spin down, naive MWI predicts that one version of you sees spin up and one version of you sees spin down. It does not explain where the 25% or 75% numbers come from. Until we have a solution to that problem (and people are trying), you don’t have a full theory that gives predictions, so how can you estimate it’s kolmogorov complexity?

  I am a physicist who works in a quantum related field, if that helps you take my objections seriously.

• titotal 10 Apr 2024 11:33 UTC

  5 points

  4

  in reply to: tangerine’s comment on: Any ev­i­dence or rea­son to ex­pect a mul­ti­verse /​ Everett branches?

  It’s the simplest explanation (in terms of Kolmogorov complexity).

  Do you have proof of this? I see this stated a lot, but I don’t see how you could know this when certain aspects of MWI theory (like how you actually get the Born probabilities) are unresolved.

• titotal 16 Mar 2024 16:35 UTC

  13 points

  −1

  on: ‘Em­piri­cism!’ as Anti-Epistemology

  The basic premise of this post is wrong, based on the strawman that an empiricist/​scientist would only look at a single piece of information. You have the empiricist and scientists just looking at the returns on investment on bankmans scheme, and extrapolating blindly from there.

  But an actual empiricist looks at all the empirical evidence. They can look the average rate of return of a typical investment, noting that this one is unusually high.They can learn how the economy works and figure out if there are any plausible mechanisms for this kind of economic returns. They can look up economic history, and note that Ponzi schemes are a thing that exists and happen reasonably often. From all the empirical evidence, the conclusion “this is a Ponzi scheme” is not particularly hard to arrive at.

  Your “scientist” and “empricist” characters are neither scientists nor empiricists: they are blathering morons.

  As for AI risk, you’ve successfully knocked down the very basic argument that AI must be safe because it hasn’t destroyed us yet. But that is not the core of any skeptics argument that I know.

  Instead, an actual empiricist skeptic might look at the actual empirical evidence involved. They might say hey, a lot of very smart AI developers have predicted imminent AGI before and been badly wrong, so couldn’t this be that again? A lot of smart people have also predicted the doom of society, and they’ve also been wrong, so couldn’t this be that again? Is there a reasonable near-term physical pathway by which an AI could actually carry out the destruction of humanity? Is there any evidence of active hostile rebellion of AI? And then they would balance that against the empirical evidence you have provided to come to a conclusion on which side is stronger.

  Which, really, is also what a good epistemologist would do? This distinction does not make sense to me, it seems like all you’ve done is (perhaps unwittingly) smeared and strawmanned scientists.

The Leeroy Jenk­ins prin­ci­ple: How faulty AI could guaran­tee “warn­ing shots”

• titotal 14 Jan 2024 13:39 UTC

  9 points

  1

  on: Against most AI risk analogies

  I think some of the quotes you put forward are defensible, even though I disagree with their conclusions.

  Like, Stuart Russell was writing an opinion piece in a newspaper for the general public. Saying AGI is “sort of like” meeting an alien species seems like a reasonable way to communicate his views, while making it clear that the analogy should not be treated as 1 to 1.

  Similarly, with Rob wilbin, he’s using the analogy to get across one specific point, that future AI may be very different from current AI. He also disclaims with the phrase “a little bit like” so people don’t take it too seriously. I don’t think people would come away from reading this thinking that AI is directly analogous to an octopus.

  Now, compare these with Yudkowsky’s terrible analogy. He states outright “The AI is an unseen actress who, for now, is playing this character.”. No disclaimers, no specifying which part of the analogy is important. It directly leads people into a false impression about how current day AI works, based on an incredibly weak comparison.