Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.
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The whiteboard in the CLR common room depicts my EA journey in meme format:

Surprising Things AGI Forecasting Experts Agree On:

I hesitate to say this because it's putting words in other people's mouths, and thus I may be misrepresenting them. I beg forgiveness if so and hope to be corrected. (I'm thinking especially of Paul Christiano and Ajeya Cotra here, but also maybe Rohin and Buck and Richard and some other people)

1. Slow takeoff means things accelerate and go crazy before we get to human-level AGI. It does not mean that after we get to human-level AGI, we still have some non-negligible period where they are gradually getting smarter and available for humans to study and interact with. In other words, people seem to agree that once we get human-level AGI, there'll be a FOOM of incredibly fast recursive self-improvement.

2. The people with 30-year timelines (as suggested by the Cotra report) tend to agree with the 10-year timelines people that by 2030ish there will exist human-brain-sized artificial neural nets that are superhuman at pretty much all short-horizon tasks. This will have all sorts of crazy effects on the world. The disagreement is over whether this will lead to world GDP doubling in four years or less, whether this will lead to strategically aware agentic AGI (e.g. Carlsmith's notion of APS-AI), etc.

I'm doubtful whether the notion of human level AGI makes much sense. In it's progression of getting more and more capability there's likely no point where it's comparable to a human.
4Lone Pine1y
Is it really true that everyone (who is an expert) agrees that FOOM is inevitable? I was under the impression that a lot of people feel that FOOM might be impossible. I personally think FOOM is far from inevitable, even for superhuman intelligences. Consider that human civilization has a collective intelligence is that is strongly superhuman, and we are expending great effort to e.g. push Moore's law forward. There's Eroom's law, which suggests that the aggregate costs of each new process node doubles in step with Moore's law. So if FOOM depends on faster hardware, ASI might not be able to push forward much faster than Intel, TSMC, ASML, IBM and NVidia already are. Of course this all depends on AI being hardware constrained, which is far from certain. I just think it's surprising that FOOM is seen as a certainty.
3Daniel Kokotajlo1y
Depends on who you count as an expert. That's a judgment call since there isn't an Official Board of AGI Timelines Experts.
Re 1: that's not what slow takeoff means, and experts don't agree on FOOM after AGI. Slow takeoff applies to AGI specifically, not to pre-AGI AIs. And I'm pretty sure at least Christiano and Hanson don't expect FOOM, but like you am open to be corrected.
5Daniel Kokotajlo6mo
What do you think slow takeoff means? Or, perhaps the better question is, what does it mean to you? Christiano expects things to be going insanely fast by the time we get to AGI, which I take to imply that things are also going extremely fast (presumably, even faster) immediately after AGI: [] I don't know what Hanson thinks on this subject. I know he did a paper on AI automation takeoff at some point decades ago; I forget what it looked like quantitatively.  
Thanks for responding! Slow or fast takeoff, in my understanding, refers to how fast an AGI can/will improve itself to (wildly) superintelligent levels. Discontinuity seems to be a key differentiator here. In the post you link, Christiano is arguing against discontinuity. He may expect quick RSI after AGI is here, though, so I could be mistaken.
3Daniel Kokotajlo6mo
Likewise! Christiano is indeed arguing against discontinuity, but nevertheless he is arguing for an extremely rapid pace of technnological progress -- far faster than today. And in particular, he seems to expect quick RSI not only after AGI is here, but before!  
I'd question the "quick" of "quick RSI", but yes, he expects AI to make better AI before AGI.
3Daniel Kokotajlo6mo
I'm pretty sure he means really really quick, by any normal standard of quick. But we can take it up with him sometime. :)
He's talking about a gap of years :) Which is probably faster than ideal, but not FOOMy, as I understand FOOM to mean days or hours.
2Daniel Kokotajlo6mo
Whoa, what? That very much surprises me, I would have thought weeks or months at most. Did you talk to him? What precisely did he say? (My prediction is that he'd say that by the time we have human-level AGI, things will be moving very fast and we'll have superintelligence a few weeks later.)

Not sure exactly what the claim is, but happy to give my own view.

I think "AGI" is pretty meaningless as a threshold, and at any rate it's way too imprecise to be useful for this kind of quantitative forecast (I would intuitively describe GPT-3 as a general AI, and beyond that I'm honestly unclear on what distinction people are pointing at when they say "AGI").

My intuition is that by the time that you have an AI which is superhuman at every task (e.g. for $10/h of hardware it strictly dominates hiring a remote human for any task) then you are likely weeks rather than months from the singularity.

But mostly this is because I think "strictly dominates" is a very hard standard which we will only meet long after AI systems are driving the large majority of technical progress in computer software, computer hardware, robotics, etc. (Also note that we can fail to meet that standard by computing costs rising based on demand for AI.)

My views on this topic are particularly poorly-developed because I think that the relevant action (both technological transformation and catastrophic risk) mostly happens before this point, so I usually don't think this far ahead.

2Daniel Kokotajlo6mo
Thanks! That's what I thought you'd say. By "AGI" I did mean something like "for $10/h of hardware it strictly dominates hiring a remote human for any task" though I'd maybe restrict it to strategically relevant tasks like AI R&D, and also people might not actually hire AIs to do stuff because they might be afraid / understand that they haven't solved alignment yet, but it still counts since the AIs could do the job. Also there may be some funny business around the price of the hardware -- I feel like it should still count as AGI if a company is running millions of AIs that each individually are better than a typical tech company remote worker in every way, even if there is an ongoing bidding war and technically the price of GPUs is now so high that it's costing $1,000/hr on the open market for each AGI. We still get FOOM if the AGIs are doing the research, regardless of what the on-paper price is. (I definitely feel like I might be missing something here, I don't think in economic terms like this nearly as often as you do so) My timelines are too short to agree with this part alas. Well, what do you mean by "long after?"  Six months? Three years? Twelve years?
Thanks for offering your view Paul, and I apologize if I misrepresented your view.
Less relevant now, but I got the "few years" from the post you linked. There Christiano talked about another gap than AGI -> ASI, but since overall he seems to expect linear progress, I thought my conclusion was reasonable. In retrospect, I shouldn't have made that comment.
But yes, Christiano is the authority here;)
I’ve begun to doubt (1) recently, would be interested in seeing the arguments in favor of it. My model is something like “well, I’m human-level, and I sure don’t feel like I could foom if I were an AI.”

I've also been bothered recently by a blurring of lines between "when AGI becomes as intelligent as humans" and "when AGI starts being able to recursively self-improve." It's not a priori obvious that these should happen at around the same capabilities level, yet I feel like it's common to equivocate between them.

In any case, my world model says that an AGI should actually be able to recursively self-improve before reaching human-level intelligence. Just as you mentioned, I think the relevant intuition pump is "could I FOOM if I were an AI?" Considering the ability to tinker with my own source code and make lots of copies of myself to experiment on, I feel like the answer is "yes."

That said, I think this intuition isn't worth much for the following reasons:

  • The first AGIs will probably have their capabilities distributed very differently than humans -- i.e. they will probably be worse than humans at some tasks and much better at other tasks. What really matters is how good they are the task "do ML research" (or whatever paradigm we're using to make AI's at the time). I think there are reasons to expect them to be especially good at ML research (relative to their general level of int
... (read more)
Counter-anecdote: compilers have gotten ~2x better in 20 years[1], at substantially worse compile time. This is nowhere near FOOM. 1. ^ Proebsting's Law gives an 18-year doubling time. The 2001 reproduction [] suggested more like 20 years under optimistic assumptions, and a 2022 informal test [] showed a 10-15% improvement on average in the last 10 years (or a 50-year doubling time...)

The straightforward argument goes like this:

1. an human-level AGI would be running on hardware making human constraints in memory or speed mostly go away by ~10 orders of magnitude

2. if you could store 10 orders of magnitude more information and read 10 orders of magnitude faster, and if you were able to copy your own code somewhere else, and the kind of AI research and code generation tools available online were good enough to have created you, wouldn't you be able to FOOM?

No because of the generalized version of Amdhal's law, which I explored in "Fast Minds and Slow Computers []". The more you accelerate something, the slower and more limiting all it's other hidden dependencies become. So by the time we get to AGI, regular ML research will have rapidly diminishing returns (and cuda low level software or hardware optimization will also have diminishing returns), general hardware improvement will be facing the end of moore's law, etc etc.
3Daniel Kokotajlo7mo
I don't see why that last sentence follows from the previous sentences. In fact I don't think it does. What if we get to AGI next year? Then returns won't have diminished as much & there'll be lots of overhang to exploit.
Sure - if we got to AGI next year - but for that to actually occur you'd have to exploit most of the remaining optimization slack in both high level ML and low level algorithms. Then beyond that Moore's law is already mostly ended or nearly so depending on who you ask, and most of the easy obvious hardware arch optimizations are now behind us.
Well I would assume a “human-level AI” is an AI which performs as well as a human when it has the extra memory and running speed? I think I could FOOM eventually under those conditions but it would take a lot of thought. Being able to read the AI research that generated me would be nice but I’d ultimately need to somehow make sense of the inscrutable matrices that contain my utility function.

Elon Musk is a real-life epic tragic hero, authored by someone trying specifically to impart lessons to EAs/rationalists:

--Young Elon thinks about the future, is worried about x-risk. Decides to devote his life to fighting x-risk. Decides the best way to do this is via developing new technologies, in particular electric vehicles (to fight climate change) and space colonization (to make humanity a multiplanetary species and thus robust to local catastrophes)

--Manages to succeed to a legendary extent; builds two of the worlds leading tech giants, each with a business model notoriously hard to get right and each founded on technology most believed to be impossible. At every step of the way, mainstream expert opinion is that each of his companies will run out of steam and fail to accomplish whatever impossible goal they have set for themselves at the moment. They keep meeting their goals. SpaceX in particular brought cost to orbit down by an order of magnitude, and if Starship works out will get one or two more OOMs on top of that. Their overarching goal is to make a self-sustaining city on mars and holy shit it looks like they are actually succeeding. Did all this on a shoestring budg... (read more)

I agree with you completely and think this is very important to emphasize.  I also think the law of equal and opposite advice applies. Most people act too quickly without thinking. EAs tend towards the opposite, where it’s always “more research is needed”. This can also lead to bad outcomes if the results of the status quo are bad.  I can’t find it, but recently there was a post about the EU policy on AI and the author said something along the lines of “We often want to wait to advise policy until we know what would be good advice. Unfortunately, the choice isn’t give suboptimal advice now or great advice in 10 years. It’s give suboptimal advice now or never giving advice at all and politicians doing something much worse probably. Because the world is moving, and it won’t wait for EAs to figure it all out.” I think this all largely depends on what you think the outcome is if you don’t act. If you think that if EAs do nothing, the default outcome is positive, you should err on extreme caution. If you think that the default is bad, you should be more willing to act, because an informed, altruistic actor increases the value of the outcome in expectation, all else being equal.
It wasn't clear what this meant. This made it seem like it was a word for a type of company.
2Daniel Kokotajlo2y
Thanks, made some edits. I still don't get your second point though I'm afraid.
The second point isn't important, it's an incorrect inference/hypothesis, predicated on the first bit of information being missing. (So it's fixed.)
It's not clear that would have been sufficient to change the outcome (above).
2Daniel Kokotajlo2y
I feel optimistic that if he had spent a lot more time reading, talking, and thinking carefully about it, he would have concluded that founding OpenAI was a bad idea. (Or else maybe it's actually a good idea and I'm wrong.) Can you say more about what you have in mind here? Do you think his values are such that it actually was a good idea by his lights? Or do you think it's just so hard to figure this stuff out that thinking more about it wouldn't have helped?
My point was just: How much thinking/researching would have been necessary to avoid the failure? 5 hours? 5 days? 5 years? 50? What does it take to not make a mistake? (Or just, that one in particular?) Expanding on what you said: Is it a mistake that wouldn't have been solved that way? (Or...solved that way easily? Or another way that would have fixed that problem faster?) For research to trivially solve a problem, it has...someone pointing out it's a bad idea. (Maybe talking with someone and having them say _ is the fix.)

Technologies I take for granted now but remember thinking were exciting and cool when they came out

  • Smart phones
  • Google Maps / Google Earth
  • Video calls
  • Facebook
  • DeepDream (whoa! This is like drug hallucinations... I wonder if they share a similar underlying mechanism? This is evidence that ANNs are more similar to brains than I thought!)
  • AlphaGo
  • AlphaStar (Whoa! AI can handle hidden information!)
  • OpenAI Five (Whoa! AI can work on a team!)
  • GPT-2 (Whoa! AI can write coherent, stylistically appropriate sentences about novel topics like unicorns in the andes!)
  • GPT-3

I'm sure there are a bunch more I'm missing, please comment and add some!

7Gordon Seidoh Worley2y
Some of my own: * SSDs * laptops * CDs * digital cameras * modems * genome sequencing * automatic transmissions for cars that perform better than a moderately skilled human using a manual transmission can * cheap shipping * solar panels with reasonable power generation * breathable wrinkle free fabrics that you can put in the washing machine * bamboo textiles * good virtual keyboards for phones * scissor switches * USB * GPS
2Daniel Kokotajlo2y
Oh yeah, cheap shipping! I grew up in a military family, all around the world, and I remember thinking it was so cool that my parents could go on "ebay" and order things and then they would be shipped to us! And then now look where we are -- groceries delivered in ten minutes! Almost everything I buy, I buy online!
Heh.  In my youth, home computers were somewhat rare, and modems even more so.  I remember my excitement at upgrading to 2400bps, as it was about as fast as I could read the text coming across.  My current pocket computer is about 4000 times faster, has 30,000 times as much RAM, has hundreds of times more pixels and colors, and has worldwide connectivity thousands of times faster.  And I don't even have to yell at my folks to stay off the phone while I'm using it! I lived through the entire popularity cycle of fax machines.   My parents grew up with black-and-white CRTs based on vacuum tubes - the transistor was invented in 1947.  They had just a few channels of broadcast TV and even audio recording media was somewhat uncommon (cassette tapes in the mid-60s, video tapes didn't take off until the late 70s).   

My baby daughter was born two weeks ago, and in honor of her existence I'm building a list of about 100 technology-related forecasting questions, which will resolve in 5, 10, and 20 years. Questions like "By the time my daughter is 5/10/20 years old, the average US citizen will be able to hail a driverless taxi in most major US cities." (The idea is, tying it to my daughter's age will make it more fun and also increase the likelihood that I actually go back and look at it 10 years later.)

I'd love it if the questions were online somewhere so other people could record their answers too. Does this seem like a good idea? Hive mind, I beseech you: Help me spot ways in which this could end badly!

On a more positive note, any suggestions for how to do it? Any expressions of interest in making predictions with me?


EDIT: Now it's done, though I have yet to import it to it works perfectly fine in spreadsheet form.

I find the conjunction of your decision to have kids and your short AI timelines pretty confusing. The possibilities I can think of are (1) you're more optimistic than me about AI alignment (but I don't get this impression from your writings), (2) you think that even a short human life is worth living/net-positive, (3) since you distinguish between the time when humans lose control and the time when catastrophe actually happens, you think this delay will give more years to your child's life, (4) your decision to have kids was made before your AI timelines became short. Or maybe something else I'm not thinking of? I'm curious to hear your thinking on this.

4 is correct. :/

Oh :0
I'm interested, and I'd suggest using [] for this
I love the idea. Some questions and their associated resolution dates may be of interest to the wider community of forecasters, so you could post them to Metaculus. Otherwise you could perhaps persuade the Metaculus admins to create a subforum, similar to, for the other questions to be posted. Since Metaculus already has the subforum functionality, it seems a good idea to extend it in this way (perhaps a user's subforum could be associated with the corresponding username: e.g. user kokotajlo can post his own questions at

I have on several occasions found myself wanting to reply in some conversation with simply this image:

I think it cuts through a lot of confusion and hot air about what the AI safety community has historically been focused on and why.

Image comes from Steven Byrnes.

I made this a while back to organize my thoughts about how all philosophy fits together:

I find the bright green text on white background difficult to read even on a large screen. I would recommend black or dark gray text instead.
Invert the colors, and it's more readable.
3[comment deleted]2y

When I first read the now-classic arguments for slow takeoff -- e.g. from Paul and Katja -- I was excited; I thought they described a serious alternative scenario to the classic FOOM scenarios. However I never thought, and still do not think, that the classic FOOM scenarios were very unlikely; I feel that the slow takeoff and fast takeoff scenarios are probably within a factor of 2 of each other in probability.

Yet more and more nowadays I get the impression that people think slow takeoff is the only serious possibility. For example, Ajeya and Rohin seem very confident that if TAI was coming in the next five to ten years we would see loads more economic applications of AI now, therefore TAI isn't coming in the next five to ten years...

I need to process my thoughts more on this, and reread their claims; maybe they aren't as confident as they sound to me. But I worry that I need to go back to doing AI forecasting work after all (I left AI Impacts for CLR because I thought AI forecasting was less neglected) since so many people seem to have wrong views. ;)

This random rant/musing probably isn't valuable to anyone besides me, but hey, it's just a shortform. If you are reading this and you have thoughts or advice for me I'd love to hear it.

So there is a distribution over AGI plan costs. The max cost is some powerful bureaucrat/CEO/etc who has no idea how to do it at all but has access to huge amounts of funds, so their best bet is to try and brute force it by hiring all the respected scientists (eg manhattan project).  But notice - if any of these scientists (or small teams) actually could do it mostly on their own (perhaps say with vc funding) - then usually they'd get a dramatically better deal doing it on their own rather than for bigcorp. The min cost is the lucky smart researcher who has mostly figured out the solution, but probably has little funds, because they spent career time only on a direct path. Think wright brothers after the wing warping control trick they got from observing bird flight. Could a bigcorp or government have beat them? Of course, but the bigcorp would have had to spend OOM more. Now add a second dimension let's call vision variance - the distribution of AGI plan cost over all entities pursuing it. If that distribution is very flat, then everyone has the same obvious vision plan (or different but equivalently costly plans) and the winner is inevitably a big central player.  However if the variance over visions/plans is high, then the winner is inevitably a garage researcher.   Software is much like flight in this regard - high vision variance. Nearly all major software tech companies were scrappy garage startups - google, microsoft, apple, facebook, etc. Why? Because it simply doesn't matter at all how much money the existing bigcorp has - when the idea for X new software thing first occurs in human minds, it only occurs in a few, and those few minds are smart enough to realize it's value, and they can implement it. The big central player is a dinosaur with zero leverage, and doesn't see it coming until it's too late. AGI could be like software because . . it probably will be software. Alternatively it could be more like the manhattan project in that it fits into a well

I just want to say: Well done, Robin Hanson, for successfully predicting the course of the coronavirus over the past year. I remember a conversation with him in, like, March 2020 where he predicted that there would be a control system, basically: Insofar as things get better, restrictions would loosen and people would take more risks and then things would get worse, and trigger harsher restrictions which would make it get better again, etc. forever until vaccines were found. I think he didn't quite phrase it that way but that was the content of what he said. (IIRC he focused more on how different regions would have different levels of crackdown at different times, so there would always be places where the coronavirus was thriving to reinfect other places.) Anyhow, this was not at all what I predicted at the time, nobody I know besides him made this prediction at the time.

2Yoav Ravid2y
I wonder why he didn't (if he didn't) talk about it in public too. I imagine it could have been helpful - Anyone who took him seriously could have done better.
He did write something along similar lines here:
2[comment deleted]2y

Product idea: Train a big neural net to be a DJ for conversations. Collect a dataset of movie scripts with soundtracks and plot summaries (timestamped so you know what theme or song was playing when) and then train a model with access to a vast library of soundtracks and other media to select the appropriate track for a given conversation. (Alternatively, have it create the music from scratch. That sounds harder though.)

When fully trained, hopefully you'll be able to make apps like "Alexa, listen to our conversation and play appropriate music" and "type "Maestro:Soundtrack" into a chat or email thread & it'll read the last 1024 tokens of context and then serve up an appropriate song. Of course it could do things like lowering the volume when people are talking and then cranking it up when there's a pause or when someone says something dramatic.

I would be surprised if this would actually work as well as I hope. But it might work well enough to be pretty funny.

4Eli Tyre1y
This is an awesome idea.
2Daniel Kokotajlo1y
Glad you like it! Hmm, should I maybe post this somewhere in case someone with the relevant skills is looking for ideas? idk what the etiquette is for this sort of thing, maybe ideas are cheap.

Perhaps one axis of disagreement between the worldviews of Paul and Eliezer is "human societal competence." Yudkowsky thinks the world is inadequate and touts the Law of Earlier Failure according to which things break down in an earlier and less dignified way than you would have thought possible. (Plenty of examples from coronavirus pandemic here). Paul puts stock in efficient-market-hypothesis style arguments, updating against <10 year timelines on that basis, expecting slow distributed continuous takeoff, expecting governments and corporations to be taking AGI risk very seriously and enforcing very sophisticated monitoring and alignment schemes, etc.

(From a conversation with Jade Leung)

It seems to me that human society might go collectively insane sometime in the next few decades. I want to be able to succinctly articulate the possibility and why it is plausible, but I'm not happy with my current spiel. So I'm putting it up here in the hopes that someone can give me constructive criticism:

I am aware of three mutually-reinforcing ways society could go collectively insane:

    1. Echo chambers/filter bubbles/polarization: Arguably political polarization is increasing across the world of liberal democracies today. Perhaps the internet has something to do with this--it’s easy to self-select into a newsfeed and community that reinforces and extremizes your stances on issues. Arguably recommendation algorithms have contributed to this problem in various ways--see e.g. “Sort by controversial” and Stuart Russell’s claims in Human Compatible. At any rate, perhaps some combination of new technology and new cultural or political developments will turbocharge this phenomenon. This could lead to civil wars, or more mundanely, societal dysfunction. We can’t coordinate to solve collective action problems relating to AGI if we are all arguing
... (read more)
5Ben Pace4y
All good points, but I feel like objecting to the assumption that society is currently sane and then we'll see a discontinuity, rather than any insanity being a continuation of current trajectories.
1Daniel Kokotajlo4y
I agree with that actually; I should correct the spiel to make it clear that I do. Thanks!
Related: "Is Clickbait Destroying Our General Intelligence?" []

I'm listening to this congressional hearing about Facebook & the harmful effects of its algorithms:

I recommend listening to it yourself. I'm sorry I didn't take timestamped notes, then maybe you wouldn't have to. I think that listening to it has subtly improved my intuitions/models/priors about how US government and society might react to developments in AI in the future.

In a sense, this is already an example of an "AI warning shot" and the public's reaction to it. This hearing contains lots of discussion about Facebook's algorithms, discussion about how the profit-maximizing thing is often harmful but corporations have an incentive to do it anyway, discussion about how nobody understands what these algorithms really think & how the algorithms are probably doing very precisely targeted ads/marketing even though officially they aren't being instructed to. So, basically, this is a case of unaligned AI causing damage -- literally killing people, according to the politicians here.

And how do people react to it? Well, the push in this meeting here seems to be to name Facebook upper management as responsible and punish them, while also r... (read more)

$100 bet between me & Connor Leahy:

(1) Six months from today, Paul Christiano (or ARC with Paul Christiano's endorsement) will NOT have made any public statements drawing a 'red line' through any quantitative eval (anything that has a number attached to it, that is intended to measure an AI risk relevant factor, whether or not it actually succeeds at actually measuring that factor well), e.g. "If a model achieves X score on the Y benchmark, said model should not be deployed and/or deploying said model would be a serious risk of catastrophe." Connor at 95%, Daniel at 45%

(2) If such a 'red line' is produced, GPT4 will be below it this year. Both at 95%, for an interpretation of GPT-4 that includes AutoGPT stuff (like what ARC did) but not fine-tuning.

(3) If such a 'red line' is produced, and GPT4 is below it on first evals, but later tests show it to actually be above (such as by using different prompts or other testing methodology), the red line will be redefined or the test declared faulty rather than calls made for GPT4 to be pulled from circulation. Connor at 80%, Daniel at 40%, for same interpretation of GPT-4.

(4) If ARC calls for GPT4 to be pul... (read more)

Regarding betting odds: are you aware of this post []? It gives a betting algorithm that satisfies both of the following conditions: * Honesty: participants maximize their expected value by being reporting their probabilities honestly. * Fairness: participants' (subjective) expected values are equal. The solution is "the 'loser' pays the 'winner' the difference of their Brier scores, multiplied by some pre-determined constant C". This constant C puts an upper bound on the amount of money you can lose. (Ideally C should be fixed before bettors give their odds, because otherwise the honesty desideratum above could break, but I don't think that's a problem here.)
4Daniel Kokotajlo1mo
I was not aware, but I strongly suspected that someone on LW had asked and answered the question before, hence why I asked for help. Prayers answered! Thank you! Connor, are you OK with Scott's algorithm, using C = $100?
5Connor Leahy1mo
Looks good to me, thank you Loppukilpailija!

This article says OpenAI's big computer is somewhere in the top 5 largest supercomputers. I reckon it's fair to say their big computer is probably about 100 petaflops, or 10^17 flop per second. How much of that was used for GPT-3? Let's calculate.

I'm told that GPT-3 was 3x10^23 FLOP. So that's three million seconds. Which is 35 days.

So, what else have they been using that computer for? It's been probably about 10 months since they did GPT-3. They've released a few things since then, but nothing within an order of magnitude as big as GPT-3 except possibly DALL-E which was about order of magnitude smaller. So it seems unlikely to me that their publicly-released stuff in total uses more than, say, 10% of the compute they must have available in that supercomputer. Since this computer is exclusively for the use of OpenAI, presumably they are using it, but for things which are not publicly released yet.

Is this analysis basically correct?

Might OpenAI have access to even more compute than that?

100 petaflops is 'only' about 1,000 GPUs, or considerably less if they are able to use lower precision modes. I'm guessing they have almost 100 researchers now? Which is only about 10 GPUs per researcher, and still a small budget fraction (perhaps $20/hr ish vs > $100/hr for the researcher).  It doesn't seem like they have a noticeable compute advantage per capita.

I keep seeing tweets and comments for which the best reply is this meme:

2Daniel Kokotajlo3mo
I don't remember the original source of this meme, alas.
Originally by Robert Wiblin [], account now deleted.

Registering a prediction: I do NOT think the true Turing Test will be passed prior to the point of no return / powerbase ability / AGI / APS-AI. I think instead that even as things go off the rails and humans lose control, the TT will still be unpassed, because there'll still be some obscure 'gotcha' areas in which AIs are subhuman, if only due to lack of training in those areas. And that's enough for the judge to distinguish the AI from the human.

Agree.  Though I don't think Turing ever intended that test to be used.  I think what he wanted to accomplish with his paper was to operationalize "intelligence".  When he published it, if you asked somebody "Could a computer be intelligent?", they'd have responded with a religious argument about it not having a soul, or free will, or consciousness.  Turing sneakily got people to  look past their metaphysics, and ask the question in terms of the computer program's behavior.  THAT was what was significant about that paper.
4Daniel Kokotajlo7mo
(I've thought this for years but figured I should state it for the record. It's also not an original thought, probably others have said it before me.)
1Alexander Gietelink Oldenziel7mo
Thanks Daniel, that's good to know. Sam Altman's tweeting has been concerning lately. But it would seem that with a fixed size content window you won't be able to pass a true Turing test. 

People from AI Safety camp pointed me to this paper:

It shows how "knowing" and "saying" are two different things in language models.

This is relevant to transparency, deception, and also to rebutting claims that transformers are "just shallow pattern-matchers" etc.

I'm surprised people aren't making a bigger deal out of this!

When I saw this cool new OpenAI paper, I thought of Yudkowsky's Law of Earlier/Undignified Failure:

WebGPT: Improving the factual accuracy of language models through web browsing (

Relevant quote:

In addition to these deployment risks, our approach introduces new risks at train time by giving the model access to the web. Our browsing environment does not allow full web access, but allows the model to send queries to the Microsoft Bing Web Search API and follow links that already exist on the web, which can have side-effects. From our experience with GPT-3, the model does not appear to be anywhere near capable enough to dangerously exploit these side-effects. However, these risks increase with model capability, and we are working on establishing internal safeguards against them.

To be clear I am not criticizing OpenAI here; other people would have done this anyway even if they didn't. I'm just saying: It does seem like we are heading towards a world like the one depicted in What 2026 Looks Like where by the time AIs develop the capability to strategically steer the future in ways unaligned to human values... they are already roaming freely around the internet, learning... (read more)

"Tool AIs want to be agent AIs."

For fun:

“I must not step foot in the politics. Politics is the mind-killer. Politics is the little-death that brings total obliteration. I will face my politics. I will permit it to pass over me and through me. And when it has gone past I will turn the inner eye to see its path. Where the politics has gone there will be nothing. Only I will remain.”

Makes about as much sense as the original quote, I guess. :P

I just found Eric Drexler's "Paretotopia" idea/talk. It seems great to me; it seems like it should be one of the pillars of AI governance strategy. It also seems highly relevant to technical AI safety (though that takes much more work to explain).

Why isn't this being discussed more? What are the arguments against it?

-1Gerald Monroe2y
Without watching the video, prior knowledge of the nanomachinery proposals show that a simple safety mechanism is feasible.         No nanoscale robotic system can should be permitted to store more than a small fraction of the digital file containing the instructions to replicate itself.  Nor should it have sufficient general purpose memory to be capable of this. This simple rule makes nanotechnology safe from grey goo.  It becomes nearly impossible as any system that gets out of control will have a large, macroscale component you can turn off.  It's also testable, you can look at the design of a system and determine if it meets the rule or not. AI alignment is kinda fuzzy and I haven't heard of a simple testable rule.  Umm, also if such a rule exists then MIRI would have an incentive not to discuss it.   At least for near term agents we can talk about such rules.  They have to do with domain bounding.  For example, the heuristic for a "paperclip manufacturing subsystem" must include terms in the heuristic for "success" that limit the size  of the paperclip manufacturing machinery.  These terms should be redundant and apply more than a single check.  So for example, the agent might:    Seek maximum paperclips produced with large penalty for : (greater than A volume of machinery, greater than B tonnage of machinery, machinery outside of markers C, greater than D probability of a human killed, greater than E probability of an animal harmed, greater than F total network devices, greater than G ..) Essentially any of these redundant terms are "circuit breakers" and if any trip the agent will not consider an action further. "Does the agent have scope-limiting redundant circuit breakers" is a testable design constraint.  While "is it going to be friendly to humans" is rather more difficult.
Will you outlaw bacteria? 
The point was to outlaw artificial molecular assemblers like Drexler described in Engines of Creation. Think of maybe something like bacteria but with cell walls made of diamond. They might be hard to deal with once released into the wild. Diamond is just carbon, so they could potentially consume carbon-based life, but no natural organism could eat them. This is the "ecophagy" scenario. But, I still think this is a fair objection. Some paths to molecular nanotechnology might go through bio-engineering, the so-called "wet nanotechnology" approach. We'd start with something like a natural bacterium, and then gradually replace components of the cell with synthetic chemicals, like amino acid analogues or extra base pairs or codons, which lets us work in an expanded universe of "proteins" that might be easier to engineer as well as having capabilities natural biology couldn't match. This kind of thing is already starting to happen. At what point does the law against self-replication kick in? The wet path is infeasible without it, at least early on.
1Gerald Monroe2y
The point was to outlaw artificial molecular assemblers like Drexler described in Engines of Creation. Not outlaw.  Prohibit "free floating" ones that can work without any further input (besides raw materials).  Allowed assemblers would be connected via network ports to a host computer system that has the needed digital files, kept in something that is large enough for humans to see it/break it with a fire axe or shotgun.   Note that making bacteria with gene knockouts so they can't replicate solely on their own, but have to be given specific amino acids in a nutrient broth, would be a way to retain control if you needed to do it the 'wet' way.   The law against self replication is the same testable principle, actually - putting the gene knockouts back would be breaking the law because each wet modified bacteria has all the components in itself to replicate itself again.  
1Gerald Monroe2y
I didn't create this rule.  But succinctly:    life on earth is more than likely stuck at a local maxima among the set of all possible self-replicating nanorobotic systems.   The grey goo scenario posits you could build tiny fully artificial nanotechnological 'cells', made of more durable and reliable parts, that could be closer to the global maxima for self-replicating nanorobotic systems.    These would then outcompete all life, bacteria included, and convert the biosphere to an ocean of copies of this single system.  People imagine each cellular unit might be made of metal, hence it would look grey to the naked eye, hence 'grey goo'.   (I won't speculate how they might be constructed, except to note that you would use AI agents to find designs for these machines.  The AI agents would do most of their exploring in a simulation and some exploring using a vast array of prototype 'nanoforges' that are capable of assembling test components and full designs.  So the AI agents would be capable of considering any known element and any design pattern known at the time or discovered in the process, then they would be capable of combining these ideas into possible 'global maxima' designs.  This sharing of information - where any piece from any prototype can be adapted and rescaled to be used in a different new prototype - is something nature can't do with conventional evolution - hence it could be many times faster )

I heard a rumor that not that many people are writing reviews for the LessWrong 2019 Review. I know I'm not, haha. It feels like a lot of work and I have other things to do. Lame, I know. Anyhow, I'm struck by how academia's solution to this problem is bad, but still better than ours!

--In academia, the journal editor reaches out to someone personally to beg them to review a specific piece. This is psychologically much more effective than just posting a general announcement calling for volunteers.

--In academia, reviews are anonymous, so you can half-ass them and be super critical without fear of repercussions, which makes you more inclined to do it. (And more inclined to be honest too!)

Here are some ideas for things we could do:

--Model our process after Academia's process, except try to improve on it as well. Maybe we actually pay people to write reviews. Maybe we give the LessWrong team a magic Karma Wand, and they take all the karma that the anonymous reviews got and bestow it (plus or minus some random noise) to the actual authors. Maybe we have some sort of series of Review Parties where people gather together, chat and drink tasty beverages, and crank out reviews for a few hours.

In general I approve of the impulse to copy social technology from functional parts of society, but I really don't think contemporary academia should be copied by default. Frankly I think this site has a much healthier epistemic environment than you see in most academic communities that study similar subjects. For example, a random LW post with >75 points is *much* less likely to have an embarrassingly obvious crippling flaw in its core argument, compared to a random study in a peer-reviewed psychology journal.

Anonymous reviews in particular strike me as a terrible idea. Bureaucratic "peer review" in its current form is relatively recent for academia, and some of academia's most productive periods were eras where critiques came with names attached, e.g. the physicists of the early 20th century, or the Republic of Letters. I don't think the era of Elsevier journals with anonymous reviewers is an improvement—too much unaccountable bureaucracy, too much room for hidden politicking, not enough of the purifying fire of public argument.

If someone is worried about repercussions, which I doubt happens very often, then I think a better solution is to use a new pseudonym. (This isn't the ... (read more)

Yeah, several those ideas are "obviously good", and the reason we haven't done them yet is mostly because the first half of December was full of competing priorities (marketing the 2018 books, running Solstice). But I expect us to be much more active/agenty about this starting this upcoming Monday.
Maybe that should be an event that happens in the garden?
Wouldn't this achieve the opposite of what we want, disincentivize reviews? Unless coupled with paying people to write reviews, this would remove the remaining incentive. I'd prefer going into the opposite direction, making reviews more visible (giving them a more prominent spot on the front page/on allPosts, so that more people vote on them/interact with them). At the moment, they still feel a bit disconnected from the rest of the site.

Maybe a tax on compute would be a good and feasible idea?

--Currently the AI community is mostly resource-poor academics struggling to compete with a minority of corporate researchers at places like DeepMind and OpenAI with huge compute budgets. So maybe the community would mostly support this tax, as it levels the playing field. The revenue from the tax could be earmarked to fund "AI for good" research projects. Perhaps we could package the tax with additional spending for such grants, so that overall money flows into the AI community, whilst reducing compute usage. This will hopefully make the proposal acceptable and therefore feasible.

--The tax could be set so that it is basically 0 for everything except for AI projects above a certain threshold of size, and then it's prohibitive. To some extent this happens naturally since compute is normally measured on a log scale: If we have a tax that is 1000% of the cost of compute, this won't be a big deal for academic researchers spending $100 or so per experiment (Oh no! Now I have to spend $1,000! No big deal, I'll fill out an expense form and bill it to the university) but it would be prohibitive for a corporat... (read more)

Would this work across different countries (and if so how)? It seems like if one country implemented such a tax, the research groups in that country would be out-competed by research groups in other countries without such a tax (which seems worse than the status quo, since now the first AGI is likely to be created in a country that didn't try to slow down AI progress or "level the playing field").
4Daniel Kokotajlo3y
Yeah, probably not. It would need to be an international agreement I guess. But this is true for lots of proposals. On the bright side, you could maybe tax the chip manufacturers instead of the AI projects? Idk. Maybe one way it could be avoided is if it came packaged with loads of extra funding for safe AGI research, so that overall it is still cheapest to work from the US.
2Daniel Kokotajlo3y
Another cool thing about this tax is that it would automatically counteract decreases in the cost of compute. Say we make the tax 10% of the current cost of compute. Then when the next generation of chips comes online, and the price drops by an order of magnitude, automatically the tax will be 100% of the cost. Then when the next generation comes online, the tax will be 1000%. This means that we could make the tax basically nothing even for major corporations today, and only start to pinch them later.

GPT-3 app idea: Web assistant. Sometimes people want to block out the internet from their lives for a period, because it is distracting from work. But sometimes one needs the internet for work sometimes, e.g. you want to google a few things or fire off an email or look up a citation or find a stock image for the diagram you are making. Solution: An app that can do stuff like this for you. You put in your request, and it googles and finds and summarizes the answer, maybe uses GPT-3 to also check whether the answer it returns seems like a good answer to the request you made, etc. It doesn't have to work all the time, or for all requests, to be useful. As long as it doesn't mislead you, the worst that happens is that you have to wait till your internet fast is over (or break your fast).

I don't think this is a great idea but I think there'd be a niche for it.

Charity-donation app idea: (ETA: If you want to make this app, reach out. I'm open to paying for it to exist.)

The app consists of a gigantic, full-screen button such that if you press it, the phone will vibrate and play a little satisfying "ching" sound and light up sparkles around where your finger hit, and $1 will be donated to GiveDirectly. You can keep slamming that button as much as you like to thereby donate as many dollars as you like.

In the corner there's a menu button that lets you change from GiveDirectly to Humane League or AMF or whatever (you can go into the settings and input the details for a charity of your choice, adding it to your personal menu of charity options, and then toggle between options as you see fit. You can also set up a "Donate $X per button press instead of $1" option and a "Split each donation between the following N charities" option.

Why is this a good idea:

I often feel guilty for eating out at restaurants. Especially when meat is involved. Currently I donate a substantial amount to charity on a yearly basis (aiming for 10% of income, though I'm not doing a great job of tracking that) but it feels like a chore, I have to remember to do it and then ... (read more)

How easy is it currently to make 1-dollar donations on a smartphone? Is there a way to do it for close to 0% fees? You likely wouldn't want to give an app store 30% of your donations. 
2Daniel Kokotajlo1mo
Good point. Maybe the most difficult part about making this app would be setting up the payments somehow so that they don't get heavily taxed by middlemen. I imagine it would be best for the app to actually donate, like, once every three months or so, and store up your dollars in the meantime.
I think this is a great idea. It could be called Give NOW or just GIVE or something. The single big satisfying button is such a stupid, great concept. The gamification aspect is good, but more importantly reducing the barrier to donating small amounts of money more often seems like a great thing to me. Often times the biggest barrier to donating more sadly is the inconvenience of doing so. Whipping our your phone, opening up GIVE and tapping the big button a few times encourages more donations and gives you that self-satisfying boost that pressing a big button and getting immediate feedback gives you these days. The social-cuing is a bonus too (and this seems far more adoptable than veganism for obvious reasons). I'd be interested in working on this. I work in enterprise application development and have TypeScript and React Native w/ Firebase experience and have built and deployed a toy app to the Apple app store before (no real Android experience though). I'd be particularly interested in working on the front-end design if someone else wants to collaborate on the back-end services we'd need to set up (payment system; auth; storage; etc.). Maybe reply here if you'd be interested?
3Aaron F25d
I would be interested in working on this with you. I'm in college for CS, and I have what I'm pretty sure is enough backend experience (and some frontend) to pull this off with you. I've never dealt with financial services before, but I've looked into payment processing a little bit and it doesn't seem too complicated. Anyway, if you'd like, DM me and maybe we can find a time to chat.
3Daniel Kokotajlo1mo
Yay! Thanks! I imagine the back-end services part is going to be the trickiest part. Maybe I should post on Bountied Rationality or EA forum looking for someone to collaborate with you.
Go for it! I'm not on either of those forums explicitly, but happy to collaborate :)
Hey! I'd be interested in working on this. My suggestion would be to use Flutter [] for front-end (React Native is perfectly fine as well, though) and especially to utilize an API like Pledge []'s one for back-end (as they've solved the tough parts of the donation process already and they don't really have any service fees when it comes to this use case). Coincidentally, I have roughly 10 years of experience of hobbyist game design, so we could think about adding e.g. prosocial features and mechanics down the line if you're interested.
2Daniel Kokotajlo23d
Nice! You both should check out this thread [] if you haven't already, and see if there are other people to possibly coordinate with. Also lots of good advice in there about the main difficulties, e.g. app store policies.
Thanks for the reply! I'm aware of the thread and I believe that we'd be able to solve the policy issues. Using an existing API like the Pledge's one mentioned above would be my strong recommendation, given that they indeed handle the heavy parts of the donation process. It would make dealing with the policies of the app stores a breeze compared to making the back-end from scratch, as in that case there would be a rather huge workload in dealing with the heavy (although necessary) bureaucracy. It would be nice if we started coordinating the development somehow. I would start with a central hub where all the comms would take place so that the discussion wouldn't become scattered and hard to follow. Maybe something like semi-open Slack or Discord server for more instant and spontaneous messaging and all the fancy extra features?
4Aaron F21d
How about a private channel in the EA Anywhere slack workspace ( We can also mention the project in their software engineering channel and see if anyone else wants to work with us. If this sounds good, join the workspace and then DM me (Aaron Fink) and I'll add you to a channel.
These all seem like great ideas! I think a Discord server sounds great. I know that @Aaron F [] was expressing interest here and on EA, I think, so a group of us starting to show interest might benefit from some centralized place to chat like you said. I got unexpectedly busy with some work stuff, so I'm not sure I'm the best to coordinate/ring lead, but I'm happy to pitch in however/whenever I can! Definitely open to learning some new things (like Flutter) too.
2Daniel Kokotajlo23d
Whatever you think is best! I don't have anything to contribute to the development except vision and money, but I'll check in as needed to answer questions about those things.

A few years ago there was talk of trying to make Certificates of Impact a thing in EA circles. There are lots of theoretical reasons why they would be great. One of the big practical objections was "but seriously though, who would actually pay money to buy one of them? What would be the point? The impact already happened, and no one is going to actually give you the credit for it just because you paid for the CoI."

Well, now NFT's are a thing. I feel like CoI's suddenly seem a lot more viable!

Here's my AI Theory reading list as of 3/28/2022. I'd love to hear suggestions for more things to add! You may be interested to know that the lessons from this list are part of why my timelines are so short.

On scaling laws:
(Original scaling laws paper, contains the IMO super-important graph showing that bigger models are more data-efficient) (Newer scaling laws paper, with more cool results and graphs, in particular graphs showing how you can extrapolate GPT performance seemingly forever) (Excellent presentation by Kaplan on the scaling laws stuff, also talks a bit about the theory of why it's happening)

Added 3/28/2022: Nice summary of the AlphaCode paper, which itself is notable for more scaling trend graphs! :)

On the bayesian-ness and simplicity-bias of neural networks (which explains why scaling works and should be expected to continue, IMO): (more like, the linked pos... (read more)

Cool list! I'll look into the ones I don't know or haven't read yet.

For the past year I've been thinking about the Agent vs. Tool debate (e.g. thanks to reading CAIS/Reframing Superintelligence) and also about embedded agency and mesa-optimizers and all of these topics seem very related now... I keep finding myself attracted to the following argument skeleton:

Rule 1: If you want anything unusual to happen, you gotta execute a good plan.

Rule 2: If you want a good plan, you gotta have a good planner and a good world-model.

Rule 3: If you want a good world-model, you gotta have a good learner and good data.

Rule 4: Having good data is itself an unusual happenstance, so by Rule 1 if you want good data you gotta execute a good plan.

Putting it all together: Agents are things which have good planner and learner capacities and are hooked up to actuators in some way. Perhaps they also are "seeded" with a decent world-model to start off with. Then, they get a nifty feedback loop going: They make decent plans, which allow them to get decent data, which allows them to get better world-models, which allows them to make better plans and get better data so they can get great world-models and make great plans and... etc. (The best agents will also be improving on their learning and planning algorithms! Humans do this, for example.)

Empirical conjecture: Tools suck; agents rock, and that's why. It's also why agenty mesa-optimizers will arise, and it's also why humans with tools will eventually be outcompeted by agent AGI.

How would you test the conjecture?
2Daniel Kokotajlo4y
The ultimate test will be seeing whether the predictions it makes come true--whether agenty mesa-optimizers arise often, whether humans with tools get outcompeted by agent AGI. In the meantime, it's not too hard to look for confirming or disconfirming evidence. For example, the fact that militaries and corporations that make a plan and then task their subordinates with strictly following the plan invariably do worse than those who make a plan and then give their subordinates initiative and flexibility to learn and adapt on the fly... seems like confirming evidence. (See: agile development model, the importance of iteration and feedback loops in startup culture, etc.) Whereas perhaps the fact that AlphaZero is so good despite lacking a learning module is disconfirming evidence. As for a test, well we'd need to have something that proponents and opponents agree to disagree on, and that might be hard to find. Most tests I can think of now don't work because everyone would agree on what the probable outcome is. I think the best I can do is: Someday soon we might be able to test an agenty architecture and a non-agenty architecture in some big complex novel game environment, and this conjecture would predict that for sufficiently complex and novel environments the agenty architecture would win.
I'd agree w/ the point that giving subordinates plans and the freedom to execute them as best as they can tends to work out better, but that seems to be strongly dependent on other context, in particular the field they're working in (ex. software engineering vs. civil engineering vs. military engineering), cultural norms (ex. is this a place where agile engineering norms have taken hold?), and reward distributions (ex. does experimenting by individuals hold the potential for big rewards, or are all rewards likely to be distributed in a normal fashion such that we don't expect to find outliers). My general model is in certain fields humans look more tool shaped and in others more agent shaped. For example an Uber driver when they're executing instructions from the central command and control algo doesn't require as much of the planning, world modeling behavior. One way this could apply to AI is that sub-agents of an agent AI would be tools.
3Daniel Kokotajlo4y
I agree. I don't think agents will outcompete tools in every domain; indeed in most domains perhaps specialized tools will eventually win (already, we see humans being replaced by expensive specialized machinery, or expensive human specialists, lots of places). But I still think that there will be strong competitive pressure to create agent AGI, because there are many important domains where agency is an advantage.
Expensive specialized tools are themselves learned by and embedded inside an agent [] to achieve goals. They're simply meso-optimization in another guise. eg AlphaGo learns a reactive policy which does nothing which you'd recognize as 'planning' or 'agentiness' - it just maps a grid of numbers (board state) to another grid of numbers (value function estimates of a move's value). A company, beholden to evolutionary imperatives, can implement internal 'markets' with 'agents' if it finds that useful for allocating resources across departments, or use top-down mandates if those work better, but no matter how it allocates resources, it's all in the service of an agent, and any distinction between the 'tool' and 'agent' parts of the company is somewhat illusory.

I came across this old Metaculus question, which confirms my memory of how my timelines changed over time:

30% by 2040 at first, then march 2020 I updated to 40%, then Aug 2020 I updated to 71%, then I went down a bit, and then now it's up to 85%. It's hard to get higher than 85% because the future is so uncertain; there are all sorts of catastrophes etc. that could happen to derail AI progress.

What caused the big jump in mid-2020 was sitting down to actually calculate my timelines in earnest. I ended up converging on something like the Bio Anchors framewor... (read more)

The International Energy Agency releases regular reports in which it forecasts the growth of various energy technologies for the next few decades. It's been astoundingly terrible at forecasting solar energy for some reason. Marvel at this chart:

This is from an article criticizing the IEA's terrible track record of predictions. The article goes on to say that there should be about 500GW of installed capacity by 2020. This article was published in 2020; a year later, the 2020 data is in, and it's actually 714 GW. Even the article criticizing the IEA for thei... (read more)

9Zac Hatfield-Dodds2y
The IEA is a running joke in climate policy circles; they're transparently in favour of fossil fuels and their "forecasts" are motivated by political (or perhaps commercial, hard to untangle with oil) interests rather than any attempt at predictive accuracy.
2Daniel Kokotajlo2y
OH ok thanks! Glad to hear that. I'll edit.
What do you mean by "transparently" in favour of fossil fuels? Is there anything like a direct quote e.g. of Fatih Birol backing this up?

Eric Drexler has argued that the computational capacity of the human brain is equivalent to about 1 PFlop/s, that is, we are already past the human-brain-human-lifetime milestone. (Here is a gdoc.) The idea is that we can identify parts of the human brain that seem to perform similar tasks to certain already-existing AI systems. It turns out that e.g. 1-thousandth of the human brain is used to do the same sort of image processing tasks that seem to be handled by modern image processing AI... so then that means an AI 1000x bigger than said AI should be able... (read more)

It is known that birds brains are much more mass-effective than mammalian.

One thing I find impressive about GPT-3 is that it's not even trying to generate text.

Imagine that someone gave you a snippet of random internet text, and told you to predict the next word. You give a probability distribution over possible next words. The end.

Then, your twin brother gets a snippet of random internet text, and is told to predict the next word. Etc. Unbeknownst to either of you, the text your brother gets is the text you got, with a new word added to it according to the probability distribution you predicted.

Then we repeat with your tr... (read more)

Has anyone done an expected value calculation, or otherwise thought seriously about, whether to save for retirement? Specifically, whether to put money into an account that can't be accessed (or is very difficult to access) for another twenty years or so, to get various employer matching or tax benefits?

I did, and came to the conclusion that it didn't make sense, so I didn't do it. But I wonder if anyone else came to the opposite conclusion. I'd be interested to hear their reasoning.

ETA: To be clear, I have AI timelines in mind here. I expect to be either ... (read more)

There's a lot of detail behind "expect to be" that matters here.  It comes down to "when is the optimal time to spend this money" - with decent investment options, if your satisfactory lifestyle has unspent income, the answer is likely to be "later".  And then the next question is "how much notice will I have when it's time to spend it all"?   For most retirement savings, the tax and match options are enough to push some amount of your savings into that medium.  And it's not really locked up - early withdrawal carries penalties, generally not much worse than not getting the advantages in the first place. And if you're liquidating because you think money is soon to be meaningless (for you, or generally), you can also borrow a lot, probably more than you could if you didn't have long-term assets to point to.   For me, the EV calculation comes out in favor of retirement savings.  I'm likely closer to it than you, but even so, the range of outcomes includes all of "unexpected death/singularity making savings irrelevant", "early liquidation for a pre-retirement use", and "actual retirement usage".  And all of that outweighs by a fair bit "marginal spending today". Fundamentally, the question isn't "should I use investment vehicles targeted for retirement", but "What else am I going to do with the money that's higher-value for my range of projected future experiences"?
4Daniel Kokotajlo1y
Very good point that I may not be doing much else with the money. I'm still saving it, just in more liquid, easy to access forms (e.g. stocks, crypto.) I'm thinking it might come in handy sometime in the next 20 years during some sort of emergency or crunch time, or to handle unforeseen family expenses or something, or to donate to a good cause.
4Daniel Kokotajlo1y
It's not obvious that unaligned AI would kill us. For example, we might be bargaining chips in some future negotiation with aliens.
2Charlie Steiner1y
My decision was pretty easy because I don't have any employer matching or any similarly large incentives. I don't think the tax incentives are big enough to make up for the inconvenience in the ~60% case where I want to use my savings before old age. However, maybe a mixed strategy would be more optimal.
1[comment deleted]1y

Years after I first thought of it, I continue to think that this chain reaction is the core of what it means for something to be an agent, AND why agency is such a big deal, the sort of thing we should expect to arise and outcompete non-agents. Here's a diagram:

Roughly, plans are necessary for generalizing to new situations, for being competitive in contests for which there hasn't been time for natural selection to do lots of optimization of policies. But plans are only as good as the knowledge they are based on. And knowledge doesn't come a priori; it nee... (read more)

6Yoav Ravid2y
Seems similar to the OODA loop []
4Daniel Kokotajlo2y
Yep! I prefer my terminology but it's basically the same concept I think.
6Gordon Seidoh Worley2y
I think it's probably even simpler than that: feedback loops are the minimum viable agent, i.e. a thermostat is the simplest kind of agent possible. Stuff like knowledge and planning are elaborations on the simple theme of the negative feedback circuit.
4Daniel Kokotajlo2y
I disagree; I think we go astray by counting things like thermostats as agents. I'm proposing that this particular feedback loop I diagrammed is really important, a much more interesting phenomenon to study than the more general category of feedback loop that includes thermostats.

In this post, Jessicata describes an organization which believes:

  1. AGI is probably coming in the next 20 years.
  2. Many of the reasons we have for believing this are secret.
  3. They're secret because if we told people about those reasons, they'd learn things that would let them make an AGI even sooner than they would otherwise.

At the time, I didn't understand why an organization would believe that. I figured they thought they had some insights into the nature of intelligence or something, some special new architecture for AI designs, that would accele... (read more)

The other day I heard this anecdote: Someone's friend was several years ago dismissive of AI risk concerns, thinking that AGI was very far in the future. When pressed about what it would take to change their mind, they said their fire alarm would be AI solving Montezuma's Revenge. Well, now it's solved, what do they say? Nothing; if they noticed they didn't say. Probably if they were pressed on it they would say they were wrong before to call that their fire alarm.

This story fits with the worldview expressed in "There's No Fire Alarm for AGI." I expect this sort of thing to keep happening well past the point of no return.

Also related: Is That Your True Rejection? [] There is this pattern when people say: "X is the true test of intelligence", and after a computer does X, they switch to "X is just a mechanical problem, but Y is the true test of intelligence". (Past values of X include: chess, go, poetry...) There was a meme about it that I can't find now.

I speculate that drone production in the Ukraine war is ramping up exponentially and will continue to do so. This means that however much it feels like the war is all about drones right now, it'll feel much more that way a year from now. Both sides will be regularly sending flocks of shahed-equivalents at each other, both sides will have reinvented tactics to center around FPV kamikaze drones, etc. Maybe we'll even see specialized anti-drone drones dogfighting with each other, though since there aren't any of those yet they won't have appeared in large numbers.

I guess this will result in the "no man's land" widening even further, to like 10km or so. (That's about the maximum range of current FPV kamikaze drones)

2Daniel Kokotajlo20d
On second thought, maybe it's already 10km wide for all I know. Hmm. Well, however wide it is now, I speculate it'll be wider this time next year.

Came across this short webcomic thingy on r/novelai. It was created entirely using AI-generated images. (Novelai I assume?)
I wonder how long it took to make.

Just imagine what'll be possible this time next year. Or the year after that.

Notes on Tesla AI day presentation: Here they claim they've got more than 10,000 GPUs in their supercomputer, and that this means their computer is more powerful than the top 5 publicly known supercomputers in the world. Consulting this list it seems that this would put their computer at just over 1 Exaflop per second, which checks out (I think I had heard rumors this was the case) and also if you look at this (read more)

In a recent conversation, someone said the truism about how young people have more years of their life ahead of them and that's exciting. I replied that everyone has the same number of years of life ahead of them now, because AI timelines. (Everyone = everyone in the conversation, none of whom were above 30)

I'm interested in the question of whether it's generally helpful or harmful to say awkward truths like that. If anyone is reading this and wants to comment, I'd appreciate thoughts.

6Steven Byrnes2y
I've been going with the compromise position of "saying it while laughing such that it's unclear whether you're joking or not" :-P
4Daniel Kokotajlo2y
The people who know me know I'm not joking, I think. For people who don't know me well enough to realize this, I typically don't make these comments.
2Steven Byrnes2y
I sometimes kinda have this attitude that this whole situation is just completely hilarious and absurd, i.e. that I believe what I believe about the singularity and apocalypse and whatnot, but that the world keeps spinning and these ideas have basically zero impact. And it makes me laugh. So when I shrug and say "I'm not saving enough for retirement; oh well, by then probably we'll all be dead or living in a radical post-work utopia", I'm not just laughing because it's ambiguously a joke, I'm also laughing because this kind of thing reminds me of how ridiculous this all is. :-P
What if things foom later than you're expecting - say during retirement? What if anti-aging enters the scene and retirement can last, much, much longer, before the foom?
2Steven Byrnes2y
Tbc my professional opinion is that people should continue to save for retirement :-P I mean, I don't have as much retirement savings as the experts say I should at my age ... but does anyone? Oh well...
"Truths" are persuasion, unless expected to be treated as hypotheses with the potential to evoke curiosity. This is charity, continuous progress on improving understanding of circumstances that produce claims you don't agree with, a key skill for actually changing your mind. By default charity is dysfunctional in popular culture, so non-adversarial use of factual claims that are not expected to become evident in short order depends on knowing that your interlocutor practices charity. Non-awkward factual claims are actually more insidious, as the threat of succeeding in unjustified persuasion is higher. So in a regular conversation, there is a place for arguments, not for "truths", awkward or not. Which in this instance entails turning the conversation to the topic of AI timelines. I don't think there are awkward arguments here in the sense of treading a social taboo minefield, so there is no problem with that, except it's work on what at this point happens automatically via stuff already written up online, and it's more efficient to put effort in growing what's available online than doing anything in person, unless there is a plausible path to influencing someone who might have high impact down the line.
It's fine to say that if you want the conversation to become a discussion of AI timelines. Maybe you do! But not every conversation needs to be about AI timelines.
I've stopped bringing up the awkward truths around my current friends. I started to feel like I was using to much of my built up esoteric social capital on things they were not going to accept (or at least want to accept). How can I blame them? If somebody else told me there was some random field that a select few of people interested in will be deciding the fate of all of humanity for the rest of time and I had no interest in that field I would want to be skeptical of it as well.  Especially if they were to through out some figures like 15 - 25 years from now (my current timelines) is when humanities rein over the earth will end because of this field. I found when I stopped bringing it up conversations were lighter and more fun. I've accepted we will just be screwing around talking about personal issues and the issues de jour, I don't mind it.  The truth is a bitter pill to get down, and if they no interest in helping AI research its probably best they don't live their life worrying about things they won't be able to change. So for me at least I saw personal life improvements on not bringing some of those awkward truths up. 
Depends on the audience and what they'll do with the reminder.  But that goes for the original statement as well (which remains true - there's enough uncertainty about AI timelines and impact on individual human lives that younger people have more years of EXPECTED (aka average across possible futures) life).
Whether it makes sense to tell someone an awkward truth depends often more on the person then on the truth.
Truths in general: This is especially true when the truth isn't in the words, but something you're trying to point at with them. Awkward truths: What makes something an awkward truth, is the person, anyway, so your statement seems tautological.

Probably, when we reach an AI-induced point of no return, AI systems will still be "brittle" and "narrow" in the sense used in arguments against short timelines.

Argument: Consider AI Impacts' excellent point that "human-level" is superhuman (bottom of this page)

The point of no return, if caused by AI, could come in a variety of ways that don't involve human-level AI in this sense. See this post for more. The general idea is that being superhuman at some skills can compensate for being subhuman at others. We should expect the point of no return to be reache... (read more)

How much video data is there? It seems there is plenty:

This says 500 hours of video are uploaded to youtube every minute. This says standard definition for youtube video is 854x480 = 409920 pixels. At 48fps, that’s 3.5e13 pixels of data every minute. Over the course of a whole year, that’s +5 OOMs, it comes out to 1.8e19 pixels of data every year. So yeah, even if we use some encoding that crunches pixels down to 10x10 vokens or whatever,... (read more)

4Gerald Monroe2y
So have you thought about what "data points" mean? If the data is random samples from the mandelbrot set, the maximum information the AI can ever learn is just the root equation used to generate the set. Human agents control a robotics system where we take actions and observe the results on our immediate environment. This sort of information seems to lead to very rapid learning especially for things where the consequences are near term and observable. You are essentially performing a series of experiments where you try action A vs B and observe what the environment does. This let's you rapidly cancel out data that doesn't matter, its how you learn that lighting conditions don't affect how a rock falls when you drop it. Point is the obvious training data for an AI would be similar. It needs to manipulate, both in sims and reality, the things we need it to learn about
2Daniel Kokotajlo2y
I've thought about it enough to know I'm confused! :) I like your point about active learning (is that the right term?). I wonder how powerful GPT-3 would be if instead of being force-fed random internet text from a firehose, it had access to a browser and could explore (would need some sort of curiosity reward signal?). Idk, probably this isn't a good idea or else someone would have done it.
3Gerald Monroe2y
I don't know that GPT-3 is the best metric for 'progress towards general intelligence'.  One example of the agents receiving 'active' data that resulted in interesting results is this []OpenAI experiment.   In this case the agents cannot emit text - which is what GPT-3 is doing that makes us feel it's "intelligent" - but can cleverly manipulate their environment in complex ways not hardcoded in.  The agents in this experiment are learning both movement to control how they view the environment and to use a few simple tools to accomplish a goal.   To me this seems like the most promising way forward.  I think that robust agents that can control real robots to do things, with those things becoming increasingly complex and difficult as the technology improves, might in fact be the "skeleton" of what would later allow for "real" sentience. Because from our perspective, this is our goal.  We don't want an agent that can babble and seem smart, we want an agent that can do useful things - things we were paying humans to do - and thus extend what we can ultimately accomplish.  (yes, in the immediate short term it unemploys lots of humans, but it also would make possible new things that previously we needed lots of humans to do.  It also should allow for doing things we know how to do now but with better quality/on a more broader scale.  ) More exactly, how do babies learn?  Yes, they learn to babble, but they also learn a set of basic manipulations of their body - adjusting their viewpoint - and manipulate the environment with their hands - learning how it responds. We can discuss more, I think I know how we will "get there from here" in broad strokes.  I don't think it will be done by someone writing a relatively simple algorithm and getting a sudden breakthrough that allows for sentience, I think it will be done by using well defined narrow domain agents that each do something extremely well - and by building higher level agen
2Daniel Kokotajlo2y
I'd be interested to hear more about this. It sounds like this could maybe happen pretty soon with large, general language models like GPT-3 + prompt programming + a bit of RL.

Came across this old (2004) post from Moravec describing the evolution of his AGI timelines over time. Kudos to him, I say. Compute-based predictions seem to have historically outperformed every other AGI forecasting method (at least the ones that were actually used), as far as I can tell.

What if Tesla Bot / Optimus actually becomes a big deal success in the near future (<6 years?) Up until recently I would be quite surprised, but after further reflection now I'm not so sure.

Here's my best "bull case:"

Boston Dynamics and things like this establish that getting robots to walk around over difficult terrain is possible with today's tech, it just takes a lot of engineering talent and effort.

So Tesla will probably succeed, within a few years, at building a humanoid robot that can walk around and pic... (read more)

3Lone Pine1y
I think the problem here is clear use cases. What is the killer app for the minimum viable robot?
2Daniel Kokotajlo1y
Yeah, that's the crux... Stocking shelves maybe? This seems like the best answer so far. Packing boxes in warehouses (if that's not already done by robots?) What about flipping burgers? What about driving trucks? Specifically, get an already-autonomous truck and then put one of these bots in it, so that if you need some physical hands to help unload the truck or fill it up with gas or do any of those ordinary menial tasks associated with driving long distances, you've got them. (A human can teleoperate remotely when the need arises) Maybe ordinary factory automation? To my surprise regular factory robots cost something like $50,000; if that's because they aren't mass-produced enough to benefit from economies of scale, then Tesla can swoop in with $20,000 humanoid robots and steal market share. (Though also the fact that regular factory robots cost so much is evidence that Tesla won't be able to get the price of their bots down so low) [] Maybe cleaning? In theory a robot like this could handle a mop, a broom, a dust wand, a sponge, a vacuum, etc. Could e.g. take all the objects off your sink, spray it with cleaner and wipe it down, then put all the objects back. Maybe cooking? Can chop carrots and stuff like that. Can probably follow a recipe, albeit hard-coded ones. If it can clean, then it can clean up its own messes. I wish I had a better understanding of the economy so I could have more creative ideas for bot-ready jobs. I bet there are a bunch I haven't thought of. ... Tesla FSD currently runs on hopium: People pay for it and provide training data for it, in the hopes that in a few years it'll be the long-prophecied robocar. Maybe a similar business model could work for Optimus. If they are steadily improving it and developing an exponentially growing list of skills

Random idea: Hard Truths Ritual:

Get a campfire or something and a notepad and pencil. Write down on the pad something you think is probably true, and important, but which you wouldn't say in public due to fear of how others would react. Then tear off that piece of paper and toss it in the fire. Repeat this process as many times as you can for five minutes; this is a brainstorming session, so your metric for success is how many diverse ideas you have multiplied by their average quality.

Next, repeat the above except instead of "you wouldn't say in public..."... (read more)

Interesting. The main reasons why I'd see something potentially falling into category 3+ (maybe 2 also) are either a) threat models where I am observed far more than otherwise expected or b) threat models where cognitohazards exist. ...which for a) leads to "write something on a piece of paper and throw it in the fire" also being insecure, and for b) leads to "thinking of it is a bad idea regardless of what you do after".
3Daniel Kokotajlo1y
It sounds like you are saying you are unusually honest with yourself, much more than most humans. Yes? Good point about cognitohazards. I'd say: Beware self-fulfilling prophecies.
I think you are underestimating how much I think falls into these categories. I suspect (although I do not know) that much of what you would call being dishonest to oneself I would categorize into a) or b). (General PSA: although choosing a career that encourages you to develop your natural tendencies can be a good thing, it also has downsides. Being someone who is on the less trusting side of things at the best of times and works in embedded hardware with an eye toward security...  I am rather acutely aware of how[1] much[2] information[3] leakage[4] there is from e.g. the phone in your pocket. Typical English writing speed is ~13 WPM[5]. English text has ~9.83 bits of entropy / word[6]. That's only, what, 2.1 bits / second? That's tiny[7][8].)  (I don't tend to like the label, mainly because of the connotations, but the best description might be 'functionally paranoid'. I'm the sort of person who reads the IT policy at work, notes that it allows searches of personal devices, and then never brings a data storage device of any sort to work as a result.) Could you please elaborate? 1. ^ [] ("whoops, brightness is a sidechannel attack to recover audio because power supplies aren't perfect". Not typically directly applicable for a phone, but still interesting.) 2. ^ [] ("whoops, you can deduce keystrokes from audio recordings" (which probably means you can also do the same with written text...)) 3. ^ [] ("whoops, wifi can be used as a passive radar") 4. ^
3Daniel Kokotajlo1y
Ah, I guess I was assuming that things in a are not in b, things in b are not in c, etc. although as-written pretty much anything in a later category would also be in all the earlier categories. Things you aren't willing to say to anyone, you aren't willing to say in public. Etc. Example self-fulfilling prophecy: "I'm too depressed to be useful; I should just withdraw so as to not be a burden on anybody." For some people it's straightforwardly false, for others its straightforwardly true, but for some it's true iff they think it is.

I recommend The Meme Machine, it's a shame it didn't spawn a huge literature. I was thinking a lot about memetics before reading it, yet still I feel like I learned a few important things.

Anyhow, here's an idea inspired by it:

First, here is my favorite right way to draw analogies between AI and evolution:

Evolution : AI research over time throughout the world

Gene : Bit of code on Github

Organism : The weights of a model

Past experiences of an organism : Training run of a model

With that as background context, I can now present the idea.

With humans, memetic e... (read more)

I spent way too much time today fantasizing about metal 3D printers. I understand they typically work by using lasers to melt a fine layer of metal powder into solid metal, and then they add another layer of powder and melt more of it, and repeat, then drain away the unmelted powder. Currently they cost several hundred thousand dollars and can build stuff in something like 20cm x 20cm x 20cm volume. Well, here's my fantasy design for an industrial-scale metal 3D printer, that would probably be orders of magnitude better in every way, and thus hopefull... (read more)

Cool sci-fi-ish idea, but my impression has been that 3D printing is viable for smaller and/or specific objects for which there is not enough demand to set up a separate production line. If economies of scale start to play a role then setting up a more specifically optimized process wins over general purpose 3D plant. 
2Daniel Kokotajlo2y
This sort of thing would bring the cost down a lot, I think. Orders of magnitude, maybe. With costs that low, many more components and products would be profitable to 3D print instead of manufacture the regular way. Currently, Tesla uses 3D printing for some components of their cars I believe; this proves that for some components (tricky, complex ones typically) it's already cost-effective. When the price drops by orders of magnitude, this will be true for many more components. I'm pretty sure there would be sufficient demand, therefore, to keep at least a few facilities like this running 24/7.

Ballistics thought experiment: (Warning: I am not an engineer and barely remember my high school physics)

You make a hollow round steel shield and make it a vacuum inside. You put a slightly smaller steel disc inside the vacuum region. You make that second disc rotate very fast. Very fast indeed.

An incoming projectile hits your shield. It is several times thicker than all three layers of your shield combined. It easily penetrates the first layer, passing through to the vacuum region. It contacts the speedily spinning inner layer, and then things get crazy..... (read more)

This is well-beyond today's technology to build. By the time we have the technology to build one of these shields we will also have prolific railguns. The muzzle velocity of a railgun today exceeds 3 km/s[1] for a 3 kg slug. As a Fermi estimate, I will treat the impact velocity of a railgun as 1 km/s. The shield must have a radial velocity much larger than the incoming projectile. Suppose the radial velocity of the shield is 100 km/s, its mass is 1000×3kg=3000kg and you cover a target with 100 such shields. The kinetic energy of each shield is E=12Iω2. The moment of inertia is I=12mr2. We can calculate the total kinetic energy of n shields. ETOT=14nmr2ω2=14nmv2=10043000kg(100,000ms)2=75TJ This is an unstable system. If anything goes wrong like an earthquake or a power interruption, the collective shield is likely to explode in a cascading failure. Within Earth's atmosphere, a cascading explosion is guaranteed the first time it is hit. Such a failure would release 75 terajoules of energy. For comparison, the Trinity nuclear test released 92 terajoules of energy. This proposed shield amounts to detonating a fission bomb on yourself to block to a bullet. Yes. The bullet is destroyed. Spinning a disk keeps its mass and weight the same. The disk becomes a gyroscope. Gyroscopes are hard to rotate but no harder to move than non-rotating objects. This is annoying for Earth-based buildings because the Earth rotates under them while the disks stay still. -------------------------------------------------------------------------------- 1. In reality this is significantly limited by air resistance but air resistance can be partially mitigated by firing on trajectories that mostly go over the atmosphere. ↩︎
2Daniel Kokotajlo3y
Lovely, thanks! This was fun to think about. I had been hoping that the shield, while disintegrating and spraying bits of metal, would do so in a way that mostly doesn't harm the thing it is protecting, since all its energy is directed outwards from the rotating center, and thus orthogonal to the direction the target lies in. Do you think nevertheless the target would probably be hit by shrapnel or something? Huh. If gyroscopes are no harder to move than non-rotating objects... Does this mean that these spinning vacuum disks can be sorta like a "poor man's nuke?" Have a missile with a spinning disc as the payload? Maybe you have to spend a few months gradually accelerating the disc beforehand, no problem. Just dump the excess energy your grid creates into accelerating the disc... I guess to match a nuke in energy you'd have to charge them up for a long time with today's grids... More of a rich man's nuke, ironically, in that for all that effort it's probably cheaper to just steal or buy some uranium and do the normal thing.
Reminds me of cookie cutters from The Diamond Age []
Somewhat similar to, [,] but I don't think it actually works.  You'd have to put actual masses and speeds into a calculation to be sure, but "spinning much faster than the bullet/shrapnel moves" seems problematic.  At the very least, you have to figure out how to keep the inner sphere suspended so it doesn't contact the outer sphere.  You might be able to ignore that bit by just calculating this as a space-borne defense mechanism: drop the outer shield, spin a sphere around your ship/habitat.  I think you'll still find that you have to spin it so fast that it deforms or disintegrates even without attack, for conventional materials.
2Daniel Kokotajlo3y
Mmm, good point about space-based system, that's probably a much better use case!
It's the easy solution to many problems in mechanics - put it in space, where you don't have to worry about gravity, air friction, etc.  You already specified that your elephant is uniform and spherical, so those complexities are already taken care of.

Searching for equilibria can be infohazardous. You might not like the one you find first, but you might end up sticking with it (or worse, deviating from it and being punished). This is because which equilbrium gets played by other people depends (causally or, in some cases, acausally) not just on what equilibrium you play but even on which equilibria you think about. For reasons having to do with schelling points. A strategy that sometimes works to avoid these hazards is to impose constraints on which equilibria you think about, or at any rate to perform ... (read more)

I'm not sure I follow the logic. When you say "searching for equilibria", do you mean "internally predicting likelihood of points and durations of an equilibrium (as most of what we worry about aren't stable)? Or do you mean the process of application of forces and observation of counter forces in which the system is "finding it's level"? Or do you mean "discussion about possible equilibria, where that discussion is in fact a force that affects the system"? Only the third seems to fit your description, and I think that's already covered by standard infohazard writings - the risk that you'll teach others something that can be used against you.
4Daniel Kokotajlo3y
I meant the third, and I agree it's not a particularly new idea, though I've never seen it said this succinctly or specifically. (For example, it's distinct from "the risk that you'll teach others something that can be used against you," except maybe in the broadest sense.)
Interesting. I'd like to explore the distinction between "risk of converging on a dis-preferred social equilibrium" (which I'd frame as "making others aware that this equilibrium is feasible") and other kinds of revealing information which others use to act in ways you don't like. I don't see much difference. The more obvious cases ("here are plans to a gun that I'm especially vulnerable to") don't get used much unless you have explicit enemies, while the more subtle ones ("I can imagine living in a world where people judge you for scratching your nose with your left hand") require less intentionality of harm directed at you. But it's the same mechanism and info-risk.
2Daniel Kokotajlo3y
For one thing, the equilibrium might not actually be feasible, but making others aware that you have thought about it might nevertheless have harmful effects (e.g. they might mistakenly think that it is, or they might correctly realize something in the vicinity is.) For another, "teach others something that can be used against you" while technically describing the sort of thing I'm talking about, tends to conjure up a very different image in the mind of the reader -- an image more like your gun plans example. I agree there is not a sharp distinction between these, probably. (I don't know, didn't think about it.) I wrote this shortform because, well, I guess I thought of this as a somewhat new idea -- I thought of most infohazards talk as being focused on other kinds of examples. Thank you for telling me otherwise!
(oops. I now realize this probably come across wrong). Sorry! I didn't intend to be telling you things, nor did I mean to imply that pointing out more subtle variants of known info-hazards was useless. I really appreciate the topic, and I'm happy to have exactly as much text as we have in exploring non-trivial application of the infohazard concept, and helping identify whether further categorization is helpful (I'm not convinced, but I probably don't have to be).

I just wanted to signal-boost this lovely "letter to 11-year-old self" written by Scott Aaronson. It's pretty similar to the letter I'd write to my own 11-year-old self. What a time to be alive!

Suppose you are the CCP, trying to decide whether to invade Taiwan soon. The normal-brain reaction to the fiasco in Ukraine is to see the obvious parallels and update downwards on "we should invade Taiwan soon."

But (I will argue) the big-brain reaction is to update upwards, i.e. to become more inclined to invade Taiwan than before. (Not sure what my all-things considered view is, I'm a bit leery of big-brain arguments) Here's why:

Consider this list of variables:

  1. How much of a fight the Taiwanese military will put up
  2. How competent the Chinese military is
  3. Wheth
... (read more)
I think it's tricky to do anything with this, without knowing the priors.  It's quite possible that there's no new information in the Russia-Ukraine war, only a confirmation of the models that the CCP is using.  I also think it probably doesn't shift the probability by all that much - I suspect it will be a relevant political/public crisis (something that makes Taiwan need/want Chinese support visibly enough that China uses it as a reason for takeover) that triggers such a change, not just information about other reactions to vaguely-similar aggression.

"One strong argument beats many weak arguments."

Several professors told me this when I studied philosophy in grad school. It surprised me at the time--why should it be true? From a Bayesian perspective isn't it much more evidence when there are a bunch of weak arguments pointing in the same direction, than when there is only one argument that is stronger?

Now I am older and wiser and have lots more experience, and this saying feels true to me. Not just in philosophy but in most domains, such as AGI timelines and cause prioritization.

Here are some speculatio... (read more)

There's also the weighting problem if you don't know which arguments are already embedded in your prior.  A strong argument that "feels" novel is likely something you should update on.  A weak argument shouldn't update you much even if it were novel, and there are so many of them that you may already have included it. Relatedly, it's hard to determine correlation among many weak arguments - in many cases, they're just different aspects of the same weak argument, not independent things to update on. Finally, it's part of the identification of strong vs weak arguments.  If it wasn't MUCH harder to refute or discount, we wouldn't call it a strong argument.
4Lukas Finnveden10mo
Some previous LW discussion on this: [] (author favors weak arguments; plenty of discussion and some disagreements in comments; not obviously worth reading)
The strongest argument is a mathematical or logical proof. It's easy to see why a mathematical proof of the Riemann hypothesis beats a lot of (indeed, all) weak intuitive arguments about what the answer might be. But this is only applicable for well-defined problems. Insofar as philosophy is tackling such problems, I would also expect a single strong argument to beat many weak arguments. Part of the goal for the ill-defined problems we face on a daily basis is not to settle the question, but to refine it into a well-defined question that has a strong, single argument. Perhaps, then, the reason why one strong argument beats many arguments is that questions for which there's a strategy for making a strong argument are most influential and compelling to us as a society. "What's the meaning of life?" Only weak arguments are available, so it's rare for people to seriously try and answer this question in a conclusive manner. "Can my e-commerce startup grow into a trillion-dollar company?" If the answer is "yes," then there's a single, strong argument that proves it: success. People seem attracted to these sorts of questions.
3Alexander Gietelink Oldenziel5mo
In the real world the weight of many pieces of weak evidence is not always comparable to a single piece of strong evidence. The important variable here is not strong versus weak per se but the source of the evidence. Some sources of evidence are easier to manipulate in various ways. Evidence manipulation, either consciously or emergently, is common and a large obstactle to truth-finding.  Consider aggregating many (potentially biased) sources of evidence versus direct observation. These are not directly comparable and in many cases we feel direct observation should prevail. This is especially poignant in the court of law: the very strict laws arounding presenting evidence are a culturally evolved mechanism to defend against evidence manipulation. Evidence manipulation may be easier for weaker pieces of evidence - see the prohibition against hearsay in legal contexts for instance. It is occasionally suggested that the court of law should do more probabilistic and Bayesian type of reasoning. One reason courts refuse to do so (apart from more Hansonian reasons around elites cultivating conflict suppression) is that naive Bayesian reasoning is extremely susceptible to evidence manipulation. 

In stories (and in the past) important secrets are kept in buried chests, hidden compartments, or guarded vaults. In the real world today, almost the opposite is true: Anyone with a cheap smartphone can roam freely across the Internet, a vast sea of words and images that includes the opinions and conversations of almost every community. The people who will appear in future history books are right now blogging about their worldview and strategy! The most important events of the next century are right now being accurately predicted by someone, somewhere, and... (read more)

There's a lot to unpack in the categorization of "important secrets".  I'd argue that the secret-est data isn't actually known by anyone yet, closely followed by secrets kept in someone's head, not in any vault or hidden compartment. Then there's "unpublished but theoretically discoverable" information, such as encrypted data, or data in limited-access locations (chests/caves, or just firewalled servers).   Then comes contextually-important insights buried in an avalanche of unimportant crap, which is the vast majority of interesting information, as you point out.
4Daniel Kokotajlo2y
I think I agree with all that. My claim is that the vast majority of interesting+important secrets (weighted by interestingness+importance) is in the "buried in an avalanche of crap" category.
Makes sense.  Part of the problem is that this applies to non-secrets as well - in fact, I'm not sure "secret" is a useful descriptor in this.  The vast majority of interesting+important information, even that which is actively published and intended for dissemination, is buried in crap.
I don't think that's true. There's information available online but a lot of information isn't. A person who had access to the US cables showing security concerns at the WIV, who had access to NSA surveilance that picked might have get alarmed that there's something problematic happening when the cell phone traffic dropped in October 2019 and they took their database down.  On the other hand I don't think that there's any way I could have known about the problems at the WIV in October of 2019 by accessing public information. Completely unrelated, it's probably just a councidence that October 2019 was also the time when the exercise by US policy makers about how a Coronavirus pandemic played out was done.  I don't think there's any public source that I could access that tells me about whether or not it was a coincidence. It's not the most important question but it leaves questions about how warning signs are handled open. The information that Dong Jingwei just gave the US government is more like a buried chest. 
2Daniel Kokotajlo2y
I mean, fair enough -- but I wasn't claiming that there aren't any buried-chest secrets. I'll put it this way: Superforecasters outperformed US intelligence community analysts in the prediction tournament; whatever secrets the latter had access to, they weren't important enough to outweigh the (presumably minor! Intelligence analysts aren't fools!) rationality advantage the superforecasters had!
When doing deep research I consider it very important to be mindful of information for a lot just not being available.  I think it's true that a lot can be done with publically available information but it's important to keep in mind the battle that's fought for it. If an organization like US Right to Know wouldn't wage lawsuits, then the FOIA requests they make can't be used to inform out decisions. While going through FOIA documents I really miss Julian. If he would still be around, Wikileaks would likely host the COVID-19 related emails in a nice searchable fashion and given that he isn't I have to work through PDF documents.  Information on how the WHO coordinate their censorship partnership on COVID-19 with Google and Twitter in a single day on the 3rd of February 2020 to prevent the lab-leak hypothesis from spreading further, needs access to internal documents. Between FOIA requests and offical statements we can narrow it down to that day, but there's a limit to the depth that you can access with public information. It needs either a Senate committee to subpena Google and Twitter or someone in those companies leaking the information. How much information is available and how easy it is to access is the result of a constant battle for freedom of information. I think it's great to encourage people to do research but it's also important to be aware that a lot of information is withheld and that there's room for pushing the available information further. There might be effect like the enviroment in which intelligence analysts operate train them to be biased towards what their boss wants to hear.  I also think that the more specific questions happen to be the more important specialized sources of information become. 
Some secrets protect themselves....things that are inherently hard to understand, things people dont want to believe...

Proposed Forecasting Technique: Annotate Scenario with Updates (Related to Joe's Post)

  • Consider a proposition like "ASI will happen in 2024, not sooner, not later." It works best if it's a proposition you assign very low credence to, but that other people you respect assign much higher credence to.
  • What's your credence in that proposition?
  • Step 1: Construct a plausible story of how we could get to ASI in 2024, no sooner, no later. The most plausible story you can think of. Consider a few other ways it could happen too, for completeness, but don't write them d
... (read more)

Science as a kind of Ouija board:

With the board, you do this set of rituals and it produces a string of characters as output, and then you are supposed to read those characters and believe what they say.

So too with science. Weird rituals, check. String of characters as output, check. Supposed to believe what they say, check.

With the board, the point of the rituals is to make it so that you aren't writing the output, something else is -- namely, spirits. You are supposed to be light and open-minded and 'let the spirit move you' rather than deliberately try ... (read more)

When God created the universe, He did not render false all those statements which are unpleasant to say or believe, nor even those statements which drive believers to do crazy or terrible things, because He did not exist. As a result, many such statements remain true.

Another hard-sci-fi military engineering idea: Shield against missile attacks built out of drone swarm.

Say you have little quadcopters that are about 10cm by 10cm square and contain about a bullet's worth of explosive charge. You use them to make a flying "blanket" above your forces; they fly in formation with about one drone per every square meter. Then if you have 1,000 drones you can make a 1,000 sq meter blanket and position it above your vehicles to intercept incoming missiles. (You'd need good sensors to detect the missiles and direct the nearest dro... (read more)

3Alexander Gietelink Oldenziel7mo
It seems that if the quadcopters are hovering close enough to friendly troops it shouldn't be too difficult to intercept a missile in theory. If you have a 10 sec lead time (~3 km at mach 1) and the drone can do 20 m/s that's 200 meters. With more comprehensive radar coverage you might be able to do much better. I wonder how large the drone needs to be to deflect a missile however. Would it need to carry a small explosive to send it off course? A missile is a large metal rod - in space with super high velocity even a tiny drone would whack a missile off course/ destroy it but in terrestial environments with a missile <=1 mach I wonder what happens. If the drone has a gun with inflammatory bullets you might be able to blow-up the very flammable fuel of a missile. ( I don't know how realistic this is but doesn't seem crazy- I think there are existing missile defense systems with inflammatory bullets?). Against a juking missile things change again. With airfins you can plausibly make a very swift & nimble juking missile. 

Another example of civilizational inadequacy in military procurement: 

Russia is now using Iranian Shahed-136 micro-cruise-missiles. They cost $20,000 each. [Insert napkin math, referencing size of Russian and Ukrainian military budgets]. QED.

How many are they sending and what/who are they paying for them?
2Daniel Kokotajlo8mo
Hundreds to thousands & I dunno, plausibly marked-up prices. The point is that if Russian military procurement was sane they'd have developed their own version of this drone and produced lots and lots of it, years ago. If Iran can build it, lots of nations should be able to build it. And ditto for Ukrainian and NATO military procurement.
Drones produced in Russia, like Orlan-10, use imported engines (and many other components). This may be the real bottleneck, rather than nominal price.
2Daniel Kokotajlo6mo
Still a massive failure of military procurement / strategic planning. If they had valued drones appropriately in 2010 they could have built up a much bigger stockpile of better drones by now. If need be they could make indigenous versions, which might be a bit worse and more expensive, but would still do the job.

I would love to see an AI performance benchmark (as opposed to a more indirect benchmark, like % of GDP) that (a) has enough data that we can extrapolate a trend, (b) has a comparison to human level, and (c) trend hits human level in the 2030s or beyond.

I haven't done a search, it's just my anecdotal impression that no such benchmark exists. But I am hopeful that in fact several do.

Got any ideas?

2Lone Pine1y
What are some benchmarks that satisfy just a & b?
2Daniel Kokotajlo1y
Basically all of them? Many of our benchmarks have already reached or surpassed human level, the ones that remain seem likely to be crossed before 2030. See this old analysis which used the Kaplan scaling laws, which are now obsolete so things will happen faster: []
2Daniel Kokotajlo1y
not basically all of them, that was hyperbole -- there are some that don't have a comparison to human level as far as I know, and others that don't have a clear trend we can extrapolate.

Self-embedded Agent and I agreed on the following bet: They paid me $1000 a few days ago. I will pay them $1100 inflation adjusted if there is no AGI in 2030.

Ramana Kumar will serve as the arbiter. Under unforeseen events we will renegotiate in good-faith.

As a guideline for 'what counts as AGI' they suggested the following, to which I agreed:

"the Arbiter agrees with the statement "there is convincing evidence that there is an operational Artificial General Intelligence" on 6/7/2030"

Defining an artificial general intelligence is a little hard and has a stro

... (read more)
2Daniel Kokotajlo1y
I made an almost identical bet with Tobias Baumann about two years ago.

Does anyone know why the Ukranian air force (and to a lesser extent their anti-air capabilities) are still partially functional?

Given the overwhelming numerical superiority of the Russian air force, plus all their cruise missiles etc., plus their satellites, I expected them to begin the conflict by gaining air supremacy and using it to suppress anti-air.

Hypothesis: Normally it takes a few days to do that, even for the mighty USAF attacking a place like Iraq; The Russian air force is not as overwhelmingly numerous vs. Ukraine so it should be expected to take at least a week, and Putin didn't want the ground forces to wait on the sidelines for a week.

The Russians did claim that they gained air supremacy but it's hard in war to know what's really true.
I am just guessing here, but maybe it's also about relative priorities? Like, for Americans the air force is critical, because they typically fight at distant places and their pilots are expensive; while Russians can come to Ukraine by foot or by tanks, and the lives of the poor conscripted kids are politically unimportant and their training was cheap, so just sending the footsoldiers will probably achieve the military goal (whatever it is) anyway.

Tonight my family and I played a trivia game (Wits & Wagers) with GPT-3 as one of the players! It lost, but not by much. It got 3 questions right out of 13. One of the questions it got right it didn't get exactly right, but was the closest and so got the points. (This is interesting because it means it was guessing correctly rather than regurgitating memorized answers. Presumably the other two it got right were memorized facts.)

Anyhow, having GPT-3 playing made the whole experience more fun for me. I recommend it. :) We plan to do this every year with whatever the most advanced publicly available AI (that doesn't have access to the internet) is.

How did GPT-3 participate?
4Daniel Kokotajlo1y
I typed in the questions to GPT-3 and pressed "generate" to see its answers. I used a pretty simple prompt.
When we remember we are all mad, all the mysteries disappear and life stands explained.

--Mark Twain, perhaps talking about civilizational inadequacy.

Came across this in a SpaceX AMA:

Q: I write software for stuff that isn't life or death. Because of this, I feel comfortable guessing & checking, copying & pasting, not having full test coverage, etc. and consequently bugs get through every so often. How different is it to work on safety critical software?
A: Having worked on both safety critical and non-safety critical software, you absolutely need to have a different mentality. The most important thing is making sure you know how your software will behave in all different scenarios. This a
... (read more)

On a bunch of different occasions, I've come up with an important idea only to realize later that someone else came up with the same idea earlier. For example, the Problem of Induction/Measure Problem. Also modal realism and Tegmark Level IV. And the anti-souls argument from determinism of physical laws. There were more but I stopped keeping track when I got to college and realized this sort of thing happens all the time.

Now I wish I kept track. I suspect useful data might come from it. Like, my impression is that these scooped ideas tend to be scoope... (read more)

Does the lottery ticket hypothesis have weird philosophical implications?

As I understand it, the LTH says that insofar as an artificial neural net eventually acquires a competency, it's because even at the beginning when it was randomly initialized there was a sub-network that happened to already have that competency to some extent at least. The training process was mostly a process of strengthening that sub-network relative to all the others, rather than making that sub-network more competent.

Suppose the LTH is true of human brains as well. Apparently at ... (read more)

Two months ago I said I'd be creating a list of predictions about the future in honor of my baby daughter Artemis. Well, I've done it, in spreadsheet form. The prediction questions all have a theme: "Cyberpunk." I intend to make it a fun new year's activity to go through and make my guesses, and then every five years on her birthdays I'll dig up the spreadsheet and compare prediction to reality.

I hereby invite anybody who is interested to go in and add their own predictions to the spreadsheet. Also feel free to leave comments ... (read more)

Hi Daniel! We (Foretold) have been recently experimenting with "notebooks", which help structure tables for things like this. I think a notebook/table setup for your spreadsheet could be a decent fit. These take a bit of time to set up now (because we need to generate each cell using a separate tool), but we could help with that if this looks interesting to you. Here are some examples: [] [] [] You can click on cells to add predictions to them. Foretold is more experimental than Metaculus and doesn't have as large a community. But it could be a decent fit for this (and this should get better in the next 1-3 months, as notebooks get improved)
5Daniel Kokotajlo3y
OK, thanks Ozzie on your recommendation I'll try to make this work. I'll see how it works, see if I can do it myself, and reach out to you if it seems hard.
Sure thing. We don't have documentation for how to do this yet, but you can get an idea from seeing the "Markdown" of some of those examples. The steps to do this: 1. Make a bunch of measurables. 2. Get the IDs of all of those measurables (you can see these in the Details tabs on the bottom) 3. Create the right notebook/table, and add all the correct IDs to the right places within them.
4Daniel Kokotajlo3y
OK, some questions: 1. By measureables you mean questions, right? Using the "New question" button? Is there a way for me to have a single question of the form "X is true" and then have four columns, one for each year (2025, 2030, 2035, 2040) where people can put in four credences for whether X will be true at each of those years? 2. I created a notebook/table with what I think are correctly formatted columns. Before I can add a "data" section to it, I need IDs, and for those I need to have made questions, right?
1. Yes, sorry. Yep, you need to use the "New question" button. If you want separate things for 4 different years, you need to make 4 different questions. Note that you can edit the names & descriptions in the notebook view, so you can make them initially with simple names, then later add the true names to be more organized. 2. You are correct. In the "details" sections of questions, you can see their IDs. These are the items to use. You can of course edit notebooks after making them, so you may want to first make it without the IDs, then once you make the questions, add the IDs in, if you'd prefer.
Well, many of them live on Metaculus [].
4Daniel Kokotajlo3y
Right, no offense intended, haha! (I already made a post about this on Metaculus, don't worry I didn't forget them except in this post here!)

I just wish to signal-boost this Metaculus question, I'm disappointed with the low amount of engagement it has so far: 

It would help your signal-boosting if you hit space after pasting that url, in which case the editor would auto-link it. (JP, you say, you're a dev on this codebase, why not make it so it auto-links it on paste — yeah, well, ckeditor is a pain to work with and that wasn't the default behavior.)

I wonder SpaceX Raptor engines could be cost-effectively used to make VTOL cargo planes. Three engines + a small fuel tank to service them should be enough for a single takeoff and landing and should fit within the existing fuselage. Obviously it would weigh a lot and cut down on cargo capacity, but maybe it's still worth it? And you can still use the plane as a regular cargo plane when you have runways available, since the engines + empty tank don't weigh that much.

[Googles a bit] OK so it looks like maximum thrust Raptors burn through propellant at 600kg... (read more)

3Lone Pine1y
My understanding is that VTOL is not limited by engine power, but by the complexity and safety problems with landing.
2Daniel Kokotajlo1y
Good point. Probably even if you land in a parking lot there'd be pebbles and stuff shooting around like bullets. And if you land in dirt, the underside of the vehicle would be torn up as if by shrapnel. Whereas with helicopters the rotor wash is distributed much more widely and thus has less deadly effects. I suppose it would still work for aircraft carriers though maybe? And more generally for surfaces that your ground crew can scrub clean before you arrive?
1Lone Pine1y
The problem isn't in the power of the engine at all. The problem historically (pre-computers) has been precisely controlling the motion of the decent given the kinetic energy in the vehicle itself. When the flying vehicle is high in the air traveling at high speeds, it has a huge amount of kinetic and potential (gravitational) energy. By the time the vehicle is at ground level, that energy is either all in kinetic energy (which means the vehicle is moving dangerously fast) or the energy has to be dissipated somehow, usually by wastefully running the engine in reverse. The Falcon 9 is solving this problem in glorious fashion, so we know it's possible. (They have the benefit of computers, which the original VTOL designers didn't.) Read the book Where is my Flying Car?
2Daniel Kokotajlo1y
Well if the Falcon 9 and Starship can do it, why can't a cargo plane? It's maybe a somewhat more complicated structure, but maybe that just means you need a bigger computer + more rounds of testing (and crashed prototypes) before you get it working.
1Lone Pine1y
Yeah, to be honest I'm not sure why we don't have passenger VTOLs yet. I blame safetism.
My guess is that we don't have passenger or cargo VTOL airplanes because they would use more energy than the airplanes we use now. It can be worth the extra energy cost in warplanes since it allows the warplanes to operate from ships smaller than the US's supercarriers and to keep on operating despite the common military tactic of destroying the enemy's runways. Why do I guess that VTOLs would use more energy? (1) Because hovering expends energy at a higher rate than normal flying. (2) Because the thrust-to-weight ratio of a modern airliner is about .25 and of course to hover you need to get that above 1, which means more powerful gas-turbine engines, which means heavier gas-turbine engines, which means the plane gets heavier and consequently less energy efficient.
Being able to land airplanes outside of formal airports would be valuable for civilian aircraft as well. I would however expect that you can't legally do that with VTOL airplanes in most Western countries. 

A current example of civilizational inadequacy in the realm of military spending:

The Ukrainian military has a budget of 4.6 billion Euro, so about $5 billion. (It also has several hundred thousand soldiers)

The Bayraktar TB2 is estimated to cost about $1-2 million. It was designed and built in Turkey and only about 300 or so have been made so far. As far as I can tell it isn't anything discontinuously great or fantastic, technology-wise. It's basically the same sort of thing the US has had for twenty years, only more affordable. (Presumably it's more afford... (read more)

3Purged Deviator1y
From things I have previously heard about drones, I would be uncertain what training is required to operate them, and what limitations there are for weather in which they can & cannot fly.  I know that being unable to fly in anything other than near-perfect weather conditions has been a problem of drones in the past, and those same limitations do not apply to ground-based vehicles.  
2Daniel Kokotajlo1y
This is a good point, but it doesn't change the bottom line I think. Weather is more often good than bad, so within a few days of the war beginning there should be a chance for 1000 drones to rise and sweep the field. As for training, in the scenario where they bought 1000 drones they'd also do the necessary training for 1000 drone operators (though there are returns to scale I think; in a pinch 1 operator could probably handle 10 drones, just have them all flock in loose formation and then it's as if you have 1 big drone with 10x the ammunition capacity.)
-1Purged Deviator1y
I do agree for the most part. Robotic warfare which can efficiently destroy your opponent's materiel, without directly risking your own materiel & personnel is an extremely dominant strategy, and will probably become the future of warfare. At least warfare like this, as opposed to police actions.
In terms of whether this is "civilizational inadequacy" or just "optimizing preparation for war is hard", do you think this advice applies to Taiwan?  It's a similar budget, but a very different theater of combat.  I'd expect drones are useful against naval units as well as land, so naively I'd think it's a similar calculation.  
4Daniel Kokotajlo1y
I mean, yeah? I haven't looked at Taiwan's situation in particular but I'd expect I'd conclude that they should be buying loads of drones. Thousands or many thousands.
This sort of thing is a pretty chaotic equilibrium.  I predict that a LOT of effort in the near future is going to go into anti-drone capabilities for larger countries, reducing the effectiveness of drones in the future.  And I find it easy to believe that most drone purchasers believed (incorrectly, it turns out) that Russia had already done this, and would have wiped out even an order of magnitude more by now. I suspect this isn't a civilizational inadequacy, but an intentional adversarial misinformation campaign.  The underinvestment may be in intelligence and information about likely opponents much more than overt spending/preparation choices.
3Daniel Kokotajlo1y
Wiping out an order of magnitude more wouldn't be enough, because 900 would still remain to pulverize Russian forces. (Currently eyeballing the list of destroyed Russian vehicles, it looks like more than 10% were destroyed by TB2s. That's insane. A handful of drones are causing 10%+ as much damage as 200,000 Ukranian soldiers with tanks, anti-tank rockets, bombers, artillery, etc. Extrapolating out, 900 drones would cause 1000%+ as much damage; Russian forces would be losing something like 500 military vehicles per day, along with their crews. If most drone purchasers believed Russia would be able to counter 1000 TB2s, well not only did they turn out to be wrong in hindsight, I think they should have known better. Also: What sort of anti-drone weapons do you have in mind, that could take out 1000 TB2s? Ordinary missiles and other anti-air weapons are more or less the same for drones as for regular aircraft, so there probably aren't dramatic low-hanging fruit to pick there. The main thing that comes to mind is cyber stuff and signals jamming. Cyber stuff isn't plausible I think because the balance favors defenders at high levels of investment; if you are buying a thousand drones you can easily afford to make them very very secure. Signals jamming seems like the most plausible thing to me but it isn't a panacea. Drones can be modified to communicate by laser link, for example. (If you have 1000 of them then they can form an unbroken web back to base.) They also can go into autonomous mode and then shoot at pre-identified targets, e.g. "Bomb anything your image recognition algorithm identifies as a military vehicle on the following highway."
There are multiple levels of strategy here.  If Ukraine had 1000 TB2s, it would be reasonable for Russia to prepare for that and be able to truly clear the skies.  In fact, many of us expected this would be part of Russia's early attacks, and it's a big surprise that it didn't happen.   My point is that it would be a different world with a different set of assumptions if much larger drone fleets were common.  ECM and ground-to-air capabilities would be much more commonly embedded in infantry units, and much more air-to-air (including counter-drone drones) combat would be expected.  Russia would have been expected (even more than it WAS expected, and even more surprising if they failed) to destroy every runway very early.  You can't look at a current outcome and project one side changing significantly without the other side also doing so. Spending a whole lot on the assumption that Russia would be incompetent in this specific way seems like a different inadequacy, even though it turned out to be true this time.
4Daniel Kokotajlo1y
I'm saying that if Ukraine had bought 1000 TB2's, whatever changes Russia would have made (if any) wouldn't have been enough to leave Ukraine in a worse position than it is in now. Sure, Russia probably would have adapted somewhat. But we are very far from the equilibrium, say I. If Russia best-responded to Ukraine's 1000 TB2s, maybe they'd be able to shoot down a lot of them or otherwise defend against them, but the Ukrainians would still come out overall better off than they are now. (They are cheap! It's hard to defend against them when they are so cheap!) TB2s don't need runways since they can fly off of civilian roads. I do think counter-drone drones are the way to go. But it's not like Ukraine shouldn't buy TB2's out of fear of Russia's hypothetical counter-drone drones! That's like saying armies in 1920 shouldn't buy aircraft because other armies would just buy counter-aircraft aircraft.

I used to think that current AI methods just aren't nearly as sample/data - efficient as humans. For example, GPT-3 had to read 300B tokens of text whereas humans encounter 2 - 3 OOMs less, various game-playing AIs had to play hundreds of years worth of games to get gud, etc.

Plus various people with 20 - 40 year AI timelines seem to think it's plausible -- in fact, probable -- that unless we get radically new and better architectures, this will continue for decades, meaning that we'll get AGI only when we can actually train AIs on medium or long-horizon ta... (read more)

The 'poverty of stimulus' argument proves too much, and is just a rehash of the problem of induction, IMO. Everything that humans learn is ill-posed/underdetermined/vulnerable to skeptical arguments and problems like Duhem-Quine or the grue paradox. There's nothing special about language. And so - it all adds up to normality - since we solve those other inferential problems, why shouldn't we solve language equally easily and for the same reasons? If we are not surprised that lasso can fit a good linear model by having an informative prior about coefficients being sparse/simple, we shouldn't be surprised if human children can learn a language without seeing an infinity of every possible instance of a language or if a deep neural net can do similar things.

2Daniel Kokotajlo2y
Right. So, what do you think about the AI-timelines-related claim then? Will we need medium or long-horizon training for a number of episodes within an OOM or three of parameter count to get something x-risky? ETA: To put it more provocatively: If EfficientZero can beat humans at Atari using less game experience starting from a completely blank slate whereas humans have decades of pre-training, then shouldn't a human-brain-sized EfficientZero beat humans at any intellectual task given decades of experience at those tasks + decades of pre-training similar to human pre-training.
I have no good argument that a human-sized EfficientZero would somehow need to be much slower than humans. Arguing otherwise sounds suspiciously like moving the goalposts after an AI effect: "look how stupid DL agents are, they need tons of data to few-shot stuff like challenging text tasks or image classifications, and they OOMs more data on even something as simple as ALE games! So inefficient! So un-human-like! This should deeply concern any naive DL enthusiast, that the archs are so bad & inefficient." [later] "Oh no. Well... 'the curves cross', you know, this merely shows that DL agents can get good performance on uninteresting tasks, but human brains will surely continue showing their tremendous sample-efficiency in any real problem domain, no matter how you scale your little toys." -------------------------------------------------------------------------------- As I've said before, I continue to ask myself what it is that the human brain does with all the resources it uses, particularly with the estimates that put it at like 7 OOMs more than models like GPT-3 or other wackily high FLOPS-equivalence. It does not seem like those models do '0.0000001% of human performance', in some sense.
3Lone Pine2y
Can EfficientZero beat Montezuma's Revenge?
Not out of the box, but it's also not designed at all for doing exploration. Exploration in MuZero is an obvious but largely (ahem) unexplored topic. Such is research: only a few people in the world can do research with MuZero on meaningful problems like ALE, and not everything will happen at once. I think the model-based nature of MuZero means that a lot of past approaches (like training an ensemble of MuZeros and targeting parts of the game tree where the models disagree most on their predictions) ought to port into it pretty easily. We'll see if that's enough to match Go-Explore.

I think it is useful to distinguish between two dimensions of competitiveness: Resource-competitiveness and date-competitiveness. We can imagine a world in which AI safety is date-competitive with unsafe AI systems but not resource-competitive, i.e. the insights and techniques that allow us to build unsafe AI systems also allow us to build equally powerful safe AI systems, but it costs a lot more. We can imagine a world in which AI safety is resource-competitive but not date-competitive, i.e. for a few months it is possible to make unsafe powerful AI systems but no one knows how to make a safe version, and then finally people figure out how to make a similarly-powerful safe version and moreover it costs about the same.

It's no longer my top priority, but I have a bunch of notes and arguments relating to AGI takeover scenarios that I'd love to get out at some point. Here are some of them:

Beating the game in May 1937 - Hoi4 World Record Speedrun Explained - YouTube
In this playthrough, the USSR has a brief civil war and Trotsky replaces Stalin. They then get an internationalist socialist type diplomat who is super popular with US, UK, and France, who negotiates passage of troops through their territory -- specifially, they send many many brigades of extremely low-tier troop... (read more)

Apparently vtubers are a thing! What interests me about this is that I vaguely recall reading futurist predictions many years ago that basically predicted vtubers. IIRC the predictions were more about pop stars and celebrities than video game streamers, but I think it still counts. Unfortunately I have no recollection where I read these predictions or what year they were made. Anyone know? I do distinctly remember just a few years ago thinking something like "We were promised virtual celebrities but that hasn't happened yet even though the tech exists. I guess there just isn't demand for it."

2Daniel Kokotajlo1y
IDK, what's gibson? It's possible I read it and didn't remember the name, I rarely remember names.
William Gibson and Idoru [].
2Daniel Kokotajlo1y
Well, it wasn't that. But cool!

Historical precedents for general vs. narrow AI

  • Household robots vs. household appliances: Score One for Team Narrow
  • Vehicles on roads vs. a network of pipes, tubes, and rails: Score one for Team General
  • Ships that can go anywhere vs. a trade network of ships optimized for one specific route: Score one for Team General

(On the ships thing -- apparently the Indian Ocean trade was specialized prior to the Europeans, with cargo being transferred from one type of ship to another to handle different parts of the route, especially the red sea which was dangerous t... (read more)

Productivity app idea:

You set a schedule of times you want to be productive, and a frequency, and then it rings you at random (but with that frequency) to bug you with questions like:

--Are you "in the zone" right now? [Y] [N]

--(if no) What are you doing? [text box] [common answer] [ common answer] [...]

The point is to cheaply collect data about when you are most productive and what your main time-wasters are, while also giving you gentle nudges to stop procrastinating/browsing/daydream/doomscrolling/working-sluggishly, take a deep breath, reconsider your priorities for the day, and start afresh.

Probably wouldn't work for most people but it feels like it might for me.

"Are you in the zone right now?" "... Well, I was."
2Daniel Kokotajlo2y
I'm betting that a little buzz on my phone which I can dismiss with a tap won't kill my focus. We'll see.
This is basically a souped up version of TagTime [] (by the Beeminder folks) so you might be able to start with their implementation.
I've been thinking about a similar idea. This format of data collection is called "experience sampling". I suspect there might be already made solutions. Would you pay for such an app? If so, how much? Also looks like your crux is actually becoming more productive (i.e. experience sampling is a just a mean to reach that). Perhaps just understanding your motivation better would help (basically