This post is a not a so secret analogy for the AI Alignment problem. Via a fictional dialog, Eliezer explores and counters common questions to the Rocket Alignment Problem as approached by the Mathematics of Intentional Rocketry Institute. 

MIRI researchers will tell you they're worried that "right now, nobody can tell you how to point your rocket’s nose such that it goes to the moon, nor indeed any prespecified celestial destination."

So the usual refrain from Zvi and others is that the specter of China beating us to the punch with AGI is not real because limits on compute, etc. I think Zvi has tempered his position on this in light of Meta's promise to release the weights of its 400B+ model. Now there is word that SenseTime just released a model that beats GPT-4 Turbo on various metrics. Of course, maybe Meta chooses not to release its big model, and maybe SenseTime is bluffing--I would point out though that Alibaba's Qwen model seems to do pretty okay in the arena...anyway, my point is that I don't think the "what if China" argument can be dismissed as quickly as some people on here seem to be ready to do.
The cost of goods has the same units as the cost of shipping: $/kg. Referencing between them lets you understand how the economy works, e.g. why construction material sourcing and drink bottling has to be local, but oil tankers exist. * An iPhone costs $4,600/kg, about the same as SpaceX charges to launch it to orbit. [1] * Beef, copper, and off-season strawberries are $11/kg, about the same as a 75kg person taking a three-hour, 250km Uber ride costing $3/km. * Oranges and aluminum are $2-4/kg, about the same as flying them to Antarctica. [2] * Rice and crude oil are ~$0.60/kg, about the same as $0.72 for shipping it 5000km across the US via truck. [3,4] Palm oil, soybean oil, and steel are around this price range, with wheat being cheaper. [3] * Coal and iron ore are $0.10/kg, significantly more than the cost of shipping it around the entire world via smallish (Handysize) bulk carriers. Large bulk carriers are another 4x more efficient [6]. * Water is very cheap, with tap water $0.002/kg in NYC. But shipping via tanker is also very cheap, so you can ship it maybe 1000 km before equaling its cost. It's really impressive that for the price of a winter strawberry, we can ship a strawberry-sized lump of coal around the world 100-400 times. [1] iPhone is $4600/kg, large launches sell for $3500/kg, and rideshares for small satellites $6000/kg. Geostationary orbit is more expensive, so it's okay for them to cost more than an iPhone per kg, but Starlink wants to be cheaper. [2] https://fred.stlouisfed.org/series/APU0000711415. Can't find numbers but Antarctica flights cost $1.05/kg in 1996. [3] https://www.bts.gov/content/average-freight-revenue-ton-mile [4] https://markets.businessinsider.com/commodities [5] https://www.statista.com/statistics/1232861/tap-water-prices-in-selected-us-cities/ [6] https://www.researchgate.net/figure/Total-unit-shipping-costs-for-dry-bulk-carrier-ships-per-tkm-EUR-tkm-in-2019_tbl3_351748799
Fabien Roger18hΩ6130
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List sorting does not play well with few-shot mostly doesn't replicate with davinci-002. When using length-10 lists (it crushes length-5 no matter the prompt), I get: * 32-shot, no fancy prompt: ~25% * 0-shot, fancy python prompt: ~60%  * 0-shot, no fancy prompt: ~60% So few-shot hurts, but the fancy prompt does not seem to help. Code here. I'm interested if anyone knows another case where a fancy prompt increases performance more than few-shot prompting, where a fancy prompt is a prompt that does not contain information that a human would use to solve the task. This is because I'm looking for counterexamples to the following conjecture: "fine-tuning on k examples beats fancy prompting, even when fancy prompting beats k-shot prompting" (for a reasonable value of k, e.g. the number of examples it would take a human to understand what is going on).
dirk17h125
2
Sometimes a vague phrasing is not an inaccurate demarkation of a more precise concept, but an accurate demarkation of an imprecise concept
nim3h20
0
I've found an interesting "bug" in my cognition: a reluctance to rate subjective experiences on a subjective scale useful for comparing them. When I fuzz this reluctance against many possible rating scales, I find that it seems to arise from the comparison-power itself. The concrete case is that I've spun up a habit tracker on my phone and I'm trying to build a routine of gathering some trivial subjective-wellbeing and lifestyle-factor data into it. My prototype of this system includes tracking the high and low points of my mood through the day as recalled at the end of the day. This is causing me to interrogate the experiences as they're happening to see if a particular moment is a candidate for best or worst of the day, and attempt to mentally store a score for it to log later. I designed the rough draft of the system with the ease of it in mind -- I didn't think it would induce such struggle to slap a quick number on things. Yet I find myself worrying more than anticipated about whether I'm using the scoring scale "correctly", whether I'm biased by the moment to perceive the experience in a way that I'd regard as inaccurate in retrospect, and so forth. Fortunately it's not a big problem, as nothing particularly bad will happen if my data is sloppy, or if I don't collect it at all. But it strikes me as interesting, a gap in my self-knowledge that wants picking-at like peeling the inedible skin away to get at a tropical fruit.

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5otto.barten17h
My current main cruxes: 1. Will AI get takeover capability? When? 2. Single ASI or many AGIs? 3. Will we solve technical alignment? 4. Value alignment, intent alignment, or CEV? 5. Defense>offense or offense>defense? 6. Is a long-term pause achievable? If there is reasonable consensus on any one of those, I'd much appreciate to know about it. Else, I think these should be research priorities.

I offer, no consensus, but my own opinions: 

Will AI get takeover capability? When?

0-5 years.

Single ASI or many AGIs?

There will be a first ASI that "rules the world" because its algorithm or architecture is so superior. If there are further ASIs, that will be because the first ASI wants there to be. 

Will we solve technical alignment?

Contingent. 

Value alignment, intent alignment, or CEV?

For an ASI you need the equivalent of CEV: values complete enough to govern an entire transhuman civilization. 

Defense>offense or offense>defense?

Of... (read more)

[Setting: a suburban house. The interior of the house takes up most of the stage; on the audience's right, we see a wall in cross-section, and a front porch. Simplicia enters stage left and rings the doorbell.]

Doomimir: [opening the door] Well? What do you want?

Simplicia: I can't stop thinking about our last conversation. It was kind of all over the place. If you're willing, I'd like to continue, but focusing in narrower detail on a couple points I'm still confused about.

Doomimir: And why should I bother tutoring an Earthling in alignment theory? If you didn't get it from the empty string, and you didn't get it from our last discussion, why should I have any hope of you learning this time? And even if you did, what...

Doomimir: No, it wouldn't! Are you retarded?

Simplicia: [apologetically] Well, actually ...

Doomimir: [embarrassed] I'm sorry, Simplicia Optimistovna; I shouldn't have snapped at you like that.

[diplomatically] But I think you've grievously misunderstood what the KL penalty in the RLHF objective is doing. Recall that the Kullback–Leibler divergence represents how surprised you'd be by data from distribution , that you expected to be from distribution .

It's asymmetric: it blows up when the data is very unlikely according to , which amounts to seei... (read more)

It seems to me worth trying to slow down AI development to steer successfully around the shoals of extinction and out to utopia.

But I was thinking lately: even if I didn’t think there was any chance of extinction risk, it might still be worth prioritizing a lot of care over moving at maximal speed. Because there are many different possible AI futures, and I think there’s a good chance that the initial direction affects the long term path, and different long term paths go to different places. The systems we build now will shape the next systems, and so forth. If the first human-level-ish AI is brain emulations, I expect a quite different sequence of events to if it is GPT-ish.

People genuinely pushing for AI speed over care (rather than just feeling impotent) apparently think there is negligible risk of bad outcomes, but also they are asking to take the first future to which there is a path. Yet possible futures are a large space, and arguably we are in a rare plateau where we could climb very different hills, and get to much better futures.

aysja44m75

I don't know what Katja thinks, but for me at least: I think AI might pose much more lock-in than other technologies. I.e., I expect that we'll have much less of a chance (and perhaps much less time) to redirect course, adapt, learn from trial and error, etc. than we typically do with a new technology. Given this, I think going slower and aiming to get it right on the first try is much more important than it normally is.  

Crosspost from my blog.  

If you spend a lot of time in the blogosphere, you’ll find a great deal of people expressing contrarian views. If you hang out in the circles that I do, you’ll probably have heard of Yudkowsky say that dieting doesn’t really work, Guzey say that sleep is overrated, Hanson argue that medicine doesn’t improve health, various people argue for the lab leak, others argue for hereditarianism, Caplan argue that mental illness is mostly just aberrant preferences and education doesn’t work, and various other people expressing contrarian views. Often, very smart people—like Robin Hanson—will write long posts defending these views, other people will have criticisms, and it will all be such a tangled mess that you don’t really know what to think about them.

For...

I couldn't swallow Eliezer's argument, I tried to read Guzey but couldn't stay awake, Hanson's argument made me feel ill, and I'm not qualified to judge Caplan. 

1FlorianH7h
Nice contrarian view on the popular contrarians - and in yours I have at least 75% faith :) : Ironically, if your elaborations are arguably themselves a bit broad brushed, as @Viliam points out, this could in an odd way also be seen as underlining your core take away: even here, where publication bias (or reading-bias induced publication-bias) is decried, maybe a hint of the bias has already sneaked in again.
2niplav17h
It seems like you're spanning up three different categories of thinkers: Academics, public intellectuals, and "obsessive autists". Notice that the examples you give overlap in those categories: Hanson and Caplan are academics (professors!), while the Natália Mendonça is not an academic, but is approaching being a public intellectual by now(?). Similarly, Scott Alexander strikes me as being in the "public intellectual" bucket much more than any other bucket. So your conclusion, as far as I read the article, should be "read obsessive autists" instead of "read obsessive autists that support the mainstream view". This is my current best guess—"obsessive autists" are usually not under much strong pressure to say politically palatable things, very unlike professors.

If we achieve AGI-level performance using an LLM-like approach, the training hardware will be capable of running ~1,000,000s concurrent instances of the model.

Definitions

Although there is some debate about the definition of compute overhang, I believe that the AI Impacts definition matches the original use, and I prefer it: "enough computing hardware to run many powerful AI systems already exists by the time the software to run such systems is developed".  A large compute overhang leads to additional risk due to faster takeoff.

I use the types of superintelligence defined in Bostrom's Superintelligence book (summary here).

I use the definition of AGI in this Metaculus question. The adversarial Turing test portion of the definition is not very relevant to this post.

Thesis

Due to practical reasons, the compute requirements for training LLMs...

Having 1.6 million identical twins seems like a pretty huge advantage though.

4lukehmiles6h
This seems correct and important to me.
9ryan_greenblatt7h
See also Before smart AI, there will be many mediocre or specialized AIs.
13faul_sname8h
I think this only holds if fine tunes are composable, which as far as I can tell they aren't (fine tuning on one task subtly degrades performance on a bunch of other tasks, which isn't a big deal if you fine tune a little for performance on a few tasks but does mean you probably can't take a million independently-fine-tuned models and merge them into a single super model of the same size with the same performance on all million tasks). Also there are sometimes mornings where I can't understand code I wrote the previous night when I had all of the necessary context fresh to me, despite being the same person. I expect that LLMs will exhibit the same behavior of some things being hard to understand when examined out of the context which generated them. That's not to say a worldin which there are a billion copies of GPT-5 running concurrently will have no major changes, but I don't think a single coherent ASI falls out of that world.
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It's important to remember that the culture we grew up in is deeply nihilistic at its core. People expect Moloch, assume Moloch as a given, even defer to Moloch. If you read enough about business and international affairs (not news articles, those don't count, not for international affairs at least, I don't know about business), and then read about dath ilan, it becomes clear that our world is ruled by Moloch cultists who nihilistically optimized for career advancement.

Humans are primates; we instinctively take important concepts and turn them into dominance/status games, including that concept itself; resulting in many people believing that important concepts do not exist at all. 

So it makes sense that Moloch would be an intensely prevalent part of our civilization, even ~a century after decision...

This is inspiring

The operation, called Big River Services International, sells around $1 million a year of goods through e-commerce marketplaces including eBay, Shopify, Walmart and Amazon AMZN 1.49%increase; green up pointing triangle.com under brand names such as Rapid Cascade and Svea Bliss. “We are entrepreneurs, thinkers, marketers and creators,” Big River says on its website. “We have a passion for customers and aren’t afraid to experiment.”

What the website doesn’t say is that Big River is an arm of Amazon that surreptitiously gathers intelligence on the tech giant’s competitors.

Born out of a 2015 plan code named “Project Curiosity,” Big River uses its sales across multiple countries to obtain pricing data, logistics information and other details about rival e-commerce marketplaces, logistics operations and payments services, according to people familiar with Big

...

Thanks for posting. I would not have seen this otherwise.

Concerns over AI safety and calls for government control over the technology are highly correlated but they should not be.

There are two major forms of AI risk: misuse and misalignment. Misuse risks come from humans using AIs as tools in dangerous ways. Misalignment risks arise if AIs take their own actions at the expense of human interests.

Governments are poor stewards for both types of risk. Misuse regulation is like the regulation of any other technology. There are reasonable rules that the government might set, but omission bias and incentives to protect small but well organized groups at the expense of everyone else will lead to lots of costly ones too. Misalignment regulation is not in the Overton window for any government. Governments do not have strong incentives...

1Amalthea4h
I think the perspective that you're missing regarding 2. is that by building AGI one is taking the chance of non-consensually killing vast amounts of people and their children for some chance of improving one's own longevity. Even if one thinks it's a better deal for them, a key point is that you are making the decision for them by unilaterally building AGI. So in that sense it is quite reasonable to see it as an "evil" action to work towards that outcome.
4Matthew Barnett3h
I think this misrepresents the scenario since AGI presumably won't just improve my own longevity: it will presumably improve most people's longevity (assuming it does that at all), in addition to all the other benefits that AGI would provide the world. Also, both potential decisions are "unilateral": if some group forcibly stops AGI development, they're causing everyone else to non-consensually die from old age, by assumption. I understand you have the intuition that there's an important asymmetry here. However, even if that's true, I think it's important to strive to be accurate when describing the moral choice here.
  1. I agree that potentially the benefits can go to everyone. The point is that as the person pursuing AGI you are making the choice for everyone else.
  2. The asymmetry is that if you do something that creates risk for everyone else, I believe that does single you out as an aggressor? While conversely, enforcing norms that prevent such risky behavior seems justified. The fact that by default people are mortal is tragic, but doesn't have much bearing here. (You'd still be free to pursue life-extension technology in other ways, perhaps including limited AI tools
... (read more)
2ryan_greenblatt6h
(Surely cryonics doesn't matter given a realistic action space? Usage of cryonics is extremely rare and I don't think there are plausible (cheap) mechanisms to increase uptake to >1% of population. I agree that simulation arguments and similar considerations maybe imply that "helping current humans" is either incoherant or unimportant.)

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