Max H

Karma: 2,270

Most of my posts and comments are about AI and alignment. Posts I’m most proud of, which also provide a good introduction to my worldview:

I also created Forum Karma, and wrote a longer self-introduction here.

PMs and private feedback are always welcome.

NOTE: I am not Max Harms, author of Crystal Society. I’d prefer for now that my LW postings not be attached to my full name when people Google me for other reasons, but you can PM me here or on Discord (m4xed) if you want to know who I am.

Max H 18 Oct 2024 1:58 UTC
2 points
1
in reply to: Matthew Barnett’s comment on: The Hidden Complexity of Wishes
My main point was that I thought recent progress in LLMs had demonstrated progress at the problem of building such a function, and solving the value identification problem, and that this progress goes beyond the problem of getting an AI to understand or predict human values.
I want to push back on this a bit. I suspect that “demonstrated progress” is doing a lot of work here, and smuggling an assumption that current trends with LLMs will continue and can be extrapolated straightforwardly.
It’s true that LLMs have some nice properties for encapsulating fuzzy and complex concepts like human values, but I wouldn’t actually want to use any current LLMs as a referent or in a rating system like the one you propose, for obvious reasons.
Maybe future LLMs will retain all the nice properties of current LLMs while also solving various issues with jailbreaking, hallucination, robustness, reasoning about edge cases, etc. but declaring victory already (even on a particular and narrow point about value identification) seems premature to me.
Separately, I think some of the nice properties you list don’t actually buy you that much in practice, even if LLM progress does continue straightforwardly.
A lot of the properties you list follow from the fact that LLMs are pure functions of their input (at least with a temperature of 0).
Functional purity is a very nice property, and traditional software that encapsulates complex logic in pure functions is often easier to reason about, debug, and formally verify vs. software that uses lots of global mutable state and / or interacts with the outside world through a complex I/O interface. But when the function in question is 100s of GB of opaque floats, I think it’s a bit of a stretch to call it transparent and legible just because it can be evaluated outside of the IO monad.
Aside from purity, I don’t think your point about an LLM being a “particular function” that can be “hooked up to the AI directly” is doing much work - input() (i.e. asking actual humans) seems just as direct and particular as llm(). If you want your AI system to actually do something in the messy real world, you have to break down the nice theoretical boundary and guarantees you get from functional purity somewhere.
More concretely, given your proposed rating system, simply replace any LLM calls with a call that just asks actual humans to rate a world state given some description, and it seems like you get something that is at least as legible and transparent (in an informal sense) as the LLM version. The main advantage with using an LLM here is that you could potentially get lots of such ratings cheaply and quickly. Replay-ability, determinism and the relative ease of interpretability vs. doing neuroscience on the human raters are also nice, but none of these properties are very reassuring or helpful if the ratings themselves aren’t all that good. (Also, if you’re doing something with such low sample efficiency that you can’t just use actual humans, you’re probably on the wrong track anyway.)

Max H 8 Oct 2024 1:51 UTC
2 points
−2
in reply to: Raemon’s comment on: “Slow” takeoff is a terrible term for “maybe even faster takeoff, actually”
Economic / atomic takeoff

Max H 8 Oct 2024 1:49 UTC
2 points
0
on: “Slow” takeoff is a terrible term for “maybe even faster takeoff, actually”
For specifically discussing the takeoff models in the original Yudkowsky / Christiano discussion, what about:
Economic vs. atomic takeoff
Economic takeoff because Paul’s model implies rapid and transformative economic growth prior to the point at which AIs can just take over completely. Whereas Eliezer’s model is that rapid economic growth prior to takeover is not particularly necessary—a sufficiently capable AI could act quickly or amass resources while keeping a low profile, such that from the perspective of almost all humanity, takeover is extremely sudden.
Note: “atomic” here doesn’t necessarily mean “nanobots”—the goal of the term is to connote that an AI does something physically transformative, e.g. releasing a super virus, hacking / melting all uncontrolled GPUs, constructing a Dyson sphere, etc. A distinguishing feature of Eliezer’s model is that those kinds of things could happen prior to the underlying AI capabilities that enable them having more widespread economic effects.

IIUC, both Eliezer and Paul agree that you get atomic takeoff of some kind eventually, so one of the main disagreements between Paul and Eliezer could be framed as their answer to the question: “Will economic takeoff precede atomic takeoff?” (Paul says probably yes, Eliezer says maybe.)
Separately, an issue I have with smooth / gradual vs. sharp / abrupt (the current top-voted terms) is that they’ve become a bit overloaded and conflated with a bunch of stuff related to recent AI progress, namely scaling laws and incremental / iterative improvements to chatbots and agents. IMO, these aren’t actually closely related nor particularly suggestive of Christiano-style takeoff—if anything it seems more like the opposite:
- Scaling laws and the current pace of algorithmic improvement imply that labs can continue improving the underlying cognitive abilities of AI systems faster than those systems can actually be deployed into the world to generate useful economic growth. e.g. o1 is already “PhD level” in many domains, but doesn’t seem to be on pace to replace a significant amount of human labor or knowledge work before it is obsoleted by Opus 3.5 or whatever.
- Smooth scaling of underlying cognition doesn’t imply smooth takeoff. Predictable, steady improvements on a benchmark via larger models or more compute don’t tell you which point on the graph you get something economically or technologically transformative.

Max H 19 Jun 2024 23:15 UTC
6 points
0
on: My AI Model Delta Compared To Christiano
I’m curious what you think of Paul’s points (2) and (3) here:
- Eliezer often talks about AI systems that are able to easily build nanotech and overpower humans decisively, and describes a vision of a rapidly unfolding doom from a single failure. This is what would happen if you were magically given an extraordinarily powerful AI and then failed to aligned it, but I think it’s very unlikely what will happen in the real world. By the time we have AI systems that can overpower humans decisively with nanotech, we have other AI systems that will either kill humans in more boring ways or else radically advanced the state of human R&D. More generally, the cinematic universe of Eliezer’s stories of doom doesn’t seem to me like it holds together, and I can’t tell if there is a more realistic picture of AI development under the surface.
- One important factor seems to be that Eliezer often imagines scenarios in which AI systems avoid making major technical contributions, or revealing the extent of their capabilities, because they are lying in wait to cause trouble later. But if we are constantly training AI systems to do things that look impressive, then SGD will be aggressively selecting against any AI systems who don’t do impressive-looking stuff. So by the time we have AI systems who can develop molecular nanotech, we will definitely have had systems that did something slightly-less-impressive-looking.
And specifically to what degree you think future AI systems will make “major technical contributions” that are legible to their human overseers before they’re powerful enough to take over completely.
You write:
I expect that, shortly after AIs are able to autonomously develop, analyze and code numerical algorithms better than humans, there’s going to be some pretty big (like, multiple OOMs) progress in AI algorithmic efficiency (even ignoring a likely shift in ML/AI paradigm once AIs start doing the AI research). That’s the sort of thing which leads to a relatively discontinuous takeoff.
But how likely do you think it is that these OOM jumps happen before vs. after a decisive loss of control?
My own take: I think there will probably be enough selection pressure and sophistication in primarily human-driven R&D processes alone to get to uncontrollable AI. Weak AGIs might speed the process along in various ways, but by the time an AI itself can actually drive the research process autonomously (and possibly make discontinuous progress), the AI will already also be capable of escaping or deceiving its operators pretty easily, and deception / escape seems likely to happen first for instrumental reasons.
But my own view isn’t based on the difficulty of verification vs. generation, and I’m not specifically skeptical of bureaucracies / delegation. Doing bad / fake R&D that your overseers can’t reliably check does seem somewhat easier than doing real / good R&D, but not always, and as a strategy seems like it would usually be dominated by “just escape first and do your own thing”.

Max H 17 Jun 2024 5:24 UTC
4 points
1
in reply to: Matthew Barnett’s comment on: Matthew Barnett’s Shortform
That sounds like a frustrating dynamic. I think hypothetical dialogues like this can be helpful in resolving disagreements or at least identifying cruxes when fleshed out though. As someone who has views that are probably more aligned with your interlocutors, I’ll try articulating my own views in a way that might steer this conversation down a new path. (Points below are intended to spur discussion rather than win an argument, and are somewhat scattered / half-baked.)
My own view is that the behavior of current LLMs is not much evidence either way about the behavior of future, more powerful AI systems, in part because current LLMs aren’t very impressive in a mundane-utility sense.
Current LLMs look to me like they’re just barely capable enough to be useful at all—it’s not that they “actually do what we want”, rather, it’s that they’re just good enough at following simple instructions when placed in the right setup / context (i.e. carefully human-designed chatbot interfaces, hooked up to the right APIs, outputs monitored and used appropriately, etc.) to be somewhat / sometimes useful for a range of relatively simple tasks.
So the absence of more exotic / dangerous failure modes can be explained mostly as a lack of capabilities, and there’s just not that much else to explain or update on once the current capability level is accounted for.
I can sort of imagine possible worlds where current-generation LLMs all stubbornly behave like Sydney Bing, and / or fall into even weirder failure modes that are very resistant to RLHF and the like. But I think it would also be wrong to update much in the other direction in a “stubborn Sydney” world.
Do you mind giving some concrete examples of what you mean by “actually do what we want” that you think are most relevant, and / or what it would have looked like concretely to observe evidence in the other direction?
A somewhat different reason I think current AIs shouldn’t be a big update about future AIs is that current AIs lack the ability to bargain realistically. GPT-4 may behaviorally do what the user or developer wants when placed in the right context, but without the ability to bargain in a real way, I don’t see much reason to treat this observation very differently from the fact that my washing machine does what I want when I press the right buttons. The novelty of GPT-4 vs. a washing machine is in its generality and how it works internally, not the literal sense in which it does what the user and / or developer wants, which is a common feature of pretty much all useful technology.
I can imagine worlds in which the observation of AI system behavior at roughly similar capability levels to the LLMs we actually have would cause me to update differently and particularly towards your views, but in those worlds the AI systems themselves would look very different.
For example, suppose someone built an AI system with ~GPT-4 level verbal intelligence, but as a natural side effect of something in the architecture, training process, or setup (as opposed to deliberate design by the developers), the system also happened to want resources of some kind (energy, hardware, compute cycles, input tokens, etc.) for itself, and could bargain for or be incentivized by those resources in the way that humans and animals can often be incentivized by money or treats.
In the world we’re actually in, you can sometimes get better performance out of GPT-4 at inference time by promising to pay it money or threatening it in various ways, but all of those threats and promises are extremely fake—you couldn’t follow through even if you wanted to, and GPT-4 has no way of perceiving your follow-through or lack thereof anyway. In some ways, GPT-4 is much smarter than a dog or a young child, but you can bargain with dogs and children in very real ways, and if you tried to fake out a dog or a child by pretending to give them a treat without following through, they would quickly notice and learn not to trust you.
(I realize there are some ways in which you could analogize various aspects of real AI training processes to bargaining processes, but I would find optimistic analogies between AI training and human child-rearing more compelling in worlds where AI systems at around GPT-4 level were already possible to bargain with or incentivize realistically at runtime, in ways more directly analogous to how we can directly bargain with natural intelligences of roughly comparable level or lower already.)

Zooming out a bit, “not being able to bargain realistically at runtime” is just one of the ways that LLMs appear to be not like known natural intelligence once you look below surface-level behavior. There’s a minimum level of niceness / humanlikeness / “do what we want” ability that any system necessarily has to have in order to be useful to humans at all, and for tasks that can be formulated as text completion problems, the minimum amount seems to be something like “follows basic instructions, most of the time”. But I have not personally seen a strong argument for why current LLMs have much more than the minimum amount of humanlike-ness / niceness, nor why we should expect future LLMs to have more.

Max H 12 Jun 2024 22:00 UTC
11 points
0
in reply to: Richard_Ngo’s comment on: ricraz’s Shortform
Suppose we think of ourselves as having many different subagents that focus on understanding the world in different ways—e.g. studying different disciplines, using different styles of reasoning, etc. The subagent that thinks about AI from first principles might come to a very strong opinion. But this doesn’t mean that the other subagents should fully defer to it (just as having one very confident expert in a room of humans shouldn’t cause all the other humans to elect them as the dictator). E.g. maybe there’s an economics subagent who will remain skeptical unless the AI arguments can be formulated in ways that are consistent with their knowledge of economics, or the AI subagent can provide evidence that is legible even to those other subagents (e.g. advance predictions).

Do “subagents” in this paragraph refer to different people, or different reasoning modes / perspectives within a single person? (I think it’s the latter, since otherwise they would just be “agents” rather than subagents.)
Either way, I think this is a neat way of modeling disagreement and reasoning processes, but for me it leads to a different conclusion on the object-level question of AI doom.

A big part of why I find Eliezer’s arguments about AI compelling is that they cohere with my own understanding of diverse subjects (economics, biology, engineering, philosophy, etc.) that are not directly related to AI—my subagents for these fields are convinced and in agreement.

Conversely, I find many of the strongest skeptical arguments about AI doom to be unconvincing precisely because they seem overly reliant on a “current-paradigm ML subagent” that their proponents feel should be dominant, or at least more heavily weighted than I think is justified.
That will push P(doom) lower because most frames from most disciplines, and most styles of reasoning, don’t predict doom.
This might be true and useful for getting some kind of initial outside-view estimate, but I think you need some kind of weighting rule to make this work as reasoning strategy even at a meta level. Otherwise, aren’t you vulnerable to other people inventing lots of new frames and disciplines? I think the answer in geometric rationality terms is that some subagents will perform poorly and quickly lose their Nash bargaining resources, and then their contribution to future decision-making / conclusion-making will be down-weighted. But I don’t think the only way for a subagent to “perform” for the purposes of deciding on a weight is by making externally legible advance predictions.

Max H 26 Mar 2024 20:49 UTC
7 points
11
on: Modern Transformers are AGI, and Human-Level
Maybe a better question than “time to AGI” is time to mundanely transformative AGI. I think a lot of people have a model of the near future in which a lot of current knowledge work (and other work) is fully or almost-fully automated, but at least as of right this moment, that hasn’t actually happened yet (despite all the hype).

For example, one of the things current A(G)Is are supposedly strongest at is writing code, but I would still rather hire a (good) junior software developer than rely on currently available AI products for just about any real programming task, and it’s not a particularly close call. I do think there’s a pretty high likelihood that this will change imminently as products like Devin improve and get more widely deployed, but it seems worth noting (and finding a term for) the fact that this kind of automation so far (mostly) hasn’t actually happened yet, aside from certain customer support and copyediting jobs.

I think when someone asks “what is your time to AGI”, they’re usually asking about when you expect either (a) AI to radically transform the economy and potentially usher in a golden age of prosperity and post-scarcity or (b) the world to end.

And maybe I am misremembering history or confused about what you are referring to, but in my mind, the promise of the “AGI community” has always been (implicitly or explicitly) that if you call something “human-level AGI”, it should be able to get you to (a), or at least have a bigger economic and societal impact than currently-deployed AI systems have actually had so far. (Rightly or wrongly, the ballooning stock prices of AI and semiconductor companies seem to be mostly an expectation of earnings and impact from in-development and future products, rather than expected future revenues from wider rollout of any existing products in their current form.)

Max H 5 Mar 2024 4:26 UTC
6 points
3
in reply to: TurnTrout’s comment on: TurnTrout’s shortform feed
I actually agree that a lot of reasoning about e.g. the specific pathways by which neural networks trained via SGD will produce consequentialists with catastrophically misaligned goals is often pretty weak and speculative, including in highly-upvoted posts like Without specific countermeasures, the easiest path to transformative AI likely leads to AI takeover.
But to expand on my first comment, when I look around and see any kind of large effect on the world, good or bad (e.g. a moral catastrophe, a successful business, strong optimization around a MacGuffin), I can trace the causality through a path that is invariably well-modeled by applying concepts like expected utility theory (or geometric rationality, if you prefer), consequentialism, deception, Goodharting, maximization, etc. to the humans involved.
I read Humans provide an untapped wealth of evidence about alignment and much of your other writing as disagreeing with the (somewhat vague / general) claim that these concepts are really so fundamental, and that you think wielding them to speculate about future AI systems is privileging the hypothesis or otherwise frequently leads people astray. (Roughly accurate summary of your own views?)
Regardless of how well this describes your actual views or not, I think differing answers to the question of how fundamental this family of concepts is, and what kind of reasoning mistakes people typically make when they apply them to AI, is not really a disagreement about neural networks specifically or even AI generally.

Max H 4 Mar 2024 23:11 UTC
LW: 16 AF: 7
8
AF
in reply to: TurnTrout’s comment on: TurnTrout’s shortform feed

one can just meditate on abstract properties of “advanced systems” and come to good conclusions about unknown results “in the limit of ML training”

I think this is a pretty straw characterization of the opposing viewpoint (or at least my own view), which is that intuitions about advanced AI systems should come from a wide variety of empirical domains and sources, and a focus on current-paradigm ML research is overly narrow.

Research and lessons from fields like game theory, economics, computer security, distributed systems, cognitive psychology, business, history, and more seem highly relevant to questions about what advanced AI systems will look like. I think the original Sequences and much of the best agent foundations research is an attempt to synthesize the lessons from these fields into a somewhat unified (but often informal) theory of the effects that intelligent, autonomous systems have on the world around us, through the lens of rationality, reductionism, empiricism, etc.

And whether or not you think they succeeded at that synthesis at all, humans are still the sole example of systems capable of having truly consequential and valuable effects of any kind. So I think it makes sense for the figure of merit for such theories and worldviews to be based on how well they explain these effects, rather than focusing solely or even mostly on how well they explain relatively narrow results about current ML systems.

Max H 1 Mar 2024 2:51 UTC
17 points
−4
on: The Parable Of The Fallen Pendulum—Part 1
There are a bunch of ways to “win the argument” or just clear up the students’ object-level confusion about mechanics:
- Ask them to predict what happens if the experiment is repeated with the stand held more firmly in place.
- Ask them to work the problems in their textbook, using whatever method or theory they prefer. If they get the wrong answer (according to the answer key) for any of them, that suggests opportunities for further experiments (which the professor should take care to set up more carefully).
- Point out the specific place in the original on-paper calculation where the model of the pendulum system was erroneously over-simplified, and show that using a more precise model results in a calculation that agrees with the experimental results. Note that the location of the error is only in the model (and perhaps the students’ understanding); the words in the textbook describing the theory itself remain fixed.
- Write a rigid body physics simulator which can model the pendulum system in enough detail to accurately simulate the experimental result for both the case that the stand is held in place and the case that it falls over. Reveal that the source code for the simulator uses only the principles of Newtonian mechanics.
- Ask the students to pass the ITT of a more experienced physicist. (e.g. ask a physicist to make up some standard physics problems with an answer key, and then challenge the students to accurately predict the contents of the answer key, regardless of whether the students themselves believe those answers would make good experimental predictions.)
These options require that the students and professor spend some time and effort to clear up the students’ confusion about Newtonian mechanics, which may not be feasible if the lecture is ending soon. But the bigger issue is that clearing up the object-level confusion about physics doesn’t necessarily clear up the more fundamental mistakes the students are making about valid reasoning under uncertainty.
I wrote a post recently on Bayesian updating in real life that the students might be interested in, but in short I would say that their biggest mistake is that they don’t have a detailed enough understanding of their own hypotheses. Having failed to predict the outcome of their own experiment, they have strong evidence that they themselves do not possess an understanding of any theory of physics in enough mechanistic detail to make accurate predictions. However, strong evidence of their own ignorance is not strong evidence that any particular theory which they don’t understand is actually false.
The students should also consider alternatives to the “everyone else throughout history has been rationalizing away problems with Newtonian mechanics” hypothesis. That hypothesis may indeed be one possible valid explanation of the students’ own observations given everything else that they (don’t) know, but are they willing to write down some odds ratios between that hypothesis and some others they can come up with? Some alternative hypotheses they could consider:
- they are mistaken about what the theory of Newtonian mechanics actually says
- they or their professor made a calculation or modelling error
- their professor is somehow trolling them
- they themselves are trolls inside of a fictional thought experiment
They probably won’t think of the last one on their own (unless the rest of the dialogue gets very weird), which just goes to show how often the true hypothesis lies entirely outside of one’s consideration.
(Aside: the last bit of dialog from the students reminds me of the beginner computer programmer whose code isn’t working for some unknown-to-them reason, and quickly concludes that it must be the compiler or operating system that is bugged. In real life, sometimes, it really is the compiler. But it’s usually not, especially if you’re a beginner just getting started with “Hello world”. And even if you’re more experienced, you probably shouldn’t bet on it being the compiler at very large odds, unless you already have a very detailed model of the compiler, the OS, and your own code.)

Max H 28 Feb 2024 5:16 UTC
8 points
5
in reply to: Nora Belrose’s comment on: Counting arguments provide no evidence for AI doom

It’s that they’re biased strongly against scheming, and they’re not going to learn it unless the training data primarily consists of examples of humans scheming against one another, or something.

I’m saying if they’re biased strongly against scheming, that implies they are also biased against usefulness to some degree.

As a concrete example, it is demonstrably much easier to create a fake blood testing company and scam investors and patients for $billions than it is to actually revolutionize blood testing. I claim that there is something like a core of general intelligence required to execute on things like the latter, which necessarily implies possession of most or all of the capabilities needed to pull off the former.

Max H 28 Feb 2024 2:17 UTC
15 points
6
on: Counting arguments provide no evidence for AI doom
Joe also discusses simplicity arguments for scheming, which suppose that schemers may be “simpler” than non-schemers, and therefore more likely to be produced by SGD.
I’m not familiar with the details of Joe’s arguments, but to me the strongest argument from simplicity is not that schemers are simpler than non-schemers, it’s that scheming itself is conceptually simple and instrumentally useful. So any system capable of doing useful and general cognitive work will necessarily have to at least be capable of scheming.
We will address this question in greater detail in a future post. However, we believe that current evidence about inductive biases points against scheming for a variety of reasons. Very briefly:
- Modern deep neural networks are ensembles of shallower networks. Scheming seems to involve chains of if-then reasoning which would be hard to implement in shallow networks.
- Networks have a bias toward low frequency functions— that is, functions whose outputs change little as their inputs change. But scheming requires the AI to change its behavior dramatically (executing a treacherous turn) in response to subtle cues indicating it is not in a sandbox, and could successfully escape.
- There’s no plausible account of inductive biases that does support scheming. The current literature on scheming appears to have been inspired by Paul Christiano’s speculations about malign intelligences in Solomonoff induction, a purely theoretical model of probabilistic reasoning which is provably unrealizable in the real world.^[16] Neural nets look nothing like this.
- In contrast, points of comparison that are more relevant to neural network training, such as isolated brain cortices, don’t scheme. Your linguistic cortex is not “instrumentally pretending to model linguistic data in pursuit of some hidden objective.”
Also, don’t these counterpoints prove too much? If networks trained via SGD can’t learn scheming, why should we expect models trained via SGD to be capable of learning or using any high-level concepts, even desirable ones?
These bullets seem like plausible reasons for why you probably won’t get scheming within a single forward pass of a current-paradigm DL model, but are already inapplicable to the real-world AI systems in which these models are deployed.
LLM-based systems are already capable of long chains of if-then reasoning, and can change their behavior dramatically given a different initial prompt, often in surprising ways.

If the most relevant point of comparison to NN training is an isolated brain cortex, then that’s just saying that NN training will never be useful in isolation, since an isolated brain cortex can’t do much (good or bad) unless it is actually hooked up to a body, or at least the rest of a brain.

Max H 18 Feb 2024 18:35 UTC
5 points
5
in reply to: Shankar Sivarajan’s comment on: Intuition for 1 + 2 + 3 + … = −1/12
My point is that there is a conflict for divergent series though, which is why 1 + 2 + 3 + … = −1/12 is confusing in the first place. People (wrongly) expect the extension of + and = to infinite series to imply stuff about approximations of partial sums and limits even when the series diverges.

My own suggestion for clearing up this confusion is that we should actually use less overloaded / extended notation even for convergent sums, e.g. $(1, \frac{1}{2}, \frac{1}{4}, . . .) \sim 2$ seems just as readable as the usual $l i m \to$ and $+ . . . +$ notation.

Max H 18 Feb 2024 18:09 UTC
18 points
21
on: Intuition for 1 + 2 + 3 + … = −1/12
In precisely the same sense that we can write
$1 + \frac{1}{2} + \frac{1}{4} + \frac{1}{8} + \frac{1}{16} + \dots = 2$ ,
despite that no real-world process of “addition” involving infinitely many terms may be performed in a finite number of steps, we can write
$1 + 2 + 3 + 4 + \dots = - \frac{1}{12}$ .
Well, not precisely. Because the first series converges, there’s a whole bunch more we can practically do with the equivalence-assignment in the first series, like using it as an approximation for the sum of any finite number of terms. −1/12 is a terrible approximation for any of the partial sums of the second series.
IMO the use of “=” is actually an abuse of notation by mathematicians in both cases above, but at least an intuitive / forgivable one in the first case because of the usefulness of approximating partial sums. Writing things as $(1, 2, 3, . . .) \sim - \frac{1}{12}$ or $R ((1, 2, 3, . . .)) = - \frac{1}{12}$ (R() denoting Ramanujan summation, which for convergent series is equivalent to taking the limit of partial sums) would make this all less mysterious.
In other words, (1, 2, 3, …) is in an equivalence class with −1/12, an equivalence class which also contains any finite series which sum to −1/12, convergent infinite series whose limit of partial sums is −1/12, and divergent series whose Ramanujan sum is −1/12.

Max H 30 Jan 2024 15:41 UTC
8 points
4
in reply to: Ege Erdil’s comment on: Processor clock speeds are not how fast AIs think
True, but isn’t this almost exactly analogously true for neuron firing speeds? The corresponding period for neurons (10 ms − 1 s) does not generally correspond to the timescale of any useful cognitive work or computation done by the brain.
Yes, which is why you should not be using that metric in the first place.

Well, clock speed is a pretty fundamental parameter in digital circuit design. For a fixed circuit, running it at a 1000x slower clock frequency means an exactly 1000x slowdown. (Real integrated circuits are usually designed to operate in a specific clock frequency range that’s not that wide, but in theory you could scale any chip design running at 1 GHz to run at 1 KHz or even lower pretty easily, on a much lower power budget.)
Clock speeds between different chips aren’t directly comparable, since architecture and various kinds of parallelism matter too, but it’s still good indicator of what kind of regime you’re in, e.g. high-powered / actively-cooled datacenter vs. some ultra low power embedded microcontroller.

Another way of looking at it is power density: below ~5 GHz or so (where integrated circuits start to run into fundamental physical limits), there’s a pretty direct tradeoff between power consumption and clock speed.
A modern high-end IC (e.g. a desktop CPU) has a power density on the order of 100 W / cm^2. This is over a tiny thickness; assuming 1 mm you get a 3-D power dissipation of 1000 W / cm^3 for a CPU vs. human brains that dissipate ~10 W / 1000 cm^3 = 0.01 watts / cm^3.
The point of this BOTEC is that there are several orders of magnitude of “headroom” available to run whatever the computation the brain is performing at a much higher power density, which, all else being equal, usually implies a massive serial speed up (because the way you take advantage of higher power densities in IC design is usually by simply cranking up the clock speed, at least until that starts to cause issues and you have to resort to other tricks like parallelism and speculative execution).
The fact that ICs are bumping into fundamental physical limits on clock speed suggests that they are already much closer to the theoretical maximum power densities permitted by physics, at least for silicon-based computing. This further implies that, if and when someone does figure out how to run the actual brain computations that matter in silicon, they will be able to run those computations at many OOM higher power densities (and thus OOM higher serial speeds, by default) pretty easily, since biological brains are very very far from any kind of fundamental limit on power density. I think the clock speed <-> neuron firing speed analogy is a good way of way of summarizing this whole chain of inference.

Will you still be saying this if future neural networks are running on specialized hardware that, much like the brain, can only execute forward or backward passes of a particular network architecture? I think talking about FLOP/s in this setting makes a lot of sense, because we know the capabilities of neural networks are closely linked to how much training and inference compute they use, but maybe you see some problem with this also?
I think energy and power consumption are the safest and most rigorous way to compare and bound the amount of computation that AIs are doing vs. humans. (This unfortunately implies a pretty strict upper bound, since we have several billion existence proofs that ~20 W is more than sufficient for lethally powerful cognition at runtime, at least once you’ve invested enough energy in the training process.)

Max H 30 Jan 2024 3:32 UTC
12 points
6
on: Processor clock speeds are not how fast AIs think
The clock speed of a GPU is indeed meaningful: there is a clock inside the GPU that provides some signal that’s periodic at a frequency of ~ 1 GHz. However, the corresponding period of ~ 1 nanosecond does not correspond to the timescale of any useful computations done by the GPU.
True, but isn’t this almost exactly analogously true for neuron firing speeds? The corresponding period for neurons (10 ms − 1 s) does not generally correspond to the timescale of any useful cognitive work or computation done by the brain.
The human brain is estimated to do the computational equivalent of around 1e15 FLOP/s.
“Computational equivalence” here seems pretty fraught as an analogy, perhaps more so than the clock speed <-> neuron firing speed analogy.
In the context of digital circuits, FLOP/s is a measure of an outward-facing performance characteristic of a system or component: a chip that can do 1 million FLOP/s means that every second it can take 2 million floats as input, perform some arithmetic operation on them (pairwise) and return 1 million results.
(Whether the “arithmetic operations” are FP64 multiplication or FP8 addition will of course have a big effect on the top-level number you can report in your datasheet or marketing material, but a good benchmark suite will give you detailed breakdowns for each type.)
But even the top-line number is (at least theoretically) a very concrete measure of something that you can actually get out of the system. In contrast, when used in “computational equivalence” estimates of the brain, FLOP/s are (somewhat dubiously, IMO) repurposed as a measure of what the system is doing internally.
So even if the 1e15 “computational equivalence” number is right, AND all of that computation is irreducibly a part of the high-level cognitive algorithm that the brain is carrying out, all that means is that it necessarily takes at least 1e15 FLOP/s to run or simulate a brain at neuron-level fidelity. It doesn’t mean that you can’t get the same high-level outputs of that brain through some other much more computationally efficient process.
(Note that “more efficient process” need not be high-level algorithms improvements that look radically different from the original brain-based computation; the efficiencies could come entirely from low-level optimizations such as not running parts of the simulation that won’t affect the final output, or running them at lower precision, or with caching, etc.)
Separately, I think your sequential tokens per second calculation actually does show that LLMs are already “thinking” (in some sense) several OOM faster than humans? 50 tokens/sec is about 5 lines of code per second, or 18,000 lines of code per hour. Setting aside quality, that’s easily 100x more than the average human developer can usually write (unassisted) in an hour, unless they’re writing something very boilerplate or greenfield.
(The comparison gets even more stark when you consider longer timelines, since an LLM can generate code ²⁴⁄₇ without getting tired: 18,000 lines / hr is ~150 million lines in a year.)
The main issue with current LLMs (which somewhat invalidates this whole comparison) is that they can pretty much only generate boilerplate or greenfield stuff. Generating large volumes of mostly-useless / probably-nonsense boilerplate quickly doesn’t necessarily correspond to “thinking faster” than humans, but that’s mostly because current LLMs are only barely doing anything that can rightfully be called thinking in the first place.
So I agree with you that the claim that current AIs are thinking faster than humans is somewhat fraught. However, I think there are multiple strong reasons to expect that future AIs will think much faster than humans, and the clock speed <-> neuron firing analogy is one of them.

Max H 16 Jan 2024 17:49 UTC
4 points
on: Air Conditioner Test Results & Discussion
I haven’t read every word of the 200+ comments across all the posts about this, but has anyone considered how active heat sources in the room could confound / interact with efficiency measurements that are based only on air temperatures? Or be used to make more accurate measurements, using a different (perhaps nonstandard) criterion for efficiency?
Maybe from the perspective of how comfortable you feel, the only thing that matters is air temperature.
But consider an air conditioner that cools a room with a bunch of servers or space heaters in it to an equilibrium temperature of 70° F in a dual-hose setup vs. 72° in a single-hose setup, assuming the power consumption of the air conditioner and heaters is fixed in both cases. Depending on how much energy the heaters themselves are consuming, a small difference in temperature could represent a pretty big difference in the amount of heat energy the air conditioner is actually removing from the room in the different setups.
A related point / consideration: if there are enough active heat sources, I would expect their effect on cooling to dominate the effects from indoor / outdoor temperature difference, infiltration, etc. But even in a well-insulated room with few or no active heat sources, there’s still all the furniture and other non-air stuff in the room that has to equilibrate to the air temperature before it stops dissipating some amount of heat into the air. I suspect that this can go on happening for a while after the air temperature has (initially / apparently) equilibrated, but I’ve never tested it by sticking a giant meat thermometer into my couch cushions or anything like that.
Anecdotally, I’ve noticed that when I come back from a long absence (e.g. vacation) and turn on my window unit air conditioner for the first time, the air temperature seems to initially drop almost as quickly as it always does, but if I then turn the air conditioner off after a short while, the temperature seems to bounce back to a warmer temperature noticeably more quickly than if I’ve been home all day, running the air conditioner such that the long-term average air temperature (and thus the core temperature of all my furniture, flooring, etc.) is much lower.

Max H 14 Jan 2024 17:59 UTC
10 points
3
on: Against most AI risk analogies
Part of this is that I don’t share other people’s picture about what AIs will actually look like in the future. This is only a small part of my argument, because my main point is that that we should use analogies much less frequently, rather than switch to different analogies that convey different pictures.
You say it’s only a small part of your argument, but to me this difference in outlook feels like a crux. I don’t share your views of what the “default picture” probably looks like, but if I did, I would feel somewhat differently about the use of analogies.
For example, I think your “straightforward extrapolation of current trends” is based on observations of current AIs (which are still below human-level in many practical senses), extrapolated to AI systems that are actually smarter and more capable than most or all humans in full generality.
On my own views, the question of what the future looks like is primarily about what the transition looks like between the current state of affairs, in which the state and behavior of most nearby matter and energy is not intelligently controlled or directed, to one in which it is. I don’t think extrapolations of current trends are much use in answering such questions, in part because they don’t actually make concrete predictions far enough into the future.
For example, you write:
They will be numerous and everywhere, interacting with us constantly, assisting us, working with us, and even providing friendship to hundreds of millions of people. AIs will be evaluated, inspected, and selected by us, and their behavior will be determined directly by our engineering.
I find this sorta-plausible as a very near-term prediction about the next few years, but I think what happens after that is a far more important question. And I can’t tell from your description / prediction about the future here which of the following things you believe, if any:
- No intelligent system (or collection of such systems) will ever have truly large-scale effects on the world (e.g. re-arranging most of the matter and energy in the universe into computronium or hedonium, to whatever extent that is physically possible).
- Large-scale effects that are orders of magnitude larger or faster than humanity can currently collectively exert are physically impossible or implausible (e.g. that there are diminishing returns to intelligence past human-level, in terms of the ability it confers to manipulate matter and energy quickly and precisely and on large scales).
- Such effects, if they are physically possible, are likely to be near-universally directed ultimately by a human or group of humans deliberately choosing them.
- The answer to these kinds of questions is currently too uncertain or unknowable to be worth having a concrete prediction about.
My own view is that you don’t need to bring in results or observations of current AIs to take a stab at answering these kinds of questions, and that doing so can often be misleading, by giving a false impression that such answers are backed by empiricism or straightforwardly-valid extrapolation.
My guess is that close examination of disagreements on such topics would be more fruitful for identifying key cruxes likely to be relevant to questions about actually-transformative smarter-than-human AGI, compared to discussions centered around results and observations of current AIs.
I admit that a basic survey of public discourse seems to demonstrate that my own favored approach hasn’t actually worked out very well as a mechanism for building shared understanding, and moreover is often frustrating and demoralizing for participants and observers on all sides. But I still think such approaches are better than the alternative of a more narrow focus on current AIs, or on adding “rigor” to analogies that were meant to be more explanatory / pedagogical than argumentative in the first place. In my experience, the end-to-end arguments and worldviews that are built on top of more narrowly-focused / empirical observations and more surface-level “rigorous” theories, are prone to relatively severe streetlight effects, and often lack local validity, precision, and predictive usefulness, just as much or more so than many of the arguments-by-analogy they attempt to refute.

Max H 13 Jan 2024 17:14 UTC
13 points
7
in reply to: Carl Feynman’s comment on: Why do so many think deception in AI is important?

a position of no power and moderate intelligence (where it is now)

Most people are quite happy to give current AIs relatively unrestricted access to sensitive data, APIs, and other powerful levers for effecting far-reaching change in the world. So far, this has actually worked out totally fine! But that’s mostly because the AIs aren’t (yet) smart enough to make effective use of those levers (for good or ill), let alone be deceptive about it.

To the degree that people don’t trust AIs with access to even more powerful levers, it’s usually because they fear the AI getting tricked by adversarial humans into misusing those levers (e.g. through prompt injection), not fear that the AI itself will be deliberately tricky.

But we’re not going to deliberately allow such a position unless we can trust it.

One can hope, sure. But what I actually expect is that people will generally give AIs more power and trust as they get more capable, not less.

Max H 1 Jan 2024 18:24 UTC
5 points
3
in reply to: jessicata’s comment on: 2023 in AI predictions
Tsvi comes to mind: https://www.lesswrong.com/posts/sTDfraZab47KiRMmT/views-on-when-agi-comes-and-on-strategy-to-reduce