Clarifying and predicting AGI

(expanding on my reply to you on twitter)

For the t-AGI framework, maybe you should also specify that the human starts the task only knowing things that are written multiple times on the internet. For example, Ed Witten could give snap (1-second) responses to lots of string theory questions that are WAY beyond current AI, using idiosyncratic intuitions he built up over many years. Likewise a chess grandmaster thinking about a board state for 1 second could crush GPT-4 or any other AI that wasn’t specifically and extensively trained on chess by humans.

A starting point I currently like better than “t-AGI” is inspired the following passage in Cal Newport’s book Deep Work:

[Deciding whether an activity is “deep work” versus “shallow work”] can be more ambiguous. Consider the following tasks:

• Example 1: Editing a draft of an academic article that you and a collaborator will soon submit to a journal.
• Example 2: Building a PowerPoint presentation about this quarter’s sales figures.
• Example 3: Attending a meeting to discuss the current status of an important project and to agree on the next steps. It’s not obvious at first how to categorize these examples.

The first two describe tasks that can be quite demanding, and the final example seems important to advance a key work objective. The purpose of this strategy is to give you an accurate metric for resolving such ambiguity—providing you with a way to make clear and consistent decisions about where given work tasks fall on the shallow-to-deep scale. To do so, it asks that you evaluate activities by asking a simple (but surprisingly illuminating) question:

How long would it take (in months) to train a smart recent college graduate with no specialized training in my field to complete this task?

In the case of LLM-like systems, we would replace “smart recent college graduate” with “person who has read the entire internet”.

This is kinda related to my belief that knowledge-encoded-in-weights can do things that knowledge-encoded-in-the-context-window can’t. There is no possible context window that turns GPT-2 into GPT-3, right?

So when I try to think of things that I don’t expect LLM-like-systems to be able to do, I imagine, for example, finding a person adept at tinkering, and giving them a new machine to play with, a very strange machine unlike anything on the internet. I ask the person to spend the next weeks or months understanding that machine. So the person starts disassembling it and reassembling it, and they futz with one of the mechanism and see how it affects the other mechanisms, and they try replacing things and tightening or loosening things and so on. It might take a few weeks or months, but they’ll eventually build for themselves an exquisite mental model of this machine, and they’ll be able to answer questions about it and suggest improvements to it, even in only 1 second of thought, that far exceed what an LLM-like AI could ever do.

Maybe you’ll say that example is unfair because that’s a tangible object and robotics is hard. But I think there are intangible examples that are analogous, like people building up new fields of math. As an example from my own life, I was involved in early-stage design for a new weird type of lidar, including figuring out basic design principles and running trade studies. Over the course of a month or two of puzzling over how best to think about its operation and estimate its performance, I wound up with a big set of idiosyncratic concepts, with rich relationships between them, tailored to this particular weird new kind of lidar. That allowed me to have all the tradeoffs and interrelationships at the tip of my tongue. If someone suggested to use a lower-peak-power laser, I could immediately start listing off all the positive and negative consequences on its performance metrics, and then start listing possible approaches to mitigating the new problems, etc. Even if that particular question hadn’t come up before. The key capability here is not what I’m doing in the one second of thought before responding to that question, rather it’s what I was doing in the previous month or two, as I was learning, exploring, building concepts, etc., all specific to this particular gadget for which no remotely close analogue existed on the internet.

I think a similar thing is true in programming, and that the recent success of coding assistants is just because a whole lot of coding tasks are just not too deeply different from something-or-other on the internet. If a human had hypothetically read every open-source codebase on the internet, I think they’d more-or-less be able to do all the things that Copilot can do without having to think too hard about it. But when we get to more unusual programming tasks, where the hypothetical person would need to spend a few weeks puzzling over what’s going on and what’s the best approach, even if that person has previously read the whole internet, then we’re in territory beyond the capabilities of LLM programming assistants, current and future, I think. And if we’re talking about doing original science & tech R&D, then we get into that territory even faster.

Here are some predictions—mostly just based on my intuitions, but informed by the framework above. I predict with >50% credence that by the end of 2025 neural nets will:

To clarify, I think you mean that you predict each of these individually with >50% credence, not that you predict all of them jointly with >50% credence. Is that correct?

A 1-year AGI would need to beat humans at... basically everything. Some projects take humans much longer (e.g. proving Fermat's last theorem) but they can almost always be decomposed into subtasks that don't require full global context (even tho that's often helpful for humans).

This seems wrong. There is a class of tasks that takes humans longer than 1 year: gaining expertise in a field. For example, learning higher mathematics from scratch, or learning to code very well, or becoming a surgeon, etc.

If AI is capable of doing any current human profession, but is incapable of learning new professions that do not yet exist (because of lack of training data, presumably), then it is not yet human-complete: humans still have relevance in the economy, as new types of professions will arise.

Good post! I think I basically agree with you except I think that I would add that the stuff that can't be done by the end of 2025 will be doable by the end of 2027 (with the possible exception of manual labor, that might take another year or two). Whereas I take it you think it'll take longer than that for e.g. robustly pursuing a plan over multiple days to happen. 

Care to say what you think there -- how long until e.g. AI R&D has been dramatically accelerated by AIs doing much of the cognitive labor? How long until e.g. a souped-up version of AutoGPT can carry out a wide range of tasks on the internet/computers, stringing them together to coherently act towards goals on timespans of multiple days? (At least as coherently as a typical human professional, let's say?)

Fits nicely into (t,n)/(w,h) - as above t is time of human, n is number of humans, w is number of watts inference costs, h is amount of time inference takes. (perhaps there are other units besides watts that matter, such as specific hardware, but imo watts are a fundamental metric in terms of ability to eat all biological life as literal food or whatever doom it is we expect these days.)

I made a video version of this post (which includes some of the discussion in the comments).
 

But in most cases I expect that the bottleneck is being able to perform a task *at all*; if they can then they'll almost always be able to do it with a negligible proportion of the world's compute.

I like your framing, and particularly like this piece of it. The thing that I've been trying to convince people of after doing a deep-dive research project over several months on this is... GPT-4 is close to the threshold of being able to do recursive self-improvement. I think that GPT-5 will be over that threshold. If not, then I'm nearly certain a GPT-6 would be. And I think that this threshold is critical not in a FOOM-within-days way, but in a human-assisted gradually-accelerating-self-improvement-over-months culminating in something roughly like 100x improvement over 6 - 18 months, and then it's a crazy singularity world and I don't know how to predict it will go other than 'uhoh'.

If I'm right that we're like 1 - 5 years away from this crazy RSI process getting started, then it would sure be nice if humanity would coordinate a bit better about how to deal with this scenario.

I tried to write markets for these when Richard originally posted then. I feel like posting a block of markets is kind of antisocial, but I think the benefits outweigh the costs.
 

English is not my first language, and I'm not very proficient in it. So there is a word that is confusing to me. Does the word 'any' mean 'all' in this context or does it refer to something that is chosen at random?

I want to point out the two main pillars I think your model has to assume for it to be the best model for prediction. (I think they're good assumptions)

1. There is a steep difficulty increase in training NNs that act over larger time spans. 
2. This is the best metric to use as it outcompetes all other metrics when it comes to making useful predictions about the efficacy of NNs. 

I like the model and I think it's better than not having one. I do think it misses out on some of the things Steven Byrnes responds with. There's a danger of it being too much of a Procrustes bed or overfitted as specific subtasks and cognition that humans have evolved might be harder to replicate than others. The main bottlenecks might then not lay in the temporal planning distance but in something else.

My prior on the t-AGI not being overfitting is probably something like 60-80% due to the bitter lesson, which to some extent tells us that cognition can be replicated quite well with Deep Learning. So I tend to agree but I would have liked to see a bit more epistemic humility to this point I guess.

I really like the t-AGI concept for a specific task or benchmark but find it difficult to generalize since capabilities are not uniform. How do you classify an AGI that is 1-second AGI at some tasks but 1-day AGI at others (e.g. due to serial processing speed advantages)?

I think coherence over time is a very difficult problem, and one humans still struggle at, even though (I assume) evolution optimized us hard for this.

Not sure about this. Did humans in the ancestral environment need more than 1-day coherence?

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Hello,

Your viewpoint is very interesting. I have two questions.

If we use an "expert threshold" to define advanced artificial intelligence, what would the threshold for superintelligence be? There are exceptional individuals, such as Leonardo da Vinci or Einstein, who could be considered superintelligent. 

I believe, in order to be a fair comparison, superintelligence should be compared to these rare cases. Yet, we lack sufficient data on such individuals, and even if we had the data, our current systems might not comprehend these unique forms of intelligence. What's your perspective on this?

Also, you characterized superintelligence as the equivalent of 8 billion AI systems working for one year. However, it's impractical to have 8 billion people collaborate on a task for a year. How should this be measured appropriately?

Thank you.

What percentage of humans does it need to beat at a given set of benchmarks?

If I wanted to prove I have a t AGI do I need to be at 99th percentile or 50th percentile for each benchmark, and out of all the benchmarks how many of them do we need to "pass"? (Pass meaning "declare victory", but the numerical score is the thing that matters)

Theres also major issues with benchmarking because current AI are stupidly good at learning to pass any test, so leaked questions cause a problem.

In a way what you are asking is for an AGI architecture: the AGI architecture would get trained on data captured before the test questions were developed, and you're measuring the ability of that architecture to use all the training data and it's cognitive architecture on these benchmark tasks.

Certain questions like "build a successful company" or other complex real world tasks have the problem that each successful company was only possible if founded for a target (product, market, time). Miss any of those and it will fail even if the AGI does a better job than human entrepreneurs.