I agree, it takes extra effort to make the AI behave like a team of experts.

Thank you :)

Good luck on sharing your ideas. If things aren't working out, try changing strategies. Maybe instead of giving people a 100 page paper, tell them the idea you think is "the best," and focus on that one idea. Add a little note at the end "by the way, if you want to see many other ideas from me, I have a 100 page paper here."

Maybe even think of different ideas.

I cannot tell you which way is better, just keep trying different things. I don't know what is right because I'm also having trouble sharing my ideas.

Does it have to be a highly sentient animal or does clam chowder count? :)

Edit: I posted without thinking. I just noticed this sounds sorta inappropriate given your serious personal stories (I should have read them before posting). Sorry, social media is a bad influence on me, and social skills are not my thing. But earnestly asking, do you think clam chowder etc. would work?

I feel your points are very intelligent. I also agree that specializing AI is a worthwhile direction.

It's very uncertain if it works, but all approaches are very uncertain, so humanity's best chance is to work on many uncertain approaches.

Unfortunately, I disagree it will happen automatically. Gemini 1.5 (and probably Gemini 2.0 and GPT-4) are Mixture of Experts models. I'm no expert, but I think that means that for each token of text, a "weighting function" decides which of the sub-models should output the next token of text, or how much weight to give each sub-model.

So maybe there is an AI psychiatrist, an AI mathematician, and an AI biologist inside Gemini and o1. Which one is doing the talking depends on what question is asked, or which part of the question the overall model is answering.

The problem is they they all output words to the same stream of consciousness, and refer to past sentences with the words "I said this," rather than "the biologist said this." They think that they are one agent, and so they behave like one agent.

My idea—which I only thought of thanks to your paper—is to do the opposite. The experts within the Mixture of Experts model, or even the same AI on different days, do not refer to themselves with "I" but "he," so they behave like many agents.

:) thank you for your work!

I'm not disagreeing with your work, I'm just a little less optimistic than you and don't think things will go well unless effort is made. You wrote the 100 page paper so you probably understand effort more than me :)

Happy holidays!

That is very thoughtful.

1.

When you talk about specializing AI powers, you talk about a high intellectual power AI with limited informational power and limited mental (social) power. I think this idea is similar to what Max Tegmark said in an article:

If you’d summarize the conventional past wisdom on how to avoid an intelligence explosion in a “Don’t-do-list” for powerful AI, it might start like this:

☐ Don’t teach it to code: this facilitates recursive self-improvement

☐ Don’t connect it to the internet: let it learn only the minimum needed to help us, not how to manipulate us or gain power

☐ Don’t give it a public API: prevent nefarious actors from using it within their code

☐ Don’t start an arms race: this incentivizes everyone to prioritize development speed over safety

Industry has collectively proven itself incapable to self-regulate, by violating all of these rules.

He disagrees that "the market will automatically develop in this direction" and is strongly pushing for regulation.

Another think Max Tegmark talks about is focusing on Tool AI instead of building a single AGI which can do everything better than humans (see 4:48 to 6:30 in his video). This slightly resembles specializing AI intelligence, but I feel his Tool AI regulation is too restrictive to be a permanent solution. He also argues for cooperation between the US and China to push for international regulation (in 12:03 to 14:28 of that video).

Of course, there are tons of ideas in your paper that he hasn't talked about yet.

You should read about the Future of Life Institute, which is headed by Max Tegmark and is said to have a budget of $30 million.

2.

The problem with AGI is at first it has no destructive power at all, and then it suddenly has great destructive power. By the time people see its destructive power, it's too late. Maybe the ASI has already taken over the world, or maybe the AGI has already invented a new deadly technology which can never ever be "uninvented," and bad actors can do harm far more efficiently.

I upvoted your post and I'm really confused why other people downvoted your post, it seems very reasonable.

I recently wrote "ARC-AGI is a genuine AGI test but o3 cheated :(" and it got downvoted to death too. (If you look at December 21 from All Posts, my post was the only one with negative votes, everyone else got positive votes haha.)

My guess is an important factor is to avoid strong language, especially in the title. You described your friend as a "Cryptobro," and I described OpenAI's o3 as "cheating."

In hindsight, "cheating" was an inaccurate description for o3, and "Cryptobro" might be an inaccurate description of your friend.

Happy holidays :)

Wow it does say the test set problems are harder than the training set problems. I didn't expect that.

But it's not an enormous difference: the example model that got 53% on the public training set got 38% on the public test set. It got only 24% on the private test set, even though it's supposed to be equally hard, maybe because "trial and error" fitted the model to the public test set as well as the public training set.

The other example model got 32%, 30%, and 22%.

Thank you for the help :)

By the way, how did you find this message? I thought I already edited the post to use spoiler blocks, and I hid this message by clicking "remove from Frontpage" and "retract comment" (after someone else informed me using a PM).

EDIT: dang it I still see this comment despite removing it from the Frontpage. It's confusing.

I think the Kaggle models might have the human design the heuristics while o3 discovers heuristics on its own during RL (unless it was trained on human reasoning on the ARC training set?).

o3's "AI designed heuristics" might let it learn a far more of heuristics than humans can think of and verify, while the Kaggle models' "human designed heuristics" might require less AI technology and compute. I don't actually know how the Kaggle models work, I'm guessing.

I finally looked at the Kaggle models and I guess it is similar to RL for o3.