Effective Altruism Forum
EA Forum

Comment Permalink
Greg_Colbourn ⏸️ 3
1
0

I guess you're sort of joking, but it should be really surprising (from an outside perspective) that biological brains have figured out how to understand neural networks (and it's taken billions of years of evolution).

Reply
See in context
by Burnydelic
3 min read 4

13

AI safetyExistential riskAI alignmentLarge Language ModelsNews relevant to effective altruismPublic communication on AI safety
Frontpage

tl;dr: OpenAI leaked AI breakthrough called Q*, acing grade-school math. It is hypothesized combination of Q-learning and A*. It was then refuted. DeepMind is working on something similar with Gemini, AlphaGo-style Monte Carlo Tree Search. Scaling these might be crux of planning for increasingly abstract goals and agentic behavior. Academic community has been circling around these ideas for a while.

https://www.reuters.com/technology/sam-altmans-ouster-openai-was-precipitated-by-letter-board-about-ai-breakthrough-2023-11-22/ 

https://twitter.com/MichaelTrazzi/status/1727473723597353386

"Ahead of OpenAI CEO Sam Altman’s four days in exile, several staff researchers sent the board of directors a letter warning of a powerful artificial intelligence discovery that they said could threaten humanity

Mira Murati told employees on Wednesday that a letter about the AI breakthrough called Q* (pronounced Q-Star), precipitated the board's actions.

Given vast computing resources, the new model was able to solve certain mathematical problems. Though only performing math on the level of grade-school students, acing such tests made researchers very optimistic about Q*’s future success."

https://twitter.com/SilasAlberti/status/1727486985336660347

"What could OpenAI’s breakthrough Q* be about?

It sounds like it’s related to Q-learning. (For example, Q* denotes the optimal solution of the Bellman equation.) Alternatively, referring to a combination of the A* algorithm and Q learning.

One natural guess is that it is AlphaGo-style Monte Carlo Tree Search of the token trajectory. 🔎 It seems like a natural next step: Previously, papers like AlphaCode showed that even very naive brute force sampling in an LLM can get you huge improvements in competitive programming. The next logical step is to search the token tree in a more principled way. This particularly makes sense in settings like coding and math where there is an easy way to determine correctness. -> Indeed, Q* seems to be about solving Math problems 🧮"

https://twitter.com/mark_riedl/status/1727476666329411975

"Anyone want to speculate on OpenAI’s secret Q* project? 

- Something similar to tree-of-thought with intermediate evaluation (like A*)? 

- Monte-Carlo Tree Search like forward roll-outs with LLM decoder and q-learning (like AlphaGo)?

- Maybe they meant Q-Bert, which combines LLMs and deep Q-learning

Before we get too excited, the academic community has been circling around these ideas for a while. There are a ton of papers in the last 6 months that could be said to combine some sort of tree-of-thought and graph search. Also some work on state-space RL and LLMs."

https://www.theverge.com/2023/11/22/23973354/a-recent-openai-breakthrough-on-the-path-to-agi-has-caused-a-stir 

OpenAI spokesperson Lindsey Held Bolton refuted it:

"refuted that notion in a statement shared with The Verge: “Mira told employees what the media reports were about but she did not comment on the accuracy of the information.”"

https://www.wired.com/story/google-deepmind-demis-hassabis-chatgpt/ 

Google DeepMind's Gemini, that is currently the biggest rival with GPT4, which was delayed to the start of 2024, is also trying similar things: AlphaZero-based MCTS through chains of thought, according to Hassabis.

Demis Hassabis: "At a high level you can think of Gemini as combining some of the strengths of AlphaGo-type systems with the amazing language capabilities of the large models. We also have some new innovations that are going to be pretty interesting."

https://twitter.com/abacaj/status/1727494917356703829

Aligns with DeepMind Chief AGI scientist Shane Legg saying: "To do really creative problem solving you need to start searching."

https://twitter.com/iamgingertrash/status/1727482695356494132

"With Q*, OpenAI have likely solved planning/agentic behavior for small models. Scale this up to a very large model and you can start planning for increasingly abstract goals. It is a fundamental breakthrough that is the crux of agentic behavior. To solve problems effectively next token prediction is not enough. You need an internal monologue of sorts where you traverse a tree of possibilities using less compute before using compute to actually venture down a branch. Planning in this case refers to generating the tree and predicting the quickest path to solution"

My thoughts:

If this is true, and really a breakthrough, that might have caused the whole chaos: For true superintelligence you need flexibility and systematicity. Combining the machinery of general and narrow intelligence (I like the DeepMind's taxonomy of AGI https://arxiv.org/pdf/2311.02462.pdf ) might be the path to both general and narrow superintelligence. 

13

0
0

Reactions

0
0

More posts like this

Comments4


Sorted by
New & upvoted
Click to highlight new comments since:
titotal4
0
5

I don't think it would very surprising if a giant pile of linear algebra calculations figured out how to do linear algebra calculations. 

Reply
Greg_Colbourn ⏸️ 3
1
0

I guess you're sort of joking, but it should be really surprising (from an outside perspective) that biological brains have figured out how to understand neural networks (and it's taken billions of years of evolution).

Reply
gwern3
1
0

Whatever the nature of Q*, there is not much evidence that it could have prompted the Altman firing. It's not clear why some very early preliminary results about a Q* as described would prompt a firing nor why the firing would be so abrupt or when it was if the research happened months ago (and Altman was alluding to it publicly weeks ago), while Sutskever's involvement & the exact timing of the firing appear to be adequately explained by other issues.

As there is still nothing leaking or confirming Q*, I'm increasingly skeptical of its relevance - for something supposedly being demoed and discussed company-wide, if it was so cosmically important or so safety-relevant, you'd think there'd be more meat on the rumor bones by now, and there wouldn't be issues like denying that Murati confirmed the rumors as opposed to merely described the rumors (which is the sort of garbling that is in line with past leaks like the initial misdescriptions of Sutskever refusing to give examples of Altman's deceptive behavior). This increasingly sounds like a real system (maybe) which has been seized on by hype and yoked to a scandal it's mostly uninvolved with. (Maybe related to issues of candor, but not 'the' reason.)

Discussing Q* seems like a big distraction and waste of time, IMO, when there are better-reported things to discuss (like Altman trying to fire Helen Toner from the OA board).

I'm confused why people are broadcasting such a tiny morsel of news so much, and it's getting so much play on places like Marginal Revolution, while other parts of the recent OA drama don't seem to rate a mention. Nobody seemed to care even 1% as much about the prior OA research like GPT-f or process evaluation...

Reply
Greg_Colbourn ⏸️ 1
0
0

Thoughts on this? Supposedly shows the leaked letter to the board. But seems pretty far out, and if true, it's basically game over (AES-192 encryption broken by the AI with new unintelligible maths; the AI proposing a new more efficient and flexible architecture for itself). Really hope the letter is just a troll!

Reply
Curated and popular this week
 ·  · 5m read
 · 
This work has come out of my Undergraduate dissertation. I haven't shared or discussed these results much before putting this up.  Message me if you'd like the code :) Edit: 16th April. After helpful comments, especially from Geoffrey, I now believe this method only identifies shifts in the happiness scale (not stretches). Have edited to make this clearer. TLDR * Life satisfaction (LS) appears flat over time, despite massive economic growth — the “Easterlin Paradox.” * Some argue that happiness is rising, but we’re reporting it more conservatively — a phenomenon called rescaling. * I test rescaling using long-run German panel data, looking at whether the association between reported happiness and three “get-me-out-of-here” actions (divorce, job resignation, and hospitalisation) changes over time. * If people are getting happier (and rescaling is occuring) the probability of these actions should become less linked to reported LS — but they don’t. * I find little evidence of rescaling. We should probably take self-reported happiness scores at face value. 1. Background: The Happiness Paradox Humans today live longer, richer, and healthier lives in history — yet we seem no seem for it. Self-reported life satisfaction (LS), usually measured on a 0–10 scale, has remained remarkably flatover the last few decades, even in countries like Germany, the UK, China, and India that have experienced huge GDP growth. As Michael Plant has written, the empirical evidence for this is fairly strong. This is the Easterlin Paradox. It is a paradox, because at a point in time, income is strongly linked to happiness, as I've written on the forum before. This should feel uncomfortable for anyone who believes that economic progress should make lives better — including (me) and others in the EA/Progress Studies worlds. Assuming agree on the empirical facts (i.e., self-reported happiness isn't increasing), there are a few potential explanations: * Hedonic adaptation: as life gets
 ·  · 38m read
 · 
In recent months, the CEOs of leading AI companies have grown increasingly confident about rapid progress: * OpenAI's Sam Altman: Shifted from saying in November "the rate of progress continues" to declaring in January "we are now confident we know how to build AGI" * Anthropic's Dario Amodei: Stated in January "I'm more confident than I've ever been that we're close to powerful capabilities... in the next 2-3 years" * Google DeepMind's Demis Hassabis: Changed from "as soon as 10 years" in autumn to "probably three to five years away" by January. What explains the shift? Is it just hype? Or could we really have Artificial General Intelligence (AGI)[1] by 2028? In this article, I look at what's driven recent progress, estimate how far those drivers can continue, and explain why they're likely to continue for at least four more years. In particular, while in 2024 progress in LLM chatbots seemed to slow, a new approach started to work: teaching the models to reason using reinforcement learning. In just a year, this let them surpass human PhDs at answering difficult scientific reasoning questions, and achieve expert-level performance on one-hour coding tasks. We don't know how capable AGI will become, but extrapolating the recent rate of progress suggests that, by 2028, we could reach AI models with beyond-human reasoning abilities, expert-level knowledge in every domain, and that can autonomously complete multi-week projects, and progress would likely continue from there.  On this set of software engineering & computer use tasks, in 2020 AI was only able to do tasks that would typically take a human expert a couple of seconds. By 2024, that had risen to almost an hour. If the trend continues, by 2028 it'll reach several weeks.  No longer mere chatbots, these 'agent' models might soon satisfy many people's definitions of AGI — roughly, AI systems that match human performance at most knowledge work (see definition in footnote). This means that, while the compa
 ·  · 4m read
 · 
SUMMARY:  ALLFED is launching an emergency appeal on the EA Forum due to a serious funding shortfall. Without new support, ALLFED will be forced to cut half our budget in the coming months, drastically reducing our capacity to help build global food system resilience for catastrophic scenarios like nuclear winter, a severe pandemic, or infrastructure breakdown. ALLFED is seeking $800,000 over the course of 2025 to sustain its team, continue policy-relevant research, and move forward with pilot projects that could save lives in a catastrophe. As funding priorities shift toward AI safety, we believe resilient food solutions remain a highly cost-effective way to protect the future. If you’re able to support or share this appeal, please visit allfed.info/donate. Donate to ALLFED FULL ARTICLE: I (David Denkenberger) am writing alongside two of my team-mates, as ALLFED’s co-founder, to ask for your support. This is the first time in Alliance to Feed the Earth in Disaster’s (ALLFED’s) 8 year existence that we have reached out on the EA Forum with a direct funding appeal outside of Marginal Funding Week/our annual updates. I am doing so because ALLFED’s funding situation is serious, and because so much of ALLFED’s progress to date has been made possible through the support, feedback, and collaboration of the EA community.  Read our funding appeal At ALLFED, we are deeply grateful to all our supporters, including the Survival and Flourishing Fund, which has provided the majority of our funding for years. At the end of 2024, we learned we would be receiving far less support than expected due to a shift in SFF’s strategic priorities toward AI safety. Without additional funding, ALLFED will need to shrink. I believe the marginal cost effectiveness for improving the future and saving lives of resilience is competitive with AI Safety, even if timelines are short, because of potential AI-induced catastrophes. That is why we are asking people to donate to this emergency appeal
Recent opportunities in AI safety
1
An International Collaborative Hub for Advancing AI Safety Research
Cody Albert
· · 6m read
0
0
170
Forethought: A new AI macrostrategy group
5 authors
· · 4m read
6
6
15
Open call: "Existential risk of AI: technical conditions"
miller-max
· · 1m read
0
0
View more