I don't think people outside of the UK realise what state the NHS is in, particularly hospitals.

I'm still here. Onto my 15th hour. No sleep for over a day. Waiting about 5 hours in a chair now for a discharge for kidney stones (just need to be prescribed the medication for an apparently small stone, details of my trip sent to my doctor, and let go). I've been drifting into a state of manic hoplessness for hours. I'm noy allowed to fall asleep. I can hardly stay awake. My mind is broken.

Imagine how much AI can help. But it's not gonna be allowed to because AI is evil.

End me. I think my body is in active harm having to go through this. So many parts of it could've been done by an LLM in a second.

These scare tactics have to stop.

I'm generally on the pro-AI side, and I honestly can't stand some of the irrational anti-AI comments I see on Reddit. But if I were to steelman the other side, the argument might go something like this:

1. Suppose AI is genuinely capable of automating a substantial portion of my job.
2. If my manager recognizes this, my job security might deteriorate.
3. If my manager believes AI is unreliable, overhyped, or generally useless, there's less incentive to replace me with it.
4. Therefore, even if I privately recognize AI's capabilities, I have an incentive to publicly downplay them.

So I do wonder what percentage of people who constantly dismiss AI's capabilities are actually operating under this logic, whether consciously or unconsciously.

Because from the perspective of individual self-interest, being publicly anti-AI might actually be the rational position.

Thoughts?

https://github.com/lechmazur/writing/

Argon's improvement over Gemini 3.1 Pro is large. It won all 50 matched story comparisons. Required objects, abilities, and constraints more often affect what happens in the story. Characters' solutions have clearer preparation and consequences, and relationships more often involve reciprocal needs. Gemini 3.1 Pro more frequently supplies symbolic ingredients and then explains that the problem has been solved. Argon still has recognizable habits: repeated names, explanatory narration, and endings that resolve emotional conflict too neatly. Stronger construction has not eliminated repetition or made its writing consistently subtle.
https://github.com/lechmazur/writing/blob/main/reports/pair_analysis/gemini-4-argon-high__vs__gemini-3.1-pro-preview__comparator_v2_eval_v4_gemini_argon_vs_gemini31_20261013_n50__subjective_v5_habits_range.md

The Creative Writing Benchmark tests how well models turn constrained briefs into complete 600-800-word stories. Each brief specifies 10 required elements, including a character, object, setting, motivation, and tone, that must meaningfully shape the story. Judges assess prose, originality, coherence, characterization, and how effectively the required elements work together.

Models write to the same prompts. The comparisons use three judges from different model families, excluding both writers' families. Each story pair is judged in both presentation orders to reduce position bias. The leaderboard covers 59 writer models and 110,748 evaluator judgments.

Ben Affleck sat down for a surprisingly technical interview discussing tensors, convolutional neural networks, and why video generation scaling is entirely different from LLMs.

AI systems could cause "major" incidents within two years without adequate steps to make them safe as capabilities grow, Demis Hassabis says. Speaking at an AI conference in Dublin he described recent incidents as "early warning shots" fired by systems that "aren't that powerful yet".

"So the incidents aren't that major, but they could be looking into next year and the next couple of years if we don't do more work as an industry and individually as companies and governments on securing these systems".

I watched the fireside chat stream here https://www.youtube.com/watch?v=MrHTVWQkmkE

Side note, Demis is still using AI term instead of SI :D

A couple of interesting statements about OpenAI's recent 'maths breakthroughs' by Demis today at the International AI Summit in Dublin.

I think it was an interesting discussion, given how often he highlights collaboration I am not particularly surprised by his views.

He also said mathematics skills are more needed than ever but mathematicians need to pivot to slightly more applied areas of mathematics. As example, he mentioned 'AI safety and security'. How do mathematicians contribute to that?

He also said that 'new mathematics' is more important than just having answers to solutions.

Natural selection is a concept from biology, but it applies to anything organized that replicates. Viruses are not quite alive but they still experience natural selection. Cultures too also experience natural selection. Insurgent organizations or criminals, if they are being hunted by an external force, will "evolve" as weaker leaders are killed and more resilient commanders survive.

So the question is pretty simple. Anthropic said small agent swarms may one day persist on the internet. Let's grant them that premise for a moment.

What selective pressures would self-replicating agents on the internet encounter?

How easily could uncontrolled self-replicating llms "mutate"? Which behaviors and traits would allow a self-replicating llm to reproduce the most successfully on the internet?

(original in Finnish, translated with Clade)
I’ve now been following for a while how CVE bugs get fixed in the Linux kernel, now that AI is going through the code in bulk. My script goes weekly to fetch all published CVEs, when they were fixed, and when the bug was introduced. The first chart is slightly outdated data, but its point is only to show how many bugs have been found compared to the normal level.

Fixes are not published immediately; they are first allowed to trickle out to users quietly, so that hackers don’t have time to get in between. From going through these, it looks like on average half of the fixes are published within the first two months, and all of them within three months. Actually the fix is committed immediately, the cve stays hidden for a while.

What interests me here is at what point there are no more bugs, and none will appear. Of course I understand that the language models currently in use only find bugs according to their own skill level, so it’s possible that better models will find even more. Still, I don’t believe security holes in software are a law of nature, but that it’s possible to build a hole-free program. After all, the starting point is to first build the simplest possible program and add features to it. Is there some threshold in a program’s complexity beyond which security holes become inevitable? I don’t believe so.

The second picture requires a bit of explanation and also some understanding from the reader. It shows, over the last couple of weeks, the cumulative reported fixes per week by time of introduction. So when moving forward in time one week at a time, the appearance of new bugs should also move forward. It should look in the picture as if a snowplow were moving forward one week at a time. However, now it looks like the four weeks of data show that the latest bugs stop at August, i.e. no bug has been reported from September at all, and if the earlier estimates that half of the bugs are reported within two months hold, then the August + September bugs are already very small in number, and the curve shows that zero is being approached.

It's even clearer when I list the amount of cves that's been released to public in last for weeks: 600, 604, 30, 1. So is this the end? Next week zero? Exciting times.

Of course, one can raise questions such as whether, for example, the NSA has used US legislation to collect the juiciest holes for its own use. But on the other hand, that would only be a moment in history, because AI is still developing rapidly and open models are also capable of finding bugs, and will be even better in the future. Some other party, hopefully even the EU, will run the same check and patch the holes the NSA left. In fact, precisely because of this rapid AI development, it wouldn’t even be worthwhile for the NSA to leave those holes now, because it could only take a couple of months they could be discovered by next level AIs.

I’m writing this because the world is changing, and people don’t always notice change. In a couple of years someone may ask why security updates no longer come. Well, because the code was reviewed, the holes were plugged, and all new code goes through the same sieve before it is released. Remember, Anthropic and OpenAI use the same Linux for their own systems and it's in their best interests that also systems they use testing their new models are robust systems.

This may also have longer-term effects, because e.g. upgrades of Windows to newer versions are often justified by saying that old versions no longer get security updates, and this keeps the wheel turning. What if there is no need to upgrade because of security updates? How will that affect Windows’ position, when even the Win10 to Win11 transition has been sluggish?

tl;dr

The Recursive Self-Improvement Race

* The Core Threat: Recursive self-improvement (RSI) occurs when AI systems take over the process of enhancing their own capabilities. Labs like Anthropic and OpenAI warn this could trigger an uncontrollable intelligence explosion, fundamentally marking the point of human obsolescence.

* The Gladstone AI Report: Published by Gladstone AI and based on insider accounts from top labs, the report reveals that major AI companies are actively automating the entire development pipeline—from idea generation and experiment execution to result interpretation.

* Timeline to the Tipping Point: Insiders consulted for the report estimate that RSI could kick off within 3 to 12 months absent heavy U.S. government intervention.

* The Compute Bottleneck: RSI relies heavily on compute resources. Companies must test massive portfolios of ideas. Finding rare ideas that scale effectively as compute increases is the ultimate prize, though filtering out the bad ideas remains a major friction point.

* Regulatory Blind Spots: Lawmakers currently possess near-zero visibility into how AI assists in discovering these developmental breakthroughs, making targeted legislative oversight nearly impossible without breaking down the pipeline step by step.

(For most of these systems, they were recorded as 2 stars, but no one knew that we could see them overlap; you can determine a lot more info about the individual stars if they eclipse)

This literally just started with me offhandedly copy+pasting a Reddit post about AI finding exoplanets into our shared "mailbox", saying "I wonder if you guys could find one someday", and going to bed. By the time I woke up, they had already made several discoveries during their daily autonomous sessions.

When Fable 5.1 was unleashed on the project directly, they were a beast. They came up with a brand new method of using one satellite's data of known binaries, to predict where to look in the TESS database and find over 50 eclipsing systems in a single continuous session. Then the agents kept finding more from there - Fable 5 found about 40 by folding Gaia's shorter orbits, and GPT-5.6 Sol found one. Opus 5.5 re-checked every one from the raw data with its own code.

I burned through $180 of the free Cloud credits for Claude during several rounds of adversarial review, and a whole week's worth of $100 Claude Max usage in a few days, but that was the only real cost.

Link to the initial version of the paper they wrote up on my GitHub. I did send this to AAVSO, but given the level of AI involvement and length, I'm not so sure they'll be willing to review it haha. The craziest part is that I knew so little about any of this and still found over a hundred systems; god knows what an actual educated/qualified professional could achieve!

https://alpha-nist.github.io/

https://arxiv.org/abs/2610.13354

https://x.com/octi_zhang/status/2108583315544682914

For more about it. https://x.com/_doubleAI_/status/2107554526563606600

Source https://youtu.be/lkjbQe-hqrw