Quick Takes

The first mention of the threat of AI (I could find) - May 17, 2000 "There is a fourth issue that does, I think, rise to the level where it can be included, and it is an issue really without a name; I call it the issue of engineered intelligence. I am going to propose to this House, I hope some of my colleagues will join me, we will have dinner, we will have a drink or two, we will think this over, not maybe a drink or two, we will think over what form this bill should take, but I am planning to introduce a bill calling for the creation of a national commission on engineered intelligence." - Rep. Sherman on May 17, 2000, during the second session of the 106th Congress. * 2 years after Google was founded * 5 years before The Singularity Is Near * He references The Age of Spiritual Machines * 2 months before MIRI was founded * ~3 years before MIRI stopped trying to build AI (from my understanding) * 16 years before the first congressional hearing on AI (2016) Based on his speech, it seems he was in circles where this issue was being discussed. I'm curious if anyone has more information. I'm also curious why he never introduced such a bill, and whether organizations pushed him towards introducing one. Brad Sherman was also the congressman who held up a copy of If Anyone Builds It, Everyone Dies. Full Speech: Mr. Speaker, let me now bring up, in the waning minutes of this brief presentation, a third topic, a topic that is very important. I have only a bit to say about it, because, frankly, it is a topic that has me stumped. Let me by way of introduction mention that this is a topic that, as far as I know, has never been addressed. It is a topic that my staff has said, BRAD, maybe you do not want to bring that up, because you will be the only one talking about it, you will look weird. It is a topic I ought to bring up, because it is one of the seminal topics. And it is only one of several seminal topics that gets no attention; by seminal topics, I mean one

Linkpost: AI-discovered PHP vulnerability is severe enough that "multiple cyber-security agencies have urged organisations unable to apply fixes in time to consider taking their sites down". https://www.bbc.co.uk/news/articles/cmly0lg3w5x2o Organizations capable of doing so should update to PHP 8.5.12, 8.4.27, 8.3.36 or 8.2.35 immediately. https://www.php.net/index.php#2026-10-21-5 describes mitigations for organizations that cannot.

OpenAI released a paper about models messing with their monitoring, and it's the first time I've seen chain of thought excerpts from GPT-6 Astra and Sol. For example: [...] Many (but not all) pairs of words are missing the space between them. I had an agent check with a common tokenizer (o200k_base), and this was mostly token-count-neutral (on average it made the token-count slightly worse). This is because tokenizers typically have a token in the lexicon with a leading space, and another variant without. By choosing which variant of the token to use, the model can squeeze in approximately one extra bit of side-channel information per token. Given the whole controversy about looped transformers and chain of thought monitorability, it's not exactly reassuring that, when we do see chains of thought, they turn out to have a prominent non-English side channel in them.

Massively undermade point in favour of the belief that AI will be a very big deal: The AI spend is the largest bet in the history of capitalism. A (peacetime) industrial effort is underway which has not had a comparison since the nineteenth century. Many of us are vaguely aware that large sums are being spent, but the scale of the undertaking is astonishing: * The six largest spenders this year are spending more on Capital expenditures this year than the Russian government, governing 11 timezones and 143 million people, fighting a war on which the fate of its regime probably depends, spent last year.  * Hyperscaler spending (Alphabet, Amazon, Microsoft, Meta and Oracle) will be $785-820bn this year, about 2.4% of US GDP. There are only 11 governments on earth who spend more than that. Next year they are projected to spend about $1.3 trillion dollars. * Google and Amazon are each spending about 0.6% of US GDP. The Manhattan project and Apollo programme each accounted for about 0.4% of US GDP in their peak years of spending. Each of these companies has announced their intention to spend much more next year.  * This money paying for thousands of data centres, the largest of which consume as much electricity as major cities and cover thousands of acres. Gas pipelines are being re-routed and nuclear power plants re-opened to fuel them. They already consume as much electricity as Britain. The increase in power demand for data centres from 2023 to 2030 will be the equivalent of adding another top ten power consuming country. This spending signals a few important things about the beliefs of the key decision makers (who are amongst the most successful capital allocators in the world)  * To recoup their spend, AI will soon have to generate far more revenue than it currently does, so they believe very powerful, very useful AI will be achieved. * Most of the money is being spent on chips which have life spans of about 6 years, so they believe this will be achieved soon

tl;dr: While attempting to reproduce/extend the recent "Already Clean" article, I found that Qwen3-32B appears to be a depth-upscaled model. One of the models that was examined is Qwen3-32B. There are neurons in the same position within their layer, but 24 layers apart, that seem to have similar explanations: L12/N12088 and L36/N12088 are both related to "Hitchiker's Guide to the Galaxy", L26/N17433 and L50/N17433 are both related to "little did they know" For every pair of the 64 layers, I calculated the cosine similarity of the weight matrices. There are two clear regimes: pairs where similarity is 0.000 ± 0.001, and pairs where similarity is 0.248 ± 0.041. The latter pairs are all between layer and layer . My best guess is that Qwen3-32B started out pretraining as a 40 layer model, and partway through pretraining, the middle 24 layers (8-31) were duplicated, becoming layers 32-55 of the final model shape, and pretraining was continued. It appears that this was noticed, indirectly, in a related model, Qwen3-30B-A3B. I don't know what impact, if any, this has on the "recursive generalization" idea, but I hadn't seen this mentioned elsewhere, including in the Qwen3 technical report, so I figured I'd mention it.

ai agents fill a very important niche in the ai research pipeline: previously, your infrastructure would constantly break every few days because of new problems that you'd have to spend hours debugging, to the point where it's easy to go weeks barely making any progress because a ton of your time is absorbed into just constantly repairing things. with ai agents, they are perfect at doing the boring job of fixing everything as it inevitably constantly breaks. for me, this is a game changer in terms of how depressed i am trying to run experiments, and my ability to run experiments on closer-to-frontier stacks without going insane

Complete replacement models (CRMs) seem to the direction the last few Transformer Circuit Thread posts from Anthropic seem to be taking us. A complete replacement model effectively replaces the MLP blocks and attentional blocks in a LLM with a series of sparse dictionary learning tools (primarily transcoders). Tracing Attention Computation Through Feature Interactions (Anthropic) was the first crack at a CRM. It used transcoders to replace the MLP blocks, and routed sparse features (using weakly causal crosscoders) found in the residual stream through the LLM's own QK circuit to find interactions between features at the query and key positions. However, this not a standalone substitution for attentional blocks because the QK circuit and OV circuit are not being replaced. Then OpenMOSS came out with 'Briding the Attention Gap' and Lorsa (low rank sparse attention) to completely replace attentional blocks. Lorsa shares a transformer's architecture, but sparsifies the QK circuit and OV circuit as to be significantly more monosemantic and interpretable than the underlying model. Although the author's cite a number of limitations with Lorsa, this seems to be the first actual CRM. In my opinion, an ideal CRM represents the dream for sparse dictionary learning. The idea is attaching some learned dictionary of features onto every MLP and attentional block of a transformer, at every layer. By doing this, you've turned dense, polysemantic neurons in every step of the process into sparse, interpretable neurons. Then, using attribution graphs you can trace downstream effects, like how important features are to the logits of a model, or how important features are to preceding features (this is key to finding concise and coherent circuits). Ultimately, you get this web of interconnected features for any prompt on a model and how they interact and affect one another. My question is this: say we ignore the whole causal vs correlational problem in SDL and for simplicity assume S