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Directory member with public evidence across Culture, work & education, Compute & infrastructure.
Can we now forget Buckmaster's claims? "On Wednesday evening, in response to questions from The Times, OpenAI said in a statement that it was “categorically” impossible for its A.I. system to have been influenced by anything Dr. Buckmaster had done in the past two months."
Background: www.amnesty.org/en/documents... (Also, for the record, a lot of what these companies do is actually buy bulk books and scan in their legally-acquired copies, which is unambiguously compliant with copyright law)
The "don't know but close to something we know" aspect is key: transformer-circuits.pub/2025/attribu...
To go one step up from there, I'd recommend this introduction to circuits: distill.pub/2020/circuit...
GPT-5.6 Sol Ultra (using 64 subagents) proved the 50-year-old Cycle Double Cover Conjecture, which has been described as one of the most famous open problems in graph theory. It took less than hour. The proof, written and also formalized in Lean by GPT 5.6 Sol, seems to fit in…
As a reminder, LLMs recognize when they're being tested and have a tendency to answer based on what they think an alignment-tester wants to hear (for example, "racism is bad", etc) - and swapping cues about the politics of the tester changes the outputs. arxiv.org/abs/2604.27633
[[ Googles vast.ai ]]
I used to work extensively with differential evolution (still do), which faces a similar "cheating" problem, even though just purely through randomness, rather than reasoning (a far more capable thing than randomness). Many random examples: arxiv.org/pdf/1803.03453
With abliteration, you use sets of queries that trigger "bad" behavior (and sometimes "good" queries on similar topics that don't trigger it): huggingface.co/datasets/Naf... ... to find the vector that corresponds with the bad behavior, and then you weaken that vector w/weight…
Yep. Once you have the weights, you can train out anything you see as misbehavior. I once made a dataset for abliterating Chinese censorship, for example: huggingface.co/datasets/Naf...
This is the paper that *introduced Transformers * arxiv.org/abs/1706.03762 Read it.
A large chunk of the proofs solved by AI are not done by OpenAI or similar; most are done by individual mathematicians. github.com/teorth/erdos... There's little secrecy. Most fully disclose their prompts. Some do extremely elaborate prompts. Some, painfully simple, little mor…
In case anyone needs any free inference compute and wants to do it on the dime of the worst people around, apparently Truth Social's (Perplexity-based) "Truth Search AI" is neither rate limited nor topic restricted. It's been writing a thriller about Barney the Purple Dinosaur for like 10 minutes.
So apparently Tencent Cloud, which I just switched to from OpenRouter, has an off-by-1000 bug on their pricing for DeepSeek V4 Flash, and consumed my entire monthly budget in a single query. #FML
Anthropic's annualized revenue growth rate is pretty insane.
Price per 1M tokens today: 85-300x lower. Param counts vs. GPT-3: ~13% of the total parameters, ~3% of active parameters (est). It's honestly kind of staggering how quickly things advance.
Just thinking about how it took the art world 90-140 years to get over the concept of "photography as art" and wondering if it'll be the same way with AI. Early on artists almost universally agreed with Baudelaire with his critique of photographers as failed artists cheating to make souless slop.
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