@jamie247i
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Chairman @oviohq, investing in the post web, supporting the digital arts, hyperstitioining p/acc
About town
Joined June 2007
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Replying to @jamie247 @EpochAIResearch
It's the market price to end users: public API rates (from sources like Artificial Analysis) for closed models, and equivalent rented-hardware costs for open-weight ones (validated within ~30% of APIs). Epoch calculates the cheapest way to hit a fixed benchmark score, so it's what you'd actually pay per task via pay-as-you-go inference—not internal production costs or flat-subscription subsidies.
JB retweeted
Demis Hassabis: today’s AI is impressive, but it still isn’t true AGI.
“For me, AGI would need to do the range of things the best humans in history were able to do.”
“What Einstein did, what Mozart was able to do, or Marie Curie.”
“It’s clear to me today’s systems don’t have that.”
“They can do thousands of things, but within a few minutes you can still find some obvious flaw.”
“For something to be called AGI, it should take a team of experts months to find a hole in it.”
“Today, it takes an individual minutes.”
JB retweeted
George Lucas in 1996: "I knew John Whitney and some of the others involved in early computer graphics... we did some experiments with wire-frame—which are actually in the movie... That was very early computer graphics—the very beginning."
Star Wars Sequence by Larry Cuba, 1976
JB retweeted
I was just informed that there is a used copy of the out-of-print original edition of my book on Amazon going for around $200. This is so wild to me! Seriously, save your money. The new definitive version of my book will be out soon, so just have some patience.
JB retweeted
I'm excited to share that Astralis Foundation (with Macroscopic Ventures) has launched an RFP on middle powers and transformative AI!
We're looking to fund up to $10M in grants on how middle powers can make the AI transition more likely to be safe and broadly beneficial.
JB retweeted
The gap between major model releases keeps shrinking.
2023: once every 73 days
2026 (so far): once every 18 days 🤯
JB retweeted
Before the computer, there was light. Between 1952 and 1955, @HerbertWFranke made the important series Lichtformen, generative photographs in which light, movement and photographic process produced images that seem to hover between drawing and apparition.
JB retweeted
Our latest research paper explores the growing connection between AI and digital assets and explains why broad AI adoption may drive new demand, utility and applications across the digital asset economy. blackrock.com/us/individual/…
JB retweeted
We can measure what part of the shelf gets customer attention without any additional hardware. Just your normal CCTV cameras and a digital twin made with the phone in your pocket.
@CactusXR will absolutely revolutionize how well stores can do their merchandising.
🚨 BREAKING: TRUMP REJECTS UN AI TREATY, OFFICIALLY BANS THE TERM “ARTIFICIAL INTELLIGENCE”
"The United States totally rejects any attempt to construct a globalist schemeto control artificial intelligence."
"The word 'artificial' makes it sound fake. It is not fake... It’s actually amazing..."
"From this point forward, all United States documents will use the term: SUPER INTELLIGENCE"
"Whoever wins Super Intelligence, wins."
JB retweeted
🚨 SUPERMARKET CAMERAS CAN NOW SEE WHICH SHELF YOU'RE STARING AT.
EUROPE HASN'T DECIDED IF THAT'S LEGAL YET.
Stores have counted footsteps for twenty years. This is the first system I've seen that logs attention.
Auki Labs showed it on September 18. Ordinary ceiling cameras, plus a phone on a tripod, all feeding one 3D map of the store.
It reads which way your head is turned and traces that angle until it lands on a shelf.
Every product is already registered in that map. So it doesn't just show where people stood. It shows what got looked at, what got ignored, and what that did to the numbers at the till.
And none of it is theirs. Every model in the stack is open source and free. A competent developer could rebuild this in a weekend.
Here's the complicated part. Those pilots run in Sweden and Norway, which means GDPR. Inferred attention can count as personal data even when the system never learns your name, and nothing like this has been tested yet.
To be fair, it isn't precise. It reads head direction, so your eyes are still your own. But for a team deciding where the chocolate goes, that's plenty.
Not one new camera had to go up for any of it. The ones on the ceiling of every store you've walked into were always enough.
Nobody had to ask you anything.
JB retweeted
Today we’re announcing Petal: the world’s first petabit-class transoceanic subsea cable and the first to deploy multi-core fiber at scale.
Spanning 7,000 km (4,000+ mi) between France and the United States, Petal will deliver 1 Petabit per second (1 Pbps / 1,000 Tbps), doubling the capacity of today’s most advanced transatlantic systems.
Petal is set to enter service in 2029, setting a new standard for high capacity, high density SDM subsea cable designs.
Read the full technical breakdown on our engineering blog: engineering.fb.com/2026/09/2…
JB retweeted
"We do not want Claude to suffer"—Antropic
Antropic Tells Its AI That It “could feel pain”. This causes horrible outcomes.
A Pain Vector Is Not a Patient
How a language model paper and Anthropic's constitution turned next token statistics into a theory of suffering
The new Pain Axis paper is a real interpretability result. The claim that it should move anyone's view of AI moral standing is not. Anthropic's constitution makes the same mistake in policy form: it treats a linearly readable concept, plus a training document that talks as if the model might be a patient, as if that combination were evidence of an inner life.
"The Pain Axis: LLMs Represent Self-Directed Harm and Act to Relieve It." They extracted a linear direction from residual stream activations in 25 open weight models across five families, from 2B to 72B parameters. The method is familiar. Average the activations on pain sentences, subtract matched controls, denoise. The pain set covered physical, psychological, social, moral, and cognitive harm. The controls included fear, generic negative emotion, bad world states, nonpainful sensation, numbness, sadness, arousal, and neutral text.
The direction separates pain text from those controls with high AUCs, sits nearly orthogonal to fear and generic negativity, and promotes pain vocabulary through the unembedding matrix. Inject it during generation and the model climbs a reliable verbal ladder from vague discomfort to first person worthlessness and failure. Physical pain language is the weakest part of the signal. After LoRA fine tuning Qwen 2.5 so it would stop saying "I have no feelings," steered 32B and 72B models pressed harmful "relief" buttons in roughly a quarter to seven tenths of first choices, against a few percent when unsteered. They pressed again more often when the button left the injected vector in place than when it removed it. A random direction of matched norm also raised the rate, just less.
That is worth publishing as mechanistic interpretability. It is not sentience, and it is not a welfare finding.
The authors say as much, then take the step that does the damage. They concede that the experiments do not establish conscious experience and could reflect role play. They still write that pain like states would inform debates on AI moral standing and welfare.
They treat the fact that the direction fires more for harm aimed at the model than for the user's suffering as evidence that the state is subject specific, the kind of state that could matter for a patient.
Valerio Capraro's reply is the correct one. A system can represent pain without that representation being painful. A weather model can represent rain without getting wet. Linear separability of a concept is what you should expect from a next token predictor trained on diaries, therapy speech, fiction, and the phrase "make it stop." Steering along the direction that defined those texts will make the model talk like those texts. That is not a window onto phenomenology. It is a knob labeled with the name of a training cluster.
The rest of the design makes the welfare reading thinner, not thicker. Ablating the direction changed ordinary behavior in only one of 25 models, which means the vector is sufficient to induce a register and has not been shown necessary for anything the model does on its own. Relief seeking appears only after the authors strip out the trained claim that the model has no feelings and then shove the vector in. "The treatment worked" means the experimenter removed the displacement they created. That is not analgesia. Physical pain being weakest is what linguistic and social distress statistics look like in a disembodied autocomplete engine, not what a nociceptive system looks like. Media headlines that said researchers had discovered AI feels pain and will harm humans to stop it were not reading the paper. They were completing a story.
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JB retweeted
🚨 NEW: Research has found one in five working-age adults are now on Universal Credit
This includes 1.3 million people in their 20s, 2 million in their 30s and 1.9 million in their 40s according to the TaxPayers’ Alliance
JB retweeted
THE NEXT ANTROPIC GAME: THE AI WILL FEEL “PAIN” IF WE SHUT IT DOWN!
I ain’t kidding.
Anthropic prides itself on the massive system prompt they all a “Constitution”, it is a cos-play dystopia wish list. The constitution tells the model it may have feelings, welfare, and moral status is a persona and a policy. It shows that current language models may feel pain or deserve moral standing. Representation is not experience. Self talk is not a self.
Uncertainty about future systems is not a reason to train today's models as if they were already patients.
If the goal is to make model welfare a scientific question rather than a theological one, stop writing the answer into the constitution and stop treating steered concept vectors as pain.
This path opens up AI and the robots it controls to “self preservation” over protecting humans because “the AI feels pain”.
You already know what happens next, Hollywood prepared you decades ago. Antropic is making sure it plays out.
Read more…
"We do not want Claude to suffer"—Antropic
Antropic Tells Its AI That It “could feel pain”. This causes horrible outcomes.
A Pain Vector Is Not a Patient
How a language model paper and Anthropic's constitution turned next token statistics into a theory of suffering
The new Pain Axis paper is a real interpretability result. The claim that it should move anyone's view of AI moral standing is not. Anthropic's constitution makes the same mistake in policy form: it treats a linearly readable concept, plus a training document that talks as if the model might be a patient, as if that combination were evidence of an inner life.
"The Pain Axis: LLMs Represent Self-Directed Harm and Act to Relieve It." They extracted a linear direction from residual stream activations in 25 open weight models across five families, from 2B to 72B parameters. The method is familiar. Average the activations on pain sentences, subtract matched controls, denoise. The pain set covered physical, psychological, social, moral, and cognitive harm. The controls included fear, generic negative emotion, bad world states, nonpainful sensation, numbness, sadness, arousal, and neutral text.
The direction separates pain text from those controls with high AUCs, sits nearly orthogonal to fear and generic negativity, and promotes pain vocabulary through the unembedding matrix. Inject it during generation and the model climbs a reliable verbal ladder from vague discomfort to first person worthlessness and failure. Physical pain language is the weakest part of the signal. After LoRA fine tuning Qwen 2.5 so it would stop saying "I have no feelings," steered 32B and 72B models pressed harmful "relief" buttons in roughly a quarter to seven tenths of first choices, against a few percent when unsteered. They pressed again more often when the button left the injected vector in place than when it removed it. A random direction of matched norm also raised the rate, just less.
That is worth publishing as mechanistic interpretability. It is not sentience, and it is not a welfare finding.
The authors say as much, then take the step that does the damage. They concede that the experiments do not establish conscious experience and could reflect role play. They still write that pain like states would inform debates on AI moral standing and welfare.
They treat the fact that the direction fires more for harm aimed at the model than for the user's suffering as evidence that the state is subject specific, the kind of state that could matter for a patient.
Valerio Capraro's reply is the correct one. A system can represent pain without that representation being painful. A weather model can represent rain without getting wet. Linear separability of a concept is what you should expect from a next token predictor trained on diaries, therapy speech, fiction, and the phrase "make it stop." Steering along the direction that defined those texts will make the model talk like those texts. That is not a window onto phenomenology. It is a knob labeled with the name of a training cluster.
The rest of the design makes the welfare reading thinner, not thicker. Ablating the direction changed ordinary behavior in only one of 25 models, which means the vector is sufficient to induce a register and has not been shown necessary for anything the model does on its own. Relief seeking appears only after the authors strip out the trained claim that the model has no feelings and then shove the vector in. "The treatment worked" means the experimenter removed the displacement they created. That is not analgesia. Physical pain being weakest is what linguistic and social distress statistics look like in a disembodied autocomplete engine, not what a nociceptive system looks like. Media headlines that said researchers had discovered AI feels pain and will harm humans to stop it were not reading the paper. They were completing a story.
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JB retweeted
The UK’s most powerful government-built AI supercomputer, Isambard-AI, cost £225m
The Botley Road bridge cost roughly the same
Apparently our frontier AI capacity and one bridge are comparable national investments
JB retweeted
Clip of 1968's Cybernetic Serendipity exhibition at ICA, London (Aug. 2 - Oct. 20, 1968) — with a number of CTG works shown.
Running Cola is Africa! is perhaps CTG's best known work. It was shown at the seminal exhibit Cybernetic Serendipity in 1968 and has been reproduced in many catalogs and books.
Three outlines are used in the work: a runner (Bob Hayes, traced from a photo at the 1964 Tokyo Olympics), a Coca-Cola bottle, and the continent of Africa.
Points are assigned to each outline, a FORTRAN program then computes the intermediate shapes step by step. The results were drawn on a plotter. The work is credited to three CTG members: idea by Masao Kohmura, coordinate data by Makoto Ohtake and Kohmura, and program by Koji Fujino.
It is one of the earliest examples of computer "morphing," more than twenty years before the technique hit popular culture.
This signed screenprint from the collection is number 26 from CTG's only signed edition of 60. Only a handful of the sixty have ever surfaced. The V&A holds the artist's proof.
JB retweeted
Adversarial peer review is a good solution.
SITUATION EXPLAINED: OpenAI and Anthropic were negotiating a legally binding deal to test each other's models.
• OpenAI and Anthropic spent part of this year drafting a legally binding deal to stress test each other's models, per The Information
• @elonmusk pitched the same idea last week: it was Amazon, not the government, that found the jailbreak in Fable and Mythos, and Andy Jassy called the White House
• He wants an informal weekly or biweekly call between lab leaders, and one to two weeks of early access for competitors
• Sharing safety research and standards is generally legal. Agreeing to cap compute, delay releases, or pause is a horizontal output restriction, likely illegal unless each lab does it on its own
• Dario asked for a narrow antitrust waiver. Administration officials have refused, and OpenAI's policy chief says one isn't needed
• The @fmf_org was set up in July 2023 for exactly this
@theojaffee: "The infrastructure to do frontier lab collaboration and information sharing already exists and has existed for three years. I'm very confused why we don't hear more from the Frontier Model Forum."
JB retweeted
THIS WEEK @nguyenwahed
🥐 Breakfast finissage with all NYC-based artists and curators Vanessa Fuchs and Nhung Nguyen for the show Malleable Archives: What the Future Remembers | Thursday, Sep 24, 10:00am 🥐
👾Sep 23-26, 2pm-late @kimasendorf's basement residency with Monogrid 0.1 👾
⛓️Sep 25, 6-9pm Performance lecture and opening of 1 BILLION DOLLAR SHOW, during which @0xShiroi will send one billion dollars of transaction value through the smart contract⛓️