✊🟥🌹 Transhumanist 👤🔁🤖 Choo Choo Choo Choo Choo Choo Choo
Based in Germany
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as i said, people are sleeping on bytedance
The Chinese AI Infrastructure Boom:
Introducing the SemiAnalysis China Datacenter Model
1,000+ facilities across 60+ operators mapped,
built retail-first and flipped by AI,
largest hyperscaler leases 1/5 national capacity, 100MW in 12 months, Eastern Data Western Compute
newsletter.semianalysis.com/…
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You remember this shit?
Opus 5.5 xhigh take - took almost an hour
opus-55-balls.netlify.app
The "Escape" mode which Opus came up completely un-prompted is a good example of why I think Opus is the first model with "Taste" and the knowledge of "why something is nice". The idea to make a game out of it, which you don't even play, which still is pleasing to watch. yeah. milestone.
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Based on my estimates, the 10,000-agent swarm that solved the Navier-Stokes problem was about 8 months of frontier progress ahead of a single GPT-6-Astra instance (with a range of 5.5 to 12.4 months)
It used 48,000 times more output tokens than the largest single model evaluation and roughly 68 times more than an entire long-horizon coding benchmark
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Um, I think we're reaching the point where if Metzger has any remaining friends they should maybe check on him.
On the contrary, I fucked your entire movement to the wall. If it wasn’t for me, you might have won. I’ve done a lot of things in my life, but this was probably the single most important one. I don’t spend a whole lot of time talking about it, because I’m not like you, but when I entered the arena the consensus of all the experts was that the fix was in and there wasn’t even a point to fighting. Now, of course, the opposition is self sustaining. A scant few years ago no one understood what was happening and the few who cared didn’t have any idea how to fight. You don’t have to believe me of course. I suspect you won’t. It doesn’t matter.
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I’ve never had any, Eliezer, I live alone under a bridge and can never hope to be as smart as a world class genius such as yourself. Nevertheless, I fucked your entire movement. You decided that you wanted a totalitarian regime to make your twisted dreams come true, and I decided to fight. It doesn’t matter if you believe me or not of course. Your movement is still fucked.
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Yudkowsky never writes something in a sentence if he can write it in a page, he never writes a paragraph if he can write a whole essay. He loves his own words; he practically wallows in them. The style also conceals his logical errors and mistaken assumptions beneath torrents of text. You are told that to really understand his genius you need to read tens of thousands of pages, and most people give up and just assume that the reason they can’t see the emperor’s clothes is their own lack of knowledge and understanding.
Several times, I have had to read one of the piles of pompous nonsense his worshippers adore, and my first step has always been to rewrite them, getting rid of the invented jargon, the unnecessary thought experiments, and the worthless digressions, replacing them with simple clean sentences.
Almost always, his essays reduce to something a tiny fraction of the length that is either trivial or wrong. There’s almost never any there there. It’s not that you’re too stupid to understand him. It never was that at all.
"yes i wrote a book saying that if anybody builds it everybody dies but stop calling it doom"
admire the jargon, this is really one of his best, you could read it ten times and not understand a fucking lick of it. this is intentional.
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Yann LeCun is moving the goalposts by ignoring 99.9% of the latent space and declaring war specifically on the unembedding layer.
Now I'm 100% convinced that this is a JEPA marketing stunt.
How can a once-respected researcher honestly believe that a space of 100,000+ tokens is a bottleneck, but our human 26-letter alphabet isn't?
And his mention of "autoregression" is even more ridiculous when you factor in "predictive coding" in the human brain.
There is zero chance that he is being serious about this. Its either PR or he has completely lost it.
P.S. I would like to quote directly but he is not man enough to unblock me.
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Did NVIDIA just "solve" RL?
So what does it do, in the extremely reduced TL;DR ELI5 version:
A lot of current LLM RL methods generate multiple attempts for the same prompt, so-called "rollouts", and use their rewards relative to each other to figure out which behavior should be reinforced.
Other approaches use a learned critic or value model to estimate how good an action or trajectory was.
Both approaches add considerable compute and/or synchronization overhead.
FlashREINFORCE instead uses just one rollout per prompt and no learned critic.
It takes a batch of rollouts from different prompts, compares each reward against the batch's mean reward, and gets a positive or negative learning signal from that.
So, extremely reductively: one attempt per problem, no critic, still enough signal to learn.
That's kinda fucking wild.
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Things are heating up on Terry Tao’s blog. In “If Math Is More Than Proof, We Need to Better Celebrate the Rest of It,” Grant Sanderson of @3blue1brown proposes “open exposition problems” -- rewarding the work of making math genuinely understandable.
terrytao.wordpress.com/2026/…
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Terence Tao: "we have to slow down AI. the pace is insane, and there's no reason to be this fast"
I disagree. We have countless problems that need solving: energy crisis, disease, world hunger. To say there are no reasons to do so ignores the current state of the world.
And that's so regrettable. Currently, the only topics discussed are the potential negative consequences of acceleration, the possible problems. But hardly any of the advantages, the approaching golden age of science that can offer us so much more.
Haider.
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I've lost a lot of respect for Noam Brown after seeing this 🤦♂️
OpenAI's Noam Brown says air-gapping the computers may not stop a misaligned AI, because two air-gapped machines can still talk by running a CPU hot and reading the temperature change
"But I think the major takeaway from the incident is that people underestimated the AI. And we never want to be in a situation again where we underestimate the AI. It's a weird world, because AI progress is so fast that people are consistently underestimating the AI."
"So to be in a situation where you don't underestimate it again, when it comes to safety and alignment, you have to have a very, very, very high bar."
"You could even go as far as to say, "Well, we should air gap the computers." And I'm not convinced that that would be sufficient."
"There are studies, and this is mostly academic, where you can have two computers next to each other that are air-gapped and they're still able to communicate with each other because they have temperature sensors."
"One of them is able to run their CPU really hot, and then the other one can actually detect the temperature change, and then that actually gives them a mechanism to communicate."
_________
Link and more key quotes from OpenAI's safety related conversations: firesidealpha.substack.com/p…
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Current LLMs aren't truly creative because they haven't implemented the "Formal Theory of Fun and Creativity" (2008) yet. See "Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes" arxiv.org/abs/0812.4360 Tweet: nitter.net/SchmidhuberAI/status/1…
Oxford researchers argue that LLMs can never invent anything.
It is mathematically impossible.
They published a paper called “Theory Is All You Need" and it argues against the claim that computational models can generate genuine novelty or new knowledge.
They analyzed the limits of generative ai, and the results are a brutal reality check for the idea that ai will replace human decision making under uncertainty.
Here is why AI is stuck and human cognition wins:
backward-looking vs forward-looking.. llms are probability machines that look backward at existing data. human cognition is forward-looking and capable of generating genuine novelty. human cognition operates theoretically "top-down" rather than "bottom-up" from data.
the "data-belief asymmetry".. the researchers use the invention of "heavier-than-air flight" to illustrate this concept. an ai relies on data-based prediction, which is largely imitative. humans, however, use theory-based causal logic that allows them to hold beliefs that go beyond existing data.
the intervention gap.. humans don't just process information; we use theory to practically "intervene" in the world. we engage in directed experimentation to generate entirely new data. ai-based models are theory-free and place primacy on existing data and prediction.
tldr?
AI uses a probability-based approach to knowledge and ia largely imitative. It can process data and make predictions, but human cognition relies on theory-based causal reasoning.
The decades-old analogy comparing human minds and computers to mere "input-output" devices is fundamentally flawed.
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I’ve genuinely never seen a model claim this low of a hallucination rate. This company was founded by Diogo Almeida, one of the researchers behind the instruction following work that led to ChatGPT, and instead of building an autoregressive chatbot that generates strings token by token, they basically throw string generation away entirely.
This model takes unstructured state in and spits out type safe probabilistic decisions in parallel, with their new RLCD training method calibrating how confident it should actually be.
And the results are kind of insane? On their workflow evals they’re claiming up to -194x faster and -445x cheaper.. while still competing with frontier models on these System One tasks. It’s also only $0.042 per million input tokens (4.2 cents) and output is literally free??
Obviously this isn’t a replacement for GPT-6/Astra style general reasoning it gives up free form generation to do this but for actual software automation? How is this not a much bigger deal?
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
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This is probably the biggest catastrophe that could hit Europe right now: Saudi Arabia is reportedly cancelling September oil cargoes to Europe after drone attacks shut its East-West pipeline. Disruptions could extend into October.
The pipeline carries crude to the Red Sea, bypassassing the severely restricted Strait of Hormuz. Now that route is down too.
AP sources estimate repairs could take three to five weeks.
The European economy is under increasing pressure. Petrol prices in Germany have already reached an all-time high, according to ADAC, and the EU is heavily dependent on imported oil.
The full consequences are not yet clear, but I can imagine a prolonged disruption pushing living costs high enough to fuel social unrest in parts of Europe.
I hope I'll find some time later to read up on the topic and report on it. I think many people don't understand how significant and catastrophic it is for Europe.
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I wish more people understood this. There's still this narrative out there that "AI might kill us all" is a niche view. It's actually a view that's been around for decades, and is shared by the most-cited AI scientists of all time, as well as a majority of surveyed AI researchers.
The average AI researcher thinks there is an ~18% chance AI will cause human extinction or similarly permanent and severe disempowerment of the human species.
That's nearly 1 in 5.
New results from the latest version of the longest running big survey of AI researchers:
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This is so depressing for a variety of reasons
First and only time UK had the top frontier model
China started climbing a few months later
stability.ai/news-updates/st…
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