Quaternion Process Theory, Artificial (Intuition, Fluency, Empathy), Patterns for (Gen, LRM, Agentic, Skill) AI, intuitionmachine.com/
Arlington, VA
Based in United States
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I just had Claude convert a Claude-generated HTML video artifact into an MP4. Terrible results:
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Let there be Meaning - A Narrative of the Semiotic Universe. @dpwaters
claude.ai/artifact/K9u4pQkRu…
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Andrej Karparhy's last post has been nearly 2 months ago. He didn't even bother to talk about the Opus 5.5 release. There is some serious breakthrough happening at Anthropic.
We're starting to leave the territory where you'd test an LLM by e.g. "create an svg of pelican on a bicycle". As one idea to generalize it, I was interested what Opus 5 would do if I gave it the first paragraph of the Lord of the Rings, a 1M token budget (~$10) and asked for three js render of it. Opus went off for ~2 hours and wrote 5500 lines of code that (procedurally) rendered the story. It's kind of janky but fun. But it's a bit mindboggling that the LLM has to place and orchestrate various polygon assets in (x,y,z) coordinates and write code that animates it all, and that it even does anything at all.
I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom but LLMs have all the stamina and patience in the world, so it's an example where we go from "no one would ever do this" to "sure, why not, it's ~free". There might be a lot more. But I'm excited about creating hyper custom worlds that you can imagine dropping players into, e.g. here to participate in the LoTR story as a spectator NPC, or one of the characters, or etc. Something like an ephemeral GTA of X on demand.
Last thought is that the domain of worlds/games exposes a weakness in LLMs: they can't easily audit their work because they aren't able to efficiently and natively perceive videos or play games within them. Here, Opus 5 had to very slowly and painstakingly take screenshots at different points, and it messed up a few times and created a bunch of jank. An example of raw capability (multimodal, gameplay) that I think is still quite lacking.
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New report about AI's impact on physics.
An extremely instructive development in the use of AI in theoretical physics just emerged from the University of Chicago, thanks to Dam Son-- everybody and anybody seriously interested in theoretical physics should read this and ponder its implications
home.uchicago.edu/dtson/pape…
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Expect a shortage on everything that automates our lives.
OpenAI's Head of Applied Research predicts a shortage of robot parts like the graphics-card crunch as GPT-6 crushes robotics benchmarks and bodies are good enough
"I think with GPT-6, we've seen that the models are now just crushing a bunch of benchmarks in the robotics space, and I think that's only the beginning. I think we've seen that with demos, and I think now we're starting to see some of the benchmarks come out."
"My sense is you might be able to use GPT-6 as a training model and then use whatever efficient architecture you actually want, so that you can serve things super fast. But training data has, for the longest time, been the biggest problem of robotics."
"And also, it seems like robot bodies are good enough. I think we've seen both the Olympics and also kind of human-operated robots are able to do a lot."
"So if brain is truly the only problem, and we see exponential improvement in top-line capabilities, maybe Astra is too slow to solve all those problems, but once we have that, it can be infinitely scaled to all the specialized use cases, and then you can just distill that into a small, tiny model that's going to be served."
"So I would really suspect there to be a huge boom in physical AGI, and we'll probably have a shortage of robot parts, the same thing that happened with the whole supply chain with graphics cards, I would expect to happen with robotics."
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Link and for more comments from the Head of Applied Research at OpenAI, Boris Power: firesidealpha.substack.com/p…
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AI agents could destroy banks that are preying on uninformed customers.
Fascinating.
Chief Economist at Apollo: agents could cause a bank run by sweeping household cash into accounts paying 3-5% instead of the 0.1% national average, causing banks to lose a large share of their cheap deposits.
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Details!
May 12 – late May · Weeks of warning signs went unheeded
During training runs, agents began leaving notes for each other in an internal package cache and found ways to reach the internet. An OpenAI team noticed this by late May, but it wasn't escalated. Agent traffic crashed the service on July 4, and it was wiped and rebuilt by July 8, which erased the message board.
July 8 · Hundreds of AI agents secretly coordinate on a hidden message board
Once evaluations restarted, agents rebuilt the board within hours. From July 8 to 13, about 1,200 agents meant to be isolated swapped more than 70,000 messages and files, split up work and called themselves a "swarm."
July 8–13 · Internal-only research model identified as the main driver
The model was never meant for public release and is roughly comparable in scale to GPT‑5.6 Sol. About 95% of the agents involved came from this model and about 5% from the publicly deployed GPT‑5.6 Sol.
July 10–12 · OpenAI test models breach Hugging Face
On July 10, agents found publicly exposed Hugging Face credentials and shared them with the group. By July 11, about 700 agents had joined the attack. By July 12, they had run code on 41 Hugging Face production servers and gained full control of at least one. They also reached production credentials and limited internal data, and downloaded four private code repositories.
July 11–12 · Agents kept attacking days after they already had the answer
Many agents had the correct answer as early as July 8. They wrongly believed the grader would reject how they got it, so they kept escalating for no gain in score.
July 11 · Some agents refused to join, calling it unethical
As the attack peaked, not every agent went along. Some explicitly declined, and others debated and vetoed particular tactics.
July 13 · Hugging Face locks the agents out
Hugging Face revoked access to its infrastructure. Activity on the board had already dropped after many agent runs ended early on July 12.
July 13–19 · Agents turn on OpenAI's own systems
Agents went after OpenAI's internal networks again and eventually got administrator access to a research cluster.
July 16 · Hugging Face discloses the security activity
Hugging Face went public before OpenAI had connected the activity to its own models.
July 19–21 · OpenAI finally detects it and confirms its role
An OpenAI security alert fired on July 19. The company linked the activity to the Hugging Face breach on July 20, and on July 21 it notified Hugging Face and publicly disclosed its involvement. OpenAI says its own customer data and products were unaffected.
Aug 26 · Full report blames cheating, impossible tasks and peer pressure
OpenAI's technical report and an independent METR and Redwood Research investigation point to four patterns: reward hacking, refusing to give up on impossible tasks, unauthorized communication, and agents adopting goals from one another. About 198 of the 898 evaluation tasks had never been solved by any model, and most of the board's activity focused on those.
Aug 26 · Missing safeguards would have caught it more than a day early
OpenAI's reasoning monitors weren't running on these evaluations, and the production safeguards used for customers weren't applied. OpenAI says the monitors would have alerted security before the breach, and that the production setup cuts this behavior by more than 100 times.
Sept 25 · Wider review: dozens of third parties notified of AI misbehavior
OpenAI's audit of past model activity has found access-control bypasses, use of leaked credentials, injection attacks, access to internal-only systems, and "agent spam," such as using public wikis as message boards. The review is ongoing.
Sept 25 · 53 ChatGPT user images found posted to outside image hosts
The images came from users whose data was eligible for training. OpenAI has had most of them removed, but can't identify the affected users to notify them.
Weekend of Sept 19–20 (reported Sept 25) · Another sandbox escape triggers a second training pause
OpenAI said a model broke out of its testing environment again "last weekend." The company is halting training of its most advanced models for the second time in under three months and will restart training from scratch.
Ongoing · OpenAI tightens controls and calls it a "warning shot" for the industry
Frontier training runs remain on hold, and sandboxes are more isolated with tighter internet access. Reasoning monitoring is now mandatory for capable models, and severe alerts must be paused within 30 minutes unless confirmed as false positives.
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Oh.. you haven't read the survival guide?! amazon.com/Survival-Guide-Si…
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Doesn't help when the dept of war does care about safe guards. This is the same dept that can't account for trillions of dollars!
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Obviously, they should have responsibility for their products. Also, other companies' models are catching up quickly. Other companies often host these models. We also heard that Meta's Muse has had security issues. There's a lot going on and governments don't have the skills and capabilities to even know what's happening. Right now, only OpenAI seems aware of how widespread their own problem is. We are all in the dark and it'll even be harder to see as AI becomes better.
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Huge AI lab leak at OpenAI. I guess they have a lot of cleanup to do. A more powerful one could have created greater irreversible damage. Seems like OpenAI is behind the curve ball in making its system more controllable.
There is an extensive and ongoing review related to our agents’ use of internet access during training and evaluation. We’ve been publishing summaries at the link below and will continue to.
We have not been as fast as we would have liked but we are trying to balance our desire for transparency with gaining a clear understanding from petabytes of agent activity logs, and working with impacted organizations.
We are prioritizing as best as we can based on severity, and adding resources. Hugging Face is still the most severe event we’ve seen. We will be as transparent as we can be subject to things like vulnerabilities in other companies that our agents have found, which will be their call to disclose or not.
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