Fortunately increased capabilities seem to make this a basic expectation with new releases.

> The six cases in this section are divided into two parts. Part I covers four cases in which the actor in question used Claude to develop software for weapons themselves: a guided rocket program, in which the actors conducted a live field test; a design and proposal work on a system to intercept torpedoes; software for a drone swarm, tested in simulation, with its code loaded onto real boards; a targeting software for electronic warfare and for suppressing air defenses.

https://www.anthropic.com/threat-intelligence-report-septemb...

While he worked at both OpenAI and Anthropic, he resigned from Anthropic. Mistaken reporting in the first few sentences, definitely a horror concept.

In earlier circles they were known as the "pytorch-pretrained-bert" guys, still under the huggingface company name. IIRC it was a health chatbot type startup.

https://news.ycombinator.com/item?id=49643513

> pretrain on user data, with users' tokens as prediction targets: high regurgitation risk, improper

> use user prompts to distill large models into small ones: low regurg. risk, some companies probably do this

> use user traces to construct RL tasks: low regurg. risk, because RL has low memorization abilities, but can extract customer IP, depending on how it's done. Ranges from benign "use explicit user feedback in reward model training" to invasive "upload user's coding environment and commit history to turn into rl envs"

source: https://x.com/johnschulman2/status/2097440545853637108

- The projects started without HF/nvidia involvement and were massively successful BECAUSE folks want to run models on their own devices.

- With highly capable agents, "hard to implement" should be less of a barrier. The inference market should in some sense become more efficient, as agents should make it easier to transition between software and hardware solutions. Sure there might be less training data for integration platforms, but if we've learned anything in the past few weeks, it's that agents can be remarkably persistent.

Comments URL: https://news.ycombinator.com/item?id=49567758

Points: 2

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Also, do you have a source for: "99% certainly european chips, almost certainly EIC-funded. (Eg. Hailo, Axelera, ..)"

For Hailo, the best I can find is https://hailo.ai/products/ai-accelerators/hailo-10h-m-2-ai-a...

Which states that this is a 40 TOPS int4 PCIe device, not what you'd put in a datacenter and certainly not what can run a frontier class model (or the model above). Embedded devices for inference are really cool! But that's the opposite end of scale for what goes into a data center.

https://artificialanalysis.ai/models/quasar-438b

The article agrees, assuming "here" is global north tech workers.