Can you be more specific? It seems to me that the there was a third party assessment, they identified risks associated with the specific risk groups, and the government therefore chose to block the model's deployment.

I feel significantly less sympathy for Anthropic's Supply Chain Risk designation if they believe the government should have this power over them. You get what you sign up for.

Current AI coding is certainly very lacking in the craftsmanship department. But it is not obvious to me that that will always be the case. I don't think there's some fundamental reason AI could never produce code that matches or exceeds the craftsmanship of human experts.

"The allocation of all metabolic resources to maintenance purposes limits the size of the smallest prokaryotes and largest unicellular eukaryotes, whereas an inability to meet the ever-increasing biosynthesis rates limits the largest prokaryotes and smallest unicellular eukaryotes. Metabolic constraints for larger eukaryotes are relieved by alternative reproductive strategies and multicellularity."

https://magic.wizards.com/en/news/making-magic/creation-magi...

This description does not seem to really match what was done in the Doom demo, and makes me skeptical that the author has actually looked into the details.

On the other hand, it seems like Dario is himself a bit more of a true believer.

https://magic.wizards.com/en/news/feature/everything-you-nee...

>We've created an all-new Games Rules Engine (GRE) that uses sophisticated machine learning that can read any card we can dream up for Magic. That means the shackles are off for our industry-leading designers to build and create cards and in-depth gameplay around new mechanics and unexpected but widly fun concepts, all of which can be adapted for MTG Arena thanks to the new GRE under the hood.

At the time, this claim of using "sophisticated machine learning" to (apparently?) translate natural language card text into code that a rules engine could enforce struck me as obviously fake. Now nearly ten years later, AI is starting to reach a level where this is plausible.

In their letter, the union writes:

>Over the past few years, pressure has ramped up from leadership to adopt LLMs and Gen AI tools in various aspects of our work at WOTC, often over the explicit concerns of impacted employees

I'm curious if this would include fighting against turning WotC's old fanciful claim into a reality as the technology matures?

Right, but the article seems to argue that there is some important distinction between natural brains and trained LLMs with respect to "niceness":

>OpenAI has enormous teams of people who spend time talking to LLMs, evaluating what they say, and adjusting weights to make them nice. They also build secondary LLMs which double-check that the core LLM is not telling people how to build pipe bombs. Both of these things are optional and expensive. All it takes to get an unaligned model is for an unscrupulous entity to train one and not do that work—or to do it poorly.

As you point out, nature offers no more of a guarantee here. There is nothing magical about evolution that promises to produce things that are nice to humans. Natural human niceness is a product of the optimization objectives of evolution, just as LLM niceness is a product of the training objectives and data. If the author believes that evolution was able to produce something robustly "nice", there's good reason to believe the same can be achieved by gradient descent.

How did brains acquire this predisposition if there is nothing intrinsic in the mathematics or hardware? The answer is "through evolution" which is just an alternative optimization procedure.