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.

Like suppose there were only two tasks, each with a baseline score of solving in 100 steps. You come along and you solve one in only 50 steps, and the other in 200 steps. You might hope that since you solved one twice as quickly as the baseline, but the other twice as slowly, those would balance out and you'd get full credit. Instead, your scores are 1.0 for the first task, and 0.25 (scoring is quadratic) for the second task, and your total benchmark score is a mere 0.625.

Who is learning this for the first time only now? Even just restricting ourselves to the current administration, look at how many times Trump has directed punitive actions against private entities! Look at his actions against law firms like Perkins Coie or Covington & Burling. This is not something that just arose out of nowhere with Anthropic.