https://bigfatgenius.com/2220%20Fall%202010/Nutt%20King%20Ph...

Cocaine (in crack form) was found to be the third most harmful recreational drug overall, after alcohol and heroin. It also caused the most direct harm to users out of all drugs. Cannabis, not so much, mid-table. Non-crack cocaine was somewhere between the two.

Note that this study is now over 15 years old.

> a tiny fractional loss in an RNG is not going to make a practical difference

I'm not so sure this is true. I don't think either of us is in a place to say whether this vulnerability has practical applications or not. A 1/65536 bias might seem like nothing important to you. It seems like potentially something to me, in a world where the attacker might control the volume of data generated.

If you want a casino example, then consider a roulette wheel that always lands on 36 but still pays out as usual. I think you'd want to play on it. Now consider one that always lands somewhere between 30 and 36. Still worth it, right? With careful bets and a good starting float you're still coming away from the table up (with a very high probability).

In fact for a roulette wheel you only need two dead pockets for the player to get an edge. Bias is exploitable.

You could be correct that the very small bias here is not enough to be exploitable. But, given the history around this, it would be wrong to handwave it away as trivial.

By your argument, it would not be a problem if the RNG never generated 0. So, it must follow that it would also not be a problem if it never generated {1, 2, 3, ..., 253}.

That means that our RNG now only generates the values 254 and 255. Which of the values is generated is unpredictable on any given call. However, 7 of the 8 output bits are now always fixed and so completely predictable. Can you imagine how an attacker could exploit that?

Failing to generate only the number 0 is a weaker version of the same class of flaw.

Yes! I believe that class of people are called "investors."

Your point is basically correct, I think, but the illusion of value that a corporate entity offers, and the reality of the value that the corporate entity is creating, are distinct things.

I think this is the former, but I want the latter.

The claim here seems to be that the human mathematicians, working with AI, developed technology to go A->B->C. By training on those conversations, OpenAI was then able to encourage the model to go A->B->C->D.

In my opinion that situation should be acceptable, if openly disclosed, because it is in the public interest to make progress on these problems and because AI is clearly an amazing tool for making progress. But the human mathematicians are saying that OpenAI is presenting as if the model got from A->D entirely independently, without acknowledging their background contributions.

I'm not at all familiar with this area, but my reading is that he appears to call it out as a relatively obvious extension of his own work:

> It is a creative and at the same time elementary construction that uses not just property (T) for an application of my result with Kun, but also for the ambient group G in order to overcome the problem, that the Γ-components might be of different size. Once this is achieved, the rest of the argument is straightforward.

Creative and at the same time elementary is where LLMs excel, generally speaking. It's why they are so good at writing code.

[1] https://arxiv.org/pdf/1312.4068

Elect to is a bit strong here. Acquiesce to, maybe, or perhaps suffer to.

For a relatively narrow subject area (e.g. construction of pipe bombs) the collation is minimal, and so the filtering and tailoring probably isn't that important; a novice doesn't learn a lot more from the LLM than they would have done from a few Google searches.

For a broad subject (practical creation and exploitation of software vulnerabilities), the collation is very significant and the filtering means that LLMs can empower a novice to act at a similar level as an expert.

To be very explicit, my problem is that the article argues that simulation will be a suitable substitute in the software space, as it is in the aviation space. I see no evidence for that; it's pure hypothesis taken as fact.

Yes, it's clear that if AI agents handle routine issues then only the most complex issues will be handled by humans. In no way does it follow that humans will be less prepared to handle those incidents than they are now; by definition, this class of problems have no rote solutions. Each issue of this type already requires deep system knowledge to remediate, and still will. Software is not aviation.

What we are seeing here is an instance of the more general trend where experts are still required to operate complex systems, but AI is destroying the career path that creates those experts. That's what we should be worrying about, not that people are going to be spending less time rebooting boxes. This isn't something you can fix with a "simulator."