The AI isn't creating a new recipe on its own. If a language model spits something out it was already available and indexable on the internet, and you could already search for it. Having a different interface for it doesn't change much.
This is false in several aspects. Not only are some models training on materials that are either not on the internet, or not easy to find (especially given Google's decline in finding advanced topics), but they also show abilities to synthesize related materials into more useful (or at least compact) forms.
In particular, consider there may exist topics where there is enough public info (including deep in off-internet or off-search-engine sources) that a person with a 160 IQ (+4SD, ~0.0032% of population) could devise their own usable recipes for interesting or dangerous effects. Those ~250K people worldwide are, we might hope & generally expect, fairly well-integrated into useful teams/projects that interest them, with occasional exceptions.
Now, imagine another 4 billion people get a 160 IQ assistant who can't say no to whatever they request, able to assemble & summarize-into-usable form all that "public" info in seconds compared to the months it'd take even a smart human or team of smart humans.
That would create new opportunities & risks, via the "different interface", that didn't exist before and do in fact "change much".
By 160 IQ, there should have been people researching ultra-safe languages with novel reflection types enhanced by brilliant thermodynamics inspired SMT solvers. More contributors to TLA+ and TCS, number theoretic advancements and tools like TLA+ and reflection types would be better integrated into everyday software development.
There would be deeper, cleverer searches across possible reagents and combinations of them to add to watch lists, expanding and improving on already existing systems.
Sure, a world where the average IQ abruptly shifts upwards would mean a bump in brilliant offenders but it also results in a far larger bump in genius level defenders.
But just a few years ago, I'd have said that prospect was "maybe 20 years away, or longer, or even never". Today, with the recent rapid progress with LLMs (& other related models), with many tens-of-billions of new investment, & plentiful gains seemingly possible from just "scaling up" (to say nothing of concommitant rapid theoretical improvements), I'd strongly disagree with "not anywhere near". It might be just a year or few away, especially in well-resourced labs that aren't sharing their best work publically.
So yes, all those things you'd expect with plentiful fast-thinking 160 IQ assistants are things that I expect, too. And there's a non-negligible chance those start breaking out all over in the next few years.
And yes, such advances would upgrade prudent & good-intentioned "defenders", too. But are all the domains-of-danger symmetrical in the effects of upgraded attackers and defenders? For example, if you think "watch lists" of dangerous inputs are an effective defense – I'm not sure they are – can you generate & enforce those new "watch lists" faster than completely-untracked capacities & novel syntheses are developed? (Does your red-teaming to enumerate risks actually create new leaked recipes-for-mayhem?)
That's unclear, so even though in general I am optimistic about AI, & wary of any centralized-authority "pause" interventions proposed so far, I take well-informed analysis of risks seriously.
And I think casually & confidently judging these AIs as being categorically incapable of synthesizing novel recipes-for-harm, or being certain that amoral genius-level AI assistants are so far away as to be beyond-a-horizon-of-concern, are reflective of gaps in understanding current AI progress, its velocity, and even its potential acceleration.
First, it's unclear how all these defensive measures are supposed to help if a bad actor is using an LLM for evil on their personal machine. How do reflection types or watch lists help in that scenario?
Second, if the model is open source, a bad actor could use it for evil before good actors are able to devise, implement, and stress-test all the defensive measures you describe.
In particular:
> If a language model spits something out it was already available and indexable on the internet,
Is patently false.
Especially considering how hard it is for humans to create something new, e.g in literature - basically all stories have been written and new ones just copy the existing ones in one way or another.
Attempts to objectively rate LLM creativity are finding leading systems more creative than average humans: https://www.nature.com/articles/s41598-023-40858-3
Have you tried leading models – say, GPT4 for text or code generation, Midjourney for images?
Isn't that exactly the point, and why there should be a 'warning/awareness' that it is not a 160 IQ AI but a very good markov chain that can sometimes infer things and other time hallucinate/put random words in a very well articulated way (echo of Sokal maybe)
Of course not. Nobody in the world could do that. But that doesn't mean it can only spit out things that are already available on the internet which is what you originally stated.
And nobody is worried about the risks of ChatGPT giving instructions for building a nuclear bomb. That is obviously not the concern here.
Then I realized it's seen literally hundreds of thousands of cooking blogs etc, so it's effectively giving you the "average" version of any recipe you ask for - with your own customizations. And that's actually well within its capabilities to do a decent job of.
I’m actually surprised no politicians have tried to crack down on open-source image generation on that basis yet.
The strongest counter-argument used was that the existence of such safe images would give cover to those who continue to abuse children to make non-fake images.
Things kind of went to shit when I pointed out that you could include an "audit trail" in the exif data for the images, including seed numbers and other parameters and even the description of the model and training data itself, so that it would be provable that the image was fake. That software could even be written that would automatically test each image, so that those investigating could see immediately that they were provably fake.
I further pointed out that, from a purely legal basis, society could choose to permit only fake images with this intact audit trail, and that the penalties for losing or missing the audit trail could be identical to those for possessing non-fake images.
Unless there is some additional bizarre psychology going on, SD might have the potential to destroy demand for non-fake images, and protect children from harm. There is some evidence that the widespread availability of non-CSAM pornography has led to a reduction in the occurrence of rape since the 1970s.
Society might soon be in a position where it has to decide whether it is more important to protect children or to punish something it finds very icky, when just a few years ago these two goals overlapped nearly perfectly.
It's a bit similar to the synthetic Rhino horn strategy intended to curb Rhino poaching[0]. Why risk going to prison or getting shot by a ranger for a 30$ horn? Similarly, why risk prison (and hurt children) to produce or consume CSAM when there is a legal alternative that doesn't harm anyone?
In my view, this approach holds significant merits. But unfortunately, I doubt many politicians would be willing to champion it. They would likely fear having their motives questioned or being unjustly labeled as "pro-pedophile".
[0] https://www.theguardian.com/environment/2019/nov/08/scientis...
And you can't attack the means of generating either, without essentially making open source code and private computers illegal. The code doesn't have to have a single line in it explicity about child porn or designer viruses etc to be used for such things, the same way the cpu or compiler doesn't.
So you would have to have hardware and software that the user does not control which can make judgements about what the user is currently doing, or at least log it.