The article mentions that the state is currently home to 257 data centers. Just one thing to note in case you aren’t aware: That’s not exclusively AI datacenters and there is a massive difference in power draw and pollution between regular data centers and the AI counterpart.
I never wanted to be anything, and I was exceptionally successful at that so far. :/
I still can’t wrap my head around how this strategy (“buy our models, they are so powerful they might kill all humans”) would work in their favor, apart from one thing: restrictions would hurt Chinese models probably more than themselves. But I find it very unlikely that characters like Altman or Amodei would be capable of playing that kind of 4d chess. It’s a weird flex
What do you think where this is going to end up? My personal assessment of the situation is that only the mega-corporations can afford enshittification like that, and even those are now getting a taste of their own medicine (Sloperators recreating their flagship products… it’s a race to the bottom with enshittification on one side and sloppy replacements on the other). Smaller sized companies will simply get buried when the quality of their products falls to garbage levels. This is what those smaller sized companies don’t realise yet: When everyone can churn out the same low quality slop, their USP and their customers are gone soon.
Let them, but insist they should also use Claude to QA their code and thus take responsibility for the whole thing. If they trust AI to be good enough, why not go full circle? That shouldn’t be a problem, right?
Joking aside, I know there is a real danger of those people shifting the responsibility for their created code mess downwards to the developers and QA, and when shit inevitably hits the fan they know who to blame.
Ghost pairing ist ein irreführender Begriff. “Digital incompetence” trifft es eher. Das potenzielle Opfer muss gleich mehrere dumme Dinge tun:
Aber tatsächlich wäre es vermutlich sicherer wenn der Nutzer im Messenger initial selbst seine Paarungswilligkeit anzeigen müsste bevor eine solche Paarungsanfrage überhaupt möglich wird. So wie bei Bluetooth, wo die Auffindbarkeit explizit eingeschaltet werden muss und nur ein paar Minuten aktiv bleibt.
It’s kind of scary to imagine living in that time and using all these products (or even worse, being a worker in an asbestos factory) cause you have no way to know this stuff causes cancer. I wonder what kind of extremely unhealthy things we are exposed to right now just because we don’t know.
Sorry bout the sarcasm. My point is, not only do you have to specifically instruct the LLM to go looking for voter data, but you’ll also have to let it roam freely and unsupervised on the internet for quite a while to get to such a result (which is a bad idea to begin with). In the end, it’s the sloperators fault to let it go off the rails and a scenario like this is entirely preventable
So, there I was, happily vibecoding another Minecraft clone when suddenly it went looking for voter data in Georgia. Yeah, happens every time. Can’t do anything about it. These Superintelligences just have it their way.
said Gemini was chosen because it was the only tool the board evaluated
We chose Gemini because we couldn’t be bothered with doing actual research. Educators of the year award.
Ok, I’ll bite. Assuming that the article means Large Language Models exclusively when mentioning “AI”.
Good specialized models aren’t that easy to build for a lot of use cases.
Coding is one of the more problematic cases. People tend to think that coding is about code, so if we just create a model that is specialized in writing code, we’re golden. However it turns out this isn’t true. Coding is not only about code itself, but about creating an application that has a specific purpose and features in a specific domain. The LLM needs an understanding of not only the plain code, but needs knowledge about the subjects the application is about, and these are mostly in the domain of general knowledge. Another issue is that the model itself needs to communicate with the user in an appropriate way, and this needs general knowledge as well.
All these examples require an LLM to have a good general knowledge to grasp what the original text is about. You won’t get a decent summary when the general knowledge of the subject at hand is subpar. My point is, bigger LLMs with more general knowledge handle these tasks better than smaller ones. I’m not saying smaller models can’t do the job, but there will be differences in quality.
It’s not that hard to let existing LLMs use specialized knowledge.
The actual problem is to prevent this specialized knowledge from being stolen by US/Chinese AI companies. So instead of focusing on building own “specialised” models, let’s work on preventing the ongoing theft of knowledge, and using the right tools for the job.