I believe there is a bit of hype on both sides, with the labs operating from the mindset of "never let a good crisis go waste".
It is a crisis and the risk of A(G)I is real. But almost all this while in the history of automation, we have blamed implementation of automation when things go wrong. Instead of attributing morality, intent and so many other things that we do with LLMs.
Without having to go in to the philosophical validity of these concepts applied to AI, I feel it's much more useful to focus on the implementation of automation here as well - what risks were opened, how was the env hardened, what was the observability like - how was it being observed anyway, and more.
Done poorly, that can be catastrophic enough. But that sounds mundane. And does not help the hype.
This led me on a 1.5 hours long, entertaining chat session with Gemini learning about stuff in Physics - hopefully without much hallucinations. But definitely with a lot of typical - "You have arrived at a great insight.." like egging on by the AI :D
But learned a bunch about bosons, fermions, QCD, gluons, Kugelblitz, composite bosons and finally Noether’s Theorem on symmetry. Was a wild ride
How do you plan to make this newfound knowledge 'stick'? Do you have enough relevant/related knowledge that you'll remember it? Or is there a way to have Gemini make a bunch of cards for use with spaced repetition software?
Oh cool, will look into this. Have only used it to make podcasts in the past. I'm a little hesitant to build in a walled garden though. I'd rather store things in an open format that can be used with many platforms, not just the one.
Yea, I like the quiz feature a lot. It feel more efficient than both the podcast one (which mostly creates a very superficial high level talk on a level I typically already grasp), and the flashcard one in my experience.
It is a crisis and the risk of A(G)I is real. But almost all this while in the history of automation, we have blamed implementation of automation when things go wrong. Instead of attributing morality, intent and so many other things that we do with LLMs.
Without having to go in to the philosophical validity of these concepts applied to AI, I feel it's much more useful to focus on the implementation of automation here as well - what risks were opened, how was the env hardened, what was the observability like - how was it being observed anyway, and more.
Done poorly, that can be catastrophic enough. But that sounds mundane. And does not help the hype.