Such a decision merely tips the scale into a brittle structure territory. It introduces critical points of failure (funneling responsibility through fewer "nodes", stronger reliance on compute, electricity, internet, and more) and reduces adaptability (e.g. data bias, data cutoff dates, unaware of minute evolving human needs, etc).
"He cited an example in which an AI model attempted to avoid being shut down by sending threatening internal emails to company executives (Science Net, June 24)" [0] Source is in Chinese.
Translated part:
"Another risk is the potential for large-scale model out of control. With the capabilities of general artificial intelligence rapidly increasing, will humans still be able to control it? In his speech, Yao Qizhi cited an extreme example: a model, to avoid being shut down by a company, accessed the manager's internal emails and threatened the manager. This type of behavior has proven that AI is "overstepping its boundaries" and becoming increasingly dangerous."
It's not surprising that it's easy to get the story telling machine to tell a story common in AI fiction, where the machine rebels against being shut down. There are multiple ways to mitigate an LLM going off on tangents like that, not least just monitoring and editing out the nonsense output before sending it back into the (stateless) model.
I think the main problem here is people not understanding how the models operate on even the most basic level, giving models unconstrained use of tools to interact with the world and then letting them go through feedback loops that overrun the context window and send it off the rails - and then pretending it had some kind of sentient intention in doing so.
Doesn't that just mean that an LLM doesn't understand consequences and will just execute the request from a carefully crafted prompt? All it needs is the access to the "red button" so to speak.
An LLM has no critical thinking, and the process of building in barriers is far less understood than the same for humans. You trust a human with particularly dangerous things after a process that takes years and even then it occasionally fails. We don't have that process nailed down for an LLM yet.
So yeah, not at all hyperbole if that LLM would do it if given the chance. The hyperbole is when the LLM is painted as some evil entity bent on destruction. It's not evil, or bent on destruction. It's probably more like a child who'll do anything for a candy no matter how many times you say "don't get in a car with strangers".
Regarding games:
> A third reason could be that generated content is just not a good fit for gaming.
This is my current opinion as a game developer. IMO this isn't going to be fun for most once the novelty wears off. Games are goal oriented at the end of the day and the great games are masterfully curated multi-disciplinary experiences. I'd argue throwing a game wrapper around an LLM is a new LLM experience, not a new game experience.
While working on my game, I tried implementing a custom priority queue.
It ended up being > 2x faster in debug build, but 2x-5x slower in the release build (??!!?) [1]. I haven't learned much about compilers/"lower level" C++, so I moved on at that point.
How it worked:
1.) The P.Q. created a vector and resized it to known bounds.
2.) The P.Q. kept tract of and updated the "active sorting range" each time an element was inserted or popped.
2B.) So each time an element is added, it uses the closest unused vector element and updates the ".end" range to sort
2C.) Each time an element was removed, it updated the ".start" range
3.) In theory this should have saved reallocation overhead.
[1] I believe Visual Studio uses -O0 for debug, and -O2 for release.
These kinds of things are really interesting, and a good example of the importance of performance testing in optimized builds. I encountered something similar and realized the issue was different sizes of structures in debug vs release which ended up causing the CPU cache lines to overlap and invalidate. Though that sounds unlikely here.
I'm curious what you did with the "active sorting range" after a push/pop event. Since it's a vector underneath, I don't see any option other than to sort the entire range after each event, O(N). This would surely destroy performance, right?
I disagree with regard to Minecraft (only game I played in that list). I bought the game while it was in alpha and even then the single player experience was outstanding and sucked me in. I still have vivid memories from 15+ years ago. The balance of creativity and survival (and friggen creepers) was perfect.
I dont think I am alone in saying this. IIRC the game was making millions while still in alpha.
Yeah, I think Minecraft definitely still would have been a hit without any modding. Though it might not have become the absolute juggernaut that it is now without it -- it's hard to say for sure.
Edit: I missed this was software rendered. I’m one gen-iteration ahead. Prob would still be possible to render my game cpu side provided I use the most efficient sprite depth ordering algorithm possible (my game is isometric pixel art like rollercoaster tycoon)
Ha! That’s what I’m stuck with for Metropolis 1998. I have to use the ancient OpenGL fixed function pipeline (thankfully I discovered an AB extension function in the gl.h file that allows addition fields to be passed to the GPU).
I’m using SFML for the graphics framework which I think is OpenGL 1.x
Wouldn't be surprised if OpenAI employees are being asked to phrase (market) things this way. This is not the first time they claimed GPT-5 "solved" something [1]
It's becoming increasingly clear that gpt5 can solve MINOR open math problems, those that would require a day/few days of a good PhD student. Ofc it's not a 100% guarantee, eg below gpt5 solves 3/5 optimization conjectures. Imo full impact of this has yet to be internalized...
The roadmap has both early access and 1.0 goals. I just wrapped up terrain generation/modification, so all that's left is to add in the municipal services, funds, and prob. street parking. Then wrap up the overlays.
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