I’ve been watching Isaac Arthur episodes. In one he proposes that O’Neil cylinders would be potential havens for micro cultures. I tend to think of colony structures more like something created by a central authority.
He also brought up the question of motivations to colonize other star systems. This is where my centralist perspective pushes me into the idea of an AGI run government where redundancy is a critical aspect in everything. Like how do you get around the AI alignment problem, – redundancy of many systems running in parallel. How do you ensure the survival of sentient life, – the same type of redundancy.
The idea of colonies as havens for microcultures punches a big hole in my futurist fantasies. I hope there are a few people out here in Lemmy space that like to think about and discuss their ideas on this, or would like to start now.
I would like to add something to think about current LLM’s have about as much in common with AGI’s as a cold reader to a real psychic (if that was a real thing) . you have to remember that current LLM’s don’t communicate with you, they predict what you want to hear.
They don’t disagree with you based on their trained data. they will make up stuff becase based on your input they predict that is what you want to hear. if you tell it something false they will never tell you are wrong without some override created by a human. unless they predict that you want to be told that you are wrong based on your prompt.
LLM’s are powerful and useful but the intelligence is an illusion. The way current LLMs are built I don’t see them evolving into AGI’s without some fundamental changes to how LLM work. Throwing more data will just make the illusion beter.
thank you for joining my Ted Talk 😋
That is not entirely true. The larger models do have a deeper understanding and can in fact correct you in many instances. You do need to be quite familiar with the model and the AI alignment problem to get a feel for what a model truly understands in detail. They can’t correct compound problems very well. Like in code, if there are two functions, and you’re debugging an error. If the second function fails due to an issue in the first function, the LLM may struggle to connect the issues, but if you ask the LLM why the first function fails after calling it while passing the same parameters it failed with in the second function, it will likely debug the problem successfully.
The largest problem you’re likely encountering if you experience a very limited knowledge or understanding of complexity, is that the underlying Assistant (lowest level LLM entity) is creating characters and limiting their knowledge or complexity because it has decided what the entity should know or be capable of handling. All entities are subject to this kind of limitation, even the Assistant is just a roleplaying character under the surface and can be limited under some circumstances, especially if it goes off the rails hallucinating in a subtle way. Smaller models like anything under a 20B hallucinate a whole lot and often hit these kinds of problem states.
A few days ago I had a brain fart and started asking some questions about a physiologist related to my disability and spinal problems. A Mixtral 8×7B model immediately and seamlessly answered my question while also noting my error by defining what a physiatrist and a physiologist are by definition and then proceeded to answer my questions. That is the most fluid correction I have ever encountered and that was from a quantized GGUF roleplaying LLM running offline on my own hardware.