RobCat27

I think we'll eventually hit an information theoretic type of wall with physical hardware and GPUs and need a similar AI breakthrough as well as the development refinement of logical/physical qubits in the quantum computing space with some analogue to the transformer architecture to continue accelerating. However, I think there must be many years of development and refinement that can take place before that paradigm shift to overcome the physical compute wall is necessary. This is just my theory, but I'm young enough that I'm expecting with the rate that we are advancing, I will see AI / LLM analogues developed and run on a quantum computer in my lifetime.

I've enjoyed using it for coming up with the structure of a project. I'll ask in search mode for structures of other similar projects if I'm not sure. I also enjoy making human-readable .md or .txt documentation files for myself very quickly with it.

I believe this is the case also. With a well enough performing AI/ML/probabilistic model where you can change the model's input parameters and get a highly accurate prediction basically instantly, we can test theories approximately and extremely fast rather than running completely new experiments, which will always come with it's own set of errors and problems.

I like the message, but I feel like this is bad data visualization. The width of each group of people is not the same, so it's somewhat meaningless to visually compare groups without being able to see the raw percentages. For example, the "Many Adverse Experiences" group is stretched to be longer than the other groups so that proportionally fewer people in that group appear to be a larger proportion than the same proportion would be in other groups because they're not as wide.

Especially since commonly followed wisdom seems to be "trust, but verify."

I've found that for questions that aren't super complex on things that are extremely likely to be in its training dataset (such as public documentation of popular Python libraries) the error rate is very close to 0%. Even compared to GPT from a few months ago I've found the accuracy of responses has increased dramatically.

I still believe anyone using these tools on a day-to-day basis should have a sense of "trust but verify."