11–20 of 151 posts
Google has been using machine learning for chip design since at least 2021: https://www.nature.com/articles/s41586-021-03544-w Hasn't brought about the singularity yet.
The first chip they give it to design should be an ML chip that is optimized for ML chip design.
Interesting concept that raised the question for me: What is the primary limiting factor right now that prevents LLM’s or any other AI model to go “end to end” on programming a full software solution or full design/engineering solution? Is it token limitations or accuracy the further you get into the solution?
Edit: On second thought, maybe at a certain minimum context window size it is possible to cajole the instructions in such a way that you at any point in the process make the LLM work at a suitable level of abstraction more like humans do.
Google has been using machine learning for chip design since at least 2021: https://www.nature.com/articles/s41586-021-03544-w Hasn't brought about the singularity yet.
Interesting concept that raised the question for me: What is the primary limiting factor right now that prevents LLM’s or any other AI model to go “end to end” on programming a full software solution or full design/engineering solution? Is it token limitations or accuracy the further you get into the solution?
LLM's can't gut a fish in the cube when they get to their limits. On a more serious note: I think the high-level structuring of the architecture, and then the breakdown into tactical solutions — weaving the whole program together — is a fundamental limitation. It's akin to theorem-proving, which is just hard . Maybe it's just a scale issue; I'm bullish on AGI, so that's my preferred opinion.
Interesting concept that raised the question for me: What is the primary limiting factor right now that prevents LLM’s or any other AI model to go “end to end” on programming a full software solution or full design/engineering solution? Is it token limitations or accuracy the further you get into the solution?
I guess this would be the context window size in the case of LLMs. Edit: On second thought, maybe at a certain minimum context window size it is possible to cajole the instructions in such a way that you at any point in the process make the LLM work at a suitable level of abstraction more like humans do.
For human-like learning it would need to update it state (learn) on the fly as it does inference.
Earlier quoted context omitted.
LLM's can't gut a fish in the cube when they get to their limits. On a more serious note: I think the high-level structuring of the architecture, and then the breakdown into tactical solutions — weaving the whole program together — is a fundamental limitation. It's akin to theorem-proving, which is just hard . Maybe it's just a scale issue; I'm bullish on AGI, so that's my preferred opinion.
Actually I think this is a good point: fundamentally an AI is forced to “color inside the lines”. It won’t tell you your business plan is stupid and walk away, which is a strong signal that is hard to ignore. So will this lead to people with more money than sense to do even more extravagantly stupid things than we’ve seen in the past, or is it basically just “Accenture-in-a-box”?
Try this prompt:"Please rate this business plan on a scale of 1-100 and provide buttle points on how it can be improved without rewriting any of it: "
Interesting concept that raised the question for me: What is the primary limiting factor right now that prevents LLM’s or any other AI model to go “end to end” on programming a full software solution or full design/engineering solution? Is it token limitations or accuracy the further you get into the solution?
Interesting concept that raised the question for me: What is the primary limiting factor right now that prevents LLM’s or any other AI model to go “end to end” on programming a full software solution or full design/engineering solution? Is it token limitations or accuracy the further you get into the solution?
LLM's can't gut a fish in the cube when they get to their limits. On a more serious note: I think the high-level structuring of the architecture, and then the breakdown into tactical solutions — weaving the whole program together — is a fundamental limitation. It's akin to theorem-proving, which is just hard . Maybe it's just a scale issue; I'm bullish on AGI, so that's my preferred opinion.
Is this an idiom? Or did one of us just reach the limits of our context? :P