101–110 of 394 posts
I just used o3 to design a distributed scheduler that scales to 1M+ sxchedules a day. It was perfect, and did better than two weeks of thought around the best way to build this.
Designing a distributed scheduler is a solved problem, of course an LLM was able to spit out a solution.
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yeah unless you have very specific requirements I think the baseline here is not building/designing it yourself but setting up an off-the-shelf commercial or OSS solution, which I doubt would take two weeks...
Dunno, in work we wanted to implement a task runner that we could use to periodically queue tasks through a web UI - it would then spin up resources on AWS and track the progress and archive the results. We looked at the existing solutions, and concluded that customizing them to meet all our requirements would be a giant effort. Meanwhile I fed the requirement doc into Claude Sonnet, and with about 3 days of promptin…
I'll take the "under" on 30 years. Demis Hassabis (who has more credibility than whoever these 3 people are combined) says 5-10 years: https://time.com/7277608/demis-hassabis-interview-time100-20...
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I am tech founder, who spends most of my day in my own startup deploying LLM-based tools into my own operations, and I'm maybe 1% of the way through the roadmap I'd like to build with what exists and is possible to do today.
What has your roadmap to do with the capabilities? LLMs still hallucinate and make simple mistakes. And the progress seams to be in the benchmarks only https://news.ycombinator.com/item?id=43603453
> And the progress seams to be in the benchmarks only
This seems to be mostly wrong given peoples' reactions to e.g. o3 that was released today. Either way, progress having stalled for the last year doesn't seem that big considering how much progress there has been for the previous 15-20 years.
I just used o3 to design a distributed scheduler that scales to 1M+ sxchedules a day. It was perfect, and did better than two weeks of thought around the best way to build this.
Your anodectical example isn't more convincing than “This machine cracked Enigma's messages in less time than an army of cryptanalysts over a month, surely we're gonna reach AGI by the end of the decade” would have.
And in 30 years it will be another 30 years away. LLMs are so incredibly useful and powerful but they will NEVER be AGI. I actually wonder if the success of (and subsequent obsession with) LLMs is putting true AGI further out of reach. All that these AI companies see are the $$$. When the biggest "AI Research Labs" like OpenAI shifted to product-izing their LLM offerings I think the writing was on the wall that they…
People will keep improving LLMs, and by the time they are AGI (less than 30 years), you will say, "Well, these are no longer LLMs."
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I feel like it's already been pretty well digested and excreted for the most part, now we're into the re-ingestion phase until the bubble bursts.
I am tech founder, who spends most of my day in my own startup deploying LLM-based tools into my own operations, and I'm maybe 1% of the way through the roadmap I'd like to build with what exists and is possible to do today.
How do you know they are possible to do today? Errors gets much worse at scale, especially when systems starts to depend on each other, so it is hard to say what can be automated and not.
Like if you have a process A->B, automating A might be fine as long as a human does B and vice versa, but automating both could not be.
AGI is here today... go have a kid.
I just used o3 to design a distributed scheduler that scales to 1M+ sxchedules a day. It was perfect, and did better than two weeks of thought around the best way to build this.
While impressive, I'm not convinced that improved performance on tasks of this nature are indicative of progress toward AGI. Building a scheduler is a well studied problem space. Something like the ARC benchmark is much more indicative of progress toward true AGI, but probably still insufficient.
And in 30 years it will be another 30 years away. LLMs are so incredibly useful and powerful but they will NEVER be AGI. I actually wonder if the success of (and subsequent obsession with) LLMs is putting true AGI further out of reach. All that these AI companies see are the $$$. When the biggest "AI Research Labs" like OpenAI shifted to product-izing their LLM offerings I think the writing was on the wall that they…
People over-estimate the short term and under-estimate the long term.