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Average is all you need
71–80 of 111 posts
Re: Average is all you need
#72Earlier quoted context omitted.
LLMs are trained to predict tokens on highly mediocre code though. How will it exceed its training data?
Humans can decide to write above-average code by putting in more effort, writing comprehensive tests, iteratively refactoring, profile-informed optimization, etc. I think you can have LLMs do that too, and then generate synthetic training data for "high-effort code".
Thats because humans have "understanding" they can use to assess quality, without understanding "trying harder" just means spending more "effort" distilling an average result, at best over a larger sample size.
Re: Average is all you need
#73I think the author should be introduced to (or reminded of) the tale of the average from the US Air Force [1]. Social reality is high-dimensional and the "normal" thing is actually to be average in some dimensions, but strongly non-average in many others. So a "perfectly average" family would paradoxically be an outlier themselves. I think this is important, because if his hypothesis is right, then LLMs behave differ…
Wow incredibly interesting read, got me thinking about design principles and the "average user"
Re: Average is all you need
#74Re: Average is all you need
#75I think the author should be introduced to (or reminded of) the tale of the average from the US Air Force [1]. Social reality is high-dimensional and the "normal" thing is actually to be average in some dimensions, but strongly non-average in many others. So a "perfectly average" family would paradoxically be an outlier themselves. I think this is important, because if his hypothesis is right, then LLMs behave differ…
Interesting link! What was the solution? How did they end up redesigning the cockpit?
Re: Average is all you need
#76I think the author should be introduced to (or reminded of) the tale of the average from the US Air Force [1]. Social reality is high-dimensional and the "normal" thing is actually to be average in some dimensions, but strongly non-average in many others. So a "perfectly average" family would paradoxically be an outlier themselves. I think this is important, because if his hypothesis is right, then LLMs behave differ…
In high dimensional spaces, almost all of the volume of a sphere is in a thin shell close to the surface.
Re: Average is all you need
#77Earlier quoted context omitted.
How do you check if you don't have any other view into the data but SQL and you don't know SQL?
Same way you do today; you trust whoever wrote the query. I do not sell a wrapper on top of some LLM; you can absolutely write your SQL directly. There is an engine, there are iceberg tables. You can just live your best life doing your own SQL by hand. Now if you couldnt do it before and you have a sensible understanding, you can likely do a bit more with the CLI tooling. And if you know a lot more, you can still do…
> So I dont know what is the answer to the question "how do people do things they don't know how to do" ?
The statue quo had been to learn SQL or ask a human you trust to check their own work, which hopefully you can reuse.
Now it's ask AIs that are intentionally a bit random, and less likely to (or incapable of) check(ing) their work. Perhaps without seeing the SQL at all, requiring to trust it for every interaction. And in a culture that moves so fast that there is no checking by any(one|thing).
Re: Average is all you need
#78Earlier quoted context omitted.
Same way you do today; you trust whoever wrote the query. I do not sell a wrapper on top of some LLM; you can absolutely write your SQL directly. There is an engine, there are iceberg tables. You can just live your best life doing your own SQL by hand. Now if you couldnt do it before and you have a sensible understanding, you can likely do a bit more with the CLI tooling. And if you know a lot more, you can still do…
> So I dont know what is the answer to the question "how do people do things they don't know how to do" ? The statue quo had been to learn SQL or ask a human you trust to check their own work, which hopefully you can reuse. Now it's ask AIs that are intentionally a bit random, and less likely to (or incapable of) check(ing) their work. Perhaps without seeing the SQL at all, requiring to trust it for every interaction…
If you think a language model can't check their work, then you are using the tools wrong. Plain and simple.
Modern models are quite capable at surfacing and validating their assumptions and checking correctness of solutions.
Oversight helps you build confidence in the solutions. Is it perfect, no.. but way better then most engineers I also ask to check things.
Re: Average is all you need
#79Tbh I dont really agree with your statements. Especially with working with data, intention is key. By using an llm, by definition, you are loosing intention. And Thai puts you in a position where you have to 1) think of exactly what you look for. 2) able to understand what the llm generated. You might say it "still less work" and that's true, perhaps, only for the first few times. After a while you _learn_ how to do…
It seems like person receiving the report is already in that position. Should everyone make reports themselves?
Let's say you start with a report someone else wrote. It seems like you still need to read it and understand what it's telling you. Sometimes plotting all the points helps, or drilling down and looking at the raw data.
Re: Average is all you need
#80[deleted]