Earlier quoted context omitted.
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It sounds completely crazy that anyone would give an LLM access to a payment or order API without manual confirmation and "dumb" visualization. Does anyone actually do this?
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Beliefs that are true for regular software but false when applied to AI
261–270 of 461 posts
Re: Beliefs that are true for regular software but false when applied to AI
#262Earlier quoted context omitted.
> minor tools for making emojis, summarizing notifications, and proof reading. The notification / email summaries are so unbelievably useless too: it’s hardly more work to skim the notification / email that I do anyway.
It does feel like somebody forgot that "from the first sentence or two of the email, you can tell what it's about" was already a rule of good writing...
I think people read texts because they want to read them, and when they don't want to read the texts they are also not even interested in reading the summaries.
Why do I think this? ...in the early 2000's my employer had a company wide license for a document summarizer tool that was rather accurate and easy to use, but nobody ever used it.
Re: Beliefs that are true for regular software but false when applied to AI
#263Earlier quoted context omitted.
This is not necessarily a problem. Any programming or mathematical question has several correct answers. The problem with LLMs is that they don't have a process to guarantee that a solution is correct. They will give a solution that seems correct under their heuristic reasoning, but they arrived at that result in a non-logical way. That's why LLMs generate so many bugs in software and in anything related to logical t…
> Any programming or mathematical question has several correct answers. Huh? If I need to sort the list of integer number of 3,1,2 in ascending order the only correct answer is 1,2,3. And there are multiple programming and mathematical questions with only one correct answer. If you want to say "some programming and mathematical questions have several correct answers" that might hold.
I think what they meant is something along the lines of:
- In Math, there's often more than one logically distinct way of proving a theorem, and definitely many ways of writing the same proof, though the second applies more to handwritten/text proofs than say a proof in Lean.
- In programming, there's often multiple algorithms to solve a problem correctly (in the mathematical sense, optimality aside), and for the same algorithm there are many ways to implement it.
LLMs however are not performing any logical pass on their output, so they have no way of constraining correctness while being able to produce different outputs for the same question.
Re: Beliefs that are true for regular software but false when applied to AI
#264For a real world example of the challenges of harnessing LLMs, look at Apple. Over a year ago they had a big product launch focused on "Apple Intelligence" that was supposed to make heavy use of LLMs for agentic workflows. But all we've really gotten since then are a couple of minor tools for making emojis, summarizing notifications, and proof reading. And they even had to roll back the notification summaries for a w…
Which is ironic, given all I really want from Siri is an advanced-voice-chat-level chat gpt experience - being able to carry on about 90% of a natural conversation with gpt, while Siri vacillates wildly between 1) simply not responding 2) misunderstanding and 3) understand but refusing to engage - feels awful .
Probably the issue is it is free. If people paid for it they could scale infra to cope.
Re: Beliefs that are true for regular software but false when applied to AI
#265My current method for trying to break through this misconception is informing people that nobody knows how AI works. Literally. Nobody knows. (Note that knowing how to make something is not the same as knowing how it works. Take humans as an obvious example.)
Well we are happy to work with other humans without knowing how they work.
Re: Beliefs that are true for regular software but false when applied to AI
#266Re: Beliefs that are true for regular software but false when applied to AI
#267For a real world example of the challenges of harnessing LLMs, look at Apple. Over a year ago they had a big product launch focused on "Apple Intelligence" that was supposed to make heavy use of LLMs for agentic workflows. But all we've really gotten since then are a couple of minor tools for making emojis, summarizing notifications, and proof reading. And they even had to roll back the notification summaries for a w…
Maybe they used their AI to design Liquid Glass. Impressive at first sight, but unusable in practice.
Re: Beliefs that are true for regular software but false when applied to AI
#268The "just find the bug and patch it" mindset is so deeply ingrained in engineering culture that it's easy to forget it doesn't apply here
Re: Beliefs that are true for regular software but false when applied to AI
#269Earlier quoted context omitted.
They have certainly gotten better, but it seems to me like the growth will be kind of logarithmic. I'd expect them to keep getting better quickly for a few more years and then kinda slow and eventually flatline as we reach the maximum for this sort of pattern matching kind of ML. And I expect that flat line will be well below the threshold needed for, say, a small software company to not require a programmer.
Ahaha, of course nothing will ever be able to do my job!
It will take at least a few more decades at least from the looks of it. I would be 6 feet under by then so yes "Nothing will ever be able to do my job".
Re: Beliefs that are true for regular software but false when applied to AI
#270The main thing is that LLMs aren't software programs and as such should not be compared to them.