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Beliefs that are true for regular software but false when applied to AI

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Re: Beliefs that are true for regular software but false when applied to AI

#231

Post by schoen »

virajk_31 wrote: post #229

I think your post is fundamentally wrong. See, you are comparing AI responses with the written code, which may not be the fair comparison. I see it as, better you could compare code generated by AI vs the code written by an engineer.

The original author seems to view the AI application as itself a software application which has desired or undesired, and predictable or unpredictable, behaviors. That doesn't seem like an invalid thing to talk about merely because there are other software-related conversations we can have about AIs (or other code-quality-related conversations).

Re: Beliefs that are true for regular software but false when applied to AI

#232

Post by Frieren »

__loam wrote: post #140
freetime2 wrote: post #122

For 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…

I'm happy they ate shit here because I like my mac not getting co-pilot bullshit forced into it, but apparently Apple had two separate teams competing against each other on this topic. Supposedly a lot of politics got in the way of delivering on a good product combined with the general difficulty of building LLM products.

> Apple had two separate teams competing against each other on this topic

That is a sign of very bad management. Overlapping responsibilities kill motivation as winning the infighting becomes more important than creating a good product. Low morale, and a blaming culture is the result of such "internal competition". Instead, leadership should do their work and align goals, set clear priorities and make sure that everybody rows in the same direction.

Re: Beliefs that are true for regular software but false when applied to AI

#233

Post by emoII »

virajk_31 wrote: post #229

I think your post is fundamentally wrong. See, you are comparing AI responses with the written code, which may not be the fair comparison. I see it as, better you could compare code generated by AI vs the code written by an engineer.

AI responses and code generated by AI are literally the same thing

Re: Beliefs that are true for regular software but false when applied to AI

#234

Post by ares623 »

andrewmutz wrote: post #128

Tremendous alpha right now in making scary posts about AI. Fear drives clicks. You don't even need to point to current problems, all you have to do is say we can't be sure they won't happen in the future.

How the tables have turned.

Re: Beliefs that are true for regular software but false when applied to AI

#235

Post by Terr_ »

harrisonjackson wrote: post #218
teeray wrote: post #194

Earlier 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.

The Ring app notification summaries still scare me. > "A bunch of people right outside your house!!!" because it aggregates multiple single person walking by notifications that way...

That makes... That makes just enough sense to become nonsense, rather than mere noise.

I mean, I could imagine a person with no common sense almost making the same mistake: "I have a list of 5 notifications of a person standing on the porch, and no notifications about leaving, so there must be a 5 person group still standing outside right now. Whadya mean, 'look at the times'?"

Re: Beliefs that are true for regular software but false when applied to AI

#236

Post by ako »

wrs wrote: post #36

My 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.)

I think there are people who know exactly how it works, they know how neural networks work, they know how the transformer architecture works, how attention works, embeddings, tokenization, etc. We just can’t define the weights of the connections between the neurons.

Re: Beliefs that are true for regular software but false when applied to AI

#237

Post by troupo »

naasking wrote: post #95
coliveira wrote: post #38

Earlier 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…

> The problem with LLMs is that they don't have a process to guarantee that a solution is correct Neither do we. > They will give a solution that seems correct under their heuristic reasoning, but they arrived at that result in a non-logical way. As do we, and so you can correctly reframe the issue as "there's a gap between the quality of AI heuristics and the quality of human heuristics". That the gap is still shrin…

Humans learn. They don't recreate the world from scratch every time they start a new CLI session.

Human errors in judgement can also be discovered, explained, and reverted.

Re: Beliefs that are true for regular software but false when applied to AI

#238

Post by troupo »

Legend2440 wrote: post #165
CobrastanJorji wrote: post #29

Earlier 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!

Since "AGI has been achieved internally" tweet I've only seen incremental improvements that are guaranteed to never be able to do my job. Or most people's jobs.

Re: Beliefs that are true for regular software but false when applied to AI

#239

Post by adrianN »

AdieuToLogic wrote: post #211
mrkmarron wrote: post #180

Earlier quoted context omitted.

[flagged]

> The goal is to build a language and system model that allows us to reliably sandbox and support agents in constructing "Trustworthy-by-Construction AI Agents." 1 - Reliability implies predictable behavior. 2 - Predictable behavior implies determinism. 3 - LLM's are non-deterministic algorithms. In the link you kindly provided are phrases such as, "increases the likelihood of successful correct use" and "structure f…

Reliability does not require determinism. If my system had good behavior on inputs 1-6 and bad behavior on inputs 7-10 it is perfectly reliable when I use a dice to choose the next input. Randomness does not imply complete unpredictability if you know something about the distribution you’re sampling.

Re: Beliefs that are true for regular software but false when applied to AI

#240

Post by eCa »

generic92034 wrote: post #93
wrs wrote: post #36

My 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.)

Nobody knows (full scope and on every level) how human brains work. Still bosses rely on their employees' brains all the time.

> Nobody knows (full scope and on every level) how human brains work.

That is what the parent meant.

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