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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

#331

Post by plasticchris »

Cthulhu_ wrote: post #305
cwillu wrote: post #283

Earlier quoted context omitted.

Unrelated, but am I the only person who finds the concept of “getting notifications for somebody walking by a house” to be really creepy?

Well yeah, but that's in part a problem with always-on doorbell cameras. On paper they're illegal in many countries (privacy laws, you can't just put up a camera and record anyone out in public), in practice the police asks people to put their doorbell cameras in a registry so they can request footage if needs be. Anyway, I get wanting to see who's ringing your doorbell in e.g. apartment buildings, and that extending…

At least in the USA it’s legal to record public spaces. So recording the street and things that can be seen from it is legal, but pointing a camera over your neighbors fence is not.

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

#332

Post by jmogly »

drpixie wrote: post #244
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…

>> a solution that seems correct under their heuristic reasoning, but they arrived at that result in a non-logical way Not quite ... LLMs are not HAL (unfortunately). They produce something that is associated with the same input, something that should look like an acceptable answer. A correct answer will be acceptable, and so will any answer that has been associated with similar input. And so will anything that fools…

Effortless lying, scary in humans, scarier in machines?

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

#333

Post by lazide »

dingdingdang wrote: post #306
rldjbpin wrote: post #286

Earlier quoted context omitted.

> to perform up to Apple's typical standards of polish and control. i no longer believe they have kept on to the standards in general. the ux/ui used to be a top priority, but the quality control has certainly gone down over the years [1]. the company is now driven by supply chain and business-minded optimizations than what to give to the end user. at the same time, what one can do using AI has large correlation with…

>> to perform up to Apple's typical standards of polish and control. >i no longer believe they have kept on to the standards in general. One 100% agree with this, if I compare AI's ability to speed up the baseline for me in terms of programming Golang (hard/tricky tasks clearly still require human input - watch out for I/O ops) with Apple's lack of ability to integrate it in even the simplest of ways.. things are jus…

Personally, I’ve never seen an AI flow of any kind that meets what would meet the quality of a typical ‘corporate’ acceptable flow. As in, reliably works, doesn’t go crazy randomly, etc.

I’ve seen a lot of things that look like they’re working for a demo, but shortly after starting to use it? Trash. Not every time (and it’s getting a little better), but often enough that personally I’ve found them a net drain on productivity.

And I literally work in this space.

Personally, I find apples hesitation here a breath of fresh air, because I’ve come to absolutely hate Windows - and everybody doing vibe code messes that end up being my problem.

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

#334

Post by danaris »

silvestrov wrote: post #313
mikkupikku wrote: post #299

Earlier quoted context omitted.

Maybe they remembered that a lot of people aren't actually good writers. My brother will send 1000 word emails that meander through subjects like what he ate for breakfast to eventually get to the point of scheduling a meeting about negotiating a time for help with moving a sofa. Mind you, I see him several times a week so he's not lonely, this is just the way he writes. Then he complains endlessly about his coworker…

I feel too many humanities teachers are like your brother. Why use 10 words when you could do 1000. Why use headings or lists, when the whole story could be written in a single paragraph spanning 3 pages.

I mean...this depends very heavily on what the purpose of the writing is.

If it's to succinctly communicate key facts, then you write it quickly.

- Discovered that Bilbo's old ring is, in fact, the One Ring of Power.

- Took it on a journey southward to Mordor.

- Experienced a bunch of hardship along the way, and nearly failed at the end, but with Sméagol's contribution, successfully destroyed the Ring and defeated Sauron forever.

....And if it's to tell a story, then you write The Lord of the Rings.

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

#335

Post by derf_ »

There are four main levers for improving an ML system:

1. You can change the training data.

2. You can change the objective function.

3. You can change the network topology.

4. You can change various hyperparameters (learning rate, etc.).

From there, I think it is better to look at the process as one of scientific discovery rather than a software debugging task. You form hypotheses and you try to work out how test them by mutating things in one of the four categories above. The experiments are expensive and the results are noisy, since the training process is highly randomized. A lot of times the effect sizes are so small it is hard to tell if they are real. The universe of potential hypotheses is large, and if you test a lot of them, you have to correct for the chance that some will look significant just by luck. But if you can add up enough small, incremental improvements, they can produce a total effect that is large.

The good news is that science has a pretty good track record of improving things over time. The bad news is that it can take a lot of time, and there is no guarantee of success in any one area.

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

#336

Post by gchamonlive »

> One popular dataset, FineWeb, is about 11.25 trillion words long3, which, if you were reading at about 250 words per minute, would take you over 85 thousand years to read. It’s just not possible for any single human (or even a team of humans) to have read everything that an LLM has read during training.

Do you have to read everything in a dataset with your own eyes to make sense of it? This would make any attempt to address bias in the dataset impossible, and I think it's not, so there should be other ways to make sense of the dataset distribution without having to read it yourself.

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

#337

Post by ubermonkey »

rldjbpin wrote: post #286
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…

> to perform up to Apple's typical standards of polish and control. i no longer believe they have kept on to the standards in general. the ux/ui used to be a top priority, but the quality control has certainly gone down over the years [1]. the company is now driven by supply chain and business-minded optimizations than what to give to the end user. at the same time, what one can do using AI has large correlation with…

>i no longer believe they have kept on to the standards in general.

They're definitely not as good as they WERE, but they're still better than anybody else.

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

#338

Post by mr_toad »

SchemaLoad wrote: post #201
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.

Like most AI products it feels like they started with a solution first and went searching for the problems. Text messages being too long wasn't a real problem to begin with. There are some good parts to Apple Intelligence though. I find the priority notifications feature works pretty well, and the photo cleanup tool works pretty well for small things like removing your finger from the corner of a photo, though it's n…

> it's not going to work on huge tasks like removing a whole person from a photo.

I use it for removing people who wander into the frame quite often. It probably wont work for someone close up, but its great for removing a tourist who spends ten minutes taking selfies in front of a monument.

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

#339

Post by 1718627440 »

plasticchris wrote: post #331
Cthulhu_ wrote: post #305

Earlier quoted context omitted.

Well yeah, but that's in part a problem with always-on doorbell cameras. On paper they're illegal in many countries (privacy laws, you can't just put up a camera and record anyone out in public), in practice the police asks people to put their doorbell cameras in a registry so they can request footage if needs be. Anyway, I get wanting to see who's ringing your doorbell in e.g. apartment buildings, and that extending…

At least in the USA it’s legal to record public spaces. So recording the street and things that can be seen from it is legal, but pointing a camera over your neighbors fence is not.

And a lot of people don't share that opinion, so this isn't the law in a lot of countries. When you wanted to suggest that it is a problem, that US companies try to extend the law of there home country to other parts of the world, then I endorse that.

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

#340

Post by 1718627440 »

skydhash wrote: post #322
gwd wrote: post #304

Earlier quoted context omitted.

But it's worse than that. Even if in theory the system could be fixed, we don't actually know how to fix it for real , the way we can fix a normal computer program. The reason we can't fix them is because we have no idea how they work; and the reason we have no idea how they work is this: 1. The "normal" computer program, which we do understand, implement a neural network 2. This neural network is essentially a diffe…

That’s a very poetic description. Mine is simpler. It’s a generator. You train it to give it the parameters (weights) of the formula thag generate stuff (the formula is known). Then you give it some input data, and it will gives you an output. Both the weights and the formula is known. But the weight are meaningless in a human fashion. This is unlike traditional software where everything from encoding (the meaning of…

> The only ways to fix it

The correct way to fix it would be to build a decompiler to normal code, that would explain what it does, but this is akin to building the everything machine.

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