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SQLite Critical CVEs or LLM Slop?

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Re: SQLite Critical CVEs or LLM Slop?

#341

Post by red75prime »

buttercraft wrote: post #324
red75prime wrote: post #241

Earlier quoted context omitted.

It's a negative in your worldview where "intelligence can't be a computation" is an axiom or something close. To me this worldview is sloppy thinking.

I did not express my opinion or "worldview." I was simply responding to this: > exclude LLMs with CoT from the category of intelligent systems with certainty

Why do you think that intelligence of a Turing-complete system trained on human data can be likened to a Russell's teapot?

At least, don't you think that the recent mathematical results of LLMs are a bit like a glimpse of something teapot-shaped in the orbit? (which makes it not a Russell's teapot, which. by definition, can't be observed).

To me, it's an expected progression of ANNs' approximation of human cognitive processes. The universal approximation theorem guaranties the existence of such ANNs barring the super-Turing or quantum superiority of the brain.

Re: SQLite Critical CVEs or LLM Slop?

#342

Post by Jensson »

estearum wrote: post #338
Jensson wrote: post #333

Earlier quoted context omitted.

LLM doesn't just produce output as a function, they are much more specific: they predict text based on text they have been trained on.

Yes that is the item (2) parameter described above.

No, brains doesn't just try to mimic pasts signals, LLMs do that but brains doesn't.

The way they make LLM solve problems is by adding a lot of logical jumps into its data, or break down different problems etc, and then as it predicts the text it predicts these logical jumps and then solves the problem. That is very different from how humans learn to solve problems, you don't feed them a billion different state transitions they have to encode to be able to navigate math, they learn to become proficient at math from a few hundred to a few thousand examples, that is fundamentally different from how LLM can learn.

That LLM are so slow learners that requires massive amount of data is a big reason its hard to make them smarter, and its caused by them being next token predictors. And the reason humans can learn with so little data is because we are not just next token predictors.

Re: SQLite Critical CVEs or LLM Slop?

#343

Post by p2detar »

Jensson wrote: post #222
gbnwl wrote: post #221

Earlier quoted context omitted.

Are they useful or not? Will they continue changing the world or not? People who choose one way or the other for describing them typically fall on one side or the other in these questions imo. What do you think? Will these next token predictors change the world or not?

They are useful. They will continue to change the world. They are still next token predictors with all the problems that comes with that. For them to change the world you have to work with them as next token predictors. Ensure that the next token predictor has enough prediction paths to solve the problems you want and so on. Since when they don't they fail spectacularly. These big companies will continue to add new s…

In all fairness humans can also be considered next token predictors. It could be said that’s how we communicate with one another today. Presently LLMs lack other things, like physical presence in the world and continuity of input sensory data.

Re: SQLite Critical CVEs or LLM Slop?

#344

Post by estearum »

Jensson wrote: post #342
estearum wrote: post #338

Earlier quoted context omitted.

Yes that is the item (2) parameter described above.

No, brains doesn't just try to mimic pasts signals, LLMs do that but brains doesn't. The way they make LLM solve problems is by adding a lot of logical jumps into its data, or break down different problems etc, and then as it predicts the text it predicts these logical jumps and then solves the problem. That is very different from how humans learn to solve problems, you don't feed them a billion different state trans…

Okay, then what part of the brain is doing anything other than transforming an input into an output based on what inputs that part of the brain has previously been exposed to?

Re: SQLite Critical CVEs or LLM Slop?

#345

Post by Jensson »

estearum wrote: post #344
Jensson wrote: post #342

Earlier quoted context omitted.

No, brains doesn't just try to mimic pasts signals, LLMs do that but brains doesn't. The way they make LLM solve problems is by adding a lot of logical jumps into its data, or break down different problems etc, and then as it predicts the text it predicts these logical jumps and then solves the problem. That is very different from how humans learn to solve problems, you don't feed them a billion different state trans…

Okay, then what part of the brain is doing anything other than transforming an input into an output based on what inputs that part of the brain has previously been exposed to?

> transforming an input into an output based on what inputs that part of the brain has previously been exposed to

You changed the definition there, for it to be like an LLM it should be:

> transforming an input into an output trying to mimic inputs that part of the brain has previously been exposed to

Anyone can see how that limits you a lot, and why that makes it so much harder for LLM to learn things properly than it is for humans.

Re: SQLite Critical CVEs or LLM Slop?

#346

Post by e12e »

elmer2 wrote: post #168
gortok wrote: post #52

We can chalk this up as another example of over-exhuberance by what folks believe LLMs can accomplish vs. what they actually are. LLM-based “AI” is able to use its vast corpus of inputs and calculate the most statistically likely output in a given situation. It is probabilistic, and when you are dealing with probabilities in a situation where certainties, not probabilities, matter, you’re going to get dinged on credi…

Many people with no skills are taking advantage of the LLM craze to artificially inflate their own value. I see it every day on LinkedIn. People that previously have barely any experience in tech, now being hired in AI startups because they are good bullshitters.

> People that previously have barely any experience in tech, now being hired in AI startups because they are good bullshitters.

I like the symmetry of people being unable to detect "skill" in people who are great at manipulating language and in LLMs who are also great at manipulating language.

Re: SQLite Critical CVEs or LLM Slop?

#347

Post by Jensson »

p2detar wrote: post #343
Jensson wrote: post #222

Earlier quoted context omitted.

They are useful. They will continue to change the world. They are still next token predictors with all the problems that comes with that. For them to change the world you have to work with them as next token predictors. Ensure that the next token predictor has enough prediction paths to solve the problems you want and so on. Since when they don't they fail spectacularly. These big companies will continue to add new s…

In all fairness humans can also be considered next token predictors. It could be said that’s how we communicate with one another today. Presently LLMs lack other things, like physical presence in the world and continuity of input sensory data.

Humans learn to be a next token predictor as a kid when they learn to speak, an LLM cannot learn to be a next token predictor or anything of the sort, we have no clue how you could have an LLM learn human language just based on a thousands conversations with a human.

You don't see how that is very different? For an LLM to be as smart as a human it has to be able to learn like a human. Like you don't evaluate how smart a human is based on how much he knows, you evaluate it based on how fast he learns. And LLM are so bad at learning its ridiculous, they lack that part of the brain that lets humans be smart and learn so fast and easily.

Re: SQLite Critical CVEs or LLM Slop?

#348

Post by neitsa »

inigyou wrote: post #79
masklinn wrote: post #29

Earlier quoted context omitted.

That is exactly why many big projects are migrating to becoming CNA, so that randos can’t get assigned unqualified CVEs which nobody has looked at or validated. Apparently RedHat is a CNA of last resort, so it might be possible to get your project under Redhat’s scope and go through them without having to be a CNA yourself.

What are the requirements to become a CNA?

The OSSF (Open Source Security Foundation) has a nice guide on how to become a CNA [0]. It's pretty involved though.

[0] https://github.com/ossf/wg-vulnerability-disclosures/blob/ma...

Re: SQLite Critical CVEs or LLM Slop?

#349

Post by noncoml »

estearum wrote: post #162
gortok wrote: post #151

Earlier quoted context omitted.

Turing-completeness is a necessary pre-requisite for being able to fulfill the requirements of a Turing machine, nothing more. In the same way that cell division is a necessary condition for life, but cell division does not mean a given life form itself is sentient. Intelligent life-forms can generate probabilistic outputs based on inputs, but being able to generate probabilistic outputs based on inputs is not what m…

> but being able to generate probabilistic outputs based on inputs is not what makes us intelligent. ??? Of course it is. The brain is mechanically not capable of doing anything other than that. Do you believe the brain is something other than a bundle of probabilistic physical interactions? Or are brains not the source of what we call intelligence?

We don’t know

Re: SQLite Critical CVEs or LLM Slop?

#350

Post by estearum »

noncoml wrote: post #349
estearum wrote: post #162

Earlier quoted context omitted.

> but being able to generate probabilistic outputs based on inputs is not what makes us intelligent. ??? Of course it is. The brain is mechanically not capable of doing anything other than that. Do you believe the brain is something other than a bundle of probabilistic physical interactions? Or are brains not the source of what we call intelligence?

We don’t know

Of course we do. Unless the brain is an object unlike anything else in the known universe, then it's an input → output machine.
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