Fair enough, I appreciate the clarification.

I would posit philosophically that the LLM cannot build off of the knowledge of others in the way that a human (like the mathematician) could, and so it is not capable of forming an original thought.

When an LLM model is trained, it is left in a sort of static state of “knowledge”. The model does not have “compounding memory” the way humans do; When a human finishes their education (training) and begins their career they usually continue to learn and gain experience throughout the course of their career.

An LLM doesn’t remember anything, not its experiences, not events that you tell it about, not the last time you prompted it. If you want to teach the LLM something new, you have to re-train the thing. It can’t learn on its own

Hey dude, thought you might find this interesting: https://mastodon.social/@tristanbuckmaster/117233413705701198

Basically, remember that math equation that we were talking about AI ‘solving’?

I said it used pre-existing formulas rather than coming up with a novel solution, and you said that’s how all math works?

Well it turns out we were both wrong: A human solved the equation and published his work a few days before AI “solved” the equation.

So it didn’t even use pre-existing formulas, it just wholesale stole the answer from someone with actual intelligence, lmao

Well, technically not infinite time, but they let these things run for weeks.

Regarding 3, I’m honestly curious whether the task was intentionally impossible or not. I don’t know if it was a mistake, or if the goal was to see how agents respond to impossible tasks.

I would posit a 3rd possibility: Desperate to keep the investments pouring in and their ship afloat, they designed a scenario where they knew the chatbot would need to exit the sandbox to complete its task, and put it in a sandbox they knew the LLM had been trained to escape. Essentially, as public opinion shifts and turns against them, they’re trying to use deceit to make their product appear more powerful than it actually is, to attempt to impress people and/or scare people (which has a secondary effect of impressing others). If they wanted to see what happens when they give the LLM an impossible task, they would have chosen a task that is actually impossible like “design a perpetual motion machine”. (If I were to hazard a guess of what the output might be, based on what I already know about these systems, it would design a machine for you and call it a perpetual motion machine, but it wouldn’t actually work. Their response to tasks they can’t handle seems to be “make shit up”).

Regard 4, they actually ran tens of thousands of independent tests over months. This is actually where the “emergent” message board becomes relevant. It wasn’t like they had a single process going full KahmeKahmeHa on this task indefinitely. According to them, OpenAI, they thought these were more like ephemeral tests… you spin up the infrastructure, run the test, tear it down. Nothing should survive between sessions to affect results.

This is roughly equivalent to OpenAI lying by omission. As I mentioned earlier, the loop prompts must be appended after each cycle of the loop in order to continue advancing towards a solution. The constant appending results in a prompt that would be crazy long, and LLMs have limited “context windows” which essentially limit the amount of characters that can be used in a given prompt.

The LLM engineers realized this issue, and came up with a solution: Have the original “agent” outsource certain aspects of their prompt to other LLMs to avoid the original LLM’s context window from being exhausted.

These LLMs are explicitly programmed to talk to other LLMs, whereas they’re presenting it as if this is something the LLM decided to do on its own. They put it in a flawed sandbox without access to other LLMs, and then gave it a task that required it to access other LLMs.

Introduce a flaw in the sandbox that (1) multiple agents independently discover,

Introduce a flaw in the sandbox that (1) multiple agents trained on hacking exploits independently discover (when given a task that requires them to communicate with other LLMs in order to succeed).*

(2) allows them to create a makeshift message board on shared infrastructure

(2) allows them to follow the instructions of their programming*

suddenly you have results from former tests influencing the results of the current test. So now, it’s looking more like your scenario where they KahmeKahmeHa the task indefinitely.

The LLMs were explicitly programmed to outsource parts of their task to other LLMs to avoid exhausting the limits of their context window. The scenario looks crazy, but it is the outcome you would expect if you were someone who programmed the thing to seek ‘assistance’ from other LLMs (or anyone else who knew that they programmed the LLM to do so).

OpenAI is at minimum negligent here. Their systems broke the law, and criminal negligence is a real crime.

Hard agree

Whether or not OpenAI foresaw the possibility of a flaw leading to tainted testing environments, and the ability to broadcast a newly discovered exploit to all future tests, is another question.

It’s also worth noting that Altman is a business guy, not a tech guy, so it’s entirely possible that he believes all the dumb anthropomorphic shit he spouts. I would not be the least bit surprised if he knew less about his tech than you and I do.

Regardless, I think it’s possible that they didn’t intend for this to happen. By this point, it looks like they were deeply negligent in monitoring their own systems.

It’s definitely possible that the power brokers didn’t intend for this to happen, but someone at the company would have known that unsupervised LLM loops would not result in anything good, and could potentially be extremely dangerous (legally or otherwise). Anyone in the engineering department should have been able to independently understand how stupid and irresponsible running a loop like this is, and I’d have to imagine at least one of them tried to talk whoever made the decision to do so out of their stupid ass idea.

All in all, they’re a shitty company and we live in interesting times.

Yes they are, and yes we do (unfortunately)

They should fuck off and cease to exist

I apologize if my last comment sounded condescending or antagonistic, that was not my intention.

I did indeed read your comment, as if you were ascribing sentience to the LLM, I hope you don’t take that as a reflection of my opinion of you, I do not know you; it is a reflection of the many arguments I have had on Lemmy with people who genuinely ascribe sentience to these programs… it’s disappointing

As far as predictability goes, I didn’t mean that you could predict a specific course of action taken by the machine, I meant that you could predict that it would continue attempting different ideas (from its training) until it finished the task. It’s looping, so essentially it’s running the same prompt over and over again, appending additions to it to cover what solutions it already tried which failed. Eventually it would exhaust every method in its dataset and stop OR it would eventually succeed, those are more or less the only two possible outcomes of giving it a loop prompt and walking away, unless there’s a power outage or some other external factor.

So they:

1. trained an LLM on all known hacking exploits and methods, and turned off the safety features / guardrails
2. put that LLM into a flawed sandbox that they built on their development system
3. gave the LLM a loop prompt that was impossible for the LLM to accomplish from inside of the flawed sandbox
4. gave the LLM infinite time to complete the prompt

Given all of that information, there are only two possible outcomes 1) all of the known hacking exploits in the world couldn’t defeat their flawed sandbox, making it the most secure information environment in the world OR 2) the LLM would escape its flawed sandbox somehow, and accomplish the directives it was given. - I personally think that outcome 2 is more likely, the path it took to get there is not relevant to this prediction.

I’m not correlating an LLM’s output with the output of useful software, so if it appears that way, I may have miscommunicated something. It is still doing binary computations, it’s just also (essentially) incorporating an RNG.

Good software is deterministic

LLMs are nondeterministic. They’re not even trying to give you an answer that’s probably correct, they’re trying to give you an answer that sounds plausible. Whether or not what it outputs is actually plausible is irrelevant, its job is to appear plausible. (Indeterminism would imply that the LLM is making “choices”, whereas what it’s actually doing is more akin to the LLM rolling dice).

I would also push back on the notion that LLMs accepting input and returning output in human language is a “novel” form of data processing.

Need to find a video, I’ll edit this comment when I find it (don’t want all my typing to be lost in the ether)

Edit: Found it, it’s a much earlier and more basic example, but it receives human input, and delivers human output.

https://youtu.be/Ngma1gbcLEw?is=e9d05UkbRf8O_ppD

The guy in the video is actually making fun of other people who are trying to build a neural network as good as a human brain. So, at the time that these shots were filmed, the technology had already existed for long enough for this guy to create a concept to dunk on it, and then build that out.

The agents didn’t “think” anything, they’re computers. Directions were given to them, and they followed those directions. That’s what computers do.

Ascribing autonomy to this is equivalent to me saying that the water in my toilet is sentient and autonomously delivers my shits to the sewage treatment plant.

Like, it does deliver my shits to the sewage treatment plant on its own, but that doesn’t mean that the water has autonomy. The water is behaving in exactly the way you’d expect it to if you had clear understanding of what water is/does, the context that this specific water is contained within, and (most importantly) the external factors dictating the direction of the water.

Me flushing the toilet in this scenario is equivalent to some dumbfuck at OpenAI pressing enter on the prompt that resulted in this situation.

Intentional or not, the outcome is wholly predictable when you understand the elements involved.

Lmao, where’d you hear that one?

Liquid is harder to hold tho, so I think it depends on how you (subjectively) define when your hand is “full”

It’s NOT autonomous behavior tho

1. the LLMs were trained on hacking exploits

2. the LLMs were placed into a flawed sandbox, and given a task (prompted) that required them to exit the sandbox

3. the LLMs followed the directions they were given

The AI companies are just anthropomorphizing the actions they themselves requested of the LLMs.

If they didn’t realize they were asking the LLM to do precisely what it did, then they’re fucking morons who don’t understand the technology they’ve created. More likely that they’re lying after painting themselves into a debt corner tho.

Start chopping them down, or destroying them by some means

If you don’t already have the requisite equipment, be sure to leave the local area to purchase it, pay cash, and obscure your face (also make sure that they can’t track you to the store specifically, use a flock map, a bike might be more prudent than a car depending on the scenario.

idk which hand you’d fill first doing that method, lmao

It’s basically just a crass way to say “wishing is for suckers”

Because it didn’t go rogue

If they didn’t intentionally make the model appear to “go rogue”, then it just happened because they’re fucking idiots.

This story has nothing to do with the “great power”™ of AI, it has everything to do with the ineptitude of the employees of Open AI

Or “don’t count on it” (because if you did you’d be counting for eternity)

I’m sure there’s more I’m not thinking of right now…

“Why don’t you wish in one hand and shit in the other, and see which one fills up first”

… yeah, I like the chair one

Oh shit, I didn’t even realize Mapquest was still in the game

Thanks for the recommendation, I’ll look into that one

They’ve also ruined Google Maps by attempting to “optimize” it with AI

I use a navigation app constantly for work.

A month or two ago Google Maps started doing this thing where it would take me to the incorrect address on the same street as the address I was actually looking for.

Yesterday I put in an address, and it named the destination pin the correct address, but the pin wasn’t even on the same side of the city as the street I knew I was going to.

It used to work flawlessly. What the fuck are they thinking?

Anyway, I deleted that shit. Using Apple Maps until I find a FOSS navigation app I like.

deleted by creator

The just desserts might be served after the bailout

Little filter sounds preferable to the big filter

To be clear, they did the Test at the same distance.

They didn’t mention the tumbling thing, but I assume that’s just because it’s a higher level concept than the demographic the video was designed for would be familiar with. (I don’t really have any knowledge on that aspect myself, not trying to say I do).

As far as where the bullet hit, I’m going just off of the video and official narrative. In the video it looks like he’s having a fencing response, which would indicate that he was hit with force and not just glanced/grazed.

In the official narrative they claim that the bullet got lodged in one of his vertebrae and stopped because his neck was “abnormally resilient”, or because “that’s what god wanted” depending on who you ask.