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redonihunter2 days ago
in which generating something with AI can be reasonably called "stealing"

Basically never. But since stealing sounds worse than plagiarism, people arguing against it, are using the worse sounding offense. Also, the stealing argument is usually used for the training data and not the output. Some models were trained with data that was not quite meant for public access. Plus the whole debate about giving consent for machines to learn from your work. All these are not stealing as such. That word has lost a lot of meaning over the years. If I steal something from you, you no longer have it.

There is no "training"

I am not sure how you mean that. Your hypderdimensional math is using matrix multiplication or something like that, is it not? How are the values in those matrixes filled? To calculate probabilities, you need data to derive the chances. And you need a mapping from natural language to whatever the system uses internally. Guess that's tokens.

But maybe you were very specific for this movie transformation thing. Maybe it does not even count as "ai" in the sense it is so often used these days. And that is "generative ai" and means using a large language model or something related to it. Whatever is under the hood there, those things are trained to have a "model" which would be the map to navigate your hyperspace.

There is no "output", only the input transformed.

i * m = o

There is an m. You need a model to transform your input into an output. And you are right in the way that if you keep record of your random seed and your exact parameters and the model, you will reproduce the exact same output. So in a way, it's just like "there is no 21", only a transformed 7.

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Durance Gaming2 days ago

@redonihunter - So forgive me, but also correct me, on my jargon here. My entry point into higher mathematics was probably the most round-about way possible. I am still adapting my vocabulary. What I speak to here, I am referring to a sense of "invariability".

Just from my own perspective, the term "training" implies something I have not yet witnessed. The weight calibrations feel like exactly that, calibrations. When I sit an think about it, I suppose I was harsh on this term. I think I may have sort of "flash-formed" direct connections to the overall epiphany because it all hit me at once. When I look at modern AI, all I can seem to see is something like this:

Vectors ~= Coordinates

Matrix ~= Map

Weighted Matrix ~= Re-drawing or re-calibrating the map

Weights ~= Calibration

Bias ~= Threshold or Condition

Knowledge Base ~= Packaged Guidance System

Temperature ~= How Far You Are Allowed To Arrive From An Intended Destination

Then you add in the softMax? That looks like some kind of Sci-Fi high-dimensional probability drive where every "neuron" is like a different landing point and every "synapse" is the jump through hyperspace. Then, at the final layer, whichever little spaceship warped closest to the desired token space wins. I mean, we're talking passing the mathematical expression of a thing through high-dimensional space, nudged along by little weighted bumps and then locked into better and better patterns of navigation. This isn't even pattern recognition, this is something else entirely... Like... It reminds me of how water can erode rock and carve channels. Almost like the frozen weights represent some sort of "tunneling" of some kind.

But anyways, it appears that nothing ever actually touches the network itself. Like, no one ever let it upload files and chew up color patterns to mimic stuff with common pattern recognition. None of that stuff. You know what I mean?

And by "there is no output" I know that's a bit of a stretch but literally speaking, the output is the input transformed by this process. Take a ChatGPT conversation for example. Every token (let's say word) activated is a lightning-fast automation of this mathematical process. Every word it prints, it has fed the entire conversation back in over and over. Including the words it, itself generated.

Talking to a chat AI is not a conversation. It is a composition. This is why context management is so important. It's why it "lies". It's why it has "memory" problems... There is no memory. There's nothing to remember.

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redonihunter2 days ago
Just from my own perspective, the term "training" implies something I have not yet witnessed. The weight calibrations feel like exactly that, calibrations.

Calibration is a feedback loop of fitting your model to the data. In statistics, that's a bad thing. For ai, that is how they are trained. The end result is the calibration values.

If you calibrate your model on weather patterns or if you calibrate it on how words appear in texts, those will be different values inside your hard coded model, that now can do this hyper dimensional math things to predit what comes next. Like an answer to a question. Or if it rains tomorrow.

But anyways, it appears that nothing ever actually touches the network itself.
There is no memory. There's nothing to remember.

Yes and no.

The model itself is not changed by using it. It really acts like a black box transformator where you put in something and get out a response specific to that something.

And it does have a memory for your session, but actually it does not have a memory. Just as you said, the previous text is just fed in again.

I recently saw a good explanation for this memory thing.

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Durance Gaming1 day ago

Ah, I see the disconnect and I think it's my own fault. Everything you are saying is totally true, but I think we are talking about two different parts of the same overall thing. You said "the model". I know exactly what you mean with your weather example and it's kind of a big deal when you think about it. I always wondered how scientists did "studies" and "research" with AI with all it's current limitations but at the time, I was still stuck on thinking AI = Chat Bot or Image Generator or what-have-you. The input vectors can represent anything and whether or not the system can produce anything useful with the data--that's all down to the training. But this isn't what I'm trying to point at.

Not the model. The model is a combination of multiple processes. There is a surrounding framework involved.

I'm talking about the system at the core of it all. Not just ChatGPT or whatever, but any feed-forward neural network at all. 

The very concept of neural network technology appears to be an invariable expression of the idea of "navigation". As a matter of fact, there's nothing stopping anyone from building a 4x4 input neural network specifically for navigating the probabilities of a 4D environment. Once you cross that 4th dimension, you are plotting points in higher-dimensional space. That, as far as I can tell, is the function of a feed-forward of a neural network. I hate to reduce such a neat thing to such a description but... It's like a fancy graphing calculator. You could do the math by hand yourself, but you wouldn't want to lol.

I hope that makes sense.

"And it does have a memory for your session, but actually it does not have a memory. Just as you said, the previous text is just fed in again."

This part, I gotta push back on--respectfully--but hear me out: That's not memory. That's repetition. Which why it only works in the same session. Like I said, it isn't a conversation it's co-creating a composition but you go once and ChatGPT goes ~34,000 times per word of it's "replies". It isn't talking to you or me, it's talking to the conversation as a whole. New session, different co-composed conversation.

I noticed this before I studied neural networks, when I got real into setting up my own models on my PC.

The more "memory" my models "remembered", the worse they performed. Tracking the source, sure enough, there was no real "memory" but instead just brute-force reapplication of data over and over and over, chewing up token limits and taking up context space.

In my opinion, "memory" is one of the worst things you could possibly give a modern AI in any business environment. Any access to irrelevant context, in any way, opens the door to compounding instability. The current way of faking "memory" is probably what leads to a lot of cybersecurity woes.

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Durance Gaming1 day ago

Forgive me for the double reply, I read your message first then replied and am now watching the video you posted.

So far, immediately, there's a problem and maybe it's half-mine. This explanation is not what a "token" is. At least not in the system architecture sense. But now I am connecting the dots... Ok, so they call the limit a "token" limit because they charge you per token (navigation point) reached at the end of each full pass. Ok... Still watching...

Yep. A token isn't always a full word. Right. Still viewing...

Yeeeaaahhh dude! They were DECIDED and realizing that blew my mind. From my perspective, it's like writing a map of a place you've never been and then navigating probability until the landscape matches the map you drew... It's wild!

Boom--right there! Did you catch that at 4:04 to 4:10? "It doesn't care, they're just input tokens" That right there, the "it" he's talking about is the neural network. It doesn't care because it's a high-dimensional probability drive. The output is repurposed through the surrounding framework into "intelligence" and "behavior". This is what I am pointing to, the actual system itself before any arbitrary tokens are involved. The tokens are part of the framework, not the system itself... Still watching...

So, I don't want anyone to take this the wrong way... I'm not saying it's a conspiracy or anything... But the very next thing the guy does if you listen to his words and also pay attention to his body language: "But that's not even the interesting part" and he waves it away with a physical gesture. Before I taught myself linear algebra, I came from a 30+ year background of psychology and philosophy (b.s. in humanities) and that dude got uncomfortable when he realized where he was about to have to go to explain "why" the tokens don't matter. I don't think it's any kind of conspiracy, I think it might be a sense of embarrassment like the Tech world doesn't want to admit how far away from AGI modern AI architecture actually is.

I did watch the rest of the video but the rest of it revolves around token pricing... Okay... I see what's going on now. There is no real measurable way to price the usage of something like a modern AI because it's literally just automated calculations. So I can see the logic behind charging by the token.

Something isn't adding up, man... You don't need all these data centers and all this stuff to achieve something like ChatGPT. Even if everyone in the world was using it, yeah you'd need to scoop up all the RAM and processing power you could but you don't need all this storage space unless you're saving every single output of the neural network thought process and every single conversation.

I dunno... Anyways, that was a good video. I get it now, the token pricing versus actual tokens. Makes sense.

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redonihunter1 day ago

In context the video was just a good explanation for the memory concept. Or rather the lack of memory. A session grows rapidly in token usage, because it has to be fed in again and again. The model is not fine tuned to you by usage, it "forgets".

It also explains, why excessive use of such a system needs so much computing hardware and electricity. And doing a little chatting with chatgpt does not do this. But trying to replace a software developer with ai will do that.

You seem to fuss about the word token or the definition. It's just the bits or atoms of language. A chatbot has to operate on those pieces, so measuring the usage of the bot is easiest done by counting how many pieces a session used.

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