You’re humanizing the software too much. Comparing software to human behavior is just plain wrong. GPT can’t even reason properly yet. I can’t see this as anything other than a more advanced collage process.
Open used intellectual property without consent of the owners. Major fucked.
If ‘anybody’ does anything similar to tracing, copy&pasting or even sampling a fraction of another person’s imagery or written work, that anybody is violating copyright.
sampling a fraction of another person’s imagery or written work.
So citing is a copyright violation? A scientific discussion on a specific text is a copyright violation? This makes no sense. It would mean your work couldn’t build on anything else, and that’s plain stupid.
Also to your first point about reasoning and advanced collage process: you are right and wrong. Yes an LLM doesn’t have the ability to use all the information a human has or be as precise, therefore it can’t reason the same way a human can. BUT, and that is a huge caveat, the inherit goal of AI and in its simplest form neural networks was to replicate human thinking. If you look at the brain and then at AIs, you will see how close the process is. It’s usually giving the AI an input, the AI tries to give the desired output, them the AI gets told what it should have looked like, and then it backpropagates to reinforce it’s process. This already pretty advanced and human-like (even look at how the brain is made up and then how AI models are made up, it’s basically the same concept).
Now you would be right to say “well in it’s simplest form LLMs like GPT are just predicting which character or word comes next” and you would be partially right. But in that process it incorporates all of the “knowledge” it got from it’s training sessions and a few valuable tricks to improve. The truth is, differences between a human brain and an AI are marginal, and it mostly boils down to efficiency and training time.
And to say that LLMs are just “an advanced collage process” is like saying “a car is just an advanced horse”. You’re not technically wrong but the description is really misleading if you look into the details.
And for details sake, this is what the paper for Llama2 looks like; the latest big LLM from Facebook that is said to be the current standard for LLM development:
Well, given how we’re the ones that developed the models, they are deterministic as we know and can save and reproduce the random weights they are given during training, and we can use a debugger to step through every single step the models makes in learning and “thinking”, yes, we understand them.
We know the input, we can set the model to save the weight in checkpoints during training and can view them any time, and we can see weights of the finished model, and we can see the code.
If what you said about LLMs being completely black box were true, we wouldn’t be able to reproduce models, and each model would be unique.
But we can control every step of the training process, and we can reproduce not just the finished model, but the model at every single step during training.
We created the math, we created the training sets, we created the code and we can see and modify the weights and any other property of the model.
Look, I understand why you think this. I thought this too when I was first beginning to learn machine learning and data science. But I’ve now been working with machine learning models including neural networks for nearly a decade, and the truth is that is nearly impossible to track the path of an input to a given output in machine learning models other than regression-based models and decision tree-based models.
There is an entire field of data science devoted to explaining how these models arrive at their conclusions. It’s called “explainable AI” or “xAI”, and I have a few papers that I’ve published in exploring the utility of them. The basic explanation for how they work is that we run hundreds of thousands of different models and then do statistical analysis to estimate why the models arrived at their conclusion. It isn’t an exact science, however.
Again, we have the input, we have the math and code that make it work, we have the weights, we have everything.
Would it take a lot of time to backtrack and check why we got a given output to an input? Yes, maybe an inordinate amount of time. But it can be done. It’s only black box because nobody has the time (likely years to decades) to wade through the layers of a finished model to check every node and weight.
The whole thing at its core is mathematics. It’s a series of steps, that can be listed and reviewed each step of the way if we wanted. It’s just that if would take too much time.
If what you said were true, we couldn’t reproduce models. And since we can…
It isn’t an exact science, however.
So if math and computer science isn’t an exact science, what is?
You’re humanizing the software too much. Comparing software to human behavior is just plain wrong. GPT can’t even reason properly yet. I can’t see this as anything other than a more advanced collage process.
Open used intellectual property without consent of the owners. Major fucked.
If ‘anybody’ does anything similar to tracing, copy&pasting or even sampling a fraction of another person’s imagery or written work, that anybody is violating copyright.
So citing is a copyright violation? A scientific discussion on a specific text is a copyright violation? This makes no sense. It would mean your work couldn’t build on anything else, and that’s plain stupid.
Also to your first point about reasoning and advanced collage process: you are right and wrong. Yes an LLM doesn’t have the ability to use all the information a human has or be as precise, therefore it can’t reason the same way a human can. BUT, and that is a huge caveat, the inherit goal of AI and in its simplest form neural networks was to replicate human thinking. If you look at the brain and then at AIs, you will see how close the process is. It’s usually giving the AI an input, the AI tries to give the desired output, them the AI gets told what it should have looked like, and then it backpropagates to reinforce it’s process. This already pretty advanced and human-like (even look at how the brain is made up and then how AI models are made up, it’s basically the same concept).
Now you would be right to say “well in it’s simplest form LLMs like GPT are just predicting which character or word comes next” and you would be partially right. But in that process it incorporates all of the “knowledge” it got from it’s training sessions and a few valuable tricks to improve. The truth is, differences between a human brain and an AI are marginal, and it mostly boils down to efficiency and training time.
And to say that LLMs are just “an advanced collage process” is like saying “a car is just an advanced horse”. You’re not technically wrong but the description is really misleading if you look into the details.
And for details sake, this is what the paper for Llama2 looks like; the latest big LLM from Facebook that is said to be the current standard for LLM development:
https://arxiv.org/pdf/2307.09288.pdf
You’re mystifying and mythologising humans too much. The learning process is very equivalent.
amazing
Well, there still a shit ton we don’t understand about human.
We do, however, understand everything about machine learning.
LOL
We understand less about how LLMs generate a single output than we do about the human brain. You clearly have no experience developing models.
Well, given how we’re the ones that developed the models, they are deterministic as we know and can save and reproduce the random weights they are given during training, and we can use a debugger to step through every single step the models makes in learning and “thinking”, yes, we understand them.
We can not however, do that for the human brain.
You really don’t understand how these models work and you should learn about them before you make statements about them.
Machine learning models are, almost by definition, non-deterministic.
We know the input, we can set the model to save the weight in checkpoints during training and can view them any time, and we can see weights of the finished model, and we can see the code.
If what you said about LLMs being completely black box were true, we wouldn’t be able to reproduce models, and each model would be unique.
But we can control every step of the training process, and we can reproduce not just the finished model, but the model at every single step during training.
We created the math, we created the training sets, we created the code and we can see and modify the weights and any other property of the model.
What exactly do we not understand?
Look, I understand why you think this. I thought this too when I was first beginning to learn machine learning and data science. But I’ve now been working with machine learning models including neural networks for nearly a decade, and the truth is that is nearly impossible to track the path of an input to a given output in machine learning models other than regression-based models and decision tree-based models.
There is an entire field of data science devoted to explaining how these models arrive at their conclusions. It’s called “explainable AI” or “xAI”, and I have a few papers that I’ve published in exploring the utility of them. The basic explanation for how they work is that we run hundreds of thousands of different models and then do statistical analysis to estimate why the models arrived at their conclusion. It isn’t an exact science, however.
Again, we have the input, we have the math and code that make it work, we have the weights, we have everything.
Would it take a lot of time to backtrack and check why we got a given output to an input? Yes, maybe an inordinate amount of time. But it can be done. It’s only black box because nobody has the time (likely years to decades) to wade through the layers of a finished model to check every node and weight.
The whole thing at its core is mathematics. It’s a series of steps, that can be listed and reviewed each step of the way if we wanted. It’s just that if would take too much time.
If what you said were true, we couldn’t reproduce models. And since we can…
So if math and computer science isn’t an exact science, what is?