No, they take exponentially increasing resources as a consequence of having imperfect recall. Smaller models have “worse” recall. They’ve been trained with smaller datasets (or pruned more).
As you increase the size of the model (number of “neurons” that can be weighted) you increase the ability of that model to retain and use information. But that information isn’t retained in the same form as it was input. A model trained on the English language (an LLM, like ChatGPT) does not know every possible word, nor does it actually know ANY words.
All ChatGPT knows is what characters are statistically likely to go after another in a long sequence. With enough neurons and layers combined with large amounts of processing power and time for training, this results in a weighted model which is many orders of magnitude smaller than the dataset it was trained on.
Since the model weighting itself is smaller than the input dataset, it is literally impossible for the model to have perfect recall of the input dataset. So by definition, these models have imperfect recall.
In other words they require exponentially more input because the AI doesn’t know what it is looking at.
It uses its perfect recollection of that input to create a ‘model’ of what a face should look like and stores that model like a collage of all the samples and then uses that to reproduce a face.
Well, what you described is simply not a perfect recollection. It is many small tidbits of information that combined together can make a larger output.
If our brains worked exactly the same as AI programming then AI wouldn’t be needed because it would be no different than how we are doing things without AI.
I feel like you keep misrepresenting what I’m saying. Nowhere did I say that our brains work completely and exactly the same as AI. However, we do learn in much the same way. By amortizing small amounts of information and drawing connections between them
No, they take exponentially increasing resources as a consequence of having imperfect recall. Smaller models have “worse” recall. They’ve been trained with smaller datasets (or pruned more).
As you increase the size of the model (number of “neurons” that can be weighted) you increase the ability of that model to retain and use information. But that information isn’t retained in the same form as it was input. A model trained on the English language (an LLM, like ChatGPT) does not know every possible word, nor does it actually know ANY words.
All ChatGPT knows is what characters are statistically likely to go after another in a long sequence. With enough neurons and layers combined with large amounts of processing power and time for training, this results in a weighted model which is many orders of magnitude smaller than the dataset it was trained on.
Since the model weighting itself is smaller than the input dataset, it is literally impossible for the model to have perfect recall of the input dataset. So by definition, these models have imperfect recall.
In other words they require exponentially more input because the AI doesn’t know what it is looking at.
It uses its perfect recollection of that input to create a ‘model’ of what a face should look like and stores that model like a collage of all the samples and then uses that to reproduce a face.
It’s perfect recollection with an extra step.
Well, what you described is simply not a perfect recollection. It is many small tidbits of information that combined together can make a larger output.
That’s exactly how our brains work too
If our brains worked exactly the same as AI programming then AI wouldn’t be needed because it would be no different than how we are doing things without AI.
I feel like you keep misrepresenting what I’m saying. Nowhere did I say that our brains work completely and exactly the same as AI. However, we do learn in much the same way. By amortizing small amounts of information and drawing connections between them