It’s an easy mistake to make: both things are called “AI”, after all. But you wouldn’t study manually-written FPS bots circa 2000s, or MNIST-classifier CNNs circa 2010s, and claim that your findings generalize to how LLMs circa 2020s work. By the same token, LLM findings do not necessarily generalize to AGI.
My understanding is that many of those studying MNIST-classifier CNNs circa 2010 were in fact studying this because they believed similar neural-net inspired mechanisms would go much further, and would not be surprised if very similar mechanisms were at play inside LLMs. And they were correct! Such studies led to ReLU, backpropagation, residual connections, autoencoders for generative AI, and ultimately the scaling laws we see today.
If you traveled back to 2010, and you had to choose between already extant fields, having that year’s GPU compute prices and software packages, what would you study to learn about LLMs? Probably neural networks in general, both NLP and image classification. My understanding is there was & is much cross-pollination between the two.
Of course, maybe this is just a misunderstanding of history on my part. Interested to hear if my understanding’s wrong!
After an exchange with Ryan, I see that I could’ve stated my point a bit clearer. It’s something more like “the algorithms that the current SOTA AIs execute during their forward passes do not necessarily capture all the core dynamics that would happen within an AGI’s cognition, so extrapolating the limitations of their cognition to AGI is a bold claim we have little evidence for”.
So, yes, studying weaker AIs sheds some light on stronger ones (that’s why there’s “nearly” in “nearly no data”), so studying CNNs in order to learn about LLMs before LLMs exist isn’t totally pointless. But the lessons you learn would be more about “how to do interpretability on NN-style architectures” and “what’s the SGD’s biases?” and “how precisely does matrix multiplication implement algorithms?” and so on.
Not “what precise algorithms does a LLM implement?”.
the algorithms that the current SOTA AIs execute during their forward passes do not necessarily capture all the core dynamics that would happen within an actual AGI’s cognition, so extrapolating the limitations of their cognition to future AGI is a bold claim we have little evidence for
I suggest putting this at the top as a tl;dr (with the additions I bolded to make your point more clear)
My understanding is that many of those studying MNIST-classifier CNNs circa 2010 were in fact studying this because they believed similar neural-net inspired mechanisms would go much further, and would not be surprised if very similar mechanisms were at play inside LLMs. And they were correct! Such studies led to ReLU, backpropagation, residual connections, autoencoders for generative AI, and ultimately the scaling laws we see today.
If you traveled back to 2010, and you had to choose between already extant fields, having that year’s GPU compute prices and software packages, what would you study to learn about LLMs? Probably neural networks in general, both NLP and image classification. My understanding is there was & is much cross-pollination between the two.
Of course, maybe this is just a misunderstanding of history on my part. Interested to hear if my understanding’s wrong!
After an exchange with Ryan, I see that I could’ve stated my point a bit clearer. It’s something more like “the algorithms that the current SOTA AIs execute during their forward passes do not necessarily capture all the core dynamics that would happen within an AGI’s cognition, so extrapolating the limitations of their cognition to AGI is a bold claim we have little evidence for”.
So, yes, studying weaker AIs sheds some light on stronger ones (that’s why there’s “nearly” in “nearly no data”), so studying CNNs in order to learn about LLMs before LLMs exist isn’t totally pointless. But the lessons you learn would be more about “how to do interpretability on NN-style architectures” and “what’s the SGD’s biases?” and “how precisely does matrix multiplication implement algorithms?” and so on.
Not “what precise algorithms does a LLM implement?”.
I suggest putting this at the top as a tl;dr (with the additions I bolded to make your point more clear)