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midlightdenight | 3 years ago
The way I’ve interpreted the scaling hypothesis is that we will see emergent intelligence with larger nets through automated training alone. If we want a model to learn images we throw a larger net at it with training data.
The way I’ve interpreted some of these newer techniques to multi modality is that they are stitched together models. If we want a model to learn images we decide this and teach one model images and then connect it to the core model. There’s not a lot of emergent behavior due to scaling in this scenario.
With that perception I don’t see how gpt4 says anything about the scaling hypothesis. However I am not in this field and would be grateful to learn more.
concinds|3 years ago
So the goal should be: we've created a "language module" (LLMs) and a "visual perception module" (computer vision), but we also need to add a "logic module", a "reasoning module", an "empathy module", etc, while continuing to improve each.
I just don't see how you could get an LLM, no matter how advanced, to recognize a car. Even if it can describe cars (wheels, windshield, doors) it doesn't know what any of those components look like. It's like that old joke about philosophers being unable to define a chair beyond "I'll know it when I see it".
avereveard|3 years ago
these net can replicate at most the first step for now, even tuning by reingofrcement learning is more of a set up batch than an ongoing thing, and certainly not something they will be able to do in the context of a single problem but as part of a retraining
agi is still a fair bit away, I'm unsure if these super large architecture will ever get to replicate the second part of our brain, the flexibility while on the job, because of their intrinsic training mechanism.