So your claim is that if a human child was given a subset of the text GPT-3 was fed — with no audio, video, or reinforcement feedback from its environment — the

So your claim is that if a human child was given a subset of the text GPT-3 was fed — with no audio, video, or reinforcement feedback from its environment — the child could learn English? The important point to observe here is that what a kid does is fundamentally different than what GPT-3 does: - GPT-3 learns the word that minimizes perplexity given some context - A kid learns the word that helps them accomplish some task in some environment. In the learning process, children get feedback from their environment - including the responses of other agents in the environment (eg Mom, Dad, friends, etc). This is going to be far more memory-intensive than some text files. Also important to observe is that the child's experience can't be reduced to the audio, video and haptic streams they get, because the audio and video stream depend on their own actions. So you need the conditional statements that were embedded in the environment the kid was learning in. All of which is to say this is a little heavier than ascii. Edit: From your other comments, it seems we agree that bigger neural networks with more training data are not going to yield some quantum leap where GPT-3 starts talking. I'm not at all suggesting that throwing more data at the existing architectures is a fruitful direction for understanding cognition. (In general I think NLP is utterly pointless as regards understanding cognition, and if that's your goal you should focus on vision and reinforcement learning.) But my point is that we can't expect that a smarter architecture on a smaller corpus will get us anywhere. If we ever develop machines that are intelligent in some robust sense of the word, their "training data" will most likely be a physical environment with other agents in it.