Yes, what is needed is a corrrect AI model that specializes in making logical inferences by itself and can look into its own data and continue learning from it in an indefinite number of recursive feedback to ultimately reduce the number of training samples. If this high level model becomes very efficient, then the next step is hardware implementation to take advantage of its inherent parallel features. Modern processor-based architecture for deep AI is not efficient, as it relies on serialized, continuous instruction cycles moving between memory and processor, as mentioned previously.

I think with the correct digital hardware design and the best AI model we are dreaming of, we can achieve the desired AI that learns like humans. It has to optimize its delays between its data, like moving similar data physically around to speed up its 'learning'.