I 90% agree, but if that were all there was to it, we should have been able to achieve the same level of success decades earlier, just by running our ML code lo

I 90% agree, but if that were all there was to it, we should have been able to achieve the same level of success decades earlier, just by running our ML code longer. Given enough time, old slow AI software should have produced the same analytical results as deep learning does today. But that's not the case. Deep nets can model vastly more information / state than any other AI/ML method. Once Hinton (and others) showed how to train NNs with more than three layers (ca. 2006) it was finally possible to learn and store all that state. Then with the rise of GPGPUs soon after, deep nets became efficient as well. Thereafter several tasks that had been infeasible even using curated information became amenable to mostly brute force learning strategies driven only by labeled examples -- just lots of 'em. The question now is how far can we extend DL's tools and examples. Are they sufficient to build higher level cognitive AI agents. Must AGI employ many thousands of deep nets? Or can all those specific-skill nets be folded together somehow into one unified "deep mind"? Like you, I'm doubtful that today's very specific successes in DL will lead to higher level cognition in the foreseeable future. That path isn't at all clear to me.