I enjoyed part 1 of Chollet's two articles today but am less fond of this one. It suggests that deep learning will expand from its present capabilities of reco

I enjoyed part 1 of Chollet's two articles today but am less fond of this one. It suggests that deep learning will expand from its present capabilities of recognizing patterns to one day master logical relations and employ a rich knowledge base of general facts, growing into a situated general problem solver that one day may equal or surpass human cognition. Maybe. But he then proposes that deep nets will rise to these heights of self-organization and purposefulness using one of the weakest and slowest forms of AI, namely evolutionary strategies? I don't think so. The many problems bedeviling the expansion of an AI's competence at one specific task into mastery of more general and more complex tasks are legend. Alas neither deep nets nor genetic algorithms have shown any way to address classic AGI roadblocks like: 1) the enormity of the possible solution space when synthesizing candidate solutions, and 2) the enormous number of training examples needed to learn the multitude of common sense facts common to all problem spaces, and 3) how to translate existing specific problem solutions into novel general ones. Wait, wait, there's more... These roadblocks are common to all forms of AI. The prospect of replacing heuristic strategies with zero knowledge techniques (like GA trial and error) or curated knowledge bases with only example-based learning is unrealistic and infeasible. Likewise, the notion that a sufficient number of deep nets can span all the info and problem spaces that will be needed for AGI is quite implausible. While quite impressive at the lowest levels of AI (pattern matching), deep learning has yet to address intermediate and high level AI implementation challenges like these. Until it does, there's little reason to believe DL will be equally good at implementing executive cognitive functions. Yes DeepMind solved Go using AlphaGo's deep nets (and monte carlo tree search). But 10 and 20 years before that IBM Watson solved Jeopardy and IBM Deep Blue solved chess. At the time, everyone was duly impressed. Yet today nobody is suggesting that the AI methods at the heart of those game solutions will one day pave the yellow brick road to AI Oz. In another 10 years, I predict it's just as likely that AlphaGo's deep nets will be a bust as a boom, at least when it comes to building deep AI like HAL 9000.