I&#x27;m glad people are working on better approaches to AI, but I don&#x27;t think articles like this, based on sources like Marcus, contribute much.<p>Mostly because Marcus isn&#x27;t willing to talk about what he&#x27;s working on right now. Hand-wavy stuff about how children learn, a tired analogy that&#x27;s been trotted out many times before. This article was too early. It doesn&#x27;t contain real news.<p>Fwiw, the statement below is simply untrue.<p>&gt; <i>“If you want to get a robot to learn to walk, or an autonomous vehicle to learn to drive, you can’t present it with a data set of a million examples of it falling over and breaking or having accidents—that just doesn’t work.”</i><p>The entire premise of Deepmind&#x27;s work combining DL and reinforcement learning shows that autonomous agents learn from millions of examples.<p>And in fact, data is not the limiting parameter here. Trying to create an AI that doesn&#x27;t need big data will be great for edge computing -- but we&#x27;re actually swimming in data, and we might as well make us of it. Ignoring big data in the pursuit of AI is like ignoring sunlight in the pursuit of clean energy. It&#x27;s everywhere. Why not use it?<p>Deep learning is one of the best ways we have to use that data. In a sense, deep is the new normal.