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, and it doesn&#x27;t contain real news. Reproducible results, or it didn&#x27;t happen...<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 using it to set new records every year -- <a href="http:&#x2F;&#x2F;deeplearning4j.org&#x2F;accuracy.html" rel="nofollow">http:&#x2F;&#x2F;deeplearning4j.org&#x2F;accuracy.html</a> -- so we might as well rely on it. Ignoring big data in the pursuit of AI is like ignoring sunlight in the pursuit of clean energy.<p>Deep learning is one of the best ways we have to use that data. In a sense, deep is the new normal.