&gt; Much higher (but still short of general intelligence, obviously) would be beating the best humans at language translation, proving math theorems, or answering factual questions phrased in natural language. Then, if and when computers can write novels which you&#x27;d prefer to read over the best human author, now we&#x27;re talking.<p>There&#x27;s a lot of similarity. Take math theorem proving, which is an active area of machine learning; the algorithms tend at the moment to look something like create a tree of possible theorems, then the algorithm tries to traverse down the tree towards the target theorem by evaluating each branch by the probability it leads towards the target. Because of the exponential explosion, it&#x27;s very important to select good theorems to expand at each level of the tree, and the ML algorithms can learn characteristics of good intermediate theorems by learning on the existing corpuses of machine-checkable proofs, which gives them much higher success rates in reaching target theorems. So right now the best theorem-provers can already reprove a lot of existing proofs. But they&#x27;re using stuff like SVMs last I saw, and were much less sophisticated than a deep q-learner.<p>Some links: &quot;Theorem Proving in Large Formal Mathematics as an Emerging AI Field&quot;, Urban &amp; Vyskocil 2012; &quot;Mathematics in the Age of the Turing Machine&quot;, Hales 2014 <a href="http:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;1302.2898v1.pdf" rel="nofollow">http:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;1302.2898v1.pdf</a> ; &quot;Josef Urban on Machine Learning and Automated Reasoning&quot; <a href="http:&#x2F;&#x2F;intelligence.org&#x2F;2013&#x2F;12&#x2F;21&#x2F;josef-urban-on-machine-learning-and-automated-reasoning&#x2F;" rel="nofollow">http:&#x2F;&#x2F;intelligence.org&#x2F;2013&#x2F;12&#x2F;21&#x2F;josef-urban-on-machine-le...</a>