The Arrival of AI
While I definitely agree that advances in ML in the last 20 extremely important (and potentially revolutionary), I think this article misses the mark in a few places.<p>&gt;Now, instead of humans designing algorithms to be executed by a computer, the computer is designing the algorithms. (Albeit guided by human-devised algorithms)<p>This line is way off in both tone and substance. On tone, it <i>really</i> underplays the human effort involved in effective machine learning (as it is practiced in 2017) and anthropomorphizes &quot;machines&quot; to an unreasonable extent. In substance, I fail to see how a machine that &quot;designs its own algorithms&quot; according to an algorithm designed and implemented by a human is <i>fundamentally</i> different than an algorithm coded directly by a human. To use the author&#x27;s example, machine learning allows humans to build complex software systems in less time just as a bicycle allows humans to cover more distance with less energy. It&#x27;s a big improvement, but it&#x27;s not, say, teleportation.<p>&gt;it is only now that the machines are creating themselves, at least to a degree. (And, by extension, there is at least a plausible path to general intelligence)<p>I could not disagree more strongly with this addendum. Simply put, I fail to see <i>any</i> path from state-of-the-art ML&#x2F;DL research today to AGI, and I would even go so far as to say that humans have made approximately zero progress on this task since it was first formulated in the 50s. I think we know about as much about &quot;intelligence&quot; (and consequently, what would constitute AGI) as star-gazers in ancient times know about the universe. That&#x27;s not to say that it will take millennia to invent AGI, but the path to get there is probably quite orthogonal to modern ML research.