We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.
For the last few years, AI has been almost synonymous with deep learning (DL). We’ve seen AlphaGo touted as an example of deep learning. We’ve seen deep learning used for naming paint colors (not very successfully), imitating Rembrandt and other great painters, and many other applications. Deep learning has been successful in part because, as François Chollet tweeted, “you can achieve a surprising amount using only a small set of very basic techniques.” In other words, you can accomplish things with deep learning that don’t require you to become an AI expert. Deep learning’s apparent simplicity–the small number of basic techniques you need to know–makes it much easier to “democratize” AI, to build a core of AI developers that don’t have Ph.D.s in applied math or computer science.
But having said that, there’s a deep problem with deep learning. As Ali Rahimi has argued, we can often get deep learning to work, but we aren’t close to understanding how, when, or why it works: “we’re equipping [new AI developers] with little more than folklore and pre-trained deep nets, then asking them to innovate. We can barely agree on the phenomena that we should be explaining away.” Deep learning’s successes are suggestive, but if we can’t figure out why it works, its value as a tool is limited. We can build an army of deep learning developers, but that won’t help much if all we can tell them is, “Here are some tools. Try random stuff. Good luck.”
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