"Solved" is pretty broad (as is "AI") but deep learning, specifically, performs well (SotA) on a number of benchmarks in speech, language and image recognition.

"Solved" is pretty broad (as is "AI") but deep learning, specifically, performs well (SotA) on a number of benchmarks in speech, language and image recognition. Some challenges immediately come to mind: 1. The pace of published research is pretty fast right now. This makes it difficult to know where the research fits in when solving problems. It'll probably take a few years before we know where to use many of the approaches published last year. 2. Iteration performance (trying many new things quickly) is improving with high-level frameworks but still lots of work to do here. Since we don't know where a lot of research fits it's not always apparent which methods work best (and "best" changes every quarter). 3. We're still missing theoretical foundations for much of deep learning. This is useful, not just for research, but to know what can and cannot work with current approaches. 4. Model architectures are still based largely on trial-and-error, intuition and search.