General purpose AI in business - too hard and waiting for its Netscape Moment

As I wrote in March, Google sees its cloud services as fueling the "democratization of AI" by abstracting many of the hard implementation details of building and using an AI software stack into cloud services. The early examples, such as Amazon AI, Azure Cognitive Services and Google Cloud Machine Learning Services are splendid examples of encapsulating sophisticated AI functions in a fairly straightforward service with an API wrapper. However, the high-level services mostly focus on the well-trodden paths of image and speech recognition; domains that have long catalyzed AI research. While such applications certainly have many uses in business, including for conversational interfaces as I detail here, they don't address the vast majority of business problems that could benefit from machine learning optimization and where applying AI still requires too much time and expertise. As the ARCHITECT blog rightly points out, AI research has often focused on games like Chess and Go, or handy add-ons to online consumer services like automatic image tagging and voice commands, not hard business problems.