A  recent article in the Financial Times called out the growing number of AI services available in the cloud, and the growing conviction at the leadership levels of Google, Microsoft and IBM that cloud-based AI services are the wave of the future.  Enabled by ubiquitous cloud servers, storage, and big data, AI services will be incorporated into programs across the enterprise, in mobile apps, and ...  Well, everywhere.  "The next great disrupter," said the FT.  As big a disrupter as electricity or the steam engine says a well-known professor at MIT. But are the tools available today really that transformative?  What about what's coming next?

Voice and Language

The tools that IBM, Microsoft and Google have made available already are truly game changers -- but are still narrow in scope compared to what is to come.  Speech Recognition and Natural Language Processing have made huge advances in recent years and are now available on several platforms for your UX-Creating pleasure. These are core AI methods behind Siri-like assistants and the new crop of email-reading task-bots like x.ai.

Computer Vision

Computer vision has also improved significantly.

The big guys and a number of startups like Clarifai and Imagga now offer image tagging services that can ingest images and identify objects or scene composition with tolerable accuracy making visual search much easier and more accurate.  These still have a way to go though, in my opinion, before they are truly disruptive or transformative.

Data Analysis

And then there are a host of services offering cloud-based data analysis aimed at analyzing large amounts of data for specific vertical applications.  Amazon has exposed many of the algorithms developed for their own use as external services.  Google has opened up their AI development tools to encourage others to develop services offering AI in the cloud.

AI in the cloud that does more, with more

The next generation of cloud-based AI services is about to arrive - and it will include many diverse services that go far beyond the relatively narrow selection currently on offer.  Our own Reality AI is a good example -- cloud-based AI services for sensor data that does the work of a signal-processing engineer.   Using Reality AI, product and application developers can train up algorithms to spot complex vibration signatures (eg inside industrial equipment), spot specific sounds despite overwhelming background noise, identify both complex and simple motions using accelerometer data, even work with AC power and RF signals.

Other sources of transformative AI services in the cloud include a new startup from NYU's Gary Marcus that looks to use insights from how children learn to help AI generalize about the world the way people do, and another startup with a sort of a meta-AI tool that looks at the available data, automatically figures out what kind of predictive model will work best, then builds it.

We are only at the beginning....