Initiatives such as language translation and image, facial, activity and emotion recognition - are based on predictive analytics that get more accurate as the data behind them gets richer.

"Looking ahead, new and established MI companies will use millions of internet images, videos and podcasts of people smiling, laughing, frowning, talking, arguing, holding hands, walking, playing football and so on as the basis for unprecedented emotion and activity recognition capabilities.

Software and hardware advances: It's long been known that neural networks and parallel processing would be important development tools of AI because they more closely resemble the way the human brain works.

Cloud business models: The emergence of machine learning business models based on the use of the cloud is the single biggest reason that the field is so energized today, the report said: "We are essentially seeing the merger of machine intelligence with cloud economics."

Before the cloud, most AI work was isolated and relatively high cost, but the economics of the cloud mean machine learning capabilities, such as recognizing faces or translating languages, will cheap and easy to use

"It is this realization that is triggering both the explosion of highly specialized MI start-ups, as well as the major machine intelligence pushes at Google, Facebook, Microsoft,Apple, IBM and their various global rivals."

Map the relevant MI services and technologies to your firm's value chain.

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