Recently I participated in the ThinkX program sponsored by SAP Innovation Partnership Program and Singularity University. The origins of ThinkX touch the xPrize competitions and other design events which seek to leverage exponential learning. Over the past 5 years, SAP and SU have hosted over 800 SAP executives, leaders and employees to events around the world. In this second article of a three-part series I will explore the big trends, themes and take-aways - including how they may impact industry and society in the coming years. Advances in cognitive science, particularly in areas such as Artificial Intelligence (AI) and blockchain will increase at an accelerated rate.

Up until the late 80s and early 90s hardware was King: IBM,Intel, Texas Instruments were market leaders. Devices from Japanese corporations like Sony, Toshiba, and Panasonic were everywhere. But the use cases of most of their devices didn't evolve significantly as the tastes of consumers around the world shifted to software and mobile devices. The shift to software, when it came, favored companies like Microsoft, Oracle, and SAP. The latter then missed the shift to mobile, opening the door for companies like Apple, Google, and Samsung.

Tech companies and investors have recently been piling money into artificial intelligence--and plenty has been trickling down to chip maker Nvidia. The company's revenues have climbed as it has started making hardware customized for machine-learning algorithms and use cases such as autonomous cars. At the company's annual developer conference in San Jose, California, this week, the company's CEO Jensen Huang spoke to MIT Technology Review about how the machine-learning revolution is just starting. Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue?

Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue? Recent research results from applying machine learning to diagnosis are impressive (see "An AI Ophthalmologist Shows How Machine Learning May Transform Medicine"). Your chips are already driving some cars: all Tesla vehicles now use Nvidia's Drive PX 2 computer to power the Autopilot feature that automates highway driving.

Tech companies and investors have recently been piling money into artificial intelligence--and plenty has been trickling down to chip maker Nvidia. The company's revenues have climbed as it has started making hardware customized for machine-learning algorithms and use cases such as autonomous cars. At the company's annual developer conference in San Jose, California, this week, the company's CEO Jensen Huang spoke to MIT Technology Review about how the machine-learning revolution is just starting. Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue?

Tech companies and investors have recently been piling money into artificial intelligence--and plenty has been trickling down to chip maker Nvidia. The company's revenues have climbed as it has started making hardware customized for machine-learning algorithms and use cases such as autonomous cars. At the company's annual developer conference in San Jose, California, this week, the company's CEO Jensen Huang spoke to MIT Technology Review about how the machine-learning revolution is just starting. Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue?