Researchers agree that unleashing deep-learning models on the vast amounts of data available today has progressed artificial-intelligence capabilities to once unattainable levels. However, deep learning isn't the ideal technique for every application in which AI could provide significant performance gains over what humans could achieve alone. Earlier this year at the AI Frontiers conference in Santa Clara, California, we sat down with AI experts from some of the world's leading technology-first organizations to learn about other techniques researchers are exploring to expand the applications of AI. An edited version of their remarks follows. Mohak Shah, lead expert, data science, Bosch Research and Technology Center, North America: In terms of technology, I think acceleration is going to come when we put all the pieces together.

Researchers agree that unleashing deep-learning models on the vast amounts of data available today has progressed artificial-intelligence capabilities to once unattainable levels. However, deep learning isn't the ideal technique for every application in which AI could provide significant performance gains over what humans could achieve alone. Earlier this year at the AI Frontiers conference in Santa Clara, California, we sat down with AI experts from some of the world's leading technology-first organizations to learn about other techniques researchers are exploring to expand the applications of AI. An edited version of their remarks follows. Mohak Shah, lead expert, data science, Bosch Research and Technology Center, North America: In terms of technology, I think acceleration is going to come when we put all the pieces together.

Deep learning has allowed technologists to employ AI in numerous business and consumer applications, but researchers are working feverishly to master other techniques that will broaden AI's reach.