From Alpha Go to cancer detection to data center efficiency, we talk to analytics practitioner and SAS CTO Oliver Schabenberger about what AI can do and what it can't do. Here's what he said.
Are killer machines coming to annihilate mankind? Are we headed for a dystopian future where robots are our overlords? Are the Cylons already among us? Are concerns voiced by industry icons such as Elon Musk, who has donated millions to The Future of Life Institute, warranted?
Oliver Schabenberger recently added a more measured voice to this debate in this commentary piece that he wrote for InformationWeek, pointing out that machines "are not surpassing us in thinking or learning." Schabenberger is the CTO of analytics software company SAS, and InformationWeek recently sat down with him to find out more about his thoughts on the opportunities presented by AI and more. Here's what he said.
First, in terms of definitions, Schabenberger notes that AI is really about using a computerized system to perform a human task. It's something that we could do ourselves but we decide to implement it through a computerized system. That definition is really different from creating a "thinking machine."
Rather, "the revolution is that we are now able to do those tasks with an accuracy that was previously not possible," Schabenberger told me in an interview. "We are interacting with our devices now with voice. Some years ago the accuracy was just not there for that to be a satisfying interaction, and now it is. And that changes our perception."
But these new functionalities are still very limited, according to Schabenberger. For instance, when it comes to understanding language, machines don't have context to understand conversation.
"The systems we are building with deep learning right now don't understand context," he said. "If you interact with a chat bot, you will learn very quickly that they don't understand the humanness of conversations. They can't understand sarcasm."
(At least one project is underway to help bots learn the art of sarcasm.)
But while machines may be limited in the art of conversation, satire, and wit, they excel at pattern recognition and processing huge volumes of data. Right now, that's where organizations are finding great opportunity to deploy AI. One application, for instance, is in looking at human moles to help detect those that exhibit the telltale signs of being melanomas.
"When a machine looks at a melanoma it doesn't see 'area, border, and color,'" Schabenberger said, referring to what doctors look for when examining human moles for signs of cancer. "It just sees patterns and pixels. When we fed it images over and over again, it recognized the difference between a mole and a melanoma because we call it that. That is top-down, supervised training." We define melanoma for the machine and show it images that fit that description.
