Picked up from the podium
Oren Etzioni, Allen Institute for Artificial Intelligence: “Machine learning today is 99 percent human work … Superhuman performance on a narrow task is not human performance.” Etzioni’s cold splash of reality is important. AI has been so subjected to hype cycles that we all have unrealistic expectations about what it can accomplish. Right now, the reality is that machine learning, conversational systems, bots, etc., are a boon to humans trying to navigate too much information within a well defined, narrow field. Will they eventually take over the world? Etzioni’s research in the field indicates that AI researchers themselves think otherwise. He asked them: “When will AI (superintelligence) arrive?” Sixty-seven percent answered that it will take more than 25 years. Twenty-five percent said that it will “never” arrive. That’s a comforting result. Humans need all the help we can get in making sense of complex information landscapes. But it sounds like the final say is still up to us.
Bias and acceptable risk also emerged as major themes in a number of presentations. If algorithms are crafted by humans and training sets are selected by humans, how can we eliminate bias? Should we and/or should our machines be urged to make inherent biases explicit? Will we develop an understood spectrum of biased positions, along the lines of how we judge whether a publication is liberal or conservative today? And what constitutes acceptable risk as we move from the digital world to the real world and errors become a matter of life and death?
The question of trust underlies both of these discussions. Can we trust the data? Can we trust the cars that drive based on the data? Should we forfeit our data privacy in exchange for better search results, shopping or restaurant recommendations or medical diagnoses? These are not technical, but personal, societal and legal problems. Solutions may partially come from technology, but we can already see that the solutions to our human societal issues will lag well behind our technology breakthroughs.