Ali Rahimi, a researcher in artificial intelligence (AI) at Google in San Francisco, California, took a swipe at his field last December—and received a 40-second ovation for it. Speaking at an AI conference, Rahimi charged that machine learning algorithms, in which computers learn through trial and error, have become a form of “alchemy.” Researchers, he said, do not know why some algorithms work and others don’t, nor do they have rigorous criteria for choosing one AI architecture over another. Now, in a paper presented on 30 April at the International Conference on Learning Representations in Vancouver, Canada, Rahimi and his collaborators document examples of what they see as the alchemy problem and offer prescriptions for bolstering AI’s rigor.
“There’s an anguish in the field,” Rahimi says. “Many of us feel like we’re operating on an alien technology.”
The issue is distinct from AI’s reproducibility problem, in which researchers can’t replicate each other’s results because of inconsistent experimental and publication practices. It also differs from the “black box” or “interpretability” problem in machine learning: the difficulty of explaining how a particular AI has come to its conclusions. As Rahimi puts it, “I’m trying to draw a distinction between a machine learning system that’s a black box and an entire field that’s become a black box.”
I have blogged about this in the past. The idea that you throw all your data into a big box and process it to come up with true insights is largely hogwash. I have spent most of my 50 year career on this subject and proven over and over again that you must first start with a hypothesis and test it to see if there is a relationship. In most cases, as you do this, you find even what you thought was a simple relationship is more complex. Then, once you truly know what is true, you can begin to build a learning algorithm that will separate the data into groups of true and false.
If your organization has a big data initiative, please reach out to me and let me help you before your black box thinking kills your credibility.