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Artificial Intelligence: Algorithms That Make Machines Intuitive
Slowly, computers are being taught intuition and reasoning. An example of an early success of this work is an algorithm developed by Max Kanter and his thesis advisor, Kalyan Veeramachaneni, at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The algorithm called the Data Science Machine, tries to find buried patterns in data analysis that typically would require guidance requiring human intuition.
The MIT algorithm was entered in three competitions, pitted against human teams, for making predictions from data sets. The algorithm performed better than 615 of the 906 teams that participated, and of the three trials, came in with accuracy ratings of 94, 96 and 87. The algorithm, when used in the trials, took anywhere from two to twelve hours of computation time, compared to, in some cases, months that were spent on the problems by humans.
Kalyan Veeramachaneni commented on the results of the competition, saying that “what we observed from our experience solving a number of data science problems for industry is that one of the very critical steps is called feature engineering. The first thing you have to do is identify what variables to extract from the database or compose, and for that, you have to come up with a lot of ideas.”
Margo Seltzer, a professor of computer science at Harvard University, said that “the Data Science Machine is one of those unbelievable projects where applying cutting-edge research to solve practical problems opens an entirely new way of looking at the problem. I think what they’ve done is going to become the standard quickly — very quickly.”
Kanter said that “we view the Data Science Machine as a natural complement to human intelligence. There’s so much data out there to be analyzed. And right now it’s just sitting there not doing anything. So maybe we can come up with a solution that will at least get us started on it, at least get us moving.”
