Give any respectable machine learning algorithm a concrete scenario to optimize and it will blow human-based heuristics out of the water. But we as humans should continue to focus on what we do best — thinking creatively, building empathy for other humans — in order to guide machines in the right directions.
A friend asks for recommendations of restaurants that are good for a romantic night out. If you’re like most people, you probably jumped to a few salient features — cozy atmosphere, fancy and non-messy food, and maybe bonus points for shareable desserts. Based on the perceived importance of each of these features, you then remembered a few restaurants that do well on each of these areas and formulated a recommendation.
The same friend asks a black box machine learning algorithm for date night recommendations. The machine ingests as many business attributes as it has access to — distance from the user, ratings, and price, among numerous others — and trains the features on all of the users who have ever searched for “date night.” It then spits out an ordered list of a couple hundred restaurants.
It’s clear from this simplistic comparison that human intuition and machine learning excel in different ways. Our strength lies in the fact that we as humans have spent a lot of time building up the implicit shared context around “date night.” We know our friend well, we know that this is a crucial second date, and we’ve thought up of all of the details that could help make this a magical evening for her. At the end of the evening, we’ll also hear about it if our friend calls us to complain about the hour-long wait and the horrible parking situation, and the next time we’ll try to remember to factor in that information when recommending a restaurant.
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