In the future envisioned by Wall Street and Silicon Valley alike, humans are just another externality. There are too many of us, asking for salaries and health care and meaningful work. Each victory we win for human labor, such as an increase in the minimum wage, makes us that much more expensive to employ, and supports the calculus through which checkout workers are replaced by touchscreen kiosks.
Where humans remain valuable, at least temporarily, is in training their replacements. Back in the era of outsourcing, domestic workers would cry foul when they were asked to train the lower-wage foreign workers who would shortly replace them. Today, workers are hardly aware of the way digital surveillance technologies are used to teach their jobs to algorithms.
This is what all the hoopla about “machine learning” is really about. The things we want our robots to do — like driving in traffic, translating languages, or collaborating with humans — are mind-bogglingly complex. We can’t devise a set of explicit instructions that covers every possible situation. What computers lack in improvisational logic, they must make up for with massive computational power. So computer scientists feed the algorithms reams and reams of data, and let them recognize patterns and draw conclusions themselves. Read more via Medium
