The idea that machines, whether physical or digital, will take over human jobs in the future is certainly not a new one, but Anthony Goldbloom's machine learning talk delves into the extent of that shift. Goldbloom himself works at a company that develops machine learning software, so he is in a unique position to understand and analyze the potential impact that machine will have on the workforce.
Though machine learning has lately become something of a buzz phrase in technology circles, it has been around for a few decades, albeit in simpler forms. In the early nineties, companies used machine learning to efficiently assess credit risk or sort through mail. Since then, the capabilities of machine learning have skyrocketed, with machines completing tasks previously thought to be the exclusive provenance of humans. For instance, Goldbloom's company helped to create a program that could grade high school essays just as accurately as human teachers could.
Goldbloom's lesson from his research is that humans have no chance of competing with machines on frequent, high volume tasks. However, humans can best machines when it comes to novel situations. These parameters have the potential to delineate the workforces of the future.
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