The upward mobility of artificial intelligence and machine learning in the mass consciousness can be described by the competitions they’ve competed in and won against humans.
The companies and games are well known: IBM Watson for chess and Jeopardy, Google DeepMind for the Chinese game Go; and the relatively new entrant OpenAI for video game Dota 2 against amateur and semi-pro players (human pros remain victors, for now).
As the wins progressed over the years, so too did the sophistication and combination of machine learning techniques, briefly: natural language processing, and supervised, unsupervised and reinforcement learning. The resulting headlines were predictably hyperbolic about the impending robot invasion and generally accompanied by “Terminator” graphics.
This is, of course, silly. Virtually every expert across the board of numerous industries, certainly in finance, will acknowledge that the true winning combination is human-machine, not one versus the other, albeit a very useful demonstration.
The “man versus machine” milestones over the years came hand in hand with putting those artificial intelligence technologies to work for humans. And though there may be a lot of cynicism about what AI can’t do (yet), what it is doing isn’t exactly chopped liver.