Coming to terms with the changing role of machines
“We have to start recognizing the inevitability of machines taking over more and more tasks that we used to do in the past. It’s called progress. Machines replaced farm animals and all forms of manual labor, and now machines are about to take over more menial parts of cognition. Big deal. It’s happening. And we should not be alarmed about it. We should just take it as a fact and look into the future, trying to understand how we can adjust.”
Garry Kasparov
The quote, said recently in a BBC interview entitled “Why the world should embrace AI” which includes a video interview, comes from a person who, in 1997, saw himself losing a tournament to a machine in a discipline, chess, in which he was the world champion.
The machine that beat Kasparov was not intelligent: it was simply able to calculate combinatorial scenarios and probabilities faster and more accurately than a human brain. A simple question of brute force to which we are already perfectly accustomed. Chess, by its very nature, is particularly sensitive to this kind of brute force: it is a task perfectly bounded and defined by clear and inflexible rules, in whose development the analysis of scenarios plays a fundamental role. A machine simply has more computing power than a person, allowing it to calculate and contemplate more scenarios.
Things have changed a lot since that game of chess. A machine is now capable of beating humans at general knowledge quizzes like Jeopardy, the millennial game Go, and even poker, but the important thing is the possibility of applying that learning ability to other tasks. A machine is no longer simply something capable of doing what a human tells it to, a repetitive task that can be carried out more quickly, more accurately, and with fewer errors, but is instead capable of doing things that humans cannot: learning from data, developing a model to explain them, and carrying out better analysis than the humans who programmed it. And no, these machines will not evolve to become Terminator, because they are not intelligent: they are simply able to carry out learning processes related to very particular tasks, with limited rules and limited scenarios, which is not to say they don’t have extraordinary possibilities, capable of differentiating competitive enterprises from those that are not.
Garry Kasparov knows this. It is not a matter of trying to stop what cannot be stopped: progress is inevitable and offers awesome possibilities. Soon, machine learning knowledge will be the new “spreadsheet management” in colleges and universities, taking us from a time not so long ago when it was something absolutely specialized and only within the reach of data scientists, to something perfectly normal, part of the day to day, something we trust for tasks of all kinds.
So start preparing…
(En español, aquí)
