Garry Kasparov: Humanity to Lose 96% Jobs To Machines
Russian chess grandmaster Garry Kasparov shared his beliefs on job market vulnerability to machine replacement and future of human-machine collaboration, during the interview to Wired’s Will Knight.
Speaking of his own experience in chess, he mentioned that he was “the first knowledge worker whose job was threatened by a machine”, referring to the times when he, being the world champion, lost a game to Deep Blue supercomputer.
In his words, this defeat helped him understand the “the future of human-machine collaboration”. In his opinion, since the most jobs do not even require human creativity, he concluded that “man loses to computer”:
“When you look at the statistics, only 4 percent of jobs in the US require human creativity. That means 96 percent of jobs, I call them zombie jobs. They’re dead, they just don’t know it. For several decades we have been training people to act like computers, and now we are complaining that these jobs are in danger. Of course they are”
Notably, in his opinion, since humanity does not even have a chance of beating AI, so it has to “recognise the element of inevitability” and to learn how to work together with machines. It could be “universal basic income” or any other form of “financial cushion”, shared grandmaster.
Talking about the ethics of AI, he referred to the notion that “progress cannot be stopped”, but humanity still holds a “monopoly on evil”: He dismisses ethical AI as “nonsense”, because in his view, the problem is never AI, but “humans using new technologies to harm other humans”.
In this context, it is also important to mention, that Garry Kasparov do not believe in AGI, or artificial general intelligence. On the other hand, according to Kasparov, there should be more control over companies that generate a lot of data, like Facebook, or Google.
As Future Time previously reported, Oxford Economics has also revealed that humanity could lose 20 million jobs this decade, complementing the research by job vulnerability index for different geographical areas.