Gary M.Shiffman, Opinion contributor Published 7:00 a.m. ET March 12, 2020

Twelve-year-oldGary Leschinskyis a nationally-ranked chess player in the U.S. He has a bright future ahead of him but it may not be in chess.

Why? The reality is that theres not much future for people in chess anymore. Machines have become so advanced that they will always beat us in the game, however smart we are. In "Range: Why Generalists Triumph in a Specialized World", David Epstein notes that this has been the case since grandmaster Garry Kasparovs loss to the IBM supercomputer in 1997, and that its a sign that perhaps we should be outsourcing tactical tasks to computers. Similarly, translation, spell checking, copyediting, transcription, and other jobs heavily reliant on rote memory have all begun to be outsourced to computers.

What Gary Leschinsky has going for him, instead, is something particularly human his creativity.Hes also aninventor, and he has patented an allergy watch that can detect food allergies.

Computers and machines can beat us in games like chess, checkersand tic-tac-toe because these games are bound by a finite number of moves and possibilities. Machines can surpass humans in anything that is bounded and literal. Theyre inductive: we train them to recognize patterns in data. Humans, on the other hand, are limited in our inductive abilities. There is so much data available today that the human mind simply cant process it all.

Heres the difference: Humans have creative, deductive, emotionaland ethical abilities. Machines dont. This is what makes humans irreplaceable, no matter how many chess matches computers win.

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What does this mean for the future of work? Will our jobs really be replaced by machines? A Pew survey found that nearlyfour in 10 Americans worry thats the case, and a 2019 Brookings Institute report concluded that a quarter of U.S. jobs will be severely disrupted by AI in the coming years.

Contrary to popular fears, however, theres a very relevant and important place for humans in a world of robots and computers. The key is that we have to be strategic about what roles and tasks we assign to the machines and what roles and decisions we protect for the humans. We should be assigning the chess-like tasks to the machine while protecting the creative and values-based tasks that are inherently human.

Human and robot connection.(Photo: PhonlamaiPhoto/Getty Images/iStockphoto)

Right now, the best use of technology is prioritization: filtering, ordering, and ranking data. Most of us, for example, take for granted that when we type cat into a Google search bar, we instantaneously have access to millions of pictures of cats. Ten years ago, this would have been impossible. It would have been one of the most impressive feats of computing ever accomplished. That a machine can accurately select a cat from billions and billions of available images is actually a remarkable thing.

Its not magic, though. Computers have this ability because humans trained them to do it; we essentially fed algorithms millions of images of cats to teach them what an image of a cat looks like. This is how machines learn.

The applications of machine learning have far greater implications than quickly finding cute cats. Today, you can build a computer model to identify virtually anything, provided you have the examples to teach the algorithm. Giant Oak, for example, developed atechnologythat enables financial institutions and government agencies to identify money launderers, human and drug traffickers, and terrorists.

When we do this, were not digging through the haystack to find the needle. Were teaching a computer to prioritize billions of documents for human investigators, agents, and analysts to review. Thecomputeris not the one deciding whether or not someone is a terrorist or a money launderer. Instead, its doing what it does best using the training data humans have given it to identify and rank potentially relevant information for humans to use in making judgment calls.

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For example, we can imagine that over 99% of all bank customers abide by all laws, but some small percentage of launder money. With proper training data, machine learning can prioritize information likely associated with money laundering over all other information. Now the human need only review the most important information when adjudicating money-laundering behaviors.

Computers promise amazing efficiencies, but we also need humans. If were deciding whether to let someone onto an airplane, open a bank account, or watch our children, we dont want a computer making the final call.

Computers, robots and machines might impact the future of work, but they will never completely replace humans. Theyll just beat us at chess.

Gary M.Shiffmanis the founder and CEO ofGiant Oakandthe author of "The Economics of Violence: How Behavioral Science Can Transform our View of Crime, InsurgencyandTerrorism."He teaches economic science and national security at Georgetown University. Follow him on Twitter:@GaryMShiffman

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Computers may surpass humans, but we'll still have jobs. Here's why. - USA TODAY