Machine Learning

How Humans Plus Machines Will Equal Amazing Advancements

How Humans Plus Machines Will Equal Amazing Advancements
Image credit: AKA
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As people seek to unlock new potential for growth -- whether in business, science or another endeavor -- their greatest collaborators may not be merely teammates or clients in their networks but rather a machine.

While much focus has been on human versus machine -- Garry Kasparov versus IBM’s Deep Blue in chess matches of the 1990s or the Watson computer pitted against human champions on Jeopardy -- the greatest potential for advancement comes from humans partnering with computers.

The truth is, in the contest between human and computer, the computer wins. But human and computer collaborating together make an unbeatable combination.

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The nature of collaboration is to partner with others to transcend our own limitations. In the past, we have done this with flesh-and-blood colleagues; one is strong where another is weak. But as machines become more sophisticated and also harness much of our skills, they begin to complement and augment us.

It is already happening -- and in the future will become even more common. Consider machines connected to the brain that help people who are deaf to hear or that operate robotic arms so that a woman who has lost use of her limbs can feed herself, as shown by neuroscientists recently.

Rather than respond with fear that smart machines will take our jobs -- or, ultimately, take our place -- we need to wrap our heads around the potential for collaboration with computers to improve human performance. To paraphrase one of the first videos of Apple CEO Steve Jobs talking about extending human potential, call it putting your brain on a bicycle.

Lumbering, flat-footed bipeds that we are, humans are certainly not the fastest creatures. Condors and cheetahs, to name a few, have us beat. But, put a human on a bicycle, and there is no question who is fastest. Human plus machine equal unparalleled performance. Brain plus smart computer achieve similar results.

The human-machine partnership makes the most of complementary strengths. “Using machine learning to find a needle in a haystack is where computers can help us transcend our limitations,” says Professor Adam Pah from Northwestern University’s Kellogg School of Management and Northwestern’s Institute on Complex Systems (NICO).

He gives the example of Foodborne Chicago, which uses computers and code to search Twitter for tweets related to food poisoning. From computer-generated leads, humans take over to determine if there was likely a case of food poisoning. The result has been additional restaurant inspections in Chicago that would not have occurred otherwise.

Such collaboration starts with understanding the key talents of the human mind and augmenting those. We humans do not rely on knowledge alone, but also draw upon our intuition and emotional intelligence.

For example, our intuition tells us that we know something even before we have the answer. That confidence allows us to pull information that is buried deep within us and even rely on our knowledge to follow our intuition. You can witness this on a TV game show such as Jeopardy, when contestants hit their response buttons before they have the answer in mind, then recall the right answer before their allotted time expires.

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Computers cannot do that. Theirs is an empirical world of seemingly unlimited data to be combed through. What they can do, however, is analyze without the interference of human emotions or external biases. In this way, machines can help humans reach conclusions that make people -- and the organizations they create -- more effective.

Recently, Thomas Oberlechner, a founding partner and chief science officer at AltX, gave the example of how human-computer collaboration can enable investment decisions that are more closely aligned with people’s decision style, investment preference, risk tolerance, crisis vulnerability, financial values, etc. Using data from psychometric assessments, behavioral knowledge is turned into investment decisions.

Humans are not good at decision-making, because our attention is fleeting. We are not necessarily rational in our decision-making process, and we do not always know what makes us happy. Humans also have the tendency to lie to themselves. For example, that we make healthier choices (eating and exercising) than we really do, or when assessing the risk we, compared to others, pose when it comes to, say, texting while driving.

Imagine, then, having a machine on your shoulder helping with myriad problems and choices. Paired with a smart machine, you would be more effective and potentially less harmful to yourself -- more people are going to die this year from “extreme selfies” than shark attacks --  and even potentially happier, healthier and wealthier.

And we want to do it fast. In 1996, when Kasparov played IBM’s Deep Blue, the majority of human viewers were rooting for Kasparov to win. In 2011, when Watson was battling the human talents, most viewers were rooting for Watson.

As computing speed doubles about every eight months, humans soon will no longer be the fastest computer. The human brain, though, is in no danger of being replaced. Humans possess the always-in-demand skill of creativity, which computers cannot master. With our creativity, we can change and adapt to an environment. Computers, though, can facilitate that change and adaptation to advance human intelligence.

Together, the “brain on a bicycle” partnership can lead to “Human Version 2.0” -- accelerating breakthroughs and advancing discoveries.

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