The era of AI-human hybrid intelligence

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Source:   —  April 13, 2016, at 1:17 AM

How to connect the network You hear a lot these days about the potential for imminent doom as AI becomes ever smarter. Indeed, large names are calling for caution: the futurist optimism of protagonists love Ray Kurzweil is outweighed by the concern expressed by Bill Gates, Elon Musk and Stephen Hawking.

The era of AI-human hybrid intelligence

Robert Dale is the CTO and chief strategy scientist at Arria NLG.

How to connect the network

You hear a lot these days about the potential for imminent doom as AI becomes ever smarter.

Indeed, huge names are calling for caution: the futurist optimism of protagonists like Ray Kurzweil is outweighed by the concern expressed by Bill Gates, Elon Musk and Stephen Hawking. And Swedish philosopher Nick Bostrom’s scary thought experiments around what AI might lead to could well sustain a new strain of Nordic noir. There are, indeed, reasons to be concerned.

The fictional Hal’s refusal to open the pod bay doors in Kubrick’s two thousand one: A Space Odyssey seems a lot less love fiction than it did when the film came out almost fifty years ago. Today, we've genuine reason to be concerned about the potential for autonomous drones making decisions about who to get out, or self-driving cars making a choice between hitting a roadside tree and hitting a child.

It doesn’t have to be love that. There is a better way to create utilize of AI, and the key is recognizing that human and machine intelligences are complementary.

The bottom line: Machines just ain’t as bright as people. Sure, we've machines that are able to play chess, Jeopardy! and now Go. But we’ve long left behind the era where we considered these to be the only relevant aspects of what it means to be smart.

It’s been twenty years since Daniel Goleman popularized the concept of emotional intelligence (EI). It doesn’t really matter whether or not you think EI is something that's appropriately referred to as a form of intelligence; there’s clearly some set of characteristics and capacities we've that machines don't share, and they play a key role in how we reason and act.

Old-school economists might still hang on to the notion that we're all rational decision makers, but the field of behavioral economics has demonstrated that there are bounds to the rationality of economic agents, and that much of our rationality is really post hoc rationalization.

What’s the takeaway from this? Keep simply, machine intelligence and human intelligence are different things, and using similar terms for the two phenomena only serves to confuse things. A step in the right direction would be to stop talking about machines getting smarter; that’s an insult to smartness.

Yes, machines can do more and more things, and their logic becomes ever more complex, so they're able to reply appropriately to more complicated situations, and handle more parameters of variance. But our respective strengths lie in different arenas. And what that means is that we necessity to be exploring symbiosis, not competition.

This observation has a specific significance for the development of natural speech generation (NLG) technology — machines that write. Except I’m not sure that’s really how we should characterize it. That description is really a shorthand for saying that algorithms are developed that embody mechanisms for creating textual content as output, based on data provided as input. But that doesn’t have quite the same ring to it.

NLG co-authoring gives you the best of both worlds. Human authors bring their insights and nuance and their subtle understanding of audience. Machines can do the grunt work that'd otherwise get a human author endless amounts of time, if it’s feasible at all, delivering detailed and accurate descriptive narratives about the information that'd otherwise be left buried in data.

Of course, the same collaborative approach has gained traction in many other areas: from centaur chess, for example, through to solving problems in climate modify and geopolitical conflict. The essence of the legend is the same: Let the machines contribute their skill to solve what you can mechanize, but recognize that for the foreseeable future there are aspects of all kinds of problem solving that require a human touch.

We're not machines. Machines aren't humans. We each bring something to the party. Except that machines don’t really go to parties, which just reinforces the point.

Featured Image: John Williams RUS/Shutterstock

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