The Human Factor (in the Age of Machines)

[caption id="attachment_69660" align="alignright" width="350"] "machine," Creative Commons license. | Credit: Flickr by Jonas Tegnerud[/caption] No matter how far AI evolves in the future, for as long as humans remain as the dominant species on this planet, machines will exist to serve the benefit of human collectives, in some form or another. That is an optimistic view and possibly the best-case scenario. Now, if we imagine the dark path as kindly illustrated in movies like "Terminator" or the "Matrix" series, AI may one day decide to eliminate humans as we are merely nuisances to them (the worst case scenario), or convert us into living, breathing battery packs to power them with our body heat (the next-worst-case scenario). Even without such doomsday predictions, it is quite feasible that machines will take jobs away from most of us, starting with menial and repetitive ones and moving on to so-called white-collar positions with thinking involved. Not quite the end-of-the-world case, but definitely the end-of-the-world-as-we-know-it situation, as the cognitive process won’t remain as a uniquely human function. Not too long ago, it was big news that AI decisively defeated one of the smartest human beings on Earth in the game of Go. It was quite an achievement — not necessarily for the machine, but for the humans who designed it. The machine, less than one year after that achievement, is now up to the level that its older version won’t able to match. The latest is that it doesn’t even play Go anymore, after having played the game by itself millions of times. Here is my take on that event: First, why is that so surprising? Yes, the game of Go is far more complex than chess, with a virtually unlimited number of outcomes. But everything happens on a game board and the rules are quite simple. Machines and humans can observe and predict events within that set boundary. If machine does nothing but “1” task within the rule set for an unlimited amount of time without being bored or getting tired, of course it will beat humans who easily get distracted or grow tired. So can we even call such a match fair? At some point in the distant past, a car passed the speed of the fastest human runner or even a man on a horse (with exactly 1 horse-power). But other than the fact that we still continue to humiliate horses by measuring the engine power in terms of “horsepower,” who cares about that? We don’t have runners compete against cars in the Olympic Games, do we? The second point is that, yes, it is newsworthy that an AI beat one of the best Go players in the world. But so what? The history of computers has been a series of human defeats in terms of speed and accuracy since the very invention of the thinking machine. Computers have been outperforming humans in many ways all along, so why does everyone get so scared them all of a sudden? Is it fear of the unknown or loss of control? We have learned how to coexist with clunky mainframes in the past, and we will learn how to live — and live well — with AI with or without cute faces. And that's if, and only if, we maintain the “human factor” in the evolution of thinking machines. So let’s stop thinking about how smart machines have become, and let’s think about what that word “smart” means. What 'Smart' Means Does it mean that it remembers things better than us? Undoubtedly. The best use of a computer is to have it remember what we don’t want to remember. Just because I can’t even remember my work number without my “smart” phone, that doesn’t mean that I became dumber. I will use the remaining memory space in my brain to store some other useless information, like the average driving distance of an old golfer or a name of an actor in some obscure movie. Then again, why even bother with all of that when I can just Google them anytime? We often say someone is “smart” when she can calculate numbers in her head really fast. During my childhood in Far East Asia, it was a fad to take abacus lessons. And some protégés could add a long series of 10-digit numbers in their heads without even touching the abacus. Where are they now? Yeah, they may still be able to invoke some “Woos” and “Ahhs” from a TV audience, but they won’t be able to get a job at NASA to calculate trajectories of a spaceship just with those skills. Again, why bother with skills that even the smallest computer could do better? But plotting new tasks for the spaceship? Now that sounds like a human function, even in the Starship Enterprise — with unlimited computing power. We also say someone is “smart” when he can connect dots among seemingly unrelated things or phenomena. Call it intuition, or pattern recognition in computing terms, but there are people who are better than others in identifying previously undetected correlations in various fields. Does one need scientific training to get better at that? Yes. Are machines catching up with us in that area, too? Definitely yes. But who would provide the “purpose” for such exploration? Who would come up with the initial hypothesis, justify the whole study, and ultimately answer the ever-important “so what” question? Machines Are Smart. So What? My dogs have Facebook accounts. (Don’t ask why.) And the other day, I noticed that Facebook put the younger one’s age in dog years on his birthday. It is quite interesting to see that now, the face recognition algorithms can differentiate different types of animals, as I’ve read a while back that it hasn’t been easy for a machine to differentiate pictures of dogs and cats. Apparently, they can do that now, and it is not that surprising. One of the best features of machine learning is pattern recognition. And with enough training iterations with empirical data, why is it even surprising that they recognized my dog’s face? Give a machine some more time to play with it, it may be able to calculate the level of cuteness of a dog, eventually (based on collected human responses, of course). But who conceived the initial idea that putting our dog’s age in dog years would indeed be “funny” to humans? I can bet my farm that it was a human decision. We also say someone is “smart” when he or she is creative, witty, humorous (without being a jackass) or intuitive. We call people “wise” when they see things beyond obvious patterns and consider even unintended consequences of actions. That's quite the opposite of one-dimensional folks. (These are the people who would instill purposes in our collective behavior and machine activities, alike.) Machines Are Smart. Humans Are Smarter. We now call machines “smart,” only because they came a long way since the invention of computers. But let’s face it. There wasn’t a single year when the storage capacity and computing speed did not improve significantly. We are just passing through the inevitable path of evolution. When we say machines are smart, it means it remembers things really well, and calculate things really fast. Now it can recognize patterns, fill in the gaps and predict bits of the future. And they are moving into the stage of improving themselves without our constant intervention. That independent part may scare some people, and that’s only natural — considering our own violent history. We call cars smart when they can navigate without a driver. But let me point out that such an intelligence level is equivalent to ants marching back to their home base. Heck, bumble bees do that in 3D space, but we don’t call them “smart.” If the machine can reason “why” it must head home at a certain time without any human instruction, I will then call it a true thinking machine. Machine to Marketer: Take Me to Your Leader Dialing back to present day, all of those analytical tools with fancy names out there? Supervised or unsupervised learnings alike, they are nothing without clear purposes. Do not think for a second that “your” problems will be defined by the machines and they will magically provide answers to you. At least not yet. And even if one day machines can do that, would you want to be defined by machines? The human factor is not to be lost in the age of abundant data, ubiquitous connections and virtually unlimited computing power. Humanization of data and analytics practices is the key for success, as setting the business purpose will remain as a human function, and the results of analyses are nothing without adaptions by human decision-makers. Blind implementation of automated learning by machines will never help any business. No matter how predictive machine learning has become, deciding what to predict is the hardest part of the process. Adaptation of the data-based decision-making process depends on the intuitiveness of solutions, not just complexity and speed factors. Therefore, taking the best-of-the-both-worlds approach, even the simplest implementation of machine learning must be justified by proper solutioning framework from a business point of view, instead of pursuing the latest technologies for the sake of being cutting-edge. Machines are becoming smarter by leaps and bounds. And maybe someday, they may even be sentient — with clear purposes of their existence. How do we human marketers keep up with such rapid evolution and stay relevant? We must stay logical and be in a position to provide purposes of their activities, as even the most powerful computer in the world won’t understand illogical instructions. The capability to translate seemingly illogical human goals into terms even a machine can understand will be the future function of computing and analytics professionals. And ironically enough, we will need to cherish the human factors to coexist with machines successfully, and harness their full potential for our benefits. And those who fear them are lost already.

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The Human Factor (in the Age of Machines)

Stephen H. Yu

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Stephen H. Yu