“If a machine can think, it might think more intelligently than we do, and then where should we be?”
– Alan Turing
How Intelligent?
When the much publicized match between IBM’s Deep Blue and the then reigning world chess champion, Garry Kasparov, came to an end in February 1997, many were beyond disbelief. Others, euphoric. The much forgotten prospect of smart machines taking over their increasingly not-so-smart human overlords, all of a sudden, seemed to be realer than many would like.
But what is this Intelligence?
The dualistic worldview of modern European civilization, now dominating much of the world, point us a direct lineage. On this path that lasts millennia, one of the most iconic figures, from whom came one of the most iconic assessments of the human existence, was a 17th century Frenchman by the name of René Descartes.
I think, therefore I am. So it went.
And how does one assess thinking? In actions.
By the same logic, if Deep Blue made moves that turned out to be game-winning, it must be because it thought, and with that thought, it is – in the sense of the French word être, which also implies a state of being.
After all, we are human beings, and if machines demonstrate the same philosophical properties that is core to our fundamental selfhood, are they not beings too? And if they are, do they have the same potential as us?
Can they replace us?
Let’s backtrack to the game for a moment.
What Happened to AI Since?
The defining difference between the current, so-called third wave approach to machine Intelligence and prior ones is that, whereas the latter still very much relied on the machine’s drilled reenactment of choreographed definitions, now we mostly leave the AIs to train themselves. More organic, some say, not that different from the way children learn. This is inseparable from progress in a number of other fields, prominent among which are the internet, mobile technology, big data, neural science, and deep learning.
IBM’s Deep Blue, at the time of defeating Kasparov, needed software updates between games to keep its strategies up to date, as its actions were guided, in exactitude, by calculating the probability of what was defined as ‘winning’ with each move. The more recent Alpha Go, a virtually undefeated Go AI, was instead given an objective – winning, and the rules of the game, then pretty much left alone to play itself until it found different ways to achieve that objective.
Underlying all this is big data, the concept that every single bit of anyone’s digital footprint is of value because encoded in them, are the millions and billions of decisions each human makes on a daily basis – and if enough context can be pieced together, the whys should be apparent.
Consequently, now, AIs are everywhere, because we are everywhere. We compose music, they compose music too; we trade stocks, they trade stocks; we make medical diagnoses, they as well – the list goes on. Anything we do, they do, and sometimes, much better and more efficient than us. And here enters our protagonists.
In a what seemed like just slightly over a decade ago a gambit, to further understand human behavioral patterns and optimize commercial strategic communication for brands, marketers decided that our human intelligence has had its run – we must now pivot to the artificial ones.
Personalized Recommendations
Now virtually and literally everywhere, until years countable by a single hand ago, personalized recommendation was still a novelty.
In an early attempt to turn around the tide of plunging ad engagement, multinational tech conglomerate Dell has since turned to AI for crafting more personalized messages. The key in this particular case, as in many others in marketing communication, is with emotion.
At a time when consumers were already inundated with irrelevant product promotion ads on a daily – if not hourly basis, the age-old carrot of clickbait was losing its appeal more rapidly than preferred. And the ability to paint impersonal objects into something that make people on the other end of the message feel – over and over again, has always been a major headache turned heartache for strategic communications companies and advertising agencies alike.
This has been especially the case with computer electronics, where products tend to be seen as quite impersonal.
With enough data and computing power to make sense of all those neatly stashed 0s and 1s—personal data, however, Dell was able to train a group of expert AIs – termed by some as AEI, or artificial emotional intelligence, to capture the quirks and likes of much more specific groups of consumers that are catalogued based on personality types, and consistently deliver the messages that not only bait their clicks, but judged by the result of a quite substantial increase in sales, captures their hearts as well.
When AI has A Body
As intelligent as they have become since the third wave began, to some, a brain in a vet is still a brain in a vet, and no matter how adapt such intelligent brains are at emulating human behavior, or even substituting genuine human interactions in the screen world, when we step away from digital devices – as people do from time to time, AIs become quickly irrelevant.
This, in a society with significantly less luddite tendencies such as Japan, is where AI meets robotics.
Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory at Osaka University, has been trying to come up with something – and eventually, a someone, that is not only artificially intelligent, but can interact with actual humans in a physically tangible way.
From the first-gen squarely cute albeit clumsy moving blocks, intelligent android has come a long way. Among the most well-known of Hiroshi Ishiguro’s creations is Erica, an android in the sense of Blade Runner android – a physical humanoid torso, supported by a computer generated female voice under a not-so-bad British accent.
Deepfake
As politics blends with consumerism, judging purely on paper, it is increasingly hard to distinguish political campaigns from product promotion campaigns. And the higher the stakes, the more indistinguishable the two becomes.
In the South Korean presidential election that had just ended, something extraordinary happened. Both leading candidates from the political spectrum opted to use deepfake – a technology that renders a highly realistic AI version of the real person that is fully capable of interacting with genuine humans real-time – on their campaign trails.
For the middle-aged, serious-looking conservative candidate Yoon Seok-youl, this was an unprecedented opportunity to connect with youngsters who had grown up in consumer society, and expected nothing less than a good show from the candidate they were going to vote for.
Last Few Thoughts
As important as data was to the revival of AI development in the early 2010s – and still is today, it also constitutes an accurate dilemma.
Data, simply put, is human actions rendered in a computationally digestible language. Unlike the offline world, in which the fundamental laws we don’t yet thoroughly comprehend, let alone control, the very foundation of the digital reality is defined by us. Every swipe, every click, every hesitation over a page is therefore, by definition, recordable, readable, and consequently recorded and read.
So, where did all the data go? And what happened to it?
In a world still very, very far away from an actual AI singularity moment, what can be achieved with those increasingly sophisticated brains in vets seem to have no clear end point. But as we have seen, as far as marketing communication on a mass scale is concerned, the future appears rather clear.
The immediate objectives for advertising agencies that have their eyes on AI is to gain more insights into human behavior with raw data and hopefully, get to know the people on the other side the screen more than they know themselves in a profitable way, so that the one-way communication most marketing plans still rely on would evolve into something truly interactive.
In the words of Mohak Shah, lead expert in data science from Bosch Center for Artificial Intelligence North America, “in terms of the things we’re doing now, we are still solving very basic problems … except that all these basic problems are becoming building blocks of achieving something much bigger”.
But, at the end of the day, something bigger or not, really intelligent or otherwise, pretty impressive feats for those millions upon millions of unassuming looking 0s and 1s, is it not?
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