The Salesforce announcement of Einstein this week — impressive as it was — reminded me that marketers sometimes use terms like machine learning, artificial intelligence (AI) and even automation interchangeably.
Businesses have long been infatuated with the word intelligence – business intelligence, relationship intelligence, and media intelligence – are all overused terms from the last decade, for example. In my mind, all of these descriptors are a stretch. Intelligence means something very specific: it’s the disposition, composition and strength of an enemy.
Yet business executives love war analogies and here we are mashing these terms together again. We like to use the term AI because it sounds more sophisticated. Why settle for mere automation or machine learning when you can have intelligence, artificial or otherwise?
Machine Learning vs. AI
The reality in marketing is that most of our tech tools are simply automation, described with great editorial liberty. Taking repeatable tasks, mapping them out, streamlining the process and then using software to get part of the work done. It’s pretty common on Twitter, for example, with all sorts of automated tools creating lists, following, unfollowing and favoriting tweets.
Google and Facebook take this to the next level. These businesses have algorithms that learn your preferences – the things you like or are likely to click – so they can better sell ads targeting you. This is an example of machine learning, although I’m sure both companies are pouring money into R&D in this area.
By contrast, artificial intelligence suggests cognition and self-awareness. It’s whole different level. Most animals don’t even have the ability to recognized themselves. Show a cat a mirror and it’ll hiss, purr or tip-toe around to look behind the mirror for another cat, which of course, doesn’t exist.
If we can create a machine with consciousness, then surely that will change the world. At what point does AI cease to be a machine and becomes instead…life or alive? The ethical implications are incredible – and maybe too are the dangers. Your job in marketing will probably be the least of concerns; Stephen Hawking, a highly revered physicist, has suggested AI could end humanity.
The chances are good marketers will have a role here — creating arguments both for and against AI. That is if the bots haven’t taken out jobs by then.
Mankind and Machine: Better Together?
Yet like so many arguments in marketing, new ideas are often cast in a dichotomy when there usually is a third option. Rather than being at extremes, the truth probably lies somewhere in the middle.
Instead of fretting about what might or might not come tomorrow, we should strive to embrace new tools and concepts, including AI and machine learning. We should seek to understand them and learn how to integrate them into what we do every day to drive value.
The unknown? That’s frightful. Knowledge is the ultimate stress relief.
And that’s the theme for this week’s Unscripted Marketing links [UML] — marketing vs. the machine. As it is every week, I’ve highlighted key points from three articles below and recommend reading them each in their entirety.
Don’t miss these related posts:
The Struggle is Real in B2B Content Marketing [UML]
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Beyond PR 101: 3 Truly Unique Pitching Tips [UML]
1) Working with the Advertising Machines
Richard Robinson, a managing director for EMEA at Turn puts the science fiction aside in a piece for the London-based Tech City News titled: Debunking the myths of machine learning in AdTech. Any technophobic fears would be better directed analyzing how to tap machines to help us do our jobs better:
“The short answer is that the media environment has become increasingly complex. It is now beyond human capability to reach an individual online via his or her various devices. To have a clear conversation with me Richard the consumer – you would have to process huge amounts of data. That’s not because I’m unusually complex, but rather because I switch between multiple devices – a trait of almost all consumers.”
That switching of devices part? It’s even more complex when we consider we switch “frequencies” on those devices as well, as we toggle between apps, news and email. I call that the frequency hop phenomenon.
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2) Focus on Things Machines Cannot Do
There’s a premium price for being able to do things others cannot. That’s my takeaway from an agency view piece for AdAge called How to Not Lose Your Marketing Job to a Machine. The author Brian Fetherstonhaugh, who is chairman and CEO of OgilvyOne Worldwide, sees risk for those that specialize in “calculate and execute” style workflows. Security rests in creativity he writes:
“In simple terms, we can divide all activities in the world into things that are ‘repeatable’ and those that are ‘creative.’ By repeatable, I mean things like mass production, performing calculations, and other mechanical tasks. In this domain, the machines dominate. They are faster, more accurate, more reliable, and more efficient. In the creative domain, humans still dominate. We are good at things like inventing new ideas, dealing with ambiguity, and building human trust.”
The science of marketing is ideal for automation; the art perhaps is not. Is the discipline of marketing more art or science?
3) Drawing Over the Machines
Technology certainly helps us understand data – that is slicing and dicing large database and rendering them in graphical form. But as far as I know, only people can create abstract shapes to convey a concept; think: leadership presentations spelling out a vision or new direction for a business.
That’s what went through my mind as I read Dan Roam’s pike on Neuromarketing called How To Influence Others by Drawing, Even if You Flunked Art.
He writes:
“Drawing is like talking, except you use shapes instead of words.”
And later adds:
“Drawing is like talking: There is silence until you say something.”
While his piece isn’t directly tied to AI or machine learning, except in the sense it’s a focus on doing what perhaps robots cannot, Mr. Roam makes an interesting case that drawing is our oldest technology.
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Market Research Mistake: Falling in Love with an Idea
Photo credit: Flickr, petcor80, 586. artificial intelligence_1_1 (Public Domain Mark 1.0)
