Election lessons. Beware who you ignore.

It was election week here in America, but unless you’ve been living under a rock for the last eight months, you already know that. Not unlike the Brexit vote from earlier this year, a lot of people were genuinely surprised by the outcome. Perhaps most surprising to me is that the people who seem to be the most surprised are the people who claimed to know—for certain—that the outcome would be otherwise. Why do you suppose that is? There is a lot of finger-pointing and head-scratching going on but from what I’ve seen so far none of these so-called experts has a clue why they were wrong.

Most of them are blaming polls for their miscalculations. And it’s starting to look like their error came not in who they polled but who they thought irrelevant and ignored. Many in the media are in denial that their efforts to shape the election may have even fueled the fire for the underdog. What has become of American Journalism is shameful. Wikileaks proves that ninety percent of the media was kissing up to the left, with pre-approved interviews, stories and marching orders to “shape the narrative.” I don’t care who you were voting for, that kind of collusion is a disgrace for democracy. Call it Pravda. But I don’t want to turn this blog into a political commentary, but it was amusing to watch them all wearing the stupid hat on Wednesday morning. What I do want to talk about, however, is how we look at data to reach a conclusion.

In a morning-after article from the LinkedIn network, futurist Peter Diamandis posted the topic, “Here’s what election campaign marketing will look like in 2020.” It was less about the election and more about future tech with an occasional reference to the election and campaign processes. He has five predictions. First is, the news flash that “Social media will have continued to explode. [and that] The single most important factor influencing your voting decision is your social network.” Diamandis says that “162 million people log onto Facebook at least once a month.” I agree with the first part of his statement but what about the people the other 50% and those that don’t share their opinions on politics. A lot of pollsters are looking at the huge disparity in projections vs. actuals in the 2016 election. They are acknowledging that a lot of people simply weren’t forthcoming in pre-election polling. Those planning to vote Trump, for example, knew that Trump was a polarizing figure and they weren’t going to get into it with their friends on social media or even a stranger taking a poll. Then, I’m willing to bet that a lot of voters who put the election over the top are in the fifty percent that isn’t on social media. Just look at the demographics for social media.

Peter Diamandis is a brilliant guy, and I’m not here to pick on him. Many of his predictions are quite conceivable. Mostly he’s talking about an increase in data mining, and AI is getting better at learning from it, with a laser focus on the individual. If you add this together with programmable avatars, facial recognition improvements and the Internet of Things, the future means that we are all going to be tracked with increasing levels of detail. And though our face is probably not something we can keep secret, if it all creeps you out, remember that much of this is based on what we choose to share. Fortunately, it will take a little bit longer than 2020 for all of these new technologies to read our minds—so until then we still hold the cards. As long as you don’t share our most private thoughts on social media or with pollsters, you’ll keep them guessing.

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Yes, you too can be replaced.

Over the past weeks, I have begun to look at the design profession and design education in new ways. It is hard to argue with the idea that all design is future-based. Everything we design is destined for some point beyond now where the thing or space, the communication, or the service will exist. If it already existed, we wouldn’t need to design it. So design is all about the future. For most of the 20th century and the last 16 years, the lion’s share of our work as designers has focused primarily on very near-term, very narrow solutions: A better tool, a more efficient space, a more useful user interface or satisfying experience. In fact, the tighter the constraints, the narrower the problem statement and greater the opportunity to apply design thinking to resolve it in an elegant and hopefully aesthetically or emotionally pleasing way. Such challenges are especially gratifying for the seasoned professional as they have developed almost an intuitive eye toward framing these dilemmas from which novel and efficient solutions result. Hence, over the course of years or even decades, the designer amasses a sort of micro scale, big data assemblage of prior experiences that help him or her reframe problems and construct—alone or with a team—satisfactory methodologies and practices to solve them.

Coincidentally, this process of gaining experience is exactly the idea behind machine learning and artificial intelligence. But, since computers can amass knowledge from analyzing millions of experiences and judgments it is theoretically possible that an artificial intelligence could gain this “intuitive eye” to a degree far surpassing the capacity of an individual him-or-her designer.

That is the idea behind a brash (and annoyingly self-conscious) article from the American Institute of Graphic Arts (AIGA) entitled Automation Threatens To Make Graphic Designers Obsolete. Titles like this are a hook. Of course. Designers, deep down assume that they can never be replaced. They believe this because inherent to the core of artificial intelligence there is a lack of understanding, empathy or emotional verve, so far. We saw this earlier in 2016 when an AI chatbot went Nazi because a bunch of social media hooligans realized that Tay (the name of the Microsoft chatbot) was in learn mode. If you told “her” Nazi’s were cool, she believed you. It was proof, again, that junk in is junk out.

The AIGA author Rob Peart pointed to AutoDesk’s Dreamcatcher software that is capable of rapid prototyping surprisingly creative albeit roughly detailed prototypes. Peart features a quote from an Executive Creative Director for techno-ad-agency Sapient Nitro. “A designer’s role will evolve to that of directing, selecting, and fine tuning, rather than making. The craft will be in having vision and skill in selecting initial machine-made concepts and pushing them further, rather than making from scratch. Designers will become conductors, rather than musicians.”

I like the way we always position new technology in the best possible light. “You’re not going to lose your job. Your job is just going to change.” But tell that to the people who used to write commercial music, for example. The Internet has become a vast clearing house for every possible genre of music. It’s all available for a pittance of what it would have taken a musician to write, arrange and produce a custom piece of music. It’s called stock. There are stock photographs, stock logos, stock book templates, stock music, stock house plans, and the list goes on. All of these have caused a significant disruption to old methods of commerce, and some would say that these stock versions of everything lack the kind of polish and ingenuity that used to distinguish artistic endeavors. The artist’s who’s jobs they have obliterated refer to the work with a four-letter word.

Now, I confess I have used stock photography, and stock music, but I have also used a lot of custom photography and custom music as well. Still, I can’t imagine crossing the line to a stock logo or stock publication design. Perish the thought! Why? Because they look like four-letter-words; homogenized, templates, and the world does not need more blah. It’s likely that we also introduced these new forms of stock commerce in the best possible light, as great democratizing innovations that would enable everyone to afford music, or art or design. That anyone can make, create or borrow the things that professionals used to do.

As artificial intelligence becomes better at composing music, writing blogs and creating iterative designs (which it already does and will continue to improve), we should perhaps prepare for the day when we are no longer musicians or composers but rather simply listeners and watchers.

But let’s put that in the best possible light: Think of how much time we’ll have to think deep thoughts.

 

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