Category Archives: privacy

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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Superintelligence. Is it the last invention we will ever need to make?

I believe it is crucial that we move beyond preparation to adapt or react to the future but to actively engage in shaping it.

An excellent example of this kind of thinking is Nick Bostrom’s TED talk from 2015.

Bostrom is concerned about the day when machine intelligence exceeds human intelligence (the guess is somewhere between twenty and thirty years from now). He points out that, “Once there is super-intelligence, the fate of humanity may depend on what the super-intelligence does. Think about it: Machine intelligence is the last invention that humanity will ever need to make. Machines will then be better at inventing [designing] than we are, and they’ll be doing so on digital timescales.”

His concern is legitimate. How do we control something that is smarter than we are? Anticipating AI will require more strenuous design thinking than that which produces the next viral game, app, or service. But these applications are where the lion’s share of the money is going. When it comes to keeping us from being at best irrelevant or at worst an impediment to AI, Bostrom is guardedly optimistic about how we can approach it. He thinks we could, “[…]create an A.I. that uses its intelligence to learn what we value, and its motivation system is constructed in such a way that it is motivated to pursue our values or to perform actions that it predicts we would approve of.”

At the crux of his argument and mine: “Here is the worry: Making superintelligent A.I. is a really hard challenge. Making superintelligent A.I. that is safe involves some additional challenge on top of that. The risk is that if somebody figures out how to crack the first challenge without also having cracked the additional challenge of ensuring perfect safety.”

Beyond machine learning (which has many facets), there are a wide-ranging set of technologies, from genetic engineering to drone surveillance, to next-generation robotics, and even VR, that could be racing forward without someone thinking about this “additional challenge.”

This could be an excellent opportunity for designers. But, to do that, we will have to broaden our scope to engage with science, engineering, and politics. More on that in future blogs.

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