summing up is a recurring series of interesting articles, talks and insights on culture & technology that compose a large part of my thinking and work. Drop your email in the box below to get it – and much more – straight in your inbox.
Big Idea Famine, by Nicholas Negroponte
I believe that 30 years from now people will look back at the beginning of our century and wonder what we were doing and thinking about big, hard, long-term problems, particularly those of basic research. They will read books and articles written by us in which we congratulate ourselves about being innovative. The self-portraits we paint today show a disruptive and creative society, characterized by entrepreneurship, start-ups and big company research advertised as moonshots. Our great-grandchildren are certain to read about our accomplishments, all the companies started, and all the money made. At the same time, they will experience the unfortunate knock-on effects of an historical (by then) famine of big thinking.
We live in a dog-eat-dog society that emphasizes short-term competition over long-term collaboration. We think in terms of winning, not in terms of what might be beneficial for society. Kids aspire to be Mark Zuckerberg, not Alan Turing.
Ask yourself: What ideas, spaces and lifestyles will you leave behind for your grandchildren?
Forget privacy: you're terrible at targeting anyway, by Avery Pennarun
The state of personalized recommendations is surprisingly terrible. At this point, the top recommendation is always a clickbait rage-creating article about movie stars or whatever Trump did or didn't do in the last 6 hours. That's not what I want to read or to watch, but I sometimes get sucked in anyway, and then it's recommendation apocalypse time, because the algorithm now thinks I like reading about Trump, and now everything is Trump. Never give positive feedback to an AI.
This is, by the way, the dirty secret of the machine learning movement: almost everything produced by ML could have been produced, more cheaply, using a very dumb heuristic you coded up by hand, because mostly the ML is trained by feeding it examples of what humans did while following a very dumb heuristic.
There's no magic here. If you use ML to teach a computer how to sort through resumes, it will recommend you interview people with male, white-sounding names, because it turns out that's what your HR department already does. If you ask it what video a person like you wants to see next, it will recommend some political propaganda crap, because 50% of the time 90% of the people do watch that next, because they can't help themselves, and that's a pretty good success rate.
There's lots of talk about advancements in artificial intelligence or machine learning, but very little about their shortcomings and effects on society. Surrounded by hysteria, mistaken extrapolations, limited imagination and many more mistakes we're distracted from thinking productively about our future.
The Bomb in the Garden, by Matthew Butterick
Now, you may say “hey, but the web has gotten so much better looking over 20 years.” And that’s true. But on the other hand, I don’t really feel like that’s the right benchmark, unless you think that the highest role of design is to make things pretty. I don’t.
I think of design excellence as a principle. A principle that asks this: Are you maximizing the possibilities of the medium?
That’s what it should mean. Because otherwise it’s too easy to congratulate ourselves for doing nothing. Because tools & technologies are always getting better. They expand the possibilities for us. So we have to ask ourselves: are we keeping up?
We somehow think, technology becomes better because it gets faster. But that is simply confusing technology consumption for technology innovation. Consumption simply allows us to do more of the same, while innovation augments us to do things that were previously impossible.
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