A centaur is the pairing of a human and an artificial intelligence to play chess. […] Kasparov realized that he could have performed better against Deep Blue if he’d had the same instant access to a massive database of all previous chess moves that Deep Blue had. If this database tool was fair for an AI, why not for a human? To pursue this idea, Kasparov pioneered the concept of man-plus-machine matches, in which AI augments human chess players rather than competes against them.

My hypertext idealism is still alive - and stirred today by Mike Caulfield’s“The Garden and the Stream: A Technopastoral”, describing his experience of maintaining a federated personal wiki. And weirdly, these links were compiled over the space of a year, just by noting things I learned or heard and linking them to things I’d heard before or that others had written. I created a wiki on issues of found art without even knowing it.

Physicist Sabine Hossenfelder offers consulting for amateur theorists, to help them connect with the mainstream research community. My clients know so little about current research in physics, they aren’t even aware they’re in a foreign country. They have no clue how far they are from making themselves understood. Their ideas aren’t bad; they are raw versions of ideas that underlie established research programmes. But those who seek my advice lack the mathematical background to build anything interesting on their intuitions.

J.D. Evans and Sam Lebovic compare “the market” to another collective system we all participate in through our daily lives: the transit system. In the United States, we are used to the term “the market” being thrown around in public discourse, as though it refers to a single thing. In fact, there are many markets, all nested and interconnected. Some parts are private, some public. Optimizing for the growth of the whole loses sight of the goals of the participating agents, which the analogy to roadways makes clear:

Camille Fournier on the value of complaining: If you do this well, you actually teach people how to understand which problems are important, and which problems are not. Letting people complain might seem like it will do nothing but encourage negativity and drama, but if you guide people to learn from their complaints it can instead help your team grow. It’s great when people can bring problems AND solutions to you simultaneously, but it’s more likely that they will need help to see the best solution.

From a Neil Postman’s “Bullshit and the Art of Crap-Detection” (emphasis mine): Each person’s crap-detector is embedded in their value system; if you want to teach the art of crap-detecting, you must help students become aware of their values. After all, Vice President, Spiro Agnew, or his writers, know as much about semantics as anyone in this room. What he is lacking has very little to do with technique, and almost everything to do with values.

“Learning How to Read”, by Niklas Luhmann: This leads to another question: what are we to do with what we have written down? Certainly, at first we will produce mostly garbage. But we have been educated to expect something useful from our activities and soon lose confidence if nothing useful seems to result. We should therefore reflect on whether and how we arrange our notes so that they are available for later access.

Three computers held sway over my brothers and I during my childhood: the Commodore 64 and its games, spread across dozens of 5 ¼ inch floppy disks; the IBM XT and the short stories I wrote in WordPerfect; and the king of them all – the Intel 386 running DOS 5. Aside from newer games with insanely cool graphics1, the 386 brought QBasic and a C compiler into our lives. From that point on, instead of Ultima IV, I watched fractals, cellular automata, and homegrown computer games develop — all from my perch behind the computer chair, watching over my brothers' shoulders2.

David Robinson wrote about his experience after a year as a Data Scientist at Stack Overflow. I haven’t read it yet, but it the opening paragraph turned me on to his series about analyzing baseball statistics using the beta distribution. Understanding the beta distribution Understanding empirical Bayes estimation Understanding credible intervals Understanding the Bayesian approach to false discovery rates Understanding Bayesian A/B testing Understanding beta binomial regression This is a great series of posts – I’ve been learning how to use the beta distribution to estimate uncertainties in clickthrough rates, so finding David Robinson’s blog was like stumbling into the lecture hall for a class I didn’t know I needed.

While home sick this week, I started learning to play Go. Partly inspired the article on AlphaGo in Wired, but also because it’s a pretty game. Hiroki Mori’s site The Interactive Way to Go is an awesome resource1. I haven’t gotten to the point of actually attempting to play a full game, but working through Go problems is super fun. Some of the patterns that emerge in Go (like “Crane in the nest” and the “Ladder”) unfold with a inevitability that makes me think of stable structures in Conway’s Game of Life.