1. How To Think
Thinking is not IQ. When people talk about thinking they make the mistake of thinking that people with high IQs think better. That’s not what I’m talking about. I hate to break it to you but unless you’re trying to get into MENSA, IQ tests don’t matter. That’s not the type of knowledge or brainpower that makes you better at life, happier, or more successful. It’s a measure sure, but a relatively useless one.
If you want to outsmart people who are smarter than you, temperament and life-long learning are more important than IQ.
Two of the guiding principles that I follow on my path towards seeking wisdom are: (1) Go to bed smarter than when you woke up; and (2) I’m not smart enough to figure everything out myself, so I want to ‘master the best of what other people have already figured out.’
Acquiring wisdom, is hard. Learning how to think is hard. It means sifting through information, filtering the bunk, and connecting it to a framework that you can use. A lot of people want to get their opinions from someone else. I know this because whenever anyone blurts out an opinion and I ask why, I get some hastily re-phrased sound-byte that doesn’t contextualize the problem, identify the forces at play, demonstrate differences or similarities with previous situations, consider base rates, or … anything else that would demonstrate some level of thinking. (One of my favorite questions to probe thinking is to ask what information would cause someone to change their mind. Immediately stop listening and leave if they say ‘I can’t think of anything.’)
Thinking is hard work. I get it. You don’t have time to think but that doesn’t mean you get a pass from me. I want to think for myself, thank you.
It’s a grounding in computational thinking—not a facility with the latest feature or product—that fosters future success in the field, whether students go on to become engineers or inventors or entrepreneurs.
That’s a powerful rationale for teaching computational thinking to our young people. But there’s a problem. In conventional computer science instruction, these principles are only accessible to those who learn how to program. This poses a big hurdle, especially for younger students. Enter Computer Science Unplugged, which has been developed at the University of Canterbury in New Zealand over the past two decades.
Professors Tim Bell, Mike Fellows and Ian H. Witten have figured out how to teach the concepts of computer science through games, puzzles and magic tricks. Taking the computer out of the picture—for the time being—allows children as young as five to learn about the basic ideas that undergird computer science. Youngsters can tackle topics as apparently abstruse as algorithms, binary numbers, Boolean circuits, and cryptographic protocols. The activities offered by Computer Science Unplugged are aimed at students in kindergarten through seventh grade, though they have been used by students in high school and even college.
Younger children might learn about “finite state automata”—sequential sets of choices—by following a pirates’ map, dashing around a playground in search of the fastest route to Treasure Island. Older kids can learn how computers compress text to save storage space by taking it upon themselves to compress the text of a book. This is done by marking repetitions of a word within a text, crossing out the word each time it reappears, and drawing an arrow back to its first appearance on the page. (Dr. Seuss books, like Green Eggs and Ham, compress especially efficiently because of their frequent repetitions.)
3. Heat Death: Venture Capital in the 1980s | Reaction Wheel
A fascinating read of the venture funding run of the 80’s
The history repeats itself crowd thinks that that there must be a bubble sooner or later. “Now?” they constantly ask, “Is it a bubble now?” as if history has to repeat whatever was most memorable about the last time. History may repeat itself, but there’s an awful lot of history that this particular venture capital cycle could repeat. Below is a short history of venture capital in the 1980s, my interpretation and comparison to the ’90s and today, and some thoughts about what that means. It’s long. If you’re attention-deprived, skip to ‘1980s v. 1990s’, about four-fifths of the way down.
Design is entering its golden age. Now, like never before, the value of the discipline is recognized. This recognition is both a welcome change and a challenge for designers as they move to designing for networked systems. Jon Follett, editor of Designing for Emerging Technologies, recently sat down with Matt Nish-Lapidus, partner and design director at Normative Design, who contributed to the book. Nish-Lapidus discusses the changing role of design and designers in emerging technology.
As Nish-Lapidus describes, we’re witnessing the evolution of product development from one crafts-person, one customer; to a one crafts-person, many customers; to a one craft-person, one product that many people will customize. He explains how the crafted object and the nature of design has changed, beginning with the pre-industrial era:
“We go from having a single pair of glasses made for a single person, handmade usually, to a pair of glasses designed and then mass-manufactured for a countless number of people, to having a pair of glasses that expresses a lot of different things. On one hand, you have something like Google Glass, which is still mass-produced, but the glasses actually contain embedded functionality. Then we also have, with the emergence of 3D printing and small-scale manufacturing, a return to a little bit of that artisan, one-to-one relationship, where you could get something that someone’s made just for you.”
Disclaimer: The selections I use to describe the links are snippets, often edited together to better describe the original piece, each of which is worth reading on it’s original site.
My interest is in the future because I am going to spend the rest of my life there. – Charles Kettering