What We Talk About When We Talk About Design History: Design Observer

From the packaging of our belongings to the presentation of our surroundings, most of us recognize that design has, over the course of the past century, become a ubiquitous component in everyday life. Design is signage and graffiti and labels and lace, posters and propaganda and toothbrushes and teapots: objects and artefacts that captivate and delight us, frustrate or provoke us, but why?

This is where design historians come in.

Design history is, after all, social history: it’s an evolutionary (and somewhat cautionary) tale of use and abuse, of innovation and migration, of the inevitable tide of obsolescence that puzzles some of us to such a vexing degree that we simply have no other choice but to become design historians to start making sense of things.

And we begin, like all historians, by doing research.

Live Like a Hydra: Thoughts on how to get stronger when things are chaotic. #2 The Chaos Monkey — Medium

Netflix has a server architecture that currently serves a pretty high percentage of all of the internet’s traffic, due to their streaming video service.

One of the most interesting things about their server architecture is that they routinely attack their own systems. They have a tool called Chaos Monkey that randomly disables their own production instances to make sure they can survive that common type of failure without any customer impact.

Because there are several ways in which servers can fail, they’ve also employed a fleet of monkeys that attack all manner of servers — some that are too slow, some that aren’t connected up to the proper server groups, some that just look weird, etc. And finally, there’s a Chaos Gorilla that doesn’t just turn off individual servers, but occasionally wipes out an entire availability zone, as if Godzilla had destroyed an entire portion of the country.

The philosophy is simple: by building a server architecture that expects failure, the system as a whole can learn how to withstand bigger and tougher obstacles even if they don’t know exactly when or how they will occur in real life.

Meet Margaret Hamilton, the badass ’60s programmer who saved the moon landing – Vox

In the early days, women were often assigned software tasks because software just wasn’t viewed as very important. “It’s not that managers of yore respected women more than they do now,” Rose Eveleth writes in a great piece on early women programmers for Smithsonian magazine. “They simply saw computer programming as an easy job. It was like typing or filing to them and the development of software was less important than the development of hardware. So women wrote software, programmed and even told their male colleagues how to make the hardware better.”

“I began to use the term ‘software engineering’ to distinguish it from hardware and other kinds of engineering,” Hamilton told Verne’s Jaime Rubio Hancock in an interview. “When I first started using this phrase, it was considered to be quite amusing. It was an ongoing joke for a long time. They liked to kid me about my radical ideas. Software eventually and necessarily gained the same respect as any other discipline.”

Hamilton is now 78 and runs Hamilton Technologies, the Cambridge, Massachusetts-based company she founded in 1986. She’s lived to see “software engineering” — a term she coined — grow from a relative backwater in computing into a prestigious profession.

Meet Margaret Hamilton, the badass ’60s programmer who saved the moon landing – Vox

Apollo was also a major software project. Astronauts used the Apollo Guidance Computer, which was placed in both the command module and the lunar module, for navigation assistance and to control the spacecraft, and someone needed to program it.

The software for the guidance computer was written by a team at the MIT Instrumentation Laboratory (now the Draper Laboratory), headed up by Margaret Hamilton. Here’s an amazing picture of her next to the code she and her colleagues wrote for the Apollo 11 guidance computer that made the moon landing possible.

The process of actually coding in the programs was laborious, as well. The guidance computer used something known as “core rope memory”: wires were roped through metal cores in a particular way to store code in binary. “If the wire goes through the core, it represents a one,” Hamilton explained in the documentary Moon Machines. “And around the core it represents a zero.” The programs were woven together by hand in factories. And because the factory workers were mostly women, core rope memory became known by engineers as “LOL memory,” LOL standing for “little old lady.”

Voight-Kampff machine – Off-world: The Blade Runner Wiki

Originating in Philip K Dick’s novel Do Androids Dream of Electric Sheep?, the Voight-Kampff machine or device (spelled Voigt-Kampff in the book) also appeared in the book’s screen adaptation, the 1982 science fiction film Blade Runner.

The Voight-Kampff is a polygraph-like machine used by the LAPD’s Blade Runners to assist in the testing of an individual to see whether they are a replicant or not. It measures bodily functions such as respiration, heart rate and eye movement in response to emotionally provocative questions.

The Voight-Kampff machine is perhaps analogous to (and may have been partly inspired by) Alan Turing‘s work which propounded an artificial intelligence test — to see if a computer could convince a human (by answering set questions, etc.) that it was another human.

How To Engineer Serendipity

I’d like to tell the story of a paradox: How do we bring the right people to the right place at the right time to discover something new, when we don’t know who or where or when that is, let alone what it is we’re looking for? This is the paradox of innovation: If so many discoveries — from penicillin to plastics – are the product of serendipity, why do we insist breakthroughs can somehow be planned? Why not embrace serendipity instead?

The final piece is the network. Google has made its ambitions clear — as far as chairman Eric Schmidt is concerned, the future of search is a “serendipity engine” answering questions you never thought to ask. “It’ll just know this is something that you’re going to want to see,” explained artificial intelligence pioneer Ray Kurzweil shortly after joining the company as its director of engineering.

One antidote to this all-encompassing filter bubble is an opposing serendipity engine proposed by MIT’s Ethan Zuckerman. In his book, Rewirehe sketches a set of recommendation and translation tools designed to nudge us out of our media comfort zones and “help us understand whose voices we’re hearing and whom we are ignoring.”

As Zuckerman points out, the greatest threats to serendipity are our ingrained biases and cognitive limits — we intrinsically want more known knowns, not unknown unknowns. This is the bias a startup named Ayasdi is striving to eliminate in Big Data.Rather than asking questions, its software renders its analysis as a network map, revealing hidden connections between tumors or terrorist cells, which CEO Gurjeet Singh calls “digital serendipity.”