Object Lessons — Bogost and Schaberg edit a series about the hidden lives of ordinary things, from advocates to attendants, heresies to shares. For anyone who cares about products.
A Data Programming CS1 Course (PDF) — We have found that students can be motivated to learn programming and computer science concepts in order to analyze DNA, predict the outcome of elections, detect fraudulent data, suggest friends in a social network, determine the authorship of documents, and more. The approach is more than just a collection of “nifty assignments”; rather, it affects the choice of topics and pedagogy.
Cars and the Future (Ben Thompson) — This generational pattern of adoption will, in the history books, look sudden, even as it seems to unfold ever so slowly for those of us in the here and now — especially those of us working in technology. The pace of change in the technology industry — which is young, hugely driven by Moore’s Law, and which has largely catered to change-embracing geeks — is likely the true aberration. After all, the biggest mistake consistently made by technologists is forgetting that for most people technology is a means to an end, and for all the benefits we can list when it comes to over-the-top video or a network of on-demand self-driving vehicles, change and the abandonment of long-held ideals like the open road and a bit of TV after supper is an end most would prefer to avoid.
CES 2016 Observations for Product People — The big challenge is no surprise. Software development is unable to keep up with the hardware. What is going to separate one device from another or one company from another will be the software execution, not just the choice of chipset or specs for a peripheral/sensor. It would be hard to overstate the clear opportunity to build winning products using stronger software relative to competitors. Said another way, spending too many cycles on hardware pits you against the supply chain for most products. The whole piece is solid.
Japanese Scientists Create Touchable Holograms (Reuters) — Using femtosecond laser technology, the researchers developed ‘Fairy Lights, a system that can fire high-frequency laser pulses that last one millionth of one billionth of a second. The pulses respond to human touch, so that – when interrupted – the hologram’s pixels can be manipulated in mid-air.
Google Cloud Vision API — classifies images into thousands of categories (e.g., “boat,” “lion,” “Eiffel Tower”), detects faces with associated emotions, and recognizes printed words in many languages.
Competitive Coding (Bloomberg) — ignore the lazy author’s patronising tone; the bit that caught my eye was: He first began freaking people out in second grade, at age 8, when he took second place in a major Belarusian coding competition. To put this achievement in perspective, the score was high enough for Korotkevich to be granted automatic enrollment in a top technical university without needing to pass any other entrance exams. That is how you value STEM education: let people test out of it if they don’t need it!
How We Pass The Buck (Anil Dash) — The thing is, technology is not neutral, algorithms are built with values, and the default choices in our software determine huge swaths of our culture. We delegate ethical decisions as consumers and citizens to people who make software, but almost no computer science program teaches ethics, and almost no major technology company has a chief ethicist.
Peer to Peer Markets (PDF) — We discuss elements of market design that make this possible, including search and matching algorithms, pricing, and reputation systems. We then develop a simple model of how these markets enable entry by small or flexible suppliers, and the resulting impact on existing firms. Finally, we consider the regulation of peer-to-peer markets, and the economic arguments for different approaches to licensing and certification, data, and employment regulation.
16 Product Things I learned at Imgur — You can A/B test individuals, but it’s nearly impossible to A/B test communities because they work based on a mutually reinforcing self-conception. Use a combination of intuition (which comes from experience), talking to other community managers and 1:1 contact with a sample of your community. But you’ll still be wrong a lot.
kaldi — a toolkit for speech recognition written in C++ and licensed under the Apache License v2.0
Google Patenting Machine Learning Developments (Reddit) — I am afraid that Google has just started an arms race, which could do significant damage to academic research in machine learning. Now it’s likely that other companies using machine learning will rush to patent every research idea that was developed in part by their employees. We have all been in a prisoner’s dilemma situation, and Google just defected. Now researchers will guard their ideas much more combatively, given that it’s now fair game to patent these ideas, and big money is at stake.
Machine Ethics (Nature) — machine learning ethics versus rule-driven ethics. Logic is the ideal choice for encoding machine ethics, argues Luís Moniz Pereira, a computer scientist at the Nova Laboratory for Computer Science and Informatics in Lisbon. “Logic is how we reason and come up with our ethical choices,” he says. I disagree with his premises.
CS 61AS — Berkeley self-directed Structure and Interpretation of Computer Programs course.
Harbingers of Failure (PDF) — We show that some customers, whom we call ‘Harbingers’ of failure, systematically purchase new products that flop. Their early adoption of a new product is a strong signal that a product will fail – the more they buy, the less likely the product will succeed. Firms can identify these customers either through past purchases of new products that failed, or through past purchases of existing products that few other customers purchase.
Eric Brewer on Kubernetes — interesting not only for insights into Google’s efforts around Kubernetes but for: There’s so much excitement we can hardly handle all the pull requests. I think we’re committing, based on the GitHub log, something like 40 per day right now, and the demand is higher than that. Each of those takes reviews and, of course, there’s a wide variety of quality on those. Some are easy to review and some are quite hard to review. It’s a success problem, and we’re happy to have it. We did scale up the team to try and improve its velocity, but also just improve our ability to interact with all of the open source world that legitimately wants to contribute and has a lot to contribute. I’m very excited that the velocity is here, but it’s moving so fast it’s hard to even know all the things that change day to day. Makes a welcome change from the code dumps that are some of Google’s other high-profile projects.
We Don’t Sell Saddles Here — Stewart Butterfield, to his team, on product development and quality. Every word of this is true for every other product, too.
What is Privacy Worth? (PDF) — When endowed with the $10 untrackable card, 60.0% of subjects claimed they would keep it; however, when endowed with the $12 trackable card only 33.3% of subjects claimed they would switch to the untrackable card. […] This research raises doubts about individuals’ abilities to rationally navigate issues of privacy. From choosing whether or not to join a grocery loyalty program, to posting embarrassing personal information on a public website, individuals constantly make privacy-relevant decisions which impact their well-being. The finding that non-normative factors powerfully influence individual privacy valuations may signal the appropriateness of policy interventions.
Tools are the Problem — Tools don’t solve problems any more; they have become the problem. There’s just too many of them, and they all include an incredible number of features that you don’t use on your site –but that users are still required to download and execute.
Elements of Scale: Composing and Scaling Data Platforms (Ben Stopford) — today’s data platforms range greatly in complexity, from simple caching layers or polyglotic persistence right through to wholly integrated data pipelines. There are many paths. They go to many different places. In some of these places at least, nice things are found. So, the aim for this talk is to explain how and why some of these popular approaches work. We’ll do this by first considering the building blocks from which they are composed. These are the intuitions we’ll need to pull together the bigger stuff later on.
Estimating Google’s 2FA Adoption — If we project out to the current day (965 days later), that’s a growth of ~25M users (25,586,975). Add that to the ~14M base number of users (13,886,058) exiting the graph and we end up at a grand total of…nearly 40 million users (39,473,033) enrolled in Google’s 2SV. NB there’s a lot on the back of this envelope.
Empathy and Product Development — None of this means that you shouldn’t A/B test or have other quantitative measure. But all of those will mean very little if you don’t have the qualitative context that only observation and usage can provide. Empathy is central to product development.