- 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.
The O'Reilly Radar Podcast: The Internet of Things ecosystem, predictive machine learning superpowers, and deep-seated love for appliances and furniture.
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In this week’s episode of the Radar Podcast, O’Reilly’s Mary Treseler chats with Mike Kuniavsky, a principal scientist in the Innovation Services Group at PARC. Kuniavsky talks about designing for the Internet of Things ecosystem and why the most interesting thing about the IoT isn’t the “things” but the sensors. He also talks about his deep-seated love for appliances and furniture, and how intelligence will affect those industries.
Here are some highlights from their conversation:
Wearables as a class is really weird. It describes where the thing is, not what it is. It’s like referring to kitchenables. ‘Oh, I’m making a kitchenable.’ What does that mean? What does it do for you?
There’s this slippery slope between service design and UX design. I think UX design is more digital and service design allows itself to include things like a poster that’s on a wall in a lobby, or a little card that gets mailed to people, or a human being that they can talk to. … Service design takes a slightly broader view, whereas UX design is — and I think usefully — still focused largely on the digital aspect of it.