The O'Reilly Radar Podcast: Matt Nish-Lapidus on design's circular evolution, and designing in the post-Industrial era.
In this week’s Radar Podcast episode, Jon Follett, editor of Designing for Emerging Technologies, chats with Matt Nish-Lapidus, partner and design director at Normative. Their discussion circles around the evolution of design, characteristics of post-Industrial design, and aesthetic intricacies of designing in networked systems. Also note, Nish-Lapidus will present a free webcast on these topics March 24, 2015.
Post-Industrial design relationships
Nish-Lapidus shares an interesting take on design evolution, from pre-Industrial to post-Industrial times, through the lens of eyeglasses. He uses eyeglasses as a case study, he says, because they’re a piece of technology that’s been used through a broad span of history, longer than many of the things we still use today. Nish-Lapidus walks us through the pre-Industrial era — so, Medieval times through about the 1800s — where a single craftsperson designed one product for a single individual; through the Industrial era, where mass-production took the main stage; to our modern post-Industrial era, where embedded personalization capabilities are bringing design almost full circle, back to a focus on the individual user:
“Once we move into this post-Industrial era, which we’re kind of entering now, the relationship’s starting to shift again, and glasses are a really interesting example. We go from having a single pair of glasses made for a single person, hand-made 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.
“These post-Industrial objects are more of an expression of the networked world in which we now live. We [again] have a way of building relationships with individual crafts-people. We also have objects that exist in the network themselves, as a physical instantiation of the networked environment that we live in.”
The future is maintenance: build for the inevitable.
Technology has had a cult of newness for centuries. We hail innovators, cheer change, and fend off critics who might think new and change are coming too fast. Unfortunately, while that drives the cycle of creation, it also creates biases that damage what we create, reducing the benefits and increasing the costs.
Formerly new things rapidly become ordinary “plumbing,” while maintenance becomes a cost center, something to complain about. “Green fields” and startups look ever more attractive because they offer opportunities to start fresh, with minimal connections to past technology decisions.
The problem, though, is that most of these new things — the ones that succeed enough to stay around — have a long maintenance cycle ahead of them. As Axel Rauschmayer put it:
“People who maintain stuff are the unsung heroes of software development.”
In a different context, Steve Hendricks of Historic Doors pointed out that:
“Low maintenance is the holy grail of our culture. We’ve gone so far that we’re willing to throw things away rather than fix them.”
That gets especially expensive. Heaping praise on the creators of new things while trying to minimize the costs of the maintainers is a recipe for disaster over the long term.
From careers to culture to code, here are key insights from the O'Reilly Software Architecture Conference 2015.
Experts from across the software architecture world came together in Boston for the O’Reilly Software Architecture Conference 2015. Below we’ve assembled notable keynotes, interviews, and insights from the event.
Software architects: post-“post-useful”
The old notion of a software architect being a non-coding, post-useful deep thinker is giving way to something far more interesting, says Neal Ford, software architect and meme wrangler at ThoughtWorks. “Architecture has become much more interesting now because it’s become more encompassing … it’s trying to solve real problems rather than play with abstractions.”
Khoi Vinh on "How They Got There," the cards interaction model, and designers as founders.
I recently sat down with Khoi Vinh, vice president of user experience at Wildcard and co-founder of Kidpost. Previously, Vinh was co-founder and CEO of Mixel (acquired by Etsy, Inc.), design director of The New York Times Online, and co-founder of the design studio Behavior, LLC. Our conversation included a discussion of career paths; the much talked about new interaction model, cards; and advice for design entrepreneurs.
Curiosity serves designers well
Vinh and I discussed the ever-evolving role of designers. He recently self-published How They Got There, a book of interviews with interaction designers who describe their career paths and offer advice and insight. Vinh explained:
“How They Got There is kind of like the book I wish I could have read when I was just starting out in my career. The central thesis is that very few careers are truly planned out, A to B, to C, to Z, and it’s usually a lot of stuff that just happens by circumstance or blind luck, or through someone who knows someone.
“As I became more and more aware of that in my career, I started to find those stories really interesting, really revealing, because they say so much about the character of people who achieve notoriety in their careers; the circumstances that led them to where they are can be fascinating. In a lot of instances, the things that get these people onto these paths are very, very minor events or minor coincidences. … There’s a serendipity, but I think, one thing that comes out when you read these stories is what serves these designers really well is curiosity, a willingness to be available to opportunities, so to speak. They go with the flow. They let one thing turn into another through their ability to acclimate themselves to various situations.
“What’s that old saying from Branch Rickey — “luck is the residue of design”? These careers are somewhat serendipitous, but they are really the result of folks who are very conscientious about making the most of whatever situation they had and working really hard and applying themselves, and looking at the world around them with great curiosity and being really willing to study what it takes to get to the next level.”
Tensor methods for machine learning are fast, accurate, and scalable, but we'll need well-developed libraries.
Data scientists frequently find themselves dealing with high-dimensional feature spaces. As an example, text mining usually involves vocabularies comprised of 10,000+ different words. Many analytic problems involve linear algebra, particularly 2D matrix factorization techniques, for which several open source implementations are available. Anyone working on implementing machine learning algorithms ends up needing a good library for matrix analysis and operations.
But why stop at 2D representations? In a recent Strata + Hadoop World San Jose presentation, UC Irvine professor Anima Anandkumar described how techniques developed for higher-dimensional arrays can be applied to machine learning. Tensors are generalizations of matrices that let you look beyond pairwise relationships to higher-dimensional models (a matrix is a second-order tensor). For instance, one can examine patterns between any three (or more) dimensions in data sets. In a text mining application, this leads to models that incorporate the co-occurrence of three or more words, and in social networks, you can use tensors to encode arbitrary degrees of influence (e.g., “friend of friend of friend” of a user).
Being able to capture higher-order relationships proves to be quite useful. In her talk, Anandkumar described applications to latent variable models — including text mining (topic models), information science (social network analysis), recommender systems, and deep neural networks. A natural entry point for applications is to look at generalizations of matrix (2D) techniques to higher-dimensional arrays. Read more…