M2M, IoT, and the invisibility of ubiquity

From the Internet to the Internet of Everything to just plain Everything.

I started writing this post to respond to the question: “What is the difference between machine-to-machine (M2M) and the Internet of Things (IoT)?” It turns out, a post answering that question isn’t really necessary. There is already a pretty good thread on Quora that answers it.

However, with the emphasis on the technologies at play, most of the answers on Quora left me a little flat. I guess it’s because, while they are correct, they tend to focus on the details and miss the big picture. They say things like, “M2M is the plumbing and IoT is the application,” or M2M is about SMS and general packet radio service (GPRS), while IoT is about the IP stack. Or, essentially, that M2M is freighted with telecom transport-layer heritage (baggage?), while the IoT is emerging out of the upper layers of the Internet’s IP stack — which would be great except for the fact that it’s not always true. Plenty of IoT devices operate with other-than-IP protocol stacks via gateways.

I think the distinction between M2M and IoT isn’t all that important with regard to the technology stacks they employ. What’s more interesting to me is that the change in language suggests a transition. It’s a signpost plunked down in the middle of an otherwise smooth continuum, where enough of us have noticed something happening to make a name for it. We used to argue about what Web 2.0 meant; now we argue about what IoT means. Regardless of what the term “Internet of Things” actually means, its growing use represents a conceptual point of departure from what came before. Something new is happening, and we are using different words to signify it. Read more…

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Four short links: 18 April 2014

Four short links: 18 April 2014

Interview Tips, Data of Any Size, Science Writing, and Instrumented Javascript

  1. 16 Interviewing Tips for User Studies — these apply to many situations beyond user interviews, too.
  2. The Backlash Against Big Data contd. (Mike Loukides) — Learn to be a data skeptic. That doesn’t mean becoming skeptical about the value of data; it means asking the hard questions that anyone claiming to be a data scientist should ask. Think carefully about the questions you’re asking, the data you have to work with, and the results that you’re getting. And learn that data is about enabling intelligent discussions, not about turning a crank and having the right answer pop out.
  3. The Science of Science Writing (American Scientist) — also applicable beyond the specific field for which it was written.
  4. earhornEarhorn instruments your JavaScript and shows you a detailed, reversible, line-by-line log of JavaScript execution, sort of like console.log’s crazy uncle.
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Biomimicry in the real world

There's good reason to believe nature has clues about how to do a good job — can it also help with web designs?

FestoRoboticBird

Festo’s Robotic Bird. Photo by Mike Loukides.

A couple of years ago, I visited the World Science Festival in New York and saw Festo’s robotic bird. It was amazing. I’ve seen things that looked more or less like a bird, and that flew, but clearly weren’t flying like a bird. An airplane has a body, has wings, and flies, but you wouldn’t mistake it for a bird. This was different: it looked like a giant seagull, with head and tail movements that were clearly modelled on a living bird’s.

Since then, Festo has built a robotic kangaroo; based on work they started in 2010, they have a robotic elephant’s trunk that learns, a robotic jellyfish, and no doubt many other animals that I haven’t yet seen.

Read more…

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Four short links: 17 April 2014

Four short links: 17 April 2014

Foresight and Innovation, Artificial Intelligence, Consumer IoT, and Gender Disparity


  1. Playbook for Strategic Foresight & Innovation — MANY pages of framework and exercises. Good for what it is, but also as a model for how to disseminate your ideas and frame for the world to consume.
  2. Why I’m a Crabby Patty About AI and Cognitive Science (Fredrik Deboer) — huzzah! the current lack of progress in artificial intelligence is not a problem of insufficient processing power. Talking about progress in artificial intelligence by talking about increasing processor power is simply a non sequitur. If we knew the problems to be solved by more powerful processors, we’d already have solved some of the central questions!
  3. Four Types of Consumer Internet of Things Things (BERG London) — nice frame for the different needs of the different types of products and services.
  4. We Can Do Bettera visualisation of the gender disparity in engineering teams in the tech industry.
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Four short links: 16 April 2014

Four short links: 16 April 2014

Time Series, CT Scanner, Reading List, and Origami Microscope

  1. morris.jspretty time-series line graphs.
  2. Open Source CT Scanner — all the awesome.
  3. Alan Kay’s Reading List — in case you’re wondering what to add to the pile beside your bed. (via Alex Dong)
  4. Foldscope — origami optical microscope, 2000x magnification for under $1.
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Disposable architecture?

Technology is now outpacing innovation, fostering a culture of disposability.

I’ve noticed a number of faint signals recently pointing to a general shift in the speed of technology and the repercussions it’s having on the products we’re seeing come to market. This recent Tweet from Tom Scott got me really thinking about it:

Scott’s comment brought me back to a recent conversation I had with Princeton architecture student Alastair Stokes. I’d asked Stokes whether the technology challenges of designing a building to last 100+ years are more difficult today than they were in, say, 1900 — or if it’s as difficult, just different. He said the challenges might be more difficult today, but regardless, maybe technology is changing the solution: we shouldn’t try to design buildings today to last 100 years, but design them so they’ll last for, say, 20 years and then be replaced. Read more…

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