"systems" entries

Four short links: 18 July 2015

Four short links: 18 July 2015

WebAssembly, Generative Neural Nets, Automated Workplace, and Conversational UIs

  1. WebAssembly (Luke Wagner) — new standard, WebAssembly, that defines a portable, size- and load-time-efficient format and execution model specifically designed to serve as a compilation target for the Web. Being worked on by Mozilla, Google, Microsoft, and Apple.
  2. Inceptionism: Going Deeper into Neural Networks (Google Research) — stunningly gorgeous gallery of images made by using a deep image-classification neural net to make the picture “more.” (So, if the classifier says the pic is of a cat, randomly twiddle pixels until the image classifier says “wow, that matches `cat’ even better!”)
  3. The Automated Workplace (Ben Brown) — What happens if this process is automated using a “bot” in an environment like Slack? — repeat for all business processes. (via Matt Webb)
  4. Conversational UIs (Matt Webb) — a new medium needs a new grammar and conversational UIs are definitely a new medium. As someone whose wedding vows were exchanged on a TinyMUSH, conversational UIs are near and dear to my heart.
Four short links: 6 March 2014

Four short links: 6 March 2014

Repoveillance, Mobiveillance, Discovery and Orchestration, and Video Analysis

  1. Repo Surveillance NetworkAn automated reader attached to the spotter car takes a picture of every ­license plate it passes and sends it to a company in Texas that already has more than 1.8 billion plate scans from vehicles across the country.
  2. Mobile Companies Work Big DataMeanwhile companies are taking different approaches to user consent. Orange collects data for its Flux Vision data product from French mobile users without offering a way for them to opt-out, as does Telefonica’s equivalent service. Verizon told customers in 2011 it could use their data and now includes 100 million retail mobile customers by default, though they can opt out online.
  3. Serfdoma decentralised solution for service discovery and orchestration that is lightweight, highly available, and fault tolerant.
  4. Longomatcha free video analysis software for sport analysts with unlimited possibilities: Record, Tag, Review, Draw, Edit Videos and much more! (via Mark Osborne)

Defining the industrial Internet

Some broad thoughts on characteristics that define the industrial Internet field.

We’ve been collecting threads on what the industrial Internet means since last fall. More case studies, company profiles and interviews will follow, but here’s how I’m thinking about the framework of the industrial Internet concept. This will undoubtedly continue to evolve as I hear from more people who work in the area and from our brilliant readers.

The crucial feature of the industrial Internet is that it installs intelligence above the level of individual machines — enabling remote control, optimization at the level of the entire system, and sophisticated machine-learning algorithms that can work extremely accurately because they take into account vast quantities of data generated by large systems of machines as well as the external context of every individual machine. Additionally, it can link systems together end-to-end — for instance, integrating railroad routing systems with retailer inventory systems in order to anticipate deliveries accurately.

In other words, it’ll look a lot like the Internet — bringing industry into a new era of what my colleague Roger Magoulas calls “promiscuous connectivity.” Read more…

Comments: 7

Interoperating the industrial Internet

If we're going to build useful applications on top of the industrial Internet, we must ensure the components interoperate.

One of the most interesting points made in GE’s “Unleashing the Industrial Internet” event was GE CEO Jeff Immelt’s statement that only 10% of the value of Internet-enabled products is in the connectivity layer; the remaining 90% is in the applications that are built on top of that layer. These applications enable decision support, the optimization of large scale systems (systems “above the level of a single device,” to use Tim O’Reilly’s phrase), and empower consumers.

Given the jet engine that was sitting on stage, it’s worth seeing how far these ideas can be pushed. Optimizing a jet engine is no small deal; Immelt said that the engine gained an extra 5-10% efficiency through software, and that adds up to real money. The next stage is optimizing the entire aircraft; that’s certainly something GE and its business partners are looking into. But we can push even harder: optimize the entire airport (don’t you hate it when you’re stuck on a jet waiting for one of those trucks to push you back from the gate?). Optimize the entire air traffic system across the worldwide network of airports. This is where we’ll find the real gains in productivity and efficiency.

So it’s worth asking about the preconditions for those kinds of gains. It’s not computational power; when you come right down to it, there aren’t that many airports, aren’t that many flights in the air at one time. There are something like 10,000 flights in the air at one time, worldwide; and in these days of big data, and big distributed systems, that’s not a terribly large number. It’s not our ability to write software; there would certainly be some tough problems to solve, but certainly nothing as difficult as, say, searching the entire web and returning results in under a second. Read more…

Comment: 1

Missing maps and the fragility of digital information

Traditional methods come through when connected systems fail.

A couple of months ago, I had a remarkable demonstration of the fragility of the "always on" connected mindset.

Comments: 41