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September 29 2013

Rolf Liebermann (1910-1999), Symphonie « Les Echanges » Komposition für 156 Maschinen (02:58) :…

Rolf Liebermann (1910-1999), Symphonie „Les Echanges“ Komposition für 156 Maschinen (02:58) : composition pour 156 #machines sur #Ubuweb
http://ubu.com/sound/liebermann.html

#Rolf_Liebermann composed this piece for the Swiss Expo (National exhibition) 1964. Scored for 156 machines - amongst which one can find 16 typewriters, 18 calculator machines, 8 accounting machines, 12 office perforators, 10 caisses enregistreuses, 8 humidificateurs-colleurs, 8 tele-scripteurs, 2 metronomes, 4 bells of signalisation, 2 entrance door gongs, 10 claxons, 16 telephones, 40 experimental signal receptors,1 fork lift, a duplicator and a monte-charge. Rolf Liebermann was also the head of the main music section of the Norddeutscher Rundfunk (NDR) from 1957 to 1959. In this function he was responsible for instigating the famous NDR Jazzworkshops. His most popular work might be his Concerto for Jazz Band and Symphony Orchestra which was premiered by Hans Rosebaud in Donaueschingen in 1954.

http://ubumexico.centro.org.mx/sound/liebermann_rolf/Liebermann-Rolf_01_Les-Echanges.mp3

#audio #musique

July 23 2013

L'abolition du système salarial une utopie ? L'empire de la domination masculine sur la sexualité…

L’abolition du système salarial une utopie ?
L’empire de la domination masculine sur la sexualité des femmes a-t-il un fondement capitaliste ?
Serons-nous éternellement prisonniers de ce que nous produisons ?

British Sounds(1969) de jean-Luc Godard, Jean-Henri Roger et Jean-Pierre Gorin (Groupe Dziga Vertov)

http://vimeo.com/54291385

Tourné en février 69, le film est revendiqué après coup par le groupe Dziga Vertov. C’est la première tentative de #Godard pour travailler en #dialogue avec quelqu’un. En fevrier 69, il se lie d’amitié avec un jeune militant #maioste, #Jean-Henri_Roger, qui n’a encore jamais tourné de #film. Il lui propose d’etre #coréalisateur d’un film commandé par une petite #telévison anglaise, #London_week-end televison.
#Jean-Henri_Roger sera ensuite l’un des membres fondateurs de #cinélutte puis coréalisateur de films avec Juliette Bertho, sa compagne, puis réalisteur de longs métrages et comédien dans #Eloge_de_l'amour.
Godard avait déjà tourné #One+one en #Angleterre à la fin de l’été #68. Il applique ici un principe de #Brecht selon lequel il ne faut pas donner d’#images trop complexes du monde. Godard et Roger vont simplifier le #monde avec une série de #plans-séquences longs avec, au maximum, une #idée par #plan.
A Londres ils ne rencontrent pas de #militants #maoïstes mais de la nouvelle #gauche #anglaise, des #trotskistes. London week-end #televison refuse de diffuser le film fini. Il ne sera que très partiellement montré, le 2 janvier 1970, au cours d’un débat qu’il illustre d’extraits. La première projection à Paris se fait lorsque le #groupe_Dziga_Vertov est formé.

#Industrie #Machines #Taylorisme #Aliénation #Travail #Exploitation #Prolétariat #Ouvriers #Bourgeoisie #Politique #Révolution #Luttes-des_classes #Domination #Féminisme #Sexualité #Propriété #Capital #Capitalisme #Marxisme #théorie_de_la_valeur #Communisme #Marchandise #Histoire #Technique #Langage_cinématographique #Documentaire #Vidéo

March 27 2013

The coming of the industrial internet

The big machines that define modern life — cars, airplanes, furnaces, and so forth — have become exquisitely efficient, safe, and responsive over the last century through constant mechanical refinement. But mechanical refinement has its limits, and there are enormous improvements to be wrung out of the way that big machines are operated: an efficient furnace is still wasteful if it heats a building that no one is using; a safe car is still dangerous in the hands of a bad driver.

It is this challenge that the industrial internet promises to address by layering smart software on top of machines. The last few years have seen enormous advances in software and computing that can handle gushing streams of data and build nuanced models of complex systems. These have been used effectively in advertising and web commerce, where data is easy to gather and control is easy to exert, and marketers have rejoiced.

Thanks to widespread sensors, pervasive networks, and standardized interfaces, similar software can interact with the physical world — harvesting data, analyzing it in context, and making adjustments in real-time. The same data-driven approach that gives us dynamic pricing on Amazon and customized recommendations on Foursquare has already started to make wind turbines more efficient and thermostats more responsive. It may soon obviate humans as drivers and help blast furnaces anticipate changes in electricity prices.

Electric furnace circa 1941

An electric furnace at the Allegheny Ludlum Steel Corp. in Brackenridge, Pa. Circa 1941.
Photo via: Wikimedia Commons.

Those networks and standardized interfaces also make the physical world broadly accessible to innovative people. In the same way that Google’s Geocoding API makes geolocation available to anyone with a bit of general programming knowledge, Ford’s OpenXC platform makes drive-train data from cars available in real-time to anyone who can write a few basic scripts. That model scales: Boeing’s 787 Dreamliner uses modular flight systems that communicate with each other over something like an Ethernet, with each component presenting an application programming interface (API) by which it can be controlled. Anyone with a brilliant idea for a new autopilot algorithm could (in theory) implement it without particular expertise in, say, jet engine operation.

For a complete description of the industrial internet, see our new research report on the topic. In short, we foresee that the industrial internet* will:

  • Draw data from wide sensor networks and optimize systems in real-time.
  • Replace both assets and labor with software intelligence.
  • Bring the software industry’s rapid development and short upgrade cycles to big machines.
  • Mask the underlying complexity of machines behind web-like APIs, making it possible for innovators without specialized training to contribute improvements to the way the physical world works.
  • Create a valuable flow of data that makes decision making easier and more accurate for the operators of big machines as well as for their clients and suppliers.

Our report draws on interviews with industry experts and software innovators to articulate a vision for the coming-together of software and machines. Download the full report for free here, and also read O’Reilly’s ongoing coverage of the industrial internet at oreil.ly/industrial-internet.

* We have adapted our style over the course of our industrial internet investigation. We now use lowercase internet to refer generically to a group of interconnected networks, and uppercase Internet to refer to the public Internet, which includes the World Wide Web.


This is a post in our industrial internet series, an ongoing exploration of big machines and big data. The series is produced as part of a collaboration between O’Reilly and GE.

January 11 2013

Defining the industrial Internet

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.”

Optimization becomes more efficient as the size of the system being optimized grows (in theory). Your software can take into account lots of machines, learning from a much larger training set and then optimizing both within the machine and for the group of machines working together. Think of a wind farm. There are certain optimizations you need to make at the machine level: the turbine turns itself to face into the wind, the blades adjust themselves through every cycle in order to account for flex and compression, and the machine shuts down during periods of dangerously high wind.

System-wide optimization means that when you can operate each turbine in a way that minimizes air disruption to other turbines (these things create wake, just like an airplane, that can disrupt the operation of nearby turbines). When you need to increase or decrease power output across the whole farm, you can do it across lots of machines in a way that minimizes wear (i.e., curtail each machine by 5% or cut off 5% of your machines, or something in between depending on differential output and the impact of different speeds on machine wear). And by gathering data from thousands of machines, you can develop highly-detailed optimization plans.

By tying machines together, the industrial Internet will encourage “platformization.” Cars have several control systems, and until very recently they’ve been linked by point-to-point connections: when you move the windshield-wiper lever, it actuates a switch that’s connected to a small PLC that operates the windshield wipers. The brake pedal is part of the chassis-control system, and it’s connected by cable or hydraulics to the brake pads, with an electronic assist somewhere in the middle. The navigation system and radio are part of the same telematics platform, but that platform is not linked to, say, the steering wheel.

The car as enabled by the industrial Internet will be a platform — a bus, in the computing sense — built by the car manufacturer, with other systems communicating with each other through the platform. The brake pedal is an actuator that sends a “brake” signal to the car’s brake controller. The navigation system is able to operate the steering wheel and has access to the same brake controller. Some of these systems will be driven by third-party-built apps that sit on top of the platform.

This will take some time to happen in cars because it takes 10 or 15 years to renew the American auto fleet, because cars are maintained by a vast network of independent mechanics that need change to happen slowly, and because car development works incrementally.

But it’s already happening in commercial aircraft, which often come from clean-sheet designs (as with the Boeing 787 and Airbus A350), and which are maintained under very different circumstances than passenger cars. In Bombardier’s forthcoming C-series midsize jet, for instance, the jet engines do nothing but propel the plane and generate electricity (they don’t generate hydraulic pressure or compress air for the cabin; these are handled by electrically-powered compressors). The plane acts as a giant hardware platform on which all sorts of other systems sit: the landing-gear switch communicates with the landing gear through the aircraft’s bus, rather than by direct connection to the landing gear’s PLC.

The security implications of this sort of integration — in contrast to effectively air-gapped isolation of systems — are obvious. The industrial Internet will need its own specially-developed security mechanisms, which I’ll look into in another post.

The industrial Internet makes it much easier to deploy and harvest data from sensors, which goes back to the system-wide intelligence point above. If you’re operating a wind farm, it’s useful to have wind-speed sensors distributed across the country in order to predict and anticipate wind speeds and directions. And because you’re operating machine-learning algorithms at the system-wide level, you’re able to work large-scale sensor datasets into your system-wide optimization.

That, in turn, will help the industrial Internet take in previously-uncaptured data that’s made newly useful. Venkatesh Prasad, from Ford, pointed out to me that the windshield wipers in your car are a sort of human-actuated rain API. When you turn on your wipers, you’re acting as a sensor — you see water on your windshield, in a quantity sufficient to cause you to want your wipers on, and you set your wipers to a level that’s appropriate to the amount of water on your windshield.

In isolation, all you’re doing is turning on your windshield wipers. But if your car is networked, then it can send a signal to a cloud-based rain-detection service that geocorrelates your car with nearby cars whose wipers are on and makes an assumption about the presence of rain in the area and its intensity. That service could then turn on wipers in other cars nearby or do more sophisticated things — anything from turning on their headlights to adjusting the assumptions that self-driving cars make about road adhesion.

This is an evolving conversation, and I want to hear from readers. What should be included in the definition of the industrial Internet? What examples define, for you, the boundaries of the field?


This is a post in our industrial Internet series, an ongoing exploration of big machines and big data. The series is produced as part of a collaboration between O’Reilly and GE.

Related

Defining the industrial Internet

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.”

Optimization becomes more efficient as the size of the system being optimized grows (in theory). Your software can take into account lots of machines, learning from a much larger training set and then optimizing both within the machine and for the group of machines working together. Think of a wind farm. There are certain optimizations you need to make at the machine level: the turbine turns itself to face into the wind, the blades adjust themselves through every cycle in order to account for flex and compression, and the machine shuts down during periods of dangerously high wind.

System-wide optimization means that when you can operate each turbine in a way that minimizes air disruption to other turbines (these things create wake, just like an airplane, that can disrupt the operation of nearby turbines). When you need to increase or decrease power output across the whole farm, you can do it across lots of machines in a way that minimizes wear (i.e., curtail each machine by 5% or cut off 5% of your machines, or something in between depending on differential output and the impact of different speeds on machine wear). And by gathering data from thousands of machines, you can develop highly-detailed optimization plans.

By tying machines together, the industrial Internet will encourage “platformization.” Cars have several control systems, and until very recently they’ve been linked by point-to-point connections: when you move the windshield-wiper lever, it actuates a switch that’s connected to a small PLC that operates the windshield wipers. The brake pedal is part of the chassis-control system, and it’s connected by cable or hydraulics to the brake pads, with an electronic assist somewhere in the middle. The navigation system and radio are part of the same telematics platform, but that platform is not linked to, say, the steering wheel.

The car as enabled by the industrial Internet will be a platform — a bus, in the computing sense — built by the car manufacturer, with other systems communicating with each other through the platform. The brake pedal is an actuator that sends a “brake” signal to the car’s brake controller. The navigation system is able to operate the steering wheel and has access to the same brake controller. Some of these systems will be driven by third-party-built apps that sit on top of the platform.

This will take some time to happen in cars because it takes 10 or 15 years to renew the American auto fleet, because cars are maintained by a vast network of independent mechanics that need change to happen slowly, and because car development works incrementally.

But it’s already happening in commercial aircraft, which often come from clean-sheet designs (as with the Boeing 787 and Airbus A350), and which are maintained under very different circumstances than passenger cars. In Bombardier’s forthcoming C-series midsize jet, for instance, the jet engines do nothing but propel the plane and generate electricity (they don’t generate hydraulic pressure or compress air for the cabin; these are handled by electrically-powered compressors). The plane acts as a giant hardware platform on which all sorts of other systems sit: the landing-gear switch communicates with the landing gear through the aircraft’s bus, rather than by direct connection to the landing gear’s PLC.

The security implications of this sort of integration — in contrast to effectively air-gapped isolation of systems — are obvious. The industrial Internet will need its own specially-developed security mechanisms, which I’ll look into in another post.

The industrial Internet makes it much easier to deploy and harvest data from sensors, which goes back to the system-wide intelligence point above. If you’re operating a wind farm, it’s useful to have wind-speed sensors distributed across the country in order to predict and anticipate wind speeds and directions. And because you’re operating machine-learning algorithms at the system-wide level, you’re able to work large-scale sensor datasets into your system-wide optimization.

That, in turn, will help the industrial Internet take in previously-uncaptured data that’s made newly useful. Venkatesh Prasad, from Ford, pointed out to me that the windshield wipers in your car are a sort of human-actuated rain API. When you turn on your wipers, you’re acting as a sensor — you see water on your windshield, in a quantity sufficient to cause you to want your wipers on, and you set your wipers to a level that’s appropriate to the amount of water on your windshield.

In isolation, all you’re doing is turning on your windshield wipers. But if your car is networked, then it can send a signal to a cloud-based rain-detection service that geocorrelates your car with nearby cars whose wipers are on and makes an assumption about the presence of rain in the area and its intensity. That service could then turn on wipers in other cars nearby or do more sophisticated things — anything from turning on their headlights to adjusting the assumptions that self-driving cars make about road adhesion.

This is an evolving conversation, and I want to hear from readers. What should be included in the definition of the industrial Internet? What examples define, for you, the boundaries of the field?


This is a post in our industrial Internet series, an ongoing exploration of big machines and big data. The series is produced as part of a collaboration between O’Reilly and GE.

Related

May 30 2012

Which is easier to tune, humans or machines?

In this new Velocity Podcast, I had a conversation with Kate Matsudaira (@katemats), Vice President of Engineering at Decide.com. This conversation centers mostly on the human side of engineering and performance. Kate has some great insights into building an environment for human performance that goes along with your quest for more performant, reliable, scalable, tolerant, secure web properties.

Our conversation lasted 00:20:00 and if you want to pinpoint any particular topic, you can find the specific timing below. Kate provides some of her background and experience as well as what she is currently doing at Decide.com here. The full conversation is outlined below.


  • Which is easier to tune for performance, humans or machines? 00:00:30

  • To achieve better performance from people, how do you teach people to trade-off the variables time, cost, quality and scope? 00:02:32

  • What do you look for when you hire engineers that will work on highly performant web properties? 00:05:06

  • In this talent-surplus economy, do you find it more difficult to hire engineers? 00:07:10

  • How do you demonstrate DevOps and Performance engineering value to an organization? 00:08:36

  • How does one go about monitoring everything and not slow down your web properties with monitoring everything? 00:12:56

  • Does continuous improvement help deliver performant properties? 00:15:14

If you would like to hear Kate speak on "Leveling up - Taking your operations and engineering role to the next level," she is presenting at the 2012 Velocity Conference in Santa Clara, Calif. on Wednesday 6/27/12 at 1:00 pm. We hope to see you there.


Velocity 2012: Web Operations & Performance — The smartest minds in web operations and performance are coming together for the Velocity Conference, being held June 25-27 in Santa Clara, Calif.



Save 20% on registration with the code RADAR20


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