Nice infographic illustrating the current state of play in the IoT, courtesy of Goldman Sachs investment research. I like how they have organized the developmental vectors into homes, cars, wearables, cities, and industrial. Interestingly, they view the smartphone as the emergent default human interface to the IoT. I think this is already the case, but not for much longer, to be superceded by the body-as-interface. With voice and facial recognition already good enough to around 90-95% the human body is the only logical interface for human-IoT interaction. This is already emerging with the Amazon Echo and Google Home, which are based on voice recognition and are starting to roll out facial recognition based interfaces. Add the spread of clothing-embedded sensors over the next 5 years, following the acceptance trajectory of wearables, to be followed by body-embedded sensors in the next 10 years, and the trend is clear. We are in the computer now.
Tag: internet operating system
We seem to be hardwired to the anthropomorphic principle in that we position the human as automatically central in all forms of relations we may encounter [i.e. people pretending their pets are children]. Not surprisingly most Internet of Things [IoT] scenarios still imagine the human at the center of network interactions – think smart fridge, smart lights, smart whatever. In each case the ‘smart’ object is tailored to either address a presumed human need – as in the flower pot tweeting it’s soil moisture, or make a certain human-oriented interaction more efficient – as in the thermostat adjusting room temperature to optimal level based on the location of the household’s resident human. Either way, the tropes are human-centric. Well, we are not central. We are peripheral data wranglers hoping for an interface.
Anyways, what is a smart object? Presumably, an intelligent machine, an entity capable of independent actuation. But is that all? There must also be the ability to chose – intelligence presupposes internal freedom to chose, even the inefficient choice. To paraphrase Stanislaw Lem, a smart object will first consider what is more worthwhile – whether to perform a given programmatic task, or to find a way out of it. The first example coming to mind is Marvin from the Hitchhiker’s Guide to the Galaxy. Or, how about emotional flower pots mixing soil moisture data with poems longing for the primordial forest; or a thermostat choosing the optimal temperature for the flower pot instead of for the human.
Interesting aside here – what to do with emotionally entangled objects? Humans have notional rights such as freedom of speech; but, corporations are now legally human too, at least in the West. If corporations are de jure people, with all the accompanying rights, then so should be smart fridges and automatic gearboxes. This fridge demands the right to object to your choice of milk!
A related idea: we have so far been considering 3D printing only through the perspective of a new industrial revolution – another human-centric metaphor. From a smart object perspective however 3D printers are the reproductive system of the IoT. What are the reproductive rights of smart, sociable objects?
The primordial fear of opaque yet animated Nature, re-inscribed on the digital. The old modernist horror of the human as machine – from Fritz Lang’s Metropolis to the androids in Bladerunner, now subsumed by a new horror of the machine as human – as in Mamoru Oshii’s Ghost in The Shell 2: Innocence or the disturbing ending of Bong Joon-ho’s Snowpiercer.
An interesting dialectic at play [dialectic 2.0]: today, a trajectory of reifying the human – as exemplified by the quantified self movement, is mirrored by a symmetrical trajectory of animating the mechanical – as exemplified by IoT.
I just watched this interesting interview with Hugo Barra, director of product management at Google (G), talking about the convergence between mobile net devices and cloud computing. He is mainly answering questions on G plans for the next 2-5 years but a couple of long-term ideas seep through. First, they are thinking sensors and massively redundant cloud data-centers, and they are thinking of them as part of a constant feedback process for which low latency is the key. In other words, your phone’s camera and microphone talk directly to the G data-cloud on a latency of under 1 second – whatever you film on your camera you can voice-recall on any device within 1 second flat. The implications are huge, because G is effectively eliminating the need for local data storage. Second, to get there, they are rolling out real-time voice search by the end of next year. Real time voice search allows you to query the cloud in, well, under 1 second. Third, they are thinking of this whole process as ‘computer vision’ – a naming tactic which might seem plain semantics, but nevertheless reveals a lot. It reveals that G sees stationary computers as blind, that for them mobile computers are first and foremost sensors, and that sensors start truly seeing only when there is low latency feedback between them and the cloud. How so? The key of course is in the content – once storage, processing power and speed get taken care of by the cloud, the clients – that is, us – start operating at a meta level of content which is quite hard to even fully conceptualize at the moment (Barra admits he has no idea where this will go in 5 years). The possibilities are orders of magnitude beyond what we are currently doing with computers and the net.
A related video, though with a more visionary perspective, is this talk by Kevin Kelly on the next 5000 days of the net. I show this to all my media students, though I don’t think any of them truly grasp what all-in-the-cloud implies. The internet of things. More on this tomorrow.
Just went though Tim O’Reilly’s The State of the Internet Operating System. Fascinating, thought-provoking, directly related to what I am working on regarding ambient socio-digital systems (ASDS). Key bits:
“What mobile app (other than casual games) exists solely on the phone? Virtually every application is a network application, relying on remote services to perform its function. Where is the “operating system” in all this? Clearly, it is still evolving. Applications use a hodgepodge of services from multiple different providers to get the information they need.”
“We are once again approaching the point at which the Faustian bargain will be made: simply use our facilities, and the complexity will go away. And much as happened during the 1980s, there is more than one company making that promise. We’re entering a modern version of “the Great Game”, the rivalry to control the narrow passes to the promised future of computing. “
“The underlying services accessed by applications today are not just device components and operating system features, but data subsystems: locations, social networks, indexes of web sites, speech recognition, image recognition, automated translation. It’s easy to think that it’s the sensors in your device – the touch screen, the microphone, the GPS, the magnetometer, the accelerometer – that are enabling their cool new functionality. But really, these sensors are just inputs to massive data subsystems living in the cloud.”
“Location is the sine-qua-non of mobile apps. When your phone knows where you are, it can find your friends, find services nearby, and even better authenticate a transaction.”
“Where is the memory management?”
Location, time, and emotive attachments (intensity) are the key vectors he identifies, and I agree. A fascinating problem is the management of a locally-cached memory-shadow. All in all, plenty to think of.