Friday, October 17. 2014This Phone App Knows If You’re Depressed | #sensing
Note: ... and of course, this could help HER too, to take better care of him/her.... Or maybe to start develop a Softlove?
----- Motion, audio, and location data harvested from a smartphone can be analyzed to accurately predict stress or depression. By Tom Simonite
Many smartphone apps use a device’s sensors to try to measure people’s physical well-being, for example by counting every step they take. A new app developed by researchers at Dartmouth College suggests that a phone’s sensors can also be used to peek inside a person’s mind and gauge mental health. When 48 students let the app collect information from their phones for an entire 10-week term, patterns in the data matched up with changes in stress, depression, and loneliness that showed up when they took the kind of surveys doctors use to assess their patients’ mood and mental health. Trends in the phone data also correlated with students’ grades. The results suggest that smartphone apps could offer people and doctors new ways to manage mental well-being, says Andrew Campbell, the Dartmouth professor who led the research. Previous studies have shown that custom-built mobile gadgets could indirectly gauge mental states. The Dartmouth study, however, used Android smartphones like those owned by millions of people, says Campbell. “We’re the first to use standard phones and sensors that are just carried without any user interaction,” he says. A paper on the research was presented last week at the ACM International Joint Conference on Pervasive and Ubiquitous Computing in Seattle. Campbell’s app, called StudentLife, collects data including a phone’s motion and location and the timing of calls and texts, and occasionally activates the microphone on a device to run software that can tell if a conversation is taking place nearby. Algorithms process that information into logs of a person’s physical activity, communication patterns, sleeping patterns, visits to different places, and an estimate of how often they were involved in face-to-face conversation. Many changes in those patterns were found to correlate significantly with changes in measures of depression, loneliness, and stress. For example, decline in exposure to face-to-face conversations was indicative of depression. The surveys used as a benchmark for mental health in the study are more normally used by doctors to assess patients who seek help for mental health conditions. In the future, data from a person’s phone could provide a richer picture to augment a one-off survey when a person seeks help, says Campbell. He is also planning further research into how data from his app might be used to tip off individuals or their caregivers when behavioral patterns indicate that their mental health could be changing. In the case of students, that approach could provide a way to reduce dropout rates or help people improve their academic performance, says Campbell. “Intervention is the next step,” he says. “It could be something simple like telling a person they should go and engage in conversations to improve their mood, or that, statistically, if you party only three nights a week you will get more decent grades.” Campbell is also working on a study testing whether a similar app could help predict relapses in people with schizophrenia. A startup called Ginger.io with an app similar to Campbell’s is already testing similar ideas with some health-care providers. In one trial with diabetics, changes in a person’s behavior triggered an alert to nurses, who reach out to make sure that the patient was adhering to his medication (see “Smartphone Tracker Gives Doctors Remote Viewing Powers”). Anmol Madan, CEO and cofounder of Ginger.io, says the Dartmouth study adds to the evidence that those ideas are valuable. However, he notes, much larger studies are needed to really convince doctors and health-care providers to adopt a new approach. Ginger.io has found similar associations between its own data and clinical scales for depression, says Madan, although results have not been published. Both Ginger.io and the Dartmouth work were inspired by research at the MIT Media Lab that established the idea that data from personal devices offers a new way to study human behavior (see “TR10: Social Physics”). Yaniv Altshuler, a researcher who helped pioneer that approach, says the Dartmouth study is an interesting addition to that body of work, but it’s also a reminder that there will be downsides to the mobile data trove. Being able to use mobile devices to learn very sensitive information about people could raise new privacy risks. Campbell—who got clearance for his study from an ethical review board—notes that his results show how existing privacy rules can be left behind by data mining. A health-care provider collecting data using standard mental health surveys would be bound by HIPAA data privacy regulations in the United States. It’s less clear what rules apply when that same data is derived from a phone app. “If you have signals you can use to work out, say, that I am a manic depressive, what governs use of that data is not well accepted,” he says. Whatever the answer, apps that log the kind of rich data Campbell collected are likely to become more common. Smartphone sensors have become much more energy-efficient, so detailed, round-the-clock data logging is now feasible without wiping out battery life. “As of six months ago phones got to the point where we could do 24/7 sensing,” says Campbell. “All the technology has now arrived that you can do these things.”
“Hello, Computer” – Intel’s New Mobile Chips Are Always Listening | #monitoring #always
Note: are we all on our way, not to LA, but to HER... ?
----- Tablets and laptops coming later this year will be able to constantly listen for voice commands thanks to new chips from Intel. By Tom Simonite
New processors: A silicon wafer etched with Intel’s Core M mobile chips.
A new line of mobile chips unveiled by Intel today makes it possible to wake up a laptop or tablet simply by saying “Hello, computer.” Once it has been awoken, the computer can operate as a voice-controlled virtual assistant. You might call out “Hello, computer, what is the weather forecast today?” while getting out of bed. Tablets and lightweight laptops based on the new Core M line of chips will go on sale at the end of this year. They can constantly listen for voice instructions thanks to a component known as a digital signal processor core that’s dedicated to processing audio with high efficiency and minimal power use. “It doesn’t matter what state the system will be in, it will be listening all the time,” says Ed Gamsaragan, an engineer at Intel. “You could be actively doing work or it could be in standby.” It is possible to set any two- or three-word phrase to rouse a computer with a Core M chip. A device can also be trained to respond only to a specific voice. The voice-print feature isn’t accurate enough to replace a password, but it could prevent a device from being accidentally woken up, says Gamsaragan. If coupled with another biometric measure, such as webcam with facial recognition, however, a voice command could work as a security mechanism, he says. Manufacturers will decide how to implement the voice features in Intel’s Core M chips in devices that will appear on shelves later this year. The wake-on-voice feature is compatible with any operating system. That means it could be possible to summon Microsoft’s virtual assistant Cortana in Windows, or Google’s voice search functions in Chromebook devices. The only mobile device on the market today that can constantly listen for commands is the Moto X smartphone from Motorola (see “The Era of Ubiquitous Listening Dawns”). It has a dedicated audio chip that constantly listens for the command “OK, Google,” which activates the Google search app. Intel’s Core M chips are based on the company’s new generation of smaller transistors, with features as small as 14 nanometers. This new architecture makes chips more power efficient and cooler than earlier generations, so Core M devices don’t require cooling fans. Intel says that the 14-nanometer architecture will make it possible to make laptops and tablets much thinner than they are today. This summer the company showed off a prototype laptop that is only 7.2 millimeters (0.28 inches) thick. That’s slightly thinner than Apple’s iPad Air, which is 7.5 millimeters thick, but Intel’s prototype packed considerably more computing power.
Posted by Patrick Keller
in Culture & society, Interaction design, Science & technology
at
08:06
Defined tags for this entry: artificial reality, culture & society, digital life, hardware, interaction design, interface, participative, robotics, science & technology, voice
Wednesday, October 15. 2014Town Built for Driverless Cars | #automated
Note: after the zoning for drones within cities, will we develop them with specific "city marks" dedicated for driverless cars? It reminds me a bit of this design research project done a few years ago, The New Robot Domesticity, which purpose was to design objects so that robots could also recognized/use them. Further away, it also remind me of a workshop we organized at the ECAL back in 2005 with researcher Frederic Kaplan (now head of Digital Humanities at EPFL) which purpose was to design artefacts for the Sony Aibo (a doc. video here). This later prtoject was realized in the frame of the research project Variable Environment.
----- Tricky intersections and rogue mechanical pedestrians will provide a testing area for automated and connected cars. By Will Knight
The site of Ann Arbor’s driverless town, currently under construction.
A mocked-up set of busy streets in Ann Arbor, Michigan, will provide the sternest test yet for self-driving cars. Complex intersections, confusing lane markings, and busy construction crews will be used to gauge the aptitude of the latest automotive sensors and driving algorithms; mechanical pedestrians will even leap into the road from between parked cars so researchers can see if they trip up onboard safety systems. The urban setting will be used to create situations that automated driving systems have struggled with, such as subtle driver-pedestrian interactions, unusual road surfaces, tunnels, and tree canopies, which can confuse sensors and obscure GPS signals. “If you go out on the public streets you come up against rare events that are very challenging for sensors,” says Peter Sweatman, director of the University of Michigan’s Mobility Transformation Center, which is overseeing the project. “Having identified challenging scenarios, we need to re-create them in a highly repeatable way. We don’t want to be just driving around the public roads.” Google and others have been driving automated cars around public roads for several years, albeit with a human ready to take the wheel if necessary. Most automated vehicles use accurate digital maps and satellite positioning, together with a suite of different sensors, to navigate safely. Highway driving, which is less complex than city driving, has proved easy enough for self-driving cars, but busy downtown streets—where cars and pedestrians jockey for space and behave in confusing and surprising ways—are more problematic. “I think it’s a great idea,” says John Leonard, a professor at MIT who led the development of a self-driving vehicle for a challenge run by DARPA in 2007. “It is important for us to try to collect statistically meaningful data about the performance of self-driving cars. Repeated operations—even in a small-scale environment—can yield valuable data sets for testing and evaluating new algorithms.” The simulation is being built on the edge of the University of Michigan’s campus with funding from the Michigan Department of Transportation and 13 companies involved with developing automated driving technology. It is scheduled to open next spring. It will consist of four miles of roads with 13 different intersections. Even Google, which has an ambitious vision of vehicle automation, acknowledges that urban driving is a significant challenge. Speaking at an event in California this July, Chris Urmson, who leads the company’s self-driving car project, said several common urban situations remain thorny (see “Urban Jungle a Tough Challenge for Google’s Autonomous Car”). Speaking with MIT Technology Review last month, Urmson gave further details about as-yet-unsolved scenarios (see “Hidden Obstacles for Google’s Self-Driving Cars”).
Such challenges notwithstanding, the first automated cars will go into production shortly. General Motors announced last month that a 2017 Cadillac will be the first car to offer entirely automated driving on highways. It’s not yet clear how the system will work—for example, how it will ensure that the driver isn’t too distracted to take the wheel in an emergency, or under what road conditions it might refuse to take the wheel—but in some situations, the car’s Super Cruise system will take care of steering, braking, and accelerating. Another technology to be tested in the simulated town is vehicle-to-vehicle communications. The University of Michigan recently concluded a government-funded study in Ann Arbor involving thousands of vehicles equipped with transmitters that broadcast position, direction of travel, speed, and other information to other vehicles and to city infrastructure. The trial showed that vehicle-to-vehicle and vehicle-to-infrastructure communications could prevent many common accidents by providing advanced warning of a possible collision. “One of the interesting things, from our point of view, is what extra value you get by combining” automation and car-to-car communications, Sweatman says. “What happens when you put the two together—how much faster can you deploy it?”
Posted by Patrick Keller
in Design, Science & technology, Territory
at
13:35
Defined tags for this entry: artificial reality, design, digital life, interface, interferences, perception, robotics, science & technology, territory, urbanism
Tuesday, October 07. 2014A Dating Site for Algorithms | #code
Note: the title of the post would tend to let us think that this is a place where algorithms could date, together... (not for humans either). It is not realy the case and it is "just" a place where you can go digg for unused algorithms. Intersting too though. But I must admit that I first rebloged this post because of its title...
----- A startup called Algorithmia wants to connect underused algorithms with those who want to make sense of data. By Rachel Metz
A startup called Algorithmia has a new twist on online matchmaking. Its website is a place for businesses with piles of data to find researchers with a dreamboat algorithm that could extract insights–and profits–from it all. The aim is to make better use of the many algorithms that are developed in academia but then languish after being published in research papers, says cofounder Diego Oppenheimer. Many have the potential to help companies sort through and make sense of the data they collect from customers or on the Web at large. If Algorithmia makes a fruitful match, a researcher is paid a fee for the algorithm’s use, and the matchmaker takes a small cut. The site is currently in a private beta test with users including academics, students, and some businesses, but Oppenheimer says it already has some paying customers and should open to more users in a public test by the end of the year. “Algorithms solve a problem. So when you have a collection of algorithms, you essentially have a collection of problem-solving things,” says Oppenheimer, who previously worked on data-analysis features for the Excel team at Microsoft. Oppenheimer and cofounder Kenny Daniel, a former graduate student at USC who studied artificial intelligence, began working on the site full time late last year. The company raised $2.4 million in seed funding earlier this month from Madrona Venture Group and others, including angel investor Oren Etzioni, the CEO of the Allen Institute for Artificial Intelligence and a computer science professor at the University of Washington. Etzioni says that many good ideas are essentially wasted in papers presented at computer science conferences and in journals. “Most of them have an algorithm and software associated with them, and the problem is very few people will find them and almost nobody will use them,” he says. One reason is that academic papers are written for other academics, so people from industry can’t easily discover their ideas, says Etzioni. Even if a company does find an idea it likes, it takes time and money to interpret the academic write-up and turn it into something testable. To change this, Algorithmia requires algorithms submitted to its site to use a standardized application programming interface that makes them easier to use and compare. Oppenheimer says some of the algorithms currently looking for love could be used for machine learning, extracting meaning from text, and planning routes within things like maps and video games. Early users of the site have found algorithms to do jobs such as extracting data from receipts so they can be automatically categorized. Over time the company expects around 10 percent of users to contribute their own algorithms. Developers can decide whether they want to offer their algorithms free or set a price. All algorithms on Algorithmia’s platform are live, Oppenheimer says, so users can immediately use them, see results, and try out other algorithms at the same time. The site lets users vote and comment on the utility of different algorithms and shows how many times each has been used. Algorithmia encourages developers to let others see the code behind their algorithms so they can spot errors or ways to improve on their efficiency. One potential challenge is that it’s not always clear who owns the intellectual property for an algorithm developed by a professor or graduate student at a university. Oppenheimer says it varies from school to school, though he notes that several make theirs open source. Algorithmia itself takes no ownership stake in the algorithms posted on the site. Eventually, Etzioni believes, Algorithmia can go further than just matching up buyers and sellers as its collection of algorithms grows. He envisions it leading to a new, faster way to compose software, in which developers join together many different algorithms from the selection on offer.
Related Links:Wednesday, October 01. 2014Humans Need Not Apply | #bots
Note: hmmmm....
Via Next Nature (Via Tegenlicht) ----- Warning, this video on the impact of automation on human labor might cause you to re-plot you professional career.
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fabric | rblgThis blog is the survey website of fabric | ch - studio for architecture, interaction and research. We curate and reblog articles, researches, writings, exhibitions and projects that we notice and find interesting during our everyday practice and readings. Most articles concern the intertwined fields of architecture, territory, art, interaction design, thinking and science. From time to time, we also publish documentation about our own work and research, immersed among these related resources and inspirations. This website is used by fabric | ch as archive, references and resources. It is shared with all those interested in the same topics as we are, in the hope that they will also find valuable references and content in it.
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