The iPhone: Tricorder Version 1.0?

by James Turner | comments: 4

The iPhone, in addition to revolutionizing how people thought about mobile phone user interfaces, also was one of the first devices to offer a suite of sensors measuring everything from the visual environment to position to acceleration, all in a package that could fit in your shirt pocket.

On December 3rd, O'Reilly will be offering a one-day online edition of the Where 2.0 conference, focusing on the iPhone sensors, and what you can do with them. Alasdair Allan (the University of Exeter and Babilim Light Industries) and Jeffrey Powers (Occipital) will be among the speakers, and I recently spoke with each of them about how the iPhone has evolved as a sensing platform and the new and interesting things being done with the device.

Occipital is probably best known for Red Laser, the iPhone scanning application that lets you point the camera at a UPC code and get shopping information about the product. With recent iPhone OS releases, applications can now overlay data on top of a real time camera display, which has led to the new augmented reality applications. But according to Powers, the ability to process the camera data is still not fully supported, which has left Red Laser in a bit of a limbo state. "What happened with the most recent update is that the APIs for changing the way the camera screen looks were opened up pretty much completely. So you can customize it to make it look any way you want. You can also programmatically engage photo capture, which is something you couldn't do before either. You could only send the UI up and the user would have to use the normal built-in iPhone UI to capture. So you can do this programmatic data capturing, and you can process those images that come in. But as it turns out, at the same time, shortly after 3.1, the method that a lot of people were using to get the raw data while it was streaming in became a blacklisted function for the review team. So we've actually had a lot of trouble as of late getting technology updates through the App Store because the function we're using is now on a blacklist. Whereas it wasn't on a blacklist for the last year."

Powers is hopeful that the next release of the OS will bring official support for the API calls that Red Laser uses, based on the fact that the App Store screeners aren't taking down existing apps that use the banned APIs. Issues with the iPhone camera sensors pose more of a problem for him. "In terms of science, it's definitely a really bad sensor, especially if you look at the older iPhone sensor, because it has what's called a rolling shutter. A rolling shutter means that as you press capture or rather as the camera is capturing video frames or as you capture a frame, the camera then begins to take an image. And it takes a finite number of milliseconds, maybe 50 or so, before it is actually exposed to the entire frame and stored that off into a sensor. Because it's doing something that's more like a serial data transfer instead of this all at once parallel capture of the entire frame, what that causes is weird tearing and odd effects like that. For photography, as long as it's not too dramatic, it's not a huge deal. For vision processing, it's a huge deal because it breaks a lot of assumptions that we typically make about the camera. That has gotten better in the 3GS camera, but it's still not perfect. It is getting better, especially when the camera's turned on the video mode."

(continue reading)

Counting Unique Users in Real-time with Streaming Databases

by Ben Lorica | @dliman | comments: 6

As the web increasingly becomes real-time, marketers and publishers need analytic tools that can produce real-time reports. As an example, the basic task of calculating the number of unique users is typically done in batch mode (e.g. daily) and in many cases using a random sample from relevant log files. If unique user counts can be accurately computed in real-time, publishers and marketers can mount A/B tests or referral analysis to dynamically adjust their campaigns.

In a previous post I described SQL databases designed to handle data streams. In their latest release, Truviso announced technology that allows companies to track unique users in real-time. Truviso uses the same basic idea I described in my earlier post:

Recognizing that "data is moving until it gets stored", the idea behind many real-time analytic engines is to start applying the same analytic techniques to moving (streams) and static (stored) data.

Truviso uses (compressed) bitmaps and set theory to compute the number of unique customers in real-time. In the process they are able to handle the standard SQL queries associated with these types of problems: counting the number of distinct users, for any given set of demographic filters. Bitmaps are built as data streams into the system and use the same underlying technology that allows Truviso to handle massive data sets from high-traffic web sites.

Once companies can do simple counts and averages in real-time, the next step is to use real-time information for more sophisticated analyses. Truviso has customers using their system for "on-the-fly predictive modeling".

The other main enhancement in this release is Truviso's move towards parallel processing. Their new execution engine processes runs or blocks of data in parallel in multi-core systems or multi-node environments. Using Truviso's parallel execution engine is straightforward on a single multi-core server, but on a multi-node cluster it may require considerable attention to configuration.

[For my previous posts on real-time analytic tools see here and here.]

Four short links: 10 November 2009

DIY Diagnostic Chips, Genetics on $5k a Genome, Cellphones as Diagnostic Microscopes, AR-Equipped Mechanics Do It Heads-Up

by Nat Torkington | @gnat | comments: 0

1. A children’s toy inspires a cheap, easy production method for high-tech diagnostic chips -- microfluidic chips (with tiny liquid-filled channels) can cost $100k and more. Michelle Khine used the Shrinky Dinks childrens' toy to make her own. "I thought if I could print out the [designs] at a certain resolution and then make them shrink, I could make channels the right size for micro­fluidics," she says. (via BoingBoing)
2. Complete Genomics publishes in Science on low-cost sequencing of 3 human genomes (press release) -- The consumables cost for these three genomes sequenced on the proof-of-principle genomic DNA nanoarrays ranged from $8,005 for 87x coverage to $1,726 for 45x coverage for the samples described in this report. Drive that cost down! There's a gold rush in biological discovery at the moment as we pick the low-hanging fruit of gross correlations between genome and physiome, but the science to reveal the workings of cause and effect is still in its infancy. We're in the position of the 18th century natural philosophers who were playing with static electricity, oxygen, anaesthetics, and so on but who lacked today's deeper insights into physical and chemical structure that explain the effects they were able to obtain. More data at this stage means more low-hanging fruit can be plucked, but the real power comes when we understand "how" and not just "what". (via BoingBoing)
3. Far From a Lab? Turn a Cellphone into a Microscope (NY Times) -- for some tests, you can use a camphone instead of a microscope. In one prototype, a slide holding a finger prick of blood can be inserted over the phone’s camera sensor. The sensor detects the slide’s contents and sends the information wirelessly to a hospital or regional health center. For instance, the phones can detect the asymmetric shape of diseased blood cells or other abnormal cells, or note an increase of white blood cells, a sign of infection, he said.
4. Augmented reality helps Marine mechanics carry out repair work (MIT TR) -- A user wears a head-worn display, and the AR system provides assistance by showing 3-D arrows that point to a relevant component, text instructions, floating labels and warnings, and animated, 3-D models of the appropriate tools. An Android-powered G1 smart phone attached to the mechanic's wrist provides touchscreen controls for cueing up the next sequence of instructions. [...] The mechanics using the AR system located and started repair tasks 56 percent faster, on average, than when wearing the untracked headset, and 47 percent faster than when using just a stationary computer screen.

Four short links: 9 November 2009

Moth Mind Readers, Shiny UI Futures, Usable Newspapers, Hardware Testing

by Nat Torkington | @gnat | comments: 0

1. A Battery-Free Implantable Neural Sensor (MIT Tech Review) -- Electrical engineers at the University of Washington have developed an implantable neural sensing chip that needs less power. Uses RFID's induction technology which means the power source can be up to a meter away. Proof of concept was implanted in a moth to sense central nervous system activity.
2. New Microsoft Interface Technology -- videos from Craig Mundie (Chief Research and Strategy Officer) on the MS Campus Tour talking about the future of UI using a sexy glass prototype that features tablet PC, gesture, speech recognition, and even eye tracking. Lustable.
3. Adding Usability to Print -- detailed description of a failed pitch to reinvent a newspaper, to bring web sensibility to print. Make the paper more usable, think cross media instead of separate media, while using the strength of the paper (pictures, info graphics, nice text) to the max… Make a product that people want to buy because it is more usable that the competitor, not because it wins graphic design prizes. (via Evolving Newsroom)
4. StressAppTest -- Google-created open source project to pound the living crap out of hardware by maximising random traffic to memory from processor and I/O, with the intent of creating a realistic high load situation in order to test the existing hardware devices in a computer.

Participatory Sensing - An Interview with Deborah Estrin

by Joshua-Michéle Ross | @jmichele | comments: 2

Subscribe to this video podcast via iTunes. Or, you may download the file.

While the iPhone doesn’t ship nearly as much as its humbler brethren - the iPhone opened up many minds about the potential of phones to do a whole lot more than talk. In that regard it is a peek into the future.

The iPhone is a rich portable computer with onboard sensors. Specifically, it is a location-aware (GPS), motion-aware (accelerometer), directionally-aware (digital compass) visually aware (camera being used to scan QA codes or serve as visual input), sonically aware (microphone and speakers), always-connected (wireless or 3Gs) handheld computer. Every operative word in that sentence is deeply meaningful and rich with possibilities we have just begun to explore. The iPhone does a whole lot more than display information. It is an environmental sensor. Its value lies just as much in sensing information as it does in displaying information.

While the iPhone has the richest set of onboard sensors even basic feature phones are allowing for some remarkable innovation (see my interview with April Allderdice of MicroEnergy Credits) This is an enormous leap forward when our devices are not only connected but context-aware. It is a core theme behind Tim O’Reilly and John Battelle’s “Web Squared” definition that see concepts of Web 2.0 moving into the world.

This concept of “humans as sensors” was the subject of the Web 2.0 Summit panel led by Radar’s Brady Forrest last week. I caught up with panelist Deborah Estrin before to discuss her UCLA group’s work on participatory sensing. Deborah is building multiple applications to express the value of the phone as a sensing device; from large group projects to collect data on an area (such as www.whatsinvasive.com) to personal applications that blend GPS and accelerometer to constantly map your location in time and space then overlay valuable information upon it such as air quality and so on. In the case of air quality - this data might help inform your decisions about where you go jogging or take your baby for that morning stroll.

Resetting Expectations: Some Augmented Reality Links

by Ben Lorica | @dliman | comments: 9

1. Mobile Devices and AR: Besides employing the location of users (Wikitude), there are generally two ways to overlay data onto the real world: through markers ( (2D) bar codes) or through automatic object/image recognition algorithms ("markerless"). The Economist gives a good overview of the different mobile applications that are starting to emerge and lists a few areas where AR makes sense such as shopping (letting house-hunters which properties are for sale) and events (giving sports fans access to stats and player bios).

2. 5 Barriers to a Web That's Everywhere: @gnat linked to a recent RWW post, that lists high-level challenges the AR industry needs to address, including spam and security, interoperability, user experience, and openess.

3. For technical challenges facing AR developers, I recently had a chance to visit with computer scientist and Everyscape CTO/founder, @mok_oh, who's also been blogging about AR. In the first of two posts, he points out that accurate object and image recognition remain formidable^† technical hurdles ("accurate registration of the virtual objects with the real-world image"). Without object and image recognition, Mok points out that some of the more well-known AR apps may not actually be augmented reality apps in the classic sense (" ... there’s not much difference between this and Google Maps on your mobile phone").

In a follow-up post, Mok warns that too much hype may be worst thing that can happen to AR. Serious technical problems need to be resolved:

I still think we need to continue to expand/expound on vision algorithms (e.g. image tracking, image detection/recognition, etc.) and couple that with other sensors (e.g. Wifi, RFID, Bluetooth, accelerators, gyros, GPS, compasses, etc.) to more precisely tell people what they’re seeing in an interactive and augmented sense. The level of precision provided by current apps are good from a mapping perspective (i.e. the 2D “aerial” view), but not good enough from a first-person’s ground perspective. ... Perhaps, we need to reset people’s expectations somehow, or rebrand the words to something else. Because I really do think that there’s plenty of use for AR-inspired technologies as being defined by Layars and Wikitudes of the world.

Everything I read indicates that the more likely scenario in the near future is that AR applications will use a combination of sensors (like a GPS) and markers. In contrast, accurate markerless AR is a distant dream, that will remain locked away in the world of science fiction for years to come.

(†) Mok was at Foo camp last month and I had a chance to talk to him about AR and related topics. Given that he has long worked in the relevant fields within computer science, I take his word on the state-of-the-art in computer vision.

Four short links: 20 August 2009

DIY SPY, Screencasting, Social Network Analysis, Term Extraction

by Nat Torkington | @gnat | comments: 1

1. DIY SPY - a homebrew 2.4GHz wi-fi spectrum analyzer -- As proof of concept (and a cool toy for anyone who has one of these lying around), I have implemented a working Wi-Fi spectrum analyzer on TI’s ez430-RF2500 development kit ($50), a 2-part USB dongle which consists essentially of a CC2500 radio strapped to an MSP430 low-power microcontroller (detachable bottom half) and a USB interface which enumerates as a virtual serial port (top half). The top half doubles as a standalone MSP430 programmer, so this kit is a great cheap way to get started playing with them. (via joshua on Delicious)
2. Screenr -- Instant screencasts for Twitter. Flash-based, uploads to their site and tweets the URL. The whole "for Twitter" thing is going a little too far: who records screencasts only for Twitter? It's like having a spellchecker only for three-letter words.
3. Social Network Analysis in R -- video and slides for talk on doing social network analysis with R.
4. We're Keeping the Term Extraction Service -- Yahoo!'s useful API gets a stay of execution. OK, we heard you. You’ve made it clear to us that shutting down the Term Extraction Service would be a mistake. So, we’ve changed our plans. We're leaving the service up and running indefinitely. (via Simon Willison)

Big Data and Real-time Structured Data Analytics

by Ben Lorica | @dliman | comments: 9

The emergence of sensors as sources of Big Data highlights the need for real-time analytic tools. Popular web apps like Twitter, Facebook, and blogs are also faced with having to analyze (mostly unstructured) data in near real-time. But as Truviso founder and UC Berkeley CS Professor Michael Franklin recently noted, there are mountains of structured data generated by web apps that lend themselves to real-time analysis:

The information stream driving the data analytics challenge is orders of magnitude larger than the streams of tweets, blog posts, etc. that are driving interest in searching the real-time web. Most tweets, for example, are created manually by people at keyboards or touchscreens, 140 characters at a time. Multiply that by the millions of active users and the result is indeed an impressive amount of information. The data driving the data analytics tsunami, on the other hand, is automatically generated. Every page view, ad impression, ad click, video view, etc. done by every user on the web generates thousands of bytes of log information. Add in the data automatically generated by the underlying infrastructure (CDNs, servers, gateways, etc.) and you can quickly find yourself dealing with petabytes of data.

In our report on Big Data, we listed some tools that can turn SQL data warehouses into real-time intelligence systems. The typical data warehouse usually reports on data that are a day, week, or even a month old. Not every company requires real-time reports, alerts, or exception tracking, but some domains may benefit from dramatically reducing latency. To supplement the typical post-campaign reports generated by traditional (static) data warehouses, advertisers and content providers could track and make adjustments to their campaigns in real-time. Web applications that rely on data generated by sensors (e.g. smart grids, location-aware mobile apps, logistics & supply-chain tracking, environmental sensors) would be able to display reports that are continuously updated in real-time. Web site performance and security reports are also natural candidates for real-time analytics.

If you desire (near) real-time analysis, traditional SQL databases and MapReduce systems are batch-oriented (load all the data, then analyze), and might not be able to deliver the low latency you're seeking. Fortunately, there are tools^† that allow structured data sets (such as data warehouses) to be easily analyzed in real-time.

Recognizing that "data is moving until it gets stored", the idea behind many real-time analytic engines is to start applying the same analytic techniques to moving (streams) and static (stored) data. Truviso separates the processing and analysis of data, and performs both in real-time. End-users and business analysts can access/query real-time data and historical data using SQL: in Truviso's case the underlying Postgres engine and optimizer have been extended to include an embedded stream processor to handle "live data" in any SQL statement's FROM clause^††. To specify how "live data" is to be processed by a database engine, most real-time analytic vendors provide SQL extensions that allow users to specify the time windows to be analyzed. As data flows continuously into the system, the results of queries involving "live data" are continuously updated in real-time. Leveraging a popular database such as Postgres means structured data warehouses can be ported and made real-time with Truviso.

A major challenge facing stream databases is what do with out-of-order data. Streams are timestamped data sets, and most systems expect data to arrive in the correct time sequence. Unfortunately, things happen when data flows in from multiple sources and it is not uncommon for timestamped data to arrive out-of-order. While some real-time analytic systems simply drop out-of-order data (potentially leading to misleading query results), Truviso has developed algorithms that look for contiguous data and produce query results that correctly handle out-of-order data.

What about real-time analysis of unstructured data? Truviso hasn't focused on unstructured data, preferring instead to target companies with existing data warehouses. After all, the general notion is that unstructured data doesn't quite fit into SQL databases like Truviso. But the perception that unstructured data isn't for relational databases may be changing slightly. Recently, a team at UC Berkeley used a SQL database to perform entity-extraction. They took unstructured text, passed it through a Conditional Random Fields algorithm (coded in SQL), and turned it into structured data.

(†) We recently had the chance to meet with the founders of Truviso. There are many other real-time analytic solutions including streambase and SQLstream.
(††) In Truviso's system, "live data" or streams can be created (CREATE stream) and accessed in SQL much like static database tables.

Four short links: 26 May 2009

Databases, Sensors, Visualization, and Patents

by Nat Torkington | @gnat | comments: 0

1. Flare -- dynamically partitioning and reconstructing key-value server. Currently built on Tokyo Cabinet, but backend is theoretically pluggable. (via joshua on delicious)
2. Implantable Device Offers Continuous Cancer Monitoring -- the sensor network begins to extend into our bodies. The cylindrical, 5-millimeter implant contains magnetic nanoparticles coated with antibodies specific to the target molecules. Target molecules enter the implant through a semipermeable membrane, bind to the particles and cause them to clump together. That clumping can be detected by MRI (magnetic resonance imaging). The device is made of a polymer called polyethylene, which is commonly used in orthopedic implants. The semipermeable membrane, which allows target molecules to enter but keeps the magnetic nanoparticles trapped inside, is made of polycarbonate, a compound used in many plastics. (via FreakLabs)
3. Visualizing Data source -- the source code to examples in Visualizing Data.
4. The First Software Patent (Wired) -- was issued on this day in 1981, for a complex full-text storage and retrieval system. Tellingly, business strategy of the owner of the first software patent was ... to become a patent lawyer. A day that will linger in irritation, if not live in infamy. (via glynmoody on Twitter)

Four short links: 1 May 2009

Smart Grids, Open Source, Stuff That Matters, and Global Culture

by Nat Torkington | @gnat | comments: 2

1. A Little Give and Take On Electricity (NY Times) -- Dennis L. Arfmann, a lawyer at the Boulder office of Hogan & Hartson who specializes in environmental law, said he had no idea how much electricity he and his wife, Dr. Julie Brown, had used before he filled his roof with solar panels producing 4.5 kilowatts of power. During the day he sells power to Xcel and at night he buys it back; his goal is to cut his use so his net sales rise. All hardware networked, everywhere!
2. Open Source World Map (Red Hat) -- very nice map showing the intensity of open source use in countries around the world. (via Flowing Data)
3. Imagine Cup -- Microsoft's contest to get students working on stuff that matters. The winners of the New Zealand leg, Team Think, tackled literacy: they devised a program for tablets that provides both handwriting recognition and audio output, eliminating the need for basic literacy to understand lessons or instructions. They hope to take this prototype to developing countries that have underutilised computers due to literacy issues. (via Idealog newsletter and Scoop)
4. UGT -- It is always morning when person comes into a channel, and it is always late night when person leaves. [...] The idea behind establishing this convention was to eliminate noise generated almost every time someone comes in and greets using some form of day-time based greeting, and then channel members on the other side of the globe start pointing out that it's different time of the day for them. Now, instead of spending time figuring out what time of day is it for every member of the channel, we spend time explaining newcomers benefits of UGT. (via migurski on delicious).

Four short links: 23 Apr 2009

by Nat Torkington | @gnat | comments: 3

Multitouch, visualizations, body hacks, and ubicomp:

1. Dell Demos Multitouch on the Studio One 19 (Engadget) -- the multitouch software on this baby is Fingertapps from the New Zealand company Unlimited Realities, whose founder was at Kiwi Foo Camp this year. Multitouch hits consumer PCs in a very mainstream way.
2. Circos -- open source Perl library to produce beautiful circular data displays. (via flowing data)
   
3. Brain Gain: The Underground World of “Neuroenhancing” Drugs (New Yorker) -- more on the body hacks theme of radical and literal self-improvement, as originally documented by Quinn Norton. What I found interesting was that when BoingBoing linked to it, they quoted the "Provigil might make us smarter" bit, and when MInd Hacks linked to it, they quoted the negative effects of amphetamine-based drugs.
4. Towards the Web of Things: Web Mashups for Embedded Devices -- slides and notes for a presentation given at MEM 2009. Basically saying that the Internet of Things should be built on JSON and REST, with demo. (via Freaklabs)

Where 2.0 Preview - Building the SENSEable City

by James Turner | comments: 2

Much of the information we have about how cities work (or don't) comes through direct, intentional observation and study--but could we learn as much or more by mining the data that citizens generate in their day-to-day lives, through cell phone traffic and internet usage? That's one of the questions that Andrea Vaccari, a research associate at the MIT SENSEable City Lab, is trying to answer. Andrea will be speaking on the research that the SENSEable City Project is doing at the O'Reilly Where 2.0 Conference in May.

James Turner: So why don't you start a little bit by talking about what the charter of the SENSEable City Lab is?

Andrea Vaccari: Sure. The SENSEable City Lab is a recent initiative; a new initiative of the Massachusetts Institute of Technology which focuses on studying how digital technologies are evolutionizing the way we live in cities. And, therefore, how we can leverage these technologies; how we can make use of it through understanding how cities are using it; how we can design better cities. And then we can create cities that are more sustainable, more livable and automatically more efficient.

JT: A lot of data that governments gather about cities -- the example I think of is the little things they put across the roads to find out traffic going over a road, but that's almost like just a point source data. Can you compare that to the kind of data that you're able to extract through the records you can get access to?

AV: Sure. The problem with past data in all aspects of the urban planning and social studies is that the data is usually punctual, so it refers to very specific points in space and also in time. And that's because the methods that were used to gather this information were very expensive. They required either to deploy infrastructures or to employ people to count manually cars, people, vehicles. And, therefore, it was impossible to have a real-time flow of information. What we are trying to do is to leverage the pervasive systems that enhance our cities today. And I'm referring to telecommunication networks, wireless networks, transportation systems or any other sort of digital system that interacts on a daily basis -- on a real-time basis -- with the citizens. What happens is that with these systems, interactions between the user and the system creates logs of their activity. And these logs can be used to understand the urban dynamics, to understand how people move in living cities and how cities themselves evolve in time.

JT: Now, you showed me some of the examples of the datasets that you've been playing with, and it seems like largely it's cell phone data and wifi data and then secondarily, things that are more voluntary like Flickr uploads.

AV: Yes.

JT: Wifi data you can pretty much get to a hotspot. And as Google has demonstrated with cell phone data, you can get fairly good positioning. But what kind of resolution do you get out of say cell phone data?

AV: Sure. The resolutions that we get for the cell phone is aggregated at the antenna level. So we don't get information about the individuals because we strongly respect privacy. And what we basically know is how many calls, how many text messages, how much traffic is served by each antenna in a city. And, of course, we know the position of the antenna and we can estimate the coverage of these antennas. So we can fairly understand what are the dynamics going on in the area of coverage. But, again, we don't get information about individuals.

(continue reading)

Four short links: 20 Mar 2009

by Nat Torkington | @gnat | comments: 4

Space, Space, Micromanufacturing, and Sensors:

1. Teens Capture Images of Space With £56 Camera and Balloon (Telegraph) -- DIY/MAKE culture at its best, four 18-19 year old Spanish students (with guidance of a teacher) rigged a balloon to carry a camera over 100,000 feet (that's twelve trillion and seven Canadian meters) above the earth, take pictures, and return to the ground. Here's their project's web page with a Google gadget to translate it into English. (via @erikapearson)
2. The Robot Who Helps Astronomers Identify Stars - IO9 interviewed Fiona Romeo, about the Royal Observatory's Astronomical Photograph of the Year contest and the astrotagging bot I linked to earlier.
3. Clive Thompson on the Revolution in Micromanufacturing -- talks about his experiences with Etsy. I was aware of the site but had dismissed it as some sort of urban-hipster thing—until I started seeing chatter about it on discussion boards for wealthy professionals and stay-at-home moms.
4. How The FitBit Algorithms Work -- The Fitbit’s primary method of collecting data is an accelerometer. Its accelerometer constantly measures the acceleration of your body and algorithms convert this raw data into useful information about your daily life, such as calories burned, steps, distance and sleep quality. How do we develop these algorithms? Our approach is that we have test subjects wear the Fitbit while also wearing a device that produces a “truth” value. [...].

Four short links: 16 Mar 2009

by Nat Torkington | @gnat | comments: 2

Non-interop earphones with DRM, HVAC swarms, paperprints, and product constipation at GOOG:

1. Apple iPod Shuffle (3rd gen) -- "Surprise: the only third-party headphones that will work are ones that haven’t even entered manufacturing yet, because they’ll need to contain yet another new Apple authentication chip, which will add to their price." It's interesting to see Apple prioritising the different interactions with the device: the ease from using standard connectors is less important than how it looks (no buttons!), and both are less important than securing those hardware margins by tithing every corner of the aftermarket thirdparty addon space. I hope users revolt, but I suspect it'll be viewed like the needless diversity of power cords--a non-fatal inconvenience and irritation. I can only imagine the state of mind that thinks it is acceptable to irk a million people so as to be able to make a few bucks on each set of third-party headphones.
2. Managing Energy with Swarm Logic -- intelligent HVAC gear that wirelessly communicates, figures out the individual power cycles of each appliance, then coordinate (with no central control) to figure out how to optimally run the appliances for maximum efficiency.
3. Fingerprinting Blank Paper Using Commodity Scanners -- Ed Felten's latest work of genius, cranking the contrast on scans of paper at different orientation to produce a texture from which can be calculated a fingerprint that survives printing, crumpling, and moisture. (via bos)
4. Tim Armstrong to Head AOL (Battelle) -- interesting quote from former Googler, "It's very hard to take risks at Google."

Radar Roundup: Sensors

by Dylan Field | @dylanjfield | comments: 7

Guest blogger Dylan Field is an intern at O'Reilly and Senior at Technology High School in Rohnert Park, CA, where he is a member of the FIRST Robotics team, Dylan is especially interested in Computer Science, Mathematics,and Statistics.

In his "Web Meets World" talk at the Web 2.0 Expo in New York last September, Tim O'Reilly described where he saw the web heading. "The next stage of Web 2.0 is going to be driven by sensors," he said. "We are moving out of the world in which people typing on keyboards are going to be driving collective intelligence applications."

Like all transitions, the incorporation of data from the physical web onto existing platforms is gradual. We are just beginning to see applications surface and the best is still ahead of us. Below are a few observations, predictions, and implementations of this emerging trend.

Sensors Help Keep Elderly Safe
This New York Times article highlights how Seniors are taking advantage of sensors so they can continue to live independently. Sensor systems are able to detect everything from neglected pills to glucose levels to falls. Seniors seem to like the systems, as do their relatives. "In the past, I tried to spend more time on, 'How are you feeling?' " Marvin Joss says. "I still ask those questions, but now it's more to an idea of having a conversation, not trying to listen for clues about whether she's O.K."

The Demon-Haunted World
If I had to use one word to describe this presentation by Dopplr's Matt Jones, it would be "Psychogeography," a term developed by French Theorist Guy Debord. Psychogeography is defined informally as "a whole toy box full of playful, inventive strategies for exploring cities...just about anything that takes pedestrians off their predictable paths and jolts them into a new awareness of the urban landscape." Jones cites examples like twittering bridges and pollution sensing robotic dogs to back up a claim by architect Richard Rogers that "Our cities are increasingly linked, and learning." "It seems to me like there are a bunch of hackers reclaiming information from the city," says Jones. "[They are] gardening it without permission."

SENSEable City Laboratory
MIT's SENSEable City Laboratory uses sensors to understand the macro-dynamics of cities. For example, in one experiment the lab collected all cellphone usage in Rome for one night. They then aggregated the data and produced a visualization showing how people moved around and where events were taking place. If we had real time access to this kind of information, how would it affect our choices? Would we decide not to eat at a particular restaurant because it is too crowded? Would we choose our entertainment based on the flow of the crowd?

AMEE and Google PowerMeter
AMEE and Google PowerMeter are two ways the "here's your data, do something with it" methodology can be used to make people aware of their carbon footprints. Both use sensors such as smart meters to track and display energy consumption over time. (Disclaimer: OATV is an investor in AMEE.)

In a previous partnership between the two companies, Google used AMEE's profiling engine to let users calculate their carbon footprints. After completing the web form, users were taken to a Google Map mashed up with the carbon footprints of those nearby. Soon, we'll be able to do this without the web form. Like O'Reilly said, we are slowly transitioning out of a world where people typing on keyboards are driving collective intelligence.

What role do you see sensors playing in your life? How do you interact with them now? Does the possibility of sensor driven collective intelligence frighten or excite you? Post a comment and let us know.

The Social Nervous System Has More Than One Sense

by Tim O'Reilly | @timoreilly | comments: 7

Radar's Joshua Michele-Ross published a fabulous piece on Forbes entitled The Rise of the Social Nervous System. His premise:

...communication is the foundation of society, business and government. When you scale up communications, you change the world....As ever more people get connected, we see an acceleration in the way the Internet is used to coordinate action and render services from human input. We are witnessing the rise of a social nervous system.

Josh focuses on now familiar examples: the Mumbai terrorist attacks as reported real-time on twitter, the Obama campaign (and in particular, the Houdini project), and Google Flu Trends. But Josh weaves them into a powerful conclusion:

Watch the news, and you will see daily evidence of how a system that connects billions of people is influencing the physical world--from recent protests in California against Proposition 8 organized by Facebook to the riots in my hometown of Oakland after several witnesses uploaded video taken from their mobile phones of a police shooting.

These examples all follow the core web 2.0 narrative, that in the era of the network, the key competency is harnessing collective intelligence. But Josh hammers home the further insight, namely that these effects are not limited to cyberspace, but are used to control and coordinate real-world activity. This is the new frontier, moving from "sensing" to "reacting," from "cognition" to "coordination" and group action.

The one area where I disagree with Josh's analysis is in his dismissal of purely machine-mediated sensing.

It is easy to confuse this concept with the emerging field of machine learning such as the smart energy grid, traffic control using the sensor Web or the Planetary Skin Initiative recently announced by Nasa and Cisco. Machine optimization is useful but hardly social: Human beings do not contribute the data, share it or act upon it. And the implications of a social nervous system are far more profound than simply a "smart" grid.

While Josh is right that a network that responds to and expands the power of human activity is uniquely powerful, that activity need not be conscious. Many of the most succesful Web 2.0 systems are derived from implicit rather than explicit data. We don't think that we are contributing to Google when we make a link from one site to another, but we are. We don't think we are contributing when we click on one link rather than another, or buy one product rather than another, but we are. You will argue, of course, that those are human actions of just the kind that Josh celebrates.

But where do you draw the line? When we make a phone call from one location rather than another, we don't think we are contributing our location, but our phone is quietly doing so nonetheless. When we make a credit-card purchase, we don't think we are contributing, but software at the bank, the merchant, and our personal finance application is listening to that credit card reader. When we turn on a light switch in a Smart-grid connected house, we won't think we are contributing, but we will be. And the refrigerator waking up and deciding to turn on its compressor will be making exactly the same kind of contribution. The Smart Grid is in fact intended to be just such a sensing-and-responding system, connecting people and machines into a new kind of super-organism.

It's important to remember that even the human brain has more than one sense. Computers will have a rich new sensorium of their own, driving increasingly autonomic applications. Those applications will share and sense not just words passed from human to human across services like twitter, or our search behavior as captured in the database of intentions, but sounds, pictures, and increasingly, data from senses that unaided humans don't possess at all, or less precisely: a sense of precise location, or the rate of speed at which we move, the power we consume, the carbon we emit, the approaching weather, the state of the financial markets, the unique sequence of our genome, or even the way we smell. I'd bet some of the next great fortunes will be made by someone discovering how to build a system that reacts to one of the internet's new senses.

Still, Josh's analogy is a powerful reminder that "collective intelligence" is not cerebral, but ultimately becomes visceral, that it affects not just what we think but what we do. I expect that many of the "new" senses that currently appear to be merely mechanical will soon develop social dynamics of their own.

Four short links: 28 Jan 2009

by Nat Torkington | @gnat | comments: 3

Sensors, games, recession indicators, and book prep in today's four short links:

1. New Networks Take Nature's Pulse - an article in Christian Science Monitor about sensor networks. Makezine pointed out that hobbyists are building low-cost versions with Arduinos. Sensor networks are part of the "Web meets World" change we're in, where the Web ceases to be something you sit down to interact with. Instead, our everyday life will inform and be informed by the Web in ways we won't realize.
2. Interactive Fiction Goes to Market - a company, Textfyre is readying new text adventure games ("interactive fiction") for the iPhone market. I dream of a day when the text adventure world becomes lucrative again (the tools like Inform are divine) but I can't help think that the iPhone is the wrong platform. The make-believe keyboard makes text entry such a chore that it would seem to count against text adventures. I hope and wish that I am proven wrong and some day the CEO of Textfyre buys the house next to me just so he can build a huge mansion and paint on the walls "Nat Torkington thought the iPhone was the wrong platform for text adventures".
3. You Know It's a Recession When More People Search for Coupons Than Britney Spears - interesting tidbit from Bo Cowgill, who runs Google's internal prediction market. His blog is full of fascinating pointers to prediction market research. Between him and David Pennock, my prediction market cup runneth over.
4. How To Write a Book - Steven Johnson writes, on BoingBoing, how he uses DevonThink to gather and organize his book thoughts and structure before actually sitting down to produce the words. I love reading about the act of literary creation (I have a long shelf of "how to write mystery novel" books that I can almost quote chapter and verse), the way it's so different for every author yet so the output is so similar.

Incredible images of the Sun

by Jesse Robbins | @jesserobbins | comments: 14

The Boston Globe has assembled a beautiful gallery of images of the Sun.

This LASCO C2 image, taken 8 January 2002, shows a widely spreading coronal mass ejection (CME) as it blasts more than a billion tons of matter out into space at millions of kilometers per hour. The C2 image was turned 90 degrees so that the blast seems to be pointing down. An EIT 304 Angstrom image from a different day was enlarged and superimposed on the C2 image so that it filled the occulting disk for effect (Courtesy of SOHO/LASCO consortium)

[link courtesy Barry Brumitt]