Category Archives: Technology

Google Wave Developer Preview Webcast

Google Wave is a new tool for communication and collaboration on the web, coming later this year. The presentation was given at Google I/O 2009. The demo shows what is possible in a HTML 5 browser. They are developing this as an open access project. The creative team is lead by the creators for Google Maps (brothers Lars and Jens Rasmussen) and product manager Stephanie Hannon.

A wave is equal parts conversation and document. People can communicate and work together with richly formatted text, photos, videos, maps, and more.

A wave is shared. Any participant can reply anywhere in the message, edit the content and add participants at any point in the process. Then playback lets anyone rewind the wave to see who said what and when.

A wave is live. With live transmission as you type, participants on a wave can have faster conversations, see edits and interact with extensions in real-time.

Very cool stuff. The super easy blog interaction is great. And the user experience with notification and collaborative editing seems excellent. The playback feature to view changes seems good though that is still an area I worry about on heavily collaborative work. Hopefully they let you see like all change x person made, search changes…

They also have a very cool context sensitive spell checker that can highlight mis-spelled words that are another dictionary word but not right in the context used (about 44:30 in the webcast).

For software developer readers they also highly recommended the Google Web Development Kit, which they used heavily on this project.

Went Walkabout. Brought back Google Wave.
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The First Web Server

photo of the first web server

Photo by sbisson from Geneva, Switzerland, November 2006 .

In a glass case at CERN is an unpreposessing little NeXT cube. It’s hard to believe that this little workstation changed the world, but it did. It’s Tim Berners Lee‘s original web server, the world’s first.

NeXT is the computer company Steve Jobs founded after he left Apple. Then he left NeXT to buy out Pixar. And then, of course, went back to Apple.

The Million Dollar Programming Prize

One of the main areas of collaborative filtering we exploited is the nearest-neighbor approach. A movie’s “neighbors” in this context are other movies that tend to be scored most similarly when rated by the same viewer. For example, consider Saving Private Ryan (1998), a war movie directed by Steven Spielberg and starring Tom Hanks. Its neighbors may include other war movies, movies directed by Spielberg, or movies starring Tom Hanks. To predict a particular viewer’s rating, we would look for the nearest neighbors to Saving Private Ryan that the viewer had already seen and rated. For some viewers, it may be easy to find a full allotment of close neighbors; for many others, we may discover only a handful of neighboring movies.
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A second area of collaborative-filtering research we pursued involves what are known as latent-factor models. These score both a given movie and a given viewer according to a set of factors, themselves inferred from patterns in the ratings given to all the movies by all the viewers [see illustration, “The Latent-Factor Approach“]. Factors for movies may measure comedy versus drama, action versus romance, and orientation to children versus orientation to adults. Because the factors are determined automatically by algorithms, they may correspond to hard-to-describe concepts such as quirkiness, or they may not be interpretable by humans at all.
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The model may use 20 to 40 such factors to locate each movie and viewer in a multidimensional space. It then predicts a viewer’s rating of a movie according to the movie’s score on the dimensions that person cares about most. We can put these judgments in quantitative terms by taking the dot (or scalar) product of the locations of the viewer and the movie.
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We found that most nearest-neighbor techniques work best on 50 or fewer neighbors, which means these methods can’t exploit all the information a viewer’s ratings may contain. Latent-factor models have the opposite weakness: They are bad at detecting strong associations among a few closely related films, such as The Lord of the Rings trilogy (2001–2003).

Because these two methods are complementary, we combined them, using many versions of each in what machine-learning experts call an ensemble approach. This allowed us to build systems that were simple and therefore easy to code and fast to run.

Interesting article. See some other posts on challenge prizes.

Read: posts on programing – Problems Programming Math – Programmers (comic)

Tiny Machine Commands a Swarm of Bacteria

Researchers in Canada have created a solar-powered micro-machine that is no bigger than the period at the end of this sentence. The tiny machine can carry out basic sensing tasks and can indirectly control the movement of a swarm of bacteria in the same Petri dish.

Sylvain Martel, Director of the NanoRobotics Laboratory at the Ã‰cole Polytechnique de MontrÃ©al, previously showed a way to control bacteria attached to microbeads using an MRI machine. His new micro-machine, which measure 300×300 microns and carry tiny solar panels, will be presented this week at ICRA ’09 in Japan.

On such a small device there is little room for batteries, sensors or transmitters. So the solar cell on top delivers power, sending an electric current to both a sensor and a communication circuit. The communication component sends tiny electromagnetic pulses that are detected by an external computer.

The sensor meanwhile detects surrounding pH levels–the higher the pH concentration, the faster the electromagnetic pulses emitted by the micro-machine. The external computer uses these signals to direct a swarm of about 3,000 magnetically-sensitive bacteria, which push the micro-machine around as it pulses. The bacteria push the micro-machine closer to the higher pH concentrations and change its direction if it pulses too slowly. This is more practical than trying to attach the bacteria onto the micro-machines, says Martel, since the bacteria only have a lifespan of a few hours. “It’s like having a propulsion engine on demand,” he says…

Meeting the Challenge of Simplicity

Interesting webcast by Meeting the Challenge of Simplicity by Giles Colborne. This session addresses abstract notion of simplicity, looks at why it is critical in modern UI design and answers questions: Why does simplicity matter? Is there a meaningful definition of simplicity? Why do design processes and good intentions undermine simplicity? What processes and techniques can software developers use to achieve simplicity?

InfoQ is a great site for watching presentations online. With a simple but superior interface showing a live video with a separate area showing the current slides.

Historical Engineering: Hanging Flume

photo of hanging flume overlook in Colorado, by John Hunter, Creative Commons Attribution.

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While driving from Dinosaur National Monument to Mesa Verde National Park last year I passed the sight above with the remnants of a hanging flume. The Montrose Placer Mining Company built a 13 mile canal and flume to deliver water from the San Miguel River for gold mining operations. The last 5 miles of the flume clung to the wall of the canyon itself, running along the cliff face in the photo above (see more photos).

Constructed between 1888 and 1891, the 4 foot deep 5 foot 4 inch wide hanging flume carried 23,640,000 gallons of water in a 24 hour period. The mining operations used water and sluice boxes to separate the gold from lighter materials (dirt and gravel).

The technology was not yet available to pump the water directly from the river at the necessary volume and pressure to wash the gold from the gravel, therefore they constructed the flume to transport the water.

Global Installed Wind Power Now Over 1.5% of Global Electricity Demand

graph of global installed wind power capacity

Chart showing global installed wind energy capacity by Curious Cat Science and Engineering Blog, Creative Commons Attribution. Data from World Wind Energy Association, for installed Mega Watts of global wind power capacity.

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Globally 27,339 MW of capacity were added in 2008, bringing the total to 121,188 MW, a 29% increase. The graph shows the top 10 producers (with the exceptions of Denmark and Portugal) and includes Japan (which is 13th).

In 2007, Europe had for 61% of installed capacity and the USA 18%. At the end of 2008 Europe had 55% of installed capacity, North America 23%, Asia 20%, Australia 1.5%, Latin America .6% and Africa .5%. Country shares of global capacity at the end of 2008: USA 21%, Germany 20%, Spain 14%, China 10%, India 8% (those 5 countries account for 73% of global capacity).

USA capacity grew 50% in 2008, moving it into the global lead for the first time in a decade. China grew 107%, the 3rd year in a row it more than doubled capacity.

Evolutionary Robotics

Evolutionary Robotics, chapter of Handbook of Robotics, is interesting and includes a good explanation of the difference between evolution and learning:

Evolution and learning (or phylogenetic and ontogenetic
adaptation) are two forms of biological adaptation that differ in space and time. Evolution is a process of selective reproduction and substitution based on the existence of a population of individuals displaying variability at the genetic level. Learning, instead, is a set of modifications taking place within each single individual during its own life time.

Evolution and learning operate on different time scales. Evolution is a form of adaptation capable of capturing relatively slow environmental changes that

might encompass several generations (e.g., the perceptual characteristics of food sources for a given species). Learning, instead, allows an individual to adapt to environmental modifications that are unpredictable at the generational level. Learning might include a variety of mechanisms that produce adaptive changes in an individual during its lifetime, such as physical development, neural maturation, variation of the connectivity between neurons, and synaptic plasticity. Finally, whereas evolution operates on the genotype, learning affects only the phenotype and phenotypic modifications cannot directly modify the genotype.
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Recent research showed that teams of evolved robots can: (a) develop robust and effective behavior, (b) display an ability to differentiate their behavior so
to better cooperate; (c) develop communication capabilities and a shared communication system.

Artificial Intelligence Finds Ancient Indus Script Matches Spoken Language

Artificial Intelligence Cracks 4,000-Year-Old Mystery by Brandon Keim

An ancient script that’s defied generations of archaeologists has yielded some of its secrets to artificially intelligent computers.
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The Indus script, used between 2,600 and 1,900 B.C. in what is now eastern Pakistan and northwest India, belonged to a civilization as sophisticated as its Mesopotamian and Egyptian contemporaries. However, it left fewer linguistic remains. Archaeologists have uncovered about 1,500 unique inscriptions from fragments of pottery, tablets and seals. The longest inscription is just 27 signs long.
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They fed the program sequences of four spoken languages: ancient Sumerian, Sanskrit and Old Tamil, as well as modern English. Then they gave it samples of four non-spoken communication systems: human DNA, Fortran, bacterial protein sequences and an artificial language.

The program calculated the level of order present in each language. Non-spoken languages were either highly ordered, with symbols and structures following each other in unvarying ways, or utterly chaotic. Spoken languages fell in the middle.

When they seeded the program with fragments of Indus script, it returned with grammatical rules based on patterns of symbol arrangement. These proved to be moderately ordered, just like spoken languages.

Keeping Out Technology Workers is not a Good Economic Strategy

The barriers between countries, related to jobs, are decreasing. Jobs are more international today than 20 years ago and that trend will continue. People are going to move to different countries to do jobs (especially in science, engineering and advanced technology). The USA has a good market on those jobs (for many reasons). But there is nothing that requires those jobs to be in the USA.

The biggest impact of the USA turning away great scientists and engineers will be that they go to work outside the USA and increase the speed at which the USA loses its place as the leading location for science, engineering and technology work. This is no longer the 1960’s. Back then those turned away by the USA had trouble finding work elsewhere that could compete with the work done in the USA. If the USA wants to isolate ourselves (with 5% of the population) from a fairly open global science and engineering job market, other countries will step in (they already are trying, realizing what a huge economic benefit doing so provides).

Those other countries will be able to put together great centers of science and engineering innovation. Those areas will create great companies that create great jobs. I can understand wanting this to be 1960, but wanting it doesn’t make it happen.

You could go even further and shut off science and engineering students access to USA universities (which are the best in the world). That would put a crimp in plans for a very short while. Soon many professors would move to foreign schools. The foreign schools would need those professors, and offer a great deal of pay. And those professors would need jobs as their schools laid off professors as students disappeared. Granted the best schools and best professors could stay in the USA, but plenty of very good ones would leave.

I just don’t think the idea of closing off the companies in the USA from using foreign workers will work. We are lucky now that, for several reasons, it is still easiest to move people from Germany, India, Korea, Mexico and Brazil all to the USA to work on advanced technology projects. The advantage today however, is much much smaller than it was 30 years ago. Today just moving all those people to some other location, say Singapore, England, Canada or China will work pretty well (and 5 years from now will work much better in whatever locations start to emerge as the leading alternative sites). Making the alternative of setting up centers of excellence outside the USA more appealing is not a good strategy for those in the USA wanting science, engineering and computer programming jobs. We should instead do what we can to encourage more companies in the USA that are centralizing technology excellence in the USA.

Comment on Reddit discussion.