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video – tshirtOS is the world’s first wearable, shareable, programmable t-shirt. A working, digital t-shirt that can be programmed by an iOS app to do whatever you can think of (by CuteCircuit, link).

It takes you 500,000 microseconds just to click a mouse. But if you’re a Wall Street algorithm and you’re five microseconds behind, you’re a loser.” ~ Kevin Slavin.

TED video lecture – Kevin Slavin (link) argues that we’re living in a world designed for – and increasingly controlled by – algorithms. In this riveting talk from TEDGlobal, he shows how these complex computer programs determine: espionage tactics, stock prices, movie scripts, and architecture. And he warns that we are writing code we can’t understand, with implications we can’t control. Kevin Slavin navigates in the “algoworld“, the expanding space in our lives that’s determined and run by algorithms (link at TED).

Video Documentary – Code Rush (, produced in 2000 and broadcast on PBS, is an inside look at living and working in Silicon Valley at the height of the dot-com era. The film follows a group of Netscape engineers as they pursue at that time a revolutionary venture to save their company – giving away the software recipe for Netscape’s browser in exchange for integrating improvements created by outside software developers.

” (…) code (…) Why is it important for the world? Because it’s the blood of the organism that is our culture, now. It’s what makes everything go.“, Jamie Zawinski, Code Rush, 2000.

The year is early 1998, at the height of dot-com era, and a small team of Netscape code writers frantically works to reconstruct the company’s Internet browser. In doing so they will rewrite the rules of software development by giving away the recipe for its browser in exchange for integrating improvements created by outside unpaid developers.  The fate of the entire company may well rest on their shoulders. Broadcast on PBS, the film capture the human and technological dramas that unfold in the collision between science, engineering, code, and commerce.

Figure – Book cover of Toby Segaran’s, “Programming Collective Intelligence – Building Smart Web 2.0 Applications“, O’Reilly Media, 368 pp., August 2007.

{scopus online description} Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting data-sets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general — all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application.

{even if I don’t totally agree, here’s a “over-rated” description – specially on the scientific side, by someone “dwa” – link above} Programming Collective Intelligence is a new book from O’Reilly, which was written by Toby Segaran. The author graduated from MIT and is currently working at Metaweb Technologies. He develops ways to put large public data-sets into Freebase, a free online semantic database. You can find more information about him on his blog: Web 2.0 cannot exist without Collective Intelligence. The “giants” use it everywhere, YouTube recommends similar movies, knows what would you like to listen and Flickr which photos are your favorites etc. This technology empowers intelligent search, clustering, building price models and ranking on the web. I cannot imagine modern service without data analysis. That is the reason why it is worth to start read about it. There are many titles about collective intelligence but recently I have read two, this one and “Collective Intelligence in Action“. Both are very pragmatic, but the O’Reilly’s one is more focused on the merit of the CI. The code listings are much shorter (but examples are written in Python, so that was easy). In general these books comparison is like Java vs. Python. If you would like to build recommendation engine “in Action”/Java way, you would have to read whole book, attach extra jar-s and design dozens of classes. The rapid Python way requires reading only 15 pages and voila, you have got the first recommendations. It is awesome!

So how about rest of the book, there are still 319 pages! Further chapters say about: discovering groups, searching, ranking, optimization, document filtering, decision trees, price models or genetic algorithms. The book explains how to implement Simulated Annealing, k-Nearest Neighbors, Bayesian Classifier and many more. Take a look at the table of contents (here:, it does not list all the algorithms but you can find more information there. Each chapter has about 20-30 pages. You do not have to read them all, you can choose the most important and still know what is going on. Every chapter contains minimum amount of theoretical introduction, for total beginners it might be not enough. I recommend this book for students who had statistics course (not only IT or computing science), this book will show you how to use your knowledge in practice _ there are many inspiring examples. For those who do not know Python – do not be afraid _ at the beginning you will find short introduction to language syntax. All listings are very short and well described by the author _ sometimes line by line. The book also contains necessary information about basic standard libraries responsible for xml processing or web pages downloading. If you would like to start learn about collective intelligence I would strongly recommend reading “Programming Collective Intelligence” first, then “Collective Intelligence in Action”. The first one shows how easy it is to implement basic algorithms, the second one would show you how to use existing open source projects related to machine learning.

[...] People should learn how to play Lego with their minds. Concepts are building bricks [...] V. Ramos, 2002.

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