I’ve just posted revision 0176 of Processing, a pre-release of what will become version 1.1 or maybe 1.5, depending on how long we bake this one before releasing the final. A list of changes can be found here.
You can download the release at android.processing.org, which (as you might guess) is the eventual home of the Android version of Processing. The Android support is very incomplete, as you can see from the warnings on the page.
But ignore for a moment that it says “Android”, the download is hosted there because at the moment, most of my energy is focused on the Android extensions. While the build also includes the incomplete Android tools (just pretend they aren’t there, unless you’re willing to read all the caveats on that page), there are many bug fixes for the regular Java version of Processing in the download too. It’s been a couple months since I’ve done a proper release, so there’s a backlog of fixed bugs and things I’ve been adding.
I’m posting the pre-release because so many things have changed, and I don’t want to do a 1.1 release, followed by an immediate 1.1.1. So please test! Then again, it’s taken me so long to explain the situation that I should have just posted it as 1.1.
And by the time you read this, it’ll probably be release 0177, or 0178, or…
Fascinating editorial from chess champion Gary Kasparov, about the relationship between humans and machines:
The AI crowd, too, was pleased with the result and the attention, but dismayed by the fact that Deep Blue was hardly what their predecessors had imagined decades earlier when they dreamed of creating a machine to defeat the world chess champion. Instead of a computer that thought and played chess like a human, with human creativity and intuition, they got one that played like a machine, systematically evaluating 200 million possible moves on the chess board per second and winning with brute number-crunching force. As Igor Aleksander, a British AI and neural networks pioneer, explained in his 2000 book, How to Build a Mind:
By the mid-1990s the number of people with some experience of using computers was many orders of magnitude greater than in the 1960s. In the Kasparov defeat they recognized that here was a great triumph for programmers, but not one that may compete with the human intelligence that helps us to lead our lives.
It was an impressive achievement, of course, and a human achievement by the members of the IBM team, but Deep Blue was only intelligent the way your programmable alarm clock is intelligent. Not that losing to a $10 million alarm clock made me feel any better.
He continues to describe playing games with humans aided by computers, and how it made the game even more dependent upon creativity:
Having a computer program available during play was as disturbing as it was exciting. And being able to access a database of a few million games meant that we didn’t have to strain our memories nearly as much in the opening, whose possibilities have been thoroughly catalogued over the years. But since we both had equal access to the same database, the advantage still came down to creating a new idea at some point.
Or some of the other effects:
Having a computer partner also meant never having to worry about making a tactical blunder. The computer could project the consequences of each move we considered, pointing out possible outcomes and countermoves we might otherwise have missed. With that taken care of for us, we could concentrate on strategic planning instead of spending so much time on calculations. Human creativity was even more paramount under these conditions. Despite access to the “best of both worlds,” my games with Topalov were far from perfect. We were playing on the clock and had little time to consult with our silicon assistants. Still, the results were notable. A month earlier I had defeated the Bulgarian in a match of “regular” rapid chess 4–0. Our advanced chess match ended in a 3–3 draw. My advantage in calculating tactics had been nullified by the machine.
The final reinforces that I’d heard others describe Kasparov’s play as machine-like in the past (in a sense, this is verification or even quantification of that idea). It also includes some interesting comments on numerical scale:
The number of legal chess positions is 1040, the number of different possible games, 10120. Authors have attempted various ways to convey this immensity, usually based on one of the few fields to regularly employ such exponents, astronomy. In his book Chess Metaphors, Diego Rasskin-Gutman points out that a player looking eight moves ahead is already presented with as many possible games as there are stars in the galaxy. Another staple, a variation of which is also used by Rasskin-Gutman, is to say there are more possible chess games than the number of atoms in the universe. All of these comparisons impress upon the casual observer why brute-force computer calculation can’t solve this ancient board game. They are also handy, and I am not above doing this myself, for impressing people with how complicated chess is, if only in a largely irrelevant mathematical way.
And one last statement:
Our best minds have gone into financial engineering instead of real engineering, with catastrophic results for both sectors.
“contrary to traditional assumptions, the uniquely human faculty of reason (conscious, intelligent, rational thought) requires very little computation, but that the unconscious sensorimotor skills and instincts that we share with the animals require enormous computational resources”
And another interesting notion:
Marvin Minsky emphasizes that the most difficult human skills to reverse engineer are those that are unconscious. “In general, we’re least aware of what our minds do best,” he writes, and adds “we’re more aware of simple processes that don’t work well than of complex ones that work flawlessly.”
An interesting op-ed by Dick Brass, a former Vice President at Microsoft on how their internal structure can get in the way of innovation, and citing specific examples. The first relates to ClearType and the difficulties of getting it integrated into other products:
Although we built it to help sell e-books, it gave Microsoft a huge potential advantage for every device with a screen. But it also annoyed other Microsoft groups that felt threatened by our success.
Engineers in the Windows group falsely claimed it made the display go haywire when certain colors were used. The head of Office products said it was fuzzy and gave him headaches. The vice president for pocket devices was blunter: he’d support ClearType and use it, but only if I transferred the program and the programmers to his control. As a result, even though it received much public praise, internal promotion and patents, a decade passed before a fully operational version of ClearType finally made it into Windows.
Or another case in attempts to build the Tablet PC, in stark contrast to Apple’s (obvious and necessary) redesign of iWork for their upcoming iPad:
Another example: When we were building the tablet PC in 2001, the vice president in charge of Office at the time decided he didn’t like the concept. The tablet required a stylus, and he much preferred keyboards to pens and thought our efforts doomed. To guarantee they were, he refused to modify the popular Office applications to work properly with the tablet. So if you wanted to enter a number into a spreadsheet or correct a word in an e-mail message, you had to write it in a special pop-up box, which then transferred the information to Office. Annoying, clumsy and slow.
Having spent time in engineering meetings where similar arguments were made, it’s interesting to see how that perspective translates into actual outcomes. ClearType has seemingly crawled its way to a modest success (though arguably was invented much earlier with Apple ][ displays), while Microsoft’s Tablet efforts remain a failure. But neither represent he common sense approach that has had such an influence on Apple’s success.
Update: A shockingly bad official response has been posted to Microsoft’s corporate blog. While I took the original article to be one person’s perspective, the lame retort (inline smiley face and all) does more to reinforce Brass’ argument.
Visualizing Data is my book about computational information design. It covers the path from raw data to how we understand it, detailing how to begin with a set of numbers and produce images or software that lets you view and interact with information. Unlike nearly all books in this field, it is a hands-on guide intended for people who want to learn how to actually build a data visualization.
The text was published by O’Reilly in December 2007 and can be found at Amazon and elsewhere. Amazon also has an edition for the Kindle, for people who aren’t into the dead tree thing. (Proceeds from Amazon links found on this page are used to pay my web hosting bill.)
The book covers ideas found in my Ph.D. dissertation, which is basis for Chapter 1. The next chapter is an extremely brief introduction to Processing, which is used for the examples. Next is (chapter 3) is a simple mapping project to place data points on a map of the United States. Of course, the idea is not that lots of people want to visualize data for each of 50 states. Instead, it’s a jumping off point for learning how to lay out data spatially.
The chapters that follow cover six more projects, such as salary vs. performance (Chapter 5), zipdecode (Chapter 6), followed by more advanced topics dealing with trees, treemaps, hierarchies, and recursion (Chapter 7), plus graphs and networks (Chapter 8).
This site is used for follow-up code and writing about related topics.