YouTubeTime.com makes it easy to link to a specific time in a YouTube video.
To link to 2 minutes & 14 seconds into this video of dancing in a Microsoft store, http://www.youtube.com/watch?v=TSAXEVXvNz8, add #t=2m14s to the end of the url:
http://www.youtube.com/watch?v=TSAXEVXvNz8#t=2m14s.
Did you catch the shoplifting? (via @codinghorror)
I did a project years ago called the “Dollar Dudes”, where we got on the subway with a bucket of dollar bills and announced that we were in the lucky “Dollar Train” and that everyone gets a dollar. Most everyone was delighted (at both the dollar and the ridiculousness of it all) but one guy refused to take the money and snapped at me. I was bummed out to get that reaction, but at the end of the day I didn’t feel that one guy getting irritated made the whole project a failure. The other 40 people had fun. I imagine the type of person who gets mad when offered a dollar by a stranger probably gets mad quite a bit throughout his day. I’m not trying or pretending to please every single person we encounter.
—Charlie Todd (of Improv Everywhere fame)
Yes, handing out a bucket of money really does upset someone. You have no chance of pleasing everyone. Make tradeoffs accordingly.
You’re looking at Anscombe’s quartet: 4 datasets with identical simple statistical properties (mean, variance, correlation, linear regression); but obvious differences when graphed.
(via Best of Wikipedia)
Graphs aren’t a substitute for numerical analysis. Graphs are not a panacea. But they’re excellent for discovering patterns, outliers, and getting intuition about a dataset. If you never graph your data, then you’ve never really looked at it.
I was working on optimizing color correction, using SSE (high performance x86 instructions). One operation required division — an expensive operation for a computer. The hardware had a divide instruction, but sometimes using the Newton-Raphson method to do the division in software is faster. You never know until you measure.
While doing the measurement, I somehow got the crazy idea to try both: I’d already unrolled the inner loop so instead of repeating the divide or Newton’s Method twice, I’d do a divide and then use Newton’s Method for the next value. Strangely enough, this was faster on the hardware I was benchmarking than either method individually. Modern hardware is a complex and scary beast.
I was fortunate enough to have a suite of very good unit tests to run against my optimized code. But there was a caveat to testing correctness. Because computers don’t have infinitely precise arithmetic, two correct algorithms might give different answers — but if the numbers they gave were close enough to the infinitely precise answer (say a couple ulps apart) it was good enough. (We can only be exact within some Tolerance!) The tests cleared my hybrid divide/Newton-Raphson function: but we couldn’t use it, because it was fundamentally broken.
Even though the error was acceptably small, it had a nasty distribution. Using divide gave color values that were a bit too light. Doing a divide in software gave values that were a bit too dark. Individually these errors were fine. Randomly spread over the image they would have been fine. But processing every other pixel differently had the effect of adding alternating light/dark stripes! We see contrast, not absolute color, so the numerically insignificant error was quite visible. Worse still, bands of 1 pixel stripes combined to form a shimmering Moiré pattern. It was totally busted. Unusable.
This was all immediately obvious when the results of the color correction were “graphed”. Actually looking at the answer caught a subtle error that our suite of unit tests missed.
To be clear, more subjective graphical analysis is not a substitute for numerical analysis and data mining. But I believe in actually looking at your data at least once. A graph is a kind of end-to-end visualization of everything, and that has value. Graphs are a cheap sanity check — does everything look right? And sometimes, they can give you real insight into a problem.
What’s a spurious relationship?
Here’s one: People who eat ice cream are more likely to drown. Both incidence of ice cream eating and rates of drowning are related to summertime. The relationship between ice cream and drowning is spurious. That is, there is no relationship. Yet they appear related because they are both related to a third variable.
(Image via the amazing Superdickery)
The Principle of Uncertainty is a bad name. In science or outside of it we are not uncertain; our knowledge is merely confined, within a certain tolerance. We should call it the Principle of Tolerance.
And I propose that name in two senses: First, in the engineering sense, science has progressed, step by step, the most successful enterprise in the ascent of man, because it has understood that the exchange of information between man and nature, and man and man, can only take place with a certain tolerance.
But second, I also use the word, passionately, about the real world. All knowledge, all information between human beings, can only be exchanged within a play of tolerance. And that is true whether the exchange is in science, or in literature, or in religion, or in politics, or in any form of thought that aspires to dogma.
It’s a major tragedy of my lifetime and yours that scientists were refining, to the most exquisite precision, the Principle of Tolerance, and turning their backs on the fact that all around them, tolerance was crashing to the ground beyond repair.
The Principle of Uncertainty or, in my phrase, the Principle of Tolerance, fixed once for all the realization that all knowledge is limited. It is an irony of history that at the very time when this was being worked out there should rise, under Hitler in Germany and other tyrants elsewhere, a counter-conception: a principle of monstrous certainty.
When the future looks back on the 1930s, it will think of them as a crucial confrontation of culture as I have been expounding it, the ascent of man, against the throwback to the despots’ belief that they have absolute certainty.
It is said that science will dehumanize people and turn them into numbers.
That is false: tragically false. Look for yourself.
This is the concentration camp and crematorium at Auschwitz. This is where people were turned into numbers.
Into this pond were flushed the ashes of four million people. And that was not done by gas. It was done by arrogance. It was done by dogma. It was done by ignorance.
When people believe that they have absolute knowledge, with no test in reality, this is how they behave. This is what men do when they aspire to the knowledge of gods.
Science is a very human form of knowledge. We are always at the brink of the known; we always feel forward for what is to be hoped. Every judgment in science stands on the edge of error, and is personal.
Science is a tribute to what we can know although we are fallible. In the end, the words were said by Oliver Cromwell: ‘I beseech you, in the bowels of Christ: Think it possible you may be mistaken.’ We have to cure ourselves of the itch for absolute knowledge and power. We have to close the distance between the push-button order and the human act. We have to touch people.
–Jacob Bronowski, “Knowledge or Certainty” episode of the 1973 BBC series The Ascent of Man
(Transcription source, formatting and emphasis mine).
Magnetoception, the ability to sense magnetic fields like a compass, is my prediction for the first widely implemented super-sense, because it’s so simple.
I’m no biologist, but it certainly seems that only a little wetware is necessary to implement magnetoception, since even bacteria have it. On the mechanical front, tiny manometers have been built into millions of devices already. I have no idea what the state of the art is, but the first 3-axis digital compass chip I found on google measures 4x4x1.3mm. They’re only getting smaller and more efficient. We already have the technology to build it into belts and clothing.
But I hope I’m wrong. Certainly, the future promises more than better compasses.
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