Thursday, July 31, 2008 at 15:16 (film & TV)
Tags: comedy, impro, TV, Whose Line
I’ve recently been watching many past episodes of Whose line is it anyway? (WLIIA), a game show where four comedians improvise around various themes in a miscellany of simple games. It’s among the funniest things on TV. Or was, as it’s been cancelled by now. Hopefully it comes back some time.
WLIIA started out as a UK radio show, before becoming a show on British television, where it ran for 10 years (1988-1998). It also emigrated to US television, where it ran for 8 years (1999-2006). The UK show was hosted by the always witty Clive Anderson, while the US show was hosted by the merry Drew Carey. With very few exceptions, the comedians who took part in the show were stellar stuff, e.g. Wayne Brady, Josie Lawrence, Paul Merton, Mike McShane, Colin Mochrie, Greg Proops, Caroline Quentin, Brad Sherwood, Tony Slattery, Ryan Stiles, Jim Sweeney, Steve Steen, and several others.
The point with the show was to have comedians improvise their lines and actions around subjects given to them by the show’s host, and occasionally the audience (who was also asked to participate now and then). In theory this sounds like the recipy for comedy disaster, but often it worked out very well. In fact, extremely well. It seldom became boring, much thanks to the many top notch comedians who were on the show. The US show seemed to manage this a little better, as it quickly developed a formula in which the number of regulars were fewer, and the same comedians kept performing the same games over and over again; thus somewhat defeating the whole purpose of an improvisational game. In effect, the US show came to rely less on improvisation than did the UK show. Still, they were both very, very funny. I do hope they bring the show back.
I don’t have any real point here, except that I wanted to rant a little bit about one of television’s funniest shows.
Thursday, July 10, 2008 at 15:08 (opinion, science)
Tags: commentary, method, science, theories
I just read a somewhat flawed paper at the Edge entitled: The end of theory, written by Chris Anderson.
In short, the paper argues that we will no longer need theories in science, because we have Google. We can now use computers to look at massive amounts of data, and use them to detect patterns for us. From this, Anderson draws the slightly irrational conclusion that we need no theories.
Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot …
There’s no reason to cling to our old ways. It’s time to ask: What can science learn from Google?
Anderson’s somewhat fallacious observation is that we don’t need scientific theories or models since Google will give us all our answers anyway. However, what Anderson is talking about is nothing new. He is merely describing a first step in a long-established scientific method called induction, or "data-to-explanation". In its modern form it has been around since at least Francis Bacon (late 16th century), sometimes referred to as the father of scientific induction.
Amputating the inductive method by removing the explanation part (the model, theory) is not the way to go, as then we would effectively be entering a stage of scientific stagnation. It would be a job half-done (to some degree even pointless) for a scientific endeavour to collect data and establish patterns and not try to explain why the patterns are there. The explanation part is essential if we want to understand *why* the patterns exist, and for that we need models. The models need not be established before-hand, of course (even though analysing data without some prior theory is virtually impossible). Finding patterns in data can be, and often is, a perfectly valid impetus for developing (new) explanatory models.
What Google, and the like, does is offer us new methods in handling much larger amounts of data than what has been possible before. With Google, we can find new, previously undetected patterns, some of which our existing theories cannot predict. These, in turn, will create a need for new explanations and new theories. Hence it is more likely that Google will foster even more theories and models, not less.