Thursday, July 27, 2017

Is Banksy's popularity evidence of inability to see art?

Found in the Guardian: Something that needed to be said about Banksy and other easy-to-assimilate art. Including music, which the iPod and iTunes have reduced to sonic wallpaper and mere ear-massage.

Wednesday, July 26, 2017

Cartoons and Comic Strips: Larson and Trudeau

     Gary Larson. Wildlife Preserves (1989) I never tire of Gary Larson. I think this is the fifth time I’ve read this collection of his cartoons. His gift is to imagine how a different context would affect the lives of people, animals, and of course monsters. Such as the unfortunate fish whose tail is embedded in two styrofoam shoes, which drag him up to “sleep with the humans.”  Or a flea painting a dogscape, which consists of acres of fur. Or Thor’s workbench, on which rest his hammer, his screwdriver, and his crescent wrench.
     Well, maybe you have to have the same sense of seeing the logically absurd.
     Recommended. ****

     G. B. Trudeau. Check Your Egos at the Door (1984, 1985) A Doonesbury collection. These strips were drawn during the reelection of Reagan. It’s depressing to see how little has changed since then. The only real difference is that liberals and conservatives were still talking to each other, whereas now they either scream at or ignore each other. The strips rely on words, so a brief quote is impossible, but I’ll try:
     Duane: I can’t get over these figures, Rick. Suburbanites went for Reagan 65% to 35%, fundamentalist 89% to11%, car dealers 54% to 46%...
     Rick: Duane, you can’t let all that get to you....

     Sounds a lot like the Dems trying to figure out how they lost to Trump. Except that Reagan won the popular vote, and Trump didn’t. ****

You may want to write a script for this

An image from this series was posted on a Usenet group. I liked it.

Wednesday, July 19, 2017

Ants and grasshoppers: A comment on our economy

In  When Republicans Take Power Geoffrey Kabaservice writes,

“Mr. Trump will not be able to bring back the manufacturing jobs he promised, but he could put his supporters to work building roads and bridges instead.”

The notion that building roads and bridges will provide large employment boost is a common misconception. As anyone who’s watched how roads and bridges are built these days knows, there are more machines and fewer people. Even the flagmen and -women who control traffic through a road-construction zone are being replaced by traffic lights powered by solar panels.

Sure, we need to repair roads and bridges, but manual labour of all kinds has been and is continuing to be replaced by machines. Machines that are increasinglty intelligent, able to perform more and more complicated tasks.

In fact, computers are replacing the professions. White-collar jobs are fading away just as blue-collar jobs did, and for the same reason: Our profit-focused economic values and business models sees people as a cost, and so seeks to eliminate them.

The malaise of a our highly technologised economy is that it produces more than we can consume, yet we operate it on the same assumptions that worked for our ancestors, that production is morally superior to consumption. Worse, too many players of the economic game believe that accumulating stuff is what it’s all about. “He who dies with the most toys wins” is taken at face value by a surprising number of people, if we take their behaviour as evidence of what values drive their choices.

But as older people will tell you, when you’re faced with getting rid of the stuff accumulated over a lifetime, you realise what a mug’s game that was. Nobody wants the stuff that you piled up. It’s obsolete, it has at most sentimental value, but even your children will want to keep only a small fraction of it.

We praise the ant, not the grasshopper. We haven’t noticed that the ant is a machine directed by a microchip.

Philosophy and ideology

Margarethe von Trotter, speaking with Michael Enright about her film on Hannah Arendt: “...Germany was known as the country of philosophers, music, and so on, how could it become such a horrible country during the Nazi time?...”

Because it was the country of philosophers. People who are word- and idea-focussed have a hard time distinguishing between the world as they think it is and the world as it really is. Ideology is the terminal disease of philosophy. It’s the condition of mistaking thought for reality.

Germany also vastly over-valued academic achievement, the assumption being that if you had a Ph. D. you were superior in every way. But academic achievement is more a matter of grinding out the work. Imagination and insight are rarely required, and even more rarely rewarded.

2014-07-20

Some theoretical talk about theories.

Theory, Model, Algorithm, and the Limits of Knowledge

These three terms that are often used interchangeably. They do have something in common, we’ll see what it is after an attempt to differentiate them, by describing how what they refer to differs.

Framework: The world we live in is “reality”. We interact with it in various ways. As we grow from infancy to adulthood, we develop various methods of predicting how reality works so that we can get what we need and want. Explicit ideas about how reality works are the theories on which we base our actions. We reason about the state of reality right now so that we can change it to suit ourselves.

For example, we plant seeds when we figure the weather is favourable so that we will get tomatoes a couple of months or so later. We add fertiliser and soil conditioners and water to ensure that the tomatoes will grow. Those actions are based on a bundle of interconnected ideas and observations that form a more or less coherent theory about how tomatoes grow from seeds.

Theory: An explanation of how something works the way it does. It’s what you get when you test a hypothesis, which is a more or less speculative explanation of some observation(s). Many hypotheses are prompted by anecdotes about some oddity, or about some claim that strikes the hypothesiser as odd. A good hypothesis links the observation(s) to some existing explanation, and predicts additional observation(s). If those links hold up, and/or the predictions prove true, then the hypothesis is confirmed and becomes a theory. A good theory implies or suggests further hypotheses, which in turn imply new observations.

When a theory is applied to some practical problem, we get a model. That, and the desire to just figure things out, are what drive science and engineering.

Model: An explanation that can be used to predict how some part of reality will work. We use this term because a conceptual model about growing tomatoes is analogous to a physical model of, say, a steam locomotive. A scale model is not a replica, it is something that looks like, and in  some specific, limited  ways works like its prototype. The model locomotive may operate on steam as the prototype does, but even so, there will be compromises. E.g., the thickness of the boiler shell will not be to scale for that would make it too weak to contain the necessary steam pressure. And so on.

We use both models and theories to plan what to do so as to get some desired result. The difference is subtle. We test a theory’s predictions in order to discover its limits, so that if necessary we can modify it or even replace it. We use a model within its limits to control some aspect of reality as much as possible. We may use a model to test a theory: an experiment is a model constructed from that part of a theory that we wish to test. It’s not easy to derive a model from a theory: models also have to be tested.

Both models and theories are true insofar as they work. When a model becomes a precise set of reliable rules, it becomes an algorithm.

Algorithm: A set of procedures applied to some inputs that will produce outputs in a predictable way. Thus, “long division” is an algorithm because it describes how to manipulate the input numbers (divisor and dividend) to get the answer (quotient). A recipe for a grilled cheese sandwich is an algorithm because it describes how to manipulate the inputs (bread, cheese, and a grill) so as to get an output (a tasty sandwich). And so on.

Algorithms are everywhere. They are especially handy for determining future values of present states. In this sense, an algorithm is a knowledge machine: input information about “this thing here and now”, turn the crank, and you get information about “this thing somewhere, somewhen, somehow else”.

If the above comments make sense, we may see a model as a set of interconnected algorithms, and a theory as a set of validated and interconnected models.

And that brings us to what they have in common: All three are modes of gaining new knowledge. All three operate on the same fundamental principle: “If you do this, you will find out that”. None of them “describe reality”. They describe only how we may interact with or observe certain aspects of reality. Which ones? Those that the theory or model or algorithm “is about.” What “is about” means is not easy to say. An example will explain (as far as the example applies, that is):

We may use Newton’s laws of motion to build a model that calculates the course of a rocket launched towards Jupiter. If we know its mass and its velocity, the varying gravitational forces of the Moon and Mars etc, we can calculate, and recalculate, its course to whatever precision we like. But the model will tell us nothing about the health of the crew. If we want to know that, we need another (and more complicated and less certain) model. The model cannot tell us what the rocket “really is”, only how it interacts with gravitational fields and the reaction forces of its engines. If we want to know other things, such as its shell’s resistance to fatigue cracking, we must use other models. What’s more, even to monitor the course of the rocket, we have to use other models, the ones that describe how our instruments work.

Thus all theories, all models, all algorithms are knowledge engines. They are epistemological devices. But they are limited. They can’t tell us what some entity really is, only how we can interact with it, and what will happen when we do so.

Even the notion of “entity” is fundamentally epistemological: An entity is a more or less consistent bundle of expected interactions. If any of them are missing or unexpected, we doubt that we are interacting with that entity. It may be an hallucination, or a dream, or a fake, or merely an image of the entity. Or a model of it.

Kant was right, I think: There is no way to know reality in itself. That doesn’t mean there is no reality “out there”. It just means that we can know only our interactions with it. That we can know even that much is I think at least as great a puzzle as what it is that we can’t know.

2016-04-07/2017-05-03/2017-07-19

Tuesday, July 18, 2017

Grieving: a poem about loss

     Joan Finnigan. In the brown cottage on Loughborough Lake (1970) A long poem or suite of poems, interspersed with photographs, expressing Finnigan’s grief on the death of her husband. It tells of the first summer spent on the lake without him. The book is more of a meditative essay, the kind that invites the reader to recall emotion rather than imagine experience. A few lines here and there pierce the heart:
     The summer turned to crabapples
 

    And the wild plums chimed on the trees
    along the stone-pile fences

    The lake chilled

    and we shortened our swims


     The book is misclassified as non-fiction on one website about Finnigan. ***

Small lives, much pain: Mareve Binchy's early Short Stories

      Maeve Binchy. Victoria Line, Central Line (1978 & 1980) These two collections were published separately, then republished in a single volume in 1993. The stories are Binchy’s earliest published fiction, and they contradict her reputation as a “sympathetic and often humorous” portrayer of life. Almost all of them describe women who are more or less unaware of why they lose out in the game of life, or who are lucky simply to endure. Like Alice Munro's, her portrayals of ordinary people is ruthless: she knows that human beings are anything but perfect, that they are weak cruel, feckless, vain, indifferent, self-centred, and more often than not unable or unwilling to change.
    Binchy’s especially good at showing how women fail to assert themselves, and define their value through their relationships with men. Some of these are heartbreaking: why do so many smart women put up with cads? Class has something to do with it: all her protagonists are middle or working class, and along with their men are constrained by aspirations of respectability which limit or distort their self-expression, and too often make them believe that they deserve the tawdry or painful love lives they settle for.
     With a few exceptions, we readers have the flash of insight at the end of the tale but the characters do not. It seems to me that Binchy in her later works learned to enlarge the sympathy and reduce the judgements. Or perhaps her growing confidence in her own abilities enabled her to write about women who knew what they wanted and set about getting it, a story that becomes the Binchy formula. At any rate, compared to these short stories, her later work seems to me to show a deliberate softening of the hard judgments that her only-too-accurate portrayals here imply. One could also argue that her work reflects the increasing power and self-awareness of women. That would make these early stories a collection of documentaries of women’s lives in the mid-20th century, accurately rendered.
     Recommended. ***

Sunday, July 16, 2017

Accidental discovery

Sometimes when searching for one thing you stumble across another. Here's Daphne Arts, one such discovery. If you like art, I think there will be something on this site for you. My rating: ***

Thursday, July 13, 2017

2001: A Space Odyssey, a flawed masterpeice

      2001: A Space Odyssey (1968) [D: Stanley Kubrick. Keir Dullea et al, and HAL-9000] A museum piece, instructive: what’s interesting is how limited Clarkes’ technical imagination was, and how his social imagination was essentially zero. Clarke could imagine technical progress, up to a point: he didn’t fully extrapolate the effects of the relentless miniaturisation of electronic devices. Fred Pohl had already written The Age of the Pussyfoot, which among other things imagined something very like a cross between a smartphone and a tablet PC, but much more powerful than what we actually have. Look it up.
     But where Clarke and Kubrick fail most is the social context. Beginning with the clothes, which are merely late 60s fashions streamlined a bit. Gender roles are still very 50s. The Cold War’s US-Russian rivalry is still going on. There is no awareness of the probable outcomes of the anti-racism movement of the 1960s. Martin Luther King was assassinated in 1968, the year of this movie’s release, and Guess Who’s Coming to Dinner was released the year before this movie. Not too late to affect the script, its ideas were very much part of public discourse.
     It was already clear than China and other Asian economies would eventually rival and even surpass the USA and Europe. The notion that the West would continue its supremacy in science and engineering was already undermined by the  achievements of Japan. All these things could have influenced the script, especially since so much of the movie is displays the engineering achievements expected by 2001.
     The decor and ambience of the movie celebrate technology. Kubrick uses music to underline the joy and grace of beautiful machines. The long sequence of the PanAm space shuttle arriving at the space station is shown to a sumptuous version of the Blue Danube waltz. The scene in which Dr Floyd calls home on video phone is there to emphasise the wonderful technology of the future, as is the space station itself, the moon shuttle, etc. Clark’s faith in the saving grace of ever more magical tech is touching, now that we have become accustomed to it, and are beginning to understand the negative effects of overly-rapid change, aptly called disruption.
     But those are minor cavils. This is a pioneer movie. Not only in its visual effects, all done with analogue techniques utilising models and matte boards, and photographic manipulations. Its story, such as it is, is about work. There’s no character conflict, there’s only work to be done. What plot tension there is comes from the character’s attempts to work out what to do when HAL goes rogue.
     The story has five parts: the discovery of tools, instigated by the mysterious black monolith. The discovery of the monolith on the Moon. The expedition to Jupiter. The rebellion of HAL, and Dave Bowman’s destruction of the computer’s personality module. Dave’s arrival and stay somewhere in orbit around Jupiter. Bowman’s aging, and the appearance of  a fetus journeying back towards Earth.
     But there’s more to the movie than its story or its plot structure. It is a celebration of technology, of the Universe, of humankind’s ability to overcome obstacles, and an expression of a mystical faith in some barely imaginable future of humankind. Clark and Kubrick wanted to foster wonder and hope. Wonder at all that human curiosity and skill and art can achieve, and hope that ultimately humans will become better than the warring semi-apes that they are.
     Worth seeing again, despite its datedness and flaws. ****

Saturday, July 01, 2017

Climate change: How fast does it happen?

A story about climate change.
A true story.

     The science department head at W C Eaket Secondary School was a member of the American Association for the Advancement of Science. She received their weekly journal, and placed it in the school library, where I read it most weeks.
     Every so often, there were papers on weather and climate modelling, which was developing quickly as computer power increased. The models were based on weather data laid down in ice-cores, tree-rings, layers of silt on lake bottoms and swamps, and so on. These data are good for many thousands of years in the past, but obviously not for millions for years. The geological record supplies data for those long range climate changes.
     Small changes in climate such as the Little Ice Age in the 1500s-1600s (which killed off the Viking Greenland colony) were used to test the models. These were strong models because they were based on large amounts of data. If they worked well, they were run backwards beyond the range for which there was much data, to see if they described the climate as known from geology. They were also run forward, to see what could happen if the CO2 continued to increase to the levels known to have existed millions of years ago.
     The tests were designed to guide further development of weather and climate models. The models varied in the weighting of different factors known to affect the weather, estimated and known rates at which the effects occurred, and different ideas about the feedback loops between these factors. As better data became available, the models were tweaked. Because of their differences, the models were in fact tests of different theories of how weather and climate change. Weather prediction models are so powerful now that we expect a three- to four-day forecast to be accurate. Back then, one day was considered good. When I was a child, we expected weather forecasts to be updated between morning and evening.
     The results of the climate models were, as they say, interesting. The authors reported on and discussed the successful models, the ones that closely described the known history of the weather and climate. Most of these models predicted continuing slow changes in climate like the ones known from the past.
     A handful of models in the early to mid-70s predicted very sudden changes in climate. Changes that didn’t take thousands or tens of thousands of years, but a few hundred years, or even less. The authors were uncertain what to do with those. It wasn’t clear how to decide whether these models were any better or worse than the ones that predicted slow changes. Their conclusions were cautious.  As I recall the theme of their discussions, it was along the lines of “If these models that predict fast changes are accurate, then the rapidly rising levels of CO2 in the atmosphere could cause rapid changes in climate.”
     Those papers have stayed with me. Some time later, I learned about the mathematics of chaos. A chaotic system cycles through a series of changes with minor variations from one cycle to the next. Think of the seasonal cycle of weather.  But if one variable exceeds some critical value, the system shifts into another state. This new state will cycle through a different series of changes.
      Climate is a chaotic system. As with any chaotic system, a fundamental question is how quickly the shift can occur. Some chaotic systems change so fast that we speak of a “tipping point”. There is increasing evidence that climate is such a system.

Links
Little_Ice_Age
Climate_model
Global Warming