Billy Liar (1963)

Dir: John Schlesinger, with Tom Courtenay and Julie Christie (DVD)

It’s many years since I saw this film, and my take on it has changed somewhat. As a teenager, I was completely on Billy’s side; who wouldn’t fantasise about escaping from such a straitjacket of a town? In identifying emotionally with Billy and Liz, I missed a great deal else. Now I see it with a wider frame of reference and a more nuanced view . . . to the point where I’m wondering if Billy’s parents had a point!

I have grown old. But I understand repression more.

Modernity is hovering at the edges of the film: demolition of old houses, construction of new flats, Shadrack’s plastic coffins, new supermarkets, Liz prefiguring the stereotypical free-spirit of the sixties (cue jazzy music as she makes her entrance). In contrast, Billy’s fantasies have an archaic air; they’re not dreams of the future but a comfort blanket from the present. His fantasies of machine-gunning his critics haven’t dated well (unless Schlesinger was predicting Columbine or “if . . .”).

With the notion of categorising people as “somewheres” and “nowheres” currently in vogue, it’s mildly interesting that Liz champions going somewhere where nobody knows you, whereas Mr (“Councillor to you”) Duxbury, in a serious moment, tells Billy that he can’t make a life without other people. You can see how Billy is conflicted: the stifling town has made him and gives him and Arthur something to mock uproariously, but London might mock him. (And his grandmother has just died. There is some importance in family.)

It’s an amusing and verbally witty film, but there’s an undertone of poignancy throughout. The funeral parlour, Mona Washbourne’s Flemish madonna face, the frustration of a clever boy trapped in a dreary job with a hectoring father and girlfriend trouble of his own making. Even his best friend is fed up with him by the end.

Oh, why not just get on that train?

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Leeds art gallery


Lady Godiva, Edward Blair Leighton, oil on canvas, 1892. How I loved this picture when I was a child!

Thanks to train and track disruption, I bagged another cathedral today. The train detoured via Lincoln, so I had a brief view of the cathedral.

Leeds art gallery has finally re-opened. It is full of images which have been familiar for ever – mostly thanks, I think, to the choice of illustrations in Arthur Mee’s children’s encyclopaedia. I’m not sure if Cordelia comforting King Lear is in there, but Lady Godiva definitely is. (The “Golden Deeds” chapter, if I recall correctly.)


Cordelia comforting her father, King Lear, in prison, George W Joy, oil on canvas, 1886 

I’d never been upstairs before; I had no idea that it had panels depicting idealised manual labourers by Frank Brangwyn. His display neatly carried on the illustrative tone of the Victorian painters downstairs.

My dislike of Matthew Smith was confirmed. It’s something about his colours combined with his inept forms. And I have noticed that all art galleries have a bust by Jacob Epstein.

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After a weekend spent in the fascinating hellhole between Euston, King’s Cross and Oxford Street, I am now heading home, expecting to arrive 5 hours late because of damaged overhead wires at Watford. I am not complaining: I’ve had an interesting time, I have a seat, I am heading in the right direction, and I am looking forward to whiling away a couple of hours in Leeds.

Mr Sleary Rules.

But I have just read the first paragraph of Patrick Hamilton’s “The Slaves of Solitude” (continuing my binge on mid-century austerity):

London, the crouching monster, like every other monster has to breathe, and breathe it does in its obscure, malignant way. Its vital oxygen is composed of suburban working men and women of all kinds, who every morning are sucked up through an infinitely complicated respiratory apparatus of trains and termini into the mighty congested lungs, held there for a number of hours, and then, in the evening, exhaled violently through the same channels.

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More of London

I noticed a leaf on the ground and looked up. Yes, they have planted ginkgo trees in London streets:


At lunchtime, to get a walk and some fresh air, I went up to Mornington Crescent to look at the old Carreras cigarette factory:


Butilt 1926-28, architects Marcus Evelyn Collins, O H Collins and Arthur George Porri, and said to be the first building in Britain to use pre-stressed concrete.

Then this evening I walked to Regent Street and could hardly miss the Christmas decorations:

Rather tasteful. Oh, where are the tacky decorations d’antan?

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Artificial Intelligence

On a whim, I decided to attend a New Scientist event to learn more about Artificial Intelligence (or Machine Learning) today to find out more. I read the newspapers (so I know stuff like Facebook using their subscribers as guinea pigs in seeing how good/bad news affects mood) and the easier articles in New Scientist, but my life doesn’t really bump up against the most recent developments. A certain limited use of an ipad is as far as I get.

It was a brilliant day – interesting and demystifying, although there was not much that was completely new to me. Nothing too techy. The good news is that the Terminator scenario is highly unlikely.

From my notes, with some gloss of my own:


Basically, machines with artificial intelligence are able to communicate with and respond to you; make decisions; determine their environment and take action. Machines such as Alpha Go from Deep Mind (now part of Google – the ownership of AI is problematic) was able to beat the world’s Go expert recently. (Go is much bigger in Korea and S.E. Asia than here. It appears to be played with mint imperials.) The Deep Mind programmers watched as their “baby” learned more about Go in 3 days than humans have learned in decades . . . and it created moves that humans had never thought of. However (as later speakers pointed out), it’s just a programme to play a game. The fact that one of the speakers illustrated Go with a picture of two middle-aged men bent companiably over a board one evening, backlit by the light from a shop made me question its value. But it is useful: it demonstrates machine learning in action.

Personal digital assistants which perform little tasks for you (which you can do perfectly well yourself, I harrumphed; the banality of some of the things AI does brings Marvin the paranoid android to mind) have been programmed to appear human friendly. So you tell it that you’re not feeling well . . . and it responds with “I’m so sorry to hear that.” (One of several WTF moments for me. It teamed nicely with the apology from my lift.)

Machine learning is also getting creative (cue the Lovelace Test, perhaps more significant than the Turing Test: could you tell a work of art had been created by artificial intelligence?). There are artists who make programmes so that computers can produce paintings in no recognisable genre that are admired by humans . . . until you tell them that a computer created it, and then they suddenly discover that they don’t like it as much as they thought. Its figurative stuff was meh, but it used colours in a pleasing way. But AI is also capable of telling you what it wanted to paint and how it feels about the finished product. This is distinctly spooky – it’s using words, but how can those words carry any sincerity?

Deep Blue beating Gary Kasparov at chess in 1997 was one of the AI high points, but everything rather stalled after that. There has been more interest in recent years – possibly linked to the exponential rise of computing power, which now gives the machines in your pocket near-magical powers. This – and, presumably, the fact that a couple of men have become rich beyond the dreams of avarice – rekindled recent development.

There was one slide of what the inside of the brain looks like when a human thinks: the phrase “neurons fire” is literal – they do suddenly light up like a firework.

Artificial intelligence machines judge each other’s work to improve it (I state that without fully understanding it); it’s called Generative Adversarial Network (ditto).

A great quote, which I’ve heard before:

The question of whether machines can think is about as relevant as whether submarines can swim.

Edsger W Dijkstra


Deep Mind is now the world Go champion, way ahead of any human. But that’s practically all it can do. It’s an algorithm that can learn and adapt, and it was developed by playing Atari games (ah, Pong – that takes me back). It got so good at them and worked out new ways of winning that its human programmers regarded it as a cheat! As it learned, it used less power, but equally as it learned new things, it forgot its previous knowledge. (Like me and logarithm tables, then.)


The popular view of robots is as “metal people”, but this is misleading. Robots have potential for defined tasks, but banal things like getting a glass of water from the kitchen is way beyond them (and likely to remain so for a long time). Robotic AI is already here, and is possibly the most fruitful use of AI: industrial, drones, domestic (e.g. vacuum cleaners – where do I get one?!), driverless vehicles (but plenty of problems there), NLP, medical, mining, drones, logistics (e.g. Amazon robots in their warehouses), and military (I learned of “slaughterbots”).

The problems with robots are:

  • Do we trust them?
  • Do they have ethics? (which means “how will they be programmed?”)
  • Will they replace us?
  • What does it mean to be human?
  • Are “killer robots” coming?

There were some amusing insights: in Japan it was discovered that the robot vacuum cleaner didn’t work as well as in Europe. This was because there was a craze in Japan in decorating them with cats’ ears, which messed up their vision system.

The speaker was particularly interested in robots working together to solve problems. She used swarming models from nature (e.g. ants, bees, starlings), and it was startling to watch miniature robots bumping off each other and communicating to solve a task.

A problem with robots is the risk of failure or sabotage; their constant connectivity makes them vulnerable to  disruption – malicious or otherwise.

I was introduced to the concept of weak/narrow AI (i.e. dedicated tasks) and strong/general AI, which is very far ahead.


The problem is that the training data sets (original data that algorithms work on) and the algorithms themselves are not transparent; often they are proprietary information and hence can legally remain opaque. This leads to problems (which I have read of); for example, AI used in the US for determining sentences for convicted offenders may be based on biased data*. Chatbots, which “learn” from their online interactions with humans, can be quickly and easily “taught” to be racist or vile in other ways if they are exposed to such utterances. Other problems are: who is liable in, e.g., the case of a driverless vehicle being involved in a death?


AI now has better success at image recognition (mostly of cats, it seems to me) than humans: about 5% error. However, unlike humans, which can learn from just one example, AI requires many examples (although it sifts through them pretty quickly).

Humans no longer necessarily have the manual or cognitive advantage over AI in certain jobs or activities.

The impact on future employment is impossible to forecast, so newspaper headlines of 15 million jobs in Britain to disappear should be taken with a pinch of salt. The data on which this figure was based include an assumption that there is a 98% probability of bicycle mechanics being replaced by AI. Well, I for one would like to see a robot change a Brompton rear wheel! I’d willingly take it on holiday.

China is going to be very significant in what happens with AI; they are investing billions. (I did read the other day that, with the government’s – ahem – relaxed attitude to data protection, China is able to work with vast amounts of data about people’s movements and activities and design their new cities accordingly. And other things besides.)


The speakers responded to written questions at the end.

What stood out for me:

There is a form of co-evolution: humans are learning from AI as well as vice versa. For example, the European Go champion is now much higher up the rankings since he started playing with (and generally losing to) Deep Mind. Or the way surgeons and AI can co-operate to produce more accurate diagnoses than either working alone.

There’s a potential problem in competing algorithms – e.g. two driverless cars in a scenario where a crash is going to happen. Which one gives way?

The behaviour of AI is not routinely foreseeable once it gets the bit between its teeth. (Ooof – that metaphor is odd.)

There is the danger with algorithms that determine, say, whether you should get a loan or what your life insurance payments should be that if someone knows how they work they can be gamed.

AI shows how far a machine can go without being intelligent (as humans understand the term) or having insight. (Hmmm . . . not only AI.)

There is a danger of humans giving up too much data to AI companies and leaving ourselves open to manipulation or control. (See China above.)

One speaker forecast that humans may end up adapting to low-level AI and, unwittingly, giving the impression that AI is rather cleverer than it really is – a kind of dominance by AI and humans adapting to AI rather than the other way round. Perhaps AI as the mediaeval aristocracy and humans as the serfs. And that’s the Terminator-lite scenario in a nutshell.

* I subsequently read of facial recognition software used by the police whose training data sets were predominantly of white faces; consequently it’s currently not very accurate at distinguishing between black faces.

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Down the rabbit hole

I’m in a world where inanimate objects write me notes:


Note the initial caps on Health Issue and Christmas Shopping. Perhaps it’s cute, but it makes me queasy.

On the plus side, his (her?) colleague has a glass wall, so every time I come up to my fourth-floor room I go via the sixteenth floor to look towards the Thames. One interesting thing from that height is to note that very tall buildings which, from the ground, are undistinguished may actually have a pleasant outline from higher up. Thus I’ve seen that the block opposite has curved sides and is more impressive from the air than the ground.

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A Far Cry from Kensington by Muriel Spark (1988)

The novel is set in war-damaged London in 1954, narrated in retrospect by a fat young war widow, Mrs Hawkins. Over the course of the novel she ceases to be a widow or fat, but her strong moral sense remains unchanged. She speaks the truth; she values honesty. It’s a delightful novel – funny, moving and evocative of the days of respectable rooming houses where people cooked on a single gas ring and austerity was the norm. The plot is the revenge that a third-rate parasitic writer takes on the narrator and the harm that he does to a Polish dressmaker, a neighbour of Mrs Hawkins. It becomes clear that the title has a double meaning.

Amongst many pleasures, it’s full of matter-of-fact advice from the narrator (how to lose weight: “You eat and drink the same as always, only half . . . After a while, if you are a perfectionist, you can consume half of that again”. How to write a novel: “You are writing a letter to a friend . . . Write privately, not publicly; without fear or timidity, right to the end of the letter, as if it was never going to be published, so that your true friend will read it over and over, and then want more enchanting letters from you”). The publishing houses she works in (as did Spark in real life in the early 1950s) are brilliantly described in all their quirks and oddities – and all done so economically.

Since it’s a novel set in the publishing world, it does occasionally become self-referential. There was one particular passage which echoed a favourite of mine from Northanger Abbey (“‘Oh! It is only a novel!’ . . . only some work in which the greatest powers of the mind are displayed, in which the most thorough knowledge of human nature, the happiest delineations of its varieties, the liveliest effusions of wit and humour, are conveyed to the world in the best-chosen language.”) One of Mrs Hawkins’s jobs is to read unsolicited manuscripts “sent to sea in a sieve”*; many of them are written by very earnest people:

In those days at least, it was not only rejection of manuscripts but of those ideas that seemed to come walking into my office every day in the shape of pensive men and women talking with judicious facial expressions about such mutilated concepts as optimist/pessimist, fascist/communist, extrovert/introvert, highbrow/middlebrow/lowbrow; and this claptrap they applied to art, literature and life to the effect that all joy, wit and the pleasures of curiosity were quite squeezed out.

* Just perfect!

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