Alex McLean

Inspired by Christof, here’s my roundup of 2000 to 2009, seriously inhibited by my terrible memory.  Will add to this as I remember events.

2000 – Discovered generative music and formed slub with ade, with the aim of making people dance to our code, generating music live according to rigorous conceptual ideals.  Most of what I’ve done since has revolved around and spun out of this collaboration.  Worked as a Perl hacker with the afore-mentioned Christof during the first Internet boom for mediaconsult/guideguide, a fun time hacking code around the clock in a beautiful office with a concrete floor and curvy walls.

2001 – slub succeeded in getting people to dance to our code, at sonic acts at the paradiso in Amsterdam.  It was around this time that I left guideguide for state51 to work on a digital platform for the independent music industry – they were very much ahead of their time then and still are now.  Got a paper accepted for a conference as an independent researcher, and met Nick Collins for the first time there, another fine inspiration.  Co-founded dorkbotlondon, co-organising over 60 events so far…

2002 – Some really fun slub gigs this year.  Followed in Ade’s footsteps by winning the Transmediale software art award for a slightly odd forkbomb, which later appeared in an exhibition curated by Geoff Cox alongside work by great artists including Ade, Sol Lewitt, Yoko Ono and some monkeys.  Met Jess.

2003 – Programmed the runme.org software art repository, together with Alexei Shulgin, Olga Goriunova and Amy Alexander.  Co-organised the first london placard headphone festival; did a few more after, but didn’t yet match the amazing atmosphere of the first.

2004 – Co-founded TOPLAP together with many amazing people, to discuss and promote the idea of writing software live while it makes music or video.  Wrote feedback.pl, my own live coding system in Perl.  Bought a house with Jess.

2005 – Started studying part time, doing a MSc Arts Computing at Goldsmiths, with help and supervision of Geraint Wiggins.  Dave Griffiths, another huge inspiration, officially joined slub for a gig at Sonar.

2006 – Fiddled around with visualisations of sound including woven sound and voronoi diagrams.  Learned Haskell.  Co-organised the first dorkcamp, which was featured on french tv.

2007 – Got interested in timbre and the voice, came up with the idea vocable synthesis.  Helped organise LOSS livecode festival with Access Space in Sheffield.  Went on a camping holiday in Wales and got married to a rather pregnant Jess.  Had a baby boy called Harvey a few months after.  Got my MSc and carried on with a full time PhD in Arts and Computational Technology, supervised again by Geraint.

2008 – Got interested in physical modeling synthesis, using it to implement my vocable synthesis idea.  Got interested in rhythm spaces too, through a great collaboration with Jamie Forth and Geraint.  Knitted my mum a pair of socks.

2009 – A bit too close, and in part painful, to summarise.  Also, it’s not over yet.

Note: this post turned out to be controversial, if you read it be sure to read the comments too, particularly this one.  This comment on reddit is interesting too.

Here’s an annoying thing.  Alan Turing was a great thinker and forefather of computation, and certainly deserves an apology for the inhumane way he was treated.  The Loebner Prize, known as the “Turing test” is a competition supposedly based on predictions Turing included in a paper on machine intelligence.

The annoying thing is that the Loebner prize completely misrepresents Turing’s paper.  Embossed in the prize medal shown is the question “Can machines think?”  Also, the introductory text on their website states:

In 1950, in the article Computing Machinery and Intelligence … Alan Turing asked the question “Can a Machine Think?” He answered in the affirmative …

Did he?  Ok, so lets look at the first sentence of Turing’s paper:

I propose to consider the question, “Can machines think?”

Note that he is considering the question, not asking it.  In that same first paragraph, he goes on to consider trying to define the words ‘machine’ and ‘think’, and concludes:

… the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another.

So, Turing felt that the question “Can machines think?” was absurd.

Whoever wrote the Loebner text has perhaps read the first sentence of this paper they describe, but not got to the end of the first paragraph.  Based on the resulting misunderstanding they have stamped the question that annoyed Turing so much right there next to his face.  This amounts to posthumous intellectual torture, and disrespectful to the work of a fine man.

A comment thread removed after it went nowhere and turned into childish name calling.  Jeepers.

There’s often seen to be a fight between symbolic AI and  artificial neural networks (ANNs).  The difference is between either modeling either within the grammar of a language, or through training of a network of connections between cells.  Both approaches have pros and cons, and you generally pick the approach that you think will serve you best. If you’re writing a database backed website you’ll probably use symbolic computation in general, although it’s possible that you’ll use an ANN in something like a recommendation system.

There is a third approach though, one I’ve fallen in love with and which unifies the other two.  It’s really simple, too — it’s geometry.  Of course people use geometry in their software all the time, but the point is that if you see geometry as a way of modeling things, distinct from symbols and networks, then everything becomes beautiful and simple and unified.  Well, maybe a little.

Here’s an example.  I’m eating my lunch, and take a bite.  Thousands of sensors on my tongue, my mouth and my nose measure various specialised properties of the food.  Each sensor contributes its own dimension to the data sent towards the brain.  This is mixed in with information from other modalities — for example sight and sound are also known to influence taste.  You end up having to process tens of thousands of data measurements, producing datapoints existing in tens of thousands of dimensions.  Ouch.

Somehow all these dimensions are boiled down into just a few dimensions, e.g. bitterness, saltiness, sweetness, sourness, sweetness and umami.  This is where models such as artificial neural networks thrive, in constructing low dimensional perception out of high dimensional mess.

The boiled-down dimensions of bitterness and saltiness exist in low dimensional geometry, where distance has meaning as dissimilarity.  For example it’s easy to imagine placing a bunch of foods along a saltiness scale, and comparing them accordingly.  This makes perfect sense — we know olives are saltier than satsumas not because we’ve learned and stored that as a symbolic relation, but because we’ve experienced their taste in the geometrical space of perception, and can compare our memories of the foods within that space (percepts as concepts, aha!).

So that’s the jump from the high dimensional jumble of a neural network to a low dimensional, meaningful space of geometry.  The next jump is via shape.  We can say a particular kind of taste exists as a shape in low dimensional space.  For example the archetypal taste of apple is the combination of particular sweetness, sourness, saltiness etc.  Some apples are sharper than others, and so you get a range of values along each such dimension accordingly, forming a shape in that geometry.

So there we have it — three ways of representing an apple, either symbolically with the word “apple”, as a taste within the geometry of perception, or in the high dimensional jumble of sensory input.  These are complimentary levels of representation — if we want to remember to buy an apple we’ll just write down the word, and if we want to compare two apples we’ll do it using a geometrical dimension — “this apple is a bit sweeter than that one”.

Well I think I’m treading a tightrope here between stating the obvious and being completely nonsensical, I’d be interested in hearing which way you think I’m falling.  But I think this stuff is somehow really important for programmers to think about — how does your symbolic computation relate to the geometry of perception?  I’ll try to relate this to computer music in a later blog post…

If you want to read more about this way of representing things, then please read Conceptual Spaces by Peter Gärdenfors, an excellent book which has much more detail than the summary here…

I’ve always wondered how we do programming. Code can be so clean and straight-faced, but when you step back and try to think about how you write it, a darkness descends. It’s tempting to think that your brain is working like a computer program, transforming a symbolic problem into a textual answer as sourcecode. But I don’t think that’s what is going on at all — if problems came specified in formal language, then programming would be a very different experience. We instead start with a mess, and try to find all the problems in it through the process of designing and writing code.

There’s a lovely paper called Mental imagery in program design and visual programming by Marian Petre and Alan F. Blackwell, with many great quotes from programmers trying to introspect on their work. Here’s some tasters:

“ … it moves in my head … like dancing symbols … I can see the strings [of symbols] assemble and transform, like luminous characters suspended behind my eyelids … ”

Programming is a dance of symbols behind the eyelids. Write that into a QA standard.

“It buzzes … there are things I know by the sounds, by the textures of sound or the loudness … it’s like I hear the glitches, or I hear the bits that aren’t worked out yet … ”

This programmer is describing re-purposing their sense of hearing to produce computer software. Quick, strap them into an fMRI machine!

“values as graphs in the head … flip into a different domain … transform into a combined graph … (value against time; amplitude against frequency; amplitude against time) … ”

Hmm programming as relationships within abstract spaces, and relating those spaces to one another. A nice model for thought in general, perhaps?

“It’s like describing all the dimensions of a problem in 2D, and in the third dimension you’re putting closeness to a solution.”

Another, rather different spatial approach, where goodness of solution is somehow represented by something like height.

“ … oh, that happens over there … it’s on the horizon, so I can keep an eye on it,but I don’t really need to know … ”

Exasperating, and sums things up nicely. This kind of introspection is just too hard, so much of these thought processes are entirely sub-conscious. For example you try for hours to solve a tricky problem, give up, then the answer pops into your head while you’re cycling home, otherwise thinking about dinner.

That said, while the above evidence is purely anecdotal, it gives some hints about what might be going on. I like to think that programmers tap into a general human ability to organise a messy world into far tidier problem spaces, and find their way around such spaces in much the same way as they do when bumping around in a pitch black room…