Harold Pinter(2015)


pinter_web
Acrylic on Canvas, 508mm x 406mm

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2014 review of the year

Trite as it may be, the end of the year offers an opportunity to review our deeds, and plot future (mis)adventures. Herewith, a review of my year of aesthetic experimentation, 2014.

The year started with messing with the media of political discourse. Both David Cameron, our Prime Minister, and Ed Miliband, the leader of the opposition, released ‘New Year’ messages. The blandification of British politics was laid bare by the similarities between the men and the vacuous messages. I’ve algorithmically blended politicians before, but this time I (mis)used the marvellous Echonest API to literally put their words into each other’s mouths.




I crashed my bike in February, getting knocked out, breaking some bones and being saddled with Trochlear Nerve Palsy. I subsequently spent 5 months with an eyepatch, inciting pirate jokes wherever I went. Not much art was produced for a while as a result, and I had to learn to paint with one eye. I did manage to speak at #pydata, whilst still somewhat concussed.

Inspired by the lies and clickbait which seem to make up much of the internet, I released a lying twitterbot. @factbot1 makes up facts, finds a suitable image, and posts them online every 4 hours. The account is still running, and as I write this has just produced it’s 1,500th lie.

Then there was @hipsterbait1 – an experiment in algo-commerce. Could a bot produce a work, and offer it for sale through a third party, automatically, without any human intervention? The bot produces t-shirts that mash up images and references, primarily in the domain of band t-shirts. Unfortunately, my plans to retire on my algo-generated fortune were nixed when Zazzle quickly refused to actually print them.

bffbot1

June brought one of the more sophisticated bots of the year, @bffbot1, an algorithmic stalker who aimed to be your best friend, writing you poems and spotting you in the street. She was very popular, particularly with the Turkish (not sure why) until she was killed by Twitter in October.


September was filled with curating and producing The New Sublime at Brighton Digital Festival – a fantastic group show of some of the finest artists working with digital technology.

It was a busy month where I finished a series of 13 paintings called ‘pissed off primates‘, and embarked on a brief international speaking tour which took me to Canada, London and Bournemouth.


At the end of October I produced another bot which also fell foul of Twitter – a simulation of social infection called @algobola

I also knocked up a bot with all the answers, painted Rik Mayall and Chris TT. I got some robots to perform Waiting for Godot, built systems to scrape folk fan art from twitter, compress great works of fiction, and most recently excise the face of Nigel Farage.



So that’s my 2014 in a nutshell, expect more of the same in 2015 – follow me on Twitter and be amongst the first to know…

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@farageblocked

Inspired by Charlie Brooker’s latest foray into our possible dystopian future, I have been pondering the role of ‘blocking’. We already decide whose opinions we wish to attend to, by curating our social media feeds. However, parts of popular culture we find objectionable still make their way into our lives, so we mute the hashtag, or even install a browser extension to eradicate links to perspectives we find unpalatable.

The flipside, of course, is when our content is filtered for us. Whether it be the ‘well intentioned’ nannying of a state, or in the case of some contries, the Orwellian filtering of entire services we take for granted.

One may also argue that the media has a similar effect when highlighting or burying a story to suit an agenda.


black_mirror

In Black Mirror, we see the effect of ‘blocking’ people in real life – literally removing them from our perception. Fortunately such technology currently lies in the realm of fiction, but the concept is in some ways appealing – at least if it were in our control.

In the UK, we are approaching a General Election, and witnessing the disturbing rise of Nigel Farage and his UKIP party. Despite only recently acquiring their first MP, the party, and Farage in particular, have garnered disproportionate coverage in the media.

As a result, I see Farage’s face numerous times a day, and whilst I like to keep abreast of current affairs, just looking at him makes my cortisol levels rise. I want the news, but I really don’t care to ever see his face again.

@farageblocked is a simple conceit – find pictures of Farage, algorithmically locate his face, and obscure it from view.


Nigel Farage on the local election campaign trail

Initially I simply pixellated his face, but this seemed unduly alluring, so I elected to use the far more damning ‘swirly face

Nigel Farage on the local election campaign trail

Since the face detection is entirely automated, sometimes it finds other faces in the picture and obscures them too, often to comic effect.

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You can follow @farageblocked here: https://twitter.com/farageblocked

UPDATE 20-01-15 I’ve been tweaking the concealment – currently he’s being obscured by emoji
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@tldrlit

Reading is hard. It takes time. Time is difficult to come by when life is filled with tweeting and snapchatting. Yet often there’s that nagging feeling that one should be ‘better read’. There are numerous books that we feel we ought to have read, if only maintain an erudite facade at our next cocktail party round at Gideon’s house.

tl;dr;lit attempts to address this problem.

This bot takes works of literature and algorithmically summarizes them, a chapter at a time, to 1% of their original length. These are then read aloud by the lovely voice of Fiona, a Scottish speech synth, and posted at on Twitter at convenient 3 hour intervals. This way entire works of literature can be consumed in bite-sized algo-chunks, giving you the gist of the book, without any troublesome cause to actually ‘read’ or ‘understand’ it at all…

Fiona is currently reading 1984 by George Orwell.

I have Moby Dick, Pride & Prejudice and 50 Shades of Grey lined up, but feel free to suggest more via @erocdrahs

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@algobola II

Algobola was an experiment investigating social contagion. Using twitter as a propagation channel, I introduced the ‘virus’ into the network, using myself as patient zero. I started the infection at 13:00GMT on 28th October 2014.

I knew from the start that there was a danger Twitter would close it down, but I didn’t expect it to happen so soon. Ironically, it appears my bot was automatically flagged and restricted by one of Twitter’s own internal bots – my bot was caught by a bot-policeman…

However, before that happened it managed to expose over 900 people to the virus, each of them being notified via a personalised notice informing them of their changing status as their infection developed.

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The parameters were modelled on Ebola, but modified to take into account the limited attention span of social media users. Once infected, the subject remained infectious to others for 72 hours. At that point, they either survived or died (30% survival rate). Twitter restricted posting rights of the account after about 70 hours, just before the first subject (me) died.

However, even though I could no longer inform the victims, I could still simulate the infection and record the way it propagated through the network. Indeed I continued until Twitter completely stopped API access for the account on 4th November. By this point, 5230 users were exposed.

What emerged is a fascinating chart of social media connections.

I’ve made a visualisaton of some of the data I collected. Each dot represents a twitter user, and the connections between them indicate the vector of infection. Click ‘start’ to cycle through the first 120 hours of infection, or use the buttons to jump to a specific day. If you hover your mouse over a dot it will give you the name of the twitter user.

Click here to launch the interactive view.

Maybe you can find yourself in there.


nodeanim

What emerges is a rapidly exploding map of social interactions. It gives a quick visual representation of the different kinds of social media users – those who communicate with a select few, and those with a larger network of contacts. It exposes the interrelatedness of the twitter users – who their friends are, how often they communicate – all derived from a very simple analysis of the ‘metadata’.

This stuff is sexy to both data scientists and governments. Which government wouldn’t want to harvest this data? As we live our lives on a connected, easily monitored infrastructure, these kinds of data become a convenient shortcut to our identity as individuals. To all intents and purposes we are our data. These kinds of data represent who we are. We are packets of data, flung into the ether, to be collated and analysed by giant server farms in hidden locations.

Once the data is collated, it is algorithmically analysed. A digital report card is produced, and based on the desires of the enquiring party, ‘persons of interest’ are identified. Sometimes these profiles are produced by marketing companies, like Facebook, hoping to sell ever more granular descriptions of us to entities that wish to advertise to us. Sometimes these profiles are produced by government agencies, hoping to identify individuals as subjects of ‘interest’.

In both cases, the raw data is the same. The data itself is benign until it is interpreted. It’s the algorithmic questions posed of it that produce the representations that humans actually use to make decisions. This recasting of information is possible because of the computational power available, it is necessary because the human mind is incapable of extracting inference from datasets of this size. An awful lot of trust is being put into this inscrutable algorithmic perception, and the track record in this area is not good.

The real issue is one of ethics – Do we want our governments to do this? Is there any evidence that it makes us ‘safer’? How does a legal system deal with humans rendered as data? Do you have the same rights over our digital selves? What is the relationship between the data-self and the real-physical self?

I shall be speaking about these ideas and more in Brighton next week.

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