The emoticon has become ubiquitous in informal text-based communications. The necessity to modify the meaning of potentially ambiguous sentences with the use of pictographic shorthand has a long history, but really came into it’s own with the democratisation of publishing which came with the advent of the internet. They are a visual shorthand which saves time in our busy lives. More recently we’ve seen the rise of the Emoji, which offers a more varied palette of face based symbols, conveying a range of emotions far greater than the manipulation of the ascii character can offer.

Emoji Portraits by Jung Jake

These symbols almost universally take the form of a circular yellow face. This is a gender and race neutral form which finds it’s roots in the counter-culture of the 60s, and more recently in the Acid House scene of the late 80s. It’s interesting to see the symbol returning in the realm of fine art, notably in the works of Ryca and Jimmy Cauty.

Alan Moore with a Jimmy Cauty riot shield

The human visual system has a lot of circuitry dedicated to the recognition of faces. Recognising the emotional states of our fellow primates clearly provides a strong evolutionary advantage. Indeed, it’s argued that the white sclera of the human eye developed to accentuate the expressiveness of our faces and make it easier for others to track our gaze. It is no surprise then that the face is used as a shorthand for emotion in written communication, it’s where we wear our emotions, and we’re built to quickly recognise them, even in the form of a yellow circle.


Because faces are so important to us, we have trained computer algorithms to detect them automatically. I’ve used this technique in a number of recent works, notably Who Watches the Watchers? and @farageblocked. These algorithms are derived statistically from millions of images of faces – completely unlike our own system which seems at least in part innate, and certainly forms part of a more general visual system which works in an entirely different manner. This is most obvious when we find images mis-identified as faces, we’re suddenly drawn to see if we can see what the machine has seen, as in Henry Cooke’s Faces in the Cloud project.


I recently modified @farageblocked to replace his face with miserable emoji and was immediately struck by how pleasing it was. I wondered whether the same technique could be applied to video, potentially providing a visual shorthand for remapping emotional states. There is a delightful tension between the obfuscation of identity and the immediate recognition of the emoticon.

2010 leader’s debate

As our social interactions move away from the realm of reality and into the online world, we are seeing an increase in social anxiety – we seem more comfortable communicating our emotions via the similey face than dealing with the reality of our fellow humans. Apps like pplkpr promise to simplify our emotional landscape by monitoring our stress levels and advising us who to avoid.

Waiting for Godot (excerpt)

Could we imagine a future where this anxiety is eased by a form of ’emoticon subtitling’? Where our already mediated interactions are automatically overlaid with a simplified palette?

Shake it off – Taylor Swift

The technique lends itself to directly inverting the intended meaning of the source, for example:

Happy (sad) – Pharrell Williams

Stagger Lee (happy) – Nick Cave and the Bad Seeds

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.


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…


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.


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.


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


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