Letterfit’s featured article : updated 30.09.18. Please don’t copy or re-distribute this without permission. You can direct comments to this link – thanks.
‘…like a mannequin in sunglasses’ *
Python isn’t exactly like other programming languages – in its wider use or in its on-going use at Letterfit. It is an accessible way into working with basic levels of AI and Machine Learning.
Put more boringly (perhaps), it’s object-oriented and it’s useful for coding fonts, amongst other things. Its task-specific pre-coded strings accessed in libraries make it similar to jQuery, which means that leaner code generates more functionality – which, in many respects, can make you feel like a mannequin in sunglasses when you venture into its deeper levels of mind-boggling complexities. 1
‘The arts are neglected because they are based on perception, and perception is disdained because it is not assumed to involve thought.’
E H Gombrich, from Art & illusion, (2002)
‘Science manipulates things and gives up living in them.’
Maurice Merleau-Ponty, from L’oeil et l’esprit, (literally ‘The eye and the spirit’ or ‘Eye and mind’), first published in Art de Frana 1, no 1 (1961)
At Letterfit, we have great respect for art and artistic methods as well as science and scientific methods. Some of our clients have devoted their efforts and minds into making things work better within science – and clearly, science and technology aren’t the same thing. We believe in living with and owning all of this, with re-thinking and re-working the shared elements of art, science & technology that we’re capable of contributing within.
What does this have to do with ‘tekoäly‘?
‘Tekoäly’ is Finnish for ‘Artificial Intelligence’. We first became aware of this language shift at Letterfit during 2013-18, when it was evident that some of AI’s automated algorithms, when attempting to auto-interpret human perceptions expressed as political views, slang, emotional outbursts, jokes and even some forms of emotional intelligence (the restraint, empathy and timing of ‘getting’ context, sensitivity, awareness & ethics), had gone somewhat awry. Digging further into this led us to tech-science articles about Finnish views of AI – and their views of ‘Havainto’ (‘Perception’).
We all need people like Jimmy McDonough (pictured above) to keep us in check. When we last visited him in 2018, we mentioned that our studio was interested in this. He said, ‘You’re not going to start wearing those weird round glasses, the kind they only make in Finland, are you?’
To which I responded, ‘No.’
‘Because that’s what happened to this group of design students I couldn’t stand. Their professor wore these round glasses that were only available in Helsinki. He had this annoying habit of half-smiling and staring off into the distance, like he knew something most of us didn’t. Some of his student-followers got the same stupid glasses – so they could wear these and walk around with the same half-grin, staring like Moomins, as if they knew something ironic none of us could possibly know.’ 1
Human perceptions : links – or barriers?
Though it’s a bit unfair to the research mentioned above (‘Havainto‘) and to Jimmy, whose words can only be paraphrased – in spite of these and other practically-specific concerns, we’re allowing ourselves to perceive AI as ‘tekoäly’, which (to us, at least) is less insurmountably complex and artificially superior than ‘AI’.2 Basically, we’re looking for hopeful clues that AI can help us to work a bit smarter, not feel distanced or ill-fitting, in any process of adapting to it. We’re using Python to access data-sets to compare published perceptions (using keyword searches) of articles and books about AI and Machine Learning that may or may not contrast between native-English speakers and bilingual and monolingual speakers of three European languages over time (1996-present day).3
It’s difficult, if not impossible, to be a practitioner of art or design for long and not be aware that perceptions, contexts and tastes affect meanings that go beyond what’s literal, factual or scientific. Less obvious is a keen awareness that the same words in their translated approximation of meaning(s) – apart from their visual style, treatment or association – can also be perceived differently.4
Non-human (rather than inhuman) perceptions
There are growing cultural expectations for AI aided by ML to better reflect the emotionally-aware and perceptually-intuitive responses of human variations. Whether or not this gap can be filled by ML remains to be realised in 2018. Regardless, whatever’s personally expressed in words by people (inarticulately, articulately or ambivalently) is unlikely to be free of unexpected shifts – or be emotionless.
‘Language is a body, a living creature… and within this creature’s home is the inarticulate as well as the articulate.’
John Berger, from ‘Confabulations‘, first published in 2016
Translating ‘a living creature’ is highly perceptual
Umberto Eco once remarked that ‘translation is the art of failure’. He compared the words traduttore, traditore (translator, traitor) to say that he thought he could only be, at best, regarded as an ‘admirable traitor’ for his translation work. In direct contrast to Eco’s views of these limits, The World Economic Forum has speculated about the possibility of Machine Learning translations opening up new possibilities for trade – in effect, lessening or ending the language barriers of the Tower of Babel analogy, once and for all.
These are intriguing times to live and work in – whatever you might think about all this.
We are admittedly designers more than coders, but this Python-enabled research supports a publishing project that we’re completing. Since this is an unpublished text, it wouldn’t be right to go into its premise or plot, but it can be said that perceptions and articulations affect this short story’s outcome.5
This text was authored by Jim Jackson, principal designer at Letterfit, on 30.09.18.
* Someone in the studio said this when asked how it felt to be coding in Python. We know we’re not alone in this (there’s a lot in the news about how popular Python is). We’re in it for the long haul – the project that this Python work will apply to will be going on for the next three years.
1 It is ironic that
‘Take care when gathering information. We live mainly on information. […] ‘
…were thoughts first published in 1647 (quoted here). Some of the functions we’re using aren’t that unknown or overly complex, either – some adaptations are partly based on the original Python-enabled functionality (now done with Amazon’s proprietary coding) that once scanned their databases to tell you ‘customers who bought this item looked at / bought these items.’ Who guessed that would apply to narrative design?
2 We don’t assume to be data scientists, but we don’t completely agree that these questions about language use and clearer meanings aren’t for all to consider, within the means that are open to us. It’s been pointed out to us that perceptually replacing two longer words in English with one shorter word in Finnish is arbitrary and doesn’t change its meaning. It’s evident that the use of ‘teko-‘ in Finnish does signify something artificial or synthetic, the suffix ‘-äly’ means ‘intelligence’ or ‘capability’. Even so, perceptions of words aren’t made null and void by their arbitrariness (dog to chien, etc). Native Finnish speakers may or may not agree, but this less-cumbersome word diffuses some of what’s perceptually daunting about ‘Artificial Intelligence‘ to us. Some professional and pragmatic objections (‘don’t criticise AI, it helps me to pay the bills’) may be expressions of hyperbolic fears more than language-based insights – but who knows? We could be wrong about this and other assumptions we all make about languages. That’s why this has to be open and broad-based research.
3 We’re also exploring present-day perceptions of English – of UK, US, Canadian, Antipodean and other variants of speaking English – as language forms that are near, but not a part of, European languages (or mainland Europe).
4 These factors are what makes human perceptions interesting rather than annoying to us: their changeable states. It’s impossible to exhaustively pin down any human perception as a measurable or value-specific state (in precise terms that AI algorithms can easily identify), since these vary and fluctuate during the same day and to the same person’s perceptions. This isn’t about rejecting AI’s ‘impressive micro-smarts’ that can be used to solve big problems, either (methodically and narrowly defined in ethical terms by us) faster and more accurately than any of us could match. This is about accepting that perceptions and emotions play into any human desires for clearer communication about what matters in all of this – meaning the changes in AI and ML applications that will happen in the near future.
5 This is a short story written by V A Anondaly. We do take Python programming seriously at Letterfit. It’s good to lighten up enough and know that those we trust as friends, authors and colleagues and those who do work for the massive tech-giants have similar problems with getting words to translate universally. Using Python to code anything new and useful is more about showing than telling, so nothing more will post here (as a link to this new work) till it’s functionally ready.
Leterfit’s code-learning recommendations
We recommend & support Free Code camp as a challenging way to learn competitively-useful coding skills and benefit charities.
Code mentor (one duplicate link on this list, but others that are useful, too)
Machine learning in Python (an introduction)
List of of machine learning algorithms (New tech dojo)
Outline of machine learning (surprisingly in-depth for Wikipedia)
CityLit London (short night course, intros only)
Github for beginners (five years old, but still valid)