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Akaltyele anthetyeke awetyeke // To teach to listen

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Kwene-akerle atnanpintyeme describes a kind of downwards motion in Eastern and Central Arrernte, the traditional language of Mparntwe in the central desert of Australia.

It is the name of a prototype for a voice-activated computer game, designed and made with p5 and ml5 by a group of 16 year-old students at Arrernte class at Centralian Senior School in Mparntwe as part of our 2021 Processing Foundation Fellowship. 
In it, atnengkwe (emoji animals) fall from the sky. To save them, you must say and pronounce their names correctly by the time they reach the ground. It’s a fun way to learn their names and practice your Arrernte. 


Eastern and Central Arrernte is one of more than 300 First Nations languages across Australia. Some are sleeping, some like ours, still wide awake. Eastern and Central Arrernte has about 2000 speakers remaining. Our language is us. It’s our relationship with our landscape, our ancient stories and who we are to each other. But we worry about our language. Our young people are surrounded by English, a dust which has blown in. We are working to wipe it back so you can see what was always there, what belongs there.


We are a collective of young people, senior linguists, artists and creative technologists that come together every now and then to experiment with language and technology. We first formed in 2018 to create Indigemoji, Australia’s first set of First Nations emoji, through eight weeks of workshops with nearly 1000 young people at the Alice Springs Public Library. We made a sticker set of 90 Arrernte emojis representing life and culture on Arrernte-kenhe ampere (Arrernte Country) available through a free app. Our goal was to decolonise the internet by embedding our language online to show our young people that their language and culture matters. While it achieved that (our app got downloaded 40,000 times on the first weekend it was launched) our senior Arrernte (or emoji bosses as we call them) were always very clear that anything that we could do to encourage our young people to actually speak, was worth trying.

Our technologies have long shaped this place. The colonial story here begins with a new kind of technology also, when thousands of poles and wires of the Overland Telegraph Line were woven through our landscape without our consent to connect our countries with the chatter of the rest of the world. The new animals that came with these work crews and the settlements that grew out from the line changed our lives and our landscape forever. 150 years on, a new kind of wires and connections has been transforming our lives, as the internet pulses through our soil and through the air, another colonising force.

The Kwene-akerle atnanpintyeme game grew from a broader project called Akaltyele anthetyeke awetyeke which roughly translates as ‘to teach to listen’ - an investigation of machine learning and broader concepts of artificial intelligence with the year 10 Arrernte class at Centralian Senior College.

We had heard about some of the work happening with First Nations languages and artificial intelligence around the world and wondered: What is it? Could it offer ways of practicing or preserving our language? Can a machine understand Arrernte? And whose place is it to teach the machines? The future of our language lies in our young people. They will be the ones to make decisions about our language, so we wanted them to understand how this technology works.


These students are our future language leaders. As part of a Certificate II in Applied Languages, the program at Centralian Senior College is all about building pathways, showing young people that their language is something that offers job opportunities as well. The students come together four times each week from different schools around Mparntwe and many speak multiple languages like Eastern and Central Arrernte, Western Arrarnta, Pitjantjatjara and Anmatyerr.


We began exploring AI bossing creative technologist/producer Caddie Brain dressed up as a robot around the classroom. We thought robots were smart, but it turns out you have to tell them exactly what to do. You have to program them. But what with? And how? We learned about data by getting a box of Smarties (a kind of lolly or candy). We classified them by colour and arranged them into data visualisations. We then used Teachable Machine to train our computers to recognise those colours. Then we ate the data.


Our language is data too. We began to experiment with building a dataset from hundreds of recordings of the different Arrernte animal names. We had to record ourselves saying the words over and over and over again to train a model. Bit by bit, the computer began to recognise our words as we spoke to it. For example, if there were too many boys' voices it had trouble understanding when a girl was speaking. We learnt that this is called bias in datasets. We also accidently mispronounced one of the animal names, so we had to start again. (‘Aherte’ and ‘arerte’ are similar but not the same. One is a bilby, the other means ‘crazy’!) We need our language bosses, people like Veronica Perrule Dobson, Kathleen Kemarre Wallace and Joel Perrule Liddle, to check our work and help build the dataset, making sure it is exactly right. It made us think more about what the cultural protocols might be to train a dataset like this. For that reason we have decided not to share our dataset for now. While we want to share our language, we also want to care for it and know what is done with it and our voices. Data sovereignty is important to us. 


We then designed the game, working with graphic designer Graham Wilfred Jnr. He took inspiration from arcade games and brought this to our landscape. We asked Western Arrarnta country singer Warren H Williams (we’re big fans!) to make us some sounds for the game. He recorded them on a keyboard for us. We then built a P5 sketch with our graphics, sound and Teachable Machine model. Yining Shi and the Processing Foundation worked with us every week helping us make changes and to try things out. And the game was made. 


While we had learnt so much about machine learning dreaming, we realised that we were only understanding it one way, through English. There is no Arrernte word for computer for example. So to truly consider these ideas, we needed to look at them through an Arrernte lens, to understand them our way. So one day in class we spent hours trying to translate key concepts into Arrernte. Could ‘computer’ translate as ‘to type’ or is it a kind of ‘a thing’? What algorithms hide in our language? What metaphors are there for data? Is it like sand? Is ‘training’ a machine the same thing as ‘teaching’ it? Is artificial intelligence even smart? Or is it ‘fake’ knowledge?


We took these ideas to Arrernte linguists and emoji bosses Veronica Perrule Dobson, Kathleen Kemarre Wallace, Joel Perrule Liddle who spent many weeks thinking through what these terms should and could be. There are of course, no direct translations for them. 'Akurrknge alharrkentye' the term of computer, translates as ‘lightning brain’ for example. This is old language. This is deep language. Every word deliberate. Every word existing in the closest relationship with the one next to it to build meaning. They are listed below. You can listen to them also. Listen to understand.


They also had another message for us.

Itirrentye arrekantherre akurnentye-ileme akurrknge alharrkentye-le akwetethe

anperlte-aneme. Akethe-ke-ame arrantherre alkngwirreke? 


Your thinking becomes bad if you’re always on the computer. 

Have you mob forgotten about the outside?

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kwenengenge-ntyele akethelhe-ileme
internet / 'coming from inside, from underneath'
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akurrknge alharrkentye
computer / 'lightning brain'
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urrtyalthe ilyernpenye
artificial intelligence / fake intelligence
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akurrknge alharrkentye akaltye-irreme tyerrtyele antheke-arle itelaretyeke
machine learning / 'lightning brain learning what the people teach it'
tyerrtyele akaltyele antheme akurrknge alharrkentye-ngentyele
programmer / 'someone who giving knowledge to the computer'
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akaltyele anthetyeke awetyeke
To teach to listen
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Untitled_Artwork 17_edited.jpg
akaltyele antheme
teaching / 'showing somebody'
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angkentye akweke-me-akweke-akaltye-irretyeke
data / 'small pieces of information'
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urlerte-ke-urlerte akaltyele anthetyeke
class / 'groups of things to give knowledge'
Illustrations: Graham Wilfred Jnr
human thinking
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Attachment-1 (10)_edited.jpg
intelhentyele aperneme
model / 'to follow a pattern'
akurrknge alharrkentye-le ngenhe mwerneme anthurre
algorithm / 'something that makes one addicted'

anwerne-akerte // team

Game co-designers: The Year 10 Arrernte class at Centralian Senior College - Delisha Malthouse, Megan Baliva, Agnes Saunders, Tanisha Davis, Alana Abbott, Amarlie Briscoe, Siobhan Breaden, Jeremiah Daniels-Pepperill, Tracyn Forrester, Kyle Maidment, Thomas Tambling, Bricarny Forrester, Miru Forrester, Emmanisha Nelson, Ella Fitz, Kaleel Ross, Lilly Mentha with David Moore and Jannette McCormack.


Emoji bosses, linguists and translation: Veronica Perrule Dobson, Kathleen Kemarre Wallace, Joel Perrule Liddle


Sound: Warren H Williams at Left Of Elephant Sound


Game design, illustrations and media production: Graham Wilfred Jnr


Production and Creative Technologist: Caddie Brain


Programming and Mentoring: Yining Shi


Supported by the Processing Foundation Fellowship 2021. 100% of stipend funds went to First Nations practitioners.

With deepest thanks to David Moore, Jannette McCormack, Susan Moore & staff of the Alice Springs Language Centre, Daniel Shiftman, Tega Brain, Sam Lavigne, Timothy Chatwin, Kyran Smith, Kate Csillag, Brendan Phelan and the Johnston Foundation

This project was made on Arrernte Country in Mparntwe and the Lenape homelands of New York. 

© indigemoji, 2023

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