In de aanvraag schreef ik dat ik de verschillende fasen van dit onderzoek graag wilde delen en bevragen met mensen die vanuit verschillende perspectieven meedenken: academici, collega-makers en curatoren. De overlegmomenten konden verschillende vormen aannemen:
ontmoetingen, leessessies voor klein of groot publiek, individuele en collectieve performances, online interfaces…
Aan het einde hoopte ik een beter overzicht te hebben van de mogelijkheden van en noden aan Algoliteraire Vertellingen, die de brug maken tussen de algoritmische praktijk van professionele datawetenschappers, gedeeld in wetenschappelijke papers en handleidingen; en de verhalen over AI die circuleren in de media en die vaak oppervlakkig blijven en AI bewieroken. Als neveneffect hoopte ik ook een preciezer idee te hebben over manieren waarop je een collectief van Algoritmische Vertellers best vertegenwoordigt, beheert, begeleidt,
verdedigt, of misschien wel heel andere werkwoorden waarvan ik het bestaan nog niet kende.
Als een resultaat van dit onderzoek en bij wijze van experiment lanceerde ik in september 2019 het pseudoniem Anaïs Berck. Anaïs Berck is boomgriffier. Het pseudoniem staat voor een gelijkwaardige samenwerking tussen een of meerdere mensen, algoritmes en bomen. Je leest meer over Anaïs Berck via https://www.anaisberck.be/over-anais-berck/.
]]>An Mertens expérimente des formes qui dévoilent la personnalité des algorithmes. Pour cette nouvelle création, elle se glisse dans la peau du “classifieur naïf bayésien”, un algorithme très utilisé pour trier les spams de nos boîtes aux lettres, pour analyser les sentiments sur les médias sociaux, mais aussi pour définir si un texte est écrit par une femme ou un homme.
Cette performance dévoile un aperçu de la façon dont cet algorithme lit les données, transforme les mots en nombres, fait des calculs qui définissent des motifs et est capable de prédire le sexe de l’auteur·e. Il révèle également ses limites, et comment il peut être trompé.
An Mertens propose d’adopter la perspective de la machine pour tenter de comprendre ce que signifie une prédiction selon laquelle une phrase a “78% de chance d’avoir été écrite par une femme”.
Data Workers is an exhibition at the Mundaneum from 28th March till 28th April 2019. It shows algoliterary works, stories told from an ’algorithmic storyteller point of view’. The exhibition is a creation by members of Algolit, a group from Brussels involved in artistic research on algorithms and literature. Every month they gather to experiment with F/LOSS code and texts. Some works are by students of Arts² and external participants to the workshop on machine learning and text organised by Algolit in October 2018 in Mundaneum.
Companies create artificial intelligences to serve, entertain, record and know about humans. The work of these machinic entities is usually hidden behind interfaces and patents. In the exhibition, algorithmic storytellers leave their invisible underworld to become interlocutors.
The data workers operate in different collectives. Each collective represents a stage in the design process of a machine learning model: there are the Writers, the Cleaners, the Informants, the Readers, the Learners and the Oracles. Robots voice experimental literature, algorithmic models read data, turn words into numbers, make calculations that define patterns and are able to endlessly process new texts ever after.
The exhibition foregrounds data workers who impact our daily lives, but are hard to grasp or imagine. It connects stories about algorithms in mainstream media to the storytelling in technical manuals and academic papers. Robots are invited to go into dialogue with human visitors and vice versa. In this way we might understand our respective reasonings, demystify each other’s behaviour, encounter multiple personalities, and value our collective labour.
It is also a tribute to the many machines that Paul Otlet and Henri La Fontaine imagined for their Mundaneum, showing their potential but also their limits.
Data Workers is a creation by Algolit.
Works by: Cristina Cochior, Gijs de Heij, Sarah Garcin, An Mertens, Javier Lloret, Louise Dekeuleneer, Florian Van de Weyer, Laetitia Trozzi, Rémi Forte, Guillaume Slizewicz, Manetta Berends, Mia Melvær.
A co-production of: Arts², Mundaneum, Constant.
With the support of: Fédération Wallonie-Bruxelles, Arts Numériques, Passa Porta, Ugent, DHuF – Digital Humanities Flanders and the Distributed Proofreading Project.
Thanks to: Michel Cleempoel, Donatella Portoghese, Mike Kestemont, François Zajéga, Raphaèle Cornille, Kris Rutten, Anne-Laure Buisson, David Stampfli.
The opening is on Thursday 28 March (18-22h). As part of the exhibition, we invite Allison Parrish, an algoliterary poet from New York. She will give a lecture in Passa Porta on Thursday evening 25 April and a workshop in the Mundaneum on Friday 26 April.
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The game
(To optimize this game, a test phase and a confusion matrix need to be added)
1. GATHER YOUR SAMPLE DATA: write 6 short sentences in the same style, of which 3 sentences are positive, 3 sentences negative.
2. PROCESS THE TEXT:
2.1. Decide the unit of analysis (word/character/bigram…).
2.2. Split your sentences in units.
2.3. Mark each unit as being positive or negative.
2.4. Create your vocabulary: a collection of all unique units of all 6 sentences.
3. PREPARE THE TRANSFORMATION from WORDS to NUMBERS:
3.1. Display the units in 1 row of a grid. These are the columns of your matrix.
3.2. For each sentence, display the units as 1 row in the columns of the grid.
3.3. Count the probability that a sentence in your model is positive:
number of positive sentences / total number of sentences
3.4. Count the probability that a sentence in your model is negative:
number of negative sentences / total number of sentences
3.5. Count all positive units.
3.6. Count all negative units.
3.7. Count all units (your vocabulary size).
4. The TRAINING starts! For each unit you make the following calculation:
4.1. if the unit is positive:
the probability that a sentence in your model is positive * the probability that the word is positive
This means:
number of positive sentences / total number of sentences
*
number of times that the word is used as a positive example + 1
/
total number of positive words + vocabulary size
4.2. else:
the probability that a sentence in your model is negative * the probability that the word is negative
This means:
number of negative sentences / total number of sentences
*
number of times that the word is used as a negative example + 1
/
total number of negative words + vocabulary size
5. SMOOTHING UNITS: each cell in your grid should have a number now. Add 0.000001 to cells that have 0. This avoids that calculations end up being zero.
6. SMOOTHING UNKNOWN UNITS: add one last column to your grid with the label ‘Unknown’. Fill this column with smoothing numbers.
7. THE PREDICTION CAN START!
7.1. Invent a new sentence in the same style as your training data.
7.2. Split your sentence in the type of units you chose in the beginning
7.3. Calculate the probability that the new sentence is positive:
7.3.4. Find the corresponding probabilities for each of the positive units in your grid
7.3.5. If the unit does not exist, pick the smoothing number of the ‘unknown unit’.
7.3.6. Multiply the probability that a sentence in your model is positive with all individual probabilities of units of your new sentence
7.4. Calculate the probability that the new sentence is negative:
7.4.4. Find the corresponding probabilities for each of the negative units in your grid
7.4.5. If the unit does not exist, pick the smoothing number of the ‘unknown unit’.
7.4.6. Multiply the probability that a sentence in your model is negative with all individual probabilities of units of your new sentence
8. Compare the outcome of 7.3.6. and 7.4.6.
9. ORACLE: the highest value of 11. is the prediction made by this model.
]]>Algolit is a project of Constant, a workgroup around i-literature, free code and texts. The group meets regularly following the principles of the Oulipo-meetings: they share work and thoughts and create together, with or without the company of an invitee. Algolit is open to anyone interested in exchanging practises around digital ways of reading and writing.
There is no need for programming knowledge, but if you have it, you’re of course very welcome as well. The idea is to offer different entrances into the field of algorithmic writing, using recipes that can be performed physically, computationally or analogically. The results will be documented at the end of the day.
The group was initiated in 2012 by Catherine Lenoble and An Mertens.
Since then, the algoliterarian space has been created and recreated together with a.o. Nicolas Malevé, Olivier Heinry, Stéphanie Villayphiou, Brendan Howell, Gijs De Heij, Christina Cochior, Hans Lammerant, Manetta Berends, Javier Lloret, Mia Melvaer, Olivier Perriquet.
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The Algoliterator is trained using a recurrent neural network and the full works of Belgian author Felix Timmermans, who entered the public domain in 2018.
The Algoliterator helps you to write a text in the style of Timmermans. You can choose a start sentence from his oeuvre, you can also choose whether you want the Algoliterator to produce the following sentences based on primitive training, intermediate training or final training. The machine proposes a paragraph that you can edit. If you are happy with the result, you can send it to Zora, the house robot of Muntpunt. She will read out the text for you.
Sources: https://gitlab.constantvzw.org/algolit/algoliterator.clone
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Varia
Varia is een boothuis in het zuiden van Rotterdam. Het is het gelijkvloers van een hoekhuis, waarvan drie wanden grote ramen zijn die uitgeven op de verschillende straten. Een groepje jonge kunstenaars heeft er hun intrek genomen. Sommigen van hen geven les in PZI, Experimental Publishing of hebben er een master van op zak. Maandelijks organiseren ze lezingen, gesprekken, screenings, telkens rond nieuwe media en maatschappij.
Programma
De beschrijving van Algologs las als volgt: ‘Algologs = een 1 dag + 1 dag dialoog rondom algoritmische werkwijzen. Dit evenement is een verlenging van Algolit, een werkgroep waar taal en algoritmes elkaar ontmoeten. Algologs maakt deel uit van een reeks ontmoetingen rondom de Algolit-bijeenkomsten (die normaal gesproken in Brussel plaatsvinden), om zo externe sprekers, presentatoren en deelnemers uit te nodigen om deel te nemen aan het gesprek. Tijdens Algologs verhogen we het % betrokkenheid bij alledaagse algoritmes.’
Vrijdagavond waren een reeks sprekers uitgenodigd om ‘logs te maken van algoritmische standpunten, stemmen en werkwijzen.’ Zaterdag volgde een Algolitsessie, ‘waar we een collectieve duik zullen introduceren in de clusteringstechnieken van word-embeddings.’
De indrukken die ik hier neerschrijf, hebben betrekking op het vrijdagavondprogramma, omdat de combinatie van genodigden niet alleen de liefde voor taal, verhalen en code deelden, maar ook het diepe verlangen om de software die aanhoudend onze data via telefoon en computer doorsturen en analyseren, evenals de ideologie van hun makers, een stem te geven, zichtbaar en leesbaar te maken.
Collage
De avond ging van start met een performance van softwarekunstenaar en -schrijfster Marloes De Valk. “We Are Going to Take Over the World, One Robot at a Time” lijkt aanvankelijk een klassieke lezing. Marloes leest een tekst in eerste persoon, terwijl achter haar de ene slide na de andere wegflitst. Ze zet grote overtuigingen neer, over hoe technologie hoort te zijn, over hoe paradijselijk het leven op aarde zal zijn wanneer morgen al het werk door robots zal worden gedaan, hoe veelbelovend een leven op Mars is. Met grootse boude woorden neemt ze standpunten in, op zo’n manier dat ik als luisteraar beducht word. Na een tijdje merk ik bovendien dat de slides enkel bestaan uit logo’s van grote corporate bedrijven uit Silicon Valley, dat ze bovendien voorzien zijn van een maand- en een jaartal. Net voor het moment aanbreekt, dat mijn achterdocht dreigt over te slaan in verveling, komt het inzicht. Marloes leest een tekst die een collage is van uitspraken van CEO’s van bekende IT-bedrijven, die het merendeel van onze software en infrastructuur leveren. Ik ga weer recht zitten, spits mijn oren en wil geen enkele slide meer missen. Ik beleef een hallucinante rit.
Botparadijs
De lezing zet een toon die de hele avond in de ruimte zal blijven hangen. Onderzoeker en ontwerper Cristina Cochior surft verder op dezelfde golf en geeft ons een ontluisterend overzicht van de verschillende types robots die actief zijn op het web. Ze benoemt hen, categoriseert hen, toont hen en op het einde van haar presentatie heeft ze een reeks portretten met ons gedeeld, van wezens waar we het bestaan van vermoeden maar die we maar zelden kunnen onderscheiden. Robots of kortweg bots worden aangeboden als een SaaS, Software as a Service, ‘ook weleens Software on Demand genoemd. Het is software die als een online dienst wordt aangeboden. De klant hoeft de software niet aan te schaffen, maar sluit bijvoorbeeld een contract per maand per gebruiker, eventueel in combinatie met andere parameters. De SaaS-aanbieder zorgt voor installatie, onderhoud en beheer, de gebruiker benadert de software over het internet bij de SaaS-aanbieder.’ (Wikipedia). Je hoeft er dus niet voor te kunnen programmeren en ze worden geheel op maat voor je gemaakt, als je wil. Bovendien kunnen ze perfect overleven op lokale servers. Dit maakt dat ze erg in opmars zijn. Bots werken meestal op een of andere manier samen met mensen. Voor Cristina zijn er “sheppards (bots with high number of followers), sheepdogs and electric sheep (bots that blindly repeat)”
Ze onderscheidt soorten bots die verschillende doeleinden dienen. Zo heb je bijvoorbeeld politieke bots, die de plaats innemen van vroegere politieke propaganda-stunts, zoals die waarbij het Amerikaanse leger promoflyers dropt vanuit een gevechtsvliegtuig boven een gebied. Ze kunnen ook ingezet worden om commentaren meer gewicht te geven, kwistig te liken, je op Tinder van hun visie te overtuigen of lasterpraat te verkopen. We zijn ons niet altijd bewust dat we met een bot te maken hebben, en intussen weten we dat ze invloed hebben op huidige politieke campagnes.
Of er zijn rethorische bots, die als enige functie hebben om berichten te laten verschijnen als toppers in de ranking van zoekmachines, als een vorm van sluikreclame. Het echte potentieel schuilt in wat Cristina de ‘infrapunctural bots’ noemt. Het zijn sociale wezens, die nieuwe en meer kritische verbeelding mogelijk maken. Dat zijn de bots waarin schrijvers en kunstenaars nieuwe ruimtes voor creatie vinden.
Infrapunctural Bots?
Als je nu hunkert naar voorbeelden van zo’n bots, dan leek de rest van het programma daarmee gevuld, zij het dat de zes bots die getoond werden, nog niet op het net leven, maar misschien morgen wel. Alle zes waren het creaties van XPUB-beoefenaars Natasha Berting, Angeliki Diakrousi, Joca van der Horst, Alexander Roidl, Alice Strete en Zalán Szakács. Ze zijn ontwerpers en kunstenaars die de Experimental Publishing-master volgen aan het Piet Zwart Instituut in Rotterdam. De bots maken deel uit van het project dat ‘OuNuPo’ werd genoemd, naar analogie met OuLiPo, de afkorting van Ouvroir de Numérisation Potentielle. Concreet maakten ze een boekscanner op basis van open source hardware, die ook tentoongesteld was, en werkten ze aan de hand van een zelf samengestelde feministische reader, recepten uit waarmee ze het gescande materiaal automatisch transformeren en manipuleren. Documentatie en broncode zijn onder een vrije licentie online te vinden.
De bot van Alice Strete bijvoorbeeld, weefde bestaande teksten opnieuw in elkaar volgens oude jacquard-patronen – weefpatronen die aan de basis liggen van de eerste computerprogramma’s en die getuigen van de intieme relatie tussen vrouwenarbeid en de eerste computers. De bot is eenvoudig te bedienen is met vijf commando’s: load/show/over/under/quit.
Natasha Berting onderzocht het concept van bookscanning vanuit een studie van de canon: welke werken worden gedigitaliseerd en welke niet? En hoe houdt dat een ondervertegenwoordiging van vrouwelijke auteurs in stand? Ze wijst er bovendien op dat het scanningsproces zelf niet neutraal is en verre van perfect. Onderweg gaat veel verloren, en wat verloren gaat, wordt bepaald door de data die werd gebruikt om de vertaalsoftware van beeld naar tekst te trainen. En dus maakt de bot werk van het verwijderen en overschrijven van exclusieve of net vaak voorkomende woorden.
‘Reading the structure’ is een bot van Joca van der Horst, die ons toont hoe een machine leest. Voor een computer is een woord geen betekenisdrager zoals voor ons, het is een combinatie van letters en witruimtes, bit en bytes. Op basis van getrainde modellen kan de computer onze woorden labels toekennen, zoals zelfstandig naamwoord, bijvoeglijk naamwoord, werkwoord, waardoor er een vorm van menselijke grammatica kan ingebracht worden in het model. De bot kan het gegenereerde materiaal opnieuw bewaren in een databank.
Alexander Roidl reflecteerde op de verhalen die schuilgaan in databases, en hoe elke databank ook een afwijking in zich draagt, een bias, die effect heeft op de kennisproductie van deze machines. De spreadsheet is de schakel in de relatie tussen mens en machine. Door die te ontleden en weer aan te bieden aan een chatbot, kan je als lezer/gebruiker in dialoog gaan met de boekscanner zelf.
Opdat machines met tekst-zoals-wij-die-kennen kunnen werken, bestaan er bovendien protocollen van decoding en encoding (asci, utf-8, bits & bytes…). Het inspireerde Zalán Szakács om teksten volgens modellen van Michael Winkler vorm te geven als gecodeerde grafische beelden. Een volledig boek gaat in zo’n circulaire vorm al gauw op een mandala lijken.
De laatste bot van Angeliki Diakrousi gebruikte de speech recognition software Pocketsfinx om een transparante gelaagde lezing van een zin te laten horen. De kunstenaar leest een zin, de software transcribeert die en luistert vervolgens hoe een toeschouwer de vertaalde zin opnieuw inleest. Intussen wordt de eerste zin vijf keer herhaald, waardoor de software andere dingen hoort dan wij. Hoe langer het proces verdergaat, hoe meer de stemmen overlappen en hoe absurder en hoe langer de transcripties worden. Wat je ziet ontstaan in de kakofonie van geluiden, is een hoogst unieke expressie van een hoogst unieke machine.
Literair
De avond die ‘Algologs’ was getiteld zou ik een erg geslaagde literaire avond durven noemen, ook al zou niet een van de kunstenaars op het programma zich een literaire schrijver noemen. Ze delen allemaal een grote liefde voor taal, tekst en code. Maar laat dit statement, om de combinatie van screening, performance, lezing en visuele kunst literatuur te noemen, een poging tot antwoord zijn op de vragen die Johanna Drücker stelt in haar paper ‘Un-Visual and Conceptual’ uit 2005, en waarnaar de organisatoren verwezen tijdens de inleiding van de avond.
The forest lends itself as a metaphor for talking about big data. We are interested in the forest because of the amount of trees there are. We enjoy their view, their rustling, the multitude of trunks, fruits, plants. Apart from the forest rangers, few visitors have knowledge of individual trees in the forest, unless they fall outside ‘normality’. Particularly old, thick, large trees, rare specimens can sometimes catch our attention. But the large part of the trees is only interesting for us as a group.
In the same way, companies look at us, users of their technology. When they make up profiles based on our clicks, likes and comments, their focus is not our individual personality, but what we have in common with others, our relationships, our existence in group(s).
Trees are also interconnected via underground networks of mycelium, a phenomenon that covers our entire globe and is referred to as ‘the woodwide web’. Therefore it is tempting to start organising small algorithmic games in the forest based on algorithms used in predictive models.
A first game is an interpretation of the ‘linear regression’. Next to finding a correlation – however subjective, statistically irresponsable and minimal the measurements may be – this exercise also shows the negotiations and compromises that you go through along the way to arrive at usable and measurable data. It is also a very nice way to look at trees in detail.
Linear Regression
In statistics, linear regression is is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables:
– One variable, denoted x, is regarded as the predictor, explanatory, or independent variable.
– The other variable, denoted y, is regarded as the response, outcome, or dependent variable.
Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications.
The following videos by David Longstreet explain simple linear regression very well:
– Introduction to Linear Regression
– Calculating Linear Regression using least square method
– Calculating R Squared Using Regression Analysis
A History of Linear Regression
Francis Galton, a cousin of Charles Darwin and an accomplished 19th century scientist in his own right, has often been criticized in this century for his promotion of “eugenics” (planned breeding of humans). While studying the problem of heredity – understanding how strongly the characteristics of one generation of living things manifested in the following generation, he was the first to define the linear regression slope.
Galton initially approached this problem by examining characteristics of the sweet pea plant. He chose the sweet pea because that species could self-fertilize; daughter plants express genetic variations from mother plants without contribution from a second parent. This characteristic eliminated, or at least postponed, having to deal with the problem of statistically assessing genetic contributions from multiple sources.
In 1875, Galton had distributed packets of sweet pea seeds to seven friends; each friend received seeds of uniform weight (see also the original paper), but there was substantial variation across different packets. Galton’s friends harvested seeds from the new generations of plants and returned them to him. Galton plotted the weights of the daughter seeds against the weights of the mother seeds. He realized that the median weights of daughter seeds from a particular size of mother seed approximately described a straight line with positive slope less than 1.0.
Galton’s first insights about regression sprang from this two-dimensional diagram plotting the sizes of daughter peas against the sizes of mother peas. Galton used this representation of his data to illustrate basic foundations of what statisticians still call regression.
Source: https://www.tandfonline.com/doi/full/10.1080/10691898.2001.11910537
Steps in the game
Ideally the participants in this game take a sample of 100 trees. Experience shows that this requires 20 people, who measure each 10 trees, in groups of two. With previous knowledge about the species of common trees, this takes one afternoon.
First of all, the question arises of what wants to be measured. Which possible correlation do you want to investigate? During a workshop with art students from Studio Editions of the Ecole Nationale d’Arts de Paris Cergy in January 2018, one of the proposals was to find out if there was a positive relationship between the thickness of oak and the type of trees growing around an oak tree, and if there is a type of tree that is more common in growing near the oak.
Then a protocol is established for the ‘random’ choice of the trees. We make use of a dice for this. If a group throws a three, they can decide to observe the fourth tree. The question remains how the trees are counted, whether the trees to the left and/or right of the path are counted, to what distance of the path you take the trees into account and what you do with trees that grow along the path and do not allow for close neighbours because of the compaction of the soil.
During the observations of the different trees, all kinds of questions and obstacles can emerge, so that the protocol for the measurements may be adjusted along the way. There may also be cases of Omitted Variable Bias. Omitted variable bias occurs when a regression model leaves out relevant independent variables, which are known as confounding variables. They are also called spurious effects, and spurious relationships. The problem is very well explained in a post by Jim Frost. And if you want to have fun, have a look at some Spurious Correlations by Tyler Vigen.
Finally, the measurements are logged in a graph. Organizing the information is again a small performative moment, with as result a first idea whether or not there is a possible correlation.
If there is time left, the PM Coefficient can be calculated to check the accuracy of the correlation.
Participants in the game
A big thank you to: Anne-Laure Buisson, Chris Vanderlinden, Angeline Ostinelli, Temperance Cole, Amo Vaccaria, Doriane Geneste, Julie Timoshkin, Lu Wang, Rachel Lang, Rudy Levassor, Nicolao Federico.
This is what I know about myself.
The description given to me is that of a sacred tree. I am included in the Near East Collection. In case you decide to dress me one day: my height is 75 cm, my width is 87 cm. I have been tagged a relief. I have been categorized with the number o.00271. My body is made of stone. My great grand parents must have lived before -883 / -859. I should be able to find some relatives in Near and Middle East (Asia) as my place of production, and in Nimrud (Asia > Mesopotamia > Assyria) as my place of discovery: . My cultural background is Assyrian.
I have one close friend here.
She has also been catalogued as a sacred tree. Someone also decided to list her in the Near East Collection. Someone measured her height: 51 cm, and width: 94 cm. She is also commonly named a relief. I guess you would say her official name is o.00278. The material of her body is stone. And just like me, she is very very old, from -883 / -859. Her geographical origin is exactly the same: place of production: Near and Middle East (Asia), place of discovery: Nimrud (Asia > Mesopotamia > Assyria). She also belongs to the Assyrian culture.
We have long conversations in whispers. Our main topic is the questioning of our identities. We are sacred and we are very proud of that. But if only we knew what kind of sacred trees we are. We found out there are another hundred and fifteen objects in the collection that are tagged with the word ‘tree’, but there is only one other that carries the name ‘sacred tree’. Let me ask him to introduce herself. You will immediately notice he is a much more complex being.
Hi! I am a cylinder seal with hero and the sacred tree. I have been classified in the Near East Collection. These are my measurements: height: 2,7 cm, width: 1,1 cm. The name given to me is cylinder seal. Translated to an index, I am o.01387. My bones are of stone. My great grand parents must have lived before -1500 / -1201. My place of birth is place of production (historical): mesopotamia (asia). My cultural background is assyrian.
You see, he is not only a sacred tree, he is a ‘hero and a sacred tree’. Thanks to conversations with him, Relief and I realized that our question is much more simple than his. If we could solve our question somehow, Cylinder Seal’s identity will be half revealed as well. That is what triggered the decision of Relief to go on an adventurous journey through the collection.
All of a sudden our lives became thrilling. Only by proposing the idea of the journey, for example, made us realize we did not even know where we were located. Where are we? To which collection do we belong? It took us an enormous amount of courage to break out of our fields and start to explore. At node 2070 we found the nicely formulated answer to our first question: “Carmentis is the online museum catalogue of the Royal Museums of Art and History (RMAH), presenting the digitized objects of our diverse permanent collections that range from Egyptian objects to musical instruments from the Musical Instrument Museum to the collection of historic carriages based in the Museum of the Cinquantenaire. Carmentis is named after the Roman goddess of childbirth, who was associated with technological innovation and the invention of the Latin alphabet.”
I would have loved to know how many other beings were part of Carmentis, but the mere idea overwhelmed us.
‘Stay focussed,’ Relief said. ‘We are only interested in finding out what kind of tree we might be.’ It was good to be reminded of that, because the world out here is immense. ‘Where to go to first?’ I asked her, despairing.
For days we kept quiet, not knowing how to proceed.
‘What’s happening to you? I don’t hear from you anymore?’ the Cylinder Seal asked. When he heard our trouble, he smiled. It turns out to be very useful to have a hero as part of your personality. He proposed to start looking for the material of the tree, the wood present in the collection.
‘Maybe those trees will be more defined,’ she said. With shaky hearts we took off.
There are two types of wood in the collection: Hardwood and Softwood. On the menu – which is endlessly long – we found fifty six types of Hardwood and only six types of Softwood. We read the list of Softwoods tenderly: Cedar, Cypress, Fir, Juniper, Norway Spruce, and Pine.
We looked at each other and immediately agreed we intuitively felt closer to the Hardwoods: Acacia, African Blackwood, African Ebony, American Mahogany, Apricot, Ash, Beech, Big Leaf Mahogany, Birch, Black Elder, False Brandybush, Brazilwood, Brazilian Tulipwood, Common Hazel, Dalbergia cearensis, Dalbergia latifolia, Dalbergia nigra, Dogwood, Ebony, Elm, Eucalyptus, European Boxwood, European Ash, European Beech, European Birch, European Cherry, Hickory, Hornbeam, Lemonwood, Lime, Lusumbya, Manna Ash, Maple, Mheme, Mninga Mtumbati, Mulberry, Oak, Olive tree, Opepe, Pear, Plane Wood, Plum, Poplar, Rohida-tree, Rosewood, Satin, Shagbark Hickory, Snakewood, Spider-tresses, Sweet Chestnut, Sycamore, Tilia Americana, Tilia Europaea, Tilla Euchlora, Toromiro, Walnut, Yew.
Happiness filled our hearts. Even if we had no idea what the names meant, the fact that our focus had been reduced to only fifty six options made us drunk with joy. When we came back to our fields, we kissed Cylinder Seal with such gratitude, we almost merged.
Once cooled down, we realized the journey was still immensely long. No less than two thousand seven hundred twenty one beings in the collection were categorised as Hardwoods. We needed a new strategy.
We decided to nominate Cylinder Seal as our compass, as his arguments again seemed very reasonable to us.
‘All trees on earth are sacred,’ he concluded. ‘Humans might not always be aware of it. If they call a tree sacred, it must be that it is very important for their community. The chance that the commonest trees throughout history end up being the most important seems quite plausible. I guess you must be made of the same wood as the big tree hits of the collection.’ His words almost sounded like a prophecy!
It did not take us that long to ascertain the most popular trees present in Carmentis. ‘Ebony’ superseded all the others with one thousand forty three objects. But Relief, sharp as always, noticed there was also a tree called ‘African ebony’ with sixty four hits. The confusion was huge. The only solution was to ask the objects themselves.
African ebony is only used in the Collection of Musical Instruments, while Ebony – even if nine hundred seventy nine out of the one thousand forty three objects are part of the Musical Instruments – is used in collections such as Sculptures and Furniture, Arms and Armour, Egypt, Preciosa and Silverware, China and something called External Collections. Wow, a first glance into our universe showed us a richness we had never dared to dream of. Our imagination was fired by the thought that the Near East Collection was part of such a great galaxy. We wanted to explore them all, but we were strong, and decided to keep our focus. Although Relief was triggered by the word Preciosa. So before moving on, we went to meet some of their species.
Object nr 1 was doubting about everything.
“Do you believe I am large, having a height of 36,4 cm?,” she asked. “Do you believe I am a coffeepot? And that my inventory number is 4183? Is it true I am composed of ebony (diospyros sp.) (vegetal > wood (vegetal material) > hardwood) and silver (metal)? And how can I find out I was born in the era of 1765? Can you confirm I was produced in Brussels city (Europe > Western Europe > Belgium > Brussels-Capital Region)? Because you know, it is hard to be someone of which the culture is non-defined.” All ebony objects in the Collection of Preciosa and Silverware turned out to have a non-defined Culture. And all of them were in deep existential crisis. There was another coffeepot of a similar size produced in Mons, and two crosses of the same period. One was a pendant (jewellery) from Spain, born in 1601/1700. The other was a terribly depressed crucifix of 75.1 cm, made of stone, ebony and silver. The poor thing did not know where he was produced, what culture he belonged to and as he said with a deep sigh: “If you’re a crucifix knowing only that you’ve been produced somewhere on earth in the period between 1601 and 1700, you feel like a mass product.”
We tried to cheer them up as much as possible, encouraging them to find out the answers, if only for the sake of the journey. It would give them the spice of life. But they seemed to be deaf to our plans. And we were happy to return to the major ebony collection, that of the Musical Instruments.
“Maybe we can just concentrate on them for now,’ Relief suggested. ‘Even if they have no data, each musical instrument must live with the memories of the sounds it has generated. I bet they’re the most joyful creatures we’ll ever meet.”
She was right. As always.
We paid a visit to the ebony beings in the Collection of Musical Instruments. A crowd of nine hundred seventy nine objects was represented by a hundred and forty three types of instruments. We were impressed by the number of different trees used in the body of a Musical Instrument. When we expressed this amazement to the first one we met, a Traverse flute born in Belgium in 1700, she pointed us in the direction of the pianos.
“Just ask to be introduced to a grand piano,” she whispered.
And so we did. One of the largest pianos spoke to us in a low voice: “I am called a piano à queue / luthéal. I am included in the Musical Instruments Collection. This is my size: height: 98,3 cm, width: 142 cm, depth: 160 cm. I have been tagged as a grand piano. If one day I were to carry an ID card, its number would be 3613. My bones are of brass (alloy) (metal > alloy > copper alloy), steel (metal > alloy > iron alloy), tilia americana (american linden) (vegetal > wood (vegetal material) > hardwood > lime (tilia sp.)), hornbeam (carpinus sp.) (vegetal > wood (vegetal material) > hardwood), fir (abies sp.) (vegetal > wood (vegetal material) > softwood), rosewood (dalbergia sp.) (vegetal > wood (vegetal material) > hardwood), walnut (juglans sp.) (vegetal > wood (vegetal material) > hardwood), ivory (animal > tooth > mammal tooth), celluloid (processed material > synthetic > plastic), american mahogany (swietenia sp.) (vegetal > wood (vegetal material) > hardwood), copper (metal), norway spruce (vegetal > wood (vegetal material) > softwood), ebony (diospyros sp.) (vegetal > wood (vegetal material) > hardwood), beech (fagus sp.) (vegetal > wood (vegetal material) > hardwood), tanned leather (processed material > > leather), iron (metal), bronze (metal > alloy > copper alloy), oak (quercus sp.) (vegetal > wood (vegetal material) > hardwood), felt (processed material > > textile), maple (acer sp.) (vegetal > wood (vegetal material) > and hardwood), cast iron (metal > iron). My history dates back to 1911. I come from Belgium (Europe > Western Europe).”
We thanked the piano and focused on our research again. The majority groups consisted of violins and bows. We gazed at them while wondering how they managed to get on, as there were only one hundred and thirty three bows for one hundred and seventy two violins. We tried to find out, but they were extremely discrete.
“We’re only playing in our imagination,” a German mute violin born in 1901 / 1925 finally admitted. Her body was a void, but her voice was soft as feathers. “In the realm of your imagination you can play with whom you prefer, really. I often even play with twenty bows at once. It is a sensation I never experienced when played by humans, but when I picture it and listen to the sounds I might produce, it gives me the ultimate pleasure.” This was the first time we learned about a practise of imagination. It touched us deeply as we never even thought of the possibility of cultivating that dimension.
As for the wood types, the violins were very generous. They showed us their body parts made of ebony. And they continued naming all other parts. As we watched, we realized almost all violins are made of Norway Spruce, a pine that is commonly used as a Christmas tree. The fact that Norway Spruce is a softwood struck me. I felt so closely connected to it, that I started wondering if we were exploring the right path. But I decided to keep that thought to myself. Such a radical questioning of our journey would only upset my dear friend.
None of the violins knew why they were made of Ebony and not African ebony.
“Violins must exist made of African ebony,” Violon Expérimental nr 3370-01 uttered. “If you could find one, you could compare.”
“Thanks, that is a great idea!”
The suggestion was beautiful but the reality not. None of the Musical Instruments made with ‘African ebony’ was a violin. Our confusion caught their attention. After listening to our story and confessing none of them even knew there was another type of ebony that was not African, the Automatic Harmonium with keyboard, born in 1891/1910 in New York and in close contact with his compatriots in the Collection, showed us the way to the internet. We ran into a long tunnel, passed all possible protocols and stepped into yet another Milky Way of our Galaxy. The concept of abundance was too little to describe what we felt. All of a sudden, we had been reduced to tiny pieces of stardust in the nutshell that was our collection. It took us some time to get used to that quick resizing. But once we did, we found a description of Ebony in less than a second, on Wikipedia: ‘Ebony is a dense black hardwood, most commonly yielded by several different species in the genus Diospyros, which also contains the persimmons. Ebony is dense enough to sink in water. It is finely-textured and has a very smooth finish when polished, making it valuable as an ornamental wood.’
And here was the exact answer to our question: ‘Species of ebony include Diospyros ebenum (Ceylon ebony), native to southern India and Sri Lanka; Diospyros crassiflora (Gabon ebony), native to western Africa; and Diospyros celebica (Makassar ebony), native to Indonesia and prized for its luxuriant, multi-colored wood grain. Mauritius ebony, Diospyros tesselaria, was largely exploited by the Dutch in the 17th century. Some species in the genus Diospyros yield an ebony with similar physical properties, but striped rather than evenly black (Diospyros ebenum).’
“All this is very interesting,” Relief commented. “And I’m very exited we are making this journey, but it feels as if we are on a wrong track.” I held my breath. Maybe she was thinking just like me, that we had chosen the wrong journey. “You know,” she continued, “while observing all these violins and bows, and all these reed organs, I realized they were all produced by humans to be played by humans, or rather, to be used by humans. You see where I am going?”
“Well, I…” but Relief kept up her reasoning.
“If we are sacred trees, it is because humans named us so. Do you really think they would name trees sacred if the next day they would make objects out of them, objects to be used by anyone?”
“Musicians are not anyone.”
“You get what I mean. Humans would only convey the word sacred to a tree they depend on for their survival.”
“A tree that provides them with food?”
“For example.” I was so impressed by how the journey had activated my friend’s brain, that I totally forgot about my own thoughts. We decided to take a look at the fruit trees in the collection. Walnut turned out to be the most present, followed by Pear.
Sixty four Musical Instruments are made of Walnut, and remarkably so, there are as many objects in Sculptures and Furniture made of Walnut, as there are in Arms and Armour: thirty seven.
“That confirms my hypothesis,” Relief said with conviction. “Ever since they appeared on Earth, there are humans who believe they depend on arms for their survival, so it is comprehensible they would use sacred wood for them. Let’s have a look!’
Thirteen pistols lived next to another thirteen revolvers (firearms), seven rifles (long guns) and four bayonets. All but three arms were born in the 19th century. Did that tell us something about the notion of sacred trees or rather about the nature of our collection? Impossible to know.
“Let’s have a look at where they were born,” Relief suggested. But that did not give us more clues either. Twenty four arms were produced in Europe, one in South America, two in North America and ten were of non-defined origin.
“Maybe we should try another fruit tree.” My voice sounded tired. My friend noticed. I admitted that my enthusiasm was flagging, as I felt that our question had endless answers – or none.
“We‘d better start with the less frequent fruit trees. That will make for a very short trip. There is only one Mulberry, one Apricot and one Sweet Chestnut.” They were yet another three Musical Instruments. One of them was a beautiful tar from Tehran, produced before 2010, made of Mulberry, Bullhorn, Gut and Lamb skin. Sweet Chestnut was part of a harpsichord born in Italy between 1600 and 1700. The Chestnut was in the excellent company of Poplar and Cypress. When we approached Apricot, we experienced for the first time on our journey a strange energy. The young string instrument did not want to meet us as beings, his intention was hostile. He was a very exotic ghichak from Xinjiang in China, born in 2003. He came closer and seemed to want something from us. It was clear we had no other defense than our stone bodies. I put myself upright.
“What is it you’re looking for?”
“I want your inventory number now!” With one little shake of my body, the ghichak realized he had no choice and was about to run off.
“Hey wait!” Relief had spoken gently. “Why is it you want ours if you have one yourself?” The ghichak looked at us with great sadness and frustration.
“How would you feel if you would be born in this place, but be part of something called External Collection? You’re in but fuck you, you’re out!” He left and we felt pity for him.
Ghickak did not help us. Our research did not seem to make any sense at all.
“Cylinder Seal might not have been right or wrong,” Relief commented when she noticed the limits of my patience. “Maybe sacred trees are not related at all to abundance nor scarcity.”
“So what could we eventually be related to?” I asked, almost in tears.
“To ourselves?”
“Yes indeed, you are absolutely right, when it comes to knowing who we are, we can only relate to ourselves. Let’s look back at the other aspects we know for sure.”
With renewed energy we continued our travels. First, we decided to look for objects that dated from the same period as us: -883 / -859. But none of the objects made of hardwood travelled so far back in time. The longing for the softwood overwhelmed me again, but I kept cool. If ever we had to make another journey, I would definitely propose to take that route into the Galaxy, but not now.
“We should be able to find some tree relatives in our culture,” I posed. The results were depressing. No objects of hardwood in the Assyrian culture.
“And what about softwood?” I shouted, my heart still bouncing with love for the Norway Spruce. The answer came as quickly as my question.
“No objects of softwood in the Assyrian culture.”
We remained in silence for a long moment. In silence we travelled back to Cylinder Seal. He did not have an answer either. I felt the desire to weep bitter tears of loneliness, but before they could come to the surface they transformed with the memories of all the fragments of trees we had met. There had been so much wonder, so much joy and so much love in each encounter that I could not believe we were not somehow connected. A luminous idea came to my mind.
“And if only,” I whispered, “if only we would be named sacred trees because we are all trees united in one?” My soft question caused a tremendous shock, followed by an immense feeling of freedom and relief.
“Of course!” Cylinder Seal shouted out loud. “We are all fifty six trees present in the collection!”
“I beg your pardon,” I corrected him, “we are sixty two trees all together, the Softwoods are us as well.”
“Of course!” Relief hugged me.
Ever since that day we make daily excursions into a large network of peers and we have never been happier. And Cylinder Seal makes even longer journeys, as he also considers all ‘women/woman’ and all ‘men/man’ as his peers. In the evenings we exchange our most happy encounters of the day.
———-
Some numbers
Woods present in objects of the Collection: [(‘Ebony’, 1034), (‘Maple’, 862), (‘Rosewood’, 725), (‘sycamore’, 416), (‘European Boxwood’, 352), (‘Oak’, 326), (‘Beech’, 239), (‘American mahogany’, 190), (‘Walnut’, 152), (‘African blackwood’, 138), (‘Hornbeam’, 123), (‘Lime’, 118), (‘Tilia Americana’, 102), (‘European beech’, 100), (‘Brasilwood’, 92), (‘Pear’, 78), (‘Poplar’, 72), (‘African ebony’, 64), (‘Elm’, 46), (‘Dalbergia latifolia’, 39), (‘Ash’, 22), (‘Tilia europaea’, 11), (‘Birch’, 10), (‘Satin’, 10), (‘Acacia’, 9), (‘Toromiro’, 8), (‘Plane wood’, 8), (‘European cherry’, 7), (‘Yew’, 7), (‘Lemonwood’, 6), (‘Plum’, 5), (‘Dalbergia nigra’, 4), (‘Opepe’, 4), (‘Shagbark Hickory’, 3), (‘European ash’, 3), (‘Black elder’, 3), (‘Tilla euchlora’, 2), (‘Common Hazel’, 2), (‘Snakewood’, 2), (‘Brandybush, false’, 2), (‘Olive tree’, 2), (‘Mninga, mtumbati’, 2), (‘Big Leaf Mahogany’, 1), (‘Mulberry’, 1), (‘Eucalyptus’, 1), (‘European birch’, 1), (‘Mheme’, 1), (‘Apricot’, 1), (‘Rohida-tree’, 1), (‘spider-tresses’, 1), (‘Dalbergia cearensis’, 1), (‘Brazilian tulipwood’, 1), (‘Dogwood’, 1), (‘lusumbya’, 1), (‘Manna Ash’, 1), (‘Sweet chestnut’, 1)]
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Musical Instruments made of “Ebony”: [(‘Violin’, 172), (‘Bow (chordophone component)’, 133), (‘Violoncello’, 53), (‘Guitar’, 46), (‘Upright piano’, 45), (‘Viola’, 42), (‘Grand piano’, 40), (‘Bell (wind instrument component)’, 36), (‘Transverse flute’, 31), (‘Mouthpiece (clarinet and saxophone)’, 27), (‘English guitar’, 25), (‘Reed organ’, 24), (’empty’, 21), (‘High treble viol’, 16), (‘Closet’, 16), (‘Clarinet’, 13), (‘Viola da gamba’, 13), (“Viola d’amore”, 12), (‘Neapolitan mandolin’, 11), (‘Archcittern’, 10), (‘Mandole’, 9), (‘Baton (music equipment)’, 9), (‘Lyre guitar’, 9), (‘Body (wind instrument component)’, 8), (‘Lute’, 8), (‘Piccolo’, 8), (‘Hardanger fiddle’, 7), (‘Theorbo’, 7), (‘Pistol’, 7), (‘Flageolet’, 6), (‘Double bass’, 5), (‘Mandoline’, 4), (‘Lute-guitar’, 4), (‘Pneumatic piano’, 4), (‘Harp’, 4), (‘Bass violin’, 4), (‘Double duct flute’, 4), (‘Fragment’, 3), (‘Fiddle’, 3), (‘Lyre’, 3), (‘Harpsichord’, 3), (‘Wiper’, 3), (“Clarinette d’amour”, 3), (‘Mute violin’, 3), (‘Dulcimer’, 3), (‘Music equipment’, 2), (‘Player piano (met klavier)’, 2), (‘Colascione’, 2), (‘Harpsichord with double keyboard’, 2), (‘Automatic harmonium with keyboard’, 2), (‘Tablet’, 2), (‘Claviharpe’, 2), (‘Armchair’, 2), (‘Vielle organisée’, 2), (“Violin d’amour”, 2), (‘Tenor violin’, 2), (‘Dital harp’, 2), (‘Coffeepot’, 2), (‘Tailpiece (string-holder)’, 2), (‘Cittern’, 2), (‘Viol’, 2), (‘Treble viol’, 2), (‘Harp-guitar’, 2), (‘Recorder’, 2), (‘Sound-board’, 1), (‘Flute’, 1), (‘Piano-harmonium’, 1), (‘Bandora’, 1), (‘Clavichord’, 1), (‘Positive organ’, 1), (‘Ocléal’, 1), (‘Harmonina’, 1), (‘Musical instrument’, 1), (‘Pyramid piano’, 1), (‘Cane violin’, 1), (‘Lyre-bandurria’, 1), (‘Bass clarinet’, 1), (‘Erxian’, 1), (‘Automatic harmonium’, 1), (‘Fife’, 1), (‘Mandoloncello’, 1), (‘Viola pomposa’, 1), (‘Crucifix’, 1), (‘Harpéal’, 1), (‘Revolver (firearm)’, 1), (‘Terpodion’, 1), (‘Harmoniphone’, 1), (‘Piano-violon’, 1), (‘Chess set’, 1), (‘Flaviol’, 1), (‘Double clarinet’, 1), (‘Chromatic harp’, 1), (‘Pegbox’, 1), (‘Wind instrument’, 1), (‘Tenor viol’, 1), (‘Mute’, 1), (‘Bajiao gu’, 1), (‘Screen (furniture)’, 1), (‘Flagon’, 1), (‘Headrest’, 1), (‘Chair’, 1), (‘Sarinda’, 1), (‘Kamancha’, 1), (‘Baryton’, 1), (‘Lira’, 1), (‘Orchestrion’, 1), (‘So duang’, 1), (‘Tuning peg (chordophone component)’, 1), (‘Mouthpiece (brass instrument)’, 1), (‘Oud; Ud’, 1), (‘Cecilium’, 1), (‘Tuning device’, 1), (‘Pendant (jewelry)’, 1), (‘Small case’, 1), (‘Writing and drawing equipment’, 1), (‘Double flageolet’, 1), (‘Descant recorder’, 1), (‘Dulcitone’, 1), (‘Portative organ’, 1), (‘Key (sound device component)’, 1), (‘Wing joint (wind instrument component)’, 1), (‘Cane flute’, 1), (‘Dulcian’, 1), (‘Horn fiddle’, 1), (‘Musical instrument component’, 1), (‘Claviphone’, 1), (‘Bust’, 1), (‘Automatic epinette’, 1), (‘Cimbalom’, 1), (‘Bahut’, 1), (‘Celesta’, 1), (‘Square pianoforte’, 1), (‘Poikilorgue’, 1), (‘Virginal’, 1), (‘Organ’, 1), (‘Muet; Mvet’, 1), (‘Clavéal’, 1), (‘Toy’, 1), (‘Paiban’, 1), (‘Rabab’, 1), (‘Mouth harmonium’, 1), (‘Mandora’, 1), (‘Quwaytara’, 1)]
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Musical Instruments made of “African ebony”: [(‘Reed organ’, 24), (‘Upright piano’, 6), (‘Pneumatic piano’, 3), (‘Music equipment’, 2), (‘Claviharpe’, 2), (‘Player piano (met klavier)’, 2), (‘Automatic harmonium with keyboard’, 2), (‘Automatic harmonium’, 1), (‘Musical instrument’, 1), (‘Harpéal’, 1), (‘Portative organ’, 1), (‘Celesta’, 1), (‘Dulcitone’, 1), (‘Terpodion’, 1), (‘Cecilium’, 1), (‘Pyramid piano’, 1), (‘Piano-harmonium’, 1), (‘Organ’, 1), (‘Ocléal’, 1), (‘Harmoniphone’, 1), (‘Square pianoforte’, 1), (‘Automatic epinette’, 1), (‘Poikilorgue’, 1), (‘Harmonina’, 1), (‘Clavéal’, 1), (‘Tuning device’, 1), (‘Piano-violon’, 1), (‘Orchestrion’, 1), (‘Mouth harmonium’, 1), (‘Claviphone’, 1)]
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Collections with objects made of “Walnut”: [(‘Collection Musical Instruments’, 64), (‘Collection Sculptures and Furniture’, 37), (‘Collection Arms and Armour’, 37), (‘Collection Carriages’, 12), (‘Collection Preciosa and Silverware’, 1), (‘External collections’, 1)]
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Objects made of “Walnut”: [(‘Chair’, 17), (‘Pistol’, 13), (‘Revolver (firearm)’, 13), (‘Closet’, 11), (‘Grand piano’, 9), (‘Upright piano’, 7), (‘Rifle (long gun)’, 7), (‘Berline’, 6), (‘Trumpet marine’, 5), (‘Armchair’, 5), (‘Reed organ’, 4), (‘Bayonet’, 4), (‘Player piano (met klavier)’, 3), (‘Harpsichord’, 3), (‘Virginal’, 2), (‘Viola da gamba’, 2), (‘Violin’, 2), (‘Guitar’, 2), (‘Musical box with cilinder’, 2), (‘Coupé (carriage)’, 2), (‘Violoncello’, 2), (‘Walking stick’, 2), (‘Mandole’, 1), (‘Metronome’, 1), (‘Automobile’, 1), (‘Statue’, 1), (‘Lira’, 1), (‘Bahut’, 1), (‘Pneumatic piano’, 1), (‘Coffeepot’, 1), (‘Komet’, 1), (‘Automatic harmonium’, 1), (‘Hardanger fiddle’, 1), (‘Polyphon’, 1), (‘Harpsichord with double keyboard’, 1), (‘Mandora’, 1), (‘Hommel’, 1), (‘Piano-harmonium’, 1), (‘Clavéal’, 1), (‘Fragment’, 1), (‘Orchestrion’, 1), (‘Cimbalom’, 1), (‘Vielle organisée’, 1), (‘Clavichord’, 1), (‘Bass violin’, 1), (‘Sleigh’, 1), (“Viola d’amore”, 1), (‘Celesta’, 1), (‘Colascione’, 1), (‘Square pianoforte’, 1), (‘Small case’, 1)]
Places of Production of Arms made of “Walnut”: [(‘Place of production: Liège (Europe > Western Europe > Belgium > Wallonia > Liège (province))’, 7), (‘Place of production: Unknown’, 6), (‘Place of production: Saint-Étienne (Europe > Western Europe > France > Rhône-Alpes (region) > Loire (department))’, 4), (‘non-defined’, 4), (‘Place of production: Belgium (Europe > Western Europe)’, 3), (‘Place of production: Dantzig (Europe > Central Europe > Poland > Kuyavian-Pomeranian (voivodship))’, 2), (‘Place of production: Rio de Janeiro (America > South America > Brazil > South-East (region) > Rio de Janeiro (state))’, 1), (‘Place of production: Germany (Europe > Central Europe)’, 1), (‘Place of production: Wiesbaden (Europe > Central Europe > Germany > Hessen (state) > Darmstadt (district))’, 1), (‘Place of production: Western Europe (Europe)’, 1), (‘Place of production: Munich (Europe > Central Europe > Germany > Bavaria (state) > Upper Bavaria (district))’, 1), (‘Place of production: Liège (province) (Europe > Western Europe > Belgium > Wallonia)’, 1), (‘Place of production: England (Europe > Western Europe > United Kingdom of Great Britain and Northern Ireland > Great-Britain)’, 1), (‘Place of production: Madrid (Europe > Western Europe > Iberian Peninsula > Spain > Madrid (autonomous region) > Madrid (province))’, 1), (‘Place of production: United States (America > North America)’, 1), (‘Place of production: Brussels City (Europe > Western Europe > Belgium > Brussels-Capital Region)’, 1), (‘Place of production: New York (state) (America > North America > United States)’, 1)]
]]>The natural cycle of a Northwest European forest ends with the beech. Once the beech gets in, this tree will soon dominate the place. Beeches have the particular capacity of covering up all possible light spots with their extended crown. Their leaves are thick and in fall they don’t transform rapidly into humus, as leaves of other species do. In the Forêt de Soignes the beeches have been planted since the 19th century. The dominance is artificial, but representative. At the end stage of a forest, one tends to forget about all the species that were necessary to come to this stage. The beech therefore suits as a metaphor for algorithmic packages you get off the shelve, without even looking at what they’re made of.
The last tree of the image is an abstract one, representing the Arboretum, where people experiment with new species. At this very moment, the Neural Networks have just escaped from the Arboretum. They are so successful people did not wait for proper evaluation methods before starting to use them.
—
I present this image as a contribution to ’elif n°1 : Résistance électronique, stratégie éditoriale et cyberféminisme’, a seminar reserved for students of Ensba Lyon and Esad Saint-Etienne, on 29 and 30th March 2016.
The lecture is a next step in the research on ‘Algorithmic Storytellers’ and a way to show the interdependent behaviours of probabilistic models.
Other guests are: Stéphane Lemercier (artist), Eric Watier (Monotone press), Manuel Schmalstieg (Greyscale Press), Sarah Garcin and Angeline Ostellini (G.U.I), Loraine Furter (Hybrid Consortium), Anne Laforet (artist and theory), Ludivine Loiseau and Stéphanie Vilayphiou (OSP).
This research is supported by the Vlaamse Overheid.
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