|Aad Björkro on Sat, 26 Oct 2019 15:32:19 +0200 (CEST)|
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|Re: <nettime> Algorithms that Matter Symposium 2020: Call for contributions|
(Hanns Holger Rutz): [...] classical works on computation by Turing, von Neumann etc. were based on the idea that data=""
[...] reminded with Hans-Jörg Rheinberger that the term should actually be 'fact' ('made') not 'data' ('given').
(Francis Hunger): What has not been seen as worthy to discuss is the long history of office and production automation [...] because it often relates not to the academic side. It relates to deeply embedded daily practices.
(Hanns Holger Rutz): [...] not to talk of "the algorithm", indeed I have looked a while back at the principles of "its" reification ...
(Francis Hunger): it is worth to look into data and the information-model much more, since obviously with the rise of Pattern Recognition (aka AI) and databases these are spaces that can be contested and subverted to a larger extend than I have seen until today.
Dear Hans, dear All,# distributed via <nettime>: no commercial use without permission
I would contest that. I have for a long time believed in it myself. The academic world is obsessed with writing about Turing and about von Neumann and about Cybernetics, and "'the algorithmic' as the specific medium of computation". And I think it shows in how the field of the computational is discussed today.I prefer to think of "the algorithmic" as the specific medium of computation (what Luciana Parisi would call 'mode of thought', I guess).
What has not been seen as worthy to discuss is the long history of office and production automation, which is less heroic than the above (although not completely disconnected), however in my understanding in the end much more influential to how computing today is shaped. It is however a much overlooked story, because it often relates not to the academic side. It relates to deeply embedded daily practices. JoAnne Yates and Thomas Haigh have written about it, for instance.
The conceptual problem from my perspective is: if the claim is to reflect on "the computational" of today, the question is then, what kind of computational are we talking about. Are we talking about concepts of the 1930s–1960s which have influenced _early_ computing, or are we talking about today's everyday practices like the use of infrastructure (banking, water supply, public transport, websites etc.) and software applications that shape these practices, beginning with well-known software such as graphic-, sound- and video-design towards the less known, deeply embedded Enterprise Resource Management, Customer Relationship Management, Supply Chain Management.
These are obviously overlapping yet distinct topical fields, so my intervention is not only about the question whether to concentrate on algorithms or the algorithmic, it is also about the question, what are the topical fields which get currently discussed. In festivals, in academic conferences and in general public. Can they be tracked back to the 'the algorithmic' as the specific medium of computation? I don't think so and suggest to talk more about data-information model-algorithm. Maybe it is not the task of a music centered festival and I was simply mislead by the call for "Algorithms that Matter".
This is to say, that the discussion reaches beyond this specific call alone. It also occured to me, to just name another instance, reading Matteo Pasquinellis recent and very relevant: Three Thousand Years of Algorithmic Rituals: The Emergence of AI from the Computation of Space. (That would lead to far now, to discuss it.) And there are many more.
I agree. I think however, it is worth to look into data and the information-model much more, since obviously with the rise of Pattern Recognition (aka AI) and databases these are spaces that can be contested and subverted to a larger extend than I have seen until today.When we conceived ALMAT back in 2015 (?), we were very much thinking of the heritage of computer (sound) art, and how things have somehow shifted in the past years in terms of the role of computation, with 'mattering' of course having the double reference of matter/meaning, referring thus, among other things, to the physical world, but also various discourses such as "new materialism". In this way, we would never assume a clear fissure between algorithm/data, and already the classical works on computation by Turing, von Neumann etc. were based on the idea that data="" Last not least, let's be reminded with Hans-Jörg Rheinberger that the term should actually be 'fact' ('made') not 'data' ('given'). In any case, we were mostly interested in coding practices, retroaction and speculative reason, and so 'data' was never in the focus of our attention (along with 'big data', 'machine learning', 'models' etc.). We don't discount it, but simply approach the theme from artistic practice as writing processes. Logic, classical cybernetics and information theory are all important for this, but form only part of the truth. I have a few objections, though. For example you write: "If we for instance look into how bias enters software, we usually won't find much in algorithms". This of course depends on the definition of bias. If you take a step back and look at "computational thinking" as a world view, then the bias is there from the very beginning in the very conception of the types of objects we're dealing with, so I think this very much applies to algorithms as well. With all the discourse on algorithmic governance, algorithmic ethics and so on, we've become accustomed to think that's just about creating 'balanced' models and data sets, but this is too short-sighted. We need to question the entire axioms of communication/control metaphors.
Of course. If it's on nettime, it's public anyway.This discussion is very important. Would you mind if I add it to the symposium RC page?
All the best,
Best, .h.h. On 10/10/2019 23:20, Francis Hunger wrote:Hi Hanns and everybody,Rather than understanding algorithms as existing and transparent tools, the ALMAT Symposium is interested in their genealogical, processual aspects and their transformative potential. We seek critical approaches that avoid both mystification and commodification, that aim at opening the black box of "wonder" that is often presented to the public when utilising algorithms.That's very much needed. And I think there is a conceptual problem, which this conference shares with many others that talk about "the algorithm". I agree, that the specialized field of generative art concentrates on algorithms (that generate the visual or auditive experience) and that algorithms on a larger scale matter in optimization (like b-tree sorting, fast gradient step method in pattern recognition). However from a perspective of "gray media" (Fuller/Goffey), "logistical media" (Rossiter) on the one hand, and "habitual media" (Wendy Hui Kyong Chun) on the other, I think "algorithm" is wrong terminology. Approaching it from a perspective of the database and referring to actual practices of application programming I would argue, that algorithms are a minor issue. Of much more importance is the information model. The information model is usually the decision, which information and subsequently data, should be included into the processable reality of computing, and what to exclude. In short: data is, what gets included according to the information model. Everything else is non-data or non-existent (under the closed world assumption) to the computer. So if you aim to look into the genealogy of algorithms, you may look into mathematics and maybe operational reserch. You will however miss out on looking at the genealogy of _data_ and the material qualities of the _information model_. If we for instance look into how bias enters software, we usually won't find much in algorithms. A b-tree sorting or the training of a neural network is always tied to weights, and actually needs and creates bias. Since a computer can not understand meaning, meaning needs to be ascribed (through classification), which is done by the mentioned algorithms moving numerical weights towards a certain result that is meaningful to humans. Much more relevant for the question of bias is, how the _information model_ is organized, because it inscribes the reality of the computable. Much more relevant is the question of how _data_ is collected, curated und used, as we can see in the current projects of Adam Harvey (https://megapixels.cc/) or !Mediengruppe Bitnik (https://werkleitz.de/en/ostl-hine-ecsion-postal-machine-decision-part-1), or the Data Workers Union (https://dataworkers.org/). I get, that 'algorithm' is often used as common notion, in a similar blurry way as is 'digital'. However a stronger concern for the information model and for data would open up the avenue for a stronger political stance, since it looks into who decides about inclusion and exclusions, and how these decisions are shaped. I'm talking about identifying addressable actors who are being hold responsible. So let's look further into the trinity: information model–––data–––algorithm (and the infrastructure in and around it). best Francis
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