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A researcher in artistic applications of AI contacted me because some of my projects may have given the impression that I was working with AI, which I don't consider to be the case. However, as our preliminary exchange clarified, there is an EU definition that seems to cast the net wide and would include even very simple Markov models. Now, in fact, my Peptalk program does use a first order Markov transition matrix for letter combinations used to create nonsense words in its own polyglottally trained language. But really, would anyone seriously hype up such a thing as being AI?
Although I don't work with AI as an artistic tool, I told the researchers that I would be happy to share my opinions, so I was offered the opportunity of a zoom interview, which I declined. Instead, I proposed to answer questions over email, which has the benefit of providing time for reflexion over such a complex topic. Here is an edited version of my replies.
I'm a composer, musician, visual artist working primarily with video, print making, and performance, and I'm also a writer.
Instead of quoting my lengthy reply, curious readers may peruse my home page and judge for themselves:
If demixing and source separation counts (assuming it is based on machine learning), then there's one example I have actually used for the purpose of remastering old mono cassette recordings. The demixing was poor, I've been able to achieve comparable or better results of remastering mono to stereo by more laborious manual techniques. In principle, source separation could be a useful application. Other than that, since I do not use what I consider to be AI, let me offer some thoughts about why I refrain from doing so.
When using software written by others, I tend to prefer that which is more transparent about what goes on inside. For example, I use a small subset of Csound for sound synthesis and processing and, when feasible, I write my own programs in C/C++ where I (hopefully) have a detailed understanding of what the program does. The approach is very different from having to deal with the black boxes of proprietary plugins, which I also use to some extent. With machine learning, it appears that no-one understands exactly how it comes that a model achieves what it does. In my algorithmic compositions, I program everything myself from low level routines for sound synthesis to the generation of the entire piece. A curiosity about algorithms or formalised representations of music has been an incentive for this work. Thus, it would be pointless to trust a third party readymade composition program with a few parameters to tweak, instead of having to engage in the modeling from the ground.
Evolutionary computing was in the vogue when I did my PhD, and since I worked on sound synthesis, feature extraction, and algorithmic composition, it was almost expected that I too should find use for evolutionary algorithms. But I didn't. Limited programming skills may have stopped me, but most of all, I needed a subjective evaluation in the loop which I never found a way to formalise away.
In general, I'm weary of advanced technology proposed as solutions to problems that may be solved by simpler means, and nudged upon us whether we like it or not. That is one of the reasons why I have resisted having a smart phone, even as today's society is arranged in a way that makes it easier to have one.
Specifically, I see a few downsides with AI for artistic use. First and foremost, there is a risk that relying on tools makes us unlearn skills that take some practice to maintain. GPS causes people having trouble navigating by a sense of direction, pocket calculators become an excuse not to practice mental arithmetic, MIDI sequencers take over the need to practice instruments, and now AI will take over some aspects of artistic creativity. I understand and expect that the creativity will be applied in other ways, at other levels of abstraction, but some skills will be at risk of atrophy.
Another downside is the reliance on big data and corpora of existing music or art, copyrighted or not. There is the so-called ethical problem of machine learning being applied to virtually any material available on the internet, without express permission, or even neglecting prohibitions against its use. But the reuse or rehashing of existing corpora is likely to produce interpolations of what already exists, instead of something radically different. If "creative" applications of AI take over in artistic circles, then there is less incentive to create something from scratch, by more laborious methods, and instead rely upon the AI tool to create its remixes.
I am aware of the high energy demands of machine learning on big data sets and I do consider it an additional, important reason to refrain from it or to be judicious in its use. I have also written on the topic in a previous glog post, from a slightly different angle:
Remember that I work in a wide range of media, each with very different impacts on the environment. With prints on paper it's quite easy to reason about resource consumption. The printing technique itself is fully mechanical, a printing press is a durable machine that lasts for many decades. Digital or electronic art and music, on the other hand, is harder to assess in terms of material, waste, and energy consumption. Clearly, most computers do not last that long (mine is miraculously alive after nine years) and then need to be replaced, implying toxic e-waste and mining for rare earths and other minerals. I am also aware that the electric power consumption of a laptop is not negligible.
Artist careers demand international presence, attending festivals, conferences, exhibitions. I have to a very large extent abstained from such activities and avoided air travel. This has been in part motivated by a concern about environmental impacts, which I have been aware of at least since the 1980's.
No. That would be nearly impossible, as well as a distraction. In particular, I do not keep track of my working hours. Such bookkeeping would only imply additional unproductive work. Artistic practice must have fluid boundaries between work and leisure.
A few things were mentioned in response to question 5, such as cutting down on long-distance travel. If CO2 emissions were the only thing to worry about, it might be enough to follow the advice of Wynes & Nicholas [1]: to fly less, avoid having a car, avoiding eating meat, and having fewer children. Unfortunately, the narrow focus on CO2 emissions tends to distract from other looming catastrophes, such as biodiversity loss, fresh water shortage, top soil depletion and quite a few others. It may not be immediately obvious how artistic practices relate to this big picture, except that advanced technology (as well as some simpler technologies such as coal fueled plants) and the demands of economic growth are generally detrimental.
I think the response to this situation is very astutely formulated as a compromise, because as I have mentioned, building an artist career demands an international presence, and it demands networking, not only by traveling but also by using the big corporate internet (social media, as opposed to the smolnet). Moreover, in music and art, the use of shiny new technologies tends to introduce a novelty value that translates into artistic value. It can be seen in the insistence on acousmoniums with 50-100 loudspeakers, the increasing image resolution in video for immersive effects, or in art's interest in science and technology for its felt relevance to the present state of society. I tend to see in it an unwitting propaganda for technology optimism. In stark contrast to this, there is arte povera, and performance traditions that require nothing but the physical presence of the performer.
As for myself, I have already committed to a few of the possible compromises, and I could take it further by turning even more to slow/simple technology. Raising awareness is also important, although pointing fingers and moralising will only put people on the defensive. I think it is better to quietly withdraw from whatever harmful circumstances one can.
[1] Wynes, S. and Nicholas, K. (2017). The climate mitigation gap: education and government recommendations miss the most effective individual actions. Environ. Res. Lett. 12 074024.
Some of my work is quite extensively documented, such as my algorithmic compositions and certain videos. Perhaps it needs to be said that documenting the _process_ toward the finished work is different from documenting the final work. The process might involve missteps, discarded attempts, failures that leave no trace in the finished work. It's not always interesting to document every little sketch that ends up in the trash can. In works involving programming, I modify the program at each iteration and don't keep backups of each earlier stage.
Some works are easier to reduce to sharable data or accounts about the creative process and artistic goals than others. Those that are difficult to speak about tend to involve more intuitive or spontaneous practices.
I often write commentaries in blog/glog format, sometimes I publish articles. Source code for several of my algorithmic compositions is available from here:
Detailed descriptions of these works are also available from links on the same web page.
That's _how_ I share information about my (algorithmic) work. But perhaps a more pertinent question would be: Why share source code at all? For me, the purpose is not so much to allow anyone to recreate exact copies of my original compositions. That wouldn't be possible, anyway, because I have submitted the output of the programs to post-processing and, in some cases, the output is different on each run. The reason for sharing the source code has more to do with archival preservation, the conceptual aspect of these pieces, and the hope that an audience of composers and programmers might find something interesting in the source code to build on.
I prefer not to think in terms of responsibilities, but rather about actions and consequences. As I have argued above, there are significant downsides with the artistic use of AI, in particular that involving models trained on large data bases of other artists' creations. But, at the very least, it would be good practice to avoid data bases sourced without the express permission of each involved creator.
Responsibility, again. It would be useful to distinguish moral and legal responsibility, given that these might not agree. However, as AI regulations largely remain to be put in place, we can only reason about responsibility in subjective moral terms. And who gets to decide whether or not "an AI artwork" has caused any harm? Cases of near intellectual property rights infringements by stylistic cloning of existing works are easily imagined, but the more systemic and subtle forms of harm I have mentioned (and also alluded to in this questionnaire's interest in environmental impacts) might be obscured by the focus on IP rights and other forms of harm to individuals.
Thus far the questionnaire.
A kind of manifesto by the title "Technological Futures: A Letter to the Smolnet" calls for rejection of generative learning models, and more generally aim at reducing resource footprints, and increasing repairability, all very sound principles.
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