Aesthetics Art Art Computing Artificial Intelligence Uncategorized

Contemporary “AI Art” In Context

The “AI” used by current “AI Art” is machine learning – recursive neural networks or linear regression if you want to deflate it. These algorithms are not “artists”, they are tools or faculties. Harold Cohen’s long-running AARON project, software written under the previous AI paradigm of “expert systems” was an apprentice or studio assistant. Its use of explicit written rules also makes it a form of discourse. Machine learning could be used to produce digital muses but for the most part AI inflates menial work rather than deflating the status of the artist or their inspiration.

Appropriating a GAN is appropriation art and, ignoring the legal status of appropriation art and the political question of who-appropriates-whom, can be evaluated as such. Appropriating kitsch or canonical high art is a critical move. The critical value of appropriating the art of peers is less clear. Art GANs have at least a claim to the status of art or artistic materials. The producers of it have at least a claim to the status of artists. To treat the products of the GAN as found objects and the GAN’s algorithm as their author is a conceptually provocative move but its precedents lie in the erasure of skilled labour in the work of Koons and Kostabi.

GANs produce pastiches and AST produces interpretations. These are robust art historical categories and are hardly unprecedented. Art that falls into these categories should not be fetishised or rejected based merely on a misapprehension of novelty.

An AI-generated pastiche is of something that (almost certainly) does not exist. This non-existence may consist in several senses:

  1. The image produced does not exist in the training set.
  2. The image produced does not exist in the oeuvre, genre, movement or medium that the training set draws from.
  3. The image did not previously exist and exists only as this image. This is trivial compared to the other senses but it the sense of existence usually meant.
  4. The entities depicted by the image do not exist in reality.
  5. The entities depicted by the image have never existed in the arrangement or event depicted.

An AI-generated interpretation is of something that (almost certainly) does not look like that interpretation.

  1. Where the interpretation is of photographic imagery (in the last moment of its popular acceptance as a mechanical capturing of reality) the results will not resemble it due to the imposition of the distortions and modulations of artistic style.
  2. Where the interpretation is of one artist’s work in the style of another, the results will not stylistically resemble the source work. This is trivial but it usefully illustrates the level at whist AST operates.

At the level of content the introduction, removal, or alteration of subjects and themes is approached more by Deep Dream’s “puppyslugs” than by other contemporary methods. Even then it is a Surrealist’s idees fixes that intrude from the AI’s “subconscious” into every image rather than a freer or more reflective play of concepts and influences.

The current tools of AI art fit neatly into the history of artistic tools and art theory but begin to problematize them.

  1. Historical styles being competently revived may no longer simply be forgery or quotation.
  2. Influence (and at the level of law, copyright infringement) becomes both mechanically explicit and operationally diffuse.
  3. The impact of AI on art is an automation of production, replacing manufacturing jobs the same as in other industries.
  4. The opacity of artist’s explanations of the construction of their work is doubled, as the artist is now using tools that perform actions for reasons that may be opaque to them.

The technology used in contemporary AI art is that which threatens democracy with facial recognition and deep fake images, video and text. Its explanatory opacity (why does the image look like this, which exact sources did it draw on, etc.) can be addressed by the same systems that are being developed to address the need to explain the operation of algorithms within corporations, law enforcement and other powerful organizations if they are to remain accountable. So this entanglement can be critically and politically positive where it is acknowledged and explored.

Current AI art works at the level of style, in the shallows of form. To extend their reach through the realm of form more profoundly and into subject and content is possible with current tools should we choose to do so. This may require more complex pipelines of generation, classification and search but these can be constructed within the same frameworks that current systems are.

The operation of GANs tends to produce art with a compositional scheme of all-overness, for the composition as a whole and for any object (rarely objects) within it. This has a deconstructive effect, deterritorializing an image corpus and reterritorializing it in novel compositions that find new local maxima in the dissolved state space of the corpus’s images. These images are latent in the corpus, generated from within it but lying outside of it. The local sense but global nonsense of markov chains and dreams. The challenge of a new metastability, but only of a new metastability.

Now, about AI curation, collection and critique…

(With thanks to Cynthia Gayton and Seryna Myers.)

Art Art Computing Generative Art Projects

Like That 2020 (3D)

“Like That” is a generative art project that started in 1996 as a series of 2d image generators and 3d animations called “The Order of Things”. It drew on the aesthetics of then-contemporary British art (Julian Opie, Rachel Whiteread, Art & Language, Bridget Riley). The 2d works in the series were written in the PostScript programming language. The 3d pieces were written in Metrowerks CodeWarrior C++ on Macintosh System 7.x using QuickDraw 3D, their source code has unfortunately long since been lost.

In 2008 I re-implemented and extended the project using Processing. That version incorporated more historical references and I renamed it “Like That”, a reference to a phrase one of my children had used as a general purpose assertion as a toddler. In 2009 I generalized Like That using a script written in Common Lisp to glue together fragments of Processing code into many different combinations of shapes, colours and movements.

In 2019 most web browsers no longer easily support the Java programming language that Processing is based on and Processing’s JavaScript replacements are either already deprecated or too different from it to make porting simple. I have therefore ported the 3D Processing code to THREE.js (I’m still thinking about whether to port the animated 2d code). Going from platform-specific compiled applications to cross-platform bytecode and then to scripting languages has been the technical journey of much of software development over the same time period.

I still find Like That visually and conceptually engaging so I was glad to be able to update it to add some contemporary references and keep it running.

Project page:


Source code:

Art Art Computing Crypto Ethereum Projects Shows Virtual Reality

Galerie Default

I created a building in CryptoVoxels using one of their default build templates and filled it with a show of Tokens Equal Text:

I’ve named it Galerie Default after how it was made. You can take a look in your web browser via the link above (and if you have a fancy VR headset you’ll soon be able to wander around it immersively). There are much more advanced uses of the CryptoVoxels system to show NFT art within it, but this was a fun experiment.

Art Art Computing Artificial Intelligence Generative Art Shows

Hacking Creative Composition at CADAF

I’ve a couple of pieces at CADAF in New York with Kate Vass Gallerie (above is one of the giclées, “Local Maxima: SFLT2, Square” (2019)):

Creative Crypto have a profile of me ahead of the event, from which I’ve stolen the title of this post:

Art Art Computing Crypto

Blockchain Aesthetics: Dogecoin and Ethereum

Two new sets of visualizations to complement the Bitcoin ones: Dogecoin and Ethereum.

You can get them via git, or view them on the Show section of this site.

Art Computing Generative Art Projects



This took a ridiculous amount of time to hack together, but here’s a Common Lisp function to decide the shortest angle between two other angles. It’s used in the example of seeking a point above. I can now add noise to this to make a more AARON-style pen.

(defun shortest-angle-difference (a1 a2)
  "Find the shortest positive or negative distance between two angles."
  ;; This was slowly assembled from various online sources, none of which worked
  ;; Normalize angles to 0..2pi
  (let ((from (mod a1 +radian+))
        (to (mod a2 +radian+)))
      ;; If from and to are equal (0 = 2pi radians) the angle is zero
      (if (or (= from to)
              (= (+ from to) +radian+))
          (let ((angle (- to from)))
            (if (> (abs angle) pi)
                ;; Please simplify me
                (* (- (signum angle)) (- +radian+ (abs angle)))
Art Computing Generative Art Projects

draw-something 2016

draw-something drawing February 2016

I’ve updated the Common Lisp version of draw-something to use modern technologies – Roswell, QuickLisp, ASDF 3, cl-cffi-gtk and the Plan testing library. The tests helped flush out bugs, changing my mis-uses of defmethod to defun silenced a lot of compiler warnings and that in turn helped find some more bugs. There’s now a bit of technical debt in the form of function and class names, I’ll address that later. Like the recent minara update, this is a bitrot update rather than a new feature release.

Running the code to test it reminded me of just how dissatisfied I was with the last version of draw-something. The image at the top of this post is one of the less bad results of running the code. This is an aesthetic / theoretic problem rather than a coding one. I need the same clarity that informed the earlier versions of the program (you can see a JavaScript version of one running on tumblr) in order to structure the code to output something you’d actually want to look at.

Art Computing Free Software Generative Art Projects Uncategorized

Minara 0.4.0


I’ve been making the regular (accidentally) six-yearly update to Minara, my vector graphics program.

The new version switches from GLUT to Gtk for the windowing system, from GLU to Cairo for the renderer, and from C to pure Scheme for the core application. It’s all written in The GNU project’s Guile Scheme system.

Minara is Lisp all the way down: the application, tools, and graphics files are all written in Scheme. It’s designed as an environment for 2D generative vector art hacking.

Aesthetics Art Art Computing Culture Politics Projects

Art For Algorithms


My first article for Furtherfield as guest editor is now online:

Rob Myers takes a look at how we can subvert the operation of the algorithms that the Digital Humanities, corporations and governments use to read, see, and draw conclusions about human expression by treating them as the true audience for contemporary art and literature.

Art Computing Crypto Projects

Easier Dogecode

I’ve added a Dogecode runner that uses‘s API rather than requiring a local dogepartyd instance to be running. You can get the runner as part of the Dogecode source code here: And you use it like this:

dcrunw DFibwNZvuJaHM9bD6x1WA63urkHiE4tWzF

which will fetch the program encoded as Dogeparty tokens at the specified address (DFib…) and run it locally. Here’s some addresses to try: