Categories
Art Crypto Projects

Certificate of Inauthenticity

“Certificate of Inauthenticity”, 2020, ERC-721 Tokens.

Provably inauthentic art.

A follow-up to my collaboration with Furtherfield.

Find out more here:

https://robmyers.org/certificate-of-inauthenticity/

Or buy here:

https://opensea.io/assets/certificate-of-inauthenticity

Categories
Shows

Crypto Manifold

2020.6.27 – 2020.10.25

Chronus Art Center (CAC)
BLDG.18, No.50 Moganshan RD., Shanghai

“crypto_manifold presents artists’ multifaceted exploration and affective investigation of the panoptic application of blockchain technology.”

Featuring !Mediengruppe Bitnik, CHEN Baoyang, Simon Denny, Grayson Earle, Sarah Friend, Marija Bozinovska Jones, Paul Kolling & Max Hampshire & Paul Seidler, Matthias Tarasiewicz, Lina Theodorou & Rob Myers

Details here:

http://www.chronusartcenter.org/en/cac-exhibition-crypto_manifold/

Curators Bi Xin and Cao Jiamin have done a wonderful job of creating an installation of Lina’s awesome illustrations for Bad Shibe, with the story readable on a tablet as part of it.

They also got Lina and I to make short video statements explaining how we approached writing and illustrating the story, which was fun.

The show is open in Shanghai until October.

Categories
Aesthetics Crypto

Aesthetic Comparison Games

Using ideas from design theory, provable computation, and calculus we can construct games of aesthetic comparison with arbitrary precision. These games can be represented in a form that allows them to be resolved using blockchain smart contracts via reference to materials submitted to set up the game, by reference to on-chain precedent, or as a last resort by appeal to a third party oracle.

Game Setup

To construct the game, two parties must agree on two images, on the properties of the elements of those images under consideration, the relationship between them that is under consideration, and the degree of that relationship.

▲ = ▲ ?

The properties must be represented electronically and atomically, for example as RGB colours, as extremely small pixmaps, or as simplified bezier curves. The hashes of these values are then arranged as the leaves of a merkle tree for each image, in lexicographic value. A third three is then constructed by the first party of the hashes of tuples of pairs of leaves from each of the first two trees, the name of the relationships that is held to be true between them, a stated tolerance for deviation from simple mathematical equality in that relationship, and the weight of these properties as evidence in the aesthetic assertion being made accorded to that relationship (this must sum to 1.0 for all leaves). A threshold for property significance is declared (e.g. 0.001), this may be updated in later rounds of the game with mutual agreement or by appeal.

These trees are then combined with the root of two further trees – the precedent tree and the adjudicator address tree – to produce the merkle root of the game setup. It is vitally important that both parties agree on the representation of each image contained in the tree and on the tolerances and weights accorded to elements from them. It is trivial to make green into red with a high enough tolerance for colour difference, for example. Tools to automate preflight tests for game trees will be important.

For a multi-stage merkle tree acceptance phase, use rounds of committing/revealing proposed trees with increasing stakes. Accepting a tree returns the stakes. There may be a time or round limit for this phase, or no hard limits on agreement only exit rules.

Compulsory/voluntary comparison games may require different agreement, comparison and appeal phases to avoid griefing. Or a single well-understood workflow with well-understood and clearly described failure modes in each contect may be easier to reason about and therefore ultimately more reliable.

Game Rounds

Once the game root has been registered, the comparison proceeds in rounds of assertions made with reference to the content of the subtrees that the root anchors.

If the comparison can be made automatically (e.g. #FF0000 == #FF0000), this proof can be offered onchain. An uncontested assertion of this form wins.

Example comparison relations and properties include: =, ≠, ≈, ≉, ⊂, ⊄, ⊃, ⊅, <, >, geometric affine transform, colour difference, freeform textual statement.

Beware of image content when comparing. Steganographic information may mislead automated comparison.

Where statements can be phrased equivalently, the one that would place the lowest value on the left branch should be used.

Unrealistic trees, e.g.

  ▲
 / \
◯   ▧

can be rejected by submitting a contradictory precedent or an appeal if evaluation is binding, can be replaced with a more accurate proposal in a multi-stage MAST acceptance opening phase for a comparison game, or simply not entered in to if an evaluation is not binding.

If comparisons can be reduced to precedents, cite them. This means that if a comparison has been resolved in a previously successfully completed comparison, submit the merkle path of that proof and the merkle path of the properties that it matches in the properties tree instead of starting an appeal. An uncontested assertion of this form wins.

If either party wishes to reject an assertion they can provide the merkle path to a contradictory assertion.

Appeals

If the content of the game tree root is exhausted by assertions without a simple winner emerging, either player may attempt to establish a new prededent by assembling a pair of merkle paths from the property trees of the attacker and the defender, staking a pre-agreed amount of value that will be forfeited if they lose the appeal, and sending the appeal to a third party tribunal implemented using prediction markets, an ombudsman oracle DAO, or some other means.

The outcome of the appeal becomes available as a precedent for future games.

Game Outcome

Ultimately a winner will emerge, in which case they can exercise the right granted to them by a pre-game commitment to update a DAO’s state or receive an amount of cryptocurency or some other action that a proof of resolution can enable. Or both parties can co-operate to declare a winner or a draw before that, either returning any stakes, burning any commitments, or co-operating to exercise the commitment that the winner would have been able to exploit.

Categories
Aesthetics Art Crypto

Intensive and Extensive Aesthetic Property Token Composition

ERC-721 tokens can be composed into tree structures using ERC-998 tokens. Where those ERC-721 tokens represent images or image elements, that tree structure becomes a rendering tree or two-dimensional scene graph (three-dimensional scene graphs will have to wait for 3D Rare Art standards to solidify). To lay out the elements of the image we must be able to transform them in various ways, changing their position, size, colour and other intensive and extensive aesthetic properties. We can represent these aesthetic properties as ERC-20 tokens with 18 digits of precision as they are continuous quantities.

To apply these properties to an ERC-721 token we can attach them using an ERC-998 composable tokens in an SVG-style tree hierarchy. Each ERC-998 token has one or more quantities of ERC-20 aesthetic property tokens attached, one or more child ERC-998 tokens, and zero or more (usually zero or one) ERC-721 tokens attached. The properties expressed by the ERC-20 tokens attached to each ERC-998 token are applied to any attached ERC-721 token(s) and transitively to the children of any attached ERC-998 tokens.

Where the values we wish to represent should be limited to a given range (e.g. 0.0 .. 1.0 or 0 .. 255), we can either assert if too many tokens are are sent to be attached to ERC-998 tokens (we might also be able to refund them in an additional transaction, but this would affect the gas required – and as per the ERC-20 standard we should not accept fewer tokens than are sent), we can treat higher values as meaning the maximum (e.g. 3.1 is 1.0, and 1337 is 255), or we can scale values relative to the largest quantity.

When the values must be both positive and negative (for example if we are representing co-ordinates around an origin, especially relative co-ordinates in a group hierarchy), we can use a second token to represent negative values (this would be better represented using ERC-1155 tokens but ERC-998 does not support this standard). If both positive and negative tokens are applied their values should be summed. For co-ordinates we can use only positive tokens by treating group origins as their top left rather than their centre and only adding positive offsets to child ERC-998 tokens.

Affine transformation ERC-20 tokens are applied as a transformation matrix to the children of the ERC-998 token they are attached to. This means that children-of-children multiply their parent matrix with their own. There is an implicit graphics state push/pop for each ERC-998 token, so transformations do not affect sibling tokens, only child ones.

For colour or alpha (transparency) values, these values are added to the colour values of the image represented by the token. This may not be the expected behaviour. As with co-ordinates, using only positive values can be achieved by carefully structuring the hierarchy of the image so that child ERC-998s only need to add rather than subtract colour values to represent their colour scheme. Alternatively we can treat colour tokens as scale, allowing both increases and decreases of colour to be expressed across the token hierarchy, and source primitive forms to be arbitrarily coloured if they are white. More complex colour interactions and other filter or layer behaviours could be specified by additional tokens.

This gives us the following ERC-20 tokens:

X x co-ordinate offset values in distance units.
Unbounded.
Y y co-ordinate offset values in distance units.
Unbounded.
WIDTH width in distance units.
Unbounded.
HEIGHT height in distance units.
Unbounded.
ROTATION rotation in degrees.
Unbounded, wraps around past 360 as usual.
RED red scale.
Unbounded, although values that multiply the source value higher than 1.0 will have no additional effect.
GREEN green scale.
Unbounded, although values that multiply the source value higher than 1.0 will have no additional effect.
BLUE blue scale.
Unbounded, although values that multiply the source value higher than 1.0 will have no additional effect.
ALPHA transparency scale.
Unbounded, although values that multiply the source value higher than 1.0 will have no additional effect.

What is to stop the owner of an artwork created in this way from breaking it up, re-arranging it, or adding to it?

Nothing at all…

Isn’t this an extremely expensive way of assembling art on-chain?

It depends on the value of the work that is made using it…

Categories
Art Art History Artificial Intelligence Crypto Philosophy

AI Art, Ownership, Blockchain

Questions of “ownership” in art can be a matter of law, of social norms, or of art theory. New art forms and new methods of producing art can fall foul of existing answers to these questions or creatively re-open them. Often they do both. “AI Art” produced using contemporary “Artificial Intelligence” artificial neural network software is a good example of this. “Rare Art” produced using blockchain token software is another, which we will consider below in relation to one particularly notorious example of AI Art.

“Portrait of Edmond Belamy” was produced by the artist group “Obvious” using an existing artificial neural network model trained on a corpus of images of classical paintings. Obvious did not credit the author of that network model, or any of the artists whose paintings were included in the corpus. At this point there are already three different layers of questions around ownership.

Firstly, the assembly of an image corpus. Accurate reproductions of paintings that are no longer in copyright should not attract copyright, and in the US at least this is quite rightly the case. A collection of such reproductions may attract copyright on the collection itself, but this should not affect individual works within the collection. If the images were of paintings that are still under copyright, copying each image might infringe that copyright. I say “might” because doing so might fall under fair use/fair dealing (hereafter just “fair use”) exceptions to copyright. These exceptions are popular both with artists who work with appropriation and with large Internet companies who work with search and advertising. Both groups, and others such as Digital Humanities scholars, might wish to assemble such corpora of images so it is difficult to generalise about motives and outcomes regarding them. In the case of art, however, fair use for artists is a key defence of artistic creativity in an age where the visual environment is dominated by corporate media.

Beyond this legal view is the ethical and art theoretic view. Is it right to treat individual artworks in a corpus as tokens of a type or as just part of a set, as fodder or as raw material for an industrial process? With apologies to Clement Greenberg, does discarding the tactile elements of painting still meaningfully capture it, and does discarding visual detail and differences in scale discard more for processes of derivation than processes of study?

Secondly, the training and use of the artificial neural network model on that image corpus. The model is trained by processing images in the corpus, by copying and reading their data. The model will contain representations of parts of the images from its training corpus, and its output will also resemble parts of the images they are trained on. Mechanical copying and creating derivatives of images are covered by copyright. Cutting up images and juxtaposing them with the work of other artists is covered by the moral rights that accompany copyright. Again, copyright does not apply to works that are out of copyright (moral rights vary by country…), and artists should have a claim to fair use of such materials. The degree to which artistic use of source materials transforms them should be a factor in establishing that such use is indeed fair use, and the output of artificial neural networks certainly transforms the images that they are trained on. Style is not copyrightable (let’s not talk about “trade dress” here), but forgery and “passing off” can be legal matters, and the application an artist’s signature style to a work that they have not made but is sold under there name is the same whether performed by human hand or algorithm.

Again, the ethical and art theoretic view raises more questions. Signature styles are a matter of pride as well as profit for artists, and while this can be critiqued within art theory it is a strongly established norm that simple imitation of style, without a critical framework for doing so, is a breach of artistic norms. Artificial neural network models need not operationalise an individual artist’s signature style in order to devalue the concept of signature styles in general.

Thirdly, Obvious’s use of existing neural network software to generate an image has caused widespread debate. “Signing” the image with the algorithm used by the artificial neural network software to produce it functions as a double-bluff whatever the intention behind doing so. We know that Obvious produced and sold the image, their attribution is not threatened by this. But it erases the work of both Robbi Barret in producing the model of art that the image is simply a product of and of the artists that the neural network’s model already erases the authorship of, both in terms of attribution and in terms of control of their work (even if from beyond the grave in the case of the corpus artists). Software authors should not be able to control uses of the tools that they produce – Microsoft should not be able to censor your writing using Word or claim joint authorship of everything you write using it. But a trained artificial neural network model is a more complex thing than a text editor from this point of view – it is as much content as it is tool. Microsoft should not be able to tell you how you change the contents of an empty text document, but changing a novel or a painting whether represented physically or digitally may infringe on the copyright and moral rights that it may have. Again fair use should be strongly considered for artistically transformative use of artificial neural network models.

Art theroretically, such direct and uncredited use of existing materials, even materials created by another artist, may count as appropriation art, which is an established category within the arts. Appropriation art is deliberately transgressive, often for critical effect. Appropriating non-art or low-art materials is very different from appropriating canonical art or the art of leading contemporaries but both can be critical moves. Artistic labour can be appropriated directly, in the case of contemporary artists who use studio assistants to produce art under their own signature such as Jeff Koons or Damien Hirst. The signature that the products of this labour are exhibited and sold under is a key part of its erasure. And where software used to make art is free software(/open source), attribution may or may not be a strong social norm but past a certain point that attribution is useful information to have for artistic, critical, and art historical engagement with the work.

Prior to this, Obvious had already encountered the question of ownership and found an answer that led directly to “Portrait of Edmond Belamy” being sold at auction. That answer was based on AI’s twin in contemporary technological hype, the blockchain.

Christie’s discovered Obvious via their work on Superrare, a blockchain-based “Rare Art” platform. Rare Art is named after the “Rare Pepe” project that developed the techniques of using cryptocurrency and blockchain token technology to record limited edition certificates for digital images. This produces “artificial scarcity” and allows a form of ownership for pieces of digital art art that would otherwise be infinitely reproducible. This use of certificates as ownership proxies for art was pioneered by conceptual art. Compared to a flammable piece of paper with a handwritten signature on, the authenticity of a blockchain transaction secured by a not inconsiderable fraction of the world’s computing power each day only increases over time. It may not be entirely clear what the authenticity is of, but the terms and conditions of Rare Art platforms and the community norms of their users and consumers do produce a vivid image of a novel and very strong concept of ownership.

“True digital ownership” on a blockchain secured by cryptographic keys is seen by its proponents as stronger, more trustworthy, and more absolute than previous conceptions of property. This makes AI art a natural fit for Rare Art because each has needs that the other fulfills: ownership in the case of the products of AI art, strongly perceptible uniqueness but also recognisability as art in the case of Rare Art. Sale at auction also provides this kind of closure for the financial value of art, but new art and in particular digital art faces a bootstrapping problem in which it must establish its value in order to be sold at auction but cannot be sold at auction without first establishing its value. Christie’s saw art by Obvious selling on Superrare and could react to that market signal more quickly and with lower risk than with signals from gallery or online sales of physical goods.

It is a truism of International Art English that art questions things. There are many questions in play in both AI Art and Rare Art. They involve the concept of ownership considered in terms of the law, of social norms, and of art theory. The answers to these questions from within each of these realms individually may be obvious and simple to their practitioners, but between them they may be more at odds than each realises. Negotiating this without closing the door to cultural creativity or opening it to corporate exploitation is a task that is of interest far beyond the artworld.

(I am not a lawyer, etc.)

Categories
Crypto

Oracles Are The Oracle Problem

In computer science, an “oracle” is a source of truth from outside the system. On a blockchain, this means that oracles provide information that is not part of the transaction protocol. This can be the price of the US dollar, the weather in a particular location, whether a particular celebrity is still alive, or other facts that are not simply protocol-level transfers of coins secured by cryptographic signatures.

The introduction of truth that cannot be enforced at the protocol level or checked entirely by reference to the prior history of the blockchain means that oracles introduce a question of trust. Like the economic trusted third parties that Satoshi Nakamoto sought to exclude from the original Bitcoin protocol – who are better understood as treacherous third parties (to channel Richard Stallman’s critique of DRM for a moment) – oracles introduce trusted third parties for knowledge. Since demand for the information that oracles provide is ultimately economic, this amounts to the reintroduction of economic trusted third parties.

Various mechanisms can be used to address the risk of trust in oracles. Reputation on- or off-chain, or economic incentives enforced using different rewards and punishments, for individual providers or communities or markets providing information. Each scheme has its failure modes, and each ultimately requires trust in the behaviour of off-chain participants to act in an economically rational manner.

Oracles are unavoidable for a large class of problems but where they can be creatively avoided it is worth exploring alternatives. The use of token trade volumes and prices over time in DeFi applications to establish interest rates is a good example of this. Where on-chain facts cannot be tautologies, if they can be inferred from other on-chain facts this will be more robust than oracles if those facts cannot easily be manipulated. Ideally this means protocol-level facts, or at least facts with robust on-chain incentives for truthfulness.

Given this, “the oracle problem” is not how best to implement trusted oracles. It is the existence of oracles. Let’s continue to find creative ways to extract off-chain information from on-chain truth.

Categories
Philosophy

It All Sounds The Same

In the early 1990s, on a show called “A Stab In The Dark” that was a disastrous attempt to revive the TW3 format, the comedian David Baddiel demanding that audience members name random acid house tracks played over the studio PA. One embarrassed young man eventually helped Baddiel out by admitting that he couldn’t.

“It all sounds the same to me” is a dismissive and often reactionary comment. But it is also a judgement and an account of identity or rather of its lack. It renders something indifferent.

In a review of an album by the electronic music outfit Autechre on The Quietus website, reviewer Charlie Frame was faced with the opposite problem when they wrote:

It would now take a machine with a capacity and patience far exceeding that of any mortal being to keep track of their increasingly arcane song-titles alone, which are deliberately alienating in their anonymity, as though they’d been randomly selected from sections of a printer test page. I’d wager Autechre themselves have trouble differentiating between their ‘Chenc9-1Dub’s and their ‘Nth Dafusederb’s…

Whether Autechre or acid house, and whatever you call it, electronic music is clearly different from, say, Shostakovich. And the first and second Autechre tracks, and the two acid house tracks, will have differences when played one after the other. You cannot identify precisely which track is which compared to the other if you are just dropped into them midway through, you may not be able to find them afterwards, you certainly won’t be able to name them, but when faced with them you would be able to tell what is different about them, even if only that they do not occupy the same moment.

You can also tell what is different between two tracks by Autechre and two classic acid house ones. You don’t even need to know that they are Autechre or acid house. Each track is different from the other in the pair, and the differences between each track in each pair are different from the differences of the other pair. If not in their immediate sound then in their production or some other property. It’s the same with the music events that these tracks were and are played at. Each event in a series of events is different from the others, and each series of events also has different differences from the other series.

This is Deleuze’s “Difference and Repetition”. Differences, differences between series, differences of differences, and repetitions that make the differences. I have named the things that are different here, but if we remove the names the point stand. It is not removing the names that removes the identities. Rather it is recognizing that the identities are neither necessary nor sufficient to identify what is named here.

If you don’t believe me, just listen to Autechre on shuffle. 😉

Categories
Crypto Philosophy

Why Bitcoin is Money According To Marx

tl;dr: whales.

In “Marx on Money“, Suzanne de Brunhoff describes the theory of money that Karl Marx presents in “Capital”.

Money, for Marx, emerges in three stages prior to capitalism.

In the first stage, gold becomes a measure of the value of all other commodities rather than simply one commodity among many.

In the second stage, gold coins become the medium of circulation. Once gold becomes de-materialized in this way its role can then be occupied by fiat currency.

In the third and final stage, the emergence of hoarding paradoxically introduces money “proper”.

de Brunhoff writes that “Hoarding is a demand for money as money…”, an interruption in the circulation of money that “…serves to ceaselessly preserve and reconstitute the money form as such, whatever the deformations, transformations, and disappearances it undergoes as a result of the other two functions. Produced by these, it becomes in its turn a condition of their functioning.”

In the crypto space, hoarders are known as whales. They remove their coins from circulation with the expectation that this will ultimately provide them with more utility than immediately spending them. In this they act just like hoarders of gold coin or fiat currency. To quote Marx, “The money becomes petrified into a hoard, and the seller becomes a hoarder of money.”

Whales therefore establish that cryptocurrency is money according to Karl Marx.

Categories
Crypto

Staking Planes

=========o========= – Art
=========/========= – Media
========oo========= – Sensate
=========o========= – Geodata

Stake conceptual dimension as well as spatial position.

Build on lower level stakes (…assertions). Sensates must be placed on geodata, media and art above that.

Lose stake to challenger if your assertion is bumped to another plane (including the Plane of Falsity).

Appeal to Aesthetic Comparison Games, or use pure stake?

Categories
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.)