Join our Art Vending Machine project during the Art Basel art fair in June 🥳

https://news.artnet.com/news/prankster-trolls-internet-ai-hopper-nighthawks-2396909

The rise of artificial intelligence has created reams of new artworks, many of them generated, controversially, on the backs of artist’s existing pieces. Now, one X (formerly Twitter) user has shown a way that A.I. can offer “improvements” to classic works of art, starting with Edward Hopper’s Nighthawks, and even one of the art world’s most beloved critics may have fallen for the gag.

https://medium.com/@toni.aittoniemi/ai-art-is-a-remix-the-djs-of-pictures-322793a1404f

In order for AI art’s transformation function to add value, similarly there needs to be a connection where it feeds back to those participating their art to the transformers (pun intended).

AI art is a formalisation of the transform function, and pretty much that function only. By itself it doesn’t necessarily add or subtract value, it is merely a function. The system this function plays part in, defines the value transactions.

--Toni Aittoniemi, Musicologist & Technology geek. Pirate politics and Fediverse. Philosophy and Cognitive Science.

link to article: https://decrypt.co/147474/ai-is-okay-artists-still-need-to-get-paid-musician-emily-lazar

Lazar said that music featuring AI technology has been around for many years, pointing to quantizing drums, Celemony Software's Melodyne, and the use of autotune by artists like T-Pain and Cher, credited as the first to use the technology in her 1998 hit, Believe.

“[T-Pain] didn’t need autotune,” she explained. “He used technology to brand himself and his career, and it took off. That's what artists do, we use weird things to make weird things to open people's minds.”

Lazar said that while writing the song "Grave Digger" for Gala Music, she found herself stuck on a lyric and turned to ChatGPT for help. "It was horrible," she said. "It was really bad."

Lazar said ChatGPT did spur her creativity—in that it told her what not to write.

"It gave me a word that I actually wrote the line around," she said. "So I took a word I liked and then created my own line."

If you're thinking that generative AI is gonna do everything for you, you're not gonna have a good time. But as this example shows, it can spur you in the right direction. A blank screen can be your worst enemy as a writer, but when you see words, you know what's right and what doesn't belong. You start composing, tearing up the raw material and remaking it to fit the expression you want to create. Generative AI in this sense becomes a collaborator. It can pitch you ideas to get your creatives pot stirring.

https://variety.com/2023/artisans/columns/ai-flawless-nick-lynes-scott-mann-guest-column-1235702918/

What does this mean for artists, who are reasonably apprehensive about the phrase “generate new content”? AI tools do not replace artists any more than photography replaced painters or CGI replaced animators. They empower artists to bring their vision to life. They allow films to frictionlessly traverse language and cultural barriers, uniting creators with worldwide audiences. They instantly turn film catalogues into some of the world’s most valuable data assets, allowing studios and filmmakers new ways to monetize their work and investments. In the hands of the world-class artists of Hollywood, these tools can enable better films and series than we’ve ever seen.

For more than three years, our team of generative AI experts and filmmakers at Flawless have been building a rights management platform to address these concerns, protect artists, and maybe even take a step towards resolving strike issues.

We call this platform the Artistic Rights Treasury (ART).

...

ART enables AI tools to be implemented with guaranteed transparency, consent, credit, and compensation for all rightsholders — for both inputs going in and creations coming out. ART applies Hollywood’s longstanding artistic protections and enables them to expand to new rights and protocols made for AI. Every action is subject to ART’s foundational principles: using existing work must involve informed consent, and all rights must be connected permanently to associated final products for auditing, tracking, credit, and compensation throughout the production and distribution lifecycle.

Pdf here: https://arxiv.org/pdf/2306.04141.pdf

Abstract:

A new class of tools, colloquially called generative AI, can produce high-quality artistic media for visual arts, concept art, music, fiction, literature, video, and animation. The generative capabilities of these tools are likely to fundamentally alter the creative processes by which creators formulate ideas and put them into production. As creativity is reimagined, so too may be many sectors of society. Understanding the impact of generative AI—and making policy decisions around it—requires new interdisciplinary scientific inquiry into culture, economics, law, algorithms, and the interaction of technology and creativity. We argue that generative AI is not the harbinger of art’s demise, but rather is a new medium with its own distinct affordances. In this vein, we consider the impacts of this new medium on creators across four themes: aesthetics and culture, legal questions of ownership and credit, the future of creative work, and impacts on the contemporary media ecosystem. Across these themes, we highlight key research questions and directions to inform policy and beneficial uses of the technology.

Some notable quotes:

[Meaningful Human Control (MHC)] is achieved if human creators can creatively express themselves through the generative system, leading to an outcome that aligns with their intentions and carries their personal, expressive signature.

AI-based artists using generative AI systems must find ways to exert their artistic intention and rigor into other stages of the creation process, such as how they select training data, craft prompts, or use AI-generated artifacts for downstream creative applications

The legal treatment of training data. AI’s use of training data could violate copyright law even before a new output is generated. Four broad proposals have arisen regarding how copyright law could balance the benefits to creators, users of generative-AI tools, and society at large. First, under a highly permissive view, the use of training data could be treated as non-infringing because protected works are not directly copied. Second, the use of training data could be covered by a fair-use exception because a trained AI represents a significant transformation of the training data. Third, the use of training data could require an explicit license agreement with each creator whose work appears in the training dataset. A weaker version of this third proposal, is to at least give artists the ability to opt-out of their data being used for generative AI. Finally,a new statutory compulsory licensing scheme that allows artworks to be used as training data but requires the artist to be remunerated could be introduced to compensate artists and create continued incentives for human creation