✘ How to build a Decentralized Future in Generative AI
- Chaos Theory - How to start Google - Why AI systems still confound researchers
Hi all, Mark Redito is back with another great piece. Last time out, he shared his insights on using AI as an artist. As a musician and creative technologist, he’s perfectly positioned to reflect on the impact of AI. Today, he takes you on a journey towards a collectively owned generative AI model. This can be a beacon of decentralization, collaboration, and true ownership by creators. Enjoy the read, his links, and the music he recommends.
Love, Maarten
0. Introduction
In the quest for innovation across art, media, and music, generative AI has emerged as a powerful force. Yet, this power is increasingly concentrated in the hands of a few — OpenAI, Microsoft, Google — shaping societal and cultural landscapes through their proprietary models. This centralization sparks concerns around ownership, privacy, and the equitable distribution of technology's benefits. My proposition? A collectively owned generative AI model can be a beacon of decentralization, collaboration, and true ownership by creators.
Let a Million Generative AI Models Bloom!
As an artist and technologist, I've observed the transformative impact of generative AI and its potential to democratize creative expression. Yet, the current landscape is dominated by large, closed-source models, limiting access and control for individual creators. One solution could be to foster a multitude of fine tuned generative AI models, owned and managed by the creators themselves. This not only challenges the status quo of centralization but also celebrates the diversity of creativity and innovation.
Why Collective Ownership Matters
Collective ownership of generative AI models empowers artists in several ways:
Control and Compensation: Imagine a creative ecosystem where artists maintain sovereignty over their data and creations, ensuring fair recognition and compensation. In this envisioned future, artists who contribute their works to a collectively owned model could see direct financial benefits, such as receiving a share of profits generated from the model's use, whether through subscriptions or other monetization strategies. This model not only facilitates an additional income stream but also contributes value to the creative ecosystem by embodying a collective point of view.
Collaborative Innovation: The introduction of advanced digital audio workstations (DAWs) and VST plugins revolutionized sound design, allowing artists to push the boundaries of music production. Similarly, the integration of MaxMSP with Ableton Live empowered musicians and visual artists to create bespoke plugins and immersive audio-visual performances. These technological advances underscore how collaboration, fueled by state-of-the-art tools, can drive innovation. By working together, artists leverage these technologies to explore new artistic and technological frontiers, enhancing both their collective creativity and individual expressions.
Accessibility for All: The challenge for many artists lies in navigating the barriers to accessing cutting-edge technology, whether due to cost, complexity, or both. In the same way ChatGPT democratized access to Large Language Models (LLMs), artistic-focused collective models aim to dismantle these barriers. This approach makes advanced tools, like bespoke AIs tailored for specific artistic styles, more accessible to every artist, regardless of their technical expertise or financial resources. It's a step towards an inclusive creative landscape where technology empowers all creators equally.
Creative Diversity: Just as the music world thrives on a wealth of genres and traditions, the diversity of collective artist-fine tuned models promises a rich tapestry of styles and perspectives. This abundance fosters a fertile ground for experimentation, leading to the emergence of new genres, styles, techniques, and models of monetization. By encouraging a broad spectrum of creative expressions, collectively owned models not only enrich the artistic community but also challenge and expand the collective imagination. The diversity of these models acts as a beacon, illuminating the myriad ways in which technology can enhance and amplify the creative voice of every artist.
A Vision for the Future
This piece extends an invitation to envision a future where generative AI is not only by and for the creators but also a gift they can offer to the world, reflecting the collective's aspirations and generosity. Drawing from a rich tapestry of ideas and the collective wisdom of the past, my goal is to spark discussions, inspire collaborations, and encourage the actualization of this vision. If this resonates with you, feel free to reach out through my socials (@markredito) or email.
I. Foundations for Collective AI Creation
Before delving into the mechanics of building a collectively owned generative AI model, it's essential to establish a solid foundation. This involves assembling the right mix of creative minds, selecting a suitable base model, and securing the necessary computational resources. Here's what we need:
LINKS
🔰 Chaos Theory (Tina Mai)
“Chaos teaches us to expect the unexpected, but what happens after that is up to us. Proactive people don’t wait for life to happen to them; they make their own luck. Serendipity finds its way into your life when you are always learning, always building, always sharing your work.”
✘ Such a lovely read on luck, chance and exercising our own agency. Beautifully written and insightful.
💡 How to Start Google (Paul Graham)
“Don't feel like your projects have to be serious. They can be as frivolous as you like, so long as you're building things you're excited about.”
✘ My takeaways? Let curiosity and interest guide you. Get good at building out projects and learning technologies.
😵💫 Why AI Systems still Confound Researchers (Ben Brubaker)
“In principle, it’s easy to follow all the simple mathematical operations that collectively generate the network’s outputs. But in large networks, it’s hard to turn all that math into a qualitative explanation of what’s responsible for any given output, and researchers have had little success in pinpointing the role of individual neurons.”
✘ The state of the art AI models that we have today are made up of really dense and complex layers of equations with billions to trillions of parameters (Claude 3 has 100T parameters). On top of that, the stochastic nature of these models makes it really difficult to understand what's going on. This doesn't deter researchers looking for ways to better understand our current systems. It's critical for practitioners of this technology to deeply understand the workings of the system to better shape it into a tool that benefits us all.
MUSIC
That funky bassline and that James Brown-like “Ow!” vocal sample got me on first listen.
This Chaz Bundick (Toro Y Moi) co-written song is my go-to for nights when I feel nostalgic. Those fuzzed-out, wall-of-sound guitars and catchy melodies just gets me.
Earth wind and fire vibes! I had to track where the sample came from and it wasn’t easy lol.
I love how you make actionable, methodical suggestions for how to build the stuff we need, Mark! Great piece