The case for Decentralized-AI

A casual weekend conversation with friends on what I call #chaipecharcha(discussion with a cup of coffee) and the topic of bit tensor blew my mind away. Intrigued with this topic started digging into what I have learned about Bittensor AI and why we need to seriously understand the architecture and the challenges and opportunities we have.

What is Bittensor?

Bittensor (TAO) is a decentralized, open-source, and permissionless machine learning network that transforms machine intelligence into a tradable commodity.

Current Limitations of the AI aka Centralized AI

• Data Silos: In a centralized system, data is often siloed within the walls of large tech companies, limiting the ability to harness the full breadth of information available. we either push models to data or need to send data to models which is a very expensive process
• Computational Bottlenecks: The immense computing power required for training and running complex AI models is concentrated in the hands of a few, creating potential bottlenecks and single points of failure. The demand for Nvidia GPU is unsatiable
• Privacy Concerns: The centralization of personal data within the control of a few entities raises significant privacy and security concerns, as evidenced by recent data breaches and misuse scandals.
• Lack of Transparency: Centralized AI systems are often opaque, making it difficult to understand how decisions are made and hindering the ability to hold these systems accountable. we are still digging into explainability.
• Environmental Concerns: We are consuming natural resources at an alarming rate and now increasingly look at solar, nuclear, and yes the 3-mile nuclear power plant shows the hunger for AI accelerated energy demands that the vendors are scrambling for. wonder will start reporting on scope1,2 and 3 emissions.
• Power disparity: AI is giving the power to individuals to dream but ultimately might end up with a few or create an economic disparity and might end up being a wealth distribution problem.

Decentralized AI and the Bit Tensor

The Bit Tensor, a unique architecture data structure that combines the properties of tensors and distributed computing, seems to address the above challenges. By decentralizing the storage and processing of AI data and models, the Bit Tensor enables a more democratized, transparent, and secure approach to AI development.

1. Distributed Data Storage: The Bit Tensor's ability to store and process data in a decentralized manner allows for the aggregation of information from multiple sources, breaking down data silos and enabling more comprehensive training datasets.
2. Scalable Computing Power: By leveraging the computing resources of a distributed network of devices, the Bit Tensor can provide the necessary computational power for training and running AI models without relying on a few centralized data centers. Existing computing can be utilized. However, the need for GPUs for LLM's is not clear to me. Maybe the models could be trained on GPU and moved to distributed mode for production.
3. Enhanced Privacy: The decentralized nature of the Bit Tensor means that personal data is stored and processed locally, reducing the risk of large-scale data breaches and providing users with greater control over their information.
4. Transparent and Accountable AI: The Bit Tensor's distributed architecture enables greater transparency in the development and deployment of AI systems, allowing for increased scrutiny and accountability.
5. Participatory wealth creation: Since the model and data are distributed and are owned by the creator. IP rights and ownership are tightly held by the creator. This helps in wealth distribution and learned a new term called participatory wealth creation.

Path Forward to 2025

As we live in hype cycles, ending the year 2024 as the era of agenticai, I do see a partnership in 2025 of agentic with decentralized AI, and yes Blockchain and AI will be in vogue. This shift towards a decentralized AI ecosystem has the potential to drive innovation, foster greater societal trust, and ensure that the benefits of AI are equitably distributed, serving the interests of all aka participatory wealth distribution or creation.