AI & Crypto: These three areas are worth following

Decentralization’s MLOps, distributed hardware, and blockchain-based traceability solutions pave the way for a more decentralized and inclusive future for AI.

By: io.net

Compilation: Alex Liu, Foresight News

Artificial intelligence has rapidly become one of the most centralized forces in the world. Developing and deploying artificial intelligence requires a large amount of resources — including substantial capital, advanced computing power, and highly specialized talent. Of course, only organizations with the most abundant funds have the ability to invest in cutting-edge infrastructure and attract top talent, while smaller enterprises struggle to keep up.

In traditional circumstances, MLOps (Machine Learning Operations, machine learning training) is controlled by large organizations, which internally manage everything from data collection to model training and deployment. This closed ecosystem monopolizes talent and resources, creating significant barriers for startups and small companies.

One of the most exciting ways to challenge this centralization in blockchain is to support Decentralization, permissionless AI models. By using a distributed community to protect, verify, fine-tune, and validate every stage of the deployment process of LLM (Large Language Models), we can prevent a minority of participants from dominating the field of artificial intelligence.

io.net is closely following the intersection of artificial intelligence and blockchain, identifying three key areas that can reshape the landscape.

Distributed MLOps

In traditional MLOps, large tech companies have the upper hand. They have a resource monopoly on talent and operate everything internally. On the other hand, decentralized MLOps uses blockchain and token incentives to create a distributed network, allowing for broader participation throughout the entire AI development lifecycle.

From data labeling to model fine-tuning, the Decentralization network can scale more effectively and fairly. The talent pool can be adjusted according to demand and complexity, making this approach particularly effective in professional fields where talent is usually concentrated in well-funded companies.

In the case of CrunchDao, they built a Kaggle-like Decentralization model where AI talents could compete to solve problems for trading companies. As specific data sets become more prevalent, companies will increasingly rely on these talent networks to provide “people in the loop” to supervise, fine-tune, and optimize. Another project, Codigo, is using a similar approach to build a Decentralization network of encryption developers who earn tokens to train and refine cryptocurrency-specific language models.

Distributed Hardware

One of the biggest obstacles to the development of artificial intelligence today is access to cutting-edge GPUs, such as Nvidia’s A100 and H100. They are crucial for training large artificial intelligence models, but their cost is prohibitively high for most startups. Meanwhile, companies like AWS are entering into direct transactions with Nvidia, further limiting access for small enterprises.

That’s why we need blockchain-based decentralization models like io.net. By allowing people to monetize idle GPUs, whether they are located in data centers, cryptocurrency mining facilities, or even game consoles, small companies can access the necessary computing power at a very low cost. It is an alternative to traditional cloud providers that is permissionless and cost-effective, without the risks of censorship or high fees.

Distributed Traceability

As Balaji Srinivasan said, “Artificial intelligence is an abundant digital product, Cryptocurrency is a scarce digital asset; AI generates, Cryptocurrency verifies.” As AI models increasingly rely on novel, private, and even copyrighted data, and with the growing threat of Depth falsification, ensuring data source and proper permission becomes even more important.

When it comes to artificial intelligence models that train on protected data without proper consent, copyright infringement is a serious issue. This is where the Decentralization tracing solution shines. By using a transparent, Decentralization ledger of blockchain, we can track and verify data throughout its entire lifecycle (from collection to deployment) without relying on centralized institutions. This adds an extra layer of trust, accountability, and respect for data rights, which is crucial for the future development of artificial intelligence.

Conclusion

The fusion of artificial intelligence and blockchain technology provides an exciting new way to challenge the centralization threat in artificial intelligence development. MLOps, distributed hardware, and blockchain-based traceability solutions for Decentralization are all playing a role in creating a fairer and more scalable artificial intelligence ecosystem. These models allow for dynamic talent networks, utilization of idle computing resources, and ensure data reliability, paving the way for a more Decentralization and inclusive future for artificial intelligence.