Three giant AI tokens are merged. Here’s how and when:

Fetch.ai, Ocean Protocol, and SingularityNET have finalized details of their plan to merge to create the Artificial Superintelligence Alliance (ASI).

With a market capitalization of $5.8 billion based on the tokens’ individual valuation as of May 28, the newly combined ASI token represents the largest open-source decentralized network focused on the AI ​​industry.

The merger, expected to be completed on June 13, will rename FET to ASI and create an Ethereum-based ERC-20 token that will allow users to exchange FET, AGIX and OCEAN tokens for ASI through a secure and audited token migration agreement. provide. This will allow tokens to be transferred to ASI from a total of over 200,000 token holders in the constituent communities.

Since each project has a different market capitalization, the conversion rate is also set differently. FET will be renamed ASI with a total token supply of 2,630.55 billion and a 1:1 conversation ratio of FET. Meanwhile, AGIX tokens can be linked to ASI at a ratio of 0.433350:1, while OCEAN tokens are converted at a ratio of 0.433226:1.

Once the migration is complete, the Artificial Superintelligence Alliance could become the most important decentralized AI ecosystem in the cryptocurrency space, with a wide range of attractive use cases from large enterprises to domestic users.

“This merger paves the way for a new era in AI by combining our strengths to achieve unprecedented advancements,” Ben Goertzel, CEO of Future ASI Alliance, said in a press release.

The convergence of research, brand, technology and products from SingularityNET, Fetch.ai and Ocean Protocol lays the foundation for an open and scalable AI infrastructure that leverages blockchain to ensure ethical and trustworthy practices in AI development and deployment. .

Humayun Sheikh, CEO and Co-founder of Fetch.ai, shares: decryption His vision behind the driving force behind this ambitious merger and how it could impact the AI ​​development scene:

“Through this merger, we are looking at three product lines: the first is agent networks and related tools that facilitate the development, connectivity, and deployment of agents in a commercial environment; the second focuses on commercial deployment of the NeuroSymbols LLM and frameworks to support this, as well as facilitate communication between AI layers, either through agents or directly. Finally, we are exploring data distribution as an independent solution for data sharing and exploitation.”

The partnership is designed to challenge Big Tech’s dominance in AI development. The ASI Alliance aims to accelerate the commercialization and monetization of each foundation’s technologies and enable broad access to cutting-edge AI platforms and extensive data sets. ASI Alliance has set clear milestones for the next two to three years with three key goals:

The first involves large-scale research that “recognizes the need to explore and develop new AI, AGI and ASI technologies to stay ahead of rapid obsolescence,” Sheik said. The second goal focuses on deploying large-scale computing resources and scaling hardware infrastructure for AI development and use. And the end goal is to focus on scaling the business model by “leveraging the synergies between data and computing and recognizing them as twin pillars of our commercialization efforts.”

ASI tokens operate on a combined, decentralized AI network, providing potentially unprecedented scale and performance on the Web3 network side. Users wishing to leverage the combined power of the Artificial Superintelligence Alliance will have to pay in tokens instead of going through a centralized entity, allowing users to decide when to join or leave the network.

“This involves two key aspects: compute for training models and compute for inference delivery. (We) want to leverage the combined power of these three elements for robust commercial deployments, creating synergies that have not yet been fully realized. “We are providing ,” Sheik explained. to decryption.

This covers many areas for the ASI team to explore. In AI, the training phase is a one-time process where the model learns everything it needs to know, like a student in a classroom. This is how AI business begins. Processing huge amounts of data, establishing connections, and learning to predict new outcomes based on knowledge databases requires a lot of computing power.

However, once the model has been trained on the data set, it enters the inference phase, where it applies the learned knowledge to analyze new, unseen data and generate results. This is how end users benefit from the model. So the model learns during training and thinks during inference. AI training is a one-time process, whereas inference is continuous and involves real-time data processing, which can be expensive in terms of computing power and energy consumption.

So while “delivering inference” focuses on the end user, “computing to train models” is closer to a business model where ASI provides the infrastructure that other developers use to build AI products.

Leaders of Fetch.ai, Ocean Protocol, and SingularityNET appear eager to officially launch ASI and welcome additional strategic collaborators to the Alliance in the near future.

“The ASI Alliance will be a game-changer in Web3 adoption for AI and data,” Bruce Pon, Ocean Protocol founder and ASI Council board member, said in a statement. “We have reviewed many details to make the process run smoothly and look forward to the formal launch of ASI.”

Editor: Andrew Hayward