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Three massive AI tokens are coming together – here’s how and when
Fetch.ai, Ocean Protocol and SingularityNET have finalized the details of theirs planned merger to create the Artificial Superintelligence Alliance (ASI).
The newly combined ASI token, which would have a market capitalization of $5.8 billion based on the respective token valuations as of May 28, represents the largest open source decentralized network focused on the artificial intelligence sector.
The merger, which will be finalized on June 13, will rename FET to ASI and provide EthereumERC-20 tokens based on ERC-20 to allow users to exchange their FET, AGIX and OCEAN tokens with ASI via a secure and controlled token migration agreement. This will enable the pipeline of tokens into ASI from over 200,000 total token holders from the constituent communities.
Since each project has a different market capitalization, different conversion rates have been set. FET will be rebranded as ASI, with a total supply of 2.63055 billion tokens and a talk rate of 1:1 from FET. Meanwhile, AGIX tokens can be linked to ASI at a rate of 0.433350:1, while OCEAN tokens will convert at a rate of 0.433226:1.
Once the migration is complete, the Artificial Superintelligence Alliance could become the most prominent decentralized AI ecosystem in the cryptocurrency industry, with a wide range of use cases appealing to both large enterprises and home users.
“This merger paves the way for a new era in artificial intelligence, joining our forces to achieve unprecedented progress,” Ben Goertzel, CEO of the future ASI Alliance, said in a press release.
The fusion of the research, brands, technologies and products of 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 the development and deployment of ‘AI.
Humayun Sheikh, CEO and co-founder of Fetch.ai, shared with Decrypt his insight into the driving force behind this ambitious merger and how it could impact the AI development scene:
“With this merger, we are looking at three distinct product lines. First is a network of agents and accompanying tools that make it easy to develop, connect, and deploy agents in commercial contexts. Second, we are focusing on commercial implementation of neural symbolic LLMs and the framework that supports them, as well as facilitating communication between AI layers, via agents or directly. Finally, we are exploring the implementation of data as an independent solution for sharing and using data data.”
This 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 technology and enable widespread access to cutting-edge AI platforms and vast data sets. Looking ahead, the ASI Alliance has set clear goals for the next two to three years, with three key objectives.
The first involves in-depth research “that recognizes the need to explore and develop new AI, AGI and ASI techniques to keep pace with rapid obsolescence,” Sheik shared. The second goal focuses on deploying heavy computing resources for the development and use of artificial intelligence and expanding its hardware infrastructure. And the end goal is more forward-looking, focused on scaling the business model by “leveraging the synergy between data and compute, recognizing them as the twin pillars of our commercialization efforts,” she said.
The ASI token will run across the combined decentralized AI network, providing potentially unprecedented scale and power in terms of a Web3 network. Users wishing to harness the combined power of the Artificial Superintelligence Alliance will have to pay in tokens instead of going through a centralized entity, giving users the power to decide when to join or leave the network.
“This includes two main aspects: computation for training models and computation for providing inference. [We’re] leveraging the combined power of these three elements for powerful commercial deployments, offering a synergy that has not yet been fully realized,” Sheik explained to Decrypt.
This covers a lot of ground for the ASI team to explore. In AI, the training phase is a one-time process in which the model learns everything it should know, like a student in a classroom. This is how artificial intelligence activities begin. This requires a lot of computing power to process a huge amount of data, establish their connection and learn to predict new outcomes based on the knowledge database.
But after the model is trained on a dataset, it enters the inference phase where it applies the knowledge it has learned to analyze new, unseen data and produce results. Here’s how end users benefit from the model. Therefore, the model learns during training and thinks during inference. While AI training is a one-time process, inference is continuous and involves real-time data processing, which can be expensive in terms of both computing power and energy consumption.
Therefore, “inference delivery” focuses on the end user, while “training model computing” is more of a business model where ASI provides the infrastructure that other developers use to build their AI products.
The leaders of Fetch.ai, Ocean Protocol and SingularityNET appear eager to formally launch the ASI and welcome additional strategic collaborators to the Alliance in the near future.
“The ASI Alliance will be a game changer for the adoption of Web3 for AI and data,” Bruce Pon, founder of Ocean Protocol and director of the ASI board of directors, said in a statement. “We have worked through many details to ensure the process runs smoothly and we look forward to formally launching the ASI.”