The combination of cryptocurrency and artificial intelligence (AI) is helping to create many practical applications in the real world. A recent example of this is the growth of networks that are not controlled by one central authority, which are used to train AI models.

Projects like Bittensor, Gensyn, and SingularityNET are showing how decentralized GPU power can help with training AI models. Inference helps run programs like chatbots, virtual assistants, and coding helpers. This is the stage where an AI model uses what it has learned.

Inference is becoming very important as AI models become more popular. Recent data shows that the AI inference market is growing quickly, with some reports predicting it will be worth $76. 25 billion this year. This market is expected to be worth $349. 49 billion by 2032.

Most AI training models are still being created by big AI companies like OpenAI, Anthropic, Meta, Google, and xAI. Fortunately, this story is getting better as decentralized networks for training AI models improve.

The Importance of AI Tokens for Learning and Making Predictions

Decentralized inference training is very different from traditional methods. One major difference is that it uses reward systems called tokens to motivate participants.

Luke Gniwecki, who leads AI and blockchain products at SingularityNET and CUDOS, explained to Cryptonews that decentralized networks for making inferences need economic organization, but do not rely on central billing, trust, or management of funds. “Tokens help organize everything,” he said.

Gniwecki explained that “AI tokens” let anyone use them without needing permission. This means that anyone can use computing power with a Web3 wallet, without needing regular payment services.

He said that AI tokens make it easy to see how much you are paying because the cost can be based on each token used instead of a confusing cloud subscription.

Gniwecki mentioned that when more people want AI services, it also makes tokens more useful and boosts the value of the network. “Also, many node operators can get paid fairly for their work that can be checked. ”

Using AI Tokens: $FET and ASI Token

To explain it simply, Gniwecki said that ASI:Cloud is a powerful cloud service that is designed for using AI and making quick decisions. He said that ASI:Cloud lets you use special tokens to access popular training models and offers a lot of GPU resources from around the world.

ASI:Cloud was created by CUDOS working together with SingularityNET. SingularityNET is a platform where people can find and use AI services and make requests to AI models that are decentralized. ASI:Cloud uses the ASI ‘$FET’ token to manage access, payments, and rewards in this shared network for processing information, Gniwecki said.

For example, the $FET token is used for “Inference-as-a-Service. ” This means that developers can use it to pay for AI tasks on shared groups of powerful computers called GPU clusters, and they pay for each piece of output from the AI models.

“Every node helps with computing power handled by CUDOS, while SingularityNET takes care of the processing and organizes everything,” Gniwecki said.

Gniwecki mentioned that the “ASI token” is used to make payments on the platform. The ASI token is the main digital coin for the Artificial Superintelligence Alliance, which is a group of projects working together on artificial intelligence. Some of these projects are Fetch. ai, SingularityNET, and CUDOS.

“The ASI token shows how the costs of making predictions are monitored and paid for using different providers,” Gniwecki said.

TAO and Bittensor

Bittensor is working on another cool project. Bittensor is a network for artificial intelligence that is not controlled by one person or company. It lets developers, miners, and validators share their machine learning models and data on the blockchain.

“TAO” is the main coin used by Bittensor. People earn TAO when their work is found helpful through a system called “proof-of-intelligence. ” TAO can have a maximum of 21 million tokens, and the amount created is cut in half roughly every four years.

Karia Samaroo, the CEO of the publicly traded company xTAO, told Cryptonews that xTAO wants to help Bittensor grow faster by owning and staking TAO. Samaroo said that xTAO is one of the top validators in the network.

“Bittensor is creating a public marketplace for machine intelligence, which is a system where anyone can share their models and earn TAO as a reward for the value they offer,” Samaroo said.

Samaroo thinks that TAO is the main part that keeps the Bittensor system running. It helps measure, reward, and protect intelligence throughout the Bittensor network.

For example, Samaroo said that TAO helps many separate computers work together to process information and make decisions without having a main control.

“Traditional AI relies on data centers that are owned by a small number of big companies. ” Bittensor changes the way things work by creating an open global market. Here, anyone can help by providing computing power, models, or data and get paid directly based on how well they perform. TAO makes intelligence more open and spread out by creating a system that encourages keeping AI accessible, shared, and free from control, he said.

Other decentralized models for making predictions using AI tokens

Gensyn is another system that helps with machine learning in a way that is not controlled by a single company. Gensyn started working in this area early on and released its first litepaper, which explained a plan for decentralized training, in February 2022.

Today, Gensyn brings together data, computing power, and money into one reliable network. This lets users create strong AI systems that work on many different devices around the world. Gensyn is now using its test network.

Jeff Amico, the COO of Gensyn, told Cryptonews that the network will soon introduce its own token. This token will help manage resources, improve security, and ensure that everyone involved has the same goals.

“Good tokens help manage value, trust, and checking information in a decentralized machine learning network,” Amico said. “They are a standard way for people who don’t know or trust each other to trade. ”

Also, Akash Network offers cloud computing that is not controlled by one company, which can be used to set up and run AI models. Most AI programs running on Akash use GPUs to process information. Apps like Venice. ai, which focuses on privacy instead of ChatGPT, use Akash to run their advanced AI models.

“AKT” is the main token used on the Akash blockchain. People who use Akash pay with AKT to access the network, and those who provide services get paid in AKT as well. Greg Osuri, who started Akash Network, told Cryptonews that AKT helps protect the Akash blockchain using a method called proof-of-stake.

“This means that without the token, there is no blockchain and no network,” said Osuri.

He said that AKT offers payment methods and rewards to help get the computing started on Akash. “You know that many people want GPUs right now. To create a network that can compete with companies like Amazon, we really need to offer some token rewards. ”

Problems Related to AI Tokens

Even though decentralized training models have moved from being just an idea to real networks, many of these projects still have a lot of room for improvement.

This happens for several reasons. According to Galaxy’s report on “Decentralized AI Training,” the way people are rewarded and checked is not keeping up with new technology. The report says that only a few networks actually offer instant token rewards on the blockchain.

Gniwecki also mentioned that there are problems with dependability and delay, balancing the system of tokens, checking things for safety, and following rules set by governments.

“For example, if the rewards focus too much on guessing market trends or giving too much to those who supply, the network could become unstable,” he said. “ASI’s method links how much people want tokens to how they are used. ” For example, using a pay-per-token system for making predictions; focusing on how much computer power you use instead of earning rewards.

Gniwecki also said that making sure calculations are done honestly is a big challenge for decentralized inference. He also said that AI tokens working with regular money and business budgets can lead to problems.

“ASI fixes this by using two types of payment systems: one with cryptocurrency and one with regular money. ” “This makes it easier for regular users to access while still keeping a decentralized system for those who are into cryptocurrency,” Gniwecki said.

AI Tokens Will Improve

Despite the challenges, decentralized training models will keep improving.

“Over the next few years, AI will move from being controlled by a few big companies to using open systems that better share important resources like computing power, data, and money,” Amico said.

He mentioned that Gensyn is mainly working on this change using tools like “RL Swarm,” which helps people learn from each other in a training system, and BlockAssist, which is a helper for training.

Gniwecki stated that in the coming year, ASI:Cloud will change from being accessed in a decentralized way to having programmable AI systems.

These changes will make the ASI token more than just a way to pay for things; it will also help with teamwork in AI, sharing models, and running independent AI systems. As the platform grows, it is expected to handle over 3 billion tokens in the first 100 days. Future rewards for staking will be based on verified computing performance.