How AI is Driving the Future of Blockchain and Digital Assets
Although artificial intelligence has been a computer science discipline for many years, over the past decade there has been a revolution of fitting AI to work on graphic processing units of all levels, from enterprise-grade hardware down to common gaming-oriented peripherals found in home computers. This unprecedented level of access to the technology has enabled both solo enthusiasts as well as developer groups to bring their own AI-enabled offerings to the community.
The role of AI in digital assets trading
Although AI is widely used in traditional finance to make trading decisions, the 24/7, always-on, and borderless nature of digital assets makes its use even more imperative. Unlike with traditional finance, DeFi doesn’t have downtime or holidays where human traders can take a breath and analyze a situation while their trading world is effectively on hold. AI allows traders to make decisions on staggering amounts of data such as market conditions, prices of asset pairs, real-world geopolitical actions such as governmental regulations, and new developments within the digital asset world itself among many other factors. All of this enables AI-assisted traders to make more accurate predictions about where the state of any given market will go.
Given that the digital asset world is extremely community-driven (much more so than traditional finance) sentiment analysis also becomes incredibly important in gauging reactions to a new development. Simply scrolling through a social media feed manually is unlikely to give you a clear picture of the community’s overall response to news, as even a truly neutral and impartial platform is going to necessarily give you a curated version of community reactions, to say nothing of one with potential political ulterior motives. Sentiment analysis models instead allow you to gather the totality of opinions on a subject along with their second-hand engagement with other users and weight all opinions appropriately to determine their prevalence within a community rather than trying to simply eyeball a comment feed and making potentially poor decisions in the heat of the moment.
AI algorithms can also accurately ingest and evaluate historical trading patterns, which are extremely important in counteracting the high level of volatility in digital asset trading which can lead to emotional, poorly thought through trades. Context and knowledge of prior trading patterns can help steel the nerves of traders when facing the potential of a sudden bear market. Moreover, AI algorithms can learn from previous trading decisions and adjust their strategies accordingly, which can result in more profitable trades. By leveraging AI technology, traders can mitigate the risks associated with emotional decision-making and increase their chances of success in the digital asset market. It is no surprise that many financial institutions are now investing heavily in AI research and development to stay competitive in this rapidly evolving industry.
Blockchain Applications of AI
Given that blockchain is at its core simply a sequential data ledger, this easy accessibility to a large amount of input has given rise to entire chains devoted specifically to working with AI. For example, SingularityNET is a blockchain network that was built to enable the creation, sharing, and deployment of AI applications. The platform allows developers and companies to share their AI technologies and services through a decentralized network, providing users with unparalleled access to advanced algorithms, models, and data. The reason this is so exciting is it allows users access to AI services without having to invest in GPU infrastructure on their end – even low-end consumer-grade GPUs cost hundreds of dollars, and for some tasks a high-end GPU costing thousands may be required just to get the job off the ground.
On the other hand, projects like DeepBrain Chain, rather than offering access to a marketplace of AI products, provides a cloud computing GPU network that aims to democratize the access to compute power to projects. The platform uses a public chain to enable distributed AI computing with the DeepBrain Coin (DBC) as the essential currency unit. It is used to pay for computing power and data storage on the network, and can also be earned by providing computing resources. By providing a platform for secure and private AI computing, DeepBrain Chain enables these entities to test and develop advanced machine learning capabilities at a fraction of the cost of traditional cloud computing platforms. With DeepBrain Chain, users have access to an extensive range of algorithms and models that can be used to enhance their AI development processes. The platform has already established strong partnerships with reputable organizations such as Microsoft and Siemens, signaling its reliability and potential for future growth as well.
Finally, Fetch.ai is a decentralized platform that aims to bring autonomous machine learning to the masses. It integrates blockchain and AI technology to create an open ecosystem for various parties to collaborate and exchange data in a trusted, secure, and decentralized manner. The platform is designed with multiple use cases in mind, including logistics, energy, finance, and healthcare. With Fetch.ai, users can easily create and deploy intelligent agents that can access and analyze data from different sources to facilitate decision-making and automate tasks. This can lead to increased efficiency, reduced costs, and improved customer experiences. Moreover, the platform rewards participants who contribute to the ecosystem with its native FET token, which can be used to access premium services and pay for transaction fees.
Conclusion
AI clearly has a massive hand in shaping the direction of blockchain and digital assets moving forward, not only through informing trading patterns but by also directing the use of decentralized and democratized utilities to individuals and businesses. With these tools available to us today it is no surprise why many financial institutions are now investing heavily in both AI research and development as well as blockchain technologies to stay competitive in this rapidly evolving digital asset market.
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