The Intersection of Crypto and Machine Learning
Cryptocurrencies and artificial intelligence are arguably the two most salient technologies of the 21st century that have captured the mindshare of public and industry alike. The two are often designated as separate developments with diverging use cases, as AI relies on substantial computational power typically provided by centralized data centers, while blockchains offer decentralized computation and privacy but struggle with high computational demands and large-scale storage. On the other hand, they are also in many aspects complementary and synergistic technologies. As machine learning advances, we have also seen a notable increase in AI + Blockchain projects. In 2024, AI-driven blockchain projects have surged, with particular focus on language learning models (LLMs) and privacy-preserving approaches like Zero-Knowledge Machine Learning (ZKML), which are proving to be effective combinations.
How Crypto Projects Can Integrate AI
LLM technology is incredibly powerful due to its ability to comprehend natural language. Developers are leveraging LLMs in two main ways — by delivering precise and up-to-date answers through massive amounts of contextual data processing, and by executing specific tasks through the integration of various agents and tools. Blockchain platforms are increasingly integrating AI functions and models directly into their infrastructure. This will allow developers to perform key machine learning tasks, such as classification, regression, text completion, and AIGC directly on-chain. Such AI functions are then able to be accessed and used via smart contracts.
LLM Use Cases for Blockchain Platforms
Analyze Transactions: Manually analyzing transaction records is a complex and time-consuming process that requires data collection, cleaning, analysis, and even possible coding skills. However, with LLMs, a new approach is now possible. LLMs can analyze and visualize data, enabling users to customize on-chain data analysis. This includes evaluating metrics like win ratios, performance ratios, or any other specific insights users want to explore. Mainstay crypto aggregation and analytics platforms like Defi Llama and Dune have integrated machine learning models to improve their data insights.
Community Building: Governance is a vital aspect of any Web3 community, as DAOs give members the ability to vote on proposals that shape the future of the products. However, extensive background information and debates often accompany the decision-making process, making it difficult for members to fully grasp the context before voting. LLM can assist by summarizing the implications of each choice, enabling members to make better decisions. Furthermore, LLMs can be used to develop advanced Q&A bots that synthesize all the available knowledge associated with a project — including presentations, podcasts, GitHub, Discord chats, and Twitter Spaces — then provide guidance to any confused community members.
Network Security: LLM’s reasoning capabilities can be leveraged to identify and filter malicious transactions, effectively serving as a firewall for smart contracts. By inputting an address, the LLM can retrieve all transaction data through a third-party plugin. It then analyzes these records and assesses the likelihood of the address being associated with a bot. This feature can be integrated into DApps that want to detect and prevent bot activity, such as those for NFT sales.
Tracking the Market: It’s no secret that crypto markets are volatile and tend to constantly shift based on the latest news or trends. Web3 KOLs are always sharing new ideas and perspectives, some of which may have significant price impact. Yet it’s unrealistic to expect a human trader to keep up to date with every tweet, update, and newsletter that pertains to their investments. LLM can help by identifying and summarizing the most critical news and insights for any trader’s specific portfolio. The same logic applies to project analysis. Information such as a project’s whitepaper, price history, founder team, and more can be fed into a LLM in order to retrieve a quick breakdown. AI can even help analyze the founder’s personality and it might support or impede the project’s future development.
Bella Protocol’s LLM Research Bot
Bella Protocol has been keen on the trend of LLM expansion into the cryptosphere. That’s why we have delved deep into AI development for DeFi, and partnered with industry leading projects like Phoenix SkyNet and ApeX Exchange in the process. In September 2024, the Bella AI Signal Bot was released to help traders time their market entries on over 13 crypto trading pairs. It has already garnered thousands of subscribers who can utilize 5 different machine learning models to generate straightforward long and short signals delivered directly through Telegram. As the next step to our AI initiative, Bella will also be launching our very own LLM Research Bot to offer qualitative analysis that supplement the AI Signal Bot. Users will be able to ask and consult the Bella LLM Research Bot on essentially any matter pertaining to real-time crypto markets as well as established Web3 projects. In the future, we plan to provide continuous support for our suite of AI-powered products by integrating expanded data sets and fine-tuning the precision of AI responses.
About Bella
Aiming to help users simplify crypto trading and optimize crypto yields across multiple chains, Bella Protocol now offers a powerful suite of streamlined tools, including the AI-powered Perpetual Trading Signal Bot, a zkSync-based yield protocol, and a Uniswap V3 simulator.
The latest AI product, Bella Signal Bot, is an AI-driven trading assistant that empowers users with real-time market insights, offering long, short, and close signals based on advanced AI models. By integrating directly with Telegram, traders can seamlessly receive alerts for their preferred token pairs and execute informed trades with ease, helping them stay ahead of market movements.
The flagship product, Bella LP Farm, is a yield protocol based on zkSync Era, Mantle Network, and Manta Pacific that optimizes returns on liquidity provision. By staking LP tokens on an intuitive portal, users can effortlessly bolster potential earnings with multiple token rewards.
Bella Protocol caters to the needs of developers and quant strategists with a unique offering called Tuner. This programmatic Uniswap V3 simulator enables users to backtest and fine-tune their quantitative strategies on a transaction-to-transaction basis. With the ability to work with arbitrary or historical data without relying on the EVM, Tuner operates independently while fully preserving the exact smart-contract behavior of the intricate design and implementation of Uniswap V3.
Bella Protocol is backed by Binance Labs, Arrington XRP Capital, and several other renowned investors.
For more information about Bella or to join our team, please contact us at contact@bella.fi
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