Potential Applications of AI Models in Blockchain and Cryptocurrency

May 17, 2024

Artificial Intelligence models are computational algorithms that aim to mimic human intelligence. Without strict programming instructions, these algorithms can identify patterns and make predictions and decisions by learning from available data. These AI models typically use machine learning and neural network techniques to extract useful information from large amounts of data.

So, an AI model can make decisions or predictions on its own and not simply copy human intelligence. The emergence of AI began with the beginning of successful computer programs for playing chess and checkers developed in the early 1950s.

These models allow the program to react to human opponents instead of simply following predetermined paths of movement or actions. Nowadays, these models are used in many industries, such as the gambling industry, which has developed online blackjack worldwide.

Types of AI models

Supervised learning

In supervised learning, the AI ​​models are trained on labeled datasets, in which each input is associated with a specific output. The model learns about the relationship between the inputs and the outputs using the instructions given in the training system. This therefore enables the model to make predictions and choices using new data.

Unsupervised learning

In the unsupervised learning process, unlabeled data sets are provided to an AI model. The model therefore has to discover the hidden patterns or structures in the data by itself. This technique is commonly used in the areas of dimension reduction, clustering and anomaly detection.

Reinforcement learning

Reinforcement learning is a trial-and-error learning method between an AI agent and the environment. In this case, the agent is instructed to maximize the total reward signal it receives over time while interacting with the environment. This method deals with the software, algorithms and other technological methods commonly used in games, robotics and autonomous systems.

Deep learning

It is a branch of machine learning that uses man-made neural networks with deep architectures to learn complex data representations. It has proven excellent in various tasks including speech recognition, visual recognition and natural language processing.

The most sophisticated AI applications based on this wave, namely the large language models (LLMs) that power the modern chatbots, operate on deep learning. However, at this current stage of development, deep learning requires a lot of computing power.

Potential applications of AI models

Let’s look at the likely applications of artificial intelligence in the blockchain and crypto industry;

Predictive analytics

Artificial intelligence models can analyze trends and past cryptocurrency price data to predict future price trends and spot potential trading opportunities. This is known as predictive analytics. Predictive analytics in cryptocurrency helps investors make better decisions, improve existing strategies and adjust where necessary during highly volatile periods.

Risk management can also be done effectively with the help of AI. AI helps detect and predict different risks, such as volatility or liquidity, by analyzing massive data sets in real time. With real-time valuable information, trading AI bots can adjust/adjust portfolios to cut trades/share to minimize losses.

Fraud detection and security

AI has made the authentication methods better, more user-friendly and more secure with facial recognition, fingerprint scanning and voice recognition.

The introduction of these features in cryptocurrency wallets and transactions appears to be a safeguard against fraud. The AI ​​authentication strategies used are difficult to crack, therefore the possibilities of unauthorized access are greatly reduced.

But that’s not all. AI-based anti-fraud systems are also applied in real-time to monitor cryptocurrency transactions which are the main source of suspicious activities and harmful security threats, as well as detect fraudulent transactions. By using the latest machine learning algorithms, the blockchain networks can improve their security level and thus the probability of cyber and financial fraud can be significantly reduced.

Decentralized Finance (DeFi) and Smart Contracts

These are two emerging trends in the technology world today. They aim to maximize the use of blockchain and eliminate intermediaries, thereby enforcing a more efficient and transparent financial system.

AI-based smart contract platforms are a good example of the AI ​​applications in the crypto industry. Through the automatic execution of the financial contracts and the transactions, the trust among the traders is strengthened and the peer-to-peer interactions become more fluid without the need for intermediaries.

At the same time, in the field of DeFi, Artificial Intelligence models are being used to improve lending protocols, predict trends and evaluate creditworthiness. As a result, these models have facilitated the development of new financial products and services.

For example, the Solano blockchain realized the potential of an AI chatbot to facilitate the user experience around decentralized finance. With the plugin, users can;

Transfer tokens Trade non-fungible tokens (NFTs) Investigate transactions Interpret public account information. Search for NFT collections cheap on the Solana blockchain.

Market Sentiment Analysis

Artificial intelligence models can be trained to analyze sentiment on social media, news articles and other sources of information to estimate market and investor sentiment regarding specific cryptocurrencies or blockchain projects.

Investor sentiments are extracted from databases such as Reddit, Twitter and news articles, and the analysis of such data helps make wiser trading decisions. The sentiment analysis will be a key source of the information needed to deal with the market dynamics, investor behavior and the possible trend points.

Tokenization and asset management

Artificial intelligence-based digital tokenization systems can easily turn real assets into digital tokens, which will be tradable and tradable in fractions on a distributed ledger platform. In addition, AI algorithms help to manage assets, rebalance the portfolios and make investment decisions based on risk preferences and predetermined criteria.

Today, there are so many crypto AI tokens and projects launched in the crypto market that have rewarded investors incredibly. The following is a list of some of the most famous AI crypto tokens:

Ocean Protocol ($OCEAN) SingularityNET ($AGI) Fetch. has ($FET)

Above all, AI has unlimited potential to open up new opportunities and drive the adoption and innovation of cryptocurrencies and blockchain. By using AI technology, institutions can detect weaknesses in security and management, increase the efficiency of operations and bridge the gap to the new value creation in finance, management and other fields.