May 17, 2024

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

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

These models enable the program to react to human opponents as opposed to simply following the predefined paths of movement or actions. Nowadays, these models are used in many industries such as the gaming 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 every input is associated with a particular output. The model learns about the connection between the inputs and the outputs by using the instructions given in the training system. Thus, it enables the model to make predictions and choices using new data.

Unsupervised Learning

In the unsupervised learning process, unlabeled datasets are provided to an AI model. Hence, the model has to discover the hidden patterns or structures in the data by itself. This technique is usually adopted in the areas of dimension reduction, clustering, and abnormality 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 that it gets over time while interacting with the environment. This method is about the software, algorithms, and other technological methods that are commonly used in gaming, robotics, and autonomous systems.

Deep Learning

This 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) fueling the modern chatbots, operate on deep learning. However, at this present stage of development, deep learning requires a lot of computational power.

Potential Applications of AI Models

Let's have a look at the likely applications of Artificial Intelligence in the blockchain and crypto industry;

Predictive Analytics

Artificial Intelligence models can analyze trends and previous cryptocurrency price data to forecast future trends in prices and spot possible trading opportunities. This is known as predictive analytics. Predictive analysis in cryptocurrency helps investors make better decisions, improve existing strategies, and adjust where necessary during very volatile periods.

Risk management can also be effectively done using AI. AI helps to track and forecast different risks, like volatility or liquidity, by analyzing massive datasets in real time. With real-time valuable information, trading AI bots can adapt/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 proves to be a safeguard against fraud. The AI authentication strategies employed are hard to crack, hence, the possibilities of unauthorized access are significantly reduced. 

But that's not all. AI-based anti-fraud systems are also applied in real time to supervise cryptocurrency transactions which are the main source of suspicious activities and harmful security threats as well as to detect fraudulent transactions. Through the use of 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 upcoming trends in the technology world today. They aim to maximize the use of blockchain and eliminate intermediaries, thus 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 enhanced 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 employed to improve lending protocols, forecast 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 are able to;

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

Market Sentiment Analysis

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

Investors' sentiments are extracted from databases such as Reddit, Twitter, and news articles, and analyzing such data helps to 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 make real-world assets into digital tokens, which will be tradable and holdable in fractions on a distributed ledger platform. In addition, AI algorithms help to manage assets, rebalance the portfolios, and to take investment decisions based on risk preferences and predefined criteria.

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

• Ocean Protocol ($OCEAN)
• SingularityNET ($AGI)
• Fetch. ai ($FET)

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