May 17, 2024
Artificial intelligence models are computational algorithms that aim to mimic human intelligence. Even without strict programming instructions, these algorithms can make predictions and decisions by identifying patterns and learning from available data. These AI models typically use machine learning and neural network techniques to extract useful information from large amounts of data.
Therefore, AI models can make decisions and predictions on their own, rather than simply copying human intelligence. The origins of AI began in the early 1950s with the development of successful computer programs for playing chess and checkers.
These models allow programs to react to human adversaries rather than simply following a predefined path of movement or action. These models are now used in many industries, including the gaming industry, which is developing online blackjack all over the world.
Types of AI models
supervised learning
In supervised learning, an AI model is trained on a labeled dataset, and every input is associated with a specific output. The model uses instructions given in the training system to learn about connections between inputs and outputs. Therefore, the model can use new data to make predictions and choices.
unsupervised learning
In an unsupervised learning process, an unlabeled dataset is provided to the AI model. Therefore, models must independently discover hidden patterns and structures in the data. This technique is commonly employed in the fields of dimensionality reduction, clustering, and anomaly detection.
reinforcement learning
Reinforcement learning is a learning method that involves repeated trial and error between an AI agent and the environment. In this case, the agent is instructed to maximize the sum of reward signals it obtains over time while interacting with the environment. This technique concerns software, algorithms, and other technical techniques commonly used in games, robotics, and autonomous systems.
deep learning
This is a field of machine learning that uses artificial neural networks with deep architectures to learn complex data representations. It has been proven to excel in a variety of tasks, including speech recognition, visual recognition, and natural language processing.
The most sophisticated AI applications from this wave, the large-scale language models (LLMs) that power modern chatbots, are powered by deep learning. However, at its current stage of development, deep learning requires large amounts of computational power.
Potential applications for AI models
Let's take a look at the application of artificial intelligence in the blockchain and crypto industry.
Predictive analytics
Artificial intelligence models can analyze trends and previous cryptocurrency price data to predict future trends in price and identify trading opportunities. This is known as predictive analytics. Predictive analytics in cryptocurrencies can help investors make better decisions, improve existing strategies, and adjust as necessary during highly volatile times.
Risk management can also be done effectively by using AI. AI helps track and predict various risks such as volatility and liquidity by analyzing huge datasets in real-time. With the help of real-time valuable information, trading AI bots can adapt/adjust their portfolios to reduce trades/shares and minimize losses.
Fraud detection and security
AI has made authentication methods better, more user-friendly, and more secure with facial recognition, fingerprint scanning, and voice recognition.
Introducing these features into cryptocurrency wallets and transactions has proven to be a safeguard against fraud. The employed AI authentication strategy is difficult to crack, so the chances of unauthorized access are significantly reduced.
But that's not all. AI-based anti-fraud systems are applied not only to detect fraudulent transactions but also to monitor cryptocurrency transactions in real-time, which are a major source of suspicious activities and harmful security threats. The use of modern machine learning algorithms increases the security level of blockchain networks and can significantly reduce the likelihood of cyber and financial fraud.
Decentralized Finance (DeFi) and Smart Contracts
These are two upcoming trends in today's technology industry. They aim to take full advantage of blockchain, eliminate intermediaries, and strengthen a more efficient and transparent financial system.
AI-based smart contract platforms are a great example of AI applications in the cryptocurrency industry. Automated execution of financial contracts and trades strengthens trust between traders and makes peer-to-peer interactions smoother without the need for intermediaries.
At the same time, the DeFi space is employing artificial intelligence models to improve lending protocols, predict trends, and assess creditworthiness. As a result, these models have facilitated the development of new financial products and services.
For example, the Solano blockchain has enabled the potential of AI chatbots to facilitate user experiences around decentralized finance. The plugin allows users to:
- Token transfer
- Trade non-fungible tokens (NFTs)
- Investigate transactions
- Interpretation of public account information
- Find cheap NFT collectibles on the Solana blockchain.
Market sentiment analysis
Artificial intelligence models can be trained to analyze sentiment on social media, news articles, and other sources to estimate market and investor sentiment regarding a particular cryptocurrency or blockchain project.
Investor sentiment is extracted from databases such as Reddit, Twitter, and news articles, and analyzing such data helps in making smarter trading decisions. Sentiment analysis provides a key source of information needed to address market dynamics, investor behavior, and possible trend points.
Tokenization and asset management
Artificial intelligence-based digital tokenization systems can easily create real-world assets into digital tokens that can be fractionated, traded, and held on a distributed ledger platform. Additionally, AI algorithms help manage assets, rebalance portfolios, and make investment decisions based on risk preferences and predefined criteria.
Currently, a huge number of crypto AI tokens and projects have been launched in the crypto market, bringing incredible rewards to investors. Below is a list of the most well-known AI crypto tokens.
- Ocean Protocol ($OCEAN)
- SingularityNET ($AGI)
- fetch. ai ($FET)
Above all, AI offers new opportunities and has endless potential to drive cryptocurrency and blockchain adoption and innovation. Using AI technology, institutions can detect weaknesses in security and governance, improve operational efficiency, and close gaps for new value creation in finance, governance, and other areas.
