Artificial Intelligence = Machine Learning?

Artificial Intelligence

This is a branch of computer science specializing in building systems capable of solving tasks at the level of human intelligence or higher. For example:

• following instructions to create new solutions, content, or projects;

• predicting future trends;

• recognizing complex patterns in presented data;

• improving performance over time without additional programming;

• working in a dynamic and unstructured environment.

AI-based systems use mathematical algorithms to work with large data sets, identify patterns in input data, and make decisions or predictions based on them. But before the system is ready for real-world use, AI must first undergo extensive training.

AI systems are trained on large data sets containing appropriate examples. During training, the system adjusts its parameters to improve the accuracy of processing or analysis. After sufficient training, AI can process new, previously unknown data in the same format as the files on which the model was trained.

The most impressive advantage of AI technologies is that they can be used to automate repetitive tasks.

Common AI application scenarios:

• analysis of medical images (X-rays, MRI) and patient data for disease diagnosis;

• assessment and prediction of financial risks based on historical data;

• recommendation of products to users based on browsing history, preferences, and shopping behavior;

• analysis of transaction patterns to detect fraudulent activities;

• navigation of autonomous vehicles, object recognition on the path, and decision-making while driving;

• assessment of assignments and exams;

• creation of music, artworks, and articles based on user recommendations;

• resume checking, interview scheduling, and assistance in recruitment.

Types of Artificial Intelligence

Theorists distinguish two types of AI: weak (or narrow artificial intelligence (ANI)) and strong (or general artificial intelligence (AGI)).

Weak AI is designed to perform one or a narrow range of tasks in a limited context. It excels at performing well-defined and repetitive tasks. Examples of ANI:

• speech recognition systems;

• spam filters;

• recommendation systems;

• image recognition in self-driving cars;

• predictive text input and auto-completion functions in smartphones and text editors;

• stock trading algorithms.

Weak AI operates based on predefined algorithms and rules. It cannot perform tasks outside its domain.

Strong AI (AGI) is called a hypothetical system that will demonstrate universal intelligence similar to human. Unlike weak AI, strong AI will have the ability to understand, learn, and apply knowledge in various fields. As a result, it will be able to perform cognitive tasks without special training. Strong AI remains a theoretical concept for now.

Machine Learning

This is a class of algorithmic methods by which computers perform tasks based on patterns and logical inferences. Training these algorithms allows creating machine learning models - programs that take previously unknown input data and come to certain conclusions.

All machine learning models perform one of two main types of tasks: classification or regression.

Common tasks for ML models:

• classification of files into different categories or classes;

• data analysis to identify hidden trends or anomalies;

• making continuous forecasts;

• grouping similar data points into clusters.

ML Algorithms

Types of ML algorithms:

• Regression algorithms (e.g., linear, polynomial, ridge, and lasso regression). They predict a continuous outcome based on one or more input variables.

• Classification algorithms (e.g., random forests, SVM, decision trees, KNN method). They classify input data into predefined classes.

• Clustering algorithms (e.g., k-means, hierarchical, and DBSCAN). They group similar data points into clusters to find natural groups in the data.

• Dimensionality reduction algorithms (e.g., LDA, PCA, and t-SNE). They reduce the number of features in the data while preserving as much information as possible.

• Reinforcement learning algorithms (e.g., Q-learning, deep Q-networks, policy gradient methods, and Actor-Critic methods). They interact with the environment and gather feedback in the form of rewards or penalties.

• Deep learning algorithms (e.g., CNN, RNN, LSTM, and GAN). They use deep neural networks (DNN) to model complex patterns in the data.

Differences between AI and ML

Although there is much in common between ML and AI, these two fields are undoubtedly different from each other.

Goals

The main goal of creating AI is to develop machines that mimic human cognitive abilities, such as:

• reasoning;

• learning;

• problem-solving;

• perception;

• natural language understanding;

• context understanding;

• decision-making in each specific case.

On the other hand, the main goal of AI model creators is to enable computers to learn and make predictions or decisions. That is, to create systems that automatically detect patterns, extract ideas, and generalize data to perform classification and regression tasks.

Learning Methods

AI encompasses a variety of learning methods:

• Symbolic learning (logic-based approaches). It involves the use of explicitly defined rules, logic, and symbolic representations to perform reasoning and decision-making. The focus is on human-created knowledge bases and inference rules for problem-solving and decision-making.

• Subsymbolic learning (data-based approaches). It involves the use of statistical and computational methods to learn patterns and representations directly from data. This approach relies on neural networks and AI models that improve their performance as they gain experience.

• Hybrid learning approaches. They combine symbolic and subsymbolic learning.

The main learning methods in AI include:

• Supervised learning. Uses labeled data during model training. Each piece of input data is matched with a corresponding output, and the model learns from the correlations between input-output pairs.

• Unsupervised learning. Relies on unlabeled data. The model must find patterns and relationships without relying on predefined input-output labels.

• Semi-supervised learning. Combined use of labeled and unlabeled training data.

• Reinforcement learning. Allows the algorithm to interact with the environment and receive penalties or rewards based on performance. The model learns by figuring out how to maximize cumulative rewards over time.

Implementation

Implementing rule-based AI systems begins with defining a comprehensive set of instructions and a knowledge base. This initial step requires significant input from highly skilled experts who translate their knowledge into formal rules.

When implementing more advanced AI systems, it is often necessary to combine various methods, such as:

• symbolic AI;

• neural networks;

• probabilistic methods for dealing with uncertainty;

• evolutionary algorithms for optimization and adaptation to changing conditions;

• fuzzy logic for handling imprecision and reasoning with uncertain or vague information;

• swarm intelligence for solving problems through the collective behavior of decentralized agents.

The implementation process often involves integrating various AI components and ensuring their smooth operation. This integration is complex as it involves different technologies and algorithms that interact and complement each other.

The implementation of machine learning is more focused on data-driven approaches. The first step of implementation is to collect large amounts of information related to the problem being solved and preprocess it.

Then, it is necessary to choose the appropriate ML algorithm, depending on the problem (e.g., classification, regression, clustering).

After selecting the algorithm, the next step is to train the model. This involves feeding the preprocessed data into the model so that it can learn patterns and relationships within the data. During training, it is necessary to ensure that the model is learning correctly.

After training, the model is tested on a separate dataset to evaluate its accuracy and generalization ability.

Requirements

AI systems require a high level of developer competence. They must use various methods to build models, and systems often require a combination of several approaches to handle perception, reasoning, and learning tasks.

AI also needs frameworks for building logic and reasoning to achieve structured intelligent behavior.

Some systems require relatively moderate computing power, but more complex ones, such as those using neural networks or running large simulations, require significant hardware resources. Specialized equipment and advanced forms of computing are standard requirements in this case.

The most important thing for machine learning is data. The success of models largely depends on the quantity and quality of the initial information. Significant computing resources are also required.

Human expertise is also important in machine learning. All ML projects require the participation of research analysts who are proficient in statistical methods (in the English-speaking world they are called data scientists). They are responsible for various tasks, including data preprocessing, selecting appropriate models and tuning their parameters, and monitoring the training process. Human expertise is also necessary for effective feature engineering and model interpretation. In addition, ML administrators must be well-versed in specialized tools and frameworks.

What to study: artificial intelligence or machine learning?

It depends on individual interests, career goals, and the type of work you want to do. Both fields offer exciting opportunities and are foundational for future technologies. Here are a few points that can help with the choice:

• AI covers a wider range of topics, including machine learning, robotics, expert systems, natural language processing, etc. The wider coverage makes it more difficult to master this field, but it provides more career opportunities.

• ML is more data-oriented. This is a great option if you enjoy working with large amounts of information, statistics, and algorithms.

• AI requires deeper knowledge beyond traditional computer science.

• Machine learning requires familiarity with fewer methods and scientific fields.

• If you are interested in such overlapping areas as psychology, neurobiology, and computer science, then artificial intelligence will be a more suitable choice.

You can start learning with ML, as it is a fundamental component of AI. This will provide a solid foundation for studying the broader aspects of AI in the future.

Can you study AI without machine learning?

You can, although then a fundamental part of the technology will be missed. Here are areas related to artificial intelligence that are not particularly related to ML, up to working on more advanced projects:

• Symbolic AI (logic-based approaches). These systems rely on predefined rules for decision-making rather than ML models.

• Classical AI methods that do not rely on machine learning.

• Expert systems. They mimic the ability of a human expert to make decisions by following a set of rules provided by domain experts. In most use cases, there is no need to tune an ML model.

• Automated reasoning. This area involves developing algorithms for solving tasks with logical reasoning, theorem proving, and constraint satisfaction (CSP).

Many aspects of artificial intelligence can be studied and implemented without delving deeply into machine learning. However, given the growing importance and applicability of ML in AI, having some knowledge of ML will still improve the overall understanding of AI.