What is Artificial Intelligence (AI) ?
Artificial Intelligence (AI) is not a technology in the strict sense of the term. Rather, it is a scientific field in which tools can be classified when they meet certain criteria. However, AI is a set of theories and techniques aimed at creating systems capable of simulating human intelligence. Moreover, it encompasses a wide range of techniques and approaches, each with its own characteristics and areas of application.
5 types of Artificial Intelligence :
- Symbolic Artificial Intelligence: based on the manipulation of symbols and logical rules to represent and solve problems.
- Machine Learning: Machine Learning involves the construction of mathematical and statistical models. These models enable a computer system to learn from data, without being explicitly programmed for each task. This includes techniques such as decision trees (Random Forest) and SVMs (Support Vector Machines).
- Deep Learning: Deep Learning involves multi-layered artificial neural networks, capable of discovering and learning complex hierarchical features in data. It is particularly effective for natural language processing and other tasks requiring sophisticated understanding of unstructured data.
- Evolutionary Artificial Intelligence: This one is inspired by the principles of biological evolution. It uses evolutionary algorithms, genetic algorithms and other techniques. The purpose? Optimize solutions to complex problems by simulating processes of natural selection and reproduction.
- Hybrid Artificial Intelligence: Hybrid AI systems combine several artificial intelligence approaches to solve complex problems. For example, a system may combine symbolic techniques with Machine Learning to leverage the advantages of each approach.
However, these types of AI are not mutually exclusive. Many systems combine several approaches to meet specific application needs. In conclusion, the landscape of AI is diverse, comprising various methodologies tailored to address distinct challenges. As it continues to evolve, the integration and advancement of these diverse approaches promise to push the boundaries of what machines can accomplish, ushering in a new era of intelligent systems capable of addressing the most complex real-world problems.