Artificial intelligence (AI) is powered by several key technologies and concepts. At its core, AI utilizes machine learning and deep learning technologies, which involve training algorithms to make predictions or decisions based on data. These algorithms learn from the data using various techniques, such as supervised learning, unsupervised learning, and reinforcement learning[5][6].
Machine learning involves creating models by training an algorithm on data to enable computers to learn from and make decisions without being explicitly programmed for tasks. This includes algorithms like neural networks, which are modeled after the human brain's structure and process complex data[5][6]. Neural networks are especially effective in recognizing patterns and relationships in large datasets.
Deep learning, a subset of machine learning, uses multilayered neural networks called deep neural networks. These networks automate feature extraction from large datasets, enabling machines to learn from unstructured data and make accurate predictions[5][6].
Additionally, artificial intelligence systems incorporate technologies for natural language processing (NLP) and computer vision, allowing machines to understand and respond to human language and identify objects in images and videos[5].
Overall, the development of AI relies on combining these technologies to enable systems to simulate human learning, comprehension, problem-solving, decision-making, and creativity[5][6].
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