Robotics and Automation in Computer Science Keynote Template

echnology learning (TL) and artificial intelligence (AI) are closely related fields that have significantly advanced in recent years, revolutionizing various industries and everyday life. Artificial intelligence refers to the broader concept of machines being able to carry out tasks in a way that we would consider "smart," while Technology learning is a subset of AI that involves the use of algorithms and statistical models to enable machines to improve their performance on specific tasks through experience. AI encompasses a wide range of technologies, including rule-based systems, natural language processing (NLP), robotics, and computer vision. ML, on the other hand, focuses on developing models that learn patterns from data, allowing systems to make predictions, classify information, or even generate new data based on learned experiences.

At its core, machine learning can be categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, models are trained on labeled data, meaning the input data is paired with the correct output. This is commonly used in tasks like spam detection, image classification, and speech recognition. Unsupervised learning, however, deals with unlabeled data, where the system tries to identify hidden patterns or groupings, often used in clustering or anomaly detection tasks. Reinforcement learning is a feedback-based system where an agent learns to take actions within an environment to maximize cumulative rewards, commonly used in robotics and game playing.

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