Learning Unit 8 - Intelligent Information Systems

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  1. 3. is a key component of AI, where algorithms are designed to learn from data without being explicitly programmed. Through training on large datasets, machine learning models can identify patterns and make predictions or decisions based on new input. Deep learning, a subset of machine learning, involves the use of artificial neural networks that mimic the structure and function of the human brain.
  2. 6. refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the development of computer systems that can perform tasks that typically require human intelligence, such as speech recognition, problem-solving, decision-making, and visual perception.
  3. 7. NLP algorithms can extract meaning and context from text, including tasks like sentiment analysis, entity recognition, and text classification.
  4. 12. Word embeddings are numerical representations of words that capture semantic relationships between them. Techniques like Word2Vec and GloVe are commonly used to create word embeddings, which enhance NLP models' understanding of language.
  5. 15. NLP models can generate human-like text or speech, including applications like chatbots, language translation, and text summarization.
  6. 16. Agents execute the chosen actions in their environment using actuators or output devices. These actions may include physical movements, generating outputs, or interacting with other systems. Intelligent agents can be found in various applications, including autonomous vehicles, smart home systems, personal assistants, and recommendation systems. They can adapt to changing environments, learn from experience, and improve their decision-making over time.
  7. 17. involves giving machines the ability to interpret and understand visual information from images or videos. This technology is used in various applications like object recognition, facial recognition, autonomous vehicles, and medical imaging.
  8. 18. focuses on enabling computers to understand, interpret, and generate human language. NLP techniques are used in applications such as virtual assistants, language translation, sentiment analysis, and text summarization.
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  1. 1. NLP models can be trained on large datasets to learn patterns in language and make predictions or classifications. Supervised learning, unsupervised learning, and reinforcement learning techniques are often used
  2. 2. Agents gather information about their environment through various sensors or input sources. This could include data from cameras, microphones, or other sensors.
  3. 4. are computer programs that mimic the decision-making processes of human experts in specific domains. They are a type of artificial intelligence (AI) technology that uses knowledge and reasoning techniques to provide advice or make decisions in a specialized area.
  4. 5. Based on their reasoning process, agents determine the best course of action to achieve their goals or objectives. They consider factors such as available resources, constraints, and potential outcomes.
  5. 8. This technique involves determining the sentiment or emotional tone of a piece of text, enabling systems to understand opinions, sentiments, or attitudes expressed by individuals or groups.\
  6. 9. NLP enables systems to search and retrieve relevant information from vast amounts of text data, improving search engines and question-answering systems.
  7. 10. are software entities that can perceive their environment, reason about it, and take actions to achieve specific goals. These agents are designed to operate autonomously and make decisions based on their observations and knowledge.
  8. 11. is a mathematical framework that deals with uncertainty and imprecision in reasoning and decision-making.
  9. 13. Deep neural networks, such as recurrent neural networks (RNNs) and transformer models, have revolutionized NLP tasks, including language modeling, machine translation, and text generation.
  10. 14. Agents use their knowledge and reasoning capabilities to process the collected information and make decisions. They may employ techniques such as logical reasoning, planning, machine learning, or other AI methods.