AI Terms Directory

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AI is when computers try to think like humans.

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Imagine a robot that can learn, talk, and make decisions—just like a person!

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A type of AI where computers learn from experience.

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Think of how you learn to ride a bike. The more you practice, the better you get!

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A subset of machine learning that uses neural networks with many layers.

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Deep learning algorithms can learn complex patterns and representations from large amounts of data.

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A type of machine learning algorithm that is inspired by the structure and function of the human brain.

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Neural networks are composed of interconnected nodes or neurons that process and transmit information.

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A field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language.

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NLP is used in applications like chatbots, language translation, and sentiment analysis.

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A type of artificial intelligence model that is trained on a massive amount of text data.

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LLMs can generate human-like text, answer questions, and perform a wide range of language-related tasks.

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Data that is used to train machine learning models.

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Training data is used to teach the model how to recognize patterns and make predictions.

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The tendency of AI systems to produce biased results due to their inherent biases.

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Bias in AI can manifest in various ways, such as biased algorithms, biased data, or biased models.

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A field of artificial intelligence that focuses on enabling computers to understand and interpret visual data.

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Computer vision is used in applications like facial recognition, object detection, and image segmentation.

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An artificial intelligence system that can engage in conversation with humans.

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Chatbots can answer questions, provide information, and even have a conversation with users.

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A type of machine learning where an agent learns to make decisions by interacting with its environment.

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Reinforcement learning is used in applications like game playing, robotics, and autonomous vehicles.

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A type of AI that can create new content, such as images, music, or text.

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Generative AI is used in applications like content creation, virtual assistants, and creative writing.

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The phenomenon where a model performs well on the training data but poorly on new, unseen data.

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Overfitting can occur when a model is too complex and captures noise in the training data, leading to poor performance on new data.

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The phenomenon where a model performs poorly on both the training data and new, unseen data.

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Underfitting can occur when a model is too simple and fails to capture the underlying patterns in the data, leading to poor performance on new data.

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A technique in machine learning where a pre-trained model is fine-tuned on a new dataset.

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Transfer learning can save time and resources by leveraging the knowledge gained from a pre-trained model on a related task.

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A technique in machine learning where a model learns to learn from its own experience.

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Meta-learning can enable models to adapt and improve their performance over time without being explicitly programmed.

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A field of artificial intelligence that focuses on making AI systems more transparent and interpretable.

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Explainable AI aims to provide insights into how AI systems make decisions or predictions, making it easier to understand and trust.

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The set of principles and guidelines that guide the development and use of AI systems.

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Ethical considerations are important in AI development to ensure that AI systems are fair, transparent, and accountable.