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"AI Fundamentals Vocabulary (*Extra Credit Assignment) ": HTML5 Crossword |
Across4. A type of ML where the model is trained on labeled data, meaning the input data is paired with corresponding output labels. The goal is to learn a mapping function from inputs to outputs, allowing the model to make predictions on new, unseen data (10,8)
5. Concerns related to AI systems exhibiting biased behavior or making unfair decisions, often influenced by biased training data or algorithmic design. Ensuring fairness and mitigating biases in AI systems is critical for ethical and equitable outcomes (4,3,8)
6. The process of assessing the performance of ML models using metrics such as accuracy, precision, recall, F1 score, and area under the ROC curve (AUC). Model evaluation helps determine how well a model generalizes to unseen data and whether it meets the desired objectives (5,10)
7. A branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP techniques are used in applications such as language translation, sentiment analysis, and chatbots (7,8,10)
8. A type of ML where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The agent's goal is to maximize cumulative rewards over time by learning optimal decision-making strategies (10,8)
9. A type of ML where the model is trained on unlabeled data, and its goal is to discover hidden patterns or structures within the data. Common tasks include clustering similar data points or dimensionality reduction (12,8)
11. A field of AI that deals with enabling computers to interpret and understand visual information from the world, such as images and videos. Computer vision techniques are used in tasks like object recognition, image classification, and image generation (8,6)
13. A subfield of ML that uses neural networks with multiple layers (deep neural networks) to extract hierarchical representations of data. Deep learning algorithms have achieved breakthroughs in tasks such as image recognition, natural language processing, and speech recognition (4,8)
14. The process of selecting or transforming relevant features (attributes or variables) from raw data that are most informative for ML models. Feature extraction helps improve model performance and reduce dimensionality (7,10)
15. The process of cleaning, transforming, and preparing raw data into a format suitable for ML algorithms. Data preprocessing steps may include handling missing values, scaling features, encoding categorical variables, and splitting data into training and testing sets (4,10)
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Down1. The simulation of human intelligence processes by machines, especially computer systems, to perform tasks such as learning, reasoning, problem-solving, perception, and language understanding (10,12)
2. The practice of developing and deploying AI systems responsibly, considering ethical implications such as fairness, transparency, accountability, privacy, and societal impacts. Ethical AI frameworks and guidelines promote ethical AI design, development, and deployment practices (7,2)
3. Common challenges in ML where a model either learns the training data too well but performs poorly on new data (overfitting) or fails to capture the underlying patterns in the data (underfitting). Techniques such as regularization, cross-validation, and adjusting model complexity can mitigate these issues (11,3,12)
10. A computational model inspired by the structure and functioning of the human brain's interconnected neurons. Neural networks learn to perform tasks by adjusting the weights of connections between neurons during training (7,8)
12. A subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. ML algorithms enable computers to learn from data and make predictions or decisions (7,8)
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ACROSS
4. A type of ML where the model is trained on labeled data, meaning the input data is paired with corresponding output labels. The goal is to learn a mapping function from inputs to outputs, allowing the model to make predictions on new, unseen data (10,8)
5. Concerns related to AI systems exhibiting biased behavior or making unfair decisions, often influenced by biased training data or algorithmic design. Ensuring fairness and mitigating biases in AI systems is critical for ethical and equitable outcomes (4,3,8)
6. The process of assessing the performance of ML models using metrics such as accuracy, precision, recall, F1 score, and area under the ROC curve (AUC). Model evaluation helps determine how well a model generalizes to unseen data and whether it meets the desired objectives (5,10)
7. A branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP techniques are used in applications such as language translation, sentiment analysis, and chatbots (7,8,10)
8. A type of ML where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The agent's goal is to maximize cumulative rewards over time by learning optimal decision-making strategies (10,8)
9. A type of ML where the model is trained on unlabeled data, and its goal is to discover hidden patterns or structures within the data. Common tasks include clustering similar data points or dimensionality reduction (12,8)
11. A field of AI that deals with enabling computers to interpret and understand visual information from the world, such as images and videos. Computer vision techniques are used in tasks like object recognition, image classification, and image generation (8,6)
13. A subfield of ML that uses neural networks with multiple layers (deep neural networks) to extract hierarchical representations of data. Deep learning algorithms have achieved breakthroughs in tasks such as image recognition, natural language processing, and speech recognition (4,8)
14. The process of selecting or transforming relevant features (attributes or variables) from raw data that are most informative for ML models. Feature extraction helps improve model performance and reduce dimensionality (7,10)
15. The process of cleaning, transforming, and preparing raw data into a format suitable for ML algorithms. Data preprocessing steps may include handling missing values, scaling features, encoding categorical variables, and splitting data into training and testing sets (4,10)
DOWN
1. The simulation of human intelligence processes by machines, especially computer systems, to perform tasks such as learning, reasoning, problem-solving, perception, and language understanding (10,12)
2. The practice of developing and deploying AI systems responsibly, considering ethical implications such as fairness, transparency, accountability, privacy, and societal impacts. Ethical AI frameworks and guidelines promote ethical AI design, development, and deployment practices (7,2)
3. Common challenges in ML where a model either learns the training data too well but performs poorly on new data (overfitting) or fails to capture the underlying patterns in the data (underfitting). Techniques such as regularization, cross-validation, and adjusting model complexity can mitigate these issues (11,3,12)
10. A computational model inspired by the structure and functioning of the human brain's interconnected neurons. Neural networks learn to perform tasks by adjusting the weights of connections between neurons during training (7,8)
12. A subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. ML algorithms enable computers to learn from data and make predictions or decisions (7,8)
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