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1. - The broad field of developing computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

2. - A subset of AI that involves developing algorithms and statistical models that enable computer systems to learn from data and improve their performance on a specific task without being explicitly programmed.

3. - A type of machine learning that uses artificial neural networks with multiple layers to learn and make intelligent decisions based on data inputs.

4. ) - A branch of AI that deals with the interaction between computers and humans using natural language, enabling tasks such as text analysis, speech recognition, and language translation.

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5. - The field of AI that enables computers to derive meaningful information from digital images, videos, and other visual inputs, enabling applications like object recognition, facial recognition, and image analysis.

6. - The branch of AI focused on designing and building robots, which are programmable machines capable of carrying out complex tasks and interacting with their environment.

7. - A type of machine learning where an AI system learns by trial and error, receiving rewards or penalties for its actions in a given environment, with the goal of maximising its cumulative reward.

8. - A type of machine learning where an AI system is trained on labelled data, allowing it to learn patterns and make predictions or decisions based on new, unlabelled data.

9. - A type of machine learning where an AI system is trained on unlabelled data, allowing it to identify patterns and relationships within the data without any prior guidance or labels.

10. - A computational model inspired by the human brain, consisting of interconnected nodes (artificial neurons) that process information and learn from data.

11. - The potential for machine learning algorithms to exhibit biases or make unfair decisions due to biases present in the training data or the algorithm itself.

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12. - A type of deep learning architecture where two neural networks compete against each other, with one generating synthetic data (e.g., images, text) and the other trying to distinguish between real and generated data.

13. - A machine learning technique that involves using knowledge gained from solving one problem and applying it to a different but related problem, potentially reducing the amount of data and time required for training.

14. - The practice of developing and deploying AI systems in a responsible and ethical manner, considering issues such as fairness, transparency, privacy, and accountability.

15. - The policies, regulations, and frameworks put in place to ensure the responsible development and use of AI technologies, addressing potential risks and ethical concerns.

16. AI Liability - The legal responsibility and accountability for any harm or damages caused by AI systems, including issues related to product liability, intellectual property, and data privacy.

17. - Techniques and strategies used to identify and mitigate biases in AI systems, such as data preprocessing, algorithm adjustments, and human oversight.

18. - The ability of an AI system to provide explanations or justifications for its decisions or outputs, enabling transparency and accountability.

19. - Instances where an AI system generates outputs or makes decisions that are nonsensical, inconsistent, or unrelated to the input data or task at hand. These hallucinations can be a result of limitations in the training data, model architecture, or the AI system's understanding of the context.