AI Glossary
Cross-Cutting Topics (Applicable at Any Level)
- AI Ethics: Ensuring responsible and fair AI practices.
- Example: Establishing guidelines to prevent AI misuse in surveillance.
- Cloud AI: Scalable AI tools hosted on cloud platforms.
- Example: Using AWS AI services for facial recognition in applications.
- Bias Mitigation: Reducing biases in AI systems.
- Example: Adjusting datasets to improve fairness in hiring algorithms.
- Human-in-the-Loop (HITL): Combining human oversight with AI systems.
- Example: AI flagging potential hate speech with human reviewers verifying results.
- Synthetic Data: Artificially generated data for training models.
- Example: Creating fake credit card transactions for fraud detection models.
- Data Drift: Changes in data affecting model performance over time.
- Example: Retail recommendation systems struggling with new shopping trends.
- Turing Test: Evaluating a machine’s ability to mimic human behavior.
- Example: ChatGPT engaging in human-like conversations but falling short in nuance.
- Augmented Intelligence: AI designed to enhance, not replace, human capabilities.
- Example: Radiologists using AI to spot abnormalities in scans.
- Explainability Gap: Difficulty in understanding complex AI model decisions.
- Example: Neural networks in finance providing credit scores without transparency.
- AI Winter: Periods of stagnation in AI research and funding.
- Example: The 1970s saw reduced AI investment after initial optimism waned.
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