Adversarial Attacks – Techniques used to fool AI models by subtly altering input data.
AI Ethics – The study of moral issues in AI, including bias, privacy, and accountability.
AI Governance – Policies and regulations guiding the development and deployment of AI.
Algorithmic Transparency – Making AI decision-making processes interpretable and understandable.
Artificial Intelligence (AI) - The simulation of human intelligence in machines.
Autoencoder – A neural network used for unsupervised learning and data compression.
Autonomous AI Agents – AI systems capable of making decisions and performing tasks independently.
Bayesian Networks – A probabilistic model representing variables and their conditional dependencies.
Bias – Systematic errors in AI models that lead to unfair or inaccurate predictions.
Big Data – Large, complex datasets that require advanced analytics techniques.