Probability is a fundamental branch of mathematics that quantifies uncertainty and models random phenomena. It is central to artificial intelligence, especially for inference, decision-making, and machine learning. Probability assigns a numerical value between 0 and 1 to the likelihood of an event, where 0 means impossible and 1 means certain. Unlike deterministic logic, probabilistic reasoning explicitly accounts for uncertainty in data, models, or the environment.
Use cases and examples
Probabilities are used in modeling uncertain systems such as speech recognition, spam filtering, content recommendation, automated medical diagnosis, and weather forecasting. For example, Bayesian networks use conditional probabilities to infer causes from observed effects. Probabilistic classification algorithms (like Naive Bayes) evaluate the likelihood of a category given observed features.
Main software tools, libraries, and frameworks
Several tools enable the use of probabilities in AI: PyMC, TensorFlow Probability, Stan, Edward, and scikit-learn for classical probabilistic models. For Bayesian inference, platforms such as JAGS or BUGS are also widely used.
Recent developments, evolutions, and trends
Recent progress includes integrating probabilities into deep learning (deep probabilistic programming), the use of generative models (such as diffusion models or variational autoencoders), and improving the efficiency of sampling and approximation methods. Interpretability of probabilistic models is also a major focus, especially for sensitive domains like health or finance.