Tim Davis | Probabilistic engineering and the 24-7 employee

Software is quietly becoming a probabilistic system. Generation has become cheap but validation has not, and correctness is becoming something you believe rather than know. The shift from deterministic to probabilistic engineering, the splitting of roles, and the 24-7 employee whose agent fleet work

GitHub - probml/pml-book: "Probabilistic Machine Learning" - a book series by Kevin Murphy

"Probabilistic Machine Learning" - a book series by Kevin Murphy - probml/pml-book

A functionally reversible probabilistic computing architecture enabled by interactions of current-controlled magnetic devices

GitHub - automata/awesome-probabilistic-programming: Carefully curated list of awesome Probabilistic Programming resources

Carefully curated list of awesome Probabilistic Programming resources - automata/awesome-probabilistic-programming

Probabilistic Artificial Intelligence

Artificial intelligence commonly refers to the science and engineering of artificial systems that can carry out tasks generally associated with requiring aspects of human intelligence, such as playing games, translating languages, and driving cars. In recent years, there have been exciting advances in learning-based, data-driven approaches towards AI, and machine learning and deep learning have enabled computer systems to perceive the world in unprecedented ways. Reinforcement learning has enabled breakthroughs in complex games such as Go and challenging robotics tasks such as quadrupedal locomotion. A key aspect of intelligence is to not only make predictions, but reason about the uncertainty in these predictions, and to consider this uncertainty when making decisions. This is what this manuscript on "Probabilistic Artificial Intelligence" is about. The first part covers probabilistic approaches to machine learning. We discuss the differentiation between "epistemic" uncertainty due to lack of data and "aleatoric" uncertainty, which is irreducible and stems, e.g., from noisy observations and outcomes. We discuss concrete approaches towards probabilistic inference and modern approaches to efficient approximate inference. The second part of the manuscript is about taking uncertainty into account in sequential decision tasks. We consider active learning and Bayesian optimization -- approaches that collect data by proposing experiments that are informative for reducing the epistemic uncertainty. We then consider reinforcement learning and modern deep RL approaches that use neural network function approximation. We close by discussing modern approaches in model-based RL, which harness epistemic and aleatoric uncertainty to guide exploration, while also reasoning about safety.