The paper delves into the realm of Artificial Intelligence, exploring the advancements in machine learning and deep learning that have transformed computer systems. It emphasizes the importance of understanding and managing uncertainty in predictions, discussing probabilistic approaches and inference techniques. The manuscript also covers how uncertainty is factored into sequential decision-making tasks through active learning, Bayesian optimization, and reinforcement learning. It highlights the integration of neural networks in deep RL approaches and concludes with a discussion on model-based RL and safety considerations. This comprehensive study on “Probabilistic Artificial Intelligence” sheds light on the cutting-edge strategies shaping the future of AI.
https://arxiv.org/abs/2502.05244