07 June 2022

Top 10 Women in AI

Top 10 Women in AI in Poland were distinguished at Perspektywy Women in Tech Summit 2022: Natalia Domagala (Ethical AI), Agata Foryciarz (Fighting AI Bias), Maria Ganzha (AI Researcher), Jagoda Kaszowska-Mojsa (AI for Change), Sylwana Kaźmierska (AI Promoter), Alicja Kwasniewska (AI Rising Star), Agnieszka Pilat (AI in Culture), Ula Sankowska (AI Entrepreneur), Agnieszka Słowik (AI Researcher), Ivona Tautkute-Rustecka (AI in Culture), Halina Kwasnicka (Distinguished Mentor in AI). The jury was led by Aleksandra Przegalinska, along with Dominik Batorski, Marzena Feldy, Borys Stokalski, and Katarzyna Szymielewicz. It was a lot of hard work to select only 10 from so many talented & accomplished women from the top 100 listed in


Handbook of Financial Stress Testing

J. D. Farmer, A.Kleinnijenhuis, T. Schuermann, T.Wetzer

Stress tests are the most innovative regulatory tool to prevent and fight financial crises. Their use has fundamentally changed the modeling of financial systems, financial risk management in the public and private sector, and the policies designed to prevent and mitigate financial crises. Despite their centrality to public policy, the optimal design and use of stress tests remains highly contested. Written by an international team of leading thinkers from academia, the public sector, and the private sector, this handbook comprehensively surveys and evaluates the state of play and charts the innovations that will determine the path ahead. This guide is essential reading for researchers, practitioners, and policymakers.

Mastering Reinforcement Learning with Python

E. Bilgin

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. Deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning are also explained. The key approaches behind successful RL implementations, such as domain randomization and curiosity-driven learning are presented.

The MACROPRU project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101023445.