A Weekend of Innovation: Highlights from the Machine Learning Prague Conference 2023

A Weekend of Innovation: Highlights from the Machine Learning Prague Conference 2023
June 16, 2023
The Machine Learning Prague Conference 2023 took place over the weekend of June 3rd and 4th at the O2 Arena in Prague, attracting a diverse group of experts and enthusiasts from around the world. Preceded by insightful workshops on Friday, June 2nd, the three-day event featured exciting talks from industry leaders, researchers, and practitioners who shared their knowledge and insights on the latest developments in Machine Learning (ML) and Artificial Intelligence (AI). In this blog post, we will recap some of the most engaging and thought-provoking talks and workshops from the conference.

The Machine Learning Prague Conference 2023 not only excelled in bringing together top experts and fascinating content, but also in its outstanding organization. Attendees were treated to excellent food and drinks throughout the event, which further enhanced the conference experience. The welcoming atmosphere created a perfect setting for networking, as participants had ample opportunities to engage in meaningful discussions about ML and AI with fellow professionals, researchers, and enthusiasts. The well-organized event and positive environment contributed to the overall success of the conference and left attendees inspired and eager to apply their newfound knowledge in their respective fields.

Friday Workshops

Aisling O'Sullivan and Michal Kubišta from Dataclair led an interactive workshop on drug discovery using Natural Language Processing (NLP). Participants explored various NLP methods and learned how these can help identify potential drug candidates and accelerate the drug development process. The main focus was on using various language models provided by Huggingface and experiencing firsthand the advantages of the Huggingface Pipelines.

Martin Plajner and Theodor Petřík from Logio hosted a workshop on the application of Bayesian Networks in business planning and risk management. Attendees learned how Bayesian Networks can be used to model complex business scenarios, understand dependencies, and make informed decisions under uncertainty.

Saturday Highlights

Martin Schmid from DeepMind explored search algorithms in imperfect information games in his talk, "Player of Games - Search in Imperfect Information Games." Michal Dufek of Analytical Platform presented how graph attention reinforcement learning can be used to make better investment decisions in finance, discussing "Boosting Investment Decisions with Graph Attention Reinforcement Learning." Fabian Kovac from St. Pölten University of Applied Sciences talked about the need for alternative baselines in AI safety research and introduced new methods for attainable utility preservation in "Standing Still Is Not An Option: Alternative Baselines for Attainable Utility Preservation."

Aimira Baitieva from Valeo showcased how ML-powered industrial vision tools can improve quality control in assembly lines with her presentation on "Multi-Model Machine Learning based Industrial Vision Tool for Assembly Part Quality Control." Piotr Skalski of Roboflow discussed advancements in 3D pose estimation techniques and their applications in sports analytics and performance tracking in his talk, "3D Pose Estimation in Sport."

Sunday Highlights

Alexander Del Toro Barba from Google shared insights into the latest developments in quantum computing and its potential impact on ML in his talk on the "State and Future of Quantum Computing & Quantum Machine Learning." Matej Murín of Meteopress presented how deep neural networks can be used to improve weather forecasting accuracy in "Probabilistic Precipitation Nowcasting with Deep Physics-Constrained Neural Networks."

Olivier Koch from Onfido discussed how deep learning is revolutionizing the identity verification process and ensuring fairness in "Bringing automation and fairness to identity verification on the internet with deep learning." Alex Athorne from Seldon introduced the Alibi library, which helps developers better understand and explain their ML model decisions, in his talk on "Open Source Explainability - Understanding Model Decisions using Alibi." Uri Rosenberg of Amazon provided an overview of explainable AI techniques and their applications in computer vision and natural language processing in his presentation on "Explainable AI for Computer Vision and NLP models."

Machine Learning Prague 2023 offered a wealth of insights and knowledge for attendees, showcasing the latest advancements in ML and AI. The diverse range of topics covered during the event, as well as the hands-on workshops, highlights the growing impact of these technologies across various industries. With a well-organized event and a welcoming atmosphere, participants had the chance to network and discuss ML and AI in depth. As ML and AI continue to evolve, events like this provide invaluable opportunities for professionals to stay informed and inspired. We look forward to next year's conference and the exciting developments it is sure to bring.

Timo is a machine learning developer from Germany and works on projects in the field of machine learning and artificial intelligence. Enjoys all kind of sports from mental exercises over Esport (aka playing video games) to actual physical exercises. Always interested in learning something new.

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