Jeremiah Lowin on designing tools to allow teams to build, run, and monitor data pipelines at scale.
Sebastian Raschka on the state of tools for training, tuning, and evaluating machine learning models.
Ade Fajemisin and Donato Maragno on recent trends and challenges in applying ML to optimization problems.
Barret Zoph and Liam Fedus on recent trends and challenges in NLP and Large Language Models.
Olivia Liao on blending recommendation and personalization models with human expertise.
Jack Clark on recent progress in deep learning, the rise of AI ethics, and what lies ahead for language models.
Ajay Kulkarni and Mike Freedman on the many benefits of data management solutions focused on temporal data.
Wendy Foster on data science use cases, Responsible AI, and emerging tools such as large language models and multimodal models.
Elham Tabassi and Andrew Burt on a new NIST framework that addresses risks in the design, development, use, and evaluation…
Amit Sharma and Emre Kiciman on making causal inference and learning more accessible.