The article discusses various open problems in machine learning inspired by a graduate class. It critiques current methodologies, emphasizing the need for a design-based perspective, better evaluation methods, and innovations in large language models. The author encourages researchers to explore these under-addressed areas.

Despite significant investments in marketing technology, many companies struggle to utilize these tools effectively, leading to limited results. A study involving 292 senior marketers reveals insights into the obstacles they face and suggests potential solutions for better leveraging martech.