The document discusses challenges and opportunities for machine learning in online advertising at scale. It notes that while ML has helped with tasks like bidding and recommendations, challenges remain around long-term effects, overfitting, personalization across devices, and optimal credit assignment and metrics. The document proposes that reinforcement learning, counterfactual analysis, transfer learning and factorization could help address issues like optimal bidding strategies, offline evaluation, and modeling long tail users and products. It concludes by inviting others to help solve remaining open challenges.