The use of machine learning technology is spreading across all areas of modern organizations, and its predictive capabilities suit the finance function's forward-looking needs. Understanding how to work with machine learning models is crucial for making informed investment decisions. Yet, for many finance professionals, successfully employing them is the equivalent of navigating the Bermuda Triangle. Does this project match the characteristics of a typical machine learning problem? Is there a solid foundation of data and experienced analysts?

Artificial intelligence (AI) isn't just the buzzword of the moment--it holds the potential to significantly change the marketing landscape in China and help companies market to the segment of one. Bean: One of our companies, OCheng, is applying AI to WeChat commerce. WeChat service accounts are allowed to send a message to their followers once a week. OCheng can take customer relationship management data, look at the content people are engaging with, their purchase history, etc., and use machine learning to personalize these weekly messages. Instead of sending one message to a million people, you can send a million messages that are unique to each user every week.

Something that I think should be fully available and would be of tremendous benefit is the use of machine learning tools to help medical diagnosis. It's quite feasible to imagine that a good fraction of the medical knowledge of which symptoms are related to which diseases. Based on this we should never be in the situation of a doctor missing' a diagnosis. Indeed, it's quite conceivable that the entire medical history of an individual should be signficantly able to help form a diagnosis.