I will also appreciate more future posts. While building sophisticated machine learning models is getting easier, understanding how models develop knowledge and arrive to conclusions remains a very difficult challenge. Typically, the more accurate the models the harder they are to interpret. KDNuggets posted an article highlighting the value and resources surrounding AI Explainability - what do you think? Are these tutorials useful for you?

Using AI on the battlefield is the stuff of 80s movie fantasies. "There will be times where the machines make the mistake. We're not looking for the omniscient machine that is never wrong. What we're looking for are machines that have been tested to the point where we have the trust that the AI will do what it is designed to do, and hopefully be able to explain why it made the decision it did." Read commentary about the expected real world impact and US policy.

The risks in the ML life cycle are also different since machine learning models have become pervasive in so many aspects of everyday consumer life – so much of which is tightly regulated. As machine learning models help automate important decisions in a wide variety of industries – banking, health care, airline schedules, telecom, shopping, entertainment, and so on – they become subject to much scrutiny about compliance, audits, needs for explainability, concerns about fairness and bias, privacy laws, security concerns, etc. Many of those activities are regulated, for important reasons. While more traditional software engineering similarly has security concerns, audits, etc., the stakes are not nearly as high: code can be debugged. Machine learning, especially when driven with large scale data, is substantially more difficult to trace and "debug" compared with coding.