Why Business Executives Should Be Hip To ML Tools

I have spent most of my professional life in the age of AI and ML. During earlier times at Uber, I worked with models that estimated ETAs, calculated dynamic pricing and even matched riders with drivers. My co-founder Jason previously led video ad company TubeMogul (acquired by Adobe), which relied on ML to ensure that its advertisers didn't waste their media spend on ads that nobody saw, or ads that only bots saw. Although ride-sharing and video advertising aren't often used in the same sentence, both Jason and I faced similar challenges in ensuring that the models our companies deployed worked effectively and without bias. When models don't work as planned and machines, trained by data, make bad decisions, there is a direct impact on business results.