Joe Schurman teaches from his deep experience in the software, machine learning, AI, and processes that organizations need today as they transition to data-driven technology companies. He names some of the cloud services and tech tools he uses to lead clients to start with a user case, break it into stories,  build a team led by the solution owner, assign the stories to developers to build, and iterate product demos until the Minimum Loved Project (MLP) is achieved. Joe offers observations on investing the “right” amount of time in projects, and wisdom on developing a learner mindset.   Key Takeaways [2:06] Joe Schurman is a 2nd-degree black belt in Kung Fu. He once judged a competition in Las Vegas. He has four children; two daughters and two sons. [2:57] Joe is an expert on the fringes of what we can do with computing technology. What we can do changes every day. In the past couple of years, from an AI perspective, with data and automation, it’s taken leaps and bounds. [4:30] We’re still pretty far away from general AI, despite Sophia, an AI robot that was granted Saudi Arabian citizenship in 2017. Today’s AI depends on the programming we give a machine and its interpretation and output. Joe’s focus is narrow or weak AI. His business colleagues call it magic. Computer vision is an area he loves. [5:45] Joe uses a lab environment across Google Cloud Platform, Microsoft Azure, and Amazon Web Services. The capabilities that have come up in the last year are “just insane” with what you can do with computer vision and building libraries of what the machine can see. [6:06] Joe loved seeing a computer vision capability demonstration at AWS re:Invent of tracking every NFL player on the field and predicting injuries and other types of output and insights in real-time. The machine used narrow AI to access a library seeded with “a ton” of data to interpret the action. [7:15] What you can do with this technology comes down to the data that you feed the engine. Think about the amounts of data that organizations have to sift through to generate reflective or predictive insights. Auto machine learning helps organize the data into useful information such as anomaly detection in software engineering. The data can also come from tools like GitHub and Jira. [8:25] Joe did a fun computer vision project on UAPs for the History Channel, working with some of the nation’s top military leaders, building a library of video and audio data to be able to detect unidentified aerial phenomena that were not supposed to be entering our airspace, and curating that library. [10:06] AI started with the idea of speeding up processes, such as getting an app to market faster or gathering insights quicker to make business decisions more timely. [11:28] AI can enhance human performance. Joe starts by finding people who know how to fail fast; to get a Minimum Viable Product (MVP) out the door. Solutions such as quality engineering automation, test automation, and monitoring services for DevOps detect bugs and performance issues quickly and ensure that the quality of the team is sound.[12:47] Joe notes the importance of individuals performing, contributing to, and collaborating as a team. Set your organization and standards governance up first. Look for a platform of technology to leverage that enables you to build and tinker. Finding the latest and greatest tool is no good unless it provides the right level of collaboration with their platform and connection to different processes. [14:53] When introducing ML to an organization, start with discovery, to understand the culture and talent within the organization. How are they communicating today? Joe sees the biggest gap between data scientists and data engineers. Projects tend to fail without collaboration, regardless of the tech. If the data scientists don’t understand the domain, then the platform is irrelevant,[17:28] Joe stresses the need for a methodology in place to make any of these aspirations work for your o