Dr. Jerry Smith welcomes you to another episode of AI Live and Unbiased to explore the breadth and depth of Artificial Intelligence and to encourage you to change the world, not just observe it!   Dr. Jerry is talking today about questions and answers in the world of data science machinery and artificial intelligence.   Key Takeaways: What are Dr. Jerry’s favorite AI design tools? Dr, Jerry shares his four primary tools: MATLAB. Is a commercial product. It has a home, academic, and enterprise version. MATLAB has toolkits and applications. The Predictive Maintenance Toolbox at MATLAB, especially the preventive failure model is of great value when we want to know why things fail, also by measuring systems performance and predicting the useful life of a product. Mathematical Modeling with Symbolic Math Toolbox is useful for algorithm-based environments. It is built on solid mathematics. R Programming is Dr. Jerry’s favorite free tool for programming with statistical and math perspectives. R is an open and free source and comes with a lot of applications. Python is a great tool for programming and is as capable as R programming to assist us in problem-solving. Python is very useful when you know your work is directed to an enterprise level. Does Dr. Jerry have any recommended books for causality? The Book of Why is foundational for both the businessperson and the data scientist. It provides a historical review of what causality is and why it is important. For a deeper understanding of causality, Dr. Jerry recommends Causal Inference in Statistics: A Primer.   Counterfactuals and Causal Inferences: Methods and Principles it is a great tool to think through the counterfactual analysis.   Behavioral Data Analysis with R and Python is an awesome book for the practitioner who wants to know what behaviors are, how they show up in data, the causal characteristics, and how to abstract behavioral aspects from data. Dr. Jerry recommends Designing for Behavior Change, it talks about the three main strategies that we use to help people change their behaviors. The seven rules of human behavior can be found in Eddie Rafii’s latest book: Behaviology, New Science of Human Behavior. Dr. Jerry shares his favorite tools for casual analysis: Compellon allows us to do performance analysis, showing the fundamental causal chains in your target of interest. It can be used by analysts. It allows users to do “what-if” analysis. Compellon is a commercial product.   Causal Nexus is an open-source package in Python that has a much deeper look at causal models than Compellon. BayesiaLab is a commercial tool that is one of the higher-end tools an organization can have. It allows you to work on casual networks and counterfactual events. It is used in AI research.   What skills are needed for data science machinery and AI developers? Capabilities can be segmented into Data-oriented, Information-oriented, Knowledge, and Intelligence. These different capabilities are used in many roles according to several levels of maturity.   Stay Connected with AI Live and Unbiased: Visit our website AgileThought.com Email your thoughts or suggestions to [email protected] or Tweet @AgileThought using #AgileThoughtPodcast!   Learn more about Dr. Jerry Smith   Mentioned in this episode: MATLAB MATLAB Mathematical Modeling Python Artificial Intelligence with R Compellon Causal Nex BayesiaLab   Dr. Jerry’s Book Recommendations: The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie   Causal Inference in Statistics: A Primer, Madelyn Glymour, Judea Pearl, and Nicholas P. Jewell   Counterfactuals and Causal Inferences: Methods and Principles,  Stephen L. Morgan and Christopher Winship   Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results, Florent Buisson   Designing for Behavior Change: Applying Psychology