Artificial intelligence (AI) and machine learning (ML) are common, but often misunderstood, terms. JMP tried to make these concepts a little clearer during our latest Statistically Speaking event, Demystifying Machine Learning and Artificial Intelligence. Keynote David Hand (Imperial College, London) and panelists Diana Ballard (Brewer Science); Jason Brinkley (Abt Associates) and Jim Georges (SAS) discussed how to get started and model more effectively with ML techniques. They also shared how cutting-edge organizations are solving problems more quickly and driving innovation more effectively and the benefits of using advanced techniques like these.
To help JMP sales team engage in more meaningful and productive post-event conversations with customers and prospects, we’ve created the following list of recommended resources to position with registrants now that the event has concluded. This list pulls appropriate assets from across our web site, and includes white papers, webinars, customer stories, blogs, and more.
If you have any questions about recommendations related to this event, please feel free to contact Larry LaRusso.
Sales Force Campaign Link (On Demand) Found Here
November 4, Demystifying Machine Learning and AI Recommendations
Expert guidance on making the most of machine learning, Stat Speaking Event
Includes Dick DeVeaux keynote, The Seven Deadly Sins of Machine Learning. Excellent panel presentation with Dick, Russ Wolfinger and Julia O’Neill
Solving science and engineering problems with machine learning, Stat Speaking Event
Very strong resource. European based speakers, but in-depth discussion (keynote) on deep learning, AI and strong/engaging panel.
What is Machine Learning (and can JMP do it), JMP Blog
1st part of Elizabeth Claassen’s blog series. Good introductory discussion of concepts around machine learning and outlines all the different machine learning algorithms JMP can do. Promotes the free trial.
Applying machine learning: Will I beat my husband at pool on my birthday? JMP Blog
2nd part of Elizabeth Claassen’s blog series. Uses a fun example of machine learning algorithm. Like the connections this blog makes. Think it does a good job of humanizing our experts and our business.
Dark Data, Book Chapter
Though the topic is not a perfect match, this book chapter is from David Hand, who provided the keynote for this event. Dark Data is Hand’s newest book. (David is the keynote speaker for this webinar) The chapter talks about missing data or data not known and the effect it can have on analysis. Along the way, Hand discusses data phenomena like p-hacking, spurious measurement errors, and false discovery rates, and “HARKing.”
Data Mining and Predictive Modeling, JMP Tutorial
This JMP Tutorial includes a manufacturing case study and includes demonstrations of a number of techniques using Graph Builder and the Prediction Profiler and JMP Pro. Discussions on topics related to machine learning and AI, including regression, Bootstrap Forest and Boosted Trees (Decision Trees) and Neural Networks.
Advancing machine learning from theory to practice, Customer Success Story
Though from the academic industry, this story shows how faculty and students at Sungkyunkwan University use JMP to deploy machine learning applications to achieve substantial cost and time savings on behalf of industry clients. In one case, the team’s efforts saved preeminent Korean steel manufacturer POSCO hundreds of thousands of dollars per line in annual savings. By using JMP as an alternative to coding-intensive R or Python, Professor Lee Jong-Seok says the team accomplished in one week what would have otherwise taken one month to complete.
Machine learning drives innovation in high-tech manufacturing, Customer Success Story
This story comes from the semiconductor industry. The story documents how the company, SK Hynix used JMP to create machine learning algorithms that can expedite the use of data in manufacturing processes. They also share how they’ve standardized the use of JMP to scale up efficiencies that have the potential to affect the whole organization.
Mission-Critical Advanced Statistics for the Defense Community, Statistically Speaking
Though this event was specific to the defense industry, it does cover concepts that would apply across industries and provides a baseline understanding of AI. During the keynote, Laura Freeman discusses how they’re using statistical thinking to use machine learning and AI in a way that helps the Department of Defense deliver on key missions, up-skill their workforce and ensure success.
Mission-Oriented Analytics: New Methods for the Intelligence Community, Analytically Speaking
Industry connection, Alyson Wilson, NCSU, works on bringing analytics more into the intelligence community to influence foreign policy and national security. Also a good discussion on differences between machine learning and AI. 32 views in 2020, not popular.
Smart Machine Learning for Manufacturers, White Paper
Not a perfect match because it’s manufacturing example, but interesting discussions around merging machine learning with statistician knowledge – human and machine.
These sessions also include discussions on tree-based models and neural networks:
Understanding and Applying Tree-Based Methods for Predictor Screening and Modeling, Mastering JMP Webinar
Discovering and Predicting Patterns Using Neural Network Models, Mastering JMP Webinar
