Reading, Watching, and Listening Sources for Our First Lecture on AI Frameworks and History for Managers
Yuri Balasanov and I are about to start our Managerial Analytics class. (We would have named it Managerial AI, but we created it in 2014.) The class aims to help future managers and leaders understand AI and Analytics.
The first three-hour lecture is an introduction to Analytics and AI. We cover different frameworks, getting the terms right, and history. It includes material we use when we talk to CEOs and executive teams.
We reference a lot of sources, including new material. I think you might enjoy seeing the sources. Here they are:
Ethan Mollick. We have a lot from him. We reference his posts on X, his blog (especially, Reshaping the tree: rebuilding organizations for AI), and a well-cited research paper he did with Harvard, MIT, and Boston Consulting Group.
Prediction Machines and Competing in the Age of AI are my top two Practical AI book recommendations. Here is a podcast version of Prediction Machines covering some of the main points. They will get their own lecture later in the class. Both books provide frameworks for how managers should think about AI.
Artificial Intelligence by Melanie Mitchell. This is another top recommendation. It covers the history of Artificial General Intelligence and will make you less worried about AI.
NEW: Debates on the nature of artificial general intelligence, a recent article by Melanie Mitchell in Science. This article argues that we don’t even have a good definition of AGI.
My view on what CEOs need to know about AI.
Interview with Radhika Kulkarni on s.t. podcast. Radhika Kulkarni was a past president of INFORMS and spent her career at SAS, a company with a front-row seat to the analytics and AI movement. She has a different take on what AI is from what I propose.
Warren Powell’s “The seven levels of artificial intelligence.” This is a great short article discussing the different levels of AI.
Competing on Analytics (paid). This Tom Davenport article in HBR is credited with helping kick off the analytics movement in ~2007.
Why Software is Eating the World is a classic Marc Andreessen essay. We use it to discuss how digital transformations in the early 2000s also helped accelerate the analytics movement.
Marc Andreessen on Tetragrammaton with Rick Rubin. At about the 2-hour, 8-minute mark, Marc Andreessen talks about the architecture of computers modeled after calculating machines vs. after the human brain (basically neural nets and what LLMs are based on). We use this to talk about possible different management use cases based on this framework.
Intro to Large Language Models. This is an excellent 1-hour tutorial on LLMs by Andrej Karpathy. We dedicate a lot more time to LLMs in the class, so this is just the intro.
Avi Goldfarb— The Economic Impact of AI. This is a podcast interview on their book Power and Predictions, a follow-up to Prediction Machines. This has the management message that you may need to reorganize your business to get the most out of AI— just like factories had to reorganize to take advantage of electricity.
Two Lex Fridman interviews on Tesla’s self-driving capability, #252 with Elon Musk and #333 with Andrej Karpathy. Both interviews detailed the opportunities and management challenges for deep learning projects. Here is my previous write-up of the lessons from the Musk interview.
Elements of Statistical Learning. Used to explain what got people excited about machine learning.
What Kaggle has learned from almost a million data scientists. In this 4.5-minute video, we use the story of used cars to introduce the importance of feature engineering.
Larry Snyder has a great and intuitive introduction to deep learning. It is “Judging a Book by Its Cover.” I’ll have to work with Larry to get a recording of this material.
Unfortunately, we can’t record and distribute this lecture. But we’ve considered making it available in some format.
Bummer, won't be able to. Next time!
Tonight?