Glenn Hopper’s newest book is AI Mastery for Finance Professionals, but the content is also for CEOs, board members, and all other organizational leaders. We keep the topics pragmatic, applicable, and example-centric in this conversation. We’ll also discover what Glenn means by saying, “AI will not replace people. People who use AI will replace those who don’t use AI.” It’s not too deep and not shallow at all. Glenn explains AI in a manner that will stick in our minds.
Interview Highlights
- NYT’s article on chatbots
- The role of curiosity and continuous learning as it applies to AI
- Glenn’s research on his new book
- The simplest definition of AI
- AI vs. Machine Learning
- A simple explanation of AGI and ASI
- AI is not Zapier or SureTriggers
- Mark’s favorite Big Blue story regarding a motorcycle dealership
- AI in the role of PE investments
- The impossibility of calculating ROI with AI
- Let’s never ignore AI governance
- Can Isaac Asimov teach us anything about AI?
- Does Glenn have a third book in him?
AI Mastery for Finance Professionals is the definitive guide for navigating the AI revolution that is reshaping the financial landscape. This book demystifies complex concepts and equips you with the knowledge and tools to leverage AI for smarter decision-making, improved risk management, and unparalleled innovation.
Mark’s Favorite Lines in the Book
Curious to know which passages Mark highlighted in Glenn’s book? These are only a few and relate more to how to think about AI, and the highlights are not in any particular order.
Our approach to AI:
“It’s not just about using AI and blindly trusting its outputs; it’s about understanding the algorithms that power it, the data it relies on, and the assumptions it makes.”
AI is not meant to replace human thought:
“Artificial Intelligence (AI) is a branch of computer science that aspires to mimic human thought.”
AI is still a theoretical concept:
“While exciting, AGI remains a largely theoretical concept; there are significant scientific, technical, and philosophical challenges to overcome along the way.”
Regarding Blockchain:
“Another trend likely to shape the future of investment firms is the rise of blockchain and decentralized finance (DeFi). Blockchain technologies, like smart contracts and tokenization, have the potential to transform many aspects of financial services, from capital raising and trading to settlement and clearing.”
Regarding the use of AI in private equity, I agree with this line, but I’d expand even more to pre-diligence filtering and related activities:
“Once a potential deal is identified, AI can streamline the due diligence process, which typically involves a comprehensive investigation of a target company’s financials, legal documents, operations, and market position to assess investment risks and rewards.”
I’m glad Glenn mentioned Monte Carlo. Where practitioners struggle with Monte Carlo is getting our inputs nailed down (see my conversation with Sam Savage on this point):
“Monte Carlo simulations are a powerful AI-based technique for modeling uncertainty in financial forecasts. These simulations involve running thousands of iterative scenarios with randomly generated values for key input variables.”
Fraud detection; yes, yes, yes …
“Deep learning’s ability to process unstructured data (e.g. text, images, and voice recordings) adds another layer of sophistication to fraud detection efforts.”
Great line on deep learning:
“Deep Learning is a powerful tool, but it’s not a magic wand. Successful application of Deep Learning requires a systematic, iterative process.”
Here’s a good, solid definition:
“It is important to understand that AI is not a singular, unified technology. It is an ever-expanding collection of tools and programs that includes a variety of technologies, techniques, and approaches, including machine learning, natural language processing, computer vision, and robotics.”
For those reading this book with a group of others, here are five questions to consider during a meetup:
- Make sure you have a good grasp of overfitting and underfitting. What does that mean in your organization? What’s your biggest takeaway of Glenn’s conceptual model on this topic?
- Can AI prevent fraud? Or can AI only detect fraud sooner than an auditor can?
- Would you use AI to screen job candidates? Why or why not?
- Why is the ROI of AI far more than a calculation? Regarding better decisions, how can speed, quality, and creativity apply in the ROI conversation?
- Search The Three Laws of Robotics by Asimov. Do they apply to AI?
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Episode Pairings
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