In part two (2) of a conversation with Dr. David Zaretsky, Dr. Tony Rhem, and Northwestern professor Dr. Zaretsky, explore one of the biggest questions in artificial intelligence today: as AI tools, AI agents, and large language models become more powerful, what still requires human judgment, critical thinking, and ethical oversight? They discuss AI in education, AGI, human-in-the-loop decision-making, AI guardrails, AI ethics, AI governance, autonomous systems, military AI, coding with AI, software testing, red teaming, and the risks of allowing AI to act without proper review and control.This conversation is essential for educators, business leaders, technologists, students, and anyone interested in responsible AI adoption. If you want insight into AI governance, AI safety, critical thinking in the age of automation, and why ethics must be built into AI from the start, this episode delivers a grounded and practical discussion on the future of work, innovation, and trustworthy AI. Like, comment, subscribe, and join the conversation on AI: past, present, and the possible future.#ArtificialIntelligence #AI #AIEthics #AIGovernance #AGI #CriticalThinking#HumanInTheLoop #ResponsibleAI #AISafety #MachineLearning#FutureOfWork #AIInEducation #GenerativeAI #LLM

How is AI transforming education, critical thinking, and the future of work? In this episode of RHEMINISCING with Dr. Tony Rhem, Dr. Tony speaks with Dr. David Zaretsky of Northwestern University about generative AI in universities, the risks of overreliance on tools like ChatGPT, the importance of critical thinking, AI as a tutor, workforce disruption, reskilling, entrepreneurship, and building a personal AI strategy for the future. This is a must-watch conversation for educators, students, business leaders, and anyone navigating the impact of AI on learning and careers.#aieducation #generativeai #ai #chatgpt #criticalthinking #futureofwork #ailiteracy #aistrategy #workforcetransformation #highereducation #entrepreneurship #aiethics #northwesternuniversity

In this episode of Rheminiscing, Dr. Tony Rhem breaks down what large language models (LLMs) are, how tools like ChatGPT, Gemini, and Claude actually work, and why the real value comes from how humans collaborate with them. You’ll learn the basics of token-by-token prediction, why LLMs feel intelligent even though they operate on probability, and how today’s models differ in strengths (reasoning, multimodal understanding, and contextual alignment). Dr. Rhem also demonstrates the power of well-structured prompting with a live example focused on patient engagement in healthcare, then closes with practical best practices: define intent, provide context, iterate, and verify outputs with trusted sources, keeping humans accountable and “AI-informed,” not AI-driven. #ArtificialIntelligence #AI #GenerativeAI #largelanguagemodels #llm #chatgpt #googlegemini #anthropicclaude #promptengineering #aiprompts #aitools #aiproductivity #ailiteracy #responsibleai #aigovernance #humanintheloop #aialignment

This episode revisits a powerful conversation with Dr. Annie Green, exploring how artificial intelligence (AI), knowledge management, and digital transformation are reshaping the future of work. Building on those insights, Tony Rhem and his AI co-host Georgia unpack why a clear, well-defined AI strategy is no longer optional for organizations or individuals.The discussion emphasizes that an AI strategy is a living framework, not a static document. As technologies evolve, so must the strategy, ensuring alignment between business objectives, workforce capabilities, data governance, and ethical AI practices. Without a plan, AI initiatives often lead to confusion, bias, duplicated efforts, and wasted investment.A strong AI strategy is framed around four foundational pillars:Purpose and alignment with measurable business or mission goalsPeople and skills, ensuring AI literacy from executives to practitionersData and infrastructure, grounded in strong data governance and securityEthics and governance, embedding transparency, explainability, accountability, and oversight from day oneThe episode also highlights why organizations frequently rush into AI tools without building a foundation—chasing innovation instead of solving the right problems. The solution is pairing AI strategy with AI policy and governance frameworks, creating clear rules of engagement that drive responsible, trustworthy, and sustainable AI adoption.Importantly, the conversation extends beyond large enterprises. Small and mid-sized businesses, professionals, and individuals are encouraged to develop their own AI strategies—“pick a lane,” build depth in a specific AI discipline (such as generative AI, AI governance, or algorithmic assessments), and align learning with clear goals.The episode closes with a direct message: AI without strategy is risk at scale. Strategy and accountability are inseparable, and ethics must be designed into AI systems—not bolted on later. AI strategy, done right, becomes ethics in action.Bottom line: Technology moves fast, but purpose, governance, and strategy are what keep AI on course.

In this episode, I speak with Dr. Annie Green about AI and how it has and will continue to influence how we work, live, and play.

AI in the late 1980s, starting with rule-based expert systems built in languages like Lisp and Prolog for Fortune 1000 companies and government agencies. Over the decades, my work has evolved from developing expert systems to integrating AI into enterprise applications, advancing through the big-data era, deep learning, and ultimately generative AI.

Later I pioneered approaches that merged knowledge management with AI, providing personalized, knowledge-driven systems. As generative AI and transformers emerged in the late 2010s and 2020s, the focus shifted toward responsible AI, AI ethics, and governance, emphasizing that advancing AI isn’t just about smarter machines—it requires guardrails to ensure alignment with human values. Without governance, AI risks scaling bad decisions at high speed, making ethical oversight essential.

Generative AI isn't just about flashy images, polished essays, or realistic voices. It’s a class of AI systems designed not merely to analyze information but to create new content—text, images, audio, video, 3D models, and even synthetic data. These models learn from massive datasets and use that knowledge to produce original outputs, making them far more than a novelty; they represent a fundamental shift in how knowledge, creativity, and digital content are generated.

Episode 1: Understanding Large Language Models — The Brains Behind Modern AI

In this debut episode of RHEMINISCING with Dr. Tony Rhem, Dr. Tony draws on over four decades of experience in artificial intelligence and knowledge management to demystify one of today’s most transformative technologies — Large Language Models (LLMs).

From his early days building expert systems in Prolog and Lisp to today’s generative AI revolution, Dr. Tony explains how LLMs work, why they matter, and what it takes to use them responsibly. Joined by his AI co-host, Georgia, they unpack the inner workings of LLMs, how they process language, generate human-like responses, and are reshaping how organizations manage, retrieve, and trust knowledge.

This episode goes beyond the hype to explore AI readiness, data integrity, and ethical oversight, tackling questions such as:

• Can we trust AI systems to make decisions in healthcare, law, or finance without human oversight?
• What happens when poor information architecture undermines AI outputs?
• How do we balance accessibility, innovation, and governance in the age of generative AI?

If you’ve ever wondered what really powers chatbots, copilots, or intelligent knowledge portals, or how AI is transforming the way we think, work, and learn, this episode is for you!

This is the trailer for RHEMINISCING, a thought-provoking podcast hosted by Dr. Tony Rhem, a globally recognized expert in AI, Knowledge Management, and ethical tech innovation. Through strategic reflection and future-forward insight, this podcast examines the transformative journey of artificial intelligence from its early roots in expert systems and neural networks to today’s generative and agentic system powerhouses and tomorrow’s possible AGI and Quantum AI solutions.