Tools Empowering Large Language Models

Artificial intelligence is no longer on the horizon; it is already reshaping our homes, workplaces, schools, hospitals, boardrooms, factories, and everyday decisions.Season 2 of RHEMINISCING with Dr. Tony Rhem: Exploring Artificial Intelligence Past, Present, and the Possible takes the conversation deeper, examining the ethical, governance, and solution challenges emerging across every area of AI, from casual personal use to enterprise-scale deployment.In season 2, I will explore:• Trust, bias, privacy, and explainability• Accountability and responsible AI use• The rapid growth of AI agents• AI’s environmental impact• Physical AI across robotics, IoT, healthcare, manufacturing, and infrastructure• What it truly means to govern AI before it governs usExpect more expert guests, sharper questions, and candid conversations about where AI is taking us and how leaders can ensure that innovation remains human-centered, trustworthy, and accountable.AI is moving fast. Responsible AI leadership must move faster.Season 2 is coming. Subscribe, share, and join the conversation.#ArtificialIntelligence #ResponsibleAI #AIGovernance #AIEthics #AgenticAI #PhysicalAI #AILeadership #RHEMINISCING #Podcast

AI without strategy creates confusion, unmanaged risk, wasted investment, and false confidence. In this special Season 1 recap of RHEMINISCING with Dr. Tony Rhem, Dr. Anthony J. Rhem connects the major ideas explored throughout the season and explains what organizations and individuals must understand to use artificial intelligence effectively and responsibly.The episode revisits the evolution of AI, from expert systems and rule-based technologies to large language models, generative AI, and AI agents. It examines how tools such as ChatGPT, Gemini, Claude, and Microsoft Copilot can support research, content creation, analysis, learning, and decision-making, while reinforcing why their outputs must still be questioned, tested, and verified.Drawing on conversations about the future of work, education, fractional executive leadership, AI transparency, and responsible implementation, Dr. Rhem identifies five defining lessons from Season 1:AI literacy is now an essential leadership skill.Knowledge management, information architecture, and trusted data matter more than ever.Human judgment remains the critical control point.Strategy and governance must come before scaling.Responsible AI must be translated into practical controls, testing, transparency, auditability, and accountability.The episode concludes with a preview of Season 2, which will explore AI governance, audits and assessments, AI risk management, AI assurance, agentic AI, environmental impacts, enterprise intelligence, and the path from experimentation to operational maturity.The central message is direct: technology is only as smart as the people, purpose, and principles behind it. #RHEMINISCING #DrTonyRhem #ArtificialIntelligence #AI  #AIGovernance #ResponsibleAI #AIStrategy #GenerativeAI #AgenticAI 

How can organizations innovate with artificial intelligence without compromising privacy, fairness, transparency, accountability, or public trust?In this episode of RHEMINISCING with Dr. Tony Rhem: Exploring Artificial Intelligence—Past, Present, and the Possible, Dr. Tony Rhem revisits a timely panel discussion from the DGIQ AIGV 2024 Conference on the ethical use of data in AI applications.Moderated by Dr. Rhem, the panel brings together experts in ethical AI, AI governance, data science, and responsible technology to examine why responsible AI begins with the data used to train, test, fine-tune, retrieve information for, and operate AI systems.The discussion explains how poor-quality, biased, improperly governed, or inadequately documented data can produce unreliable outcomes, discriminatory decisions, privacy violations, security vulnerabilities, and significant organizational risk.The panelists also explore how the rapid adoption of generative AI, retrieval-augmented generation, copilots, chatbots, automation, and agentic AI has raised the stakes for organizations. AI systems are no longer limited to generating predictions. They can now create content, interact with customers, retrieve enterprise knowledge, call tools, influence workflows, and participate in operational decisions.Topics discussed include:• What the ethical use of data in AI means• Why ethical AI requires more than regulatory compliance• Bias, fairness, justice, and discrimination in AI systems• Data privacy, consent, provenance, and security• The difference between AI transparency and explainability• The importance of model cards and documented design decisions• Monitoring AI systems for model drift and unintended outcomes• The roles of the EU AI Act, GDPR, NIST AI RMF, and AI assurance frameworks• Risks associated with generative AI and autonomous AI agents• How retrieval-augmented generation can expose conflicting or outdated information• Why “AI hallucinations” are often data, design, governance, or tool-selection problems• The importance of multidisciplinary and culturally diverse AI teams• Developing meaningful ethical AI metrics and key performance indicators• AI’s potential impact on employment, inequality, privacy, sustainability, and society• Why organizations of every size need responsible AI controlsA central message from this conversation is that AI ethics cannot be added at the end of an AI project. It must be designed into the data lifecycle, model-development process, governance framework, deployment environment, organizational culture, and ongoing monitoring program.Organizations must be able to explain what their AI systems do, what data they use, how decisions are made, what actions the systems can take, who may be affected, what controls are in place, and who is accountable when something goes wrong.This episode is essential viewing for board members, executives, AI leaders, data professionals, governance practitioners, compliance officers, risk managers, technology leaders, researchers, and anyone responsible for building, buying, deploying, auditing, or overseeing artificial intelligence.Subscribe to RHEMINISCING with Dr. Tony Rhem for informed conversations connecting AI’s history, current developments, and possible future. Share your perspective in the comments: What is the most significant ethical risk your organization faces as it adopts generative AI or agentic AI?

In part two (2) of the conversation with Chris Bosco, Dr. Tony sits down with Chris to continue the conversation on the evolving role of fractional executives, AI readiness, ethical AI, and the importance of transparency in algorithmic decision-making. Together, they explore why organizations must understand how AI reaches its conclusions, the need for subject matter experts in validating AI outputs, and why “minimal viable governance” can help companies move fast without sacrificing accountability.The discussion also highlights how experienced executives bring practical wisdom, knowledge transfer, and change leadership into organizations—especially when companies are adopting AI, digital transformation, and new operating models. Chris shares insights on fractional leadership, client readiness, AI-assisted executive services, and how organizations can move from experimentation to real business value.Topics covered include: AI transparency, algorithm assessment, AI governance, fractional executives, AI readiness, knowledge transfer, change management, and the future of AI-assisted leadership.Watch, like, comment, and subscribe for more conversations on artificial intelligence, knowledge management, leadership, and the future of work.

What does it really take to make AI work inside an organization?In this episode of RHEMINISCING, Dr. Tony Rhem is joined by Chris Bosco, Chief Commercial Officer of Act Digital Consulting, for a practical conversation about fractional executives, AI strategy, and why so many companies struggle to turn promising ideas into successful business outcomes. Together, they explore how experienced fractional leaders can help organizations move faster, reduce risk, and deliver real execution instead of just recommendations.This discussion explains why fractional executives are becoming a strategic solution for companies that need senior-level expertise without the cost, delay, or long hiring cycle of a full-time executive. Dr. Rhem and Chris Bosco also dig into the realities of AI implementation, including the need for clear business objectives, strong governance, reliable data, curated content, source-of-truth alignment, and hands-on operationalization.They also address one of the biggest reasons AI initiatives fail: organizations rush to deploy tools without first establishing the right strategy, data foundation, testing discipline, and organizational structure. From AI governance and data governance to information architecture, knowledge transfer, KPIs, and business value, this conversation is packed with insights for leaders who want to implement AI the right way.If you are a business leader, board member, CIO, CTO, Chief AI Officer, consultant, or digital transformation professional, this episode will help you think more clearly about how to align AI with business outcomes and build solutions that actually work.Topics covered:Fractional executives, fractional CIO, fractional CTO, fractional Chief AI Officer, AI governance, data governance, AI implementation, digital transformation, knowledge management, information architecture, source of truth, enterprise AI, business strategy, KPI development, operational execution, and AI readiness. #ArtificialIntelligence #AIStrategy #AIGovernance #DataGovernance #FractionalExecutive #DigitalTransformation #EnterpriseAI#KnowledgeManagement #InformationArchitecture #BusinessTransformation 

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.