Pondering AI

Pia Lauritzen questions our use of questions, the nature of humanity, the premise of AGI, the essence of tech, if humans can be optimized and why thinking is required. Pia and Kimberly discuss the function of questions, curiosity as a basic human feature, AI as an answer machine, why humans think, the contradiction at the heart of AGI, grappling with the three big Es, the fallacy of human optimization, respecting humanity, Heidegger’s eerily precise predictions, the skill of critical thinking, and why it’s not really about the questions at all. Pia Lauritzen, PhD is a philosopher, author and tech inventor asking big questions about tech and transformation. As the CEO and Founder of Qvest and a Thinkers50 Radar Member Pia is on a mission to democratize the power of questions. Related ResourcesQuestions (Book): https://www.press.jhu.edu/books/title/23069/questions TEDx Talk: https://www.ted.com/talks/pia_lauritzen_what_you_don_t_know_about_questions Question Jam: www.questionjam.comForbes Column: forbes.com/sites/pialauritzen LinkedIn Learning: www.Linkedin.com/learning/pialauritzen Personal Website: pialauritzen.dk A transcript of this episode is here.   

Michael Strange has a healthy appreciation for complexity, diagnoses hype as antithetical to innovation and prescribes an interdisciplinary approach to making AI well.  Michael and Kimberly discuss whether AI is good for healthcare; healthcare as a global system; radical shifts precipitated by the pandemic; why hype stifles nuance and innovation; how science works; the complexity of the human condition; human well-being vs. health; the limits of quantification; who is missing in healthcare and health data; the political-economy and material impacts of AI as infrastructure; the doctor in the loophole; the humility required to design healthy AI tools and create a resilient, holistic healthcare system. Michael Strange is an Associate Professor in the Dept of Global Political Affairs at Malmö University focusing on core questions of political agency and democratic engagement. In this context he works on Artificial Intelligence, health, trade, and migration. Michael directed the Precision Health & Everyday Democracy (PHED) Commission and serves on the board of two research centres: Citizen Health and the ICF (Imagining and Co-creating Futures). Related Resources If AI is to Heal Our Healthcare Systems, We Need to Redesign How AI Is Developed (article): https://www.techpolicy.press/if-ai-is-to-heal-our-healthcare-systems-we-need-to-redesign-how-ai-itself-is-developed/ Beyond ‘Our product is trusted!’ – A processual approach to trust in AI healthcare (paper) https://mau.diva-portal.org/smash/record.jsf?pid=diva2%3A1914539 Michael Strange (website): https://mau.se/en/persons/michael.strange/  A transcript of this episode is here.     

Andriy Burkov talks down dishonest hype and sets realistic expectations for when LLMs, if properly and critically applied, are useful. Although maybe not as AI agents.  Andriy and Kimberly discuss how he uses LLMs as an author; LLMs as unapologetic liars; how opaque training data impacts usability; not knowing if LLMs will save time or waste it; error-prone domains; when language fluency is useless; how expertise maximizes benefit; when some idea is better than no idea; limits of RAG; how LLMs go off the rails; why prompt engineering is not enough; using LLMs for rapid prototyping; and whether language models make good AI agents (in the strictest sense of the word). Andriy Burkov holds a PhD in Artificial Intelligence and is the author of The Hundred Page Machine Learning and Language Models books. His Artificial Intelligence Newsletter reaches 870,000+ subscribers. Andriy was previously the Machine Learning Lead at Talent Neuron and the Director of Data Science (ML) at Gartner. He has never been a Ukrainian footballer. Related Resources The Hundred Page Language Models Book: https://thelmbook.com/ The Hundred Page Machine Learning Book: https://themlbook.com/  True Positive Weekly (newsletter): https://aiweekly.substack.com/  A transcript of this episode is here.

In this engaging conversation with Ravit Dotan, an AI ethics researcher and founder of TechBetter, the importance of embedding responsibility into AI innovation is front and center. Ravit argues that all algorithms carry inherent values and that clarity of purpose is essential for beneficial AI use. He critiques the pitfalls of ethics washing and highlights the gap between ethical intentions and actual practices within organizations. The discussion also emphasizes the role of individual advocacy and the need for a collaborative approach to responsibly harness AI.

Dr. Ash Watson studies how stories ranging from classic Sci-Fi to modern tales invoking moral imperatives, dystopian futures and economic logic shape our views of AI. Ash and Kimberly discuss the influence of old Sci-Fi on modern tech; why we can’t escape the stories we’re told; how technology shapes society; acting in ways a machine will understand; why the language we use matters; value transference from humans to AI systems; the promise of AI’s promise; grounding AI discourse in material realities; moral imperatives and capitalizing on crises; economic investment as social logic; AI’s claims to innovation; who innovation is really for; and positive developments in co-design and participatory research.  Dr. Ash Watson is a Scientia Fellow and Senior Lecturer at the Centre for Social Research in Health at UNSW Sydney. She is also an Affiliate of the Australian Research Council (ARC) Centre of Excellence for Automated Decision-Making and Society (CADMS). Related Resources:Ash Watson (Website): https://awtsn.com/The promise of artificial intelligence in health: Portrayals of emerging healthcare technologies (Article): https://doi.org/10.1111/1467-9566.13840An imperative to innovate? Crisis in the sociotechnical imaginary (Article): https://doi.org/10.1016/j.tele.2024.102229A transcript of this episode is here.   

Robert Mahari examines the consequences of addictive intelligence, adaptive responses to regulating AI companions, and the benefits of interdisciplinary collaboration. Robert and Kimberly discuss the attributes of addictive products; the allure of AI companions; AI as a prescription for loneliness; not assuming only the lonely are susceptible; regulatory constraints and gaps; individual rights and societal harms; adaptive guardrails and regulation by design; agentic self-awareness; why uncertainty doesn’t negate accountability; AI’s negative impact on the data commons; economic disincentives; interdisciplinary collaboration and future research.  Robert Mahari is a JD-PhD researcher at MIT Media Lab and the Harvard Law School where he studies the intersection of technology, law and business. In addition to computational law, Robert has a keen interest in AI regulation and embedding regulatory objectives and guardrails into AI designs. A transcript of this episode is here.   Additional Resources:The Allure of Addictive Intelligence (article): https://www.technologyreview.com/2024/08/05/1095600/we-need-to-prepare-for-addictive-intelligence/Robert Mahari (website): https://robertmahari.com/

Phaedra Boinodiris minds the gap between AI access and literacy by integrating educational siloes, practicing human-centric design, and cultivating critical consumers. Phaedra and Kimberly discuss the dangerous confluence of broad AI accessibility with lagging AI literacy and accountability; coding as a bit player in AI design; data as an artifact of human experience; the need for holistic literacy; creating critical consumers; bringing everyone to the AI table; unlearning our siloed approach to education; multidisciplinary training; human-centricity in practice; why good intent isn’t enough; and the hard work required to develop good AI.  Phaedra Boinodiris is IBM’s Global Consulting Leader for Trustworthy AI and co-author of the book AI for the Rest of Us. As an RSA Fellow, co-founder of the Future World Alliance, and academic advisor, Phaedra is shaping a future in which AI is accessible and good for all. A transcript of this episode is here.    Additional Resources: Phaedra’s Website -  https://phaedra.ai/ The Future World Alliance - https://futureworldalliance.org/ 

Ryan Carrier trues up the benefits and costs of responsible AI while debunking misleading narratives and underscoring the positive power of the consumer collective.  Ryan and Kimberly discuss the growth of AI governance; predictable resistance; the (mis)belief that safety impedes innovation; the “cost of doing business”; downside and residual risk; unacceptable business practices; regulatory trends and the law; effective disclosures and deceptive design; the value of independence; auditing as a business asset; the AI lifecycle; ethical expertise and choice; ethics boards as advisors not activists; and voting for beneficial AI with our wallets.  A transcript of this episode is here.    Ryan Carrier is the Executive Director of ForHumanity, a non-profit organization improving AI outcomes through increased accountability and oversight. 

Olivia Gambelin, a leading AI ethicist and founder of Ethical Intelligence, sheds light on the fusion of ethics and technology. She passionately discusses philogagging and the dangers of contrasting humans with AI, advocating for a values-driven approach in AI development. The conversation touches on cultivating curiosity, accountability in tech, and the importance of emotional intelligence and creativity in humans. Olivia also introduces the Values Canvas as a tool for aligning organizational values with ethical AI practices, inspiring innovation that reflects human intentions.

Helen Beetham, an influential educator and consultant on digital education, discusses the future of higher education in the context of AI. She challenges the traditional purposes of learning and highlights the need for diversity in course offerings. Helen critiques the misconception of AI as a panacea, emphasizing the importance of human interaction and critical digital literacy. The conversation also addresses the disparities in education systems and advocates for adaptable teaching methods that recognize students as active participants in their learning journey.