“The Secret Life of Data: Navigating Hype and Uncertainty” with Aram Sinnreich and Jesse Gilbert

Secret of Data Book Reviewed at Bridging the Gaps

As fresh algorithms and new analytical methods emerge, existing datasets can uncover insights beyond their original purpose. Regardless of their intended use, data often possess hidden potentials and always have a “secret life”. “How this data will be used, by other people in other times and places, has profound implications for every aspect of our lives—from our intimate relationships to our professional lives to our political systems”, argue Professor Aram Sinnreich and Jesse Gilbert in their new book “The Secret Life of Data: Navigating Hype and Uncertainty in the Age of Algorithmic Surveillance”. This phenomenon raises various moral, ethical, and legal questions: Should we use datasets for unexpected and unforeseen insights? Should we create new frameworks to control and regulate using datasets for purposes beyond their original intent? Ignoring opportunities to analyse data in new ways might limit their potential, but pursuing them introduces ethical dilemmas. In this episode of Bridging the Gaps, I speak with Professor Aram Sinnreich and Jesse Gilbert.

Aram Sinnreich is an author, professor, and musician. He is Chair of Communication Studies at American University. Jesse Gilbert is an interdisciplinary artist exploring the intersection of visual art, sound, and software design at his firm Dark Matter Media. He was the founding Chair of the Media Technology department at Woodbury University.

I begin the conversation by setting the stage for our listeners and delving into the idea of the “secret life of data” as discussed in the book. We then delve deeper into the nature and scale of the challenge posed by this phenomenon. One key point highlighted in the book is that the challenge is primarily conceptual—we’re uncertain about what exactly we’re aiming to control, handle, and regulate. We thoroughly explore this aspect.

To address this “conceptual challenge,” the book provides a comprehensive overview of various frameworks and approaches that have been developed and are currently utilised to tackle similar challenges. The book then extends these concepts to envision and propose a framework that could aid us in addressing this particular challenge. We discuss in detail a number of existing frameworks outlined in the book, and then explore the possibilities to address these emerging challenges. Overall, this conversation has been immensely interesting and enlightening.

Complement this discussion with “Dark Data: Why What You Don’t Know Matters” with Professor David Hand and then listen to Reclaiming Human Intelligence and “How to Stay Smart in a Smart World” with Prof. Gerd Gigerenzer

By |May 20th, 2024|Artificial Intelligence, Computer Science, Future, Information, Podcasts|
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“Evolutionary Intelligence: How Technology Will Make Us Smarter” with Professor W. Russell Neuman

Evolutionary Intelligence book reviewed at Bridging the Gaps

Artificial intelligence (AI) has emerged as one of the most remarkable advancements of our time. It is a powerful evolving technology that has transformed the way we interact with machines and perceive the capabilities of computer systems. However, with this newfound power comes a natural apprehension. There is a noticeable fear surrounding the unintentional consequences and unintended implications of Artificial Intelligence. As this technology becomes increasingly integrated into our daily lives, the question is: how justified are our fears and just how tangible, and how real is the threat posed by this revolutionary technology? Perhaps, the underlying cause of these fears is our tendency to unjustifiably attribute human traits to the machines we may construct.

A compelling new perspective suggests that human intelligence will evolve alongside digital technology, leading to a transformative coevolution of human and artificial intelligence. This augmented intelligence will reshape our thinking and behaviour. In his recent book “Evolutionary Intelligence: How Technology Will Make Us Smarter” Professor W. Russell Neuman offers a remarkably optimistic perspective where computational intelligence not only addresses the well-known limitations of human judgement but also enhances decision-making capabilities and expands our capacity for action. In this episode of Bridging the Gaps, I speak with Professor W. Russell Neuman. We discuss how our future depends on our ability to computationally compensate for the limitations of the human cognitive system. We explore Neuman’s viewpoint that “if intelligence is the capacity to match means with ends, then augmented intelligence can offer the ability to adapt to changing environments as we face the ultimate challenge of long-term survival”. Professor Neuman’s distinctive approach to explain complex concepts through narratives and anecdotes adds an engaging layer of interest to this discussion. This highly informative discussion makes a powerful argument for the continued coexistence of humans and their machines.

W. Russell Neuman is Professor of Media Technology at New York University. He is a specialist in new media and digital education. He is a founding faculty of the MIT Media Lab. He served as a Senior Policy Analyst in the White House Office of Science and Technology Policy, working in the areas of information technology, broadband policy, and technologies for border security.

Complement this discussion with Reclaiming Human Intelligence and “How to Stay Smart in a Smart World” with Prof. Gerd Gigerenzer and then listen to “Working with AI: Real Stories of Human-Machine Collaboration” with Professor Thomas Davenport and Professor Steven Miller.

By |March 31st, 2024|Artificial Intelligence, Computer Science, Future, Information, Knowledge, Podcasts, Technology|
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“The AI Playbook: Mastering the Rare Art of Machine Learning Deployment” with Eric Siegel

The AI Playbook featured on Bridging the Gaps

The most powerful tool often comes with the greatest challenges. In recent times Machine learning has emerged as the world’s leading general-purpose technology, yet its implementation remains notably complex. Beyond the realm of Big Tech and a select few leading enterprises, many machine learning initiatives don’t succeed, failing to deliver on their potential. What’s lacking? A specialised business approach and development & deployment strategy tailored for widespread adoption. In his recent book “The AI Playbook: Mastering the Rare Art of Machine Learning Deployment” acclaimed author Eric Siegel introduces a comprehensive six-step methodology for guiding machine learning projects from inception to implementation. The book showcases the methodology through both successful and unsuccessful anecdotes, featuring insightful case studies from renowned companies such as UPS, FICO, and prominent dot-coms. In this episode of Bridging the Gaps, I speak with Eric Siege. We discuss this disciplined approach that empowers business professionals, and establishes a sorely needed strategic framework for data professionals.

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker.

We begin our discussion by addressing Eric’s notable observation, highlighted both in his presentations and book, that the “AI Hype” is a distraction for companies. Eric elaborates on this notion, providing detailed insights. Additionally, we explore the suggestion to shift focus from the broad term “AI” to the more specific “Machine Learning.” Our conversation then delves into the challenges faced by companies and professionals in conceptualising and deploying AI-driven ideas and solutions. This then leads to the consideration of whether forming specialised teams and developing focused strategies are necessary to address these challenges effectively. Next, we delve into the intricacies of the six-step BizML process introduced by Eric in his book, comparing it to the concept of MLOps. We then thoroughly examine the BizML process, dissecting its components and implications. Overall, this has been a highly enlightening and informative discussion.

Complement this discussion with “Working with AI: Real Stories of Human-Machine Collaboration” with Professor Thomas Davenport and Professor Steven Miller and then listen to “Machines like Us: TOWARD AI WITH COMMON SENSE” with Professor Ronald Brachman

By |February 11th, 2024|Artificial Intelligence, Computer Science, Podcasts, Technology|
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