“Artificial Intelligence: A Guide for Thinking Humans” with Professor Melanie Mitchell

Recent developments in the field of Artificial Intelligence are fascinating as well as terrifying; there are extravagant promises as well as frustrating setbacks; there is great progress in narrowly focused AI applications, and there is lack of progress in the field of Artificial General Intelligence. In this episode of Bridging the Gaps I speak with professor Melanie Mitchell and we discuss the history, recent successes, huge expectations and emerging fears and frustrations in the field of Artificial Intelligence. We discuss fascinating and intriguing research that professor Melanie Mitchell discusses in her book “Artificial Intelligence: A Guide for Thinking Humans”.

Melanie Mitchell is a professor of complexity at the Santa Fe Institute in New Mexico. Her research focuses on genetic algorithms, conceptual abstraction, analogy-making and visual recognition in Artificial Intelligence Systems. Professor Mitchell originated the Santa Fe Institute’s Complexity Explorer project, an online learning resource for complex systems.

We begin our discussion by reviewing the history of this fascinating field and by discussing initial claims and hype that emerged at the start. We then discuss the transition from rule-based AI systems to machine learning approaches. We look into the successes of AI in narrowly defined task-based systems; we discuss the anomalies that emerge when the data is mildly changed. We then discuss the future development in this field and the challenges involved in making any meaningful progress towards Artificial General Intelligence and creating common sense in AI systems. The challenge of creating common sense seems similar to the challenge of finding and understanding dark matter in the field of physics, we discuss this. We look into the profound disconnect between the continuing hype and the actual achievements in AI, what the field has accomplished and how much further it has to go. We also discuss the approach of conceptual abstraction and incorporating analogy-making in AI systems. This has been a fascinating discussion about this ambitious and thought-provoking field.

Complement this discussion with “Artificial Intelligence: Fascinating Opportunities and Emerging Challenges with Professor Bart Selman and then listen to “2062: The World That AI Made” with Professor Toby Walsh

By |May 1st, 2022|Artificial Intelligence, Computer Science, Information, Podcasts, Research, Technology|
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“Spark: The Life of Electricity and the Electricity of Life” with Professor Timothy Jorgensen

Spark Book Review at Bridging the Gaps

When we think about electricity, we most often think of the energy that powers various devices and appliances around us, or perhaps we visualise the lightning-streaked clouds of a stormy sky. But there is more to electricity and “life at its essence is nothing if not electrical”. In this episode of Bridging the Gaps, I speak with Professor Timothy Jorgensen and we discuss his recent book “Spark: The Life of Electricity and the Electricity of Life ”. The book explains the science of electricity through the lenses of biology, medicine and history. It illustrates how our understanding of electricity and the neurological system evolved in parallel, using fascinating stories of scientists and personalities ranging from Benjamin Franklin to Elon Musk. It provides a fascinating look at electricity, how it works, and how it animates our lives from within and without.

We start by discussing the earliest known experiences that humans had with electricity using amber. Amber was most likely the first material with which humans attempted to harness electricity, mostly for medical purposes. Romans used non-static electricity from specific types of fish. Moving on to Benjamin Franklin, we discuss how he attempted to harner the power of electricity and we discuss the earliest forms of devices to store electric charge. We then discuss experiments conducted by Luigi Galvani on dead frogs and by his nephew on dead humans using electricity. As interest in electricity grew, many so-called treatemnts for ailments such as headaches, for bad thoughts and even for sexual difficulties also emerged that were based on the use of electricity; we discuss few interesting examples of such treatments. We then move on to reviewing the cutting edge use of electricity in medical science and discussed medial implants, artificial limbs and deep stimulation technologies and proposed machine-brain interfaces. This has been a fascinating discussion.

Complement this discussion by listening to he Spike: Journey of Electric Signals in Brain from Perception to Action with Professor Mark Humphries and then listen to On Public Communication of Science and Technology with Professor Bruce Lewenstein

By |March 17th, 2022|Artificial Intelligence, Biology, Future, Podcasts, Research|
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“The Self-Assembling Brain” and the Quest for Artificial General Intelligence with Professor Peter Robin Hiesinger

How does a network of individual neural cells become a brain? How does a neural network learn, hold information and exhibit intelligence? While neurobiologists study how nature achieves this feat, computer scientists interested in artificial intelligence attempt to achieve it through technology. Are there ideas that researchers in the field of artificial intelligence borrow from their counterparts in the field of neuroscience? Can a better understanding of the development and working of the biological brain lead to the development of improved AI? In his book “The Self-Assembling Brain: How Neural Networks Grow Smarter” professor Peter Robin Hiesinger explores stories of both fields exploring the historical and modern approaches. In this episode of Bridging the Gaps, I speak with professor Peter Robin Hiesinger about the relationship between what we know about the development and working of biological brains and the approaches used to design artificial intelligence systems.

We start our conversation by reviewing the fascinating research that led to the development of neural theory. Professor Hiesigner suggests in the book that to understand what makes a neural network intelligent we must find the answer to the question: is this connectivity or is this learning that makes a neural network intelligent; we look into this argument. We then discuss “the information problem” that how we get information in the brain that makes it intelligent. We also look at the nature vs nurture debate and discuss examples of butterflies that take multigenerational trip, and scout bees that inform the bees in the hive the location and distance of the food. We also discuss the development of the biological brain by GNOME over time. We then shift the focus of discussion to artificial intelligence and explore ideas that the researchers in the field artificial intelligence can borrow from the research in the field of neuroscience. We discuss processes and approaches in the field of computing science such as Cellular Automata, Algorithmic Information Theory and Game of Life and explore their similarities with how GENOME creates the brain over time. This has been an immensely informative discussion.

Complement this discussion by listening to The Spike: Journey of Electric Signals in Brain from Perception to Action with Professor Mark Humphries and then listen to On Task: How Our Brain Gets Things Done” with Professor David Badre.

By |November 7th, 2021|Artificial Intelligence, Biology, Computer Science, Neuroscience, Podcasts|
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