“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.

Quantum Computers: Building and Harnessing the Power of Quantum Machines with Professor Andrea Morello

Quantum computers store data and perform computations by utilizing properties of quantum physics. Quantum computations are performed by these machines by utilizing quantum state features such as superposition and entanglement. Traditional computers store data in binary “bits,” which can be either 0s or 1s. A quantum bit, or qubit, is the fundamental memory unit in a quantum computer. Quantum states such as the spin of an electron or the direction of a photon, are used to create qubits. This could be very useful for specific problems where quantum computers could considerably outperform even the most powerful supercomputers. In this episode of Bridging the Gaps I speak with professor Andrea Morello and we discuss fascinating science & engineering of conceptualizing and building quantum computers. Professor Andrea Morello helps us to unpack and tackle questions such as what a quantum computer is and how we build a quantum computer.

Andrea Morello is the professor of Quantum Engineering in the School of Electrical Engineering and Telecommunications at the University of New South Wales Sydney, Australia.

I begin our conversation by asking professor Morello what a quantum computer is, and how it differs from classical and conventional computers. The no-cloning theorem’s implications in the field of quantum computers are next discussed. The no-cloning theorem states that it is impossible to create an independent and identical copy of an unknown quantum state. Professor Morello’s team uses single-spin in silicon to construct quantum computers, and we go over their approach in depth. The true value of quantum computers can only be realised if we develop creative algorithms that make effective use of quantum computers’ exponentially huge information space and processing capability. We discuss this in detail. We also touch upon the concept of quantum chaos and discuss research in this area. This has been a fascinating discussion.

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

Artificial Intelligence: Fascinating Opportunities and Emerging Challenges with Professor Bart Selman

Research and development in the field of Artificial Intelligence is progressing at an amazing pace. These developments are moving beyond simple applications such as machine vision, autonomous vehicles, natural language processing and medical diagnosis. Future AI systems will be able to use reasoning to make decisions; will employ innovative models of non-human intelligence; will augment human intelligence through human centric AI Systems. These systems will enable us to discover solutions to scientific and social problems, and will enable us to understand the physical world around us that has never been possible up-to this point in time. In this episode of Bridging the Gaps, I speak with Professor Bart Selman to discuss these fascinating opportunities as well as emerging challenges in the field of Artificial Intelligence.

Bart Selman is a Professor of Computer Science at Cornell University. He is a Fellow of the American Association for Artificial Intelligence and a Fellow of the American Association for the Advancement of Science. Professor Bart Selman is the president-elect of The Association for the Advancement of Artificial Intelligence. We begin our conversation by going through some of the recent developments in the field of Artificial Intelligence and how far we are from achieving the goal of developing Artificial General Intelligence.

We discuss in detail artificial reasoning, non-human intelligence and human centric AI. We also discuss state of the art research on the topic of explainable AI. We then discuss challenges posed by applying research in the field of AI to develop systems such as autonomous weapons, weaponized AI and other similar and sensitive domains. This has been a fascinating discussion about cutting edge research in the field of Artificial Intelligence.

Compliment Professor Selman’s insights with equally fascinating discussion with Professor Toby Walsh “2062: The World That AI Made”.

By |August 14th, 2020|Artificial Intelligence, Computer Science, Podcasts|