Peter Robin's guest appearances

Peter Robin

United Public Radio 

UFO Paranormal Radio & United Public Radio

Bridging the Gaps: A Portal for Curious Minds

Brain Inspired

Melanie Mitchell

Mark Humphries

Christopher Knowles

Laird Scranton

Lon Strickler

Stormy Daniels

Dylan Avery

Nelson Dellis

Derrel Sims

Brian Allen

Robert Duncan

Julie Parker

David Taylor

Heidi Hollis

Anil Seth

OwlTail

Peter Robin Hiesinger

Support the show to get full episodes and join the Discord community.  Robin and I discuss many of the ideas in his book The Self-Assembling Brain: How Neural Networks Grow Smarter. The premise is that our DNA encodes an algorithmic growth process that unfolds information via time and energy, resulting in a connected neural network (our brains!) imbued with vast amounts of information from the "start". This contrasts with modern deep learning networks, which start with minimal initial information in their connectivity, and instead rely almost solely on learning to gain their function. Robin suggests we won't be able to create anything with close to human-like intelligence unless we build in an algorithmic growth process and an evolutionary selection process to create artificial networks. Hiesinger Neurogenetics LaboratoryTwitter: @HiesingerLab.Book: The Self-Assembling Brain: How Neural Networks Grow Smarter 0:00 - Intro3:01 - The Self-Assembling Brain21:14 - Including growth in networks27:52 - Information unfolding and algorithmic growth31:27 - Cellular automata40:43 - Learning as a continuum of growth45:01 - Robustness, autonomous agents49:11 - Metabolism vs. connectivity58:00 - Feedback at all levels1:05:32 - Generality vs. specificity1:10:36 - Whole brain emulation1:20:38 - Changing view of intelligence1:26:34 - Popular and wrong vs. unknown and right

<a href="https://www.patreon.com/braininspired" rel="nofollow" target="_blank">Support the show</a> to get full episodes and join the Discord community. <a href="https://www.patreon.com/braininspired" rel="nofollow" target="_blank"></a> Robin and I discuss many of the ideas in his book <a href="https://www.amazon.com/gp/product/0691181225/ref=as_li_qf_asin_il_tl?ie=UTF8&amp;tag=pmiddlebroo09-20&amp;creative=9325&amp;linkCode=as2&amp;creativeASIN=0691181225&amp;linkId=7d9b2d30de97530b49d1c6e179db0c3f" rel="nofollow" target="_blank">The Self-Assembling Brain: How Neural Networks Grow Smarter</a>. The premise is that our DNA encodes an algorithmic growth process that unfolds information via time and energy, resulting in a connected neural network (our brains!) imbued with vast amounts of information from the "start". This contrasts with modern deep learning networks, which start with minimal initial information in their connectivity, and instead rely almost solely on learning to gain their function. Robin suggests we won't be able to create anything with close to human-like intelligence unless we build in an algorithmic growth process and an evolutionary selection process to create artificial networks. <ul><li><a href="http://www.flygen.org/" rel="nofollow" target="_blank">Hiesinger Neurogenetics Laboratory</a></li><li>Twitter: <a href="https://twitter.com/HiesingerLab" rel="nofollow" target="_blank">@HiesingerLab.</a></li><li>Book: <a href="https://www.amazon.com/gp/product/0691181225/ref=as_li_qf_asin_il_tl?ie=UTF8&amp;tag=pmiddlebroo09-20&amp;creative=9325&amp;linkCode=as2&amp;creativeASIN=0691181225&amp;linkId=7d9b2d30de97530b49d1c6e179db0c3f" rel="nofollow" target="_blank">The Self-Assembling Brain: How Neural Networks Grow Smarter</a></li></ul> 0:00 - Intro 3:01 - The Self-Assembling Brain 21:14 - Including growth in networks 27:52 - Information unfolding and algorithmic growth 31:27 - Cellular automata 40:43 - Learning as a continuum of growth 45:01 - Robustness, autonomous agents 49:11 - Metabolism vs. connectivity 58:00 - Feedback at all levels 1:05:32 - Generality vs. specificity 1:10:36 - Whole brain emulation 1:20:38 - Changing view of intelligence 1:26:34 - Popular and wrong vs. unknown and right

BI 124 Peter Robin Hiesinger: The Self-Assembling Brain

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” available at: https://www.bridgingthegaps.ie/2021/06/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” available at: https://www.bridgingthegaps.ie/2021/02/on-task-how-our-brain-gets-things-done-with-professor-david-badre/

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” available at: <a href="https://www.bridgingthegaps.ie/2021/06/the-spike-journey-of-electric-signals-in-brain-from-perception-to-action-with-professor-mark-humphries/" rel="nofollow" target="_blank">https://www.bridgingthegaps.ie/2021/06/the-spike-journey-of-electric-signals-in-brain-from-perception-to-action-with-professor-mark-humphries/</a> And then listen to “On Task: How Our Brain Gets Things Done” with Professor David Badre” available at: <a href="https://www.bridgingthegaps.ie/2021/02/on-task-how-our-brain-gets-things-done-with-professor-david-badre/" rel="nofollow" target="_blank">https://www.bridgingthegaps.ie/2021/02/on-task-how-our-brain-gets-things-done-with-professor-david-badre/</a>

"The Self-Assembling Brain" and Quest for Improved AI with Professor Peter Robin Hiesinger

Richard Dolan

Karyn Dolan

Through The Keyhole w/ Karyn Dolan Guest Richard Dolan &amp; Peter Robin 082709

Through The Keyhole w/ Karyn Dolan Guest Richard Dolan & Peter Robin 082709