Peter Robin Hiesinger's guest appearances

Peter Robin Hiesinger

Bridging the Gaps: A Portal for Curious Minds

Brain Inspired

James FitzGerald

Jessica Thompson

David Rosen

Ryan Poplin

Blake Richards

Terrence Sejnowski

David Ferrucci

Raia Hadsell

Melanie Mitchell

Matt Smith

Alison Gopnik

Brian Christian

Robert Wilson

Anil Seth

Jeff Hawkins

OwlTail

Peter Robin

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