x

75% of people globally want unbiased news (Pew Research) but are concerned they're not getting that.

Get balanced coverage with Symby. See how stories are covered across diverse outlets and spot when one side covers a story the other skips.

PODCAST

Brain Inspired

Paul Middlebrooks

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational

science natural sciences

All Episodes

01:25:02

BI 110 Catherine Stinson and Jessica Thompson:...

Brain Inspired ·

2021/07/06

Catherine, Jess, and I use some of the ideas from their recent papers to discuss how different types of explanations in neuroscience and AI could be unified into explanations of intelligence, natural or artificial. Catherine has written about how models are related to the target system they are built to explain. She suggests both the model and the target system should be considered as instantiations of a specific kind of phenomenon, and explanation is a product of relating the model and the target

en-US

02:03:43

BI 109 Mark Bickhard: Interactivism

Brain Inspired ·

2021/06/26

Mark and I discuss a wide range of topics surrounding his Interactivism framework for explaining cognition. Interactivism stems from Mark's account of representations and how what we represent in our minds is related to the external world - a challenge that has plagued the mind-body problem since the beginning. Basically, representations are anticipated interactions with the world, that can be true (if enacting one helps an organism maintain its thermodynamic relation with the world) or false (if it

en-US

01:26:12

BI 108 Grace Lindsay: Models of the Mind

Brain Inspired ·

2021/06/16

Grace's websiteTwitter:@neurograce.Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain.We talked about Grace's work using convolutional neural networks to study vision and attention way back on episode 11. Grace and I discuss her new book Models of the Mind, about the blossoming and conceptual foundations of the computational approach to study minds and brains. Each chapter of the book focuses on one major topic and provides historical

en-US

01:29:24

BI 107 Steve Fleming: Know Thyself

Brain Inspired ·

2021/06/06

Steve and I discuss many topics from his new book Know Thyself: The Science of Self-Awareness. The book covers the full range of what we know about metacognition and self-awareness, including how brains might underlie metacognitive behavior, computational models to explain mechanisms of metacognition, how and why self-awareness evolved, which animals beyond humans harbor metacognition and how to test it, its role and potential origins in theory of mind and social interaction, how our metacognitive

en-US

01:31:53

BI 106 Jacqueline Gottlieb and Robert Wilson: Deep...

Brain Inspired ·

2021/05/27

Jackie and Bob discuss their research and thinking about curiosity. Jackie's background is studying decision making and attention, recording neurons in nonhuman primates during eye movement tasks, and she's broadly interested in how we adapt our ongoing behavior. Curiosity is crucial for this, so she recently has focused on behavioral strategies to exercise curiosity, developing tasks that test exploration, information sampling, uncertainty reduction, and intrinsic motivation. Bob's

en-US

01:01:43

BI 105 Sanjeev Arora: Off the Convex Path

Brain Inspired ·

2021/05/17

Sanjeev and I discuss some of the progress toward understanding how deep learning works, specially under previous assumptions it wouldn't or shouldn't work as well as it does. Deep learning theory poses a challenge for mathematics, because its methods aren't rooted in mathematical theory and therefore are a "black box" for math to open. We discuss how Sanjeev thinks optimization, the common framework for thinking of how deep nets learn, is the wrong approach. Instead, a promising alternative focuses

en-US

01:50:32

BI 104 John Kounios and David Rosen: Creativity,...

Brain Inspired ·

2021/05/07

What is creativity? How do we measure it? How do our brains implement it, and how might AI?Those are some of the questions John, David, and I discuss. The neuroscience of creativity is young, in its "wild west" days still. We talk about a few creativity studies they've performed that distinguish different creative processes with respect to different levels of expertise (in this case, in jazz improvisation), and the underlying brain circuits and activity, including using transcranial direct current

en-US

01:27:26

BI 103 Randal Koene and Ken Hayworth: The Road to...

Brain Inspired ·

2021/04/26

Randal, Ken, and I discuss a host of topics around the future goal of uploading our minds into non-brain systems, to continue our mental lives and expand our range of experiences. The basic requirement for such a subtrate-independent mind is to implement whole brain emulation. We discuss two basic approaches to whole brain emulation. The "scan and copy" approach proposes we somehow scan the entire structure of our brains (at whatever scale is necessary) and store that scan until some future date

en-US

01:32:20

BI 102 Mark Humphries: What Is It Like To Be A Spike?

Brain Inspired ·

2021/04/16

Mark and I discuss his book, The Spike: An Epic Journey Through the Brain in 2.1 Seconds. It chronicles how a series of action potentials fire through the brain in a couple seconds of someone's life. Starting with light hitting the retina as a person looks at a cookie, Mark describes how that light gets translated into spikes, how those spikes get processed in our visual system and eventually transform into motor commands to grab that cookie. Along the way, he describes some of the big ideas

en-US

01:45:22

BI 101 Steve Potter: Motivating Brains In and Out of...

Brain Inspired ·

2021/04/06

Steve and I discuss his book, How to Motivate Your Students to Love Learning, which is both a memoir and a guide for teachers and students to optimize the learning experience for intrinsic motivation. Steve taught neuroscience and engineering courses while running his own lab studying the activity of live cultured neural populations (which we discuss at length in his previous episode). He relentlessly tested and tweaked his teaching methods, including constant feedback from the students, to optimize

en-US

00:50:03

BI 100.6 Special: Do We Have the Right Vocabulary and...

Brain Inspired ·

2021/03/28

We made it to the last bit of our 100th episode celebration. These have been super fun for me, and I hope you've enjoyed the collections as well. If you're wondering where the missing 5th part is, I reserved it exclusively for Brain Inspired's magnificent Patreon supporters (thanks guys!!!!). The final question I sent to previous guests: Do we already have the right vocabulary and concepts to explain how brains and minds are related? Why or why not? Timestamps: 0:00 - Intro 5:04 - Andrew

en-US

01:04:26

BI 100.4 Special: What Ideas Are Holding Us Back?

Brain Inspired ·

2021/03/21

In the 4th installment of our 100th episode celebration, previous guests responded to the question: What ideas, assumptions, or terms do you think is holding back neuroscience/AI, and why? As usual, the responses are varied and wonderful! Timestamps: 0:00 - Intro 6:41 - Pieter Roelfsema 7:52 - Grace Lindsay 10:23 - Marcel van Gerven 11:38 - Andrew Saxe 14:05 - Jane Wang 16:50 - Thomas Naselaris 18:14 - Steve Potter 19:18 - Kendrick Kay 22:17 - Blake Richards 27:52 - Jay

en-US

01:08:43

BI 100.3 Special: Can We Scale Up to AGI with Current...

Brain Inspired ·

2021/03/17

Part 3 in our 100th episode celebration. Previous guests answered the question: Given the continual surprising progress in AI powered by scaling up parameters and using more compute, while using fairly generic architectures (eg. GPT-3): Do you think the current trend of scaling compute can lead to human level AGI? If not, what's missing? It likely won't surprise you that the vast majority answer "No." It also likely won't surprise you, there is differing opinion on what's

en-US

01:25:00

BI 100.2 Special: What Are the Biggest Challenges and...

Brain Inspired ·

2021/03/12

In this 2nd special 100th episode installment, many previous guests answer the question: What is currently the most important disagreement or challenge in neuroscience and/or AI, and what do you think the right answer or direction is? The variety of answers is itself revealing, and highlights how many interesting problems there are to work on. Timestamps: 0:00 - Intro 7:10 - Rodrigo Quian Quiroga 8:33 - Mazviita Chirimuuta 9:15 - Chris Eliasmith 12:50 - Jim DiCarlo 13:23 - Paul Cisek 16:42 -

en-US

00:42:32

BI 100.1 Special: What Has Improved Your Career or...

Brain Inspired ·

2021/03/09

Brain Inspired turns 100 (episodes) today! To celebrate, my patreon supporters helped me create a list of questions to ask my previous guests, many of whom contributed by answering any or all of the questions. I've collected all their responses into separate little episodes, one for each question. Starting with a light-hearted (but quite valuable) one, this episode has responses to the question, "In the last five years, what new belief, behavior, or habit has most improved your career or well

en-US

01:46:35

BI 099 Hakwan Lau and Steve Fleming: Neuro-AI...

Brain Inspired ·

2021/02/28

Hakwan, Steve, and I discuss many issues around the scientific study of consciousness. Steve and Hakwan focus on higher order theories (HOTs) of consciousness, related to metacognition. So we discuss HOTs in particular and their relation to other approaches/theories, the idea of approaching consciousness as a computational problem to be tackled with computational modeling, we talk about the cultural, social, and career aspects of choosing to study something as elusive and controversial as

en-US

01:32:38

BI 098 Brian Christian: The Alignment Problem

Brain Inspired ·

2021/02/18

Brian and I discuss a range of topics related to his latest book, The Alignment Problem: Machine Learning and Human Values. The alignment problem asks how we can build AI that does what we want it to do, as opposed to building AI that will compromise our own values by accomplishing tasks that may be harmful or dangerous to us. Using some of the stories Brain relates in the book, we talk about: The history of machine learning and how we got this point;Some methods researches are creating to

en-US

01:23:57

BI 097 Omri Barak and David Sussillo: Dynamics and...

Brain Inspired ·

2021/02/08

Omri, David and I discuss using recurrent neural network models (RNNs) to understand brains and brain function. Omri and David both use dynamical systems theory (DST) to describe how RNNs solve tasks, and to compare the dynamical stucture/landscape/skeleton of RNNs with real neural population recordings. We talk about how their thoughts have evolved since their 2103 Opening the Black Box paper, which began these lines of research and thinking. Some of the other topics we discuss: The idea of

en-US

01:34:10

BI 096 Keisuke Fukuda and Josh Cosman: Forking Paths

Brain Inspired ·

2021/01/29

K, Josh, and I were postdocs together in Jeff Schall's and Geoff Woodman's labs. K and Josh had backgrounds in psychology and were getting their first experience with neurophysiology, recording single neuron activity in awake behaving primates. This episode is a discussion surrounding their reflections and perspectives on neuroscience and psychology, given their backgrounds and experience (we reference episode 84 with György Buzsáki and David Poeppel). We also talk about their divergent paths - K

en-US

01:25:28

BI 095 Chris Summerfield and Sam Gershman: Neuro for AI?

Brain Inspired ·

2021/01/19

It's generally agreed machine learning and AI provide neuroscience with tools for analysis and theoretical principles to test in brains, but there is less agreement about what neuroscience can provide AI. Should computer scientists and engineers care about how brains compute, or will it just slow them down, for example? Chris, Sam, and I discuss how neuroscience might contribute to AI moving forward, considering the past and present. This discussion also leads into related topics, like the role of

en-US

234 results

Similar Podcasts

X Minus 1 - Old Time Radio Show

Inception Point Ai

Science, Spoken

WIRED

This Week in Space (Audio)

TWiT

DarshanTalks Podcast

Darshan Kulkarni

The Best of Coast to Coast AM

iHeartPodcasts and Coast to Coast AM