Brain Inspired

Brain Inspired

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Brain Inspired episodes

  • BI 105 Sanjeev Arora: Off the Convex Path

    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 on the learning trajectories that occur as a result of different learning algorithms. We discuss two examples of his research to illustrate this: creating deep nets with infinitely large layers (and the networks still find solutions among the infinite possible solutions!), and massively increasing the learning rate during training (the opposite of accepted wisdom, and yet, again, the network finds solutions!). We also discuss his past focus on computational complexity and how he doesn't share the current neuroscience optimism comparing brains to deep nets.

    • Sanjeev's website.
    • His Research group website.
    • His blog: Off The Convex Path.
    • Papers we discuss
      • On Exact Computation with an Infinitely Wide Neural Net.
      • An Exponential Learning Rate Schedule for Deep Learning
    • Related
      • The episode with Andrew Saxe covers related deep learning theory in episode 52.
      • Omri Barak discusses the importance of learning trajectories to understand RNNs in episode 97.
      • Sanjeev mentions Christos Papadimitriou.

    Timestamps

    0:00 - Intro
    7:32 - Computational complexity
    12:25 - Algorithms
    13:45 - Deep learning vs. traditional optimization
    17:01 - Evolving view of deep learning
    18:33 - Reproducibility crisis in AI?
    21:12 - Surprising effectiveness of deep learning
    27:50 - "Optimization" isn't the right framework
    30:08 - Infinitely wide nets
    35:41 - Exponential learning rates
    42:39 - Data as the next frontier
    44:12 - Neuroscience and AI differences
    47:13 - Focus on algorithms, architecture, and objective functions
    55:50 - Advice for deep learning theorists
    58:05 - Decoding minds

    1 hr 2 min
  • BI 104 John Kounios and David Rosen: Creativity, Expertise, Insight

    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 stimulation to alter the creative process. Related to creativity, we also discuss the phenomenon and neuroscience of insight (the topic of John's book, The Eureka Factor), unconscious automatic type 1 processes versus conscious deliberate type 2 processes, states of flow, creative process versus creative products, and a lot more.

    • John Kounios.
    • Secret Chord Laboratories (David's company).
    • Twitter: @JohnKounios; @NeuroBassDave.
    • John's book (with Mark Beeman) on insight and creativity.
      • The Eureka Factor: Aha Moments, Creative Insight, and the Brain.
    • The papers we discuss or mention:
      • All You Need to Do Is Ask? The Exhortation to Be Creative Improves Creative Performance More for Nonexpert Than Expert Jazz Musicians
      • Anodal tDCS to Right Dorsolateral Prefrontal Cortex Facilitates Performance for Novice Jazz Improvisers but Hinders Experts
      • Dual-process contributions to creativity in jazz improvisations: An SPM-EEG study.

    Timestamps

    0:00 - Intro
    16:20 - Where are we broadly in science of creativity?
    18:23 - Origins of creativity research
    22:14 - Divergent and convergent thought
    26:31 - Secret Chord Labs
    32:40 - Familiar surprise
    38:55 - The Eureka Factor
    42:27 - Dual process model
    52:54 - Creativity and jazz expertise
    55:53 - "Be creative" behavioral study
    59:17 - Stimulating the creative brain
    1:02:04 - Brain circuits underlying creativity
    1:14:36 - What does this tell us about creativity?
    1:16:48 - Intelligence vs. creativity
    1:18:25 - Switching between creative modes
    1:25:57 - Flow states and insight
    1:34:29 - Creativity and insight in AI
    1:43:26 - Creative products vs. process

    1 hr 51 min
  • BI 103 Randal Koene and Ken Hayworth: The Road to Mind Uploading

    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 when we have figured out how to us that information to build a substrate that can house your mind. The "gradual replacement" approach proposes we slowly replace parts of the brain with functioning alternative machines, eventually replacing the entire brain with non-biological material and yet retaining a functioning mind.

    Randal and Ken are neuroscientists who understand the magnitude and challenges of a massive project like mind uploading, who also understand what we can do right now, with current technology, to advance toward that lofty goal, and who are thoughtful about what steps we need to take to enable further advancements.

    • Randal A Koene
      • Twitter: @randalkoene
      • Carboncopies Foundation.
      • Randal's website.
    • Ken Hayworth
      • Twitter: @KennethHayworth
      • Brain Preservation Foundation.
        • Youtube videos.

    Timestamps

    0:00 - Intro
    6:14 - What Ken wants
    11:22 - What Randal wants
    22:29 - Brain preservation
    27:18 - Aldehyde stabilized cryopreservation
    31:51 - Scan and copy vs. gradual replacement
    38:25 - Building a roadmap
    49:45 - Limits of current experimental paradigms
    53:51 - Our evolved brains
    1:06:58 - Counterarguments
    1:10:31 - Animal models for whole brain emulation
    1:15:01 - Understanding vs. emulating brains
    1:22:37 - Current challenges

    1 hr 28 min
  • BI 102 Mark Humphries: What Is It Like To Be A Spike?

    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 throughout the history of studying brains (like the mechanisms to explain how neurons seem to fire so randomly), the big mysteries we currently face (like why do so many neurons do so little?), and some of the main theories to explain those mysteries (we're prediction machines!). A fun read and discussion. This is Mark's second time on the podcast - he was on episode 4 in the early days, talking more in depth about some of the work we discuss in this episode!

    • The Humphries Lab.
    • Twitter: @markdhumphries
    • Book: The Spike: An Epic Journey Through the Brain in 2.1 Seconds.
    • Related papers
      • A spiral attractor network drives rhythmic locomotion.

    Timestamps:

    0:00 - Intro

    3:25 - Writing a book
    15:37 - Mark's main interest
    19:41 - Future explanation of brain/mind
    27:00 - Stochasticity and excitation/inhibition balance
    36:56 - Dendritic computation for network dynamics
    39:10 - Do details matter for AI?
    44:06 - Spike failure
    51:12 - Dark neurons
    1:07:57 - Intrinsic spontaneous activity
    1:16:16 - Best scientific moment
    1:23:58 - Failure
    1:28:45 - Advice

    1 hr 33 min
  • BI 101 Steve Potter: Motivating Brains In and Out of Dishes

    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 their learning experiences. He settled on real-world, project-based learning approaches, like writing wikipedia articles and helping groups of students design and carry out their own experiments. We discuss that, plus the science behind learning, principles important for motivating students and maintaining that motivation, and many of the other valuable insights he shares in the book.

    The first half of the episode we discuss diverse neuroscience and AI topics, like brain organoids, mind-uploading, synaptic plasticity, and more. Then we discuss many of the stories and lessons from his book, which I recommend for teachers, mentors, and life-long students who want to ensure they're optimizing their own  learning.

    • Potter Lab.
    • Twitter: @stevempotter.
    • The Book: How to Motivate Your Students to Love Learning.
    • The glial cell activity movie.

    0:00 - Intro

    6:38 - Brain organoids
    18:48 - Glial cell plasticity
    24:50 - Whole brain emulation
    35:28 - Industry vs. academia
    45:32 - Intro to book: How To Motivate Your Students To Love Learning
    48:29 - Steve's childhood influences
    57:21 - Developing one's own intrinsic motivation
    1:02:30 - Real-world assignments
    1:08:00 - Keys to motivation
    1:11:50 - Peer pressure
    1:21:16 - Autonomy
    1:25:38 - Wikipedia real-world assignment
    1:33:12 - Relation to running a lab

    1 hr 46 min
  • BI 100.6 Special: Do We Have the Right Vocabulary and Concepts?

    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 Saxe
    7:04 - Thomas Naselaris
    7:46 - John Krakauer
    9:03 - Federico Turkheimer
    11:57 - Steve Potter
    13:31 - David Krakauer
    17:22 - Dean Buonomano
    20:28 - Konrad Kording
    22:00 - Uri Hasson
    23:15 - Rodrigo Quian Quiroga
    24:41 - Jim DiCarlo
    25:26 - Marcel van Gerven
    28:02 - Mazviita Chirimuuta
    29:27 - Brad Love
    31:23 - Patrick Mayo
    32:30 - György Buzsáki
    37:07 - Pieter Roelfsema
    37:26 - David Poeppel
    40:22 - Paul Cisek
    44:52 - Talia Konkle
    47:03 - Steve Grossberg

    51 min
  • BI 100.4 Special: What Ideas Are Holding Us Back?

    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 McClelland
    30:13 - Jim DiCarlo
    31:17 - Talia Konkle
    33:27 - Uri Hasson
    35:37 - Wolfgang Maass
    38:48 - Paul Cisek
    40:41 - Patrick Mayo
    41:51 - Konrad Kording
    43:22 - David Poeppel
    44:22 - Brad Love
    46:47 - Rodrigo Quian Quiroga
    47:36 - Steve Grossberg
    48:47 - Mark Humphries
    52:35 - John Krakauer
    55:13 - György Buzsáki
    59:50 - Stefan Leijnan
    1:02:18 - Nathaniel Daw

    1 hr 5 min
  • BI 100.3 Special: Can We Scale Up to AGI with Current Tech?

    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 missing.

    Timestamps:

    0:00 - Intro

    3:56 - Wolgang Maass
    5:34 - Paul Humphreys
    9:16 - Chris Eliasmith
    12:52 - Andrew Saxe
    16:25 - Mazviita Chirimuuta
    18:11 - Steve Potter
    19:21 - Blake Richards
    22:33 - Paul Cisek
    26:24 - Brad Love
    29:12 - Jay McClelland
    34:20 - Megan Peters
    37:00 - Dean Buonomano
    39:48 - Talia Konkle
    40:36 - Steve Grossberg
    42:40 - Nathaniel Daw
    44:02 - Marcel van Gerven
    45:28 - Kanaka Rajan
    48:25 - John Krakauer
    51:05 - Rodrigo Quian Quiroga
    53:03 - Grace Lindsay
    55:13 - Konrad Kording
    57:30 - Jeff Hawkins
    102:12 - Uri Hasson
    1:04:08 - Jess Hamrick
    1:06:20 - Thomas Naselaris

    1 hr 9 min
  • BI 100.2 Special: What Are the Biggest Challenges and Disagreements?

    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 - Nathaniel Daw
    17:58 - Jessica Hamrick
    19:07 - Russ Poldrack
    20:47 - Pieter Roelfsema
    22:21 - Konrad Kording
    25:16 - Matt Smith
    27:55 - Rafal Bogacz
    29:17 - John Krakauer
    30:47 - Marcel van Gerven
    31:49 - György Buzsáki
    35:38 - Thomas Naselaris
    36:55 - Steve Grossberg
    48:32 - David Poeppel
    49:24 - Patrick Mayo
    50:31 - Stefan Leijnen
    54:24 - David Krakuer
    58:13 - Wolfang Maass
    59:13 - Uri Hasson
    59:50 - Steve Potter
    1:01:50 - Talia Konkle
    1:04:30 - Matt Botvinick
    1:06:36 - Brad Love
    1:09:46 - Jon Brennan
    1:19:31 - Grace Lindsay
    1:22:28 - Andrew Saxe

    1 hr 25 min
  • BI 100.1 Special: What Has Improved Your Career or Well-being?

    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 being?" See below for links to each previous guest. And away we go...

    Timestamps:

    0:00 - Intro

    6:13 - David Krakauer
    8:50 - David Poeppel
    9:32 - Jay McClelland
    11:03 - Patrick Mayo
    11:45 - Marcel van Gerven
    12:11 - Blake Richards
    12:25 - John Krakauer
    14:22 - Nicole Rust
    15:26 - Megan Peters
    17:03 - Andrew Saxe
    18:11 - Federico Turkheimer
    20:03 - Rodrigo Quian Quiroga
    22:03 - Thomas Naselaris
    23:09 - Steve Potter
    24:37 - Brad Love
    27:18 - Steve Grossberg
    29:04 - Talia Konkle
    29:58 - Paul Cisek
    32:28 - Kanaka Rajan
    34:33 - Grace Lindsay
    35:40 - Konrad Kording
    36:30 - Mark Humphries

    43 min

About Brain Inspired

From the publisher's feed

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…

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