BI 104 John Kounios and David Rosen: Creativity, Expertise, Insight

05/07/21 • 110 min

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:

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

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:

Previous Episode

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

Next Episode

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