By James Robinson
Coffee table conversations with people thinking about foundational issues. Multiverses explores the limits of knowledge and technology. Does quantum mechanics tell us that our world is
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The podcast currently has 34 episodes available.
There is no consensus on what minds are, but there is plenty of agreement on where they can be found: in humans. Yet human consciousness may account for only a small proportion of the consciousness on our planet.
Our guest, Kristin Andrews, is a Professor of Animal Minds at the University of York, Ontario, Canada. She is a philosopher working in close contact with biologists and cognitive scientists and has spent time living in the jungle to observe research on orangutans.
Kristin notes that comparative psychology has historically resisted attributing such things as intentions, learning, consciousness, and minds to animals. Yet she argues that this is misguided in the light of the evidence, that often the best way to make sense of the complexity of animal behavior is to invoke minds and intentional concepts.
Recently Kristin has proposed that the default assumption — the null hypothesis — should be that animals have minds. Currently, biologists examine markers of consciousness on a species-by-species basis, for example looking for the presence of pain receptor skills, and preferential tradeoffs in behavior. But everywhere we have looked, even in tiny nematode worms, we find multiple markers present. Kristin reasons that switching the focus from asking "where are the minds?" to "what sort of minds are there?" would prove more fruitful.
The question of consciousness and AI is at the forefront of popular discourse, but to make progress on a scientific theory of mind we should draw on the richer data of the natural world.
Things happen. Or they don’t. How then should we make sense of claims that something might happen?
If all these claims do is express doubt, then the puzzle can be easily resolved. But if the claims capture some objective feature of the world, what is it?
Our guest is Alastair Wilson, a professor of philosophy at the University of Leeds. He takes chance seriously, in particular, he is a realist about our modal claims (claims like “either candidate could win” or “if Szilard hadn’t got Spanish flu, the atom bomb would not have been invented”) may be true or false, not just opinions or expressions of ignorance.
Alastair does this by connecting our modal talk to Everettian quantum mechanics. He argues that modal claims are assertions about the many worlds within the universal wavefunction. If in all worlds where Szilard did not succumb to Spanish flu, the atom bomb was never invented, then this claim would be true.
It is a bold and fascinating way of bringing physics and metaphysics together. What can happen, what is possible, what could have been? These become questions for natural science.
The launch of ChatGPT was a "Sputnik moment". In making tangible decades of progress it shot AI to the fore of public consciousness. This attention is accelerating AI development as dollars are poured into scaling models.
What is the next stage in this journey? And where is the destination?
My guest this week, Nell Watson, offers a broad perspective on the possible trajectories. She sits in several IEEE groups looking at AI Ethics, safety & transparency, has founded AI companies, and is a consultant to Apple on philosophical matters. Nell makes a compelling case that we can expect to see agentic AI being soon adopted widely. We might even see whole AI corporations. In the context of these possible developments, she reasons that concerns of AI ethics and safety — so often siloed within different communities — should be understood as continuous.
Along the way we talk about the perils of hamburgers and the good things that could come from networking our minds.
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Physics helps get stuff done. Its application has put rockets in space, semiconductors in phones, and eclipses on calendars.
For some philosophers, this is all physics offers. It is a mere instrument, albeit of great power, giving us control over tangible things. It is a set of gears and widgets (wavefunctions, strings, even electrons) to crank out predictions. In contrast to instrumentalists, scientific realists argue that the success of theories shows that they map onto the structure of the world, symbols in equations carry the imprint of real entities.
This is an old debate in the philosophy of science. While we touch on some arguments for either position, this episode focuses on the phenomenology of physics researchers. What do physicists believe?
Céline Henne is a philosopher at the University of Bologna. Alongside Hannah Tomczyk and Christopher Sperber she has fielded the most comprehensive survey of the attitudes of physicists towards the reality of the objects of their study. From looking at the answers to dozens of questions from several hundred physicists, they have distinguished several camps of belief.
It's an elegantly designed survey, simply reading the questions forces a consideration of one's own position.
It can be tempting to consider language and thought as inextricably linked. As such we might conclude that LLM's human-like capabilities for manipulating language indicate a corresponding level of thinking.
However, neuroscience research suggests that thought and language can be teased apart, perhaps the latter is more akin to an input-output interface, or an area of triage for problem-solving. Language is a medium into which we can translate and transport concepts.
Our guest this week is Anna Ivanova, Assistant Professor of Psychology at Georgia Institute of Technology. She's conducted experiments that demonstrate how subjects with severe aphasia (large-scale damage to the language area of their brains) remain able to reason socially. She's also studied how the brains of developers work when reading code. Again the language network is largely bypassed.
Anna's work and other research in cognitive science suggest that the modularity of brains is central to their ability to handle diverse tasks.
Brains are not monolithic neural nets like LLMs but contain networked specialized regions.
Words. (Huh? Yeah!) What are they good for? Absolutely everything.
At least this was the view of some philosophers early in the 20th century, that the world was bounded by language. ("The limits of my language mean the limits of my world" to use Wittgenstein's formulation over the Edwin Starr adaptation)
My guest this week is Nikhil Krishnan a philosopher at University of Cambridge and frequent contributor to the The New Yorker His book A Terribly Serious Adventure, traces the path of Ordinary Language Philosophy through the 20th century.
We discuss the logical positivists (the word/world limiters) and their high optimism that the intractable problems of philosophy could be dissolved by analysis. Their contention that the great questions of metaphysics were nonsense since they had no empirical or logical content.
That program failed, but its spirit of using data and aiming for progress lived on in the ordinary language philosophers who put practices with words under the microscope. Hoping to find in this data clues to the nuances of the world.
This enterprise left us with beautiful examples of the subtleties of language. But more importantly, it is a practice that continues today, of paying close attention to our everyday behaviors and holding our grand systems of philosophy accountable to these.
Listen to discover things you know, but didn't know you knew — like the difference between doing something by accident vs by mistake.
Do check out Nikhil's own podcast, Minor Books, on iTunes or Acast
(00:00) Intro
(02:49) Start of conversation: Philosophical background and history
(04:47) The Evolution of Philosophy: From Ancient Texts to Modern Debates
(16:46) The Impact of Logical Positivism and the Quest for Scientific Philosophy
(38:35) J.L. Austin's Revolutionary Approach to Philosophy and Language
(48:43) The Power of Everyday Language vs the Abstractions of Philosophy
(49:11) Why is ordinary language so effective — Language Evolution?
(52:30) Philosophical Perspectives on Language's Utility
(53:28) The Intricacies of Language and Perception
(54:48) Scientific and Philosophical Language: A Comparative Analysis
(57:14) Legal Language and Its Precision
(01:07:33) LLMS: The Future of Language in Technology and AI
(01:10:33) Intentionality and the Philosophy of Actions
(01:18:27) Bridging Analytic and Continental Philosophy
(01:33:46) Final Thoughts on Philosophy and Its Practice)
Music may be magical. But it is also rooted in the material world. As such it can be the subject of empirical inquiry.
How does what we are told of a performer influence our appreciation of the performance? Does sunshine change our listening habits? How do rhythms and melodies change as they are passed along, as in a game of Chinese whispers?
Our guest is Manuel Anglada Tort, a lecturer at Goldsmiths, University of London. He has investigated all those topics. We discuss the fields of Empirical Aesthetics and cultural evolution experiments as applied to music.
Chapters
(00:00) Intro
(03:35) Start of conversation: Music Psychology and Empirical Aesthetics
(07:54) Genomics and Musical Ability
(18:25) Weather's Influence on Music Preferences
(31:57) The Repeated Recording Illusion
(43:24) Empirical Aesthetics: Does Analysis Boost or Deflate Wonder?
(49:59) Music Evolution and Cultural Systems
(52:18) Simulating Music Evolution in the Lab
(1:01:27) The Role of Memory and Cognitive Biases in Music
(1:05:33) Comparing Language and Music Evolution
(1:20:37) The Impact of Physical and Cognitive Constraints on Music
(1:31:37) Audio Appendix
If all my beliefs are correct, could I still be prejudiced?
Philosophers have spent a lot of time thinking about knowledge. But their efforts have focussed on only certain questions. What makes it such that a person knows something? What styles of inquiry deliver knowledge?
Jessie Munton is a philosopher at the University of Cambridge. She is one of several people broadening the scope of epistemology to ask: what sort of things do we (and should we) inquire about and how should we arrange our beliefs once we have them?
Her lens on this is in terms of salience structures. These describe the features and beliefs that an individual is likely to pay attention to in a situation. They are networks that depend on the physical, social, and mental worlds.
In a supermarket aisle, what is salient to me depends both on how products are arranged and my food preferences. Very central nodes in my salience structure (for example this podcast) might be awkwardly linked to many things (multigrain rice ... multiverses).
This is a rare and wonderful thing. Philosophy that is at once interesting and useful.
Links
Chapters
(04:20) Welcome and Introduction to the Discussion
(04:53) Exploring the Essence of Epistemology
(06:31) Expanding the Boundaries of Traditional Epistemology
(10:50) Understanding vs. Knowledge: Diving Deeper into Epistemology
(12:42) The Role of Evidence and Justification in Beliefs
(23:59) Salience Structures: A New Perspective on Information Processing
(34:22) Applying Network Science to Understand Salience Structures
(43:41) Exploring Social Salience Structures and the Impact of Cities
(48:15) Exploring the Complexity of Attention and Salience
(48:30) The Challenge of Modeling Attention Mathematically
(48:57) Linking Attention to Real-world Outcomes
(50:01) Differentiating Causes of Attention and Their Impacts
(50:53) The Role of Individual and Social Responsibility in Shaping Attention
(52:19) Influence of Media and Technology on Salience Structures
(55:44) The Potential of Augmented Reality and Large Language Models
(00:47) The Personalization Dilemma of Search Engines and Social Media
(01:05:38) Exploring the Ethical and Practical Implications of Information Access
(01:22:53) Concluding Thoughts on Salience and Information Consumption
Why do whales live longer than hummingbirds? What makes megacities more energy efficient than towns? Is the rate of technological innovation sustainable?
Though apparently disparate the answer to these questions can be found in the work of theoretical physicist Geoffrey West. Geoffrey is Shannan Distinguished Professor at the Santa Fe Institute where he was formerly the president.
By looking at the network structure of organisms, cities, and companies Geoffrey was able to explain mathematically the peculiar ways in which many features scale. For example, the California Sea Lion weighs twice as much as an Emperor Penguin, but it only consumes 75% more energy. This sub-linear scaling is incredibly regular, following the same pattern across many species and an epic range of sizes. This is an example of a scaling law.
The heart of the explanation is this: optimal space-filling networks are fractal-like in nature and scale as if they acquire an extra dimension. A 3D fractal network scales as if it is 4D.
Chapters
(00:00) Introduction
(02:56) Start of conversation: Geoffrey's Career Journey
(03:25) Transition from High Energy Physics to Biology
(09:05) Exploring the Origin of Aging and Death
(11:20) Discovering Scaling Laws in Biology
(12:30) Understanding the Metabolic Rate and its Scaling
(25:40) The Impact of the Molecular Revolution on Biology
(28:39) The Role of Networks in Biological Systems
(49:07) The Connection between Fractals and Biological Systems
(01:00:29) Understanding the Growth and Supply of Cells
(01:01:07) The Impact of Size on Energy Consumption
(01:01:46) The Role of Networks in Growth and Supply
(01:02:30) The Universality of Growth in Organisms
(01:03:13) Exploring the Dynamics of Cities
(01:06:12) The Scaling of Infrastructure and Socioeconomic Factors in Cities
(01:07:36) The Implications of Superlinear Scaling in Cities
(01:11:50) The Future of Cities and the Need for Innovation
It's easy to recognize the potential of incremental advances — more efficient cars or faster computer chips for instance. But when a genuinely new technology emerges, often even its creators are unaware of how it will reshape our lives. So it is with AI, and this is where I start my discussion with Peter Nixey.
Peter is a serial entrepreneur, angel investor, developer, and startup advisor. He reasons that large language models are poised to bring enormous benefits, particularly in enabling far faster & cheaper development of software. But he also argues that their success in this field will undermine online communities of knowledge sharing — sites like StackOverflow — as users turn away from them and to LLMs. Effectively ChatGPT will kick away one of the ladders on which its power is built.
This migration away from common forums to sealed and proprietary AI models could mark a paradigm shift in the patterns of knowledge sharing that extends far beyond the domain of programming.
We also talk about the far future and whether conflict with AI can be avoided.
Chapters
(00:00) Introduction
(02:44) Start of Conversation
(03:20) The Lag Period in Technology Adoption
(06:48) The Impact of the Internet on Productivity
(11:30) The Curious UX of AI
(19:25) The Future of AI in Coding
(29:06) The Implications of AI on Information Sharing
(41:27) AI and Socratic learning
(46:57) The Evolution of Textbooks and Learning Materials
(49:01) The Future of AI in Software Development
(51:11) The Existential Questions Surrounding AI
(01:05:16) Evolutionary Success as a lens on AI
(01:13:29) The Potential Conflict Between Humans and AI
(01:14:24) An (almost) Optimistic Outlook on AI and Humanity
The podcast currently has 34 episodes available.