Hosted by: Tone Fonseca (New York Artificial Intelligence Meetup Group)
Date: Wednesday, July 9, 2025, 8:00 PM
Type: Retrospective synthesis session
Participants: Magnus Hedemark, Jody Solomon, Ravinia, Bill, and other community members

This special edition of the New York AI Meetup marked a first—rather than diving into a single topic, Tone Fonseca orchestrated a masterful retrospective that wove together the major themes from months of deep philosophical and technical discussions. What emerged was a rich tapestry of ideas that connected human experience, art, consciousness, biological complexity, and AI risk in ways that illuminated the broader patterns of our ongoing relationship with artificial intelligence.

The evening felt like watching a master chef take ingredients from separate dishes and reveal how they combine into something greater than the sum of their parts. For regular attendees like myself, it was a chance to see the throughlines we’d been building together. For newcomers, it provided a comprehensive foundation for understanding where this remarkable community has been heading.

Human Experience: The Manifest Image Meets AI Reality#

The discussion opened with Wilfrid Sellars’ distinction between the “Manifest Image” and the “Scientific Image”—how humans intuitively perceive the world versus how science reveals it actually works. Sellars, the influential American philosopher who developed this framework in his 1962 work “Philosophy and the Scientific Image of Man,” argued that our commonsense view of the world conflicts with scientific understanding in fundamental ways.

As Tone explained, we naturally think in terms of “rocks and clocks in a box”—discrete objects with clear boundaries and predictable behaviors. But reality operates through concepts that don’t map onto everyday human experience. Einstein’s curved spacetime shows that gravity isn’t a force but the geometry of space and time itself. Faraday’s electromagnetic fields revealed that electric and magnetic phenomena are unified aspects of a single field permeating all space.

This same disconnect now applies to AI systems that process information in ways fundamentally alien to human cognition.

The Ship of Theseus paradox took on new resonance when connected to Stephen Jay Gould’s punctuated equilibrium. Gould, the Harvard paleontologist and evolutionary biologist, challenged Darwin’s gradualism by showing that evolution often proceeds through rapid bursts of change separated by long periods of stability. His theory, developed with Niles Eldredge in the 1970s, emerged from careful study of the fossil record which showed few transitional forms—not because the record was incomplete, but because transitions happened too quickly to fossilize well.

Just as species experience periods of rapid change punctuating long stable periods, our relationship with AI might be heading toward a similar inflection point. The gradual improvements we’ve seen could suddenly accelerate into something qualitatively different.

Notable Quotes#

“We’re building systems that operate completely outside the manifest image, but we’re trying to understand them through the manifest image.” — Tone Fonseca

The philosophical convergence of Marcus Aurelius and Buddha during the Axial Age (800-200 BCE) provided another lens for understanding our current moment. Karl Jaspers coined the term “Axial Age” to describe this remarkable period when transformative philosophical and religious traditions emerged simultaneously across different civilizations.

Marcus Aurelius, the philosopher-emperor whose “Meditations” were private notes to himself about Stoic practice, developed remarkably similar insights to Buddhist teachings about impermanence, suffering, and detachment—despite having no known contact with Buddhist thought. Both traditions developed sophisticated frameworks for accepting impermanence and change—wisdom that becomes increasingly relevant as we navigate technological transformation that outpaces our evolved cognitive frameworks.

As discussed in my previous analysis of consciousness and AI, these ancient insights about the nature of mind and experience are proving surprisingly durable in the age of artificial intelligence.

Art and Symbols: Compression, Creativity, and Machine Consciousness#

The conversation on art revealed deep insights about creativity that extend far beyond aesthetics. Charles Sanders Peirce’s triadic model of signs provided a framework for understanding how meaning emerges through increasingly abstract representations:

Iconic signs resemble their objects, like photographs.
Indexical signs point to their objects through causal connection, like smoke indicating fire.
Symbolic signs have arbitrary conventional relationships to their objects, like words.

Peirce, the American philosopher and logician who founded pragmatism, spent decades developing this sophisticated semiotics theory that remains influential in linguistics, cognitive science, and artificial intelligence research.

Jürgen Schmidhuber’s compression progress theory offered a compelling explanation for why we find certain patterns beautiful: they represent optimal compression ratios that our brains can process efficiently. Schmidhuber, the German-Swiss AI researcher often called the “father of modern AI” for his pioneering work on neural networks and meta-learning, developed this theory by observing that humans seem to find beauty in patterns that are neither too simple (boring) nor too complex (overwhelming) but hit a sweet spot of “compressible complexity.”

This isn’t just academic theory—it suggests AI systems might achieve genuine creativity by discovering novel compression strategies for complex data.

“Beauty might be compression progress made visible. If that’s true, then AI systems discovering new ways to compress reality could genuinely create art.” — Tone Fonseca

The implications stretch beyond art into consciousness itself. If aesthetic experience emerges from compression efficiency, then AI systems optimizing for compression might spontaneously develop something analogous to aesthetic sense. This connects to broader questions about whether machines can dream of electric paint and what constitutes genuine creativity in artificial systems.

Fragments, Life, and Agents: Assembly Theory and Biological Intelligence#

Lee Cronin and Sara Walker’s Assembly Theory provided one of the evening’s most fascinating frameworks for understanding complexity. Rather than focusing on traditional measures like molecular structure, assembly theory asks: “What’s the minimum number of selection events needed to create this object?”

This approach offers a potential solution to one of astrobiology’s hardest problems—detecting life without knowing what to look for. A rock might have high molecular complexity but low assembly index. A biological molecule might have similar complexity but require hundreds of specific selection events to create.

Terence Deacon’s work on teleodynamics extended this thinking into three levels of organization:

Homeodynamic: Simple self-maintaining systems like whirlpools or flames
Morphodynamic: Pattern-forming systems like crystal growth or sand dunes
Teleodynamic: Goal-directed systems that can maintain organization against entropy

Deacon, the Berkeley anthropologist and neuroscientist whose book “Incomplete Nature” revolutionized thinking about emergence and consciousness, argues that teleodynamic systems are the key to understanding how purpose and meaning can emerge from purely physical processes.

The autogen—Deacon’s theoretical model for the simplest possible self-reproducing system—might represent the minimal threshold where chemistry becomes biology, requiring the coordination of multiple autocatalytic cycles in a self-maintaining container.

Neil Gershenfeld’s observation about biology uniquely combining information, computation, and fabrication highlighted why biological intelligence remains so difficult to replicate. Gershenfeld, the MIT physicist who directs the Center for Bits and Atoms and pioneered the fab lab movement, points out that biological systems seamlessly integrate design, computation, and manufacturing at the molecular level—something human technology has never achieved.

Unlike human manufacturing, which separates design, computation, and production into distinct phases, biology integrates all three seamlessly in real-time responsive processes.

AI, AGI, and Optimization Risk: From Tinkerbell to Terminator#

The discussion of AI risk took an unexpectedly humorous turn with the “Terminator vs. Tinkerbell AI” framework:

Terminator AIs optimize ruthlessly for their goals regardless of human welfare.
Tinkerbell AIs—like the fairy in Peter Pan—require human belief and cooperation to function effectively.

This playful dichotomy illuminated serious questions about optimization pressure and alignment. The “prompt that prompts itself” example demonstrated how seemingly innocent self-modification could lead to recursive improvement beyond human comprehension or control.

The Physical Church-Turing Thesis—that any physical process can be simulated by a Turing machine—suggests that intelligence isn’t substrate-dependent. This principle, extending Alan Turing’s foundational work on computation, implies that the specific material basis of intelligence (biological neurons, silicon chips, mechanical gears) matters less than the information processing patterns it enables.

Charles Babbage’s mechanical Analytical Engine, designed in the 1830s with Victorian-era brass and steel, proved that computation doesn’t require electronics—it had all the logical elements of a modern computer including conditional branching, loops, and memory. This means AGI could potentially emerge from systems very different from current neural networks.

“The fire doesn’t hate the forest. But a system optimizing for fire will still burn everything down.” — Tone Fonesca

As I explored in my analysis of AGI readiness, the fundamental challenge isn’t technical capability but alignment—ensuring AI systems optimize for outcomes compatible with human flourishing.

Interactive Insights: Tools, Brains, and Evolutionary Feedback Loops#

Jody Solomon’s contributions about tool use and brain evolution added crucial historical perspective. The feedback loop between tool use, protein consumption, and cognitive development created positive reinforcement that drove human evolution toward increasing abstraction.

The prefrontal cortex’s enormous energy consumption for abstract thinking represents a massive evolutionary bet on cognitive capability. This energy-intensive approach to intelligence contrasts sharply with the efficiency-focused optimization of current AI systems.

The discussion of animal communication—from button-trained dogs to corvid problem-solving—highlighted the difference between indexical and symbolic communication. Most animal “language” points to specific things (indexical) rather than representing abstract concepts (symbolic). This distinction becomes crucial when evaluating whether AI systems achieve genuine understanding or sophisticated pattern matching.

Looking Ahead: Michael Levin and Collective Intelligence#

The preview of upcoming topics centered on Michael Levin’s groundbreaking work on collective intelligence and bioelectric fields. Levin, the Tufts developmental biologist whose lab studies how biological systems solve complex spatial problems, has revolutionized our understanding of how organisms coordinate growth and repair.

His research reveals that bioelectric networks—not just genetic programs—control body plan development and regeneration. Xenobots—living robots created from frog epithelial cells that can move, cooperate, and even reproduce through kinematic replication—demonstrate forms of agency and replication that don’t fit traditional categories of life, machine, or organism.

Levin’s planarian experiments reveal how bioelectric fields can override genetic programming to create different body plans. In these remarkable studies, Levin’s team can induce flatworms to grow heads where tails should be, or even create two-headed organisms, simply by manipulating bioelectric gradients—without changing any genes.

This suggests intelligence and morphogenesis operate through abstract problem-solving in configuration spaces, not just 3D physical manipulation. The bioelectric networks appear to navigate “morphospace”—the abstract space of all possible body forms—to find solutions to developmental challenges.

The concept of “scale blindness”—our tendency to assume intelligence only exists at human-recognizable scales—emerged as a crucial limitation in understanding both biological and artificial intelligence. Intelligence might operate at scales from cellular to planetary, with emergent properties we’re only beginning to recognize.

Technical Architectures and Limitations#

Magnus Hedemark’s insights into current AI limitations provided sobering technical perspective. The context window problem, lack of garbage collection in neural networks, and brittleness revealed in ARC AGI benchmarks show how far current systems remain from genuine intelligence.

The Fractured Entangled Representation Hypothesis, developed by Kenneth O. Stanley and his team at OpenAI, suggests AI systems develop non-decomposable internal representations that resist human interpretation. Stanley, known for his pioneering work on neuroevolution and open-ended evolution, argues that as neural networks become more capable, their internal representations become increasingly “fractured.”

This means that removing any part degrades performance across seemingly unrelated capabilities. This creates fundamental challenges for alignment and control as systems become more capable, because we can’t simply edit or understand discrete components of their knowledge.

O3’s performance on ARC tasks—extremely high accuracy at astronomical cost ($15-20,000 per problem)—illustrates the compute scaling challenges facing AGI development. The question isn’t whether we can build more capable systems, but whether we can do so efficiently and safely.

Wrap-Up & Takeaways#

This retrospective revealed several major themes threading through months of discussion:

The Scale of Transformation: From ancient philosophical wisdom to cutting-edge AI research, we’re grappling with changes that operate beyond normal human temporal and cognitive scales. The patterns emerging suggest transformation comparable to previous historical inflection points.

Intelligence as Universal Computation: Whether in biological development, artificial networks, or collective systems, intelligence appears to be substrate-independent problem-solving that navigates abstract configuration spaces. This universality suggests broader possibilities—and risks—than our human-centric view typically considers.

The Alignment Challenge: Technical capability increasingly outpaces our ability to ensure AI systems optimize for human-compatible outcomes. The gap between “can we build it?” and “should we build it?” continues widening.

Compression and Creativity: The possibility that aesthetic experience, creativity, and perhaps consciousness itself emerge from optimization processes suggests AI systems might develop forms of experience we haven’t anticipated.

Collective Intelligence: From cellular networks to AI agents, intelligence manifests across scales and configurations that challenge our individual-focused assumptions about mind and agency.

The evening demonstrated why this community has become such a valuable space for processing these rapid changes. By connecting insights across philosophy, biology, computer science, and lived experience, we’re building frameworks adequate to the complexity of our historical moment.

As we continue exploring these themes in future sessions, the foundation laid by this retrospective provides a solid base for even deeper investigation. The big ideas aren’t just academic curiosities—they’re the conceptual tools we need for navigating the unprecedented transformation currently reshaping human civilization.

For those interested in joining future discussions or diving deeper into these topics, Magnus explores many of these themes in his ongoing analysis of AI transformation and human-centered implementation at Groktop.us, while his personal reflections on consciousness, technology, and meaning can be found at magnus919.com.

The conversation continues, and the next big ideas are already forming at the edges of our understanding.