Introduction and Levels of Explanation
- A major myth in biology is that the best explanations are always at the molecular level. Different levels of explanation (biology, chemistry, physics) offer different insights, and higher levels have autonomy.
- Emergence is a measure of surprise for the observer – how much a system does that wasn’t anticipated from the properties of its parts. It is relative, not absolute.
- Cognitive functions (learning, memory, conditioning) can be found even in very simple systems like gene regulatory networks. It is important to go past the levels.
Unifying Themes: Embodied Mind and Intelligence
- Levin’s work across various fields (cancer, development, regeneration, AI) is unified by an effort to understand embodied mind and intelligence in diverse, unconventional forms.
- Goals of organisms are typically attributed to evolutionary history. Synthetic constructs (xenobots, anthropods) allow studying the origins of goals in systems *without* such history.
- The “cognitive light cone” represents the size of the largest goal a system can pursue, in space and time. This concept helps understand cancer as a *shrinking* of this light cone, where cells revert to individualistic goals.
- Cancer cells aren’t necessarily more *selfish*; they have *smaller selves*. This leads to research on reconnecting cancer cells to bioelectrical networks, normalizing them *without* killing them.
Defining Intelligence and the Platonic View
- Intelligence (following William James) is the ability to reach the same goal by different means. This definition emphasizes problem-solving in a specific problem space, regardless of the physical substrate (brain, synthetic system, etc.).
- Levin leans toward a Platonic view of intelligence, akin to the mathematical Platonism, there exists, in fact, a space where mathematical properties of computation that think and compute “live” in such that we merely discover. He suggests that not only do rules of maths “live” there, but other cognitive states live as well, not limited by material states, we don’t just invent minds; physical systems can “harness” pre-existing intelligence.
- Estimates of intelligence are *not* objective properties of a system. They are guesses about its problem-solving capabilities, reflecting *our* knowledge (or lack thereof).
Self, Memory, and Dynamic Interpretation
- We are not static entities, but rather collections of interacting perspectives. “Selflets” are thin slices of experience.
- The continuity of self is perceived by *other observers* based on the consistency of behaviors and properties. We are interested on how the system behaves to ourselves, how to better get “messages” (engrams).
- Our access to the past is through memory engrams – *interpreted* traces, not direct access. These traces must be dynamically reinterpreted in new contexts.
- Our current actions are like messages to our future selves, constraining or enabling their possibilities. This creates a symmetry between our future self and *others’* future selves, with ethical implications.
- Under resource constraints, agents *must* coarse-grain; they cannot track everything. They create compressed representations (like memory engrams), focusing on salience, not fidelity.
Compression, Perspectives, and Synchronicity
- Highly compressed data can appear random because correlations are removed. This has implications for interpreting potential signals from advanced civilizations.
- What we experience depends on an interpretive agent. What we compress for memories, have to be de-compressed by us in future times, thus leading us to needing a reinterpretation of the rule to the given observation.
- Perspectives are fundamental: commitments to what to measure, what to pay attention to, and how to weave that into a model. Every perspective necessarily shuts out more than it lets in.
- If we are part of a larger cognitive system, recognizing that might look like *synchronicity* – meaningful events without apparent causal connection at our level.
Bioelectricity as Cognitive Glue
- Bioelectricity is *not* magic, but a crucial mechanism for enabling collective intelligence, by being used to create a policy for cooperation.
- Bioelectricity allows the cognitive light cone to scale up. Cells connected in electrical networks form larger emergent individuals with higher-level goals and capabilities.
- Cells create their agency with signals that enable other parts of their larger organism to move.
Examples from the Levin Lab
- Early work showed bioelectricity’s role in left-right asymmetry in chicken embryos, manipulating this with ion channel constructs.
- “Electric face”: Bioelectric patterns in nascent ectoderm *prefigure* the formation of facial features. Birth defects disrupting these patterns can be corrected bioelectrically.
- The first demonstration of gaining the regeneration functions: Bioelectric manipulation can *induce* tail regeneration in tadpoles, demonstrating the control over large-scale anatomical outcomes. A 24-hour bioelectrical stimulation to frogs triggers *a year and a half* of leg growth, showing a high-level command (“build a leg”) without micromanaging the process.
- Bioelectric signaling is linked to cancer. Cells can be induced to become metastatic with *inappropriate* bioelectric cues. Conversely, bioelectrical connections can *normalize* cells expressing strong human oncogenes.
- Planaria: Two-headed worms show a *permanent, non-genetic* change in target morphology. This demonstrates physiological memory. Planaria’s highly chaotic genome and remarkable regenerative abilities suggest a prioritization of *algorithmic competency* over genetic fidelity.
- Anthropods (human-derived organoids) demonstrate that *tracheal cells* can exhibit novel behaviors, including neural repair, *without* any genetic changes. This showcases the plasticity and emergent capabilities of cell collectives.
Implications and Future Directions
- Evolution creates *problem-solving agents*, not just solutions to specific problems. These agents have tools (cytoskeleton, gene regulatory networks, bioelectricity) to handle novel circumstances.
- Work is shifting towards clinically relevant models: human cancer cells and spheroids for cancer research, and mice for regeneration.
- Understanding how information flows across levels (embryos communicating with each other, forming “hyper-embryos”) is a key focus.
Advice for Newcomers to Biology
- Paths are in science hard to predict, be vary of following peoples research agendas instead of making your own based on your goals and aspirations, follow your own “guts”.
- Develop your own intuition about which paths to take in science, and test those intuitions through experiment.
- Prioritize specific, technical critiques to improve your craft (how to make an experiment better), but be wary of general advice about career direction.
- Unifying seemingly disparate phenomena (like the different forces responsible for apple falls and planetary motions) is a major scientific achievement. Be open to this possibility.