Researchers
1 Curated Dialogues
Akarsh Kumar
Key Views & Dialogues
What Most People Get Wrong About Evolution | Akarsh Kumar
LLM-guided evolution beat 96% of roughly 300 human-written Core War programs, showing how objective verification and cumulative selection can amplify weak model priors. Artificial-life research extends discovery into possible physics and intelligence, but its strongest behaviors occupy a narrow region of rule space, making automated search valuable while leaving AGI a long-term, unresolved wager.
View summary (7)
- Tim Scarfe’s closing correction is that evolution is “anything but random”: selection preserves partial wins, allowing some exponential combinatorial searches to become linear ones. In Core War, Akarsh Kumar’s LLM-guided evolutionary loop beat 96% of roughly 300 human-written programs even though the model was poor at zero-shot Redcode. The engineering signal is the combination of imperfect model priors, objective verification, and cumulative selection—not the LLM alone.
- Artificial life expands the research target from life as observed on Earth to “life as it could be,” searching across possible physics, chemistries, organisms, and intelligences. Kumar argues that one cannot derive general principles from a single specimen any more than one can inspect one car and explain all cars. For investors, the important optionality is a computational laboratory for testing which rule systems generate persistence, self-organization, and open-ended complexity.
- Kumar distinguishes today’s “statistical intelligence” from a path-dependent, regularity-based intelligence whose structure records how it was acquired. Humans and evolution complexify sequentially, while models can be trained on calculus without first learning arithmetic; Kumar allows that this alien approach “could just be better,” but suspects the path supplies unusually strong regularization, robustness, and generalization.
- Foundation models can serve as scalable proxies for human judgment when properties such as emergence, complexity, or interestingness resist clean mathematical objectives. The workflow is deliberately irreducible: “Just simulate it. See what happens,” let a vision-language model judge the result, then search again. This broad semantic target also makes optimization easier—“any cat” is more reachable than one exact pixel-level cat—and reduces the risk of finding what was specified rather than what was wanted.
- Open-ended behavior appears concentrated in narrow regions of rule space, not spread uniformly across it. After running and embedding all 260,000 rules in one experiment, Kumar’s team found a large island of non-open-ended systems and a much smaller island containing the most compelling simulations. That “Goldilocks zone” makes automated discovery valuable: the scarce object is not compute alone, but the right artificial physics.
- The simulations repeatedly show global organization emerging from minimal local interactions, including cell-like structures that exploit their artificial physics to persist. Game of Life needs only binary cells and a few neighborhood rules; Boids turns local steering into flocking; Particle Life uses a 6×6 interaction matrix among six particle types and can produce moving objects with membrane-like coatings and differentiated interiors.
- Artificial life remains a long-duration AGI wager rather than a near-term claim that particle-level simulation will directly yield intelligence. Kumar’s agenda is to extract an efficient abstraction of evolution that preserves its useful properties, potentially combining statistical models as critics with organically factored systems as the thing being created. The exchange also cautions against putting all eggs in one basket.