Blaise Agüera y Arcas: Computing, Life, and Intelligence

In the mid-20th century, Alan Turing and John von Neumann developed the theoretical underpinnings of computer science, neuroscience, and AI. They also founded the field of theoretical biology, showing how living systems must necessarily be computational in order to grow, heal, and reproduce. Recent experiments by Blaise Agüera y Arcas’ team at Google have drawn new connections between theoretical biology and computer science, showing how “digital life” can evolve in a purely random universe. Such artificial life doesn’t evolve the way Darwinian evolutionary theory usually presumes, through random mutation and selection, but rather through symbiogenesis, wherein small replicating entities merge into progressively bigger ones. This may be the creative engine behind biological evolution too. In this lecture, Agüera y Arcas will describe how symbiosis explains both life’s origins and its increasing complexity. He’ll also draw connections to social intelligence theories, which suggest that similar symbioses have powered intelligence explosions in humanity’s lineage and those of other big-brained species. Finally, he’ll argue that both modern human intelligence and AI are best understood through this symbiotic lens.

Watch at: www.youtube.com

Evidence of equilibrium dynamics in human social networks evolving in time

Miguel A. González-Casado, Andreia Sofia Teixeira & Angel Sánchez 
Communications Physics volume 8, Article number: 227 (2025)

How do networks of social relationships evolve over time? This study addresses the lack of longitudinal analyses of social networks grounded in mathematical modelling. We analyse a dataset tracking the social interactions of 900 individuals over four years. Despite shifts in individual relationships, the macroscopic structure of the network remains stable, fluctuating within predictable bounds. We link this stability to the concept of equilibrium in statistical physics. Specifically, we show that the probabilities governing link dynamics are stationary over time, and that key network features align with equilibrium predictions. Moreover, the dynamics also satisfy the detailed balance condition. This equilibrium persists despite ongoing turnover, as individuals join, leave, and shift connections. This suggests that equilibrium arises not from specific individuals but from the balancing act of human needs, cognitive limits, and social pressures. Practically, this equilibrium simplifies data collection, supports methods relying on single network snapshots (like Exponential Random Graph Models), and aids in designing interventions for social challenges. Theoretically, it offers insights into collective human behaviour, revealing how emergent properties of complex social systems can be captured by simple mathematical models.

Read the full article at: www.nature.com

Machines All the Way Up and Cognition All the Way Down: Updating the machine metaphor in biology

Michael Levin and Richard Watson

Cell and developmental biology offer numerous remarkable examples of collective intelligence and adaptive plasticity to novel circumstances, as cells implement large-scale form and function. Many of these capabilities seem different from the behavior of machines or the results of computations. And yet, they are implemented by biochemical, biophysical, and bioelectrical events which are often interpreted with the machine metaphor that dominates molecular and cell biology. The seeming incongruity between molecular mechanisms and the emergence of self-constructing and goal-driven intentional living agents has driven a perennial debate between mechanist and organicist thinkers. Here, we discuss the inadequacies of, on the one hand, the (unminded) mechanist and computationalist frameworks, and on the other, dualistic conceptions of machine vs. mind. Both fail to provide an integration of agential and mechanistic aspects evident in biology. We propose that a new kind of cognitivism, cognition all the way down, provides the necessary unification of ‘bottom-up’ and ‘top-down’ causal flows evident in living systems. We illustrate how the organizational layers between genotype and phenotype provide problem-solving intelligence, not merely complexity, and discuss the benefits and inadequacies of specific machine metaphors in this context. By taking a pragmatist approach to the hypothesis that life and mind are fundamentally the same problem, formalisms are emerging that embrace the unique quality of the agential material of life while fully benefitting from the advances of modern machine science. New ways to map formal concepts of machine and data to biology provide a route toward unifying evolutionary and developmental biology, and rich substrates for the use of truly bio-inspired principles to advance engineering and computer science.

Read the full article at: osf.io

Emergent social conventions and collective bias in LLM populations

ARIEL FLINT ASHERY, LUCA MARIA AIELLO, AND ANDREA BARONCHELLI

SCIENCE ADVANCES 14 May 2025 Vol 11, Issue 20

Social conventions are the backbone of social coordination, shaping how individuals form a group. As growing populations of artificial intelligence (AI) agents communicate through natural language, a fundamental question is whether they can bootstrap the foundations of a society. Here, we present experimental results that demonstrate the spontaneous emergence of universally adopted social conventions in decentralized populations of large language model (LLM) agents. We then show how strong collective biases can emerge during this process, even when agents exhibit no bias individually. Last, we examine how committed minority groups of adversarial LLM agents can drive social change by imposing alternative social conventions on the larger population. Our results show that AI systems can autonomously develop social conventions without explicit programming and have implications for designing AI systems that align, and remain aligned, with human values and societal goals.

Read the full article at: www.science.org