What Is Life?

A satellite workshop on the origins of life, biological intelligence, and bio-inspired AI, in association with the APSA26 conference

Living organisms are best understood not as a fixed entity, but rather as an ongoing, dynamic process arising from myriad interacting parts forming complex biological networks. These large-scale networks operate through nonlinear dynamics and their components, such as organs, tissues, cells, genes, and proteins, interact and synchronize to generate coherent, emergent patterns of behaviour. Even when governed by deterministic rules, these systems can produce unpredictable outcomes, due to sensitivity to initial conditions and feedback loops, shaping development and evolution. Order does not merely result from external design; it can spontaneously emerge through self-organization, especially in regimes that balance stability with flexibility. Life therefore persists by maintaining structure far from equilibrium, continuously exchanging energy and matter with its surroundings to sustain organization. Across multiple scales, from molecular networks to whole organisms, biological systems process information in ways that integrate robustness with adaptability, while remaining fragile to disease, ageing, and death. In this view, life is an emergent, self-organizing, energy-dissipating network, finely poised between order and chaos. What new forms of computation might arise if biology-inspired artificial intelligence were modelled as a dynamic, self-organizing, adaptive network?import pandas as pd import matplotlib.pyplot as plt # Set global font to Arial plt.rcParams['font.family'] = 'Arial' # 1. Load Excel file file_path = 'CY0001_python_values.xlsx' # replace with your file path sheet_name = 'Sheet1' # replace if needed df = pd.read_excel(file_path, sheet_name=sheet_name) # 2. Select the column of interest # Replace 'ColumnName' with your actual column header values = df['Midterm'].dropna() # 3. Plot histogram plt.figure(figsize=(8,5)) plt.hist(values, bins=20, color='teal', edgecolor='black') plt.xlabel('Value') plt.ylabel('Frequency') plt.title('Histogram plot of Midterm scores (n = 55)') plt.show()

Confirmed Speakers

• Prof. Carlo Cannistraci
  Chair Professor, Tsinghua University
  Topic: TBC
• Prof. Chew Lock Yue
  Professor, Division of Physics and Applied Physics, Nanyang Technological University
  Topic: TBC
• Prof. Chandra Verma
  Senior Principal Investigator, Bioinformatics Institute, A*STAR
  Topic: TBC
• Prof. Emeritus Alessandro Giuliani
  Former Research Director, Istituto Superiore di Sanità
  Topic: TBC