Emergent Intelligence and Synthetic Biology

This unit examines the intersection of advanced AI, synthetic biology, robotics, and energy systems to explore how accelerating capabilities create abundance, novel risks, and new economic and social structures. Students synthesize technical concepts (self-play, synthetic data, robo-labs, protein design, energy sources) with ethical, safety, and deployment considerations to prepare for applied design and policy work in the final unit.

Learning Objectives

• Analyze scenarios in which advanced AI and automated laboratories (self-play, AI swarms, robo-labs, QuanGeni) accelerate discovery, and quantify likely economic or operational impacts using simple models or simulations
• Evaluate ethical, safety, and societal risks arising from synthetic data, biological design, longevity-as-code, and large-scale automation (combinatorial disasters, data escape, copies), and propose concrete mitigation or governance strategies for specified stakeholders
• Apply design-space and computational reasoning to create a prototype workflow that integrates synthetic data, automated experimentation, or protein/genetic design for a defined use case (e.g., drug lead generation, customized material), including inputs, outputs, and failure modes
• Demonstrate systems-level analysis of energy and infrastructure trade-offs (solar, natural hydrogen, compute/robotics power) by modeling constraints and recommending sustainable deployment strategies for AI-enabled technologies