Complex systems: What data science can learn from astrophysics with Rachel Losacco

The AI Fundamentalists

Our special guest, astrophysicist Rachel Losacco, explains the intricacies of galaxies, modeling, and the computational methods that unveil their mysteries. She shares stories about how advanced computational resources enable scientists to decode galaxy interactions over millions of years with true-to-life accuracy. Sid and Andrew discuss transferable practices for building resilient modeling systems.

• Prologue: Why it's important to bring stats back [00:00:03]
  • Announcement from the American Statistical Association (ASA): Data Science Statement Updated to Include “ and AI”
• Today's guest: Rachel Losacco [00:02:10]
  • Rachel is an astrophysicist who’s worked with major galaxy formation simulations for many years. She hails from Leiden (Lie-den) University and the University of Florida. As a Senior Data Scientist, she works on modeling road safety.
• Defining complex systems through astrophysics [00:02:52]
  • Discussion about origins and adoption of complex systems
  • Difficulties with complex systems: Nonlinearity, chaos and randomness, collective dynamics and hierarchy, and emergence.
• Complexities of nonlinear systems [00:08:20]
  • Linear models (Least Squares, GLMs, SVMs) can be incredibly powerful but they cannot model all possible functions (e.g. a decision boundary of concentric circles)
  • Non-linearity and how it exists in the natural world
• Chaos and randomness [00:11:30]
  • Enter references to Jurassic Park and The Butterfly Effect
  • “In universe simulations, a change to a single parameter can govern if entire galaxy clusters will ever form” - Rachel
• Collective dynamics and hierarchy [00:15:45]
  • Interactions between agents don’t occur globally and often is mediated through effects that only happen on specific sub-scales
  • Adaptation: components of systems breaking out of linear relationships between inputs and outputs to better serve the function of the greater system
• Emergence and complexity [00:23:36]
  • New properties arise from the system that cannot be explained by the base rules governing the system
• Examples in astrophysics [00:24:34]
  • These difficulties are parts of solving previously impossible problems
  • Consider this lecture from IIT Delhi on Complex Systems to get a sense of what is required to study and formalize a complex system and its collective dynamics (https://www.youtube.com/watch?v=yJ39ppgJlf0)
• Consciousness and reasoning from a new point of view [00:31:45]
  • Non-linearity, hierarchy, feedback loops, and emergence may be ways to study consciousness. The brain is a complex system that a simple set of rules cannot fully define.
  • See: Brain modeling from scratch of C. Elgans

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Chapter

1. Complex systems: What data science can learn from astrophysics with Rachel Losacco (00:00:00)

2. Prologue: Why it's important to bring stats back (00:00:03)

3. Today's guest: Rachel Losacco (00:02:10)

4. Defining complex systems through astrophysics (00:02:52)

5. Complexities of nonlinear systems (00:08:20)

6. Chaos and randomness (00:11:30)

7. Collective dynamics and hierarchy (00:15:45)

8. Emergence and complexity (00:23:36)

9. Examples in astrophysics: Small modeling steps to find the unpredictable (00:24:34)

10. Consciousness and reasoning from a new point of view (00:31:45)

11. Artificial general intelligence: Best next step (00:34:50)