[[The need for a paradigm shift from a mechanistic to a systemic understanding of the world to address global challenges]] ## Physics: From Newton to Quantum - Deterministic mindset: Classical physics (Newtonian mechanics) assumed a clockwork universe—if we know initial conditions, we can predict the future exactly. - Stochastic mindset: Quantum mechanics introduced irreducible randomness (e.g., Heisenberg uncertainty principle). Even with all knowledge, some outcomes are only probabilistic. Implication: We no longer expect to predict with certainty but instead calculate likelihoods. ## AI & Machine Learning - Deterministic AI (Rule-based systems): Early AI relied on logical rules and symbolic manipulation. Every input led to a predictable output. - Stochastic AI (Modern ML): Today’s AI models, like neural networks and transformers, are fundamentally probabilistic. Outputs are distributions (e.g., next-word prediction), not certainties. Key mindset shift: - Old AI: “What’s the correct answer?” - New AI: “What’s the most likely answer, given the data?” Examples: - Generative AI (like ChatGPT) doesn’t “know” answers but samples from a learned distribution. - Computer vision models estimate likelihoods of classifications. - Reinforcement learning includes exploration—accepting that acting under uncertainty is necessary for learning. Complex, adaptive systems (like the economy, climate, human behavior) cannot be understood deterministically. Probabilistic systems are better suited for dynamic, uncertain environments.