AI's Progress Depends on Physics, Not Just Trillions of Parameters
AI dominates headlines, yet outside a few bright spots the measurable impact is thinner than the hype. Researchers argue the balance is off: physics has more to offer AI right now than AI has to offer physics. Scaling parameters gets you correlations, not the scientific principles that make predictions hold up under stress.
Current systems simulate intelligence without grasping the laws of the systems they model. That gap shows up as brittleness in science and healthcare, where errors, rare events, and nonlinear behavior are the norm-not the edge case.
Where scaling stalls
- Shortcut learning: foundation models fit patterns but miss governing laws.
- Distribution mismatch: baked-in Gaussian assumptions meet non-Gaussian data and break under extremes.
- Weak uncertainty handling: confidence is poorly calibrated, especially out-of-distribution.
- Opaque internals: billions of parameters, little mechanistic insight.
Evidence: pattern fit without physical law
In tests on orbital trajectories, a foundation model nailed position predictions yet failed to infer gravity's inverse-square law. Even with access to second derivatives, it preferred task-specific curves over the underlying equation-like adding epicycles instead of discovering Newton.
The issue isn't data volume; it's what the model is optimized to learn. If the objective rewards prediction error alone, shortcuts win and transfer collapses on new physics tasks.
Big AI: theory-first, data-smart
Big AI blends scientific theory with machine learning. The idea is simple: let established laws constrain flexible models so they generalize beyond the training set and stay faithful to causality.
- Physics-informed neural networks to encode PDEs and conservation laws.
- Hybrid stacks: differentiable simulators + learned surrogates for speed and fidelity.
- Symmetry-aware architectures (e.g., equivariance, invariants) to reduce sample waste.
- Beyond Gaussian priors: heavy tails, mixtures, and copulas for real-world variability.
- Uncertainty quantification and out-of-distribution checks baked into evaluation.
High-impact applications
- Digital twins for personalized healthcare: disease risk, treatment optimization, and safety checks.
- Drug and molecule design guided by chemical constraints, not trial-and-error patterns.
- Materials discovery with property targets enforced by physics and chemistry.
- Weather and climate extremes: better tails, better preparedness.
- Chaotic systems with quantum-informed ML where small errors explode if left unconstrained.
What to change in your workflow
- Start with the equations: list conservation laws, symmetries, and known constraints before model selection.
- Constrain training: penalize law violations; use physics residuals as loss terms.
- Test extrapolation explicitly: train on one regime, score on another; report failures, not just averages.
- Fix your priors: if tails matter, model them; stop forcing normality on non-normal data.
- Quantify uncertainty: predictive intervals, calibration plots, and scenario analysis by default.
- Stress-test with synthetic edge cases and adversarial perturbations that target the laws you care about.
- Benchmark against mechanistic baselines; if a simple simulator wins on transfer, learn from it.
- Track data provenance and units end-to-end; silent unit errors tank scientific claims.
Limits of current reasoning models
Reasoning-tuned models beat standard LLMs on medium difficulty scientific tasks, but gains fade as complexity rises. They still output ambiguous or incorrect steps and rarely surface the mechanistic story behind an answer. Better prompts don't fix missing physics.
Trust comes from theory, not scale
If your application is safety-critical, theory isn't optional-it's the guardrail. The path forward is to make learning obey the laws we already know, then use data to fill the gaps, not the other way around.
Further reading
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