Perfect AI Simulation — Pareto Frontier of Accuracy, Speed & Robustness

Population

Population size 40

Mutation rate 15%

"Perfect AI" Weights

Accuracy 33%

Speed 33%

Robustness 34%

Leading Candidate

gen 0 — evaluating…

Pareto front size

0 / 40

Generation

Frontier quality

0.000

Best weighted score

0.000

How it works

Each dot is a candidate AI configuration described by three genes in [0,1]: capacity c, compute budget b, and robustness effort r. They map to three competing objectives:

accuracy    = clamp(0.20 + 0.75·(0.6c + 0.4b) − 0.25r)
speed       = clamp(0.95 − 0.70b − 0.25c + 0.05r)
robustness  = clamp(0.15 + 0.80r − 0.30·c·b)

More capacity and compute raise accuracy but cost speed; hardening a model against adversarial/edge-case inputs (robustness) trades away some accuracy; large, high-budget models are also harder to keep robust. This mirrors real deployment trilemmas — bigger models score higher on benchmarks but are slower and more brittle at the edges, exactly why production ML systems trade accuracy for latency (quantization, pruning, distillation) and for robustness (adversarial training).

A point Pareto-dominates another only if it is at least as good on all three objectives and strictly better on one. The Pareto frontier (gold dots) is the set no other point dominates — every generation, a small genetic algorithm (tournament selection biased toward lower Pareto rank, blend crossover, Gaussian mutation, elitism that always keeps the current frontier) pushes the population toward and along that frontier.

The three weight sliders compute a single scalar utility = wA·accuracy + wS·speed + wR·robustness and crown whichever point scores highest (the pulsing ring). Slide the weights and watch the "winner" jump to a different point on the very same frontier — the demonstration behind the sim's premise: there is no coordinate-free "perfect AI". Every candidate on the gold frontier is optimal for some honest set of priorities and suboptimal for another; picking a winner always means picking weights first.

Pareto-optimal (frontier) Dominated candidate Best for current weights

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Perfect AI Simulation — the Pareto Frontier of Accuracy, Speed & Robustness