Non-determinism
Same input → DIFFERENT output. That is the AI half of the 98/2. When you ask Roost the same question twice you can get two different replies — because the next word is sampled from a distribution, not computed from a rule. This is a feature, not a bug. It is also why AI cannot live alone on the regulated paths.
A characteristic of generative models where the inference process involves sampling from a probability distribution rather than selecting the single highest-probability token. This variability is intentionally preserved to ensure output diversity, distinguishing it from the deterministic logic found in traditional software stacks.
A property of a stochastic computational system wherein the mapping from input to output is not a fixed function, but a conditional probability distribution. By sampling from the model's output logits—often modulated by temperature or top-k/p parameters—the system produces non-identical outputs for identical inputs, a fundamental requirement for generative fluidity that necessitates architectural separation from deterministic execution layers.
evolution
- 1956 · historyDartmouth Workshop
Early AI research established the use of probabilistic models and random search strategies to simulate intelligent behavior.
- 1988 · historyProbabilistic Reasoning in Intelligent Systems
Judea Pearl formalized Bayesian networks, shifting AI from rigid symbolic logic to uncertainty-based inference.
- 2014 · historyGenerative Adversarial Networks (GANs)
Ian Goodfellow introduced a framework where stochastic sampling became central to generating novel, non-deterministic data outputs.
- 2017 · historyTransformer Architecture
The 'Attention Is All You Need' paper popularized temperature-based sampling, making non-deterministic text generation a standard feature of LLMs.