# opML **Optimistic Machine Learning (opML)** Optimistic machine learning is another solution to decentralizing AI inference, with “optimistic” referring to the idea of optimistically trusting the results of an inference unless (or until) someone challenges the result. In the event of a challenge, the challenger first puts up a financial stake and a verification game is played, where a bisection protocol is used to locate the disputed step that caused the divergence in computation. The arbitrator contract resolves the challenge, heavily slashing the inference computer or the challenger depending on the success or failure of the challenge respectively. This optimistic security model comes with both pros and cons compared to zkML. The biggest advantage is the lower time/computational cost that comes with adopting a model that doesn’t generate a proof on every inference, which is significantly lower especially with larger models where both the inference and proof generation can be extremely expensive. However, the biggest disadvantage is the lack of the immediate cryptographic security that zkML offers that high-stakes use-cases may require. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.vannalabs.ai/key-concepts/opml.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.