Summary of Control When Confidence Is Costly, by Itzel Olivos-castillo et al.
Control when confidence is costly
by Itzel Olivos-Castillo, Paul Schrater, Xaq Pitkow
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a framework that combines concepts from stochastic control and efficient coding to account for computational costs of inference in Linear Quadratic Gaussian (LQG) control. The authors develop a version of LQG control with an internal cost on the relative precision of posterior probability, leading to a trade-off between task performance and computational efficiency. They show that the rational strategy that solves the joint inference and control problem goes through phase transitions depending on task demands, resulting in suboptimal inferences related by rotation transformations. The authors’ work provides a foundation for rational computations that could be used by both brains and machines for efficient but computationally constrained control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make smart decisions when you have limited computer power or brain resources. It combines two ideas: controlling things (like robots or autonomous vehicles) while also considering the cost of processing information. The authors use a special kind of control called LQG and add a new twist that makes the agent think carefully before making decisions. They find that the best strategy depends on how hard the task is, but ultimately, the agent will try to make fewer decisions if it can. |
Keywords
» Artificial intelligence » Inference » Precision » Probability
