AI, how can humans communicate better with you?
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Artificial intelligence(AI) systems and humans communicate more and more with each other. AI systems are optimized for objectives such as error rate in communication or effort, eg. computation. In contrast, inputs created by humans are often treated as a given. We investigate how humans providing information to an AI can adjust to reduce miscommunication and improve efficiency while having to change their behavior as little as possible. These objectives result in trade-offs that we investigate using handwritten digits. To create examples that serve as demonstrations for humans to improve, we develop a model based on a conditional convolutional autoencoder (CCAE). Our quantitative and qualitative evaluation shows that in many occasions the generated proposals lead to lower error rates, require less effort to create and differ only modestly from the original samples.
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