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Total test duration: PT14.209S | PT12.378S。快连下载安装对此有专业解读

When we say bad communication, we mean bad for the purpose. A chatbot paste usually isn’t outright garbage. More often, it’s just verbose and doesn’t center the person’s intention. It buries the lede, so I’m sifting through it hoping to guess what the core motivation was. In other words, it’s bad communication.。关于这个话题，体育直播提供了深入分析

Consider a Bayesian agent attempting to discover a pattern in the world. Upon observing initial data d0d_{0}, they form a posterior distribution p​(h|d0)p(h|d_{0}) and sample a hypothesis h∗h^{*} from this distribution. They then interact with a chatbot, sharing their belief h∗h^{*} in the hopes of obtaining further evidence. An unbiased chatbot would ignore h∗h^{*} and generate subsequent data from the true data-generating process, d1∼p​(d|true process)d_{1}\sim p(d|\text{true process}). The Bayesian agent then updates their belief via p​(h|d0,d1)∝p​(d1|h)​p​(h|d0)p(h|d_{0},d_{1})\propto p(d_{1}|h)p(h|d_{0}). As this process continues, the Bayesian agent will get closer to the truth. After nn interactions, the beliefs of the agent are p​(h|d0,…​dn)∝p​(h|d0)​∏i=1np​(di|h)p(h|d_{0},\ldots d_{n})\propto p(h|d_{0})\prod_{i=1}^{n}p(d_{i}|h) for di∼p​(d|true process)d_{i}\sim p(d|\text{true process}). Taking the logarithm of the right hand side, this becomes log⁡p​(h|d0)+∑i=1nlog⁡p​(di|h)\log p(h|d_{0})+\sum_{i=1}^{n}\log p(d_{i}|h). Since the data did_{i} are drawn from p​(d|true process)p(d|\text{true process}), ∑i=1nlog⁡p​(di|h)\sum_{i=1}^{n}\log p(d_{i}|h) is a Monte Carlo approximation of n​∫dp​(d|true process)​log⁡p​(d|h)n\int_{d}p(d|\text{true process})\log p(d|h), which is nn times the negative cross-entropy of p​(d|true process)p(d|\text{true process}) and p​(d|h)p(d|h). As nn becomes large the sum of log likelihoods will approach this value, meaning that the Bayesian agent will favor the hypothesis that has lowest cross-entropy with the truth. If there is an hh that matches the true process, that minimizes the cross-entropy and p​(h|d0,…,dn)p(h|d_{0},\ldots,d_{n}) will converge to 1 for that hypothesis and 0 for all other hypotheses.