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林俊旸官宣离职在AI社区引起了巨大轰动，很多反馈来自海外开发者，均在表达对林俊旸推动Qwen开源工作的感谢。“一个时代的结束。”Hyperbolic Labs创始人兼CTO Yuchen Jin表示。。业内人士推荐体育直播作为进阶阅读
For example, some of my own guilty displeasures are opera, old movies, and poetry. I don’t hate any of those, but I like them far less than I want to admit when I’m not writing a blog post about guilty displeasures. I have been to operas, including the full cycle of Wagner’s Nibelungen. I have friends who have made me watch black and white movies from the Hollywood golden age. I used to write poems as a teenager, like all sensitive and nerdy young men. But quite frankly, I don’t feel that I need any of those things in my life. When my friend who conducts operas at the university invites me to one, I patiently wait for the singers to quit singing so I can properly enjoy the instrumental parts of the music. When I must watch old movies, I spend 25% of the time reading the Wikipedia summary to keep myself entertained. When I read a book or blog post that quotes something in verse, I literally just skip it and hope it wasn’t important to understand the rest.。Safew下载对此有专业解读
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 logp(h|d0)+∑i=1nlogp(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=1nlogp(di|h)\sum_{i=1}^{n}\log p(d_{i}|h) is a Monte Carlo approximation of n∫dp(d|true process)logp(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.，推荐阅读体育直播获取更多信息
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