←搜索结果

搜索“AWS”

按匹配度 · 共 1542 条结果 清除搜索

星期五

  1. Sam Altman Blog

    Sam Altman：人类与 AI 的融合已经开始

    Sam Altman 认为，人类与 AI 的融合并非单一时刻，而是已经开始的渐进过程，手机、社交媒体和搜索算法正持续影响人类。随着算力和 AI 研究人才快速增长，超级人类 AI、基因增强与脑机接口或将到来，全球应尽早协调应对。

    wer and run the AI on it, and it figures out how to build even better chips. This probably cannot be stopped. As we have learned, scientific advancement eventually happens if the laws of physics do not prevent it. More important than that, unless we destroy ourselves first, superhuman AI is going to happen, genetic enhancement is going to happen, and brain-machine interfaces are going to happen. It is a failure of human imagination and human arrogance to assume that we will never build things sm

    匹配字段正文

    #editorial#大佬观点

星期二

  1. Lilian Weng：Lil'Log

    如何解释机器学习模型的预测？| Lil'Log

    Lilian Weng 在 Lil'Log 撰文梳理机器学习模型的可解释性方法，从线性回归、朴素贝叶斯、决策列表到随机森林等本身可解释的模型入手。文章引用 Lipton (2017) 提出的可模拟性、算法透明性与可分解性三项标准，并以金融放贷、医疗诊断、司法再犯预测和自动驾驶等场景说明解释模型决策的必要性。

    the corresponding interpretable data representation $x b$, let us sample instances around $x b$ by drawing nonzero elements of $x b$ uniformly at random where the number of such draws is also uniformly sampled. This process generates a perturbed sample $z b$ which contains a fraction of nonzero elements of $x b$. Then we recover $z b$ back into the original input $z$ and get a prediction score $f(z)$ by the target model. Use many such sampled data points $z b \in \mathcal{Z} b$ and their model p

    匹配字段正文

    #analysis#教程/实践#推理