AI多久能学完？

在深入学习AI时，许多朋友发现需要编程，变得头大。同时，各类教程都默认了你会打命令行，导致入门十分困难。鉴于此，就有了这份简明入门，旨在让大家更快掌握Python和AI的相互调用。并使你在接下来的20分钟内，循序渐进的完成以下任务：完成一个简单程序完成一个爬虫应用，抓取公众号文章完成一个AI应用，为公众号文章生成概述[heading2]一些背景[content]知己知彼，百战不殆[heading3]关于Python[content]Python就像哆拉A梦，它:拥有一个百宝袋，装满了各种道具，被称为标准库。当遇到问题时，都可以拿出来直接使用。如果百宝袋里的道具不够用，还可以打电话给未来百货，去订购新道具。在这里：打电话：对应pip一类的工具，可以用来订购任何的道具。未来百货：对应GitHub一类的分享代码的平台，里面啥都有。被全世界广泛使用，尤其是在AI领域，所以遍地是大哥[heading3]关于OpenAI API[content]OpenAI通过两种方式提供服务:其一：通过ChatGPT，提供开箱即用的服务，直接对话即可，简单直观。其二：通过OpenAI API，提供更加灵活的服务，通过代码调用，来完成更多自动化任务，比如全自动将本地的1万本小说，从中文翻译成英文。发现了没，这里的OpenAI API，对应着上面未来百货道具。

在深入学习AI时，许多朋友发现需要编程，变得头大。同时，各类教程都默认了你会打命令行，导致入门十分困难。鉴于此，就有了这份简明入门，旨在让大家更快掌握Python和AI的相互调用。并使你在接下来的20分钟内，循序渐进的完成以下任务：完成一个简单程序完成一个爬虫应用，抓取公众号文章完成一个AI应用，为公众号文章生成概述

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective,and by a large margin.The ultimate reason for this is Moore's law,or rather its generalization of continued exponentially falling cost per unit of computation.Most AI research has been conducted as if the computation available to the agent were constant(in which case leveraging human knowledge would be one of the only ways to improve performance)but,over a slightly longer time than a typical research project,massively more computation inevitably becomes available.Seeking an improvement that makes a difference in the shorter term,researchers seek to leverage their human knowledge of the domain,but the only thing that matters in the long run is the leveraging of computation.These two need not run counter to each other,but in practice they tend to.Time spent on one is time not spent on the other.There are psychological commitments to investment in one approach or the other.And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation.There were many examples of AI researchers'belated learning of this bitter lesson,and it is instructive to review some of the most prominent.In computer chess,the methods that defeated the world champion,Kasparov,in 1997,were based on massive,deep search.At the time,this was looked upon with dismay by the majority of computer-chess researchers who had pursued methods that leveraged human understanding of the special structure of chess.When a simpler,search-based approach with special hardware and software proved vastly more effective,these human-knowledge-based chess researchers were not good losers.They said that``brute force"search may have won this time,but it was not a general strategy,and anyway it was not how people played chess.These researchers wanted methods based on human input to win and were disappointed when they did not.A similar pattern of research progress was seen in computer Go,only delayed by a further 20 years.Enormous initial efforts went into avoiding search by taking advantage of human knowledge,or