AI is the new electricity, as Andrew Ng styles it. Every day another application is disrupted by cutting-edge machine learning research. Trying to keep up with the endless stream of new technologies is heart-breaking. If you fancy trying to cope with it, here I present you with what are my top 5 resources for AI-related news and knowledge.

正如吴安德(Andrew Ng)所说， 人工智能是新的力量。 每天，其他应用程序都会被尖端的机器学习研究破坏。 努力跟上不断涌现的新技术令人心碎。 如果您想解决这个问题，在这里，我向您介绍我与AI相关的新闻和知识的前5个资源。

＃1批处理 (#1 The Batch)

The deeplearning.ai weekly newsletter. Each week, it packs recent break-throughs and noteworthy news alongside a short intro by Andrew Ng himself. The best thing about The Batch is that it is quick, self-contained, and always includes some perspective on the news, such as “why it matters.”

deeplearning.ai每周新闻 。 每周，它都会收录最近的突破和值得注意的新闻，以及Andrew Ng本人的简短介绍。 The Batch的最好之处在于它快速，独立且始终包含对新闻的一些看法，例如“为什么重要”。

This is the best place to start, as it is not overwhelming, is self-contained, and is not focused on any particular sub-topic.

这是最好的起点 ，因为它不占优势，自成体系，并且不专注于任何特定的子主题。

＃2中 (#2 Medium)

Yeah, this place is incredible. Make sure you check AI and Data Science as topics of interest and start following people and publications that have similar interests as you. Most of what I see on The Batch I also see here but in more detail. Sometimes I find stuff here before it even gets to The Batch.

是的，这个地方真是不可思议。 确保将AI和数据科学作为感兴趣的主题，并开始关注与您有相似兴趣的人和出版物。 我在The Batch上看到的大部分内容也在这里看到，但更详细。 有时，我什至在找到The Batch之前就在这里找到东西。

The distinctive feature of Medium are the voices. You often see people that work at major companies, startup CEOs, or Ph.D. students of top universities. The tech stuff is more often than not being written by people that are actually in tech. They are not written by trained journalists but by real damn nerds. The cherry goes to the comment section. You are not only reading from these folks, but you also get the chance to talk to them directly and ask anything.

媒介的特色是声音 。 您经常会看到在大型公司，初创公司首席执行官或博士学位中工作的人。 顶尖大学的学生。 科技人员通常不是真正由技术人员撰写的东西。 它们不是由训练有素的记者撰写的，而是由真正可恶的书呆子撰写的。 樱桃进入评论部分。 您不仅在向这些人阅读，而且还有机会直接与他们交谈并提出任何问题。

＃3 Twitter (#3 Twitter)

Find the people whose work you like and follow them on Twitter. This is enough for the platform to email you their most important tweets. I was surprised by the number of useful links I started receiving after I started doing this. If you don’t know where to start, begin by following the guests of #5.

找到您喜欢的人，并在Twitter上关注他们。 这足以使平台通过电子邮件将最重要的推文发送给您。 开始执行此操作后，我收到的有用链接数量让我感到惊讶。 如果您不知道从哪里开始，请先关注＃5的来宾。

Twitter is where the news is born. After I created my account and started following AI people, I began to see stories that would only pop up on larger channels after weeks. Before anyone can write a piece on something, its author has already twitted about it. I don’t use twitter for anything else.

Twitter是新闻的发源地。 创建帐户并开始关注AI人员后，我开始看到几周后才会在更大的频道上弹出的故事。 在任何人都可以在某件东西上写一篇文章之前，它的作者已经对它进行了花招。 我什么都不要用twitter。

＃4 YouTube (#4 YouTube)

Similar to Medium, YouTube has many tech people doing essays on AI. The bad thing about YouTube is that it doesn’t have as much curation nor a helpful email digest. The positive side is that it is less demanding on you. Watching a video is way more relaxing to the eyes than reading articles.

与Medium相似，YouTube上有许多技术人员都在撰写AI文章。 YouTube的坏处是它没有那么多的策划，也没有有用的电子邮件摘要。 积极的一面是它对您的要求较低。 观看视频比阅读文章更令人放松。

My recommendations are TwoMinutesPapers and Yannic Kilcher. The former focuses on presenting eye-pleasing research on AI and graphics, and the latter is more in-deep on analyzing AI papers down to the math. I confess that I don’t really like Yannic’s videos that much, as they are too long, but I love his selection. Whenever he uploads something, I search for the paper to read.

我的建议是 TwoMinutesPapers和Yannic Kilcher 。 前者着重于介绍令人愉悦的AI和图形研究，而后者则更深入地分析AI论文直至数学。 我承认我不太喜欢Yannic的视频，因为它们太长了，但是我喜欢他的选择。 每当他上传东西时，我都会搜索要阅读的论文。

＃5 AI播客 (#5 AI Podcast)

Lex Fridman is a former MIT professor that runs a podcast on AI and Computer Science. The selection of guests is outstanding. It includes Turing Award winners, Nobel winners, and many brilliant individuals, such as 3Blue1Brown’s host Grant Sanderson and Elon Musk. To me, the beauty of this podcast is that Lex always asks his guests to explain fundamental concepts, but the conversation never shies away from going technical.

莱克斯·弗里德曼 ( Lex Fridman)是前麻省理工学院的教授，负责播报AI和计算机科学的播客。 客人的选择是杰出的。 它包括图灵奖的获得者，诺贝尔奖获得者以及许多杰出的人物，例如3Blue1Brown的主持人Grant Sanderson和Elon Musk 。 对我来说，这个播客的美妙之处在于，Lex总是要求客人解释基本概念，但是对话永远不会避开技术性。

This is the place to get perspective. Where else can you find Noam Chomsky and Daniel Kahneman discussing AI’s future? Or maybe the lead engineer behind TensorFlow discussing its history in-depth and not being shy of talking technical? Reading papers is nice, but hearing what the people behind some of the most ground-breaking research to date have to say helps a lot with understanding what is happening now and what will likely happen next.

这是获得观点的地方。 您还能在哪里找到Noam Chomsky和Daniel Kahneman讨论AI的未来？ 还是TensorFlow背后的首席工程师深入讨论其历史而又不怕谈论技术问题？ 阅读论文固然不错，但是了解一些迄今为止最具开创性研究背后的人们所说的话，对理解现在正在发生的事情以及接下来可能发生的事情有很大帮助。

＃6奖金：社区建议 (#6 Bonus: Community Suggestions)

Here I feature additional resources mentioned by other users on the comments section. Please let me know if you would like to add to this list.

在这里，我将介绍其他用户在评论部分提到的其他资源。 如果您想添加到此列表，请告诉我。

Davide Giordano suggested AI Trends, MIT Technology Review, and Papers with code. This last one has a newsletter which I was unaware off. Just signed in myself.

Davide Giordano建议使用代码编写 AI趋势 ，《 麻省理工学院技术评论 》和论文 。 这最后一个有我不知道的新闻通讯。 刚刚登录自己。

Marc von Wyl recommended the TWILM podcast and the Twitter profiles @omarsar0, and @seb_ruder, gold mines for NLP news.

Marc von Wyl推荐了TWILM播客和Twitter个人资料@ omarsar0和@seb_ruder ，这是NLP新闻的金矿。

Gaurav Ragtah added CatalyzeX, a curator/search engine for AI models and ML repositories. Browser plugins are available for Chrome and Firefox.

拉夫Ragtah添加CatalyzeX ，为AI模式和ML库策展人/搜索引擎。 浏览器插件可用于Chrome和Firefox 。

Unmesh Kurup highlighted the Data Science Weekly and Data Elixir newsletters, two other sources of curated news for practitioners.

Unmesh Kurup着重介绍了Data Science Weekly和Data Elixir新闻通讯，这是从业人员精选新闻的另外两个来源。

A huge thanks to everyone’s suggestions and permission to add them here in this bonus section.

非常感谢每个人的建议和允许在此奖金部分中将其添加到此处。

The Batch has the general news, Medium features all voices, Twitter has the latest things, and YouTube shows you the shiniest results. On top of that, hearing the podcast helps to put it all together by giving you some perspectives from significant figures in the field.

吨他批具有一般新闻，中拥有所有的声音，微博拥有最新的东西，你的YouTube节目最闪亮的结果。 最重要的是，聆听播客有助于您从该领域的重要人物那里获得一些观点，从而将其整合在一起。

The real magic happens when you start to consume these sources regularly. Over time, you will realize that some of the news you saw in one place are popping up on the other, but in different amounts of detail. This dramatically helps to get a deeper level of understanding as you are exposed to the same ideas over and over in various degrees of depth. You start to draw connections.

当您开始定期消耗这些来源时，真正的魔力就会发生。 随着时间的流逝，您将意识到在一个地方看到的一些新闻在另一个地方突然出现，但是细节却不同。 当您以不同的深度反复接触相同的想法时，这将极大地帮助您加深理解。 您开始绘制连接。

Happy reading/listening/watching :)

快乐阅读/收听/观看:)