译者语
Swami Chandrasekaran 画了一张漂亮的AI全景图。你要不要来看一下
Demystifying Artificial Intelligence. What is Artificial Intelligence & explaining it from different dimensions.
当我写博客文章“通过Metromap成为数据科学家—课程”时，我几乎不知道它将收到令人振奋的反馈。首先，非常感谢！多年来，许多人以非常友善的话语与我联系，以及他们如何将其用作数据科学家之旅的指南。同样，许多人在演示文稿中寻求使用Metromap图片的许可，还有一些大学也伸出手将其用作课程提纲的一部分。写这篇文章使我意识到两件事，
When I wrote my blog post Becoming a Data Scientist — Curriculum via Metromap, little did I know that it will receive a rousing feedback. So a big THANK YOU first of all! Over years a lot of people reached out to me with very kind words and how they use it as a guide in their data scientist journey. Also, many who sought permission to use the Metromap picture in their presentations as well as a few universities that also reached out to use it as part of their syllabus. Writing that post made me realize two things,
- 以消耗性和美学方式使用大图片隐喻呈现一个复杂的主题具有价值+使用价值。 Taking a step back, presenting a complex topic using a big-picture metaphor in a consumable and aesthetic fashion has value + use.
- 吓坏我了，写我的下一篇文章。 Scared the pants off me to write my next post.
现在，我在将近四年的时间里写了一个与我的内心非常接近的话题，但又一次看到了很多令人困惑的毛病-人工智能（AI）。我敢肯定，包括我在内的许多人都会对以下内容说“是”，
Now here I’m after almost 4 years to write on a topic that is very close to my heart and yet again see a lot of confusing fluff floating around — Artificial Intelligence (A.I). I’m pretty sure many of you including me would say yes to the following,
- LinkedIn连接列表中的每个人的标题都带有AI。
- 大量有关AI变革行业的文章和世界末日的文章齐头并进。
- 看到的文章不仅令人迷惑和误导，而且往往不全面。
- 听到有人正在研究“ AI for X”，其中X可以是从治疗癌症到订购午餐的任何地方。
- Everyone in your LinkedIn connections list has AI in their title.
- Are getting flooded with articles that talk about A.I transforming industries and doomsday articles that go hand in hand.
- See articles that are not only confusing and misleading but also don’t tend to be comprehensive.
- Hear from someone is working on “AI for X”, where X can be anywhere from treating cancer to ordering lunch.
请原谅我过于概括了，但是我也看到有些人非常松散地使用AI一词，对他们在说什么绝对不知道/有什么想法。如果您试图避免使用它们，并试图找到“什么是AI？”的答案，那么您注定会充满矛盾的观点以及非常混淆的术语和定义。仅仅因为有人在使用深度学习库/软件包，并不意味着他们的系统是智能的。还有更多。因此，这是我通过图片进行的另一种适度尝试-“揭开AI的神秘面纱”。
Pardon me for overgeneralizing, but I also see of folks who very loosely use the word A.I and have absolutely no clue/idea about what they are talking about. If you try to avoid them and try to seek the answer for “What is AI?”, you are bound to get flooded with conflicting views and very obfuscated terms and definitions. Just because someone is using a deep learning library/package, that doesn’t mean their system is intelligent. There is more to it. So here is my yet another modest attempt to convey via a picture — “Demystifying AI”.
如果我必须为AI选择一个很好的开始定义，那么我将投票给John McCarthy's。他可能给出了最深刻但最简单的AI定义，
If I have to pick a great starting definition for AI, I would vote for John McCarthy’s. He probably gave the most profound and yet a simple definition of A.I,
“制造智能机器，特别是智能计算机程序的科学与工程”
“science and engineering of making intelligent machines, especially intelligent computer programs”
人工智能是一个引人入胜的领域，我个人认为，如果不从多个角度审视人工智能，就无法公正地对其进行解释。我从以下几个方面对AI提出了自己的看法，
AI is a fascinating area and I personally feel it will not do justice to explain it without looking at it from multiple dimensions. I have provided my point of view on AI in the following dimensions,
- 用于AI的Guardrails （从“开始，只是因为您可以做到并不意味着您应该这样做”）
- 核心和必要的构建基块
- 数据AI系统的工作类型
- 人工智能系统的主要特征
- 不同类型的AI（打哈欠！）
- 训练/教导AI系统的方法类型
- 热门算法
- 最常见的AI工作负载/任务
- 工作中的AI系统的常见示例
- 面向AI的开发运营-人工智能系统如何构建？
- 流行平台，API，库和框架
- 您需要花费一些时间来了解的一些绝对概念和主题
- 人工智能的下一步是什么？
- Guardrails for AI (starting with, “just because you can doesn’t mean you should”)
- Core & essential building blocks
- Types of data AI systems work on
- Primary characteristics of an AI system
- Different types of AI (yawn!)
- Types of approaches to train / teach AI systems
- Top Algorithms
- Most common AI workloads/tasks
- Common examples of AI systems at work
- Dev Ops for AI — how are AI systems built?
- Popular Platform, API’s, Libraries & Frameworks
- Some of the absolute concepts and topics you need to take time in knowing
- What’s next for AI?
我的目标是为大家提供查看AI全景图的能力，同时又可以从各个角度对其进行查看。我有意识地没有深入，而是坚持相当高的层次来清楚地传达这些概念。我可以轻松地在多个细节层次上进行分解。我将来可能会尝试这样做，或者可能会尝试写一本书。随时留下您的评论和建设性的反馈。
My goal with this visual is to provide you all with an ability to look at the big picture of AI and yet look at it from various dimensions. I have consciously not gone into great depth and detail, but stuck to a fairly high-level to convey the concepts clearly. I could easily take each of these dimensions and blow it up in multiple levels of detail. I may try to do that in the future or might try to write a book. Feel free to leave your comments and constructive feedback.
