跟读练习: What is AI and ML? [Pt 2] | Generative AI with .NET for Beginners - 通过视频学习英语口语
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Hello and welcome back to this beginner series on machine learning and AI.
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In this video, I'm going to give you a quick introduction to what AI and machine learning are.
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In this video, some of the things that you're going to be learning about are, what is AI and ML?
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We're also going to learn about what you can do with AI and ML, and as a .NET developer, how you can get started with AI and ML.
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All right, so first off, what is AI and ML?
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You can look at it as different layers of basically the same thing.
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And at the top level, you have AI or artificial intelligence.
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And these are typically agents that automate tasks that are typically performed by humans.
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So you can think of this as your spam filter in your email, right?
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So at the end of the day, once you set some rules as to how you want your email organized, It's some sort of autonomous agent that's performing these actions on your behalf,
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as opposed to you having to do it by yourself.
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At the next level, you have machine learning or ML.
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And these are essentially systems that use algorithms and data to infer patterns.
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Now, the reason why you have machine learning is, again, think of your email inbox.
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There's a certain point where rules become so overwhelming
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that it's really difficult to sort of make any sort of meaning out of those rules and manage that.
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So that's sort of where machine learning comes in.
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And again, using data and algorithms, it's able to learn what those rules are and infer those patterns.
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A level lower here, you have deep learning.
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It's a machine learning technique that uses specialized algorithms known as neural networks.
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And then last but not least, you have generative AI, such as the one used in models like GPT and ChatGPT.
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And essentially this uses deep learning algorithms, typically known as transformers, to generate new content like text, images, and other content.
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So what can you do with AI and ML?
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At a high level, you can essentially extract key insights and summarize information with AI and ML.
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Some examples of that are language.
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So you could put, for example, analyze the sentiment of customer reviews.
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You can build virtual agents or chatbots, like the one pictured here, Bing Chat.
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You can translate text or code, and you can even use these models to explain code.
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When it comes to computer vision, you can use these models to identify objects in an image or a video.
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Similarly, for classification, if you need to tag customer reported issues with some severity level, you can leverage machine learning for that and AI.
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You can also do anomaly detection, tasks like predictive maintenance, when a certain piece of equipment is going to fail.
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Last but not least, you have forecasting.
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So if you're looking at quarterly sales or some sort of target that you're looking to forecast or number, you can use AI and ML to do that.
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Now, this is not a complete list, but this is just a relatively good overview of the things that you can do with AI and ML.
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So now that you know what AI and ML is and what you can do with it, how can you get started as a.NET developer?
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There's a few ways and we're going to go over a few of these throughout this entire series.
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So first and foremost, you have services such as Azure OpenAI for working with generative AI models,
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and then you have Azure Conduct Services or AI services.
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What these allow you to do is they allow you to perform task-specific tasks and workflows using the power of AI.
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You also have frameworks and libraries like Symantec Kernel for working with generative AI models.
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You have ML.NET for training custom machine learning models.
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You have libraries like AutoML, Torch Sharp, and SciSharp, which help you train custom machine learning and deep learning models.
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You have the Open Neural Network Exchange or ONNX, which helps you when inferencing and bringing models trained in other platforms over to .NET,
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and you have other libraries like plali.net, which help you with graphing and visualizing your data.
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Then last but not least, there's a series of tools to help you with these workflows, such as ModelBuilder and the ML.NET CLI, when it comes to training custom machine learning models or deep learning models.
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Then there's Polyglot Notebooks, which help with that experimentation process, and just writing code in general.
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That brings to the end of this video, and let's recap what we learned here.
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We learned what AI NML is.
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We learned the things that you can do with AI NML.
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And then last but not least, we wrapped it up with how you as a .NET developer can get started building AI applications.
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Thanks for watching.
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See you in the next video.
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本课的词汇与口语要点
这段视频共有 52 个句子、835 个单词可供跟读。 讲话部分时长为 4:19。 说话人语速较快，每分钟约 193 个词，会出现较多连读和弱读。 83% 的单词属于英语最常用的 3,000 词，其余的词建议在练习前先学一下。
视频中的重点词汇
视频中 15 个值得学习的单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| algorithm 名词 | /ˈælɡəɹɪðm̩/ | 算法, 演算法 |
| custom 名词 | /ˈkʌstəm/ | 習慣 /习惯, 習俗 /习俗 |
| essentially 副词 | /ɪˈsɛnʃəli/ | 本質上 /本质上, 基本上 |
| forecast 名词 | /ˈfɔːkɑːst/ | 預報 /预报, 預測 /预测 |
| technique 名词 | /tɛkˈniːk/ | 技術 /技术, 技巧 |
| layer 名词 | /ˈleɪ̯ɚ/ | 層 /层 |
| similarly 副词 | /ˈsɪm.ə.lə.li/ | 類似地 /类似地, 同樣地 /同样地 |
| framework 名词 | /ˈfɹeɪm.wɜːk/ | 骨架, 框架 |
| filter 名词 | /ˈfɪltɚ/ | 過濾器 /过滤器, 濾過器 /滤过器 |
| artificial 形容词 | /ˌɑː.tɪˈfɪʃ.əl/ | 人造的 |
| wrap 动词 | /ɹæp/ | 包裝 /包装, 包 |
| insight 名词 | /ˈɪnsaɪ̯t/ | 見地 /见地 |
| classification 名词 | /ˌklæsɪfɪˈkeɪʃən/ | 分類 /分类 |
| overview 名词 | /ˈoʊ.vɚˌvjuː/ | 概觀 /概观 |
| graph 名词 | /ɡɹæf/ | 圖表 /图表 |
需要注意的发音
说话人用了 6 次缩略和弱读形式，例如 you're, we're, I'm。请按听到的简短形式来说。
- “sh” 和 “zh” 音: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/, detection /dɪˈtɛk.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/
- 长单词——注意重音位置: essentially /ɪˈsɛnʃəli/, developer /dɪˈvɛləpɚ/, similarly /ˈsɪm.ə.lə.li/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 algorithm, custom, essentially 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。