쉐도잉 연습: What is AI and ML? [Pt 2] | Generative AI with .NET for Beginners - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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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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이 레슨의 어휘와 말하기 포인트
이 영상에는 섀도잉할 문장 52개와 단어 835개가 있습니다. 말하는 구간의 길이는 4:19입니다. 화자는 분당 약 193단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 단어의 83%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.
이 영상의 핵심 어휘
영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| algorithm 명사 | /ˈælɡəɹɪðm̩/ | 알고리즘, 알고리듬 |
| custom 명사 | /ˈkʌstəm/ | 습관, 풍속 |
| forecast 명사 | /ˈfɔːkɑːst/ | 예상, 예보 |
| technique 명사 | /tɛkˈniːk/ | 기술 |
| layer 명사 | /ˈleɪ̯ɚ/ | 층 |
| virtual 형용사 | /ˈvɜɹt͡ʃuəl/ | 사실상의 |
| filter 명사 | /ˈfɪltɚ/ | 여과기, 필터 |
| artificial 형용사 | /ˌɑː.tɪˈfɪʃ.əl/ | 인공, 인조 |
| wrap 동사 | /ɹæp/ | 싸다 |
| classification 명사 | /ˌklæsɪfɪˈkeɪʃən/ | 분류 |
| overview 명사 | /ˈoʊ.vɚˌvjuː/ | 개요 |
| detection 명사 | /dɪˈtɛk.ʃən/ | 탐지, 수색 |
| graph 명사 | /ɡɹæf/ | 그래프, 도표 |
| spam 명사 | /spæm/ | 스팸, 스팸 메일 |
| organize 동사 | /ˈɔɹɡənaɪz/ | 조직하다 |
주의할 발음
화자는 you're, we're, I'm 같은 축약형과 약화된 형태를 6번 사용합니다. 들리는 대로 짧게 발음하세요.
- “sh”와 “zh” 소리: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/, detection /dɪˈtɛk.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/
- 긴 단어 — 강세 위치에 주의: essentially /ɪˈsɛnʃəli/, developer /dɪˈvɛləpɚ/, similarly /ˈsɪm.ə.lə.li/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
- 자신의 목소리를 녹음해 원본과 비교하고, algorithm, custom, forecast 같은 단어에 특히 주의합니다.
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.