Shadowing Practice: What is AI and ML? [Pt 2] | Generative AI with .NET for Beginners - Learn English Speaking with Video
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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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Vocabulario y notas de pronunciación para esta lección
Este vídeo tiene 52 frases y 835 palabras para practicar shadowing. La parte hablada dura 4:19. El hablante habla rápido, unas 193 palabras por minuto, así que espera sonidos enlazados y reducidos. El 83 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar.
Vocabulario clave de este vídeo
15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| algorithm sustantivo | /ˈælɡəɹɪðm̩/ | algoritmo |
| custom sustantivo | /ˈkʌstəm/ | costumbre, usanza |
| essentially adverbio | /ɪˈsɛnʃəli/ | esencialmente, en esencia |
| developer sustantivo | /dɪˈvɛləpɚ/ | desarrollador |
| forecast sustantivo | /ˈfɔːkɑːst/ | pronóstico, vaticinio |
| neural adjetivo | /ˈnʊɹəl/ | neural |
| technique sustantivo | /tɛkˈniːk/ | técnica |
| layer sustantivo | /ˈleɪ̯ɚ/ | capa, tongada |
| virtual adjetivo | /ˈvɜɹt͡ʃuəl/ | virtual |
| similarly adverbio | /ˈsɪm.ə.lə.li/ | similarmente, igualmente |
| framework sustantivo | /ˈfɹeɪm.wɜːk/ | entramado, armazón |
| filter sustantivo | /ˈfɪltɚ/ | filtro |
| artificial adjetivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificial |
| generate verbo | /ˈd͡ʒɛn.ə.ɹeɪt/ | generar |
| wrap verbo | /ɹæp/ | envolver, fajar |
Phrasal verbs que vas a escuchar
| Palabra | Significado |
|---|---|
| go over verbo | sentar, pasar |
Pronunciación a tener en cuenta
El hablante usa 6 contracciones y formas reducidas, como you're, we're, I'm. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “sh” y “zh”: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/, detection /dɪˈtɛk.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/
- Palabras largas — cuida el acento: essentially /ɪˈsɛnʃəli/, developer /dɪˈvɛləpɚ/, similarly /ˈsɪm.ə.lə.li/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
- Grábate y compara con el original, prestando atención a palabras como algorithm, custom, essentially.
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.