跟读练习: Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners - 通过视频学习英语口语

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Hi everyone and welcome to the first lesson of the Generative AI for Beginners course.

This course is based on an open source curriculum with the same name available on GitHub

that you can find at the link on the screen.

I'm Carlotta Castelluccio, I'm a cloud advocate at Microsoft focused on artificial intelligence technologies

and in this video I'm going to introduce you to generative AI and large language models.

Large language models represent the pinnacle of AI technology, pushing the boundaries of what was once thought impossible.

They've conquered numerous challenges that older language models struggled with, achieving human-leavened performance in various tasks.

They have similar capabilities and applications, but for the sake of this course,

we'll explore how large language models are revolutionizing education through a fictional startup that we'll be referring to as Our Startup.

Our startup works in the education domain with the ambitious mission of improving accessibility in learning on a global scale,

ensuring equitable access to education, and providing personalized learning experiences to every learner, according to their needs.

In this course, we'll delve into how our startup harnesses the power of generative AI to unlock new possibilities in education.

We'll also examine how they address the inevitable challenges tied to the social impact of this technology and its technological limitations.

But let's start by defining some basic concepts we'll be using throughout the course.

Despite the relatively recent hype surrounding generative AI,

we can say that in the last couple of years we have really heard of generative AI everywhere and every time.

But this technology has been decades in the making, with its origins tracing back to the 1950s, 1960s.

The earliest AI prototypes consisted of typewritten chatbots relying on knowledge bases maintained experts.

These chatbots generated responses based on keywords found in user input,

but it soon became clear that this approach had scalability limitations.

A significant turning point arrived in the 1990s when a statistical approach was applied to text analysis.

And this gave birth to machine learning algorithms which could learn patterns from data without explicit programming.

And these algorithms allowed machines to stimulate human language understanding, having the way for the eye we know today.

In more recent times, advancements in hardware technology allowed for development of advanced machine learning algorithms, particularly neural networks.

These innovations significantly improved natural language processing, enabling machines to understand the context of words in sentences.

This breakthrough technology powered the birth of virtual assistants in the early 21st century.

This virtual assistants excelled at interpreting human language, identifying needs, and taking actions to fulfill them,

such as answering queries with predefined scripts or connecting to third-party services.

And so we arrived at Generative AI, a subset of deep learning.

After decades of AI research,

a new model architecture known as the Transformer emerged transformers could handle longer text sequences as input

and work based on the attention mechanism, enabling them to focus on the most relevant information,

regardless of its order in the input text.

Today, mass-generative AI models, often referred to as large language models,

are built upon the transformer architecture, and that's what the T in GPT actually means.

These models trained on vast amounts of data from sources like books, articles, and websites possess a unique adaptability.

They can tackle a wide range of tasks and generate chromatically correct text with a hint of creativity.

But let's dive deeper into the mechanism of large language models

and shed light on the inner workings of models like the OpenAI GPT's.

One of the key concepts to grasp is tokenization.

Large language models receive text as input and produce text as output, if you want to really simplify the mechanism.

However, these models work much more efficiently with numbers rather than with raw text sequences.

And that's where the tokenizer comes into play.

Text prompts are chunked into tokens, helping the model in predicting the next token for completion.

Models also have a maximum length of token window,

and model pricing is also tightly computed by the number of tokens used in output and inputs.

So tokenization is really an important concept in large language models and generative AI domain.

Now, a token is essentially a chunk of text which can vary in length and typically consists of a sequence of characters.

And the tokenizer's primary job is to really break down the input text into an array of those tokens,

which are then further mapped to token indices.

These token indices are essentially integer and coding of the original text chunks, making it easier for the model to process and understand.

Now let's move to predicting the output tokens.

Given an input sequence of and tokens with the maximum and varying from one model to another according to the maximum

content window length of for one model, the model is designed to predict a single token as its output.

But here's where it gets interesting.

The predicted token is then incorporated into the input of the next iteration, creating an expanding window pattern.

And this pattern allows the model to provide more coherent and contextually relevant responses,

often extending to one or multiple sentences.

Now let's delve into the selection process.

The model chooses the output token based on its probability of occurring after the current text sequence.

This probability distribution is calculated using the model's training data.

However, here's the twist.

The model doesn't always choose the token with the highest probability from the distribution.

To simulate the process of creative thinking, a degree of randomness is introduced into the selection process.

This means that the model doesn't produce the exact same output for the same input every time.

That's the element that allows generative AI to generate text that feels, you know, creative and engaging.

Now, we see that the main capability of a large language model is generating text from scratch, starting from a textual input written in natural language.

But what kind of textual input and output?

First of all, let me say that the input of a large language model is known as prompt, while the output is known as completion,

a term that refers to the model mechanism of generating the next token to complete the current input.

Let's do some examples of prompts and completion by using the OpenAI ChatGPT Playground, always in our educational scenario.

Now, a prompt may include an instruction specifying the type of output we expect from the model.

In the example we are seeing, we are asking to write an assignment for high school students,

including four open-ended questions about Louis XIV and his court.

You can see that the output is exactly what I'm asking for.

The model was able to generate an assignment with the questions.

Now, another prompt might be a question, asking in the form of a conversation with an agent.

In this example, we are asking about in a question.

So we asked who is Louis XIV and why he is an important historical character?

And we've got an answer.

Another type of prompt might be a chunk of text to complete.

So an incipit of a text to complete.

And you can see now that we used an incipit of a text to complete to complete as prompt,

and we've got a whole paragraph to complete the current input.

So this is basically an implicit ask for writing assistance.

Now, the examples I just did are quite simple, and don't want to be an exhaustive demonstration of large language models capabilities.

They just want to show you the potential of using Generative AI,

in particular, but not limited in a context such as the educational context we have used today as an example.

That's all for now.

In the following lesson, we are going to explore different types of generative AI models.

And we're going to cover also how to test, to iterate, and to improve the performance,

and compare also different models to find the most suitable one for a specific use case.

Thank you.

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本课的词汇与口语要点

这节 C1 级别的口语课以视频“Introduction to Generative AI and LLMs”为素材。视频中反复出现的词有：model, text, token, language, input。这段视频共有 93 个句子、1349 个单词可供跟读。讲话部分时长为 10:25。说话人语速平稳，每分钟约 129 个词，很适合跟读。只有 75% 的单词属于英语最常用的 3,000 词，词汇难度较高。

视频中的重点词汇

视频中最难的 15 个单词，附发音和释义：

单词	发音	释义
token 名词	/ˈtoʊkən/	代幣 /代币, 籌碼 /筹码
chunk 名词	/t͡ʃʌŋk/	塊 /块
startup 名词	/ˈstɑɹtˌʌp/	初創公司 /初创公司, 新創企業 /新创企业
predict 动词	/pɹɪˈdɪkt/	預言 /预言
transformer 名词	/tɹænsˈfoɹməɹ/	變壓器 /变压器
probability 名词	/ˌpɹɑ.bəˈbɪl.ə.ti/	可能性
algorithm 名词	/ˈælɡəɹɪðm̩/	算法, 演算法
chatbot 名词	/ˈtʃætbɑt/	聊天機器人 /聊天机器人
limitation 名词	/lɪmɪˈteɪʃən/	限制
consist 名词	/kənˈsɪst/	編組 /编组
adaptability 名词	/ədæptəˈbɪlɪti/	適應性 /适应性
compute 动词	/kəmˈpjuːt/	計算 /计算
implicit 形容词	/ɪmˈplɪsɪt/	隱含的 /隐含的, 隐含的
integer 名词	/ˈɪn.tɪ.d͡ʒə(ɹ)/	整數 /整数
iteration 名词	/ˌɪt.əˈɹeɪ.ʃən/	疊代 /叠代, 迭代

视频中出现的短语动词

单词	释义
break down 动词	壞了 /坏了, 出故障

视频中的语法

说话人最常用的结构，并附上视频中的原话：

结构	视频中的用法
被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的	is based · was applied · are built
现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事	They've conquered · have really heard · has been

需要注意的发音

说话人用了 16 次缩略和弱读形式，例如 we'll, I'm, doesn't。请按听到的简短形式来说。

“th” 音: algorithm /ˈælɡəɹɪðm̩/, breakthrough /ˈbɹeɪk.θɹuː/
“sh” 和 “zh” 音: limitation /lɪmɪˈteɪʃən/, iteration /ˌɪt.əˈɹeɪ.ʃən/, revolutionize /ˌɹɛv.əˈl(j)u.ʃəˌnaɪz/, fictional /ˈfɪkʃənəl/, ambitious /æmˈbɪʃ.əs/
长单词——注意重音位置: generative /ˈd͡ʒɛnəɹətɪv/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, limitation /lɪmɪˈteɪʃən/, adaptability /ədæptəˈbɪlɪti/

中文母语者容易读错的音：

/v/ — 不要读成 /w/，上齿轻触下唇: generative /ˈd͡ʒɛnəɹətɪv/, delve /dɛlv/, exhaustive /ɪɡˈzɔstɪv/, leaven /ˈlɛv.ən/, revolutionize /ˌɹɛv.əˈl(j)u.ʃəˌnaɪz/
词尾辅音 — 要读清楚，后面不要加元音: generative /ˈd͡ʒɛnəɹətɪv/, prompt /pɹɑmpt/, chunk /t͡ʃʌŋk/, startup /ˈstɑɹtˌʌp/, predict /pɹɪˈdɪkt/
/r/ 和 /l/ 的区别: probability /ˌpɹɑ.bəˈbɪl.ə.ti/, algorithm /ˈælɡəɹɪðm̩/, chromatically /kroʊˈmæt̬.ɪk.li/, learner /ˈlɜɹnəɹ/, revolutionize /ˌɹɛv.əˈl(j)u.ʃəˌnaɪz/

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。
用正常速度逐句跟读，每句重复到你的节奏与说话人一致为止。
录下自己的声音并与原声对比，特别注意 token, chunk, startup 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。

影子跟读法: 阅读完整分步指南 →

跟读练习: Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners - 通过视频学习英语口语

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