Shadowing Practice: Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners - Learn English Speaking with Video

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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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אוצר מילים והערות הגייה לשיעור הזה

שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “Introduction to Generative AI and LLMs”. המילים שחוזרות הכי הרבה: model, text, token, language, input. בסרטון הזה יש 93 משפטים ו-1349 מילים לתרגול Shadowing. הדיבור נמשך 10:25. הדובר שומר על קצב יציב של כ-129 מילים בדקה, קצב נוח לתרגול Shadowing. רק 75% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית, כך שאוצר המילים מאתגר.

אוצר מילים מרכזי בסרטון הזה

15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:

מילה	הגייה	משמעות
token שם עצם	/ˈtoʊkən/	אֲסִימוֹן, זִ׳יטוֹן
startup שם עצם	/ˈstɑɹtˌʌp/	סטארטאפ, חברת סטארטאפ
transformer שם עצם	/tɹænsˈfoɹməɹ/	שַׁנַּאי, טְרַנְסְפוֹרְמָטוֹר
algorithm שם עצם	/ˈælɡəɹɪðm̩/	אַלגוֹרִיתְם
integer שם עצם	/ˈɪn.tɪ.d͡ʒə(ɹ)/	שלם, מספר שלם
keyword שם עצם	/ˈkiː.wɜːd/	מִלַּת מַפְתֵּחַ
leaven שם עצם	/ˈlɛv.ən/	שאור
typewrite פועל		הִקְלִיד
accessibility שם עצם	/əkˌsɛs.əˈbɪl.ə.ti/	נְגִישׁוּת, הַנְגָּשָׁה
subset שם עצם	/ˈsʌbˌsɛt/	תת קבוצה
coherent שם תואר	/koʊˈhɛɹ.ənt/	עקבי, עקיב
harness פועל	/ˈhɑɹ.nəs/	רָתַם
excel פועל	/ɪkˈsɛl/	הצטיין
conquer פועל	/ˈkɑŋkɚ/	כָּבַשׁ
interpret פועל	/ɪnˈtɜː.pɹɪt/	דּוֹבֵב

דקדוק בסרטון הזה

המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:

מבנה	בסרטון
סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה	is based · was applied · are built
הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו	They've conquered · have really heard · has been

הגייה ששווה לשים לב אליה

הדובר משתמש ב-16 קיצורים וצורות מקוצרות, כמו we'll, I'm, doesn't. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.

הצלילים של “th”: algorithm /ˈælɡəɹɪðm̩/, breakthrough /ˈbɹeɪk.θɹuː/
הצלילים של “sh” ו-“zh”: limitation /lɪmɪˈteɪʃən/, iteration /ˌɪt.əˈɹeɪ.ʃən/, revolutionize /ˌɹɛv.əˈl(j)u.ʃəˌnaɪz/, fictional /ˈfɪkʃənəl/, ambitious /æmˈbɪʃ.əs/
מילים ארוכות — שימו לב להטעמה: generative /ˈd͡ʒɛnəɹətɪv/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, limitation /lɪmɪˈteɪʃən/, adaptability /ədæptəˈbɪlɪti/

צלילים שדוברי עברית מתקשים בהם:

/w/ — שפתיים מעוגלות, לא /v/: equitable /ˈɛk.wɪ.tə.bəl/, keyword /ˈkiː.wɜːd/, query /ˈkwɪɹ.i/
/æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: transformer /tɹænsˈfoɹməɹ/, algorithm /ˈælɡəɹɪðm̩/, chatbot /ˈtʃætbɑt/, adaptability /ədæptəˈbɪlɪti/, chromatically /kroʊˈmæt̬.ɪk.li/
/r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: generative /ˈd͡ʒɛnəɹətɪv/, prompt /pɹɑmpt/, startup /ˈstɑɹtˌʌp/, predict /pɹɪˈdɪkt/, transformer /tɹænsˈfoɹməɹ/

איך לתרגל עם הסרטון הזה

הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
תרגלו Shadowing משפט אחר משפט במהירות רגילה, וחזרו על כל משפט עד שהקצב שלכם תואם את הדובר.
הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו token, startup, transformer.

מהי טכניקת השאדואינג?

שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.

טכניקת Shadowing: קרא את המדריך המלא שלב אחר שלב →

Shadowing Practice: Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners - Learn English Speaking with Video

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