Pratique du Shadowing: Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners - Apprendre l'anglais à l'oral avec la vidéo

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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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Phonétique (IPA)Maîtrisez chaque son

VocabulaireEnrichissez votre lexique

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Vocabulaire et conseils d’expression pour cette leçon

Cette leçon d’expression orale de niveau C1 s’appuie sur la vidéo « Introduction to Generative AI and LLMs ». Les mots qui reviennent le plus souvent : model, text, token, language, input. Cette vidéo contient 93 phrases et 1349 mots à répéter en shadowing. La partie parlée dure 10:25. Le locuteur parle à un rythme régulier d’environ 129 mots par minute, confortable pour le shadowing. Seuls 75 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.

Vocabulaire clé de cette vidéo

Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :

Mot	Prononciation	Sens
token nom	/ˈtoʊkən/	symbole
generative adjectif	/ˈd͡ʒɛnəɹətɪv/	génératif
prompt verbe	/pɹɑmpt/	inciter
chunk nom	/t͡ʃʌŋk/	pièce, morceau
startup nom	/ˈstɑɹtˌʌp/	mise en route, mise en train
predict verbe	/pɹɪˈdɪkt/	prédire
transformer nom	/tɹænsˈfoɹməɹ/	transformateur
probability nom	/ˌpɹɑ.bəˈbɪl.ə.ti/	probabilité
algorithm nom	/ˈælɡəɹɪðm̩/	algorithme
capability nom	/ˌkeɪ.pəˈbɪl.ɪ.ti/	capacité
chatbot nom	/ˈtʃætbɑt/	chatbot, dialogueur
delve verbe	/dɛlv/	creuser
textual adjectif	/ˈtɛks.tju.əl/	textuel
limitation nom	/lɪmɪˈteɪʃən/	limitation
consist nom	/kənˈsɪst/	rame

Les verbes à particule que vous entendrez

Mot	Sens
break down verbe	tomber en panne

La grammaire de cette vidéo

Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :

Structure	Dans la vidéo
Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait	is based · was applied · are built
Present perfect have/has + participe passé — une action passée qui compte encore maintenant	They've conquered · have really heard · has been

Prononciation à surveiller

Le locuteur utilise 16 contractions et formes réduites, comme we'll, I'm, doesn't. Prononcez-les sous leur forme courte, telles que vous les entendez.

Les sons « th »: algorithm /ˈælɡəɹɪðm̩/, breakthrough /ˈbɹeɪk.θɹuː/
Les sons « sh » et « zh »: limitation /lɪmɪˈteɪʃən/, iteration /ˌɪt.əˈɹeɪ.ʃən/, revolutionize /ˌɹɛv.əˈl(j)u.ʃəˌnaɪz/, fictional /ˈfɪkʃənəl/, ambitious /æmˈbɪʃ.əs/
Mots longs — placez bien l’accent: generative /ˈd͡ʒɛnəɹətɪv/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, limitation /lɪmɪˈteɪʃən/, adaptability /ədæptəˈbɪlɪti/

Les sons difficiles pour les francophones :

/h/ — il se souffle, il n’est pas muet: coherent /koʊˈhɛɹ.ənt/, harness /ˈhɑɹ.nəs/, hype /haɪp/, hint /hɪnt/
/tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: generative /ˈd͡ʒɛnəɹətɪv/, chunk /t͡ʃʌŋk/, chatbot /ˈtʃætbɑt/, integer /ˈɪn.tɪ.d͡ʒə(ɹ)/, emerge /ɪˈmɝd͡ʒ/
/r/ anglais — langue recourbée, sans frotter la gorge: generative /ˈd͡ʒɛnəɹətɪv/, prompt /pɹɑmpt/, startup /ˈstɑɹtˌʌp/, predict /pɹɪˈdɪkt/, transformer /tɹænsˈfoɹməɹ/

Comment s’entraîner avec cette vidéo

Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
Répétez phrase par phrase à vitesse normale, en reprenant chacune jusqu’à ce que votre rythme corresponde à celui du locuteur.
Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme token, generative, prompt.

Qu'est-ce que la technique du Shadowing ?

Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.

Technique du shadowing : lire le guide complet étape par étape →

Pratique du Shadowing: Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners - Apprendre l'anglais à l'oral avec la vidéo

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