Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners

Summary

In this introductory lesson of the 'Generative AI for Beginners' course, Carot Cuchu, a Cloud Advocate at Microsoft, presents an open-source curriculum exploring generative AI and large language models. These models, built on the Transformer architecture, revolutionize education by improving accessibility and personalizing learning experiences. The course will examine how these technologies address challenges and limitations while transforming the educational landscape.

Takeaways

• 📘 The course is an introduction to Generative AI for beginners, based on an open-source curriculum available on GitHub.
• 👋 The instructor, Carot Cuchi, is a Cloud Advocate at Microsoft with a focus on artificial intelligence technologies.
• 🧠 Generative AI and large language models are at the forefront of AI technology, achieving human-level performance in various tasks.
• 🌟 Large language models have capabilities and applications that are revolutionizing education and improving accessibility and personalized learning experiences.
• 🔍 The course will explore how a fictional startup uses generative AI to innovate in education and address social and technological challenges.
• 📚 The origins of generative AI date back to the 1950s and 1960s, evolving from rule-based chatbots to statistical machine learning algorithms.
• 💡 The breakthrough in AI came with the introduction of neural networks and the Transformer architecture, which improved natural language processing significantly.
• 🔢 Large language models work with tokens, breaking text into chunks that are easier for the model to process and understand.
• 🔮 The predictive process involves creating an expanding window pattern, allowing the model to generate coherent and contextually relevant responses.
• 🎲 A degree of randomness is introduced in the selection of output tokens to simulate creative thinking and ensure variability in output.
• 📝 Examples of using large language models include generating assignments, answering questions, and providing writing assistance in an educational context.

Q & A

What is the main focus of the 'Generative AI for Beginners' course?

The course focuses on introducing generative AI and large language models, exploring their capabilities and applications, particularly in revolutionizing education through a fictional startup.

Who is Carot Cuchu and what is his role?

Carot Cuchu is a Cloud Advocate at Microsoft, specializing in artificial intelligence technologies. He introduces the concept of generative AI in the course.

What is the ambitious mission of the fictional startup mentioned in the script?

The startup's mission is to improve accessibility in learning on a global scale, ensuring equitable access to education and providing personalized learning experiences to every learner according to their needs.

How does the course plan to address the challenges associated with generative AI?

The course will examine the social impact of the technology and its technological limitations, discussing how the fictional startup harnesses the power of generative AI while addressing these challenges.

What is the significance of the 1990s in the development of AI technology as mentioned in the script?

The 1990s marked a significant turning point with the application of a statistical approach to text analysis, leading to the birth of machine learning algorithms that could learn patterns from data without explicit programming.

What advancements in hardware technology allowed for the development of advanced machine learning algorithms?

Advancements in hardware technology enabled the development of neural networks, which significantly improved natural language processing capabilities.

What is the Transformer architecture and its role in generative AI?

The Transformer architecture is a new model that emerged after decades of AI research. It can handle longer text sequences as input and is based on the attention mechanism, which allows it to focus on the most relevant information in the input text.

What is tokenization and why is it important in large language models?

Tokenization is the process of breaking down input text into an array of tokens, which are then mapped to token indices. This process is crucial as it converts text into a numerical format that the model can process and understand more efficiently.

How does a large language model predict the output token?

The model predicts the output token based on the probability distribution calculated from its training data. It introduces a degree of randomness to simulate creative thinking, ensuring the model does not always choose the token with the highest probability.

What are the different types of textual inputs and outputs for a large language model?

The input is known as a 'prompt', and the output is known as 'completion'. Prompts can include instructions, questions, or text to complete, and the model generates the next token to complete the current input.

What will be covered in the following lessons of the course?

In the following lessons, the course will explore different types of generative AI models, how to test, iterate, and improve performance, and compare different models to find the most suitable one for specific use cases.

Outlines

00:00

🤖 Introduction to Generative AI and Large Language Models

In this introductory lesson, Carot Cucho, a Cloud Advocate at Microsoft, welcomes viewers to the Generative AI for Beginners course. The course is based on an open source curriculum available on GitHub. The focus is on generative AI and large language models, which are described as the pinnacle of AI technology. These models have surpassed previous capabilities, achieving human-like performance in various tasks. The course will explore how these models are transforming education, specifically through a fictional startup aimed at improving accessibility and personalizing learning experiences globally. The technology's origins trace back to the 1950s and 1960s, evolving from early chatbots to machine learning algorithms and eventually to the Transformer architecture, which underpins modern generative AI models. These models are trained on vast amounts of data and can perform a wide range of tasks with a degree of creativity.

05:03

📚 Understanding Large Language Models and Tokenization

This paragraph delves deeper into the mechanics of large language models, emphasizing the importance of tokenization. Language models process text by converting it into tokens, which are chunks of text that the model can work with more efficiently. The tokenizer breaks down input text into tokens, which are then mapped to token indices, making it easier for the model to process. The model predicts the next token based on the input sequence, incorporating it into the input for the next iteration, allowing for coherent and contextually relevant responses. The model's output selection process is based on probability, with a degree of randomness introduced to simulate creative thinking. Examples are provided to illustrate how prompts and completions work in the context of education, demonstrating the model's ability to generate assignments, answer questions, and provide writing assistance.

10:05

🔍 Exploring Generative AI Models in Education

The final paragraph of the script wraps up the current lesson and teases the next. It highlights the potential of generative AI in educational contexts, as demonstrated in the examples provided. The speaker also mentions that future lessons will explore different types of generative AI models, how to test and iterate to improve performance, and how to compare models to find the most suitable one for specific use cases. This sets the stage for a deeper exploration of the practical applications and challenges of generative AI in education.

Keywords

💡Generative AI

Generative AI refers to artificial intelligence systems that can create new content, such as text, images, or music, that was not previously in existence. In the context of this video, it is the focus of the course and is used to highlight how AI can generate creative and engaging text. The script mentions that generative AI models, often referred to as large language models, are built upon the Transformer architecture, which allows them to handle longer text sequences and focus on relevant information.

💡Large Language Models

Large language models are AI systems that have been trained on vast amounts of text data and can perform tasks such as language translation, text summarization, and even creative writing. They represent a pinnacle of AI technology, pushing boundaries previously thought impossible. The video discusses how these models are revolutionizing education by providing personalized learning experiences and improving accessibility in learning.

💡Education Domain

The education domain in the video refers to the application of AI technologies, specifically large language models, in the field of education. The script introduces a fictional startup that works in this domain with the mission of improving learning accessibility and providing personalized experiences. This highlights the potential of AI to transform how education is delivered and consumed.

💡Tokenization

Tokenization is the process of breaking down text into smaller units, called tokens, which can be words, phrases, or even individual characters. This is a key concept in large language models as it allows the models to work more efficiently with numerical representations of text. The script explains that tokens are used by the model to predict the next token in a sequence, which is crucial for tasks like text completion.

💡Transformer Architecture

The Transformer architecture is a type of deep learning model that has revolutionized natural language processing. It is capable of handling longer text sequences and focuses on the most relevant parts of the input text through an attention mechanism. The video mentions that modern generative AI models, such as large language models, are often built upon this architecture, enabling them to perform complex language tasks.

💡Prompt

In the context of AI and large language models, a prompt is a textual input provided to the model to generate a response or complete a task. The video script provides examples of prompts, such as asking the model to write an assignment or answer a question about a historical figure. This demonstrates how prompts can guide the model to produce specific types of outputs.

💡Completion

Completion, in the context of AI, refers to the output generated by a language model in response to a prompt. The script shows how the model can complete a text based on the initial prompt, such as generating an assignment or answering a question. This illustrates the model's ability to understand context and generate relevant text.

💡Social Impact

The social impact of technology, including AI, refers to the effects these technologies have on society, both positive and negative. The video mentions that the course will examine the challenges tied to the social impact of generative AI, suggesting that there are ethical and societal considerations that must be addressed when using these technologies in education.

💡Natural Language Processing

Natural language processing (NLP) is a field of AI that focuses on enabling computers to understand, interpret, and generate human language. The script discusses how advancements in hardware technology and machine learning algorithms have significantly improved NLP, allowing machines to understand the context of words in sentences and perform tasks like language translation.

💡Creative Thinking

Creative thinking in the context of AI refers to the ability of models to generate outputs that are not just predictable but also innovative and engaging. The script explains that large language models introduce a degree of randomness in their selection process to simulate creative thinking, allowing them to produce varied and creative text outputs.

💡Virtual Assistance

Virtual assistance refers to AI systems that can interact with users, interpret their needs, and take actions to fulfill them. The video script mentions that advancements in AI, particularly in natural language processing, have powered the birth of virtual assistants that can answer queries and connect to third-party services, demonstrating the practical applications of AI in everyday life.

Highlights

Introduction to the Generative AI for Beginners course, an open source curriculum.

Course instructor Carot Cuchu is a Cloud Advocate at Microsoft focusing on AI technologies.

Generative AI and large language models are pushing the boundaries of what was once thought possible.

Large language models have achieved human-level performance in various tasks.

The course explores how generative AI is revolutionizing education through a fictional startup.

The startup aims to improve accessibility in learning on a global scale and provide personalized experiences.

Generative AI's origins trace back to the 1950s and 1960s with early AI prototypes.

A significant turning point in AI was the introduction of statistical approaches to text analysis in the 1990s.

Advancements in hardware technology enabled the development of advanced machine learning algorithms like neural networks.

Neural networks significantly improved natural language processing, leading to the birth of virtual assistance.

Generative AI is a subset of deep learning, with models like the Transformer emerging in recent years.

Transformer models can handle longer text sequences and are based on the attention mechanism.

Large language models are built upon the Transformer architecture, enabling unique adaptability.

Tokenization is a key concept in large language models, breaking down text into tokens for easier processing.

Models predict the next token in a sequence, incorporating it into the input for the next iteration.

A degree of randomness is introduced in the selection process to simulate creative thinking.

Large language models can generate text from scratch, starting from a textual input in natural language.

Examples of prompts and completions demonstrate the potential of using generative AI in educational contexts.

Upcoming lessons will explore different types of generative AI models and improving their performance.

Transcripts

hi everyone and welcome to the first

lesson of the generative AI for

beginners course uh this course is based

on an open source curriculum with the

same name available on gab that you can

find at a link on the screen I'm carot

cucho I'm A Cloud Advocate at Microsoft

focusing on artificial intelligence

Technologies and in this video 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 for

possible they've conquered numerous

challenges that older language models

struggled with achieving human L

performance in various

stas they have sever capabilities and

applications but for the sake of this

course we'll explore how Lar 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 Ambi 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 to how our startup

harnesses the power of generative AI to

unlock new possibilities in

education we'll also examine how they

address the enevitable challenges tied

to the social impact of this technology

and its technological limitations but

let's start by defining some basic

concept we'll be using throughout the

course despite the uh relatively recent

hype surrounding generative AI we can

say that in the last couple of years we

have really uh heard of generative AI

everywhere and every time um but this

technology has been decades in the

making with its Origins tracing back to

the 1950s

1960s uh the early AI Prof types

consisted of typ PR chatbots relying on

knowledge bases maintained by experts uh

this 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 uh which could learn patterns

from data without explicit programming

and these algorithms allowed machines to

simulate human language understanding

ping the way for the eye we know today

in more recent times advancements in

Hardware technology allowed for

development of advanced masch 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 viritual assistance

in the early 21st century this viral

assistance 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 um

emerged and Transformers could handle

longer text sequences as input and were

based on the attention mechanism

enabling them to focus on the most

relevant information regardless of its

order in the input

text today M generative AI models often

referred to as large language models are

built upon the Transformer architecture

and that's uh what the T in gbt uh

actually

means um these models trained on vast

amounts of data from sources like 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 o the open

gbds one of the key concept to grasp is

tokenization L language models receive

text as input and produce text as output

if we want to really simplify the

mechanism however these models work much

more efficiently with numbers rather

than with row text sequences and that's

where the talken ISAC comes into play

text prompts are chunked into tokens uh

helping the model in predicting the next

token for

completion models also have a maximum a

Max maximum length of token window and

model pricing is also typically computed

by the number of tokens used in output

and inputs so um tokenization is really

an important Concept in large language

models and generative I

domain now a token is essentially a

chunk of text which can VAR in length uh

and typically consist of a sequence of

characters and the tokenizer primary job

is to really is to really break down the

input text into an array of those tokens

um 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 um given an input sequence of n

tokens with the maximum n varing from

one model to another according to the

maximum um content window length or for

for one model uh the model is designed

to predict a single token as its

output but here's where it gets

interesting the predictive token is then

incorporated into the input of the next

iteration creating an exp window pattern

and this pattern allows the model to

provide more coh 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 calculating 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

tax that feels you know creative and

engaging now we said that the main

capability of a large language model is

generating a 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

input of a large language model is known

as prompt while the output is known as

completion um 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 open AI Char gbt

playground um 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 a high for high

school students including four

open-ended questions um about Louis 14

and his court and you can see that the

output is exact ly what I'm asking for

um so the model was able to generate an

assignment with the

questions now another kind of prompt

might be uh a question asked in the form

of a conversation with an agent in this

example we are asking about Lis 14 um in

a question so we asked who is Luis 14

and why he is an important historical

character and we've got an

answer another type of trump might be a

text to complete so an incipit of a text

to complete and you can see now that we

um used a an insit of a text to complete

as prompt and we've got a whole

paragraph to um um complete the 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 you know an itive

demonstration of large language models

capabilities they just want to show you

the potential of using generative a in

particular but not limited in a context

such as the educational context we have

used today as example that's all for now

uh 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 uh to iterate and to improve

the performance and compare also

different mod us to find the most

suitable one for a specific use case

thank you