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

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