Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners
Summary
TLDRIn 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
🤖 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.
📚 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.
🔍 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.
Mindmap
Keywords
💡Generative AI
💡Large Language Models
💡Education Domain
💡Tokenization
💡Transformer Architecture
💡Prompt
💡Completion
💡Social Impact
💡Natural Language Processing
💡Creative Thinking
💡Virtual Assistance
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
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