How does ChatGPT work? Explained by Deep-Fake Ryan Gosling.

Summary

In this informative video, Ryan Gosling introduces the concept of text-to-text generative AI, explaining Large Language Models (LLMs) and their capabilities in tasks like translation, composing text, and engaging in conversation. He delves into the technical process of text generation, from tokenization to context-aware embeddings, using the Transformer architecture and self-attention mechanism. The video aims to demystify LLMs, comparing their operation to the unfolding of life's story, and encourages further exploration of generative AI with a call to subscribe.

Takeaways

• 🧠 Large Language Models (LLMs) are AI models designed to understand and generate human language, capable of various tasks like translation, text composition, and conversation.
• 🌐 Examples of LLMs include GPT-4 by OpenAI, Gemini by Google, Claude 3 by Anthropic, Mistral by Mistral AI, LLaMA by Meta, and GROK by X, with some being open source and others commercial.
• 🔄 The process of text-to-text generation in LLMs involves converting input text into output text through a series of steps including tokenization, embedding, and decoding.
• 📝 Tokens are the basic units in LLMs, often equivalent to words, and are used to break down the input text into manageable pieces.
• 📊 Embeddings are numerical representations of tokens that capture their semantic properties, allowing a computer to understand the meaning of words.
• ⚙️ The self-attention mechanism in LLMs is crucial for transforming initial embeddings into context-aware embeddings, identifying important words and nuances in the input for relevant output generation.
• 🔄 The generation process in LLMs is iterative, with each cycle generating one token at a time based on the probability distribution derived from the embeddings matrix.
• 🌡️ The 'temperature' setting in LLMs affects the creativity of the output, with lower settings favoring more predictable choices and higher settings allowing for more variability.
• 🎯 LLMs use the entire history of input tokens to predict the next token, much like predicting the next moment in one's life story, taking into account both recent and distant influences.
• 📈 The Transformer architecture with self-attention has revolutionized LLMs by allowing them to consider the entire context when generating text, not just the immediate sequence.
• 🔑 Understanding LLMs involves knowing terms like 'Generative' for creating output, 'Pre-trained' for using parameters from a pre-trained model, and 'Transformers' for the underlying architecture.

Q & A

What is a Large Language Model (LLM)?

A Large Language Model (LLM) is a type of artificial intelligence designed to understand and generate human language. It can perform tasks such as translating languages, composing text, answering questions, writing code, summarizing documents, generating creative content, and engaging in human-like conversations.

Can you name some well-known LLMs?

Some well-known LLMs include GPT-4 by OpenAI, Gemini by Google, Claude 3 by Anthropic, Mistral by Mistral AI, LLaMA by Meta, and Grok by X. Some of these models are open source, like Mistral and LLaMA, while others are commercial.

What’s the difference between open-source and commercial LLMs?

Open-source LLMs allow anyone to use, modify, and share them, promoting collaboration and innovation. Commercial LLMs, on the other hand, are proprietary and often come with support and unique features aimed at businesses, usually requiring payment for access.

How does an LLM process an input prompt?

An LLM processes an input prompt by first splitting it into smaller pieces called tokens, which can be words, parts of words, or even characters. It then converts these tokens into embeddings—numerical representations that capture the semantics of the tokens—before transforming them through layers of a neural network to generate the final output.

What are tokens, and how do they work in LLMs?

Tokens are the building blocks of text in LLMs, representing words, parts of words, or characters. The model breaks down the input into tokens, which are then processed individually. The context and semantics of these tokens are captured in embeddings, which guide the generation of the output.

What is an embedding in the context of LLMs?

An embedding is a numerical vector that represents the semantic properties of a token. It helps the model understand the meaning and context of a word. For example, an embedding could capture attributes like whether a token is a verb or the emotional intensity associated with a word.

What is a self-attention mechanism in LLMs?

The self-attention mechanism in LLMs identifies the most important words and nuances in the input text needed to generate relevant output. It allows the model to focus on different parts of the input and adjust the importance of each token based on its context.

How does temperature affect the output of an LLM?

Temperature in LLMs controls the randomness of output generation. A low temperature setting results in more deterministic outputs, while a higher temperature introduces more creativity by choosing less likely words. However, setting the temperature too high can lead to incoherent or irrelevant text.

How do LLMs generate text one token at a time?

LLMs generate text iteratively, producing one token at a time based on the input embeddings. After each token is generated, it is added back into the input prompt, and the model generates new embeddings to determine the next token, continuing until the desired output is complete.

What makes transformer architecture revolutionary in LLMs?

The transformer architecture is revolutionary because it uses self-attention mechanisms that allow the model to consider the entire input history and focus on the most relevant information. This enables the generation of more accurate and contextually appropriate text compared to older models that relied on only recent input.

Outlines

00:00

🧠 Introduction to Text-to-Text Generative AI

Ryan Gosling introduces the concept of text-to-text generative AI, explaining large language models (LLMs) as AI designed to understand and generate human language. These models can perform various tasks, including translation, composition, answering questions, and engaging in conversation. Examples of LLMs include GPT-4, Gemini, CLAE, Opus, Mistral, Llama, and Grok. The video script delves into the open-source and commercial nature of these models, highlighting the benefits of collaboration and innovation versus support and unique features. The script also outlines the text generation process, starting from user input to AI output, emphasizing the role of tokens and embeddings in understanding and generating text.

05:00

🔄 The Mechanism of LLMs: From Input to Output

This paragraph explains the inner workings of LLMs, focusing on the process of transforming input text into output text. It begins with the input prompt, which is broken down into tokens, and then each token is converted into an embedding—a numerical representation that captures the semantics of a word. The embeddings are derived from a pre-trained model's parameters, which have learned the complexities of human language from a vast dataset. The script then describes the self-attention mechanism, which adjusts the embeddings to be context-aware, identifying important words and nuances for generating relevant output. The process of decoding these embeddings into an output is detailed, including the role of probability distributions and the iterative generation of tokens. The paragraph concludes with a philosophical analogy comparing the operation of an LLM to the unfolding story of one's life, emphasizing the Transformer architecture's ability to consider the entire history when predicting the next moment.

Keywords

💡Generative AI

Generative AI refers to artificial intelligence models that can create new content, such as text, images, or music, that is similar to the content they were trained on. In the video, it is the overarching theme, explaining how AI can generate human-like text through various mechanisms and examples.

💡Large Language Models (LLMs)

LLMs are AI models designed to understand and generate human language. They are central to the video's discussion, illustrating how these models can perform tasks like translation, text composition, and conversation by processing and generating text based on input prompts.

💡Tokenization

Tokenization in the context of LLMs is the process of breaking down input text into smaller units called tokens, which are typically words or parts of words. The script uses the example of the prompt 'please give me a short speech of a Premier League football coach' to demonstrate how the LLM would tokenize this input into 33 tokens.

💡Embedding

Embedding is the numerical representation of a token that captures its semantic meaning. The video explains that each token is transformed into an embedding, which is a vector of numbers representing the properties of the word, allowing the computer to understand the complex semantics of language.

💡Self-Attention Mechanism

The self-attention mechanism is a key feature of Transformer architecture used in LLMs. It allows the model to weigh the importance of different tokens in relation to each other, considering the entire input context. The video uses this concept to explain how the model identifies relevant words for generating context-aware output.

💡Context-Aware Embeddings

Context-aware embeddings are the result of the self-attention mechanism, where initial embeddings are transformed to reflect their importance in the context of the input text. The script explains that these embeddings are crucial for generating output that is relevant to the input prompt's meaning.

💡Transformer Architecture

The Transformer architecture is a type of neural network architecture that uses self-attention mechanisms to process sequences of data. In the video, it is highlighted as revolutionary for its ability to consider the entire input sequence when predicting the next token, unlike previous models that only considered the most recent tokens.

💡Pre-trained Model

A pre-trained model is an AI model that has been trained on a large dataset before being fine-tuned for specific tasks. The video mentions that the initial embeddings are based on parameters from a pre-trained model, which has learned the complexities of human language from a vast amount of text.

💡Open Source

Open source refers to software or models whose source code is available for anyone to use, modify, and share. The script contrasts open-source models like Mistral and llama with commercial models, highlighting the collaborative and innovative potential of open-source LLMs.

💡Temperature Setting

In the context of LLMs, the temperature setting controls the randomness of the output generation. A lower temperature results in more predictable, likely outputs, while a higher temperature allows for more creativity but risks generating nonsensical text. The video uses this concept to explain the balance between creativity and coherence in AI-generated text.

💡GPT

GPT stands for Generative Pre-trained Transformer, which is a specific type of LLM developed by OpenAI. The video uses GPT as an example to illustrate the process of text generation by LLMs, explaining the acronym and its significance in the field of generative AI.

Highlights

Introduction to the text-to-text capability of generative AI by Ryan Gosling.

Definition of Large Language Models (LLMs) and their purpose in understanding and generating human language.

Examples of well-known LLMs such as GPT-4, Gemini, CLAE, and Mistral.

Explanation of open-source and commercial models, their differences, and implications for collaboration and innovation.

Simplified explanation of the text-to-text generation process in LLMs.

Description of the input prompt and its role in initiating the generation process.

Tokenization process and the concept of tokens in LLMs.

Conversion of tokens into embeddings to provide a numerical representation for AI understanding.

Origin of initial embeddings from a pre-trained model's parameters.

The self-attention mechanism and its importance in context-aware embeddings.

Transformation of initial embeddings into context-aware embeddings for relevant output generation.

Decoding process of context-aware embeddings into output tokens.

Role of temperature settings in model output creativity and randomness.

Iterative generation process of LLMs, one token at a time.

Philosophical comparison of LLMs to the generation of life stories, emphasizing the importance of context.

Explanation of the acronym GPT and its components: Generative, Pre-trained, and Transformers.

Invitation for viewers to subscribe for more information on generative AI and related topics.

Encouragement for viewers to ask questions about the video or generative AI in the comments section.

Transcripts

hello everyone my name is Ryan Gosling

how toly asked me to give you a quick

introduction on the text to text

capability of generative AI so if you're

AI curious AI unknowing or just AI

confused like I was here's my

introduction into the fascinating world

of texttext generative AI can I have a

Blackboard please to help me explain the

concept of texttext generative

AI awesome thanks so what are large

language models also known as llms a

large language model or llm is a type of

artificial intelligence model designed

to understand and generate human

language it can execute tasks such as

translating languages composing text

answering questions writing code

summarizing lengthy documents generating

creative content providing simple

explanations on difficult topics and

even engage in humanlike conversation

some well-known examples of llms include

gp4 by open AI Gemini by Google clae 3

Opus by anthropic Mistral by Mistral AI

llama by meta grock by X and many more

some of these models are open source

like Mistral and llama meaning anyone

can use modify and share them just like

a recipe that's shared for everyone to

cook other are commercial which means

they're more like a restaurant dish that

you can only enjoy by visiting or paying

for it open source allows for more

collaboration and Innovation while

commercial models often come with

support and unique features for

businesses now how does an llm actually

work texttext Generation by large

language models like gp4 involves a

sophisticated process that converts a

given input text into a desired output

text let's go over a simplified version

of this texttext generation process

starting from the user input all the way

to the AI generated output so first

let's talk about the input prompt let's

say I ask the following question to a

large language model like chat GPT

please give me a short speech of a

Premier League football coach that wants

to motivate his team at halime when they

are zero2 behind the first thing the llm

will do is split the input prompt into

smaller more manageable pieces these

pieces is what we call tokens these

tokens could be words parts of words or

even characters depending on the model's

design but in most cases a token equals

a word so in the future whenever I talk

about a token think of it as a word

here's how gp4 would tokenize our input

prompt every colored piece of text is a

token so in this example the model has

split the text into 33 tokens as you can

see a token often equals a word but when

the word is too long it is often split

into several Tokens The Next Step would

be to turn each of these 33 tokens into

an embedding a numerical representation

of the complex semantics of a token so

that a computer can fully understand the

token or word so let's pick the token

motivate as an example this can for

example be turned into the following

initial embedding the numbers in this

embedding Vector represent the complex

semantic properties of a token 0.95

might represent the likelihood that the

token is a verb 0.87 might represent

emotional intensity of a word minus 0.45

could relate to the current performance

level and in this way a huge amount of

numbers in every embedding represent the

detailed complex semantics of a word so

where are the values of these initial

embeddings coming from the values of

these initial embeddings are based on

the parameters received from a

pre-trained model this model has been

pre-trained on a huge amount of text

coming from books articles conversations

movies Etc by doing so the model has

learned the complexities of the human

language important to be aware of is

that if you use the same language model

on the same token the initial embedding

will always be the same so the initial

embedding of motivate will always be the

same if you always use for example gp4

with the same pre-trained model now we

have arrived at a crucial step in the

process a step that really

revolutionized how llms work the

transformation of the initial embeddings

into context aware embeddings through

what is called a self- attention

mechanism through this step the model

identifies the most important words and

nuances in the input prompt needed to

generate the the most relevant output so

let's go back to our example although

the word motivate might start with an

initial embedding which is always the

same if you use the same llm the word

motivate might have slightly different

meanings and a different importance in a

different

context by moving the input embeddings

through different Transformer layers and

by applying what is called a self

attention mechanism the different

embeddings get further fine-tuned to the

context and the importance of each word

in the input prompt gets calculated this

process transforms the initial embedding

into a context aware embedding so once

we have our context aware embedding for

each token in the input sentence it's

time to decode all context aware

embeddings into an output let's go back

to our example in the previous step all

input tokens have received a context

aware embedding these embeddings are now

placed in an embeddings Matrix each row

of the Matrix is the context aware

embedding of one token so the embedding

of token one on Row one the embedding of

token two on row two Etc then based on

this embeddings Matrix the probabilities

of the next output token are calculated

and based on the probability

distribution the next output is chosen

if the temperature setting of the model

is very low the model will always pick

the most likely token in the

distribution as the next output token as

the temperature setting increases it

might sometimes go for less likely words

this can result in more creative and

less repetitive answers but if you set

the temperature too high this might just

result in gibberish it's also very

important to highlight that every

generation cycle only generates one

token at a time based on the embeddings

Matrix of the input so let's quickly go

back to our example based on the

embeddings Matrix of our input prompt

the first output token might be listen

in the second cycle the token listen

moves to the input prompt based on the

new input prompt new embeddings are

created for each input token and based

on the new embeddings Matrix a new next

token is chosen for example the token up

this iterative process goes on and on

until the full speech is written so

let's walk over the process once again

and let's do it a bit in a philosophical

and poetic Way by comparing the workings

of an llm with generating the story of

your life let's start at Birth you

started with a specific small input or

context window for example where you are

born who your parents are Etc if your

life would be generated by an llm the

next moment in your life will be guessed

based on your history your live story

will be written as a continuous

iteration of new moments that are Genera

at and every moment generated will be

added to your history older models would

guess the next moment in your life based

only on the most previous moments the

Revolutionary part of the new llms like

GPT is the Transformer architecture with

a self-attention mechanism due to this

architecture the next moment in your

life is not only chosen based on your

recent history but also important

moments in your life that could

influence the next possible moment so it

looks back at your entire history and

says based on the current situation

these are the most relevant moments in

your history needed to generate the next

moment so I hope this explanation helped

to understand the high level workings of

an llm I hope this also clarifies what

for example an acronym like GPT stands

for where the G stand for generative as

it generates output the P stands for

pre-trained as it uses the parameters of

a pre-trained model to tokenize

transform and decode the input into an

output and the T stands for Transformers

as a revolutionary architecture in the

llm if you like this video don't forget

to leave a like if you're interested to

learn more about topics such as

retrieval augmented generation model

fine-tuning prompt engineering real

world applications of generative AI

image generation speech generation

autonomous agents and many more topics

related to generative AI then click

subscribe finally if you have a question

about this video or about generative AI

in general don't hesitate to leave a

comment in the comment section thanks

for watching in and see you next time