How does ChatGPT work? Explained by Deep-Fake Ryan Gosling.
Summary
TLDRIn 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
🧠 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.
🔄 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.
Mindmap
Keywords
💡Generative AI
💡Large Language Models (LLMs)
💡Tokenization
💡Embedding
💡Self-Attention Mechanism
💡Context-Aware Embeddings
💡Transformer Architecture
💡Pre-trained Model
💡Open Source
💡Temperature Setting
💡GPT
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
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