Coding with OpenAI o1

Summary

In this video, the presenter discusses their experience using a new AI model, O1 Preview, to create an interactive visualization of the self-attention mechanism in Transformers, a technology behind models like GPT-3. They describe their initial lack of skills to visualize this complex process but how O1 Preview's thoughtful approach to coding allowed them to successfully develop a tool that visualizes word relationships in a sentence, such as 'the quick brown fox'. The tool dynamically shows attention scores when hovering over words, providing a valuable educational resource for their teaching sessions on Transformers.

Takeaways

• 💡 The speaker is showcasing a code example for visualizing the self-attention mechanism in Transformers, which is a technology behind models like GPT.
• 📚 The speaker teaches a class on Transformers and wanted to visualize self-attention to better explain the concept to students.
• 🤖 The speaker acknowledges a lack of personal skills in creating such visualizations and seeks help from a new model, O1 Preview.
• 💻 The speaker demonstrates the use of a command to engage the model's thinking process, which is a feature of O1 Preview that sets it apart from previous models like GPT-40.
• 🔍 The speaker provides specific requirements for the visualization, such as using the example sentence 'the quick brown fox' and visualizing attention scores with varying edge thicknesses.
• 📈 The visualization is interactive, with the ability to hover over tokens to see the attention scores and edges, which is a key feature of the visualization tool.
• 🛠️ The speaker uses the D editor of 2024 to implement the code provided by the model, indicating a futuristic or advanced tool for coding.
• 🌐 The visualization is viewable in a web browser, suggesting that the tool is web-based and can be accessed through a browser interface.
• 🔧 There is a mention of a minor rendering issue with overlapping, but overall, the speaker is satisfied with the model's output and its utility.
• 🎓 The speaker plans to use this visualization tool in teaching sessions, highlighting its potential educational value.

Q & A

What is the main topic of the video script?

The main topic of the video script is the visualization of a self-attention mechanism in Transformers, a technology behind models like GPT, using an interactive component.

Why is visualizing self-attention important for understanding Transformers?

Visualizing self-attention is important because it helps to understand how Transformers model the relationship between words in a sentence, which is crucial for tasks like language translation or text summarization.

What is the example sentence used in the script to demonstrate the visualization?

The example sentence used is 'the quick brown fox', which is a pangram often used to demonstrate the use of all letters of the alphabet.

What is the interactive component mentioned in the script?

The interactive component is the ability to hover over a word token in the visualization, which then displays edges with thicknesses proportional to the attention scores between words.

How does the new model O1 Preview help in creating the visualization?

The new model O1 Preview assists by carefully thinking through the requirements and generating code that can be used to create the visualization, including handling the interactive components.

What is a common failure mode of existing models when given many instructions?

A common failure mode is that existing models may miss one or more instructions when given too many at once, similar to how humans can overlook details when presented with complex tasks.

How does the model O1 Preview reduce the chance of missing instructions?

The model O1 Preview reduces the chance of missing instructions by thinking slowly and carefully, going through each requirement in depth before generating the output code.

What editor does the speaker use to implement the visualization code?

The speaker uses the 'D editor of 2024' to implement the visualization code, which is a fictional editor mentioned in the script.

What is the outcome when the speaker hovers over a word in the visualization?

When hovering over a word, the visualization shows arrows representing the edges between words, with thicknesses indicating the strength of the attention scores between them.

What is the speaker's overall assessment of the model O1 Preview's performance in creating the visualization?

The speaker is pleased with the model O1 Preview's performance, noting that it produced a correct and useful visualization that could be beneficial for their teaching sessions.

Outlines

00:00

💡 Visualizing Self-Attention Mechanism

The speaker introduces a project to visualize the self-attention mechanism used in Transformer models, which are foundational to technologies like GPT. They express a desire to create an interactive visualization but lack the skills to do so. They decide to use a new model, 'o1 preview,' to assist with the task. The speaker outlines specific requirements for the visualization, such as using the sentence 'the quick brown fox' and visualizing attention scores as proportional edge thicknesses when hovering over words. They highlight the advantage of the new model over previous ones, which is its ability to 'think' before responding, reducing the chance of missing instructions. The speaker then demonstrates the successful implementation of the visualization in a web browser, showing that it meets the specified requirements, including displaying attention scores upon clicking.

Keywords

💡Transformers

Transformers are a type of deep learning model introduced in the field of natural language processing (NLP). They are designed to handle sequential data and are particularly effective for tasks like translation, text summarization, and understanding the context within sentences. In the video, the speaker teaches a class on Transformers, emphasizing their importance in technologies like GPT and their role in understanding the relationships between words in a sentence.

💡Self-attention

Self-attention is a mechanism used in Transformer models that allows the model to weigh the importance of different words within a sentence relative to the word being processed. This is crucial for understanding the context and relationships between words. The video script describes a visualization of self-attention to help students grasp how Transformers process language.

💡Visualization

Visualization in the context of the video refers to the graphical representation of data or processes to make them easier to understand. The speaker wants to visualize the self-attention mechanism of Transformers to help students see how the model processes and understands sentences, such as 'the quick brown fox'.

💡Interactive components

Interactive components are elements within a visualization that allow users to interact with the data, such as hovering or clicking, to reveal additional information. The speaker's goal is to create a visualization with interactive components that show the attention scores between words when a user hovers over a word in the sentence.

💡Attention score

The attention score in Transformer models represents the relevance or importance of the relationship between two words. A higher score indicates a stronger relationship. In the video, the speaker wants the visualization to show thicker edges between words when their attention scores are higher, indicating a more relevant connection.

💡Model

In the context of the video, a 'model' refers to an artificial intelligence system, likely a type of neural network, that can process and learn from data. The speaker mentions a new model, 'O1 preview,' which is designed to think carefully before outputting an answer, unlike previous models like GPT-40.

💡Failure modes

Failure modes refer to the ways in which a system can fail to perform as expected. The speaker mentions that existing models can sometimes miss instructions when given too many at once, which is a failure mode they want to avoid with the new model that can think more carefully.

💡Code

Code in this context refers to the programming instructions written to create the visualization. The speaker uses the output from the model to create a code that, when executed, produces the desired interactive visualization of the self-attention mechanism.

💡D editor of 2024

The 'D editor of 2024' is a hypothetical text editor or integrated development environment (IDE) mentioned in the script, which the speaker uses to write and save the code for the visualization. It represents the tools that developers use to create and test their code.

💡Rendering

Rendering in the context of the video refers to the process of generating the visual output on the screen based on the code and data. The speaker mentions that when they hover over a word in the visualization, arrows are rendered to show the attention scores, which is a part of the interactive experience.

💡Teaching sessions

Teaching sessions are the classes or lectures where the speaker educates students. The speaker aims to use the visualization tool created with the help of the model to enhance their teaching sessions, making complex concepts like self-attention in Transformers more accessible to students.

Highlights

Introduction to the example of writing code for visualization.

Teaching a class on Transformers, which is behind models like GPT.

Explaining the need for understanding word relationships in sentences.

Transformers use self-attention to model word relationships.

The idea of visualizing self-attention mechanism interactively.

Lack of personal skills to create such visualizations.

Asking the new model O1 Preview for help with visualization.

Demonstrating the command input to generate visualization code.

O1 Preview's ability to think before outputting an answer, unlike previous models.

Providing detailed requirements for the visualization.

Using the example sentence 'the quick brown fox'.

Visualizing edges proportional to attention scores when hovering over tokens.

Addressing common failure modes of existing models when given many instructions.

Reasoning model's ability to reduce chances of missing instructions by thinking carefully.

Copying and pasting the generated code into a terminal.

Using the D editor of 2024 to save and open the visualization.

Correctly rendered interactive visualization when hovering and clicking.

Potential issues with rendering, such as overlapping.

Positive evaluation of the model's performance in creating visualization tools.

Anticipating the use of this model for creating various visualization tools for teaching.

Transcripts

[Music]

all right so the example I'm going to

show is a writing a code for

visualization so I sometimes teach a

class on Transformers which is a

technology behind models like chipt and

when you give a sentence to Chach PT it

has to understand the relationship

between the words and so on so it's a

sequence of words and you just have to

model that and Transformers utilize

what's called a self attention to model

that so I always thought okay if I can

visualize a self attention mechanism and

with some interactive components to it

it will be really great I just don't

have the skills to do that so let's ask

our new model o1 preview to help me out

on that so I just typed in uh this

command uh and see how the model does so

unlike the previous models like GPT 40

it will think before outputting an

answer so it starts started thinking as

this thinking let me uh show you what

are some of these uh requirements I'm

giving a bunch of requirements to think

through so first one is like use an

example sentence the quick brown fox and

second one is like when hovering over a

token visualize the edges whose

thicknesses are proportional to the

attention score and that means just if

the two words are more relevant then

have a thicker edges and so on so the

one common failure modes of the existing

modles is that when you give a lot of

the instructions to follow it can miss

one of them just like humans can miss

one of them if you give too many of them

at once once so because this reasoning

model can think very slowly and

carefully it can go through each

requirement uh in depth and that reduces

the chance of missing um the instruction

so this output code let me copy paste

this into a terminal so I'm going to use

the D editor of 2024 so Vim HTML so I'm

just going to paste this thing into that

and just save it out uh and on the

browser I'll just try to open this up

and you can see that uh when I Hoover

over this thing it shows the arrows um

and then quick and brown and so on and

when I Hoover out of it it goes away so

that's a correctly rendered um version

of it now when I click on it it shows

the tension scores as just just as I

asked for and maybe there's a little bit

of rendering like it's overlapping but

other than that is actually much better

than what I could have done yeah so this

model did uh really nicely I think this

can be a really useful tool for me to

come up with a bunch of different

visualization tools for uh my new

teaching sessions