Coding with OpenAI o1
Summary
TLDRIn 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
💡 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.
Mindmap
Keywords
💡Transformers
💡Self-attention
💡Visualization
💡Interactive components
💡Attention score
💡Model
💡Failure modes
💡Code
💡D editor of 2024
💡Rendering
💡Teaching sessions
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
Ver Más Videos Relacionados
ChatGPT o1 vs ChatGPT4 | Is it even better? | OpenAI launches new model GPT-o1
Cursor AI - Code siêu nhanh siêu nhàn với sự trợ giúp của AI Text Editor
GPT2 implemented in Excel (Spreadsheets-are-all-you-need) at AI Tinkerers Seattle
Stanford CS25: V1 I Transformers United: DL Models that have revolutionized NLP, CV, RL
15 Powerful Claude Artifacts Use Cases You Should Try
Transformers, explained: Understand the model behind GPT, BERT, and T5
5.0 / 5 (0 votes)