Latent Space Podcast 7/10/23 [Summary] - Code Interpreter == GPT 4.5 (w/ Simon Willison, Alex Volkov, Aravind Srinivas, Alex Graveley, et al.)

Original Link: Code Interpreter == GPT 4.5 (w/ Simon Willison, Alex Volkov, Aravind Srinivas, Alex Graveley, et al.)

ChatGPT Code Interpreter Feature Discussion:

Alex Volkov announces a public release of a new beta feature for ChatGPT, which is a code interpreter. He describes how users can enable this feature and highlights its capabilities.

Key Features of the Code Interpreter:

1. It allows users to upload files.

2. Code is run in a secure environment.

3. Users can download files generated by ChatGPT.

Simon Willison's Experience:

• Simon has been using this tool frequently and finds it revolutionary. It offers capabilities beyond just ChatGPT, especially for developers.

• He also points out its wide file compatibility, from CSV to SQLite. It can analyze, run SQL queries, and even generate graphs.

• There was a feature (now apparently disabled) where users could upload Python packages to the interpreter.

• A workaround for file size limit: Compressing files to zip and then uploading them.

Swyx's Notes:

• Swyx had documented his experiences and observations, which were displayed on the jumbotron. He mentions the early days of the interpreter and how it could write, run, and iteratively fix code.

Interaction with the Code Interpreter:

• Users can guide the interpreter, almost like mentoring an intern.

• Asking it to "do better" can often result in improved results.

• Alex mentions how users can still utilize familiar prompts like "act as a senior developer" for better outputs.

Practical Applications:

• Simon uses it for actual coding tasks. It can test its code, identify bugs, and rectify them.

• This tool saves developers time by quickly handling edge cases and providing accurate code.

Daniel Wilson's Perspective:

• Daniel praises the tool, calling it the "most advanced agent the world has seen".

• He hints at the significance of such a feature rollout for the entire user base and the potential DevOps implications.

Dependencies Exploration:

• Daniel and Nin seemingly uncovered the complete list of dependencies for the code interpreter.

The discussion showcases the excitement around ChatGPT's new code interpreter feature, emphasizing its transformative potential for developers and the broader tech community.

Code Interpreter Conundrums

The discussion revolves around the capabilities and limitations of a new Code Interpreter. Key points include:

• Uploading Different Languages: Simon Willison experimented by uploading binaries of Deno (an alternative to Node.js) and a Lua interpreter to the Code Interpreter, allowing it to execute JavaScript and Lua for a while before these functionalities were restricted.

• Safety and Restrictions: Simon muses about why there are certain limitations since the platform appears to use containers (possibly Kubernetes) that are restricted in networking and have limited CPU and disk space. The Code Interpreter also has timeouts on code execution.

• Loss of State: Users discuss instances where the Code Interpreter disconnects, leading to loss of the data or files uploaded. However, the conversation history remains, which can sometimes lead to confusing loops where the system behaves as if it still has access to the data.

• Context Window: There is speculation on the context window of the interpreter. It's believed to be around 8,000 tokens, similar to GPT-4, but this hasn't been confirmed.

• Refactoring and File System: Simon observes that the interpreter often rewrites whole functions for small changes, so he sometimes instructs it to refactor the code into smaller functions for efficiency. He also advises against pasting large amounts of text directly and instead suggests uploading as a file to conserve tokens and speed.

• Git Repos: Daniel Wilson discusses the potential of uploading git repos. Although the interpreter can read content and suggest changes, it cannot directly commit. If it had bindings to Git, it could potentially commit changes.

• Security: The panel discusses the security of the Code Interpreter, speculating it's robust due to its wide rollout. They believe it's running in a well-sandboxed environment, possibly using technologies like firecracker or Kubernetes, making breaches unlikely.

Exploring the Depths of Code Interpreter's Capabilities and Potential

In a discussion revolving around the capabilities of a 'Code Interpreter', participants delved into the following:

1. Visualization Libraries: Alex Volkov mentioned that many users find it difficult to work with libraries for data visualization, particularly plotting data on maps. Some libraries cited were Pandas and Matplotlib, with Simon Willison noting that the tool only supports image outputs, specifically those like PNG or GIF. Matplotlib is an older Python plotting library that fits well within the training of GPT.

2. Generating Interactive Diagrams: Swyx highlighted that the interpreter is mainly equipped with Python libraries from its requirements. There's also the ability to generate HTML, CSS, and JavaScript files. By utilizing a 'hack' from Ethan, users can render JavaScript to produce interactive visuals.

3. Audio and Video Features: The interpreter also boasts Torch and Torchaudio libraries. Nisten expressed excitement about its potential speech library, while Alex Volkov touched on the interpreter's ffmpeg capability, which allows interaction with video files. Users can split videos, convert formats, and more.

4. Data Analysis Strength: Simon Willison shared an anecdote about the code interpreter's prowess in data analysis. Using a dataset of police calls from San Francisco, Simon asked the tool to visualize crime reports around two locations over time. The tool processed a sizable dataset efficiently and produced a relevant chart, all based on a single prompt. This efficiency led Simon to reconsider the future direction of his open-source project, Dataset, as the interpreter covered most of its intended functionalities.

5. Potential Network Access: Alex Volkov and Daniel Wilson hinted at the interpreter possibly gaining internet access in the future, which could further enhance its capabilities.

The conversation showcased the power and flexibility of the Code Interpreter, hinting at a future where data analysis and visualization could become more accessible and efficient for a wider range of users.

Exploring the Capabilities of Code Interpreter

• Daniel Wilson discussed how he and others probed the system's prompts, expressing surprise at how readily the system divulged its last few prompts.

• Alex Volkov suggested the current model could be a fine-tuned earlier checkpoint.

• Nisten confirmed that OpenAI uses Kubernetes and referenced a famous OpenAI blog post about their Kubernetes cluster.

• The group discussed the system's ability to execute certain functions, with several members experimenting with its limitations and potential.

• They discussed the possibility of the system having variable CPU performance, suggesting that it might sometimes operate on shared instances.

• The conversation veered into the topic of system specs, with Simon Willison sharing a method he used to determine the system's RAM.

• There was a humorous interjection about replacing certain technical terms with "SpongeBob."

• Daniel Wilson provided insights into his experiments with image recognition using OpenCV's pre-trained models.

• A notable omission from the model's capabilities is the absence of hugging face transformers and datasets.

• The potential for the code interpreter to function as a debugger in software companies was highlighted.

Exploring Code Interpreter's Potential: From Analysis Choices to Vector Database Integration

Code Interpreter Behavior: Swyx mentions that the Code Interpreter sometimes doesn't do the whole analysis but rather gives the user multiple options to choose from. Simon confirms this, likening it to how a real data analyst would ask for more specifics when given a vague question.

Product Design: Daniel Wilson praises the design of such agents, particularly how they decide to either proceed independently or ask for user guidance.

Tips on Interaction: Simon gives a tip for users to ask the AI for multiple options, suggesting that this could speed up the process and offer more diverse results. Daniel agrees, mentioning that vague prompts can lead to tangential but useful suggestions.

Code Interpreter Applications:

• Simon introduces the idea of uploading project documentation to the Code Interpreter to help it answer questions about the docs.

• Alex and Simon discuss the potential of integrating vector databases with the Code Interpreter, hinting at its vast applications.

Tokens & Model Discussion:

• Swyx brings up a topic about extracting tokens from the Code Interpreter environment and the possible implications of such an act.

• Daniel Wilson and Alex discuss the possibility of the Code Interpreter using a different model, with Daniel providing evidence based on his client-side observations.

Working with Code: Simon and Daniel discuss uploading code to the AI. Simon typically copy-pastes code while Daniel suggests that users can upload a zip file full of Python code.

Tinygrad on Code Interpreter: Daniel proposes the idea of running Tinygrad, an alternative to PyTorch, on the Code Interpreter.

Feature Request: Surya Danturi introduces the idea of combining the Code Interpreter with plugins. He has been working on a plugin that essentially creates a user's vector database, and combining this with the Code Interpreter might enhance results. He also hints at the potential of uploading an entire GitHub repository to the Code Interpreter.

Quorum of Models, Continuous Learning, and The Future of External Interactions

Quorum of Models: Oren discusses how a model can bounce ideas off various sub-models to refine its output. This continuous feedback system means the model continuously improves its suggestions, moving closer to a near-perfect code output every time.

Memory Limitations & Solutions: Simon Willison touches on session memory limitations and suggests a potential solution using SQLite to save a session's data and reload it later.

OCR and Language Learning: Daniel Wilson introduces a use case where a model, through OCR, interprets and understands old grammatical structures of languages that lack machine translation tools. He particularly mentions languages from Nigeria and Indonesia and the challenges and achievements they've faced.

Enhancements through Plugins: Surya Danturi and Alex Volkov highlight the potential of plugins, where a model can communicate with an external plugin that can call another external API. This recursive approach could open doors to increased functionality, provided the security concerns are addressed.

Extended Memory Techniques: There's mention of a potential hack to expand the model's memory using an external text file, which can be read or written to by the code interpreter. This offers a workaround to context length limitations.

Code Interpreter Insights: Bugs, Use Cases, and Education Impact

ChatGPT iOS App Bug Discussion:

• There's a reported bug in the ChatGPT iOS app. When users prompt on the web and then switch to the app, the system may continuously self-prompt.

• The app seems to generate multiple messages before the user interacts. The history of this behavior is visible on the web.

InstructorEmbeddings:

• A discussion on "instructor embeddings" took place, which is different from "hugging face." It tops the leaderboard in its category and may potentially be integrated into a code interpreter.

Code Interpreter's Capabilities:

• Gabriel shared his experiment with the code interpreter on sentiment analysis. The system tried various libraries and even created a basic sentiment analysis mechanism when it couldn't access a specific lexicon.

• Simon Willison found it amusing to watch the system try different methods and commented on its adaptability.

• Simon also shared how he used the interpreter to construct a tool that searches Python code based on the Python's Abstract Syntax Tree (AST).

Personalized Languages and AST/Graphs:

• The potential of creating personalized languages that compile to machine code or LLVM was discussed.

• The system's understanding of graphs and the AST was highlighted.

Feature Request: Token Streaming/Interruption:

• There's a desire for token streaming on the ChatGPT interpreter, allowing users to interrupt the system if it veers off course.

OCR from a Graph:

• A user expressed interest in extracting values directly from a graph using the code interpreter. Simon was skeptical but encouraged experimentation.

Impact of Code Interpreter on Education:

• Shyamal from OpenAI emphasized the transformative potential of the code interpreter in the field of education. He cited personal experiences of individuals learning Python and data analysis through the interpreter.

• Simon supported the education perspective, noting the tool bypasses the complexities of setting up a development environment.

Feature Requests Recap:

• Users requested more extensibility, such as the reintroduction of binary execution and support for other programming languages like Node and Deno.

Exploring ChatGPT for Business: Insights, Feedback, and Novel Use-Cases

ChatGPT for Business Discussion:

• The conversation opens with a query about potential B2B applications for ChatGPT, noting that current discussions have been very B2C focused.

• Shyamal acknowledges the early phase of exploring B2B use cases for ChatGPT. Potential extensions for business might involve plugins and other integrations.

• The business version might also emphasize enterprise-level features, such as advanced data security and options to license the software for entire teams.

Code Interpreter Feedback:

• A developer, Alex, gives feedback on the Code Interpreter, praising its ability to process and edit videos efficiently. However, he notes memory limitations that restrict file sizes and the inconvenience of the interpreter timing out during prolonged periods of inactivity.

• Alex expresses interest in a version of the software that offers dedicated, more robust hardware capabilities.

Alternative Solutions:

• Shouminik mentions having created a sandboxing environment for a Discord bot which users can interact with, as an alternative to ChatGPT.

Observations on ChatGPT's Performance:

• A noteworthy point is brought up about the perceived decline in ChatGPT's performance quality in recent times. Some users find that the Code Interpreter model provides the level of quality that they originally experienced with ChatGPT.

• While some claim to have evidence of this decline, Simon Willison remains skeptical, noting the challenge in measuring such changes due to the non-deterministic nature of the model.

Takeaway: The community is actively exploring and providing feedback on OpenAI's offerings, particularly around business use cases and the performance of ChatGPT.

Feature Requests and the Power of Code Interpreter

Maxim opens the discussion with a feature request for the platform to support the ability to create music compositions using ABC files. By generating ABC notations and converting them into MP3 files, Maxim believes that this would enhance his learning experience with the piano. There's a shared sentiment that while a built-in player might not be readily available, working with downloadable files could be an alternative.

The conversation shifts as Aravind Srinivas from Perplexity joins. He touches upon the similarities of the challenges they faced in integrating visual representations, like plotting distributions, with GPT-4. There's skepticism about the added value for seasoned coders but also acknowledgment of the advantages for those who don't want interruptions in their thought processes.

Simon Willison offers personal insights on the Code Interpreter. For him, the feature makes coding more ambitious by tackling tedious tasks. Whether it's generating a nested list for a website's table of contents or working with the Python AST library, the Code Interpreter's self-debugging capability is a highlight. Simon humorously compares the experience to mentoring an intern, with the tool sometimes making mistakes and learning from them, albeit much faster.

Al Chang and Simon further discuss the process and psychology of the Code Interpreter. There's an art to guiding and tricking it into solving complex problems. The conversation ends with an emphasis on the value of the tool in writing and running code, highlighting its efficiency and reduced error rate.

AI Innovations: Code Interpreter & Multimodal Capabilities Discussed

During a discussion on Code Interpreter, several speakers shared insights:

Alex Graveley's View: He opined that Code Interpreter is valuable due to its feedback loop. Earlier models like Codex wouldn't confirm if the generated code worked. Now, with users running and critiquing the code, the tool is bound to improve significantly in code generation.

Context Window Update: An update was shared about the context window for the code interpreter. It was tested and confirmed that the code interpreter has an 8K context window, similar to some existing models. However, GPT4 default has a 4K context window.

Carl's Input: Carl highlighted the potential of Code Interpreter for feature requests and its benefit in the OpenAI UI. He also discussed challenges with rendering graphics and data visualizations via an API. Additionally, Carl expressed interest in image processing capabilities, pointing to the potential for image captioning and processing with the OpenAI ecosystem.

Simon Willison's Thoughts: Simon believed that the maximum potential of Code Interpreter is constrained by the libraries currently installed. He was eager to explore GPT-4's vision capabilities. Simon also requested direct access to the fine-tuned model used in Code Interpreter, so developers could extend its capabilities.

Kyle's Perspective: Emphasizing the multi-modal capabilities, Kyle spoke about combining data analysis with vision capabilities to provide more insightful analyses.

API Access Discussion: There was a collective agreement on the need for direct API access to the Code Interpreter model. Some developers expressed interest in building unofficial APIs if official access isn't provided.

Cryp Law Review: Mentioned a use case of creating a video from a sample photo and praised the self-debugging nature of the model, echoing Simon's views on the tool's ability to refine and improve on tasks iteratively.

Overall, the conversation centered around the potential of Code Interpreter, its current capabilities, and the hopes and wishes of the developer community for its future iterations.

The Social Leap for ChatGPT and Future Aspirations

In a deep discussion about OpenAI's ChatGPT, there is a call for making the AI model more social. The idea stems from allowing users to collaborate with others prompting the model with similar intentions, effectively "tenderizing" the AI experience. The goal is not only about sharing individual AI interactions but more about discovering like-minded analysts and forming connections.

There's also a mention of Shared GT, a way to share one's interactions up to a certain point, which, though effective, has limitations. Simon Willison touches upon a unique experience in the Stable Diffusion discord, emphasizing the value of a public learning sphere where individuals learn from each other.

Further discussion revolves around the 'code interpreter' and how it can be beneficial for business analysts. Gabriel, a participant, points out that while the current capabilities of code interpreter are advanced, there's still a long way to go for seamless data integration and manipulation. An intriguing use-case of the code interpreter is mentioned, where a user uploaded a swift file for a simple game, prompting the AI to analyze and suggest improvements.

The conversation closes with Simon Willison's enthusiasm for the tool and an encouragement for users to explore, share, and collaborate to harness the full potential of the technology.

What about Phase 5? and AI.Engineer Summit

At a discussion led by Swyx, speakers discuss the current and future state of OpenAI developments. They refer to the current stage as "Phase 4" and express anticipation for the subsequent "Phase 5". In the current phase, OpenAI is rolling out a vision model, introducing fine-tuning features, and is expected to release a new instruct model. Swyx expresses a desire for more speculative talks on what's next, likening the progression of these phases to the MCU's phases and movies.

Swyx also announces his upcoming conference, "AI Engineer", scheduled for October. The conference aims to explore the intersection of coding and large language models, offering both application-based sessions and online streaming. Swyx encourages attendees to check out the event by visiting the "ai.engineer" domain, expressing his elation over securing such a relevant domain name.

The conversation concludes with some acknowledgments and thank-yous, with mentions of other AI-focused spaces and events where industry enthusiasts gather to discuss latest advancements. They also touch upon the practicalities and features of the new tools that OpenAI offers, emphasizing their versatility and utility. The overarching sentiment is one of excitement for the current state and future potential of AI.