[ML News] Devin AI Software Engineer | GPT-4.5-Turbo LEAKED | US Gov't Report: Total Extinction
TLDRThe transcript discusses the emergence of Devon, an AI software engineer that combines a large language model with tools for coding tasks, web lookups, and debugging. It highlights the potential of AI in scripting and workflow automation, while cautioning against the hype around marketing videos. The conversation also touches on the release of Inflection's 2.5 model, the controversy around its independence from other models, and the growing interest in model merging and structured prompts for improved AI outputs. Additionally, it mentions the US government's concerns about AI's national security risks and the launch of an open source robotics project by Hugging Face.
Takeaways
- 🤖 Devon, the first AI software engineer, is an autonomous programming system that combines a large language model with tools for communication, command execution, code editing, and web browsing.
- 🔍 Devon can autonomously debug and fix errors by reading error messages, inserting print statements, and iterating on code until the bug is resolved.
- 🚀 The system is currently in private beta and has generated positive surprises among early users, with an open-source repository on GitHub receiving significant attention.
- 🎥 Marketing videos for Devon showcase its capabilities, but they represent the best-case scenarios, and the technology may not yet be capable of such tasks for all users.
- 💡 The hype around Devon's release appears to be part of a coordinated campaign, with media coverage and influencers promoting the technology.
- 🌐 Inflection released a custom LLM (Large Language Model) called the world's best personal AI, which performs closely to GPT-4, aiming to be a personal assistant for users.
- 🔗 Model merging is becoming a significant technique in LLM research, combining different fine-tuned models to create new, potentially superior models without additional training.
- 🛠️ Auto-merger is a tool that automates the model merging process, allowing for faster experimentation and evaluation of combined models.
- 📈 Prompt as WebAssembly programs aim to structure LLM outputs into more reliable formats, potentially replacing the need for manual prompt optimization.
- 🔐 Research has shown that parts of production language models can be recovered with API access, raising concerns about the security and proprietary information protection of AI models.
- 📚 A US government commission report emphasizes the need for decisive action to address national security risks posed by AI, comparing the potential impact to the introduction of nuclear weapons.
Q & A
What is Devon and how does it function?
-Devon is an AI software engineer that combines a large language model with tools such as a chat interface, console, code editor, and web browser to perform autonomous programming tasks. It can plan its actions, execute code, debug errors, and even ask for user intervention when necessary. The system is designed to handle basic scripting and workflow tasks, with the ability to look up specifications and implement code iteratively.
How does Devon handle errors during programming?
-When Devon encounters an error, it can autonomously read the error message, enter a print statement, run the program again to identify the issue, and determine the necessary steps to fix the bug. This capability for iterative debugging is part of its autonomous programming system.
What is the current availability of Devon?
-Devon is currently not widely available. It is in a private beta phase, and only a select few individuals have access to it. The public has mainly seen it through marketing videos and demonstrations.
How does the Meta gpt's data interpreter differ from Devon?
-Meta gpt's data interpreter, also known as an open-source version of Devon, focuses more on mathematical reasoning and machine learning tasks. It is less flashy and front-end oriented compared to Devon, and is more of a research investment into planning and reasoning using LLMs in the domain of machine learning.
What is the controversy surrounding Inflection's AI model?
-Inflection was accused of not having their own model and merely being a front end for Claude by Anthropic. However, Inflection responded by explaining that a user had copy-pasted a response from Claude into an Inflection chat, which led to the same output. This incident highlighted Inflection's conversational memory feature and confirmed that they do have their own models.
What is the significance of model merging in AI research?
-Model merging is a technique in AI research where different fine-tuned versions of a base model are combined to create a new model that often performs better than the individual models or even the base model. This method is akin to building an ensemble and has become a significant approach in pushing the boundaries of AI capabilities.
What is ACI (Autoprompt and CLI) and how does it work?
-ACI, or Autoprompt and CLI, is a system that enforces specific output formats for AI models. Instead of manually optimizing the output formatting in the prompt, ACI allows users to write a program that enforces the desired style, making it easier to structure prompts and produce more reliable structured information.
What was the discovery made by the researchers from the University of Southern California and Google DeepMind?
-The researchers discovered that they could recover the embedding projection layer of transformer models, such as OpenAI's GPT-3 and GPT-3.5 Turbo, with typical API access for under $20. This confirmed that these 'black box' models have a hidden dimension and allowed them to estimate the cost of recovering the entire projection matrix.
What is the main concern raised by the US government commission report on AI?
-The US government commission report raises concerns about the national security risks posed by artificial intelligence. It suggests that AI could potentially destabilize global security in ways similar to the introduction of nuclear weapons, and in the worst-case scenario, pose an existential threat to humanity. The report calls for quick and decisive action to mitigate these risks.
What is Hugging Face's new initiative in robotics?
-Hugging Face is launching an open-source robotics project, led by a former Tesla scientist. This initiative marks the company's expansion into the field of robotics, potentially integrating their expertise in AI with robotic technologies.
What are the implications of the discovery that API-protected LLMs can leak proprietary information?
-The discovery that API-protected LLMs can leak proprietary information, such as the embedding projection layer, raises concerns about the security and privacy of AI models. It suggests that with limited API access, adversaries could potentially reverse-engineer and steal critical components of AI models, which could have significant commercial and ethical implications.
Outlines
🤖 Introduction to Devon: The AI Software Engineer
The video begins with an introduction to Devon, an AI software engineer that has garnered significant attention. Described as an autonomous programming system capable of performing well in software engineering benchmarks, Devon combines a large language model with the ability to plan tasks, using tools such as a chat interface, console, code editor, and web browser for web lookups. It can autonomously debug Python scripts, ask for user intervention when needed, and create web apps for displaying results. While the video acknowledges that Devon is currently only available in private beta and the showcased capabilities are cherry-picked, it also highlights the excitement around this technology and its potential for basic scripting and workflow automation. The video also mentions that Devon's marketing videos present the best-case scenarios, urging viewers to take the hype with a grain of salt.
📈 Hype and Reactions to Devon and Other AI Developments
The speaker discusses the hype surrounding Devon, suspecting a coordinated campaign due to simultaneous press coverage and social media endorsements. Despite the hype, the speaker acknowledges Devon's novelty and potential. The conversation shifts to OpenAI, with the speaker sharing an anecdote about the company's public image and the scrutiny it faces. The video then covers Inflection's release of a custom LLM, which, despite accusations of being a front for another model, is confirmed to have its own model based on user experiments. The speaker also mentions various tools and libraries like MLX server, Auto Merger, and ACI, which are designed to enhance interaction with and manipulation of large language models.
🧠 Advancements in LLM Research and Model Merging
This section delves into the concept of model merging in LLM research, where different fine-tuned models are combined to create a new, often superior model without additional training. The speaker explains the process and its implications, likening it to an ensemble approach. Tools such as Auto Merger are introduced, which can automatically merge models and evaluate the results. The concept of 'prompt as wasm' programs is also discussed, which aims to structure LLM outputs into more reliable, structured information. The speaker touches on the potential for model stealing attacks as more models become commercially valuable and accessible only through APIs.
🔍 Analysis of Model Vulnerabilities and AI Risks
The speaker presents research on extracting information from API-protected LLMs, highlighting the potential to recover model parameters with limited API access. Two papers are discussed, one from the University of Southern California and another from Google DeepMind, both exploring the 'softmax bottleneck' and the possibilities of obtaining model outputs and parameters. The speaker also mentions a cached blog post about GPT 4.5 turbo, speculating on its authenticity and the implications of such leaks. The segment concludes with a discussion on a US government report that warns of national security risks from AI, emphasizing the need for decisive action to mitigate potential threats.
🤖 New Releases and Initiatives in AI and Robotics
The video covers various new releases and initiatives in the AI field. Anthropic's release of Claude 3, a smaller and more cost-effective model, is mentioned, as well as Coher's release of Command R, a 35 billion parameter multilingual model. Pelican releases a powerful open-source Hebrew base model, and Genr Struct 7B, an instruction generation model, is introduced. Google DeepMind announces SEMA, a scalable instructable multi-world agent capable of navigating Unity environments. The speaker also discusses the launch of an open source robotics project by Hugging Face, led by a former Tesla scientist, and the formation of a new board for OpenAI, which is expected to be more favorable towards Sam Altman.
🌐 European LLM Collaboration and Hardware Market Developments
The speaker talks about the European research collaborative, Oxy Glo, focusing on the development of large language models for Europe. The potential benefits of a multilingual model tailored to Europe's linguistic diversity are discussed. The video also mentions the use of Intel chips for training and inference in stable diffusion 3, indicating a potential shift in the hardware market and offering an alternative to Nvidia's dominance. The speaker expresses optimism about increased competition in the hardware market for large models.
Mindmap
Keywords
💡Devon
💡AI software engineer
💡Benchmarking
💡APIs
💡Debugging
💡Open source
💡Model merging
💡Conversational memory
💡Hugging Face
💡Intel chips
💡Large language models (LLMs)
Highlights
Devon, the first AI software engineer, is a combination of a large language model with autonomous programming capabilities.
Devon can plan its actions, communicate with users, run commands, edit code, and perform web lookups.
Devon demonstrates the ability to autonomously debug Python scripts and fix bugs.
The marketing videos for Devon showcase its capabilities in a cherry-picked example, indicating potential for basic scripting and workflow automation.
Devon is available in private beta and has received positive feedback from early users.
OpenDev, an open-source repository related to Devon, has quickly gained popularity with over 800 stars on GitHub.
Meta gpt's data interpreter is an open-source project focused on mathematical reasoning and machine learning tasks.
Inflection released a custom LLM, the 2.5 model, which is a competitive personal AI assistant.
Inflection addressed accusations of being a front-end for Claude by demonstrating their independent model through a user interaction.
MLX server is a library that simplifies the process of setting up a server with any model from the Hugging Face Hub and providing an API.
Auto Merger is a Hugging Face tool that combines two billion-parameter models using merging techniques to improve performance.
ACI by Microsoft is a tool for building and experimenting with controller strategies to improve LLM generations.
Research by the University of Southern California and Google DeepMind demonstrates the possibility of extracting the embedding projection layer from API-accessible language models.
The US government report emphasizes the need for decisive action to mitigate national security risks from AI, likening the rise of advanced AI to the introduction of nuclear weapons.
Hugging Face launches an open-source robotics project, led by a former Tesla scientist, marking their entry into robotics.
Anthropic releases Claude 3, a smaller model focusing on speed, efficiency, and cost-effectiveness, comparing favorably to GPT-3.5 and Gemini 1.0 Pro.
Coher releases Command R, a 35 billion parameter model optimized for reasoning, summarization, and question answering.
Genr Struct 7B is an instruction generation model designed to create valid instructions from a raw text corpus, useful for constructing synthetic training data.
Google DeepMind announces SEMA, a scalable, instructable multi-world agent capable of navigating within Unity environments and generalizing to unseen environments.
The e Open Foundation Models by 01 is a family of 6 billion and 34 billion parameter language models trained primarily on English and Chinese.
Oxy GloT, a research collective for open-source development of large language models, aims to create Europe-based or multilingual models.
Emot of Stability tweets about using Intel chips for stable diffusion 3, indicating a potential shift from Nvidia's monopoly on AI hardware.
