[AINews] 5 small news items • ButtondownTwitterTwitter

The AI Twitter Recap section provides a snapshot of recent developments shared on Twitter related to AI models and architectures. Some highlights include new models like Uncertainty Quantification in LLMs by @arankomatsuzaki, efficiency improvements with reduced matrix multiplications in LLMs by @omarsar0, and discussions on concepts' geometry in LLMs. Discussions also cover Parameter-Efficient Fine-Tuning methods and the importance of alignment and safety in AGI development.

Here is a summary of the recent AI developments discussed across various AI Discord channels. Content includes discussions on finetuning techniques, model integration, model training issues, new tools and resources in AI, community concerns, collaborative projects, security, and ethical discussions in AI. Members shared insights on topics like efficient VRAM management, troubleshooting CUDA library mismatches, new tool releases like Stable Audio Open for audio generation, and the release of comprehensive resources such as a LLM resource guide by William Brown. Collaborative efforts were also observed in learning new AI features and models, addressing concerns about credit distribution and server performance, and debating ethical issues surrounding AGI development incentives. Security concerns, ethical debates, and the rapid advancement of AI technologies were key focuses of the discussions.

OpenAI's ChatGPT 4 introduces impressive new voice generation features, while concerns are raised over the quality degradation in DALLE3 outputs. Community frustrations arise over non-commercial AI model licenses, and a collective paper exposes a breakdown in reasoning by large language models. An issue with WebSockets in the WhisperSpeech project is addressed.

A member praised a YouTube tutorial highlighting the safety of Rust in systems development through FFI encapsulation, showcasing the engineering community's interest in secure and efficient systems programming. Additionally, discussions included a Python to Mojo transition guide on YouTube for non-CS engineers moving to Mojo, the accommodation of Variants in Mojo in the absence of Enum types, a new release of the Mojo compiler, and the urgency for a cryptography library in Mojo. These discussions reflect the continuous evolution and community engagement within the Mojo Discord.

The LLM Finetuning groups led by Hamel and Dan engage in discussions covering various approaches like fine-tuning, RAG contextualization, and prompt engineering. The groups discuss a range of topics including Hainan departure, Modal credits confusion, deploying Shiny apps, and privacy policy queries. They also share resources like the Ultimate LLM Resource Guide, LangChain multi-modal RAG notebook link, Mistral AI customization, and recommendations on CUDA books and GPU setups. Users seek assistance on issues like CUDA version mismatches, Axolotl local installations, and memory management optimizations. The interactions involve clarifications on credits, troubleshooting technical challenges, and sharing tips for efficient use of platforms like Valgrind, Modal, and Langsmith.

Current Status and Updates on Credit Distribution:

• Updates were given on the distribution statuses, confirming credits from Langsmith and Fireworks. Users were advised to create necessary platform accounts and follow up if they hadn't received their credits.

LLM Finetuning (Hamel + Dan):

• Excitement for Synthetic Data Prep: Multiple users expressed excitement for the talk on synthetic data preparation, calling it an 'exciting' topic. One user noted a preference for 'ML Librarian' as a dream job concept.
• Links and Tools Shared: Users shared various tools and links relevant to data handling, including Lilac, Huggingface's dataset security, and examples of structured text generation like Outlines.
• Discussing Dataset Generation: Participants discussed using GPT-4 with human evaluations to create synthetic datasets, highlighting the high costs and potential benefits. One user mentioned using a combination of DPO and PPO approaches to improve dataset quality.
• Knowledge Graph Enthusiasm: Users expressed interest in building Knowledge Graphs for improved data structuring and retrieval, mentioning tools like LlamaIndex and LangChain notebooks for enhancing RAG.
• Crucial Resources on RLHF and Alternatives: The conversation included references to RLHF and its alternatives from Argilla's blog series, with users requesting further clarification on using Distilabel for generating synthetic chat datasets in JSONL format.

• Script to test HF Models generates buzz: Discussion on a GitHub pull request adding a script to test model evaluation with torchao APIs, encountering Out of Memory errors with large models on GPU.
• OOM Problems with Large Models: Note on OOM errors when quantizing and evaluating large models, advice to load models on CPU before quantization.
• Investigating OOM Issues: Root cause exploration of OOM issues related to EleutherAI/lm-evaluation-harness repository, highlighting the impact of large vocab sizes.
• Specific Task Memory Usage: Comparison of memory issues in tasks like wikitext and hellaswag based on logits tensors and sequence lengths.
• Optimizations and Discussions on Quantization: Technical discussion on applying torch.compile() before quantization, recommendation to disable fast math for Intel CPU backend.

This section covers various discussions within different channels on the HuggingFace Discord server. Topics include the early testing of CogVLM2-LLaMA3-Chat-19B model, Microsoft's TextDiffuser projects on GitHub, an AI assistant for climate-aware investments, and more. There are conversations about installing Apache Airflow on Windows, a comprehensive resource guide for LLM explainers, and upcoming events by the Human Feedback Foundation. Additionally, users discuss model performance, troubleshooting techniques, and hardware preferences, such as the new ASRock Radeon RX 7900 Workstation GPUs. Controversial views on Linux, privacy concerns related to the Windows Recall feature, and light-hearted stories from IT support roles are also highlighted.

• AI-powered display walls: A member suggested utilizing AI in a giant display wall to enhance daily life with color changes and automatic art displays.
• Critique of overhyped 'breakthrough' technology: Discussion on an RLCD product, critiquing the CEO's mystic image and exaggerated claims, while discussing modifications and similarities to Samsung's displays.
• AGI race heating up: Reference to a blog discussing increased investments leading to anticipated AGI advancements by 2025/26, highlighting concerns about the growing gap and hiring signals.
• Debate on IQ tests and high agency in open source: Members debated the usage and legality of IQ tests for hiring, emphasizing high agency's importance over high IQ alone.
• Interest in KANs: Discussion on the potential and limitations of Koopman Operator-Based Neural Networks (KANs), focusing on interpretability, efficiency, and concluding that they won't replace traditional networks soon.

A member announced the release of a new paper under the Open-Sci collective, focusing on the 'dramatic breakdown' of reasoning capabilities in state-of-the-art large language models. They linked to the paper, its code, and the project page.

This chunk of discussions covers a wide range of topics from different Discord channels. It includes insights on new papers like the 'autoguidance' approach for image quality improvement, critiques on NVIDIA's VRAM usage, challenges with WebSocket connections in services like WhisperSpeech, and discussions on Rust FFI, Mojo's vectorization desires, and backend feasibility. Other topics include Mojo's roadmap, AI in robotics, AGI speculation, model comparisons, and technological updates like the new Mojo compiler release. Furthermore, there are conversations around datasets like the HuggingFace FineWeb datasets, access limitations, and interest in GLM-4 9B model experiences. Issues with deep learning frameworks like Deepspeed and Runpod for fine-tuning and slow boot times are also highlighted.

In this section, various updates and discussions on AI-related topics were shared in different Discord channels. Highlights include LiveKit raising $22.5M for AI transport layer, Twelve Labs securing $50M funding for Marengo 2.6 launch, and Microsoft Research introducing Aurora for better weather predictions. Other discussions revolved around OpenAI's transparency, Storyblok's $80M funding for an AI-powered content platform, alongside AI community discussions on topics like Skill Persistence, RAG's reliability, and synthetic data generation tools. Additionally, there were updates on projects like Tinygrad, LangChain AI, OpenRouter, MLOps events, Mozilla AI llamafile, and Right to Warn AI project.

• Discussing the feasibility of a German 'PaliGema' clone: Members debated creating a German version called 'Sauerkraut Gemma' and discussed the process of replacing the base Gemma.
• Link shared for PaliGemma Model: Suggestions were made to follow the approach of freezing the vision and training the chat after translation, with reference to the PaliGemma-3B-Chat-v0.2 model.
• Comprehensive AI Resource Guide Released: A resource guide titled 'genai-handbook' by William Brown was shared, aiming to explain key concepts in modern AI systems in a textbook-style format.