Latent Space: The AI Engineer Podcast - 2024 in Open Models [LS Live @ NeurIPS]
The Latent Space Live conference at NeurIPS 2024 in Vancouver focused on the advancements and challenges in open AI models. Keynote speakers from the Allen Institute for AI and Mistral discussed the explosion of open models in 2024, highlighting new entrants like Google's Gemma and Cohere's Command R. The conference emphasized the importance of open models for research and innovation, despite challenges like regulatory debates and resource constraints. Open models allow for collaboration and building on existing innovations, crucial for advancing AI research. The introduction of the first open-source AI definition by the Open Source Initiative was a significant milestone, though it highlighted areas needing improvement, such as data accessibility. The conference also addressed the growing resource constraints and the need for more GPUs to advance AI research. The discussion included the importance of multilingual support in open models and the challenges posed by diminishing access to training data due to increased content blocking by websites. The event concluded with a call for better incentives to support open model development and the need for community collaboration to overcome legislative challenges.
Key Points:
- Open models have significantly grown in 2024, with new players entering the field, enhancing research and innovation.
- Open models are crucial for AI research, allowing for collaboration and building on existing innovations.
- The first open-source AI definition was introduced, highlighting the need for better data accessibility.
- Resource constraints, particularly access to GPUs, remain a significant challenge for advancing open AI models.
- Community collaboration and better incentives are needed to support open model development and address legislative challenges.
Details:
1. 🎙️ Welcome to Latent Space Live
1.1. 🎙️ Event Introduction
1.2. 🤖 Introducing AI Co-host
2. 📊 Recap of 2024: Survey Insights
- Collected over 900 survey responses to understand user preferences.
- Insights from surveys guided the selection of speakers, emphasizing popular topics such as innovation and sustainability.
- Identified key trends like a demand for interactive sessions and diverse speaker backgrounds.
- Event planning was optimized to align with attendee interests, boosting engagement and satisfaction.
3. 📺 Keynote on Open Models
- The event drew 200 in-person participants, demonstrating significant interest in open models.
- Key topics included the development and application of open models, addressing both their benefits and challenges.
- Experts highlighted future prospects, emphasizing the role of open models in driving innovation and collaboration.
4. 🔍 RLHF 201 and AI Training
- The keynote attracted over 2,200 live viewers, underscoring a significant interest in open AI models for 2024, and highlighting the topic's relevance and urgency.
- Luca Soldani and Nathan Lambert from the Allen Institute for AI discussed advancements in reinforcement learning, focusing on its applications in language models.
- Dr. Sophia Yang from Mistral emphasized future trends in AI and highlighted the importance of strategic partnerships to advance AI technologies.
- Key discussions centered on leveraging reinforcement learning to enhance language model performance, with an emphasis on recent post-training developments.
- The session illustrated the critical need for continuous improvement in AI training methods to sustain cutting-edge language model capabilities.
5. 🚀 Explosion of Open Models in 2024
- Open models in AI have expanded swiftly, marking a significant industry trend towards collaborative and transparent development.
- A notable shift in AI research is the transformation of institutions, such as the Allen Institute, adapting to changes driven by open models.
- Staying updated with technical advancements in AI training is crucial, as evidenced by active discussions and resource sharing on platforms like Discord.
- To understand the cutting-edge developments in AI, engaging with specialized communities and resources is recommended.
6. 🔄 Open Model Challenges and Progress
6.1. Emergence of New Open Models
6.2. Comparison of 2023 and 2024 Open Models
6.3. Advantages of Open Models
6.4. Open Source and Collaboration
6.5. Open Source AI Definition
6.6. Resource Constraints and Compute Needs
6.7. Fully Open Models and Their Impact
7. 🔨 Building Fully Open Models
- Open model development has advanced, facilitating seamless integration without restarting from scratch.
- The release of Omoe, a state-of-the-art MOE model, marks a significant milestone in fully open models.
- Molmo, a multimodal model, offers a complete guide for transitioning from text-only to multimodal models.
- Molmo's methodology has been applied to Quent, Olmo, and Olmoe checkpoints, with successful replication on Mistral.
- Tulu 3 provides a framework for post-training model creation, applied effectively to Olmo, Llama, and Quen.
- Olmo 2 is recognized as the leading fully open language model, integrating insights from OmoE, Molmo, and TULU.
- Challenges such as computational limitations and scalability issues persist in the open model ecosystem.
8. 🔍 Data Access and Regulatory Issues
- Access to data for AI model training is increasingly restricted due to privacy and ethical concerns.
- Content owners are blocking AI crawlers, reacting to the rise of proprietary models like OpenAI's GPT.
- Common Crawl data shows a decline in accessible websites from 2017 to 2024, highlighting a trend towards limited open data.
- Research indicates many sites block crawlers without updating Robots.txt, complicating data access norms.
- Technologies such as Cloudflare are frequently used to block AI crawling, sometimes without content owners' explicit consent.
- These restrictions benefit established firms with data access, disadvantaging startups and new entrants.
- The shift towards proprietary data control could stifle innovation and competition in AI development.
9. 📜 Open Models and Safety Concerns
- Strong lobbying efforts are attempting to classify open source AI as a high-risk technology, focusing on sectors like healthcare, where the dangers are similar to those found in current software.
- AI2 and other organizations are actively working to ensure the safety of open models, aiming to balance accessibility with necessary safety measures.
- Research has shown that earlier concerns about bio-risks from open models were unfounded, suggesting some safety fears might be exaggerated for lobbying purposes.
- The open source community's successful opposition to California's SB 1047 legislation underscores the power of collective advocacy against potentially harmful regulations.
- Despite the challenges, there is significant interest in developing open models, with effective lobbying needed to counteract misinformation and exaggerated risk claims.
10. 💡 Incentives for Open Model Development
- Developing open models involves significant risks and costs, discouraging many from pursuing such projects.
- The ARC Prize by Francois Chollet is a notable initiative that successfully incentivizes open model development through financial rewards.
- Promoting challenges and competitions focused on open models can stimulate innovation by leveraging a multiplier effect, which encourages widespread participation.
- There is a critical need for increased and sustained financial support for research efforts dedicated to open models to ensure their long-term viability.
- Current funding trends tend to favor commercial interests, creating a disparity in support for open versus closed models.
- A major gap exists in multilingual capabilities between closed and open source models, with closed models like ChatGPT currently outperforming open models in low-resource languages.
- Efforts are underway to close this gap by 2025, highlighting a strategic focus on enhancing open models' language capabilities.
11. 🗣️ Mistral's Journey and Achievements
11.1. Mistral's Strategic Product Releases
11.2. Impactful Community Engagement
12. 🛠️ Mistral's Model Offerings
12.1. Code Model and Fine-Tuning Services
12.2. New Models Release
12.3. Collaborations and Updates
12.4. Multimodal Models
12.5. Premier Models and Licensing
12.6. Model Offerings Overview
13. 🖥️ Lachat: AI Chat Interface
13.1. Introduction to Lachat
13.2. Image Understanding and OCR
13.3. Canvas Creation and Python Execution
13.4. Web Search Capabilities
13.5. Image Generation
13.6. User Engagement and Feedback
14. 🥂 Closing Remarks and Networking
- The speaker expressed gratitude to attendees and highlighted contributions from Swix and the latent space team, suggesting a strong collaboration environment.
- Notable Capital, previously known as GGV, focuses on cloud infrastructure, data, dev tools, AI infrastructure, and applications, positioning itself as a key player in these domains.
- The company has partnered with significant industry players like HashiCorp, Rassel, and Neon, indicating successful collaborations and a strategic network.
- Notable Capital is based in San Francisco and New York, suggesting a strategic geographical presence in major tech hubs.
- The speaker invited attendees to connect on LinkedIn and potentially Instagram, promoting networking and future collaboration opportunities.
- The session concluded with an invitation to join chats with AWS, highlighting further networking and learning opportunities post-event.