EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
MIT Technology Review·Aug 10·5 min read
These startups are chasing the next big thing in LLMs
TL;DR
Startups are exploring the future of large language models (LLMs) as the limitations of transformer technology become apparent. Innovations are emerging to address these flaws and enhance model capabilities.
✦ Why It Matters
Explore new LLM architectures to stay ahead in AI development and application.
Key Takeaways
- Investigate sparse attention mechanisms for efficient LLMs.
- Consider power retention for managing large data contexts.
- Explore liquid neural networks for smaller, adaptable models.
- Experiment with diffusion techniques for faster text generation.
How It Works
Subquadratic's sparse attention mechanism selectively processes word pairings, reducing computations while maintaining meaning. Manifest AI's power retention summarizes relevant information dynamically, allowing models to manage context without overwhelming data.