EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
MIT Technology Review·Aug 11·5 min read
The Download: the next big thing in LLMs and how AI academic research is shifting
TL;DR
Startups are exploring innovative approaches to large language models (LLMs) nine years after the introduction of transformers, aiming to redefine AI capabilities. This shift in AI academic research is poised to unlock new applications and enhance existing technologies.
✦ Why It Matters
Evaluate your current AI tools and consider integrating emerging LLM technologies to stay competitive.
Key Takeaways
- Investigate new LLM architectures to improve efficiency.
- Engage with funding initiatives like AI2050 for research support.
- Stay updated on AI developments to leverage new technologies.
- Collaborate with other researchers to enhance project outcomes.
Full Summary
Transformers, introduced by Google researchers nine years ago, are the backbone of current large language models (LLMs). However, as LLMs grow, transformers are becoming a bottleneck due to their dense attention mechanism, which is costly and struggles to manage extensive information.
Researchers are exploring four innovative approaches to enhance LLM efficiency and intelligence. Concurrently, AI academics are facing new challenges in research dynamics, particularly as funding initiatives like the Schmidt Sciences AI2050 program emerge, aimed at supporting AI research.
This program highlights the need for collaboration among leading AI researchers to adapt to the evolving landscape of academic research and funding. The implications of these developments could significantly shape the future of AI technology and its applications.