EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
MIT Technology Review·Aug 10·5 min read
AI for science needs reasoning, not just data
TL;DR
Demis Hassabis and John Jumper from Google DeepMind received a Nobel Prize for AlphaFold, a neural network that predicts protein structures. However, the success of AlphaFold highlights the challenges of applying similar AI methods across other scientific fields due to data limitations.
✦ Why It Matters
Evaluate your data collection methods to ensure they meet the standards needed for AI applications.
Key Takeaways
- Investigate AI agents for enhancing research methodologies.
- Utilize large language models to synthesize diverse scientific data.
- Focus on reproducibility by adopting AI tools that log processes.
- Encourage collaboration to build comprehensive datasets in your field.
How It Works
AI agents function as reasoning engines that utilize various digital and physical tools to mimic the human scientific process. They can autonomously generate hypotheses, evaluate them, and refine results based on iterative testing, significantly reducing the time required for traditional research.