EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
MIT Technology Review·10d ago·5 min read
The Download: a biological de-aging contest and why LLMs don’t reason
TL;DR
A new competition challenges participants to reverse their biological age over six months, using various health metrics to track progress. With around 500 competitors, the contest aims to explore the feasibility of measuring biological youth.
✦ Why It Matters
Consider tracking your own biological age to explore personal health optimization strategies.
Key Takeaways
- Explore biological age metrics for health tracking.
- Investigate new methods for reversing biological age.
- Understand the limitations of current AI reasoning.
- Consider fresh approaches to machine learning architectures.
How It Works
Participants in the biological age competition will use various health metrics and interventions to attempt to lower their biological age, which is assessed through specific biological markers rather than just chronological age.