This week, an intriguing competition has emerged, challenging participants to reverse their biological age—a measure that aims to better assess the health of our organs and overall vitality compared to chronological age. With around 500 entrants, including myself, we will embark on a six-month journey employing various techniques and strategies to achieve this goal. Notably, the competition features a leaderboard to track progress, raising the question: can biological age truly be measured and reversed?
The concept of biological age is gaining traction as a more accurate indicator of health than simply counting years. While there are numerous methods to potentially slow aging or even rejuvenate oneself, the validity of these approaches remains under scrutiny. In a personal experiment, I aim to explore the efficacy of these biological age measurements firsthand. For those interested in the nuances of this contest, I invite you to follow my journey as I delve into the evolving landscape of health and aging.
In a related discussion, Thore Graepel, a prominent figure in AI and machine learning, has expressed his concerns regarding the reasoning capabilities of today’s large language models (LLMs). Drawing from his experiences with AlphaGo—the groundbreaking AI that defeated Go champion Lee Sedol—Graepel argues that current AI systems lack the true reasoning abilities that once elevated AlphaGo beyond conventional algorithms. Following his departure from Google DeepMind, he advocates for a new paradigm in machine reasoning that could leverage the architectural insights gained from AlphaGo.
As we navigate these advancements in both biological age measurement and artificial intelligence, it is crucial to remain vigilant and informed. Both fields present exciting opportunities and challenges, prompting us to rethink our understanding of health and machine intelligence.
Source: The Download: a biological de-aging contest and why LLMs don’t reason via MIT Technology Review