AI Signals From Tomorrow
Signals from Tomorrow is a podcast channel designed for curious minds eager to explore the frontiers of artificial intelligence. The format is a conversation between Voyager and Zaura discussing a specific scientific paper or a set of them, sometime in a short format and sometime as a deep dive.
Each episode delivers clear, thought-provoking insights into how AI is shaping our world—without the jargon. From everyday impacts to philosophical dilemmas and future possibilities, AI Signals from Tomorrow bridges the gap between cutting-edge research and real-world understanding.
Whether you're a tech enthusiast, a concerned citizen, or simply fascinated by the future, this podcast offers accessible deep dives into topics like machine learning, ethics, automation, creativity, and the evolving role of humans in an AI-driven age.
Join Voyager and Zaura as they decode the AI signals pointing toward tomorrow—and what they mean for us today.
AI Signals From Tomorrow
The Illusion of Thinking
This Apple paper (https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf) examines the reasoning capabilities of Large Reasoning Models (LRMs) compared to standard Large Language Models (LLMs) by testing them on controlled puzzle environments. The researchers found that LRM performance collapses entirely beyond a certain complexity, and surprisingly, their reasoning effort decreases as problems become too difficult. The study reveals three complexity regimes: standard LLMs perform better on low complexity, LRMs are advantageous at medium complexity, and both fail at high complexity. Analysis of intermediate "thinking" steps shows LRMs can exhibit "overthinking" on simple tasks and inconsistent reasoning across different puzzles. The findings suggest current LRMs may have fundamental limitations in generalizable reasoning and exact computation.