As Shumer told VentureBeat over DM: “I’ve been thinking about this idea for months now. LLMs hallucinate, but they can’t course-correct. What would happen if you taught an LLM how to recognize and fix its own mistakes?”

Hence the name, “Reflection” — a model that can reflect on its generated text and assess its accuracy before delivering it as outputs to the user.

The model’s advantage lies in a technique called reflection tuning, which allows it to detect errors in its own reasoning and correct them before finalizing a response.

Podcaster clones his voice, hooks it up to ChatGPT, & the bot does his phone calls & interviews.

One man secretly hands off more and more of his life to an AI voice clone.

How an alga synchronizes its two flapping cilia to propel itself is revealed in a tabletop experiment with chains of mobile robots.

The freshwater alga Chlamydomonas reinhardtii swims by flapping its two cilia in a motion akin to the breaststroke. Unlike a human, C. reinhardtii lacks a brain to coordinate its limbs. The synchronization is automatic. To uncover its origin, Mingcheng Yang of the Institute of Physics of the Chinese Academy of Sciences and his collaborators built mechanical algae whose cilia are made of chains of cockroach-sized toy robots [1]. By adjusting the cilia’s flapping frequency and other parameters, the researchers reproduced the alga’s swimming gaits and identified the conditions that favor them.

Yang’s mechanical algae each consists of a puck-like base, on the sides of which are attached two chains of four robots. Each robot’s underside bristles with elastic hairs set at an angle. When a mechanical alga is placed on a tabletop and an internal electric motor is switched on, each bristly robot vibrates vertically. On the upstroke, the hairs push the robot toward the base, setting up the possibility that the chains could buckle.