“Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM) promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory”

– A compute-in-memory chip based on resistive random-access memory. [Paper PDF]

The front runners for fusion power.

History of Chile’s Project Cybersyn, an early internet. [Part 1] [Part 2] [Part 3]

“We present a novel approach, developed in partnership with Everyday Robots, that leverages advanced language model knowledge to enable a physical agent, such as a robot, to follow high-level textual instructions for physically-grounded tasks, while grounding the language model in tasks that are feasible within a specific real-world context. We evaluate our method, which we call PaLM-SayCan, by placing robots in a real kitchen setting and giving them tasks expressed in natural language.”

– Google AI: Using language models for robots.

NeRF in the Dark: A ML model that can see in the dark. [Paper PDF] [Two Minute Papers summary]

Myth of the Norden Bombsight. [YouTube]