An electronic stacking technique has the potential to exponentially boost the number of transistors on chips, paving the way for more efficient AI hardware.

The electronics industry is approaching a limit to the number of transistors that can be packed onto the surface of a computer chip. So, chip manufacturers are looking to build up rather than out.

Instead of squeezing ever-smaller transistors onto a single surface, the industry is aiming to stack multiple surfaces of transistors and semiconducting elements — akin to turning a ranch house into a high-rise. Such multilayered chips could handle exponentially more data and carry out many more complex functions than today’s electronics.

While most humans can innately use their hands to communicate with others or grab and manipulate objects, many existing robotic systems only excel at simple manual tasks. In recent years, computer scientists worldwide have been developing machine learning-based models that can process images of humans completing manual tasks, using acquired information to improve robot manipulation, which could in turn enhance a robot’s interactions with both humans and objects in its surroundings.

OpenAI’s CEO Sam Altman reveals the company already has its eyes set on achieving Artificial Superintelligence (ASI). Watch to find out more!

*Correction: Joshua Achiam is not the Head of Alignment he is the Head of Mission Alignment. He says \.

The race to replace human workers continues in Big Tech, but not everyone is convinced it will happen so soon.

Specialized microchips that manage signals at the cutting edge of wireless technology are astounding works of miniaturization and engineering. They’re also difficult and expensive to design.

Now, researchers at Princeton Engineering and the Indian Institute of Technology have harnessed artificial intelligence to take a key step toward slashing the time and cost of designing new wireless chips and discovering new functionalities to meet expanding demands for better wireless speed and performance.

In a study published in Nature Communications, the researchers describe their methodology, in which an AI creates complicated electromagnetic structures and associated circuits in microchips based on the design parameters. What used to take weeks of highly skilled work can now be accomplished in hours.

Unitree Robotics introduces a revolutionary open-source data set allowing humanoid robots to move more naturally.