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NVIDIA, TSMC Develop Advanced Silicon Photonic Chip Prototype, Says Report

NVIDIA and TSMC have developed a silicon photonics-based chip prototype, according to a report in the Taiwanese press. TSMC is the world’s leading contract chip manufacturer, and with Intel’s troubles, it has also established itself as the most advanced chip manufacturer on the planet. Silicon photonics is an emerging chip manufacturing technology that blends photonic circuits with traditional circuits to overcome physical limitations with semiconductor fabrication. According to the report, the prototype was developed late last year, with NVIDIA and TSMC also working on optical packaging technologies to improve AI chip performance.

NVIDIA & TSMC Are Working On Advanced Packaging Technologies, Says Report

TSMC’s latest chip manufacturing technology, the 2-nanometer node, is believed to have a minimum gate and metal pitches of 45 and 20 nanometers, respectively. In semiconductor fabrication, a gate pitch measures the distance between two gates on a chip, while a metal pitch measures the distance between two metal interconnects. A gate controls the flow of electrons on a transistor, while an interconnect ensures inter-transistor communication on a chip.

Scientists develop technology to control cyborg insect swarms

Scientists have developed an advanced swarm navigation algorithm for cyborg insects that prevents them from becoming stuck while navigating challenging terrain.

Published in Nature Communications, the new algorithm represents a significant advance in . It could pave the way for applications in , search-and-rescue missions, and infrastructure inspection.

Cyborg insects are real insects equipped with tiny electronic devices on their backs—consisting of various sensors like optical and infrared cameras, a battery, and an antenna for communication—that allow their movements to be remotely controlled for specific tasks.

Sam Altman’s STUNNING New Statement “EVERYTHING is About to Change”

The latest AI News. Learn about LLMs, Gen AI and get ready for the rollout of AGI. Wes Roth covers the latest happenings in the world of OpenAI, Google, Anthropic, NVIDIA and Open Source AI.

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00:00 singularity is near.
01:37 Sam Altman’s Blog Post \

SpaceX to attempt first payload deployment, engine reuse during Starship Flight 7

Get ready to be inspired by Camp Peavy, a well-known figure in the robotics industry and a passionate member of the HomeBrew Robotics Club in Silicon Valley! 🤖✨

In this engaging Q\&A, Camp dives into his incredible robotics journey, offering unique insights into the world of innovation and technology. He also shares invaluable tips for those who are new to robotics, feeling unsure, or looking for a spark of motivation to dive into this exciting field.

Learn about the amazing work happening at the HomeBrew Robotics Club and discover how you, too, can become part of this thriving community. Whether you’re a seasoned techie or just starting out, this video has something for everyone.

Sign Up for Page Launch: https://forms.gle/ijzxGKNJUj5dCLnL7

HomeBrew Robotics Website: https://www.hbrobotics.org/

Camp Peavy’s Homepage: https://camppeavy.com/

Sam Altman expects first AI workers this year; OpenAI closer to AGI

OpenAI CEO Sam Altman said the first artificial intelligence agents might enter the workforce this year as his company inches closer to developing humanlike artificial general intelligence (AGI).

“We believe that, in 2025, we may see the first AI agents ‘join the workforce’ and materially change the output of companies,” said Altman in a blog post titled “Reflections” on Jan. 6.

AI agents or agentic AI refers to artificial intelligence systems that exhibit autonomous decision-making and goal-directed behavior. They can autonomously understand complex goals, make decisions, take actions with minimal human intervention and execute multi-step reasoning processes.

A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models. A Multi-Objective Framework for Balancing Fairne

Machine learning algorithms significantly impact decision-making in high-stakes domains, necessitating a balance between fairness and accuracy. This study introduces an in-processing, multi-objective framework that leverages the Reject Option Classification (ROC) algorithm to simultaneously optimize fairness and accuracy while safeguarding protected attributes such as age and gender.

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