An AI researcher going back to school for immunology
Category: robotics/AI – Page 3
ML Systems Textbook
Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML) systems. This textbook bridges the gap between theoretical foundations and practical engineering, emphasizing the systems perspective required to build effective AI solutions. Unlike resources that focus primarily on algorithms and model architectures, this book highlights the broader context in which ML systems operate, including data engineering, model optimization, hardware-aware training, and inference acceleration. Readers will develop the ability to reason about ML system architectures and apply enduring engineering principles for building flexible, efficient, and robust machine learning systems.
Hyundai joins US’ 11 gigawatt nuclear reactor project in Texas
Hyundai Engineering & Construction (Hyundai E&C) has taken a major step in expanding its global nuclear energy footprint.
On October 26, the company announced that it signed a basic design contract with Fermi America, a U.S.-based energy development firm, for the construction of four large nuclear reactors in Texas. The project will form part of what is expected to be the world’s largest integrated energy and artificial intelligence (AI) campus.
Teen builds advanced robotic hand from LEGO parts
A talented teenager from the UK has built a four-fingered robotic hand from standard Lego parts that performs almost as well as research-grade robotic hands. The anthropomorphic device can grasp, move and hold objects with remarkable versatility and human-like adaptability.
Jared Lepora, a 16-year-old student at Bristol Grammar School, began working on the hand a couple of years ago with his father, who works at the University of Bristol. Called the Educational SoftHand-A, it is made entirely of LEGO MINDSTORMS components and is designed to mimic the shape and function of the human hand. The only non-LEGO parts are the cords that act as tendons.
The hand’s four fingers (an index, middle, pinkie and opposing thumb) and twelve joints (three on each finger) are driven by two motors that control two sets of tendons. One tendon opens the hand while the other closes it, similar to the push-pull system of our own muscles.
Beyond electronics: Optical system performs feature extraction with unprecedented low latency
Many modern artificial intelligence (AI) applications, such as surgical robotics and real-time financial trading, depend on the ability to quickly extract key features from streams of raw data. This process is currently bottlenecked by traditional digital processors. The physical limits of conventional electronics prevent the reduction in latency and the gains in throughput required in emerging data-intensive services.
The answer to this might lie in harnessing the power of light. Optical computing—or using light to perform demanding computations—has the potential to greatly accelerate feature extraction. In particular, optical diffraction operators, which are plate-like structures that perform calculations as light propagates through them, are highly promising due to their energy efficiency and capacity for parallel processing.
However, pushing these systems to operating speeds beyond 10 GHz in practice remains a technical challenge. This is mainly due to the difficulty of maintaining the stable, coherent light needed for optical computations.
Unified memristor-ferroelectric memory developed for energy-efficient training of AI systems
Over the past decades, electronics engineers have developed a wide range of memory devices that can safely and efficiently store increasing amounts of data. However, the different types of devices developed to date come with their own trade-offs, which pose limits on their overall performance and restrict their possible applications.
Researchers at Université Grenoble Alpes (CEA-Leti, CEA List), Université de Bordeaux (CNRS) and Université Paris-Saclay (CNRS) recently developed a new memory device that combines two complementary components typically used individually, known as memristors and ferroelectric capacitors (FeCAPs). This unified memristor-ferroelectric memory, presented in a paper published in Nature Electronics, could be particularly promising for running artificial intelligence (AI) systems that autonomously learn to make increasingly accurate predictions.
“The ‘ideal’ memory would be high-density, non-volatile, capable of non-destructive readout, and offer virtually infinite endurance,” Elisa Vianello, senior author of the paper, told Tech Xplore.
Supercomputer-developed AI learns the intricate language of biomolecules
Scientists at the University of Glasgow have harnessed a powerful supercomputer, normally used by astronomers and physicists to study the universe, to develop a new machine learning model which can help translate the language of proteins.
In a new study published in Nature Communications, the cross-disciplinary team developed a large language model (LLM), called PLM-Interact, to better understand protein interactions, and even predict which mutations will impact how these crucial molecules “talk” to one another.
Early tests of PLM-interact, a protein language model (PLM), show that it outperforms competing models in understanding and predicting how proteins interact with one another. The team’s research demonstrates PLM-interact could help us better understand key areas of medical science, including the development of diseases such as cancer and virus infection.
Quantum Echo: Nobel Prize in Physics Goes to Quantum Computer Trio (Two from Google) Who Broke Through Walls Forty Years Ago
Editor’s Note: EDRM is proud to publish Ralph Losey’s advocacy and analysis. The opinions and positions are Ralph Losey’s copyrighted work. All images in the article are by Ralph Losey using AI. This article is published here with permission.]
The Nobel Prize in Physics was just awarded to quantum physics pioneers John Clarke, Michel H. Devoret, and John M. Martinis for discoveries they made at UC Berkeley in the 1980s. They proved that quantum tunneling, where subatomic particles can break through seemingly impenetrable barriers, can also occur in the macroscopic world of electrical circuits. So yes, Schrödinger’s cat really could die.