Synchron’s electrodes are delivered via blood vessel.
Until now, only about 50 humans have ever had BCIs implanted in their brains.
A tool made by grad students is the ultimate “geoguesser,” using its knowledge of Google Street View to track down any photo’s location.
A new ISO standard aims to provide an overarching framework for the responsible development of AI.
The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) have approved a new international standard, ISO/IEC 42001. This standard is designed to help organizations develop and use AI systems responsibly.
ISO/IEC 42,001 is the world’s first standard for AI management systems and is intended to provide useful guidance in a rapidly evolving technology area. It addresses various challenges posed by AI, such as ethical considerations, transparency and continuous learning. For organizations, the standard is intended to provide a structured way to balance the risks and opportunities associated with AI.
The CHOOSE system, an innovative approach combining brain organoids and genetics, transforms autism research by allowing detailed analysis of mutations and their effects on brain development.
Does the human brain have an Achilles heel that ultimately leads to Autism? With a revolutionizing novel system that combines brain organoid technology and intricate genetics, researchers can now comprehensively test the effect of multiple mutations in parallel and at a single-cell level within human brain organoids.
This technology, developed by researchers from the Knoblich group at the Institute of Molecular Biotechnology (IMBA) of the Austrian Academy of Sciences and the Treutlein group at ETH Zurich, permits the identification of vulnerable cell types and gene regulatory networks that underlie autism spectrum disorders. This innovative method offers unparalleled insight into one of the most complex disorders that challenge the human brain with implications that bring autism clinical research much-needed hope.
Laser retroreflector arrays (LRAs) are advancing GPS satellite capabilities, crucial for accurate Earth measurements in geodesy. This technology enables precise tracking of Earth’s shape, rotation, and environmental changes.
The best known use of GPS satellites is to help people know their location whether driving a car, navigating a ship or plane, or trekking across remote territory. Another important, but lesser-known, use is to distribute information to other Earth-viewing satellites to help them pinpoint measurements of our planet.
NASA and several other federal agencies, including the U.S. Space Force, U.S. Space Command, the U.S. Naval Research Laboratory, and the National Geospatial-Intelligence Agency are improving the location accuracy of these measurements down to the millimeter with a new set of laser retroreflector arrays, or LRAs.
A new paper in the journal Genome Biology and Evolution, published by Oxford University Press, finds that genetic material from Neanderthal ancestors may have contributed to the propensity of some people today to be “early risers,” the sort of people who are more comfortable getting up and going to bed earlier.
Human Evolution and Genetic Adaptation
All anatomically modern humans trace their origin to Africa around 300 thousand years ago, where environmental factors shaped many of their biological features. Approximately seventy thousand years ago, the ancestors of modern Eurasian humans began to migrate out to Eurasia, where they encountered diverse new environments, including higher latitudes with greater seasonal variation in daylight and temperature.
Advanced infrared mirrors enhance climate and biofuel research via precision trace gas sensing.
An international team of researchers from the United States, Austria, and Switzerland has demonstrated the first true supermirrors in the mid-infrared spectral region. These mirrors are key for many applications, such as optical spectroscopy for environmental sensing, as well as laser cutting and welding for manufacturing.
Achieving Near-Perfect Reflectivity
Harvard’s breakthrough in quantum computing features a new logical quantum processor with 48 logical qubits, enabling large-scale algorithm execution on an error-corrected system. This development, led by Mikhail Lukin, represents a major advance towards practical, fault-tolerant quantum computers.
In quantum computing, a quantum bit or “qubit” is one unit of information, just like a binary bit in classical computing. For more than two decades, physicists and engineers have shown the world that quantum computing is, in principle, possible by manipulating quantum particles – be they atoms, ions or photons – to create physical qubits.
But successfully exploiting the weirdness of quantum mechanics for computation is more complicated than simply amassing a large-enough number of physical qubits, which are inherently unstable and prone to collapse out of their quantum states.
In a new study published in The Journal of Finance and Data Science, a researcher from the International School of Business at HAN University of Applied Sciences in the Netherlands introduced the topological tail dependence theory—a new methodology for predicting stock market volatility in times of turbulence.
“The research bridges the gap between the abstract field of topology and the practical world of finance. What’s truly exciting is that this merger has provided us with a powerful tool to better understand and predict stock market behavior during turbulent times,” said Hugo Gobato Souto, sole author of the study.
The release of Transformers has marked a significant advancement in the field of Artificial Intelligence (AI) and neural network topologies. Understanding the workings of these complex neural network architectures requires an understanding of transformers. What distinguishes transformers from conventional architectures is the concept of self-attention, which describes a transformer model’s capacity to focus on distinct segments of the input sequence during prediction. Self-attention greatly enhances the performance of transformers in real-world applications, including computer vision and Natural Language Processing (NLP).
In a recent study, researchers have provided a mathematical model that can be used to perceive Transformers as particle systems in interaction. The mathematical framework offers a methodical way to analyze Transformers’ internal operations. In an interacting particle system, the behavior of the individual particles influences that of the other parts, resulting in a complex network of interconnected systems.
The study explores the finding that Transformers can be thought of as flow maps on the space of probability measures. In this sense, transformers generate a mean-field interacting particle system in which every particle, called a token, follows the vector field flow defined by the empirical measure of all particles. The continuity equation governs the evolution of the empirical measure, and the long-term behavior of this system, which is typified by particle clustering, becomes an object of study.
