Connecticut-based WaveAerospace recently unveiled a multi-role unmanned multi-copter in development for the last five years.
Huntress Turbojet’s flight tests will take place in the summer and it is expected to be delivery-ready by early 2025.
The all-weather hybrid-electric drone has a top speed of Mach 0.7 (467 knots) and flight endurance of two hours.
The British Antarctic Survey (BAS) said successful tests would allow the drones to undertake research such as surveying marine ecosystems and studying glaciers, while reducing CO2 emissions by approximately 90%.
The Windracers Ultra UAV (uncrewed aerial vehicle) is a twin-engine, 10-metre aircraft that can carry up to 100kg of cargo or sensors for distances of 1,000km and does not require a human pilot to take off, fly or land as it is equipped with a sophisticated autopilot system.
Unlike piloted Twin Otter aircraft, which are costly to operate and face logistical challenges in the extreme environment, the BAS said the “groundbreaking” unmanned drones are safer and “could enable dramatic increases in flight time”
A collaborative group of researchers has potentially developed a means of controlling spin waves by creating a hexagonal pattern of copper disks on a magnetic insulator. The breakthrough is expected to lead to greater efficiency and miniaturization of communication devices in fields such as artificial intelligence and automation technology.
Details of the study were published in the journal Physical Review Applied on January 30, 2024.
In a magnetic material, the spins of electrons are aligned. When these spins undergo coordinated movement, they generate a kind of ripple in the magnetic order, dubbed spin waves. Spin waves generate little heat and offer an abundance of advantages for next-generation devices.
Basic biology textbooks will tell you that all life on Earth is built from four types of molecules: proteins, carbohydrates, lipids, and nucleic acids. And each group is vital for every living organism.
But what if humans could actually show that these “molecules of life,” such as amino acids and DNA bases, can be formed naturally in the right environment? Researchers at the University of Florida are using the HiPerGator—the fastest supercomputer in U.S. higher education—to test this experiment.
HiPerGator—with its AI models and vast capacity for graphics processing units, or GPUs (specialized processors designed to accelerate graphics renderings)—is transforming the molecular research game.
Are you curious about the future of Artificial Intelligence (AI) and how it will be impacted by Quantum Computing? Join us on an exciting journey into the world of technology as we explore how Quantum Computing is set to revolutionize AI by the year 2027. In this video, we will delve into the fascinating realm of Quantum Computing and its implications for the future of AI.
Quantum Computing, a cutting-edge field in computer science, harnesses the principles of quantum mechanics to perform computations at speeds unimaginable with traditional computers. By leveraging the power of quantum bits or qubits, Quantum Computing has the potential to exponentially increase processing power, enabling AI systems to tackle complex problems with unprecedented efficiency and accuracy. Imagine a world where AI algorithms can analyze vast amounts of data in seconds, leading to groundbreaking discoveries and innovations across various industries.
Continue reading “Quantum Computing Will Transform AI by 2027” »
Mark Zuckerberg’s company wants to take the AI fight to tech giants and has the monetary backing to fuel it for years.
Meta is willing to spent an additional $9 billion a year to train AI models that beat Google, Microsoft and integrate into existing products like Instagram.
OpenAI notes that information related to bioterrorism is easily accessible to anybody, even without AI, as there is dangerous content in online resources and databases.
Explore OpenAI’s study that reveals how AI could inadvertently assist in creating biological threats.
Photonic integrated circuits are an important next-wave technology. These sophisticated microchips hold the potential to substantially decrease costs and increase speed and efficiency for electronic devices across a wide range of application areas, including automotive technology, communications, health care, data storage, and computing for artificial intelligence.
Photonic circuits use photons, fundamental particles of light, to move, store, and access information in much the same way that conventional electronic circuits use electrons for this purpose. Photonic chips are already in use today in advanced fiber-optic communication systems, and they are being developed for implementation in a broad spectrum of near-future technologies, including light detection and ranging, or LiDAR, for autonomous vehicles; light-based sensors for medical devices; 5G and 6G communication networks; and optical and quantum computing.
Given the broad range of existing and future uses for photonic integrated circuits, access to equipment that can fabricate chip designs for study, research and industrial applications is also important. However, today’s nanofabrication facilities cost millions of dollars to construct and are well beyond the reach of many colleges, universities, and research labs.
A renowned Chinese scholar has unveiled what he’s calling the world’s first AI child — and she might bring the technology into a new age.
In a groundbreaking study published in the journal Science, researchers have developed a machine learning model that mimics the way children learn language, offering new insights into early language acquisition. Using video and audio recordings from a young child’s perspective, the model successfully learned to associate words with visual objects, a feat that sheds light on the mysterious process of how children begin to understand and use language.
Understanding how children learn language has long been a fascinating subject for scientists and educators alike. At the heart of this is the phenomenon of connecting words to their meanings – a process seemingly simple yet incredibly complex. This study sought to demystify this process using the latest advancements in artificial intelligence.
The motivation behind this research lies in the need for a deeper understanding of early language acquisition. Traditionally, studies in this field have been conducted in controlled laboratory settings, which may not accurately reflect the natural environment in which children learn language.
