news 2024

Machine learning-equipped camera systems can be an effective and low-cost flood defence tool, researchers show Smart CCTV systems trained to spot blockages in urban waterways could become an important future tool in flood prevention, new research published today has found. Researchers at the University of Bath have shown that their AI-enabled detection software, 'AI on The River' trained to accurately detect natural debris, litter or waste blocking trash screens mounted in culverts, can be integrated to existing CCTV systems to provide an early warning of likely flooding.

Dr. Barbara Gravel of the AI Laboratory at the Vrije Universiteit Brussel (VUB) and the Interuniversity Institute of Bioinformatics at the Université Libre de Bruxelles (ULB), has developed innovative bioinformatics techniques that shed new light on the genetic causes of complex diseases. Her PhD research focused on understanding how combinations of genetic variants, rather than single mutations, contribute to conditions such as cardiac disorders, hearing loss, visual impairment, and male infertility.

University of Waterloo engineers are turning to augmented/virtual reality (AR/VR) to better understand - and maintain - the physical reality of Canada's critical infrastructure.

EPFL professor's passion for sustainable flying and expertise in machine learning and computer vision drives innovation in green aviation design and beyond. Aerodynamic shape optimization (ASO) is a key technique in aerodynamic design aimed at enhancing an object's physical performance while adhering to specific constraints.

Integrating post-quantum security algorithms into hardware was previously considered a challenge. A research team at Graz University of Technology has developed standardized hardware with additional security measures. They are not yet a reality, but in the not too distant future, sophisticated, powerful quantum computers will be available.

An AI research collaboration led by EPFL professor Alexander Mathis creates a model which provides deep insights into hand movement, which is an essential step for the development of neuroprosthetics and rehabilitation technologies.

MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy. Imagine you're tasked with sending a team of football players onto a field to assess the condition of the grass (a likely task for them, of course). If you pick their positions randomly, they might cluster together in some areas while completely neglecting others.

Many areas in North Rhine-Westphalia are paved over by impervious surfaces such as roads, housing estates and industrial sites. While this is obvious from aerial photographs, it is difficult to analyze. In accordance with the German Sustainability Strategy, new impervious surfaces are to be limited to less than 30 hectares per day nationwide.

Help in the search for missing persons: New JKU method enables three-dimensional perception of drone images in real time. This works even with strong occlusion and at long distances. Human visual depth perception, i.e. the ability to perceive objects at different distances, is essentially based on the fact that our eyes see slightly different images in terms of perspective.

New dataset of "illusory" faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces. In 1994, Florida jewelry designer Diana Duyser discovered what she believed to be the Virgin Mary's image in a grilled cheese sandwich, which she preserved and later auctioned for $28,000.

Will AI soon surpass the human brain? If you ask employees at OpenAI, Google DeepMind and other large tech companies, it is inevitable. However, researchers at Radboud University and other institutes show new proof that those claims are overblown and unlikely to ever come to fruition. Their findings are published in Computational Brain & Behavior today.

The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model. Deep-learning models are being used in many fields, from health care diagnostics to financial forecasting. However, these models are so computationally intensive that they require the use of powerful cloud-based servers.

Using smartly trained neural networks, researchers at TU Graz have succeeded in generating precise real-time images of the beating heart from just a few MRI measurement data. Other MRI applications can also be accelerated using this procedure. Medical imaging using magnetic resonance imaging (MRI) is very time-consuming since an image has to be compiled from data from many individual measurements.

Quantum information is fragile and often difficult to protect during experiments. Protecting qubits from accidental measurements is essential for controlled quantum operations, especially during state-destroying measurements or resets on adjacent qubits in protocols like quantum error correction. Current methods to preserve atomic qubits against disturbances can waste coherence time, extra qubits, and introduce errors.

MIT researchers speed up a novel AI-based estimator for medication manufacturing by 60 times. The pharmaceutical manufacturing industry has long struggled with the issue of monitoring the characteristics of a drying mixture, a critical step in producing medication and chemical compounds. At present, there are two noninvasive characterization approaches that are typically used: A sample is either imaged and individual particles are counted, or researchers use a scattered light to estimate the particle size distribution (PSD).

Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos. A new study from researchers at MIT and Penn State University reveals that if large language models were to be used in home surveillance, they could recommend calling the police even when surveillance videos show no criminal activity.

UdeM literature professor Marcello Vitali-Rosati looks at how, for better or worse, computerized large language models are changing how we write - and what we think about it. Computerized large language models (LLMs) are making inroads into the realm of literature. Their ability to generate coherent texts and mimic all manner of writing styles has sparked lively debate among writers, literary theorists and researchers.

Researchers have found that AI large language models, like GPT-4, are better at predicting what comes next than what came before in a sentence. This "Arrow of Time" effect could reshape our understanding of the structure of natural language, and the way these models understand it. Large language models (LLMs) such as GPT-4 have become indispensable for tasks like text generation, coding, operating chatbots, translation and others.

Deepfake videos generated by artificial intelligence grow increasingly difficult to identify as false, a challenge that could significantly skew the results of the upcoming presidential election. Laurie Heller , Carnegie Mellon University professor of psychology, collaborated with Hafsa Ouajdi, Oussama Hadder, Modan Tailleur and Mathieu Lagrange of  École Centrale Nantes to analyze the errors made by the first deep neural network detector the research team developed to automatically classify environmental sounds as either real or AI-generated.

Researchers have developed a wearable 'smart choker' that uses a combination of flexible electronics and artificial intelligence techniques to allow people with speech impairments to communicate by detecting tiny movements in the throat. The smart choker, developed by researchers at the University of Cambridge, incorporates electronic sensors in a soft, stretchable fabric, and is comfortable to wear.