Articles tagged with Artificial Neural Networks
Insects can perform numerical estimation and compare sets of objects, recognizing larger quantities. Researchers found a simple model for neural networks to learn numerical cognition tasks using a single neuron.
A novel nanoelectronics device has enabled brain neurons and artificial neurons to communicate with each other over the internet. This breakthrough study shows how three key emerging technologies can work together: brain-computer interfaces, artificial neural networks and advanced memory technologies.
A new artificial neural network model has been developed to solve inverse problems, demonstrating accuracy comparable to the maximum entropy (MaxEnt) approach. The model's versatility and robustness against noisy data have been showcased in various tests, including recovering electron single-particle spectral densities.
Researchers at Otto-von-Guericke-Universität Magdeburg are using brain research methods to analyze artificial neural networks and improve explainable AI. The Cognitive neuroscience inspired techniques project aims to understand the internal processes of ANNs and identify malfunctions.
Researchers at Politecnico di Milano developed a novel circuit that can execute advanced AI operations in one operation, reducing energy consumption and paving the way for more sustainable AI computing accelerators. This breakthrough enables faster and more efficient training of neural networks, crucial for applications like facial rec...
Researchers have developed a custom artificial neural network that can analyze molecular signals controlling gene function, enabling biologists to understand complex mechanisms of gene regulation. This breakthrough enables the creation of machine learning algorithms that reflect common concepts in biology.
Researchers at the University of Pittsburgh have developed an artificial synapse that mimics the human brain's ability to create neuronal connections. This breakthrough technology could revolutionize AI and cognitive computing, enabling faster and more efficient processing of complex tasks.
Researchers developed a novel computational approach using deep artificial neural networks to predict neural responses to images. The study found that certain stimuli, such as checkerboards or sharp corners, elicit strong responses from neurons, contradicting current dogma in the field.
Researchers trained neural networks to predict plant growth patterns using computer vision algorithms and efficient graphics processing units. The system uses Raspberry Pi with Intel Movidius graphics card to calculate and predict the optimal ratio of nutrients, enabling continuous monitoring and prediction in artificial growing systems.
A new study reveals that human brains process faces in a similar way to artificial intelligence systems, with unique activation patterns playing a key role in recognition. The researchers found parallels between the human visual system and deep neural networks, which can improve face recognition capabilities.
Artificial neural networks successfully identify minor changes in DNA structure caused by UV radiation, enabling early detection of potential cancer risks. The technique uses surface-enhanced Raman spectroscopy and has the potential to be used for medical diagnostics.
Researchers at the University of Delaware are developing new memory devices that can support neural networks in low-power embedded systems. These advancements aim to improve the lifetime and reliability of IoT devices, which currently struggle with battery power and memory constraints.
Researchers use deep neural networks to simulate light-induced molecular reactions on long time scales, accelerating computation by up to 19 years. This method enables better understanding of biological processes like carcinogenesis and ageing, with potential applications in material ageing and photosensitive drugs.
Bengio will discuss recent successes and limitations of deep learning, as well as research directions for building human-level AI. The event is part of the Heidelberg Laureate Forum, a networking event bringing together young researchers and award recipients.
A two-layer all-optical artificial neural network has been successfully demonstrated for complex classification tasks, outperforming computer-based neural networks. The researchers plan to expand this approach to large-scale optical deep neural networks for specific practical applications.
Researchers believe that studying animal brains can improve AI's ability to tackle complex tasks like dish-washing. By understanding how biological neural networks work, AI systems may be able to overcome barriers and achieve superhuman performance.
Researchers have developed an effective method to monitor carbon nanotube films using artificial neural networks (ANN). The technique can help predict the efficiency of single-walled carbon nanotubes synthesis and improve the overall production framework, leading to new horizons for real-life applications.
Researchers employed machine learning to analyze images of quantum systems and identify the most predictive theory. The study used artificial neural networks to distinguish between competing theories, selecting the one that best described observed phenomena in high-temperature superconductors.
Scientists from Russia and Greece have successfully implemented a spiking neural network based on memristors, demonstrating the feasibility of local learning rules. The research enables autonomous unsupervised learning of complex neural networks, paving the way for new applications in AI.
Scientists have made a breakthrough in understanding the behavior of strongly interacting electrons using machine learning techniques, discovering a new state called Vestigial Nematic State. The technique uses artificial neural networks to recognize different forms of electronic matter and reveals symmetries of complex image-arrays fro...
Researchers controlled specific neurons in macaques' brains with images generated by artificial neural networks, demonstrating a new tool for neuroscientists to design experiments. This breakthrough uses current computer vision applications to predict and control visually evoked neural responses in primate brains.
A team of scientists at MIT developed a neural network that can read scientific papers and generate a plain-English summary. The system, called RUM, uses vectors rotating in multidimensional space to represent words and improve memory and recall capabilities.
Researchers developed Bright-field Holography to overcome limitations of holographic 3D imaging. The method combines the image contrast advantage of bright-field microscopy with the snapshot volumetric imaging capability of holography, allowing for rapid creation of images equivalent to those from a bright-field microscope.
A team at NYU Tandon School of Engineering has designed an artificial neural network approach that can predict the elastic modulus of graphene-enhanced composites from just one sample, streamlining materials testing. This reduces the need for extensive experimentation, lowering costs and accelerating product development.
Researchers used artificial neural networks to learn atomic interactions from quantum mechanics, bypassing complex calculations. The ANN was used as a surrogate model to account for errors and improve predictions.
A team of researchers developed a neuroinspired hardware-software co-design approach that can make neural network training more energy-efficient and faster. The approach uses a type of energy-efficient neural network called spiking neural networks, combined with the soft-pruning algorithm to minimize computing power and time.
Researchers at NYU Tandon School of Engineering have developed a machine learning system that pairs artificial neural networks with infrared imaging to control and interpret small-scale chemical reactions. This technique can reduce the decision-making process from one year to weeks, saving tons of chemical waste and energy.
Researchers at Osaka University have developed an AI-based system to track individual fluorescently labeled molecules in living cells. The system can analyze hundreds of thousands of molecules in a short period, providing reliable data on molecule status and dynamics.
A study by Cameron Buckner using deep neural networks suggests that human knowledge stems from sensory experience, a school of thought known as empiricism. The networks demonstrate how abstract knowledge is acquired and can be used to understand complex tasks in neuroscience and psychology.
Researchers use artificial neural networks to predict crystal stability in garnets and perovskites, achieving accuracy up to 10 times that of previous models. The team's web application allows for fast computation of material properties on various devices.
Researchers at MIT develop Temporal Relation Network (TRN) module to help CNNs recognize activities by observing key frames. The module achieves top accuracy of 95% in activity recognition on Jester dataset, outperforming existing models.
Using deep learning algorithms, researchers have developed a system that forecasts aftershocks significantly better than random assignment. By analyzing earthquake data and physics-based models, they identified the second invariant of the deviatoric stress tensor as an important factor in predicting aftershock locations.
Researchers use deep neural networks to recognize images transmitted over optical fibers, achieving high accuracy despite distortions caused by environmental factors. The technique has potential for improving endoscopic imaging in medical diagnosis and increasing the information-carrying capacity of fiber-optic telecommunication networks.
The UCLA-developed artificial neural network device can analyze large volumes of data and identify objects at the speed of light. It uses diffraction of light to process images without advanced computing programs or energy consumption.
A new 3D-printed optical deep learning network called Diffractive Deep Neural Network (D2NN) has been developed by Xing Lin and colleagues. This system processes information through layers of optically diffractive surfaces that work together to recognize handwritten digits with high accuracy.
Researchers developed an artificial synapse inspired by the human brain, which efficiently processes information and demonstrates excellent energy efficiency. This breakthrough could lead to the development of energy-efficient neuromorphic computing, revolutionizing AI devices and transforming industries.
Artificial neural networks can now be trained directly on an optical chip, paving the way for less expensive, faster, and more energy-efficient AI. This breakthrough enables complex tasks like speech or image recognition to be performed more efficiently.
A new approach uses machine learning to generate computer-generated X-rays to supplement real images, increasing the size of training sets for AI systems. This method improves classification accuracy for common and rare conditions by up to 40%, overcoming a challenge in applying artificial intelligence to medicine.
Researchers at Caltech developed an artificial neural network made of DNA that can accurately identify handwritten numbers. The network, designed by Kevin Cherry, uses a 'winner take all' competitive strategy and undergoes complex reactions to classify molecular information.
Scientists developed an AI program to categorize volcanic ash particle shapes, helping provide information on eruptions and aid hazard mitigation. The program achieved a success rate of 92% in classifying basal shapes with probability ratios.
A team at Plymouth University used artificial neural networks to classify five planets as potentially habitable, estimating a probability of life in each case. The technique may aid in selecting targets for future space missions with improved spectral detail.
Researchers developed a visible neural network, DCell, that uses real-world cellular behaviors and constraints to predict cellular growth. The system can simulate cellular growth nearly as accurately as a real cell grown in a laboratory.
Researchers at MIT have designed an artificial synapse that can precisely control the strength of an electric current flowing across it, similar to the way ions flow between neurons. The team found that their chip and its synapses could recognize samples of handwriting with 95% accuracy.
Researchers at the University of Michigan have created a new type of neural network made with memristors that can dramatically improve the efficiency of teaching machines to think like humans. The system, called reservoir computing, uses fewer nodes and requires less training time than traditional neural networks.
Researchers developed a neural network-based model to assess coastal communities' resiliency to hurricanes. The model forecasts storms in terms of impacts, rather than just wind speed, and has been tested during real-time storms, including Hurricane Harvey.
Researchers at Lobachevsky University are creating a neurochip that can transmit signals to healthy brain cells, potentially replacing damaged areas. The team aims to develop an artificial neural network of 100 nerve cells within three years.
A new algorithm, developed by a team at the University of Maryland, uses artificial neural networks to address multiple flaws in a single image. The algorithm can be trained on high-quality images and then applied to any image with imperfections.
Researchers at Lobachevsky University are developing a neural network prototype based on memristors that can analyze and classify living cell culture dynamics. The project aims to create compact electronic devices that function as part of bio-like neural networks in conjunction with living biological cultures.
Researchers developed an AI-based approach to extract lung nodules from chest CTs, improving diagnosis accuracy and reducing false positives. The method can be integrated into existing CADe systems and accommodate new data streams, potentially increasing the five-year survival rate for lung cancer patients.
The University of Texas at San Antonio is developing an artificial neural network called NFrame to monitor and detect 'bad behavior' in computer systems. The system will learn normal behaviors and flag anomalies, allowing it to predict potential issues and prevent security breaches.
Scientists at Stanford University and Sandia National Laboratories have developed an artificial synapse that mimics the human brain's efficient processing. This innovation could lead to the creation of more brain-like computers that can interpret visual and auditory signals with improved accuracy.
Researchers at Binghamton University developed a new multilevel input layer artificial neural network to predict flight delays. The model outperformed traditional networks in terms of accuracy and training time, predicting delay lengths with about 20% more accuracy than traditional models.
Researchers at the University of Southampton have demonstrated a nanoscale device called a memristor to power artificial neural networks, enabling efficient learning and pattern recognition. The study showcases the potential of memristive synapses in compact volumes and low energy costs.
A research group has made progress in obtaining bio-oils and raw materials from biomass using its patented reactor. Artificial neural networks are being used to calculate the gross calorific value of biomass, which is essential for designing and improving biomass pyrolysis, gasification, and combustion systems.
Researchers used a new artificial neural network method to simulate the atomic interactions of water molecules, explaining its melting temperature and density maximum. The study provides insights into the unusual properties of water, which cannot be understood solely on the basis of its chemical composition.
POSTECH researchers developed an organic nanofiber-based artificial synapse that emulates both important functions and energy consumption of biological synapses. The device enables high memory density and low energy consumption, potentially leading to advancements in AI computing and neuromorphic electronics.
New research reveals that hierarchy in biological networks arises due to cost constraints on connections, leading to more efficient networks. This finding may accelerate the development of complex computational brains in AI and robotics.
Researchers developed AI software to teach a quadrocopter to autonomously recognize and follow forest trails. The drone was able to find the correct direction in 85% of cases, outperforming humans who guessed correctly 82% of the time. This breakthrough enables drones to complement rescue teams and accelerate searches for missing people.
Darwin is a neuromorphic hardware co-processor developed by Chinese researchers that enables efficient execution of Spiking Neural Networks in resource-constrained embedded systems. It supports flexible configuration and has potential applications in intelligent hardware systems, robotics, brain-computer interfaces, and more.
PhD student Elise Hampton uses AI to analyze thousands of galaxy spectra, identifying the most turbulent and messy galaxies. Her goal is to understand how galaxies form, live, and die, and how different processes compete in these galactic systems.