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With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
Are humans or machines better at recognizing speech? A new study shows that in noisy conditions, current automatic speech recognition (ASR) systems achieve remarkable accuracy and sometimes even surpass human performance. However, the systems need to be trained on an incredible amount of data, while humans acquire comparable skills in less time.
As the use of generative AI continues to grow, Lincoln Laboratory's Vijay Gadepally describes what researchers and consumers can do to help mitigate its environmental impact.
Imagine a future where your phone, computer or even a tiny wearable device can think and learn like the human brain -- processing information faster, smarter and using less energy. A breakthrough approach brings this vision closer to reality by electrically 'twisting' a single nanoscale ferroelectric domain wall.
AI-powered algorithm can analyze video recordings of clinical sleep tests and more accurately diagnose REM sleep behavior disorder.
Facing high employee turnover and an aging population, nursing homes have increasingly turned to robots to complete a variety of care tasks, but few researchers have explored how these technologies impact workers and the quality of care. A new study on the future of work finds that robot use is associated with increased employment and employee retention, improved productivity and a higher quality of care.
Researchers have harnessed artificial intelligence to take a key step toward slashing the time and cost of designing new wireless chips and discovering new functionalities to meet expanding demands for better wireless speed and performance.
Nvidia will launch Jetson Thor for humanoid robots in H1 2025, entering a growing market where Google is also active. The robotics sector is projected for substantial growth. Nvidia offers integrated hardware and software solutions. Simultaneously, China's rapidly developing domestic humanoid robot market presents emerging competition.
The post Nvidia Intensifies Robot Push with New Humanoid Platform as Industry Giants Eye Lucrative Future first appeared on Synced.
In the era of AI, chatbots have revolutionized how we interact with technology. Perhaps one of the most impactful uses is in the healthcare industry. Chatbots are able to deliver fast, accurate information, and help individuals more effectively manage their health. In this article, we’ll learn how to develop a medical chatbot using Gemini 2.0, […]
The post Building a Medical Chatbot with Gemini 2.0, Flask and Vector Embedding appeared first on Analytics Vidhya.
Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors. This is the result of AutoPET, an international competition in medical image analysis. The seven best autoPET teams report on how algorithms can detect tumor lesions in positron emission tomography (PET) and computed tomography (CT).
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
Associate Professor Matteo Bucci’s research sheds new light on an ancient process, to improve the efficiency of heat transfer in many industrial systems.
A research team introduces Automated Search for Artificial Life (ASAL). This novel framework leverages vision-language FMs to automate and enhance the discovery process in ALife research.
The post Automating Artificial Life Discovery: The Power of Foundation Models first appeared on Synced.
Researchers from the University of Texas at Austin and NVIDIA proposes upcycling approach, an innovative training recipe enables the development of an 8-Expert Top-2 MoE model using Llama 3-8B with less than 1% of the compute typically required for pre-training.
The post Llama 3 Meets MoE: Pioneering Low-Cost High-Performance AI first appeared on Synced.
Bio-inspired wind sensing using strain sensors on flexible wings could revolutionize robotic flight control strategy. Researchers have developed a method to detect wind direction with 99% accuracy using seven strain gauges on the flapping wing and a convolutional neural network model. This breakthrough, inspired by natural strain receptors in birds and insects, opens up new possibilities for improving the control and adaptability of flapping-wing aerial robots in varying wind conditions.
A DeepMind research team introduces JetFormer, a Transformer designed to directly model raw data. This model maximizes the likelihood of raw data without depending on any pre-trained components, and is capable of both understanding and generating text and images seamlessly.
The post DeepMind’s JetFormer: Unified Multimodal Models Without Modelling Constraints first appeared on Synced.
An NVIDIA research team proposes the normalized Transformer, which consolidates key findings in Transformer research under a unified framework, offering faster learning and reduced training steps—by factors ranging from 4 to 20 depending on sequence length.
The post NVIDIA’s nGPT: Revolutionizing Transformers with Hypersphere Representation first appeared on Synced.
Even highly realistic androids can cause unease when their facial expressions lack emotional consistency. Traditionally, a 'patchwork method' has been used for facial movements, but it comes with practical limitations. A team developed a new technology using 'waveform movements' to create real-time, complex expressions without unnatural transitions. This system reflects internal states, enhancing emotional communication between robots and humans, potentially making androids feel more humanlike.
Corvus Robotics, founded by Mohammed Kabir ’21, is using drones that can navigate in GPS-denied environments to expedite inventory management.
Artificial intelligence that is as intelligent as humans may become possible thanks to psychological learning models, combined with certain types of AI.
Researchers developed a laser-based artificial neuron that fully emulates the functions, dynamics and information processing of a biological graded neuron, which could lead to new breakthroughs in advanced computing. With a processing speed a billion times faster than nature, chip-based laser neuron could help advance AI tasks such as pattern recognition and sequence prediction.
MIT engineers developed AI frameworks to identify evidence-driven hypotheses that could advance biologically inspired materials.
An electronic stacking technique could exponentially increase the number of transistors on chips, enabling more efficient AI hardware.
A research team at Meta introduces the Large Concept Model (LCM), a novel architecture that processes input at a higher semantic level. This shift allows the LCM to achieve remarkable zero-shot generalization across languages, outperforming existing LLMs of comparable size.
The post From Token to Conceptual: Meta introduces Large Concept Models in Multilingual AI first appeared on Synced.
Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations
Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations
Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations
Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations
Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations