Researchers find large language models process diverse types of data, like different languages, audio inputs, images, etc., similarly to how humans reason about complex problems. Like humans, LLMs integrate data inputs across modalities in a central hub that processes data in an input-type-agnostic fashion.
Researchers have unveiled a transformative framework for understanding complex systems. This pioneering study establishes the new field of higher-order topological dynamics, revealing how the hidden geometry of networks shapes everything from brain activity to the climate and artificial intelligence (AI).
A study showed that chatbots alone outperformed doctors when making nuanced clinical decisions, but when supported by artificial intelligence, doctors performed as well as the chatbots.
Researchers developed an automated system to help programmers increase the efficiency of their deep learning algorithms by simultaneously leveraging two types of redundancy in complex data structures: sparsity and symmetry.
AI agents trained in simulations that differ from the environments where they are deployed sometimes perform better than agents trained and deployed in the same environment, research shows.
In the same way that terrestrial life evolved from ocean swimmers to land walkers, soft robots are progressing, too, thanks to recent research in battery development and design.
Scientists have developed a computing chip that can learn, correct errors, and process AI tasks.
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.
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.
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.
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.
Reinforcement Learning, an artificial intelligence approach, has the potential to guide physicians in designing sequential treatment strategies for better patient outcomes but requires significant improvements before it can be applied in clinical settings, finds a new study.
A study is the first-of-its-kind to recognize American Sign Language (ASL) alphabet gestures using computer vision. Researchers developed a custom dataset of 29,820 static images of ASL hand gestures. Each image was annotated with 21 key landmarks on the hand, providing detailed spatial information about its structure and position. Combining MediaPipe and YOLOv8, a deep learning method they trained, with fine-tuning hyperparameters for the best accuracy, represents a groundbreaking and innovative approach that hasn't been explored in previous research.
The rapidly increasing aging population will lead to a shortage of care providers in the future. While robotic technologies are a potential alternative, their widespread use is limited by poor acceptance. In a new study, researchers have examined a user-centric approach to understand the factors influencing user willingness among caregivers and recipients in Japan, Ireland, and Finland. Users' perspectives can aid the development of home-care robots with better acceptance.