Korean researchers develop AI chip to alter mobile device images
Professor Yoo Hoi-jun and his research team with the Korea Advanced Institute of Science and Technology (KAIST) have created a…
neural networks
BrainChip showcases Akida neural processing capabilities, opens developer environment
AI and machine learning application developer BrainChip has unveiled its latest technology at a top industry event in California and…
BrainChip patents dynamic neural network model enabling edge biometrics and AI applications
BrainChip has been granted United States Patent number 10,410,117 for the dynamic neural networks which are a valuable feature of…
UK academics introduce improved neural network for highly precise facial recognition
Researchers from the University of Surrey’s Centre for Vision, Speech, and Signal Processing have been working on a more precise…
Ceva launches AI processor architecture and API for edge computer vision applications
Wireless connectivity and smart sensing technology provider Ceva has introduced its second-generation AI processor architecture, NeuPro-S, to support deep neural…
DarwinAI and Intel generate neural network with 16X faster image classification inferencing speed
Artificial intelligence startup DarwinAI has announced that its Generative Synthesis platform has been used with Intel technology and optimizations to…
Intel and Baidu collaboration on neural network platforms could boost biometric performance
Intel and Baidu have formed a partnership to collaborate on hardware and software platforms for artificial intelligence, which could improve…
Researchers develop AI method for movement identification and tracking without facial recognition
A team of Portuguese researchers have developed a way to identify and track individual animals with artificial intelligence but without…
Neurotechnology launches robotic navigation SDK
Neurotechnology has released the new SentiBotics Navigation Software Development Kit to give researchers and engineers the tools to develop pathway…
Facial recognition 20 times more accurate with advances in convolutional neural networks, NIST finds
Facial recognition algorithms can identify matches with error rates as low as 0.2 percent given good quality photos, 20 times…
