neural networks

Fig. 1 Optical microscopy images of the 3D polymer wiring between a top electrode (TE) and three bottom electrodes (BEs) at the vertical distance from the surface of glass substrate z = 0 and 100 ?m. Credit: 2023 Naruki Hagiwara et al., Advanced Functional Materials
Neuromorphic wetware for artificial neural networks that overcome the limits of traditional computer hardware
Restoring damaged neurons with nano-magnets
At the intersection of engineered materials and computation, spin-glass systems comprise a disordered system of nanomagnets arising from random interactions and competition between two types of magnetic order in the material. Credit: Jenna Maria Rantala, Aalto University
A new paradigm in artificial intelligence?
Brain-inspired computers get a training algorithm that greatly boosts efficiency and complex problem-solving
Astrophysics gets a huge simulation boost: 560 hours of work with current methods now can take 36 minutes
The world’s fastest and most powerful optical neuromorphic processor for artificial intelligence operates faster than 10 trillion operations per second
Photonic networks will really speed up pattern recognition and save energy
Understanding when not to trust AI-assisted decision-making
Enabling artificial intelligence on household appliances while enhancing data security and energy efficiency
Fewer neurons equal more intelligence with new deep learning models
Do artificial brains need to sleep?
Cutting the energy required for training and running neural networks
A technique for brain training capable to induce changes of neural networks in less than one hour
Neural networks can be designed by artificial intelligence up to 200 times faster
Neuroengineers have created a system that translates thought into intelligible and recognizable speech