Learn what CNN is in deep learning, how they work, and why they power modern image recognition AI and computer vision programs.

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...

Researchers propose a synergistic computational imaging framework that provides wide-field, subpixel resolution imaging ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

Organoid Intelligence (OI) represents a groundbreaking convergence of biology and technology, aiming to redefine biocomputing using brain organoids—three-dimensional neural structures derived from ...

Microscopy plays a pivotal role in modern biomedical research, enabling the visualization of fine structures in complex ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

An MIT spinoff co-founded by robotics luminary Daniela Rus aims to build general-purpose AI systems powered by a relatively new type of AI model called a liquid neural network. The spinoff, aptly ...

Artificial intelligence systems designed to physically imitate natural brains can simulate human brain activity before being trained, according to new research from Johns Hopkins University. “The work ...