Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data.

Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could.

Amid all the hype and hysteria about ChatGPT, Bard, and other generative large language models (LLMs), it’s worth taking a step back to look at the gamut of AI algorithms and their uses. After ...

Neuton is a neural network framework, which Bell Integrator claims is far more effective than any other framework and non-neural algorithm available on the market.

Artificial neural networks, the underlying structure of deep learning algorithms, roughly mimic the physical structure of the human brain.

The algorithm taps into these human capabilities via “an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality.

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

If successful, DeepMind's goal to bridge deep learning and classical computer science could revolutionize AI and software as we know them.

Discover how Google's neural matching algorithm might affect your website and why understanding your customers' journey is the best way to move forward.