Recently, researchers introduced a new representation learning framework that integrates causal inference with graph neural networks—CauSkelNet, which can be used to model the causal relationships and ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

A new AI Drug Discovery tool called PDGrapher can predict more accurate drug combinations up to 25 times faster than previous ...

The core of quantum network research lies in efficiently and reliably establishing entanglement between nodes; however, the challenges of maintaining fragile quantum states are far more complex than ...

Firms that fail to shine light on their dark data risk ceding the high ground in insights and inviting risk exposures lurking ...

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...

Google DeepMind and Intrinsic developed AI that uses graph neural networks and reinforcement learning to automate multi-robot ...

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

Scientists at UCL, Google DeepMind and Intrinsic have developed a powerful new AI algorithm that enables large sets of ...

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 ...