Data science and machine learning features: Notebooks and Graph Neural Networks GQL still has some way to go. Standardization efforts are always complicated , and adoption is not guaranteed across ...

This article explores what knowledge graphs are, why they are becoming a favourable data storage format, and discusses their potential to improve artificial intelligence and machine learning ...

Combining graphs and machine learning has been getting a lot of attention lately, especially since the work published by researchers from DeepMind, Google Brain, MIT, and the University of Edinburgh.

As machine learning becomes more pervasive in the data center and the cloud there will be a need to share and aggregate information and knowledge but without exposing or moving the underlying data.

Swami Sivasubramanian, vice president of data and machine learning at AWS, announced the new tool onstage at the AI keynote at re:Invent, saying that the cloud giant wanted to create something ...

TensorFlow 2.0, released in October 2019, revamped the framework significantly based on user feedback. The result is a machine learning framework that is easier to work with—for example, by ...

Machine learning typically requires tons of examples. To get an AI model to recognize a horse, you need to show it thousands of images of horses. This is what makes the technology computationally ...

New features in graphics – Stata 19 offers new graphs, including heat maps, bar plots with confidence intervals, and range and point plots with spikes or capped spikes.

Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language ...