Institutions should embed an AI use framework into syllabi, program handbooks, IRB protocols, and dissertation templates.

Without a proper data foundation, even the most advanced AI tools will fail to deliver consistent, predictable results, writes Dun & Bradstreet’s David Marshall.

Abstract: In response to the escalating threat of fake news on social media, this systematic literature review analyzes the recent advancements in machine learning and deep learning approaches for ...

Lemon Learning announces its acquisition of Aidaxis to expand desktop application coverage and strengthen its leadership in ...

MSP360 Backup is a powerful Time Machine alternative for advanced Mac users, offering scripting, strong encryption, and broad cloud support – but there's a steep learning curve.

The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a strictly ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

Density Functional Theory (DFT) is the most widely used electronic structure method for predicting the properties of molecules and materials. Although DFT is, in principle, an exact reformulation of ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.