Read more about how machine learning and deep learning differ, where each is used, and how businesses choose between them in ...

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...

In machine learning, privacy risks often emerge from inference-based attacks. Model inversion techniques can reconstruct sensitive training data from model outputs. Membership inference attacks allow ...

Real-world deployments show 40% test cycle efficiency improvement, 50% faster regression testing, and 36% infrastructure cost savings.

Umbrella or sun cap? Buy or sell stocks? When it comes to questions like these, many people today rely on AI-supported recommendations. Chatbots such as ChatGPT, AI-driven weather forecasts, and ...

For at least a decade, much of the must-have cybersecurity tools available have been powered by machine learning, predictive analytics, and pattern recognition—subsets of the broader bucket of ...

The FBI is scaling AI in areas that generate investigative leverage, particularly biometrics and data triage, while ...

Some school district IT teams have been experimenting with using generative AI tools for cybersecurity, for example to ...

A new publication DOI 10.29026/oet.2025.250010 discusses how a photonic vibration perception system achieves stable ...

What originated as a narrow procurement notice has grown into a broader effort that links machine-learning models with ...