For more than a decade, scientists have described neural networks that could run on quantum computers. The promise was clear: quantum bits can exist in combinations of states and can share linked relationships that ordinary computers cannot copy easily. Those features might let a quantum network solve certain pattern problems more effectively than any classical system. Until recently almost every discussion of the idea stayed on paper or on ordinary computers that only simulated quantum behav
machine learning (4)
With everything turning digital, Cyber Security threats have been growing each day as the attack surface is massive and continuing to grow and evolve rapidly. In response to this unprecedented challenge, Artificial Intelligence (AI) based tools for cyber security have emerged to help information security teams reduce breach risk and improve their security posture efficiently and effectively.
See: https://redskyalliance.org/xindustry/ai-s-impact-on-cyber
AI is helping firms to become more resil
As automotive engineering progresses and moves away from traditional mechanism, the integration of Artificial Intelligence (AI) and Machine Learning (ML) is not just a futuristic possibility; it is increasingly becoming a reality. These technologies are not only reshaping how vehicles operate but are also enhancing safety, efficiency, and performance.
Dr. Vish Vadari, Senior Technical Specialist Noise, Vibration and Harshness (Global) at ZF Group, explains the potential of AI and ML in optimizi
Microsoft, in collaboration with MITRE, IBM, NVIDIA, and Bosch, has released a new open framework that aims to help security analysts detect, respond to, and remediate adversarial attacks against machine learning (ML) systems. Called the Adversarial ML Threat Matrix, the initiative is an attempt to organize the different techniques employed by malicious adversaries in subverting ML systems.
Just as artificial intelligence (AI) and ML are being deployed in a wide variety of novel applications, t
