The wide adoption of AI in biomedical research raises concerns about misuse risks. Trotsyuk, Waeiss et al. propose a framework that provides a starting point for researchers to consider how risks ...

Abstract: Despite the advancements of autonomous systems from decades of engineering, there is always the need to make them even more efficient and reliable. Machine learning holds great potential to ...

Advances in machine intelligence often depend on data assimilation, but data generation has been neglected. The authors discuss mechanisms that might achieve continuous novel data generation and the ...

Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. As an enthusiastic digital marketer who is passionate about search engine optimization (SEO) ...

This paper comprehensively surveys existing works of chip design with ML algorithms from an algorithm perspective. To accomplish this goal, the authors propose a novel and systematical taxonomy for ...

Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning ...

Intelligent organizations prioritize investments in machine learning and real-time data to improve decision making, accelerate revenue generation efforts, reduce operational expenses and protect ...

Machine learning is based on the idea that a system can learn to perform a task without being explicitly programmed. Machine learning has a wide range of applications in the finance, healthcare, ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...