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 ...

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 ...

Artificial Intelligence (AI) has long ceased to be the stuff of science fiction and is now deeply embedded in our daily lives. While it's essential to understand AI's incredible capabilities, it's ...

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 ...

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

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...

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 ...

As an enthusiastic digital marketer who is passionate about search engine optimization (SEO) and machine learning, I've continued my education with some awesome artificial intelligence-related ...

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests. Systems controlled by next-generation computing ...