MIT researchers have developed a method that generates more accurate uncertainty measures for certain types of estimation.

Researchers have determined how to build reliable machine learning models that can understand complex equations in real-world situations while using far less training data than is normally expected.

Ben Khalesi covers the intersection of artificial intelligence and everyday tech at Android Police. With a background in AI and data science, he enjoys making technical topics approachable for those ...

MIT researchers are pioneering a novel approach to train machine learning models by utilizing synthetic imagery, surpassing the efficacy of traditional methods relying on real images. The key to this ...

From large language models to brain-machine interfaces, students work with faculty on cutting-edge research. UT College of Natural Sciences undergraduates Prasann Singhal, Nihita Sarma, Shankar ...

Antigen presentation by major histocompatibility complex (MHC) proteins is the caller ID of the immune system. On the cell surface, MHCs display peptides derived from cellular components or foreign ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes. Machine learning technology ...

In a new study published in Nature titled, “Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning,” researchers from Massachusetts General Hospital (MGH) and Harvard Medical ...

Booming interest in artificial intelligence (AI) and machine learning (ML) has led to a shortage of hardware resources and exorbitant cloud service costs, but decentralized infrastructure could ...