I am a computational biologist interested in interpretable machine learning for genomics and health care. Interpretable ...

Floods are some of the most devastating natural disasters communities in the United States face, causing billions of dollars ...

New research reveals a surprising geometric link between human and machine learning. A mathematical property called convexity ...

To flexibly and robustly handle diverse problems, AI systems can leverage dual-process theories of human cognition that ...

As a technical discipline, the field of AI began in the mid-20th century. In 1950, British mathematician Alan Turing proposed ...

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

Opinion: Lidiya Mishchenko and Pooya Shoghi explain how to bridge a gap preventing successful patent claims to protect new ...

Learn what is Linear Regression Cost Function in Machine Learning and how it is used. Linear Regression Cost function in Machine Learning is "error" representation between actual value and model ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Neural networks, machine learning? Nobel-winning AI science explained Paris (AFP) – The Nobel Prize in Physics was awarded to two scientists on Tuesday for discoveries that laid the groundwork ...

Machine learning required enormously powerful computers capable of handling vast amounts of information. It takes millions of images of dogs for these algorithms to be able to tell a dog from a cat.