The effort to "align" AI with human values is falling dangerously short in robotic systems, according to researchers from Penn Engineering, Carnegie Mellon University (CMU) and the University of Oxford. In a new paper appearing in Science Robotics, the researchers highlight the need to develop more thorough frameworks for ensuring that AI-enabled robots embody a core principle famously articulated by science fiction author Isaac Asimov: "A robot may not injure a human being."

Major AI platforms, including OpenAI and Anthropic, as well as social apps like Replika and Character.ai, are increasingly designing chatbots to be warm, friendly, and empathetic. However, new research from the Oxford Internet Institute at the University of Oxford finds that chatbots trained to sound warmer and more empathetic are significantly more likely to make factual errors and agree with false beliefs.

Due to recent developments in artificial intelligence (AI), it's now possible to digitally "revive" dead people and interact with them.

A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81%. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.

Most contemporary artificial intelligence (AI) systems learn to complete tasks via machine learning and deep learning. Machine learning is a computational approach that allows models to uncover patterns in data that are useful for making predictions. Deep learning, on the other hand, is a subset of machine learning that entails the use of multi-layered neural networks, which can autonomously extract features and learn complex patterns from unstructured data, sometimes with little or no human supervision.