While most humans can innately use their hands to communicate with others or grab and manipulate objects, many existing robotic systems only excel at simple manual tasks. In recent years, computer scientists worldwide have been developing machine learning-based models that can process images of humans completing manual tasks, using acquired information to improve robot manipulation, which could in turn enhance a robot's interactions with both humans and objects in its surroundings.

In the grand scheme of things, 45 years is not a long time. Back in 1980, it would take me three weeks to run a computer program, written in the programming language Cobol, that worked using punched cards. Each card represented one line of code and it required multiple re-punches to correct errors. Eventually, I would have a working program that did something useful.

If 2023 was a year of wonder about artificial intelligence, 2024 was the year to try to get that wonder to do something useful without breaking the bank.

Researchers have developed a new radar signal–processing technology that can dramatically enhance the resolution of existing low-resolution radars. This technology enables precise object recognition using existing hardware specifications without the need for bandwidth expansion.

As artificial intelligence continues to grow at a rapid pace, more and more businesses are grappling with how to adapt both quickly and responsibly.

Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed machine learning-based methods to predict material properties even with limited data. This can aid in the discovery of materials with desired properties, such as semiconductors.

Dr. Chi-Young Jung's research team from the Hydrogen Research & Demonstration Center at the Korea Institute of Energy Research (KIER) has successfully developed a method to analyze the microstructure of carbon fiber paper, a key material in hydrogen fuel cells, at a speed 100 times faster than existing methods. This was achieved by utilizing digital twin technology and artificial intelligence (AI) learning.

The near future could see AI assistants that forecast and influence our decision-making at an early stage, and sell these developing "intentions" in real-time to companies that can meet the need—before we even realize we have made up our minds.

Federated Learning (FL) has gained significant attention as a novel distributed machine learning paradigm that enables collaborative model training while preserving data privacy. However, traditional FL methods face challenges such as data heterogeneity, system heterogeneity, and labeled data scarcity.

Artificial intelligence (AI) and AI-enabled robots are becoming a bigger part of our daily lives. Real-time, flexible interactions between humans and robots are no longer just science fiction. As robots become smarter and more human-like in both behavior and appearance, they are transforming from mere tools to potential partners and social entities.

ChatGPT-maker OpenAI laid out plans Friday for a new corporate structure shifting away from control by a non-profit—an issue of contention for the artificial intelligence trailblazer.

I research the intersection of artificial intelligence, natural language processing and human reasoning as the director of the Advancing Human and Machine Reasoning lab at the University of South Florida. I am also commercializing this research in an AI startup that provides a vulnerability scanner for language models.

You're running late at the airport and need to urgently access your account, only to be greeted by one of those frustrating tests—"Select all images with traffic lights" or "Type the letters you see in this box." You squint, you guess, but somehow you're wrong. You complete another test but still the site isn't satisfied.

A new artificial intelligence (AI) model has just achieved human-level results on a test designed to measure "general intelligence."

Billionaire tech mogul Elon Musk's startup xAI said it had raised $6 billion from investors in its latest funding round, as it battles stiff competition in the artificial intelligence market.

Here's one definition of science: it's essentially an iterative process of building models with ever-greater explanatory power.

The emergence of generative artificial intelligence tools that allow people to efficiently produce novel and detailed online reviews with almost no work has put merchants, service providers and consumers in uncharted territory, watchdog groups and researchers say.

The field of machine learning is traditionally divided into two main categories: "supervised" and "unsupervised" learning. In supervised learning, algorithms are trained on labeled data, where each input is paired with its corresponding output, providing the algorithm with clear guidance. In contrast, unsupervised learning relies solely on input data, requiring the algorithm to uncover patterns or structures without any labeled outputs.

I'm a computer scientist and a bad Christmas shopper. Over the weekend, I wondered whether AI systems might be able to help me out.

Artificial intelligence (AI) has already become an invisible but indispensable collaborator in our lives. It helps filter spam from your inbox, improves your Netflix recommendations, and, as an automotive copilot, suggests optimal routes, monitors blind spots, and assists with parking.

Drug trafficking and other criminal activities on social media have become a growing social concern. To evade detection by law enforcement and automated monitoring systems, offenders use dark jargons, often combining multiple common words to form "dark jargon"—covert dark jargons that are difficult to identify. Once authorities recognize these dark jargons, offenders quickly switch to new ones, making it a constant race to keep up with the evolving words.

The analysis of a person's individual walking pattern, or gait, can reveal details about their identity and reflect differences between individuals, groups and even populations.

A recent perspective published Nov. 13 in Intelligent Computing, asserts that today's artificial intelligence systems have finally realized Alan Turing's vision from over 70 years ago: machines that can genuinely learn from experience and engage in human-like conversation.

Italy's data protection watchdog said Friday it has fined OpenAI 15 million euros ($15.6 million) after wrapping up a probe into the collection of personal data by the U.S. artificial intelligence company's popular chatbot ChatGPT.

As artificial intelligence (AI) becomes more prevalent, a new Rutgers University-New Brunswick survey sheds light on public attitudes, revealing widespread concerns about its impact on politics and the media, alongside an increasing adoption of AI tools in daily life.

An international team of scientists has used machine learning to help them develop perovskite solar cells with near-record efficiency. In their paper published in the journal Science, the group describes how they used the machine-learning algorithm to help them find new hole-transporting materials to improve the efficiency of perovskite solar cells.

New research from EPFL demonstrates that even the most recent large language models (LLMs), despite undergoing safety training, remain vulnerable to simple input manipulations that can cause them to behave in unintended or harmful ways.

Artificial intelligence has the potential to improve doctors' ability to diagnose and treat sleep apnea. But the technology is not widely adopted due to fears that it does not safeguard patient data.

Johns Hopkins computer scientists have created an artificial intelligence system capable of "imagining" its surroundings without having to physically explore them, bringing AI closer to humanlike reasoning.

Crafting a unique and promising research hypothesis is a fundamental skill for any scientist. It can also be time consuming: New Ph.D. candidates might spend the first year of their program trying to decide exactly what to explore in their experiments. What if artificial intelligence could help?