Police departments around the world are increasingly using body-worn cameras in an attempt to improve public trust and accountability. But this has created huge amounts of data, about 95% of which is never reviewed or even seen.

"We have entered the Intelligence Age," proclaimed Sam Altman, the chief executive of OpenAI, in September. "Deep learning worked," he explained, and this breakthrough in learning from data will unleash a smart era in which the more data becomes available, "the better it gets at helping people solve hard problems."

Large language models are everywhere, including running in the background of the apps on the device you're using to read this. The auto-complete suggestions in your texts and emails, the query responses composed by Gemni, Copilot and ChatGPT, and the images generated from DALL-E are all built using LLMs.

Road surface information plays an essential role across multiple sectors, influencing everything from transportation safety to economic growth and environmental sustainability. Knowing whether a road is paved or unpaved can impact decision-making for route planning and emergency responses.

Researchers at the National Institute of Standards and Technology (NIST) have developed a way to use sound to detect when lithium-ion batteries are about to catch fire. The NIST team included Wai Cheong "Andy" Tam and Anthony Putorti.

A former CEO and executive chairman of Google, recently suggested that the tech giant's apparent lag in AI development was due to the company prioritizing employees' personal well-being over progress. Eric Schmidt told an audience: "Google decided that work-life balance and going home early and working from home was more important than winning."

The rigid structures of language we once clung to with certainty are cracking. Take gender, nationality or religion: these concepts no longer sit comfortably in the stiff linguistic boxes of the last century. Simultaneously, the rise of AI presses upon us the need to understand how words relate to meaning and reasoning.

Imagine this: You're at an office job, wearing noise-canceling headphones to dampen the ambient chatter. A co-worker arrives at your desk and asks a question, but rather than needing to remove the headphones and say, "What?", you hear the question clearly.

Scientists from the University of Surrey are teaching artificial intelligence models to re-light different body types and clothing without the need for 3D modeling. This may have a positive impact on the film, television and gaming industries in the near future.

What happens when the technology meant to protect marginalized voices ends up silencing them? Rebecca Dorn, a research assistant at USC Viterbi's Information Sciences Institute (ISI) has uncovered how large language models (LLMs) that are used to moderate online content are failing queer communities by misinterpreting their language.

Technology firms are ceaselessly promoting new AI products, but climate activist Sage Lenier says AI is useless, unsustainable and has given the industry an existential problem.

As the International Rescue Committee copes with dramatic increases in displaced people in recent years, the refugee aid organization has looked for efficiencies wherever it can—including using artificial intelligence.

Think back to a time when you needed a quick answer, maybe for a recipe or a DIY project. A few years ago, most people's first instinct was to "Google it." Today, however, many people are more likely to reach for ChatGPT, OpenAI's conversational AI, which is changing the way people look for information.

With artificial intelligence increasingly permeating every aspect of our lives, experts are becoming more and more concerned about its dangers. In some cases, the risks are pressing, in others they won't emerge until many months or even years from now.

Imagine using artificial intelligence to compare two seemingly unrelated creations—biological tissue and Beethoven's "Symphony No. 9." At first glance, a living system and a musical masterpiece might appear to have no connection. However, a novel AI method developed by Markus J. Buehler, the McAfee Professor of Engineering and professor of civil and environmental engineering and mechanical engineering at MIT, bridges this gap, uncovering shared patterns of complexity and order.

A team of roboticists and engineers at MIT CSAIL, Institute for AI and Fundamental Interactions, has developed a generative AI approach to teaching robots how to traverse terrain and move around objects in the real world.

A perspective suggests that "digital twins" are not simply tools for science but are an example of the integration of complexity science and data science into a new scientific field. A "digital twin" is a digital representation of a real-world object or system.

Meta will make its generative artificial intelligence (AI) models available to the United States' government, the tech giant has announced, in a controversial move that raises a moral dilemma for everyone who uses the software.

In the race to develop robust perception systems for robots, one persistent challenge has been operating in bad weather and harsh conditions. For example, traditional, light-based vision sensors such as cameras or LiDAR (Light Detection And Ranging) fail in heavy smoke and fog.

Researchers have trained an e-nose, which detects chemicals in the air, to identify the origin of oil based on the proportion of volatile organic compounds. Since oil is a mixture of different hydrocarbons present in varying proportions depending on its source, the e-nose could be used to locate soil-contaminating oil spills, monitor the environment at refineries, and conduct oil field studies.

The field of AI is advancing rapidly, with ongoing improvements in AI models and sensors presenting exciting prospects for enhancing manufacturing and design processes. This expansion of AI has the potential to greatly improve how we approach conception, creation and building, resulting in innovative and efficient developments in these areas. As AI techniques mature and become more accessible and widespread, its impact is likely to grow.

A robot, trained for the first time by watching videos of seasoned surgeons, executed the same surgical procedures as skillfully as the human doctors.

Anyone following the rhetoric around artificial intelligence in recent years has heard one version or another of the claim that AI is inevitable. Common themes are that AI is already here, it is indispensable, and people who are bearish on it harm themselves.

The Vatican and Microsoft on Monday unveiled a digital twin of St. Peter's Basilica that uses artificial intelligence to explore one of the world's most important monument's while helping the Holy See manage visitor flows and identify conservation problems.

When you have a conversation today, notice the natural points when the exchange leaves open the opportunity for the other person to chime in. If their timing is off, they might be taken as overly aggressive, too timid, or just plain awkward.

Robots are supposed to do boring or unpleasant jobs for us. However, tedious tasks such as cleaning the bathroom are challenging to automate. How is it possible to calculate the movement of a robot arm so that it can reach every part of a washbasin? What if the basin has unusually curved edges? How much force should be applied at which point?

Many artists and creative professionals are concerned that artificial intelligence (AI) will take over more and more tasks. For instance, several major players have already used AI images in advertising campaigns, instead of hiring illustrators.

Affective computing, a field focused on understanding and emulating human emotions, has seen significant advancements thanks to deep learning. However, researchers at the Technical University of Munich caution that an over-reliance on deep learning may hinder progress by overlooking other emerging trends in artificial intelligence.

A recent study by researchers from CSIRO and the University of Melbourne has made progress in quantum machine learning, a field aimed at achieving quantum advantage to outperform classical machine learning.

Two of the trickiest qualities to balance in the world of machine learning are fairness and accuracy. Algorithms optimized for accuracy may unintentionally perpetuate bias against specific groups, while those prioritizing fairness may compromise accuracy by misclassifying some data points.