Automatic speech recognition (ASR) has made incredible advances in the past few years, especially for widely spoken languages such as English. Prior to 2020, it was typically assumed that human abilities for speech recognition far exceeded automatic systems, yet some current systems have started to match human performance.

A recent study involving Central European University (CEU) is paving the way for a new field of research that merges artificial intelligence and complexity science to understand how ongoing interaction between humans and algorithms can profoundly impact social dynamics.

A team of AI researchers at Mohamed bin Zayed University of AI, in Abu Dhabi, working with a colleague from the University of Central Florida, has developed a curriculum learning–based LLM, called LlamaV-o1, that its makers claim shows the benefits of step-by-step reasoning in AI systems. In their study, published on the arXiv preprint server (and also on GitHub), the group built their LLM with a new level of step-by-step reasoning to understand how it arrives at its answers.

President Joe Biden on Tuesday signed an ambitious executive order on artificial intelligence that seeks to ensure the infrastructure needed for advanced AI operations, such as large-scale data centers and new clean power facilities, can be built quickly and at scale in the United States.

Vijay Gadepally, a senior staff member at MIT Lincoln Laboratory, leads a number of projects at the Lincoln Laboratory Supercomputing Center (LLSC) to make computing platforms, and the artificial intelligence systems that run on them, more efficient.

Years ago, as she sat in waiting rooms, Maytal Saar-Tsechansky began to wonder how people chose a good doctor when they had no way of knowing a doctor's track record on accurate diagnoses. Talking to other patients, she found they sometimes based choices on a physician's personality or even the quality of their office furniture.

A study from Oxford University has uncovered why the deep neural networks (DNNs) that power modern artificial intelligence are so effective at learning from data.

AI applications like ChatGPT are based on artificial neural networks that, in many respects, imitate the nerve cells in our brains. They are trained with vast quantities of data on high-performance computers, gobbling up massive amounts of energy in the process.

Last week the billionaire and owner of X, Elon Musk, claimed the pool of human-generated data that's used to train artificial intelligence (AI) models such as ChatGPT has run out.

Birth asphyxia (BA) is a condition that occurs when newborn babies do not receive enough oxygen during delivery, and it's one of the primary causes of neonatal death. Developing countries, particularly in sub-Saharan Africa, experience the highest under-five mortality rates. Researchers from Carnegie Mellon University are developing a new mobile application, called HumekaFL, to detect BA.

The progress of artificial intelligence (AI) has been relentless. With OpenAI's latest model, o3, recently breaking records yet again, it raises urgent questions about safety, as well as the future of humanity.

Federated learning represents a significant shift in the landscape of machine learning, enabling many clients or devices to collaboratively train models while substantially enhancing the privacy of the data involved.

The demand for electricity to power AI data centers is skyrocketing, placing immense pressure on traditional energy sources.

The process of updating deep learning/AI models when they face new tasks or must accommodate changes in data can have significant costs in terms of computational resources and energy consumption. Researchers have developed a novel method that predicts those costs, allowing users to make informed decisions about when to update AI models to improve AI sustainability. The study is published on the arXiv preprint server.

Prime Minister Keir Starmer on Monday laid out his vision to harness the power of artificial intelligence to boost Britain's flagging economy, promising "pro-growth" regulations amid concerns about the technology's unchecked use.

The language "engines" that power generative artificial intelligence (AI) are plagued by a wide range of issues that can hurt society, most notably through the spread of misinformation and discriminatory content, including racist and sexist stereotypes.

News that Dutch publishing house Veen Bosch & Keuning (VBK) has confirmed plans to experiment using AI to translate fiction has stirred up a thought-provoking debate. Some believe it marks the beginning of the end for human translators, while others see this as the opening up of a new world of possibilities to bring more literature to even more people. These arguments are becoming increasingly vocal as the advance of AI accelerates at an ever-increasing rate.

The Los Angeles Rams defense appeared to be content to drop back in coverage and prevent San Francisco's Brock Purdy from completing a Hail Mary on the final play of their Thursday night game on Dec. 12.

Today's robots perform safety checks at industrial plants, conduct quality control in manufacturing, and are even starting to keep hospital patients company.

Group activity recognition (GAR), which aims to identify activities performed collectively in videos, has gained significant attention recently. Existing GAR datasets typically annotate only a single Group Activity (GA) instance per sample, carefully selected from original videos.

A team of math and AI researchers at Microsoft Asia has designed and developed a small language model (SLM) that can be used to solve math problems. The group has posted a paper on the arXiv preprint server outlining the technology and math behind the new tool and how well it has performed on standard benchmarks.

Trying to spot contraband is a tricky business. Not only is identifying items like narcotics and counterfeit merchandise difficult, but the current most used technology—X-rays—only gives a 2D view, and often a muddy one at that.

Imagine you're filling out an online survey about the future of your city. You thoughtfully answer each question, adding your personal insights.

Researchers have been analyzing how humanoid robots relate to people and say teaching the machines how to understand emotion may be essential in getting the best from them.

Researchers at the University of California- Los Angeles (UCLA) have recently developed TeamCraft, a new open-world environment for the training and evaluation of algorithms for embodied artificial intelligence (AI) agents, including teams of multiple robots. This benchmark, introduced in a paper published on the arXiv preprint server, is based on the popular videogame Minecraft.

Artificial intelligence is moving from data centers to "the edge" as computer makers build the technology into laptops, robots, cars and more devices closer to home.

Whether on your bank's website or your telephone provider's help line, interactions between humans and chatbots have become part of our daily lives. But do we trust them? And what factors influence our trust? Researchers at the University of Basel recently examined these questions.

Filling gaps in data sets or identifying outliers—that's the domain of the machine learning algorithm TabPFN, developed by a team led by Prof. Dr. Frank Hutter from the University of Freiburg. This artificial intelligence (AI) uses learning methods inspired by large language models. TabPFN learns causal relationships from synthetic data and is therefore more likely to make correct predictions than the standard algorithms that have been used up to now.

In November 2023, the estates of two now-deceased policyholders sued the US health insurer, United Healthcare, for deploying what they allege is a flawed artificial intelligence (AI) system to systematically deny patient claims.

Whether you're describing the sound of your faulty car engine or meowing like your neighbor's cat, imitating sounds with your voice can be a helpful way to relay a concept when words don't do the trick.