Dodging potholes is a familiar routine for drivers. But, behind every bump and crack in the pavement is a bigger issue: many communities lack the tools and data they need to maintain their roads effectively. Traditional pavement assessments rely on expensive, specialized vehicles outfitted with high-resolution sensors. These resources are typically reserved for major highways and high-traffic corridors, leaving neighborhood streets and local roads under-monitored and under-maintained.

A study of Chile's workforce finds that AI could "accelerate" nearly half of the tasks performed by the country's 100 most common jobs.

Generative artificial intelligence (AI) has the potential to revolutionize fashion design. By recognizing patterns in data and generating new text and images, AI models powered by deep learning algorithms can help fashion designers develop new catalogs, expanding their creativity, with added efficiency, helping to bring products to the market faster.

Artificial intelligence is advancing at a dizzying speed. Like many new technologies, it offers significant benefits but also poses safety risks. Recognizing the potential dangers, leading researchers from Google DeepMind, OpenAI, Meta, Anthropic and a coalition of companies and nonprofit groups have come together to call for more to be done to monitor how AI systems "think."

Imagine an ant crawling in sand, tracing a path that happens to look like Winston Churchill. Would you say the ant created an image of the former British prime minister? According to the late Harvard philosopher Hilary Putnam, most people would say no: The ant would need to know about Churchill, and lines, and sand.

Despite suggestions it has been losing its edge, California is way ahead of others when it comes to the hottest technology right now: artificial intelligence.

Machine learning is an integral part of high-stakes decision-making in a broad swath of human-computer interactions. You apply for a job. You submit a loan application. Algorithms determine who advances and who is declined.

Imagine a future where artificial intelligence quietly shoulders the drudgery of software development: refactoring tangled code, migrating legacy systems, and hunting down race conditions, so that human engineers can devote themselves to architecture, design, and the genuinely novel problems still beyond a machine's reach.

Large language models (LLMs) excel at using textual reasoning to understand the context of a document and provide a logical answer about its contents. But these same LLMs often struggle to correctly answer even the simplest math problems.

Teaching a robot new skills used to require coding expertise. But a new generation of robots could potentially learn from just about anyone.

Today's robots are stuck—their bodies are usually closed systems that can neither grow nor self-repair, nor adapt to their environment. Now, scientists at Columbia University have developed robots that can physically "grow," "heal," and improve themselves by integrating material from their environment or from other robots.

Just as people from different countries speak different languages, AI models also create various internal "languages"—a unique set of tokens understood only by each model. Until recently, there was no way for models developed by different companies to communicate directly, collaborate or combine their strengths to improve performance.

Large language models (LLMs) sometimes lose confidence when answering questions and abandon correct answers, according to a new study by researchers at Google DeepMind and University College London.

As more and more sectors experiment with artificial intelligence, one of the areas that has most quickly adopted this new technology is law enforcement. It's led to some problematic growing pains, from false arrests to concerns around facial recognition.

In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial task. In this context, Korean researchers are gaining attention for newly implementing an artificial sensory nervous system that mimics the sensory nervous system of living organisms without the need for separate complex software or circuitry. This breakthrough technology is expected to be applied in fields such as in ultra-small robots and robotic prosthetics, where intelligent and energy-efficient responses to external stimuli are essential.

How do you develop an AI system that perfectly mimics the way humans speak? Researchers at Nagoya University in Japan have taken a significant step forward to achieve this. They have created J-Moshi, the first publicly available AI system specifically designed for Japanese conversational patterns.

Imagine a self-driving car navigating downtown traffic. To avoid a collision, it must judge whether the pedestrian at the corner is about to cross. Or consider an investment algorithm trading stocks—it needs to anticipate how human investors will react to news before making a move.

US President Donald Trump went to Pennsylvania on Tuesday to announce $92 billion in energy and infrastructure deals intended to meet big tech's soaring demand for electricity to fuel the AI boom.

A Stanford study captures the gap between worker desires and AI's abilities and highlights areas ripe for research and development.

Assessing the progress of new AI language models can be as challenging as training them. Stanford researchers offer a new approach.

The Pentagon announced contracts on Monday with multiple leading US artificial intelligence firms including Elon Musk's xAI, which has faced intense scrutiny in recent days over anti-Semitic posts by its Grok chatbot.

A survey of 23 machine learning (ML) practitioners across the U.K. and other parts of the world, conducted by King's College London, highlighted feelings of alienation from the sustainability of models, suggesting environmental credentials were not viewed as part of an AI's performance.

A new Cornell study has revealed that Amazon's AI shopping assistant, Rufus, gives vague or incorrect responses to users writing in some English dialects, such as African American English (AAE), especially when prompts contain typos.

Researchers at The University of Osaka have developed a novel framework for measuring occupancy in open-plan offices with unprecedented precision. This innovative system uses computer vision and AI to analyze occupancy at a micro-scale level, focusing on specific functional zones within the office.

Many years ago, long before the internet or artificial intelligence, an American engineer called Vannevar Bush was trying to solve a problem. He could see how difficult it had become for professionals to research anything, and saw the potential for a better way.

A new approach using generative AI platforms such as ChatGPT is showing promise in enhancing communication for people with language disorders.

Mexican actors protested the growing threat artificial intelligence poses to their industry, calling on Sunday for better regulations to prevent voice cloning without consent.

Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and reasoning models like ChatGPT. The principle: during a training phase, the parameters of the network's artificial neurons are optimized in such a way that they can carry out specific tasks, such as autonomously discovering objects or characteristic features in images.

When ChatGPT or Gemini give what seems to be an expert response to your burning questions, you may not realize how much information it relies on to give that reply. Like other popular generative artificial intelligence (AI) models, these chatbots rely on backbone systems called foundation models that train on billions, or even trillions, of data points.

Over the past years, computer scientists have introduced increasingly sophisticated generative AI models that can produce personalized content following specific inputs or instructions. While image generation models are now widely used, many of them are unpredictable and precisely controlling the images they create remains a challenge.