Australia's growing mountain of solar panel waste could become far more valuable, with University of Newcastle researchers taking a major step toward making solar panel recycling economically sustainable. The team has successfully scaled up a process that recovers valuable silver from end-of-life solar panels, demonstrating almost 100% silver recovery during the world's first continuous pilot-scale flotation trial for solar-panel recycling.
One of the most promising technologies is perovskite solar cells. They are highly efficient at converting sunlight into electricity, can be manufactured as thin and flexible devices, and require less energy and lower production costs than conventional silicon modules. However, their commercial adoption has long been limited by a fundamental challenge—insufficient long-term stability when exposed to moisture, heat and oxygen.
Artificial intelligence models are jacks of many trades, including writing, generating images, and creating 3D models. But they aren't as helpful when it comes to testing robots or designs for vehicles in diverse environments, since they don't understand physics as well as they do pixels or text.
The widespread use of conversational platforms such as ChatGPT and Gemini is raising important new ethical, anthropological and legal questions regarding the potential risks, misuses and shortcomings of artificial intelligence (AI). In discussing their personal experiences, some people have shared that they formed emotional attachments to AI agents or even felt that they had formed romantic relationships with them.
Most batteries spend more than 70% of their lifetime at rest. Yet few studies have examined calendar aging, which is how a battery degrades when it is neither charging nor being used.
Researchers at Aalto University, together with international partners, have developed the most accurate model yet of how humans read. The new model uses reinforcement learning, a type of AI used in robotics, to explain—and recreate—the choices readers make as they move through text.
The multiplication of matrices and higher-dimensional arrays called tensors lies at the heart of modern computing. Matrix or tensor multiplication is, in fact, the most common operation carried out in artificial intelligence applications, as well as in scientific simulations and computer graphics. High-performance computers, especially those equipped with graphics processing units (GPUs), are designed to work with so-called dense matrices or tensors—those that have relatively few zero elements.
Meta Platforms on Monday announced the release of a new artificial intelligence model that's open for other software developers to use as CEO Mark Zuckerberg warned of the risks if control of advanced AI is concentrated under a select few companies, institutions or governments.
A new study by University of Mannheim researchers Gunther Glenk, Philip Holler and Stefan Reichelstein examines how the assessment of carbon emissions affects incentives for producing electrolytic hydrogen. They found that even stringent accounting rules generally provide sufficient investment incentives. However, they do not necessarily guarantee low-carbon hydrogen. The study is published in the journal Nature Communications.
The ability to change focus instantly is something most people take for granted. Every day, our eyes effortlessly switch between reading a book, recognizing a face across the room or watching a bird fly overhead. Replicating that remarkable technological flexibility, however, has proved far more difficult.
In the last decade, scientists have shown that fusion energy can work as a physical process. Next question: Can it work economically? A study co-authored by MIT professors Dennis Whyte and Andrew W. Lo proposes a framework for understanding what's needed to make fusion energy commercially viable in the marketplace. The method considers the physical inputs needed to sustain controlled fusion energy production, as well as the cost of building power plants that can compete in energy markets.
While asking questions to ChatGPT and similar tools to generate images has become part of daily life, behind this convenience lies the massive power consumption of huge data centers.
A new technology has been developed that enables pretrained AI models to be expanded into larger models with specialized expert modules without training the models from scratch.
A team of researchers is harnessing artificial intelligence (AI), data science and advanced mathematics to better predict how fires behave in modern homes, work that could ultimately help save lives during emergencies.
Why do we breeze through some sentences in a book or article but have to reread others to comprehend their meaning? A team of linguists and data scientists has found a partial answer in AI—some of this processing parallels that of neural-network-based large language models (LLMs). However, other aspects of why we read this way cannot be explained by these technologies, revealing where human and AI language processing diverge and maintaining the mystery of some stages of the reading process.
Artificial intelligence users are shopping around as U.S. giants face fierce competition from Chinese rivals—and in Beijing's tech district, drinkers at an AI-themed bar can even plug in to DeepSeek for free.
Large language models (LLMs), the models that underpin platforms such as ChatGPT and Gemini, are now used daily by more than a billion people worldwide. Some computer scientists have also been combining several of these models to create multi-agent systems (MAS), in which many LLM-based agents work together to tackle complex problems.
A teenager joins an online video chat. Within seconds, artificial intelligence analyzes their face and gives them a score out of 10 for "attractiveness." They are compared in real time with another user who scores higher and is declared the winner. The game is live-streamed, and the audience ridicules the loser in the chat.
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