The COVID-19 pandemic changed the way many people approached their work across the globe. For Carnegie Mellon University Africa's Jema Ndibwile, it changed the way he viewed a completely different type of virus: a computer virus.
In the animal kingdom, energy conservation is essential for survival. That's why our brains have evolved to be extremely energy- and resource-efficient at storing and processing information. Since the 1980s, computational scientists have tried to mimic brain structure and function in the hopes of achieving such efficient, fast processing of complex data.
Experts have shown that the most powerful security defenses in Windows 11 can be dismantled by an attacker who never touches the target computer.
AI chatbots powered by large language models (LLMs) are widely used as recommender systems. While accelerating access to information, AI can also give factually incorrect responses or propagate societal biases.
Next-generation science experiments will collect more data than ever—so much so that they'll surpass the capabilities of current data storage and analysis methods. To help, researchers at the Department of Energy's SLAC National Accelerator Laboratory developed an AI method to compress large amounts of raw data without losing subtle details critical to scientific discovery. They published the work in Nature Machine Intelligence.
For decades, chess has served as a laboratory for studying intelligence and decision-making. It is well established that today's chess engines can outperform even the strongest grandmasters. But far less is understood about how their play actually differs.
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 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 trail of likes, shares and downloads we leave across the internet could help predict successful innovations years in advance, Cornell researchers showed by curating two datasets that allowed them to compare early engagement with future impact.
Artificial intelligence is proving to be transformative in its ability to work with language and images. Now, with a growing push to apply AI to scientific discovery, Caltech's Anima Anandkumar says there is a crucial ingredient missing from most AI models: the ability to understand the physical world. Take, for example, weather models, says Anandkumar, Caltech's Bren Professor of Computing and Mathematical Sciences. If you want an AI model to predict weather, it must understand chaotic physical systems, like how the atmosphere changes around the planet and over time.
Among the many predictions about the future of artificial intelligence is that models will one day be able to conduct scientific research on their own, leaving humans out of the equation. Already, they can write code, run experiments and search scientific literature, but carrying out open-ended research would require a significant leap in ability.
Large language models (LLMs), the computational models that underpin conversational agents such as Gemini and ChatGPT, are now widely used by people worldwide to rapidly find information, summarize documents and generate texts for specific purposes. Some computer scientists are now combining two or more of these models to create multi-agent systems, which prompt multiple artificial intelligence (AI) agents to interact, cooperate and/or compete with the goal of completing specific tasks.
Many software tools, including compilers, optimizers and synthesizers, have a common task at their core. They must transform long, complicated programs into simplified equivalents. These scaled-down programs run faster and on a wider range of hardware but must deliver the same results as their original versions.
In recent years, computer scientists have developed a wide range of artificial intelligence (AI) models that can rapidly recognize patterns in data, generate content and solve other computational problems. Many of these AI systems are based on deep neural networks (DNNs), brain-inspired computational models that can make predictions based on specific data, or LLMs, models that can process human language, answer queries and generate text.
Artificial intelligence can generate videos so realistic that distinguishing them from authentic footage is becoming increasingly difficult. But a computer science team led by researchers at UC Riverside has developed a tool that moves beyond simply identifying whether a video is fake. It also determines which AI system created it.
---- End of list Tech Xplore Computer Science News Articles on this page 2 of 2 total pages ----