Researchers from Skoltech (part of the VEB.RF group) and Central University have developed an automated coin-grading approach that minimizes the subjectivity of manual assessments and overcomes the limitations of computer vision algorithms typically used for this purpose. The new approach provides more accurate and detailed grading across a wide variety of coin types. The approach was presented in the Journal on Computing and Cultural Heritage and further refined in two subsequent studies published in Expert Systems with Applications and IEEE Transactions on Instrumentation and Measurement.Researchers from Skoltech (part of the VEB.RF group) and Central University have developed an automated coin-grading approach that minimizes the subjectivity of manual assessments and overcomes the limitations of computer vision algorithms typically used for this purpose. The new approach provides more accurate and detailed grading across a wide variety of coin types. The approach was presented in the Journal on Computing and Cultural Heritage and further refined in two subsequent studies published in Expert Systems with Applications and IEEE Transactions on Instrumentation and Measurement.[#item_full_content]

From searching disaster zones and responding to chemical spills to monitoring fragile ecosystems, future robot swarms may have to act in places where direct human control is difficult or dangerous. To operate autonomously, the robots must be able to decide together which problem to address and where to go next. But collective decision-making creates its own vulnerability: Robots improve their decisions by sharing information, yet faulty machines, inaccurate observations or manipulated messages can mislead the entire swarm.From searching disaster zones and responding to chemical spills to monitoring fragile ecosystems, future robot swarms may have to act in places where direct human control is difficult or dangerous. To operate autonomously, the robots must be able to decide together which problem to address and where to go next. But collective decision-making creates its own vulnerability: Robots improve their decisions by sharing information, yet faulty machines, inaccurate observations or manipulated messages can mislead the entire swarm.[#item_full_content]

On a sunny summer’s day, children build sandcastles on the beach. But behind every castle and crumbling dune lies a surprisingly difficult scientific problem. In fact, the physics of sand is not yet fully understood—which also means that computer graphics lacks effective ways to simulate it.On a sunny summer’s day, children build sandcastles on the beach. But behind every castle and crumbling dune lies a surprisingly difficult scientific problem. In fact, the physics of sand is not yet fully understood—which also means that computer graphics lacks effective ways to simulate it.[#item_full_content]

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.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.[#item_full_content]

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.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.[#item_full_content]

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.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.[#item_full_content]

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.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.[#item_full_content]

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.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.[#item_full_content]

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.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.[#item_full_content]

Chipmakers and operating system developers have spent years building defenses. A new study from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) shows that a key assumption behind many of them doesn’t hold.Chipmakers and operating system developers have spent years building defenses. A new study from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) shows that a key assumption behind many of them doesn’t hold.[#item_full_content]