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]

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.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.[#item_full_content]

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.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.[#item_full_content]

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.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.[#item_full_content]

Artificial neural networks were originally inspired by the human brain, but they are still far less efficient at processing information. One reason the human brain is so efficient is its ability to focus only on the most relevant information and allocate cognitive effort based on the task.Artificial neural networks were originally inspired by the human brain, but they are still far less efficient at processing information. One reason the human brain is so efficient is its ability to focus only on the most relevant information and allocate cognitive effort based on the task.[#item_full_content]