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]

Airports could become better able to withstand major operational disruptions under a new computing system designed to keep digital services running during periods of intense pressure. Details are reported in the International Journal of Reasoning-based Intelligent Systems.Airports could become better able to withstand major operational disruptions under a new computing system designed to keep digital services running during periods of intense pressure. Details are reported in the International Journal of Reasoning-based Intelligent Systems.[#item_full_content]

Large language models (LLMs), the artificial intelligence (AI) systems underpinning ChatGPT and similar conversational platforms, are now used by many people worldwide to find and summarize information and generate different types of text. Despite their widespread use, these models still have notable limitations.Large language models (LLMs), the artificial intelligence (AI) systems underpinning ChatGPT and similar conversational platforms, are now used by many people worldwide to find and summarize information and generate different types of text. Despite their widespread use, these models still have notable limitations.[#item_full_content]

Generating an image of a person on a computer using text prompts is easy. Generating one with two people is similarly simple. But creating an image of multiple people actually doing something, and faithfully reproducing not just the people but the thing they’re doing? Not so easy.Generating an image of a person on a computer using text prompts is easy. Generating one with two people is similarly simple. But creating an image of multiple people actually doing something, and faithfully reproducing not just the people but the thing they’re doing? Not so easy.[#item_full_content]

A drone swoops low over an alpine forest. It climbs suddenly to follow the contours of the sharply rising landscape. Pulses from its lidar—a laser mapping instrument—rapidly scan the trees below.A drone swoops low over an alpine forest. It climbs suddenly to follow the contours of the sharply rising landscape. Pulses from its lidar—a laser mapping instrument—rapidly scan the trees below.[#item_full_content]

The capabilities of large AI systems are constantly improving, but they consume a great deal of energy during training and operation. The human brain, by contrast, is extremely energy-efficient: It requires only around 20 watts.The capabilities of large AI systems are constantly improving, but they consume a great deal of energy during training and operation. The human brain, by contrast, is extremely energy-efficient: It requires only around 20 watts.[#item_full_content]

A new study led by Fabrice Niyigaba ’27 reports the first digital tool for identifying online propaganda in Kinyarwanda, the national language of Rwanda’s 15 million people—and possibly the first for any of the Bantu languages spoken by 350 million Africans.A new study led by Fabrice Niyigaba ’27 reports the first digital tool for identifying online propaganda in Kinyarwanda, the national language of Rwanda’s 15 million people—and possibly the first for any of the Bantu languages spoken by 350 million Africans.[#item_full_content]