Quantum error correction must detect and correct errors without directly reading the quantum information. Classical low-density parity check (LDPC) codes have a well-established design theory: by choosing how many checks connect to each bit, retaining appropriate randomness, and avoiding short loops, designers can pursue both a large minimum distance and a threshold phenomenon in which the decoding failure rate drops sharply below a predicted noise level.
When a website loads quickly or an operating system runs smoothly, you can thank caching—a widely used computing process for fast data access that works by storing frequently used pieces of data in a computer's memory.
A guiding principle for many software engineers—"What you see is what you get" —means creating programs where the content you're editing looks the same as the final product. But when you're using generative AI (genAI) systems to 3D-print, say, a mug, you'll likely get a cup that can't hold your coffee. Why is that?
AI systems that generate video frame by frame could follow a user's camera commands far more accurately, thanks to a new training method developed by researchers from the University of Surrey and NVIDIA. This improvement matters most when someone steers a generated scene rather than simply watching it. That includes video games built on AI-generated worlds, virtual production sets where a director can move a camera around, and simulated environments used to train robots. The findings are posted on the arXiv preprint server.
New research suggests that generative artificial intelligence (AI) could weaken not only employment opportunities for junior software developers but also the pathway through which they develop into senior professionals.
Networks are everywhere. They connect computers across the internet, carry electricity through power grids and represent transport routes for moving people and goods. Yet many problems that appear to have nothing to do with networks can also be represented and solved as networks. Examples include matching passengers with drivers in a ride-hailing app or distributing computing tasks across servers.
Researchers from the University of Warwick report that how faithfully we can build a "digital twin" of a human brain depends not only on computing power but fundamentally on how much of the living brain we can measure, validate and update over time.
Modern computational tools let scientists explore huge numbers of possible molecules, materials and chemical reactions. But testing every combination in the lab is slow and costly. So how do researchers choose the best "recipe" for their experiment?
Computer scientists worldwide have been developing a wide range of artificial intelligence (AI) systems. Some of these systems rely on an individual AI agent, while others consist of multiple interacting agents that exchange information, cooperate and revise each other's responses or predictions.
Large language models (LLMs), the artificial intelligence systems underpinning the functioning of ChatGPT, Gemini and other similar conversational agents, are now widely used worldwide. In addition to processing, interpreting and generating texts, some of these models can solve basic logical problems and answer some user questions with striking accuracy.
"Find the best-selling product from last year." When an AI system attempts to answer a question like this by querying a company database, even a single reference to a nonexistent item can cause the query to fail. Until now, correcting such an error often required regenerating the entire SQL query from scratch.
While many fear artificial intelligence will replace humans, using AI to take over some human roles has benefits. Companies can use the technology to conduct surveys and polls, while behavioral scientists can run experiments on digital twins to gather faster insights without risking harm or distress to real participants.
The results are in: Which AI model is the most fallible? Persuadable? Correctible? University of Arizona researchers assessed seven different generative AI large language models, or LLMs, for these three qualities during lengthy conversations. Their work, published in Nature's Scientific Reports, reveals intrinsic limitations that might go undetected during one-off interactions.
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.
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.
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