AI system tagged posts

New AI system fixes 3D printing defects in real time

AI saves 3D prints in real time
LLMs in continuous improvement cycle. LLM-based supervisor agents can be employed at each step of the continuous improvement cycle. The cycle involves evaluating print quality, identifying failure modes, gathering relevant information, and planning and solving the issues by adjusting the print parameters, ensuring high-quality defect-free parts. Credit: Additive Manufacturing (2025). DOI: 10.1016/j.addma.2025.105027

Additive manufacturing has revolutionized manufacturing by enabling customized, cost-effective products with minimal waste. However, with the majority of 3D printers operating on open-loop systems, they are notoriously prone to failure...

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An AI System has reached Human Level on a Test for ‘General Intelligence’—here’s what that means

A new artificial intelligence (AI) model has just achieved human-level results on a test designed to measure “general intelligence.”

On December 20, OpenAI’s o3 system scored 85% on the ARC-AGI benchmark, well above the previous AI best score of 55% and on par with the average human score. It also scored well on a very difficult mathematics test.

Creating artificial general intelligence, or AGI, is the stated goal of all the major AI research labs. At first glance, OpenAI appears to have at least made a significant step towards this goal.

While skepticism remains, many AI researchers and developers feel something just changed. For many, the prospect of AGI now seems more real, urgent and closer than anticipated. Are they right?

Generalization and intelligence
To understand ...

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Seeing the light: Researchers develop new AI System Using Light to Learn Associatively

Seeing the light: researchers develop new AI system using light to learn associatively
Credit: James Tan You Sian

Researchers at Oxford University’s Department of Materials, working in collaboration with colleagues from Exeter and Munster, have developed an on-chip optical processor capable of detecting similarities in datasets up to 1,000 times faster than conventional machine learning algorithms running on electronic processors.

The new research published in Optica took its inspiration from Nobel Prize laureate Ivan Pavlov’s discovery of classical conditioning. In his experiments, Pavlov found that by providing another stimulus during feeding, such as the sound of a bell or metronome, his dogs began to link the two experiences and would salivate at the sound alone...

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