Category: Machine Learning

Researchers at Keio University have overcome a significant hurdle in RNA design by implementing a factorization machine with a quadratic optimization annealing (FMQA) framework, providing a more efficient alternative to traditional methods. A team led by Project Lecturer Shuta Kikuchi and Professor Hide Tanaka tackled the challenge of reverse folding and identified RNA sequences that […]

Engineers at Northwestern University have developed an artificial intelligence device that identifies abnormal heart rhythms within one-fifth of a heartbeat with more than 98% accuracy. Inspired by the cerebellum, the brain region responsible for reflex responses, the device achieves this near-instant detection with about 10,000 times fewer computer operations than traditional AI. This energy efficiency […]

Artificial intelligence (AI) is rapidly changing the landscape of cancer treatment, particularly in the field of radiopharmaceutical treatments. This targeted approach, which uses radioactive substances to selectively destroy cancer cells, faces challenges due to the slow and resource-intensive nature of drug development. However, the integration of machine learning, particularly deep learning, and generative AI is […]

“Edge AI” directly executes machine learning calculations Analyzes heart rate and sleep patterns in real time Processes data without a smartphone Detects signs of abnormalities such as falls and arrhythmia Home [Image generated by AI] As the global running craze has recently swept the world, filling parks and athletic fields with runners, the smartwatches we […]

In the rapidly evolving landscape of artificial intelligence, the ability of humans to truly understand and interact with the code generated by AI has emerged as a critical bottleneck. Geoffrey Litt, a design engineer at Notion, addressed this challenge at the AI ​​Engineer World Fair, arguing that “understanding is the new bottleneck.” He emphasized that […]

Enhanced delegation with subagents For developers leveraging GPT-5.6 within Codex, the model’s ability to delegate work to subagents has been significantly improved. Previous models also had access to this feature, but GPT-5.6 specifically “They are trained to decide for themselves when delegation is helpful.” In the card game demonstration, the model leveraged subagents to divide […]

As the era of infinite training data draws to a close, the diminishing returns of the laws of scaling are forcing a critical reassessment of how large-scale language models (LLMs) are built. Future profits will depend not on expanding datasets, but on leveraging better quality data more effectively. Existing refinement methods, whether strict rule-based systems […]

CNRS researchers, in collaboration with Fujitsu Research of Europe Ltd, Terra Quantum GmbH and the University of Edinburgh Quantum Research Center, have created a new way to process relational data using quantum computing. The team demonstrates a quantum graph neural network that can perform message passing and achieve permutation homoscedasticity. The method addresses the limitations […]

QuEra’s Aquila Neutral Atom Processor is currently being used to transform molecular data in a new approach to toxicity prediction, taking advantage of the molecular toxicity dataset, a specific collection of molecular structures used in an experiment, to test whether quantum computers can generate more informative features than classical methods. Instead of directly determining whether […]

Model customization transforms general-purpose AI models into specialized enterprise assets. By fine-tuning foundation models (FMs) on domain-specific data, businesses teach AI their unique workflows, terminology, and deep domain specialization, along with strict adherence to brand voice and fewer hallucinations. For enterprises, this is more than an optimization. It’s the creation of proprietary intellectual property. A […]