Browsing: Machine Learning

As AI models devour the internet, a looming crisis emerges: a shortage of data. Experts are turning to synthetic data, but this solution introduces new risks and challenges, particularly in the context of decentralized technologies.

A new machine-learning algorithm could predict neutron star collisions, allowing telescopes to observe these rare events in real time.

A Melbourne-based publisher’s move to partner with AI companies and seek consent from its authors to use their work for AI training has sparked critical debate about the future of authorship and copyright.

Richard Sutton and Andrew Barto have been awarded the 2024 Turing Award for their foundational work in reinforcement learning, a critical area that has driven advancements in artificial intelligence.

MiTAC Computing Technology Corp. will present its latest AI and HPC server solutions, the G4520G6 and TN85-B8261, at Supercomputing Asia 2025 in Singapore. These servers are designed to meet the growing demands of AI, machine learning, and high-performance computing applications.

An examination of the seventeen most pressing AI research topics, as identified by the Association for the Advancement of Artificial Intelligence (AAAI). This article explores the significance of these areas and why they matter for the future of AI.

A new method for training AI systems, ‘Chain of Draft’ (CoD), developed by Zoom Communications, drastically reduces the resources required compared to the conventional ‘Chain of Thought’ (CoT) approach, potentially leading to lower processing costs and faster results.

An examination of recent claims that generative AI and large language models (LLMs) suffer from cognitive decline, exploring the nuances of AI development and comparing it to human cognitive processes.

Researchers at Weill Cornell Medicine have developed LILAC, an innovative AI tool that can analyze image data to identify subtle changes and patterns, even when little is known about the processes being studied.

The Financial Times reports on the trend of AI companies utilizing model distillation to create more cost-effective AI models.