As artificial intelligence continues to transform industries across the globe, Asian economies strive to harness its full potential. However, a key challenge remains. Limitations in current network infrastructure could stifle these ambitions, posing a serious hurdle to successful AI implementation
In a revolutionary development set to transform artificial intelligence (AI), researchers at the Technical University of Munich (TU Munich) have pioneered a method that drastically accelerates the training of neural networks. Traditional neural network training is both time-consuming and
On April 14, 2025, an international drone racing event in Abu Dhabi marked a groundbreaking achievement in the field of artificial intelligence (AI) and robotics. For the first time, an autonomous drone decisively outperformed human pilots in a high-speed competitive race, heralding a new era in
Inephany, a promising AI startup based in London, has made significant strides with its innovative platform aimed at optimizing neural network training, particularly for large language models (LLMs). With a recent pre-seed funding round of $2.2 million led by Amadeus Capital Partners, alongside
AI is evolving rapidly and challenging long-held beliefs about the nature of intelligence. This progression necessitates a reexamination of human cognitive capabilities and their interplay with machine intelligence. As artificial intelligence continues to develop, its advancements compel both
Artificial Intelligence (AI) has transitioned from a mere concept in science fiction to a potent force reshaping present-day reality. At the heart of this transformation is Deep Learning, a sophisticated subset of AI that autonomously learns from data to improve its performance over time. This