qbiq, a leader in automating architectural planning, has raised $16 million in a Series A round led by Insight Partners, with contributions from JLL Spark, 10D, Ocean Azul, Random Forest, and M-Fund. The funding will support qbiq’s mission to make architectural planning faster, more accessible, and efficient for both designers and non-designers in the commercial real estate sector.
Oxylabs, a leader in web intelligence solutions, has gathered insights from its esteemed AI/ML Advisory Board members, including Adi Andrei, Co-founder of Technosophics; Ali Chaudhry, Research Fellow at University College London (UCL); and Julius Černiauskas, CEO of Oxylabs. As 2025 approaches, experts agree that while the excitement around Generative AI (Gen AI) and Large Language Models (LLMs) will likely face hurdles, the year ahead promises exciting breakthroughs in AI, machine learning (ML), and other tech fields.
Nosana is excited to announce the official launch of its GPU Marketplace, a game-changing platform designed to democratize access to AI compute power. This innovative network allows GPU owners to register as Hosts, while empowering AI companies with decentralized and affordable GPU resources to meet the growing demand for inference workloads. After a year-long beta testing phase with a closed group, Nosana’s mainnet launch marks the culmination of efforts to refine a decentralized solution for AI compute challenges.
As artificial intelligence (AI) continues to transform enterprises, the speed of innovation brings with it new safety concerns and security threats. Traditional security measures are often ill-equipped to handle these evolving risks. To address these challenges, Cisco has introduced Cisco AI Defense, a cutting-edge solution that empowers enterprises to develop, deploy, and secure AI applications with confidence, without compromising speed or efficiency.
New research commissioned by SambaNova Systems sheds light on a critical readiness gap as enterprises navigate the rising power demands associated with artificial intelligence (AI). Despite growing concerns, a large portion of businesses are unprepared for the energy challenges that accompany the widespread adoption of AI, particularly as inference workloads increase. The findings call attention to the urgent need for proactive strategies to manage AI’s energy consumption effectively.
