Netweb Technologies has launched Skylus.ai, a composable GPU aggregation appliance aimed at simplifying AI infrastructure. This vendor-agnostic platform enhances resource management, allowing dynamic allocation of GPU and CPU resources. With features like Tyrone Kubyts, it streamlines application development, optimizes costs, and accelerates AI adoption for enterprises.
In 2024, AI investment soared to $110B, highlighting the rise of humanoid robots as a key trend for 2025. With projections estimating the market could reach $40 billion by 2035, innovations from companies like Oversonic and Giskard AI are set to transform industries and enhance AI applications.
Elon Musk’s xAI has launched Grok 3, a next-generation AI chatbot boasting enhanced reasoning and DeepSearch capabilities. With ten times the computing power of its predecessor, Grok 3 aims to outperform competitors like ChatGPT, offering unfiltered responses and advanced research abilities. A smaller version, Grok 3-mini, is also available.
The EU has launched the InvestAI initiative, aiming to mobilise €200 billion for artificial intelligence investments, including a €20 billion fund for AI gigafactories. This initiative will enhance Europe’s AI infrastructure, enabling collaborative development of complex AI models and ensuring access to large-scale computing power for all companies.
New AI tools are revolutionizing software engineering in banks, enhancing efficiency and productivity. With large language models transforming coding practices, banks can leverage these advancements to modernize legacy systems and improve software testing. As AI continues to evolve, it opens up new possibilities for faster and better program development.
Innovaccer has launched ‘Agents of Care™’, a suite of AI-driven tools designed to automate repetitive tasks in healthcare, reducing administrative burdens and enhancing patient experiences. This initiative aims to support clinicians and care teams, allowing them to focus on patient care while improving operational efficiency.
Organizations must optimize data center networking to handle the unique demands of AI workloads, which require high bandwidth, low latency, and reliable connectivity. This involves evolving both back-end and front-end networks, leveraging Ethernet technologies, and implementing flexible hardware and automation tools to support AI training and inference effectively.
