Compliance has long been the thorn in the side of wealth and investment management firms: slow, manual, and often reactionary. Hadrius, an AI-powered compliance automation platform, thinks it has the answer—and it’s now teaming up with BridgeFT, a cloud-native, API-first wealth infrastructure provider, to prove it.
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South Korean quantum computing firm Norma has reported a dramatic performance leap in drug development, achieving over 73× faster results by running its quantum AI algorithms on NVIDIA GH200 Grace Hopper Superchips. The breakthrough comes via testing on NVIDIA’s CUDA-Q platform, which streamlines integration between GPUs and QPUs and enables hybrid quantum-classical operations.
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Finding the right drug candidate can take months, if not years, of painstaking research, outreach, and due diligence. But a Stanford professor–founded biotech may have just shown how much faster the process can move with AI.
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The AI data center processor market remains on a steep growth trajectory, but Omdia’s latest forecast signals the first signs of moderation.
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London-based InvroMining, a technology and digital asset platform founded in 2016, has announced a major expansion of its AI-driven quant infrastructure. The upgrade is designed to make quantitative digital asset participation—once the domain of hedge funds and PhDs—more accessible to a wider global audience.
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For small and mid-sized businesses (SMBs), keeping up with customer relationships is often the most rewarding—and the most exhausting—part of running a company. Intuit thinks it has a solution.
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Two power players in biotech innovation are teaming up: SmartLabs and Sonrai Analytics have announced a global partnership that will embed Sonrai’s advanced AI platform directly into SmartLabs’ growing network of managed research centers.
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It’s been just over a year since the European Union’s AI Act was introduced—the world’s first sweeping attempt to regulate artificial intelligence. And according to new research from SAP Emarsys, the impact on UK marketing has been both transformative and divisive.
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1.How are AI workloads exposing hidden inefficiencies in traditional data center infrastructure? The massive parallelism in the GPUs processing AI workloads is perhaps the root cause of exposing the limitations of the traditional compute-storage-networking infrastructure. GPUs, with their thousands of processor cores working in parallel, stress the infrastructure differently than traditional CPUs with a few dozen...
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