Proscia®, a global leader in AI-enabled pathology solutions for precision medicine, today announced the launch of Concentriq® Embeddings and the Proscia AI Toolkit, enabling life sciences organizations to accelerate the discovery and development of novel therapies and diagnostics.
Proscia Launches Concentriq Embeddings and Developer Toolkit to Unleash Pathology AI Development
Will the Blueprint for an AI Bill of Rights Help or Hinder Fraud Prevention?
In this contributed article, Tamas Kadar, CEO and co-founder of SEON, suggests that as AI technologies continue to advance, they are emerging as powerful tools not only for online fraudsters but also for those dedicated to preventing fraud. Here, Kadar assesses the recent Blueprint for an AI Bill of Rights in the US and examines what its impact will be across the domain of fraud.
Moveworks Launches Agentic Automation: A Platform for Building AI Agents
Moveworks, a leading generative AI copilot for employee support, announced the launch of Agentic Automation, a first-of-its-kind automation platform designed specifically for building complex agentic AI automations.
Team Led by UMass Amherst Debunks Research Showing Facebook’s News-Feed Algorithm Curbs Election Misinformation
An interdisciplinary team of researchers led by the University of Massachusetts Amherst recently published work in the prestigious journal Science calling into question the conclusions of a widely reported study — published in Science in 2023 and funded by Meta — finding the social platform’s algorithms successfully filtered out untrustworthy news surrounding the 2020 election and were not major drivers of misinformation.
Podcast: Intel Unveils Next-gen Solutions with Xeon 6 processors and Gaudi 3 to Tackle Enterprise Needs
Enterprises are increasingly in need of AI infrastructure that is both cost-effective and available for rapid development and deployment. To meet this demand head-on, Intel today launched Xeon 6 processors with Performance-cores (P-cores) and Gaudi 3 AI accelerators, bolstering the company’s commitment to deliver powerful AI systems with optimal performance per watt and lower total cost of ownership.
Landbase Announces GTM-1 Omni Action Models: The Future of Go-To-Market Automation
Landbase, a pioneer in intelligent automation solutions, today announced the launch of GTM-1 Omni, the world’s first and most powerful AI action model specifically designed to transform the way businesses approach their go-to-market strategies. GTM-1 Omni is currently only available to select businesses on a waitlist.
The Good, the Bad, and the Future of Data AI
In this contributed article, Paul Scott-Murphy, chief technology officer at Cirata, discusses key best practices for applying generative AI in today’s enterprises. The key to harnessing the explosion of AI is recognizing the good, bad, and future, letting those influence how and where we securely utilize it. Time invested now in doing this proactively will benefit you and your organization tomorrow.
DDN Achieves Unprecedented Performance in MLPerf™ Benchmarking, Empowering Transformative AI Business Outcomes
DDN®, provider of the data intelligence platform, proudly announces a groundbreaking achievement in the MLPerf™ Storage Benchmark, setting new standards for performance and efficiency. DDN’s A3I™ (Accelerated Any-scale AI) systems demonstrated unmatched capabilities in multi-node configurations, solidifying its role as essential drivers for high-demand machine learning (ML) workloads and transformative business outcomes. “Our MLPerf results emphatically showcase DDN’s […]
Report Findings – Security Pros Identify GenAI as the Most Significant Risk for Organizations
HackerOne, a leader in human-powered security, revealed data that found 48% of security professionals believe AI is the most significant security risk to their organization.
New MLPerf Storage v1.0 Benchmark Results Show Storage Systems Play a Critical Role in AI Model Training Performance
MLCommons® announced results for its industry-standard MLPerf® Storage v1.0 benchmark suite, which is designed to measure the performance of storage systems for machine learning (ML) workloads in an architecture-neutral, representative, and reproducible manner.