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  • IBM and Groq Partner to Accelerate Enterprise AI Deployment with Speed and Scale

    IBM and Groq Partner to Accelerate Enterprise AI Deployment with Speed and Scale

    Partnership aims to deliver faster agentic AI capabilities through IBM watsonx Orchestrate and Groq technology, enabling enterprise clients to take immediate action on complex workflows

    Oct 20, 2025

    ARMONK, N.Y. and MOUNTAIN VIEW, Calif., Oct. 20, 2025 /PRNewswire/ — IBM (NYSE: IBM) and Groq today announced a strategic go-to-market and technology partnership designed to give clients immediate access to Groq’s inference technology, GroqCloud, on watsonx Orchestrate – providing clients high-speed AI inference capabilities at a cost that helps accelerate agentic AI deployment. As part of the partnership, Groq and IBM plan to integrate and enhance RedHat open source vLLM technology with Groq’s LPU architecture. IBM Granite models are also planned to be supported on GroqCloud for IBM clients.

    Enterprises moving AI agents from pilot to production still face challenges with speed, cost, and reliability, especially in mission-critical sectors like healthcare, finance, government, retail, and manufacturing. This partnership combines Groq’s inference speed, cost efficiency, and access to the latest open-source models with IBM’s agentic AI orchestration to deliver the infrastructure needed to help enterprises scale.

    Powered by its custom LPU, GroqCloud delivers over 5X faster and more cost-efficient inference than traditional GPU systems. The result is consistently low latency and dependable performance, even as workloads scale globally. This is especially powerful for agentic AI in regulated industries.

    For example, IBM’s healthcare clients receive thousands of complex patient questions simultaneously. With Groq, IBM’s AI agents can analyze information in real-time and deliver accurate answers immediately to enhance customer experiences and allow organizations to make faster, smarter decisions.

    This technology is also being applied in non-regulated industries. IBM clients across retail and consumer packaged goods are using Groq for HR agents to help enhance automation of HR processes and increase employee productivity.

    “Many large enterprise organizations have a range of options with AI inferencing when they’re experimenting, but when they want to go into production, they must ensure complex workflows can be deployed successfully to ensure high-quality experiences,” said Rob Thomas, SVP, Software and Chief Commercial Officer at IBM. “Our partnership with Groq underscores IBM’s commitment to providing clients with the most advanced technologies to achieve AI deployment and drive business value.”

    “With Groq’s speed and IBM’s enterprise expertise, we’re making agentic AI real for business. Together, we’re enabling organizations to unlock the full potential of AI-driven responses with the performance needed to scale,” said Jonathan Ross, CEO & Founder at Groq. “Beyond speed and resilience, this partnership is about transforming how enterprises work with AI, moving from experimentation to enterprise-wide adoption with confidence, and opening the door to new patterns where AI can act instantly and learn continuously.”

    IBM will offer access to GroqCloud’s capabilities starting immediately and the joint teams will focus on delivering the following capabilities to IBM clients, including:

    • High speed and high-performance inference that unlocks the full potential of AI models and agentic AI, powering use cases such as customer care, employee support and productivity enhancement.
    • Security and privacy-focused AI deployment designed to support the most stringent regulatory and security requirements, enabling effective execution of complex workflows.
    • Seamless integration with IBM’s agentic product, watsonx Orchestrate, providing clients flexibility to adopt purpose-built agentic patterns tailored to diverse use cases.

    The partnership also plans to integrate and enhance RedHat open source vLLM technology with Groq’s LPU architecture to offer different approaches to common AI challenges developers face during inference. The solution is expected to enable watsonx to leverage capabilities in a familiar way and let customers stay in their preferred tools while accelerating inference with GroqCloud. This integration will address key AI developer needs, including inference orchestration, load balancing, and hardware acceleration, ultimately streamlining the inference process.

    Together, IBM and Groq provide enhanced access to the full potential of enterprise AI, one that is fast, intelligent, and built for real-world impact.

    Statements regarding IBM’s and Groq’s future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

    About IBM

    IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs, and gain a competitive edge in their industries. Thousands of governments and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM’s long-standing commitment to trust, transparency, responsibility, inclusivity, and service. Visit www.ibm.com for more information.

    About Groq

    Groq is the inference infrastructure powering AI with the speed and cost it requires. Founded in 2016, Groq developed the LPU and GroqCloud to make compute faster and more affordable. Today, Groq is trusted by over two million developers and teams worldwide and is a core part of the American AI Stack.

    Media Contact:

    Elizabeth Brophy

    elizabeth.brophy@ibm.com

    SOURCE IBM

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  • UN Day 2025: Building South Sudan’s Health Future Together | WHO

    Article by Dr Humphrey Karamagi, WHO Representative for South Sudan

    As the United Nations marks its 80th anniversary on 24 October under the theme “Building Our Future Together,” South Sudan also stands at a defining moment. This young nation…

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  • Groundbreaking Study in the Journal of Clinical Investigation Advances Cell Therapy for Huntington’s Disease Treatment

    SHANGHAI, Oct. 20, 2025 /PRNewswire/ — The research team led by Dr. Yuejun Chen, founder of UniXell Biotech, published online an article titled “3D-Cultured Human Medium Spiny…

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  • UK ‘deeply concerned’ about Gaza clashes in spite of Trump’s peace deal  – POLITICO

    UK ‘deeply concerned’ about Gaza clashes in spite of Trump’s peace deal  – POLITICO

    The strikes killed at least 26 people, according to Reuters.

    Cooper, Britain’s top diplomat, said that the ceasefire “must hold and humanitarian aid must get through to those in need.”

    She urged that “all parties” uphold the…

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  • Sensors Information | AZoSensors.com – Page not found

    Sensors Information | AZoSensors.com – Page not found

    While we only use edited and approved content for Azthena
    answers, it may on occasions provide incorrect responses.
    Please confirm any data provided with the related suppliers or

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  • IFLScience We Have Questions: Can Burying Scientists Alive In The Snow Help Us Protect Polar Bears?

    IFLScience We Have Questions: Can Burying Scientists Alive In The Snow Help Us Protect Polar Bears?

    Polar Bears International (PBI) is serious about protecting bears, and in the pursuit of reliable data, has gone to some extremes in the past. From burying scientists alive out in the snow to novel collar-camera setups that have enabled them to…

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  • How AI is Reshaping Commercial Insurance and Risk Assessment – with Sidharth Ojha of AXA XL

    How AI is Reshaping Commercial Insurance and Risk Assessment – with Sidharth Ojha of AXA XL

    Commercial insurance has long struggled to adopt new technology at the pace of other financial services. Manual workflows, outdated mainframes, and fragmented systems from years of mergers have slowed modernization efforts. Many insurers still view underwriting as an “art” rather than a process, which has historically delayed even basic digital upgrades.

    Industry data underscores the substantial adoption gap across the insurance sector and beyond. In MIT Center for Information Systems Research’s global study of enterprise AI maturity, only 7% of organizations have fully embedded AI across operations, while most remain in pilot or mid-stage phases. At the same time, regulatory agendas are finally catching up. 

    The EU AI Act came into effect in 2025, requiring insurers to categorize AI systems by risk level and comply with strict transparency rules. Meanwhile, the vast majority of enterprise data — more than 90% — is unstructured, stored in documents, contracts, and PDFs that are difficult to analyze without advanced tools.

    This mix of legacy systems, compliance demands, and data challenges creates a critical inflection point for insurers. How can they adopt AI responsibly while ensuring ROI and minimizing risk? Drawing on insights from Sidharth Ojha, Head of Process Optimization, Data & AI at AXA XL, in a recent episode of the AI in Business podcast, this article explores how commercial insurers can modernize operations, empower teams to experiment, and lay the foundations for scaling.

    This article examines three key insights from Ojha’s perspective on AI adoption in insurance:

    • Empowering business users with low-code AI: Provide underwriters a compliant sandbox to experiment safely and uncover constraints early.
    • Turning data into a strategic asset: Map data end to end and convert unstructured contracts into structured insights that drive growth.
    • Building foundations for scalable AI: Standardize roles, processes, and data definitions to prevent pilots from stalling and unlock enterprise adoption.

    Listen to the full episode below:

    Guest: Sidharth Ojha, Head of Process Optimization, Data & AI, Global Chief Underwriting Office, AXA XL.

    Expertise: Commercial Insurance Transformation, Process Optimization, and Applied AI

    Brief Recognition: At AXA XL, Ojha leads initiatives to apply AI in underwriting and operations, balancing compliance with efficiency and cultural change. His experience spans legacy process modernization, regulatory alignment, and enabling practical AI adoption in one of the world’s largest commercial insurers.

    Empowering Business Users with Low-Code AI 

    Ojha sees that, among the clearest challenges for driving AI adoption in insurance, is cultural inertia. Executives often recognize AI’s potential but hesitate to let non-technical staff engage with it directly, which Ojha sees as a missed opportunity.

    He describes the importance of creating “safe lanes” where underwriters and business users can test AI tools in controlled environments. By embedding low-code platforms into existing systems, insurers can enable experimentation without risking data leaks or regulatory breaches.

    “Think of it like bowling with bumpers,” Ojha explains. “You want to let people take the shot, but keep them from rolling into the gutter.” His approach builds confidence and helps uncover limitations early, before a project absorbs significant budget or time.

    In the past, insurance tech projects relied on extended handoffs: business analysts translated requirements, developers built systems, and architects ensured alignment. By the time solutions reached production, critical context was often lost. Low-code AI tools enable underwriters to interact with technology directly, bypassing translation layers and accelerating actionable feedback.

    Ojha stresses that leaders should not rush to pilots or MVPs. Instead, they should allocate more time to exploration and failure in the sandbox phase.

    “The more time you spend failing your hypotheses, the less time you waste scaling something that doesn’t work,” he notes. For an industry where “failure” carries negative connotations, reframing the need for failure tolerance as controlled testing can help insurers adopt AI more comfortably.

    This cultural shift is essential for adoption. By giving underwriters direct but safeguarded access, organizations create buy-in and align tools with real business needs — rather than building in isolation and hoping for adoption later.

    Turning Data into a Strategic Asset 

    Ojha insists – as many previous podcast guests have – that technology alone cannot deliver ROI without clean, usable data. He notes that Insurance companies face a particularly steep challenge because most of their critical information is locked in unstructured formats, such as policy documents, endorsements, quotes, and schedules of values.

    Ojha points out that five years ago, insurers struggled to do something as basic as reading a table in a PDF. Generative AI has solved many of these hurdles, but unstructured data remains diverse and inconsistent, making transformation into structured formats difficult:

    “Most of the data insurers rely on isn’t even in their systems — it’s trapped in PDFs, Word documents, and scanned contracts. The real challenge isn’t reading it, it’s standardizing it. Each policy is unique, often written like a legal manuscript. Until we can consistently turn that unstructured data into structured information, every downstream AI use case — from risk analysis to pricing — will be operating in the dark.”

    — Sidharth Ojha, Head of Process Optimization, Data & AI, AXA XL

    The payoff is significant. With structured data, insurers can answer portfolio questions in seconds, such as: “Which policies exclude communicable disease?” or “How much exposure do we have across a region?”

    During the COVID-19 pandemic, many organizations could not respond quickly to such queries. Today, AI tools offer the chance to avoid that blind spot.

    Ojha also describes new possibilities in summarization capabilities among these systems. Beyond condensing documents, he notes that AI can compare client submissions against internal appetite and compliance rules. 

    For high-volume underwriting teams, these capabilities mean touching more submissions per day, declining unsuitable risks faster, and focusing on profitable opportunities. “That’s not just efficiency,” Ojha stresses. “That’s real growth potential.”

    For leaders, the mandate is clear: treat data as a first-class asset. Inventory policy wordings, target high-volume pain points, and build systems that push structured outputs back into core platforms. Done well, these steps transform AI from a cost-saving tool into a revenue driver.

    Building Foundations for Scalable AI 

    While pilots are familiar with insurance, scaling remains rare. Ojha estimates that “80-90%” of AI projects stall between proof of concept and deployment. The reasons are less about technology and more about organizational readiness.

    He outlines the data infrastructure bottlenecks that often derail scaling AI operations in insurance:

    • Unclear accountability for data fields, leading to inconsistent inputs.
    • Fragmented processes, where teams record different levels of detail for the same product.
    • Legacy stacks that are expensive to integrate with new AI models.
    • Inconsistent definitions of key metrics across business units.

    Without fixing these foundations, even promising pilots fail to expand. Ojha advises leaders to ask: If this solution went live across three countries tomorrow, what would break first? Addressing gaps in that framework upfront prevents costly surprises later.

    Regulation also plays a role, and Ojha sees the EU AI Act as a turning point, providing categories that boards and regulators alike can trust. 

    “If you are compliant with EU rules, you are largely compliant globally,” he notes, insisting that having such assurance can ease executive concerns and accelerate project approvals.

    Ultimately, success comes from patience. Insurers are often eager to jump from idea to MVP, but Ojha emphasizes the value of deeper exploration and testing. Companies that invest in clarity of roles, process alignment, and data quality will find it easier to move AI from experimentation to enterprise-wide adoption.

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  • 2025 World Gymnastics Championships: Skye Blakely’s steady climb from setback to strength

    2025 World Gymnastics Championships: Skye Blakely’s steady climb from setback to strength

    Skye Blakely: “I really like being successful”

    Now, she heads to the World Championships as she continues to write the story of her career.

    In Jakarta, she’ll be a contender to make the finals on the uneven bars and could fight for a medal…

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  • Don’t miss the Orionid meteor shower peak overnight tonight under a moonless sky

    Don’t miss the Orionid meteor shower peak overnight tonight under a moonless sky

    Get ready stargazers! The Orionid meteor shower peak begins tonight, welcoming a spectacular natural light show that could see a flurry of shooting stars spawned by Halley’s Comet brighten the dark, moonless sky.

    The 2025 Orionid meteor shower

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  • A personal account of getting a massage at Surya Wellness Spa

    A personal account of getting a massage at Surya Wellness Spa

    This story is part of Image’s October Abundance issue, reveling in indulgence, maximalism and the deliciously impractical.

    At the Proper Hotel in Santa Monica, an employee in a white polo and khaki…

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