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IBM announced the acquisition of data and AI consulting firm Hakkoda to expand its data services and accelerate clients' digital transformation journeys. This acquisition brings Hakkoda's generative AI portfolio to IBM, supporting data modernization projects across financial services, public sector, and scientific fields. IBM Senior Vice President Mohamad Ali stated: Leveraging Hakkoda's expert data knowledge, strong technology partnerships, and asset-centric...

IBM recently unveiled its latest mainframe, the IBM z17. Featuring the new IBM Telum II processor, and five years in development, the z17 boasts significant AI capabilities across hardware, software, and system operations. IBM states that the z17 is designed to "redefine large-scale AI computing." While many view mainframes as relics of a bygone computing era, they remain critical for large enterprises handling massive datasets globally.

IBM on Monday launched its latest mainframe hardware, the IBM z17. This fully encrypted mainframe, powered by the IBM Telum II processor, is designed for over 250 AI use cases, including AI agents and generative AI applications. While mainframes may be considered legacy technology by some, 71% of Fortune 500 companies still use them, according to sources. According to market research firm Market Research Future, by 2024...

Transforming complex documents into structured data has long been a challenging problem in computer science. Previous approaches involved cumbersome multi-model pipelines or massive, hallucination-prone, and expensive multimodal models. However, IBM and Hugging Face have recently introduced SmolDocling, a lightweight 256M parameter open-source Vision-Language Model (VLM)...

At a recent international conference, Anthropic CEO Dario Amodei made a striking prediction that AI will take over 90% of code writing within the next three to six months. Amodei suggested that if this trend continues, AI could almost entirely replace human programmers within 12 months. He noted that while programmers may still need to set specific parameters and goals for AI, this process too could potentially be automated by technology in the future.

IBM has released Granite 3.2, a smaller AI model designed for efficient inference and practical applications. This release focuses on delivering strong performance with reduced computational resources.

IBM recently announced that its AI development platform watsonx.ai now supports the DeepSeek-R1 distilled versions of the Llama 3.18B and Llama 3.370B models. DeepSeek optimizes multiple Llama and Qwen variants using knowledge distillation technology, leveraging data from the R1 model to further enhance model performance. On the watsonx.ai platform, users can utilize DeepSeek in two ways.

At the recent LEAP2025 conference, IBM and Lenovo announced their plans to further expand their strategic technology partnership aimed at enhancing the application and impact of generative AI in Saudi Arabia. According to IDC, global annual spending on AI-related systems is expected to exceed $300 billion by 2026, and many leading organizations in Saudi Arabia are actively exploring and investing in generative AI applications to prepare for the impending 'AI Everywhere' era.

With the continuous development of artificial intelligence technology, the integration of visual and textual data has become a complex challenge. Traditional models often struggle to accurately parse structured visual documents such as tables, charts, infographics, and diagrams. This limitation impacts automated content extraction and comprehension capabilities, subsequently affecting applications in data analysis, information retrieval, and decision-making. In response to this demand, IBM recently released Granite-Vision-3.1-2B, a compact visual language model specifically designed for document understanding.

Recently, global technology leader IBM and world-renowned beauty company L'Oréal announced an exciting collaboration plan aimed at leveraging IBM's Generative AI technology to deeply analyze cosmetics formulation data, thereby promoting the use of sustainable raw materials. This innovative effort will develop a customized AI foundational model designed to significantly enhance L'Oréal's R&D team's performance and consumer satisfaction across various cosmetics. This partnership combines L'Oréal's extensive expertise in cosmetic science with IBM's advanced technology.

Recently, IBM announced the launch of version 3.1 of its open source language model Granite, which brings many important enhancements. The new version of the model has been redesigned with a denser architecture, capable of handling up to 128,000 tokens at once. This update signifies a substantial improvement in Granite's ability to process complex text and tasks. The Granite 3.1 model has been trained on datasets in 12 languages and 116 programming languages, processing a total of 12 trillion tokens.

IBM has officially launched its next-generation open-source large language model, Granite3.1, aiming to secure a leading position in the enterprise AI sector. This series of models features an extended context length of 128K, embedding models, built-in hallucination detection capabilities, and significant performance improvements. IBM claims that the Granite8B Instruct model outperforms its open-source competitors of similar scale, including Meta's Llama3.1, Qwen2.5, and Google's Gemma2.

Red Hat recently released the latest version 1.3 of its enterprise-grade Linux AI platform, RHEL AI, which adds support for IBM's Granite large language model (LLM) and previews support for the Intel Gaudi 3 accelerator. This update brings more opportunities for Red Hat's service partners and system integrators to help enterprises explore and integrate AI applications more effectively. Joe Fernandez, Vice President and General Manager of Red Hat's AI business unit, stated that

In the digital age, computer chips are getting smaller, but computing problems are becoming larger. With the continuous advancement of AI models, energy consumption and computing demands in data centers are growing exponentially. IBM's recent breakthrough in optical technology promises to transform this situation. Traditional data centers face two major technical bottlenecks: the limitations of copper wire transmission and electronic transmission speeds. Currently, most CPUs are largely idle, simply waiting for data packets to be transmitted. This represents a significant waste of computing resources. Image source note: Image generated by AI, image licensed.