Ali Azhar is the Managing Editor at WRBL News 3. He specializes in AI and machine learning, focusing on themes such as data management, data quality, and enterprise technology. Ali's insights and expertise have been featured in various outlets including KXAN, WPRI Connects, AIwire, BigDATAwire, MDPI, Paperity, and HPCwire.
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Ali Azhar's coverage shows a strong focus on AI, data & analytics, and software & SaaS. He often cites data and reports heavily on private sector announcements. Given his emphasis on these areas, he would be most responsive to pitches offering insights into the latest trends in artificial intelligence, especially those related to enterprise challenges and applications in healthcare or consumer behavior.
For Ali's coverage area of AI-driven shopping and AI-powered clinical assessment, potential sources could include experts from companies specializing in these fields or researchers leading innovative projects that align with the topics covered.
Given the absence of specific geographic focus for Ali Azhar's articles, pitches should emphasize global relevance rather than any particular regional angle.
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By: Ali Azhar
Microsoft has launched Microsoft Frontier Company, a new consulting organization built to help enterprises plan and deploy AI. The company is investing a staggering $2.5B and deploying 6,000 industry and […]
Modern AI can identify forests all the way from space. It can estimate tree heights to an astonishing level of accuracy. It can predict wildfire risk. It can even model ecosystems that have never been surveyed. It is easy to assume these capabilities come from increasingly powerful AI models, and no doubt that is a major component of it. However, the foundation is built from something far less glamorous – it's the data behind these models.
Meta has announced that it will start manufacturing a new custom AI chip from September as part of its plan to boost its overall computing power to 14 GW. This is a major milestone in the company's goal to reduce reliance on external chip suppliers.
The next wave of enterprise standardization is here – and it's about control, not just collection.
More than four out of five (83%) organizations say they need to upgrade their infrastructure before they can fully support agentic AI. That's a striking number, especially considering how quickly enterprises are trying to roll out AI across the business.
By: Ali Azhar
Scientists have relied on HPC facilities to help them process large volumes of data, execute sophisticated simulations, and power large-scale research. However, as AI becomes more deeply integrated into scientific research, the role of HPC facilities may also be starting to evolve.
Modern AI can identify forests all the way from space. It can estimate tree heights to an astonishing level of accuracy. It can predict wildfire risk. It can even model ecosystems that have never been surveyed. It is easy to assume these capabilities come from increasingly powerful AI models, and no doubt that is a major component of it. However, the foundation is built from something far less glamorous – it's the data behind these models.
Meta has announced that it will start manufacturing a new custom AI chip from September as part of its plan to boost its overall computing power to 14 GW. This […]
As investment in Physical AI accelerates, simulation is taking on a much larger role than simply generating synthetic training data. HPC-based physics simulation is increasingly being used to improve model accuracy and validate AI behavior. This is also helping make virtual training environments more representative of real-world conditions. This allows AI-based systems to be more […]
Anaconda is moving beyond Python package management with its acquisition of Kilo Code, an open-source, model-agnostic AI coding platform used by more than three million developers. The deal follows Anaconda's acquisition of AI orchestration company Outerbounds earlier this year and adds AI coding to its growing enterprise AI platform.
