There is often no straightforward explanation for the various types of violence that occur around the world. In fact, even when using clear definitions (such as “Civil War,” “Invasion,” or “Local ...
From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...
Smartphone cameras are leaning hard on AI, but is it helping or hurting image quality? I look at why hardware still matters ...
Opinion
The Business & Financial Times on MSNOpinion
Learning from the global AI race: Why we must lead on AI governance in Africa
By Harold Kwabena FEARONArtificial intelligence is reshaping the world in profound ways, and the countries that act decisively today will shape the direction of global innovation for decades to come.
Emirates News Agency on MSN
SCC delegation reviews FNC AI experience
SHARJAH, 22nd January, 2026 (WAM) -- A delegation from the Sharjah Consultative Council (SCC) visited the Federal National ...
CES 2026 showcases the latest AI-powered devices and systems, from vision chips for automotive to sensing solutions for AI ...
Features: High-performance computing is helping Space agencies and universities compress simulation cycles, train AI models faster, and enable more autonomous missions.
Discusses Technology and Digital Innovation Driving Growth in Animal Health January 23, 2026 4:00 PM ESTCompany ...
AZoLifeSciences on MSN
Mapping the human E3 ligase landscape
Maintaining cellular order is a major logistical challenge: Individual mammalian cells contain billions of protein molecules, which must be synthesized, deployed, and removed with precision.
How Do Non-Human Identities Revolutionize Cloud Security? What are Non-Human Identities (NHIs), and why do they hold the key ...
Illegal mining is not only an environmental issue. It is an economic, social and public-health crisis. Leveraging modern surveillance technology, alongside firm enforcement and sustained community ...
Tech Xplore on MSN
Mistaken correlations: Why it's critical to move beyond overly aggregated machine-learning metrics
MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...
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