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Multimodal Generative AI: How Text, Image and Audio Converge
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Multimodal Generative AI: How Text, Image and Audio Converge

Ask a model to summarize a PDF, explain a chart within it, and read a paragraph aloud in a specific voice—without switching tools—and you have the promise of multimodality. In 2025, systems that understand and generate text, images, audio, and video are moving from demos to production, with practical limits and clear performance trade-offs that...

The Future of AI Hardware: Chips, Architectures, and Edge to Cloud
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The Future of AI Hardware: Chips, Architectures, and Edge to Cloud

In AI training today, a single accelerator can stream multiple terabytes per second from stacked memory while drawing hundreds of watts; a full cluster can push tens of megawatts. That hard physics—bandwidth, energy, heat—now shapes model sizes, formats like FP8 and INT4, and where models run, from hyperscale datacenters to 5‑watt phone NPUs. The Future...

Measuring AI Performance: My Guide from Testing to Quality Control
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Measuring AI Performance: My Guide from Testing to Quality Control

When a customer-support assistant I deployed began fielding 18,000 chats per week, two numbers decided whether we kept it online: a 2.4% hallucination rate that generated 160 escalations, and a p95 latency of 1.1 seconds that customers tolerated. Shrinking hallucinations to under 1% and keeping p95 below 1.0 seconds dropped escalations by 28% and saved...

Rebuilding Data Infrastructure for AI Success: My Playbook
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Rebuilding Data Infrastructure for AI Success: My Playbook

In the past three years I’ve shipped AI systems that served 40ms fraud scores, refreshed a 2-billion vector index nightly, and survived a compliance audit that asked for column-level lineage across 11,000 tables. The lesson is unglamorous but liberating: AI succeeds or fails on data plumbing, not model theatrics. “Data Infrastructure for AI Success” is...

Shadow AI: How to Spot, Stop, and Safely Harness It at Work
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Shadow AI: How to Spot, Stop, and Safely Harness It at Work

Shadow AI is already in your company: expense reports with unapproved chatbot subscriptions, browser extensions quietly calling large language model APIs, and chat logs that include confidential snippets pasted to “get help fast.” In network logs, it’s common to see dozens of distinct AI endpoints touched by a single department in a week. If your...

AI Regulation in Europe: The AI Act, Risks, and Opportunities
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AI Regulation in Europe: The AI Act, Risks, and Opportunities

By mid-2025, several clauses of the EU’s AI Act will already be biting: prohibited practices apply roughly six months after entry into force, and general‑purpose AI obligations begin around the 12‑month mark. For anyone building, buying, or deploying AI in or into the EU, the question is no longer “if,” but “how” to comply without...

AI’s Environmental Footprint: Carbon, Water, and Infrastructure
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AI’s Environmental Footprint: Carbon, Water, and Infrastructure

Every AI query rides an invisible supply chain: electricity drawn from grids with wildly different carbon intensities, water evaporated at cooling towers, and silicon manufactured in energy‑intensive fabs. A single month‑long training run on 10,000 GPUs can consume around 3–4 GWh after cooling overheads, emitting over a thousand tons of CO2e on a moderately clean...

Agentic AI
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Agentic AI: The Next Stage of Artificial Intelligence Explained

Agentic AI systems don’t just draft emails or summarize documents—they open browsers, log into tools, schedule follow-ups, and keep working until the job is done. In one real-world pilot, an autonomous support agent resolved 28% of tickets end-to-end while obeying a cost cap of $0.70 per case; in another, a coding agent safely merged trivial...