AI at/on the edge
Digital transformation #mindcandy 3 June 2025
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Digital transformation #mindcandy 3 June 2025
Today’s topics are AI at the edge, intelligent data capture, clean data, real-time data architecture, agentic AI, and self healing infrastructure.
Act On Data Faster: AI At The Edge
https://www.forbes.com/sites/delltechnologies/2025/05/30/act-on-data-faster-ai-at-the-edge/
Data isn’t just everywhere — it’s everything. Yet many organizations struggle to harness its full potential. The answer? Edge AI. The edge is where data originates, decisions happen in real time, and innovation takes flight.
How Intelligent Data Capture Transforms Information Chaos into Clarity
https://www.rtinsights.com/how-intelligent-data-capture-transforms-information-chaos-into-clarity/
By automating the extraction, validation, and integration of information from various sources, businesses enhance accuracy, reduce operational costs, and make data management scalable.
Why Clean Data Is the True Backbone of AI in ERP
https://techbullion.com/why-clean-data-is-the-true-backbone-of-ai-in-erp/
AI is only as powerful as the data it uses. That’s why clean, accurate, and well-structured data is the true backbone of AI in ERP.
Why Real-Time Data Architecture Is the Cornerstone of AI Success
How real-time data architecture empowers CxOs to scale AI, elevate customer experience, and lead in the AI-driven enterprise era.
Agentic AI Marks a Turning Point for Business – But Not Without Real-Time Dynamic Access to Business Data
How approaching data movement from an event-focused perspective is crucial for intelligent and adaptive Agentic AI frameworks to solve business issues that require intricate problem-solving.
Self-Healing Infrastructure Could Be the Future of Data Management
https://hackernoon.com/self-healing-infrastructure-could-be-the-future-of-data-management
Our efforts focus on building self-healing data infrastructure. We’re actively experimenting with reinforcement learning models capable of dynamically adjusting cluster parameters, cache sizes, write concerns, and sharding strategies based on live workload patterns and historical outcomes.