Introduction: Why Enterprises Need an ADP Layer Now Enterprise document volumes are exploding, yet back-office workflows are still clogged with manual routing, data re-entry, and error-prone approvals. Finance teams waste hours reconciling mismatched invoices. Operations pipelines stall when exceptions pile up. IT leaders struggle to maintain brittle integrations every time a vendor shifts a template…
Introduction
Understanding how the brain builds internal representations of the visual world is one of the most fascinating challenges in neuroscience. Over the past decade, deep learning has reshaped computer vision, producing neural networks that not only perform at human-level accuracy on recognition tasks but also seem to process information in ways that resemble our…
Science
Published
4 September 2025
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Last week, the NVIDIA robotics team released Jetson Thor that includes Jetson AGX Thor Developer Kit and the Jetson T5000 module, marking a significant milestone for real‑world AI robotics development. Engineered as a supercomputer for physical AI, Jetson Thor brings generative reasoning and multimodal sensor processing to power inference and decision-making at the edge.
Architectural…
Introduction: Document Processing is the New Data Infrastructure Document processing has quietly become the new data infrastructure of modern enterprises—no longer a clerical back-office chore, but a strategic layer that determines speed, accuracy, and compliance at scale. Consider this: At 9:00 AM, a supplier emails a scanned invoice to the accounts payable inbox. By 9:02,…
Why Data Extraction Is the First Domino in Enterprise AI Automation Enterprises today face a data paradox: while information is abundant, actionable, structured data is scarce. This challenge is a major bottleneck for AI agents and large language models (LLMs). Automated data extraction solves this by acting as the input layer for every AI-driven workflow.…
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Data science projects are notorious for their complex dependencies, version conflicts, and "it works on my machine" problems. One day your model runs perfectly on your local setup, and the next day a colleague can't reproduce your results because they have different Python versions, missing libraries, or incompatible system configurations.
This…
Embedding models act as bridges between different data modalities by encoding diverse multimodal information into a shared dense representation space. There have been advancements in embedding models in recent years, driven by progress in large foundation models. However, existing multimodal embedding models are trained on datasets such as MMEB and M-BEIR, with most focus only…