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Yesterday, Liquid AI released LFM2.5-VL-3B. It is a 3.1B-parameter vision-language model built for on-device deployment. The model reads digital screens across mobile, web, and desktop. It grounds objects to coordinates, parses documents and charts, and calls tools from text or image input. Liquid AI reports an average of 69.4 across 28 vision benchmarks. That matches
About a month ago, Baidu (often called the “Google of China”) introduced Unlimited-OCR, an advancement over DeepSeek OCR. The model was designed to transcribe long, multi-page documents with high accuracy while delivering fast and stable inference. Unlike conventional vision-language OCR systems, Unlimited-OCR addresses a major bottleneck in long-document transcription: the rapidly growing Key-Value (KV) cache,
How Baidu Unlimited-OCR Works: Solving Long-Document Transcription Read More »
Ensure workplace safety with AI-driven helmet detection. Explore challenges in computer vision PPE monitoring & discover solutions.
Helmet Detection with Computer Vision: Issues and Solutions Read More »
Google’s research medical AI system, AMIE (Video), conducted synchronous video consultations with professional patient actors and received clinical evaluator ratings on par with primary care physicians across several core measures. Fifteen trained actors portrayed conditions across cardiopulmonary, abdominal, HEENT, neurological or psychiatric, and musculoskeletal presentations. Google says studies involving real patients and their own health
Google tests AMIE for clinical video consultations Read More »
Object removal models have improved faster than the metrics used to judge them. Diffusion erasers now reconstruct shadows, reflections and occluded structure convincingly, yet PSNR, SSIM, LPIPS, ReMOVE and CFD frequently rank their outputs the wrong way. The root cause is structural: erasure is an ill-posed, one-to-many task, so no single ground truth exists to
Explore computer vision applications for heavy industries across construction, manufacturing, mining, oil & gas, warehouse, and logistics.
Real-World Computer Vision Applications for Heavy Industries Read More »
In this tutorial, we explore adaptive experimentation using Meta’s Ax with the modern Client API. We work through a complete workflow where we tune a RandomForest model on a synthetic classification dataset while balancing predictive accuracy against model footprint. We begin by defining a mixed search space with integer, float, log-scaled, and categorical parameters, then
Adaptive Experimentation with Meta’s Ax: A Practical Coding Guide Read More »
MIT researchers and collaborators found that AI explainability tools in the health sector can produce sharply different results depending on who uses them. When applied to skin disease diagnosis, non-experts improved their accuracy with AI assistance, although the improvement largely came from deferring to the model. Primary care providers showed a different pattern: they performed
Why health AI interfaces must adapt to user expertise Read More »
Onton, a San Francisco-based search and discovery company, has released Ontology 1, a neurosymbolic model for complex, conversational, multimodal product search. On a 90-query benchmark scored by three independent LLM judges, Ontology 1 reached a mean precision@10 of 0.630, against 0.543 for Google Shopping and 0.469 for Amazon. It did this while indexing roughly 1%
In this tutorial, we design a complete GeoAI workflow for extracting building footprints from high-resolution NAIP aerial imagery. We begin by configuring the geospatial deep learning environment, downloading raster imagery and vector labels, and inspecting their spatial properties before generating georeferenced image chips and segmentation masks. We then train a U-Net model with a ResNet-34
