2. DeepEncoder – The Optical Compressor Meet the star: DeepEncoder. It uses two backbones SAM (for perception) and CLIP (for global vision) bridged by a 16× convolutional compressor. This allows it to maintain high-res understanding without exploding activation memory. The
MACHINE LEARNING
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Technical Analysis of DeepSeek-OCR Vision-Text Compression
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1. Vision-Text Compression: The Core Idea LLMs struggle with long documents because token usage scales quadratically with length. DeepSeek-OCR flips that: instead of reading text, it encodes full documents as vision tokens each token representing a compressed piece of visual
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DeepSeek Introduces High-Efficiency OCR System Using Vision Tokens
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DeepSeek just did something wild. They built an OCR system that compresses long text into vision tokens literally turning paragraphs into pixels. Their model, DeepSeek-OCR, achieves 97% decoding precision at 10× compression and still manages 60% accuracy even at 20×. That
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High School Geometry Improves Spatial Intelligence in AI Models
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Can high school geometry teach AI to understand space? A new study tackles the critical challenge of spatial intelligence in Multimodal Large Language Models (MLLMs). Researchers found that fine-tuning models on Euclid30K, a new dataset of ~30,000 Euclidean geometry
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Transformers Learn In-Context Through Gradient Descent
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Transformers learn in-context by gradient descent Oswald et al.: https://
arxiv.org/abs/2212.07677 #ArtificialIntelligence #DeepLearning #MachineLearning -
Hallucinations vs Confabulation: Rethinking RNN Behavior Terminology
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It’s been a decade but yes I believe I hallucinated the term in my 2015 post on unreasonable effectiveness of RNNs. I later became aware that Geoff Hinton used “confabulate”, which is often (but I think not always) a better analogue in human psychology. It’s a bit too specific,
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Domain-Specific AI Essential for Industrial Fault Detection
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Vertical knowledge matters. An AI trained on generic data won’t grasp a PLC fault or a quality deviation. Industrial AI needs domain context from the start. https://t.co/lhIvF2AZpS #sponsored #atelic_iiot #IndustrialAI #IndustrialData @IIoT_World via @fogoros pic.twitter.com/FbxS1KGWNe
— Lucian Fogoros (@fogoros) 19 octobre 2025Vertical knowledge matters. An AI trained on generic data won’t grasp a PLC fault or a quality deviation. Industrial AI needs domain context from the start. https://
buff.ly/KhIf1RL #sponsored #atelic_iiot #IndustrialAI #IndustrialData @IIoT_World via @fogoros -
Douglas Adams and Reinforcement Learning for Robotics
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Douglas Adams invented reinforcement learning for robotics in 1992 🙂 https://
en.wikipedia.org/wiki/Mostly_Ha
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Deep Learning Course Pricing Comparison and Affordability
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That's about 1/10th the price of most high quality courses fwiw, including what the previous http://
fast.ai deep learning courses cost. -
Deep Agents Evolution: AI Architecture Scaling from 15 to 500+ Steps
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Deep Agents Evolution A breakthrough in AI architecture enabling agents to scale from 15 to 500+ steps through advanced planning and memory systems, revolutionizing how AI handles complex tasks. Learn more about this evolution https://
philschmid.de/agents-2.0-dee
p-agents
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