Research across 1,372 participants and 9K+ trials details "cognitive surrender", where most subjects had minimal AI skepticism and accepted faulty AI reasoning (Kyle Orland/Ars Technica)
Kyle Orland / Ars Technica : Research across 1,372 participants and 9K+ trials details cognitive surrender , where most subjects had minimal AI skepticism and accepted faulty AI reasoning When it comes to large language model-powered tools, there are generally two broad categories of users.
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modellanguage modelreasoningVLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors
Vision Language Models struggle with fine-grained visual perception tasks due to their language-centric training approach, performing poorly on unnamed visual entities despite having relevant information in their representations. (1 upvotes on HuggingFace)

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The Real Size of AI Frameworks: A Wake-Up Call
You Think You Know What You're Installing When someone says "just install PyTorch," you probably think "how bad can it be?" It's a deep learning library, right? A few hundred megabytes, maybe? Think again. I built pip-size to expose the hidden cost of Python packages. And what I found in the AI ecosystem is... shocking. The Numbers Don't Lie I ran pip-size on the most popular AI frameworks. Here are the results: Framework Package Size Total (with deps) torch 506.0 MB 2.5 GB 🤯 tensorflow 545.9 MB 611.9 MB paddlepaddle 185.8 MB 212.1 MB jax 3.0 MB 137.1 MB onnxruntime 16.4 MB 39.5 MB transformers 9.8 MB 38.4 MB keras 1.6 MB 29.5 MB The PyTorch Surprise Here's what happens when you pip install torch : torch==2.11.0 506.0 MB (total: 2.5 GB) ├── nvidia-cudnn-cu13==9.19.0.56 349.1 MB ├── nvidia




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