Along with developing a framework for scalable oversight, we also conduct a proof of concept experiment that demonstrates a couple of question-answering tasks that work well under this paradigm with current language models:
MACHINE LEARNING
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Challenges in Studying Model Assistance: Task Selection and Experimental Design
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It’s also challenging to study: For most tasks today, we don’t actually need our model’s help in this way. So testing these methods will require us to be clever about how we choose our tasks and design our experiments.
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AI Systems Improving Human Oversight of Large Language Models
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In "Measuring Progress on Scalable Oversight for Large Language Models” we show how humans could use AI systems to better oversee other AI systems, and demonstrate some proof-of-concept results where a language model improves human performance at a task.
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How Much Does Attention Actually Attend in Transformers?
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How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers Hassid et al.: https://
arxiv.org/abs/2211.03495 #ArtificialIntelligence #DeepLearning #MachineLearning -
Transfer Learning for Medical Imaging and X-Ray Diagnosis with AI
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It's World Radiography Day Radiographs have come a long way since its inception. Today with the help of AI, medical diagnosis has had a lot of innovations. Transfer Learning is one such innovation that finds use in medical imaging like X-Rays. https://
learnopencv.com/transfer-learn
ing-for-medical-images/
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YOLOv7 Pose: Single-Stage Multi-Person Keypoint Detection
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Unlike conventional Pose Estimation algorithms, YOLOv7 pose is a single-stage multi-person keypoint detector. It is similar to the bottom-up approach but heatmap free. It is an extension of the one-shot pose detector – YOLO-Pose. https://
learnopencv.com/yolov7-pose-vs
-mediapipe-in-human-pose-estimation/
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#yolov7 #poseestimation -
Catherine Adenle Daily: AI and Machine Learning Updates
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The latest The Catherine Adenle Daily! https://
paper.li/CatherineAdenl
e?edition_id=c9e86830-5f2a-11ed-9d70-fa163ed80008
… #ai #machinelearning -
Smaller Models with Better Data Can Outperform Larger Ones
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Great point. We are seeing more and more than smaller models with better objectives or data can beat big ones! My main point is that an approach shouldnt go away as models get better. Scale is just one way of getting better
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Reverse Engineering Systems: Breaking Down to Rebuild Better
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Breaking down already built projects, and systems into smaller components and learning them individually to understand how it was engineered then redesign and build it again