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From Supervised Learning to Contextual Bandits: The Evolution of AI Decision-Making

Towards AI

Originally published on Towards AI. Supervised Learning: Train once, deploy static model; Contextual Bandits: Deploy once, allow the agent to adapt actions based on content and its corresponding reward. This blog explores the differences between supervised learning and contextual bandits.

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Introducing ‘Algorithm of Thoughts’ – How Microsoft’s human-like reasoning algorithm could make AI smarter

Data Science Dojo

Virginia Tech and Microsoft unveil the Algorithm of Thoughts, a breakthrough AI method supercharging idea exploration and reasoning prowess in Large Language Models (LLMs). Empowering Language Models with In-Context Learning At the heart of this pioneering approach lies the concept of “in-context learning.”

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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

In the recent discussion and advancements surrounding artificial intelligence, there’s a notable dialogue between discriminative and generative AI approaches. These methodologies represent distinct paradigms in AI, each with unique capabilities and applications. What is Generative AI?

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Pushing the Boundaries of AI-based Lossy Compression

IBM Data Science in Practice

Currently, hand-crafted compression algorithms, often designed for general image data like JPEG2000, are applied. On March 10, 2025, the Embed2Scale consortium launched the 2025 CVPR EARTHVISION Data Challenge , inviting researchers and AI practitioners to develop innovative EO data compression techniques.

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Exploring All Types of Machine Learning Algorithms

Pickl AI

Summary: Machine Learning algorithms enable systems to learn from data and improve over time. These algorithms are integral to applications like recommendations and spam detection, shaping our interactions with technology daily. These intelligent predictions are powered by various Machine Learning algorithms.

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How Should Self-Supervised Learning Models Represent Their Data?

NYU Center for Data Science

Self-supervised learning (SSL) has emerged as a powerful technique for training deep neural networks without extensive labeled data. However, unlike supervised learning, where labels help identify relevant information, the optimal SSL representation heavily depends on assumptions made about the input data and desired downstream task.

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Semi- and Self-Supervised Learning Help Clinicians Minimize Manual Labeling in Medical Image…

NYU Center for Data Science

Our study demonstrates that machine supervision significantly improves two crucial medical imaging tasks: classification and segmentation,” said Cirrone, who leads AI efforts at the Colton Center for Autoimmunity at NYU Langone. “The