Author: MLDS
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A Survey of Deep Learning: From Activations to Transformers
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Discover the crucial strategies behind the critical advancements in deep learning, from the initial activations to the sophisticated transformers of today.
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Understanding Prompt Engineering in Zero-Shot Learning
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We examine why prompt engineering in zero-shot learning provides robust generalisation without overfitting, backed by classical PAC-Bayes bounds.
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Cloudy with High Chance of DBMS: A 10-Year Prediction for Enterprise-Grade ML
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This paper explores the intersection of machine learning and data management over the next decade and predicts key trends and technical challenges in enterprise scenarios.
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Transformers are SSMs: Connecting Models and Enhancing Efficiency
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Discover the groundbreaking synergy between Transformers and state-space models, paving the way for faster and more efficient deep-learning algorithms.
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Public Sentiments on Generative AI in Journalism Across Six Countries
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Through a detailed cross-national survey, discover the global public’s take on generative AI’s impact on journalism and other societal sectors.
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Postgres: A Comprehensive Solution for Vector Storage
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Leverage Postgres with pgvector for a simplified, performant, and consistent embedding storage solution—here’s why you might not need specialised vector databases.
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A Mathematical Framework for Understanding Transformer Circuits
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This intricate research delves into transformers’ internal computations, unraveling their mechanisms to better understand and anticipate their behavior in AI models.
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Generative AI in Banking: Potential and Pitfalls
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Generative AI is revolutionising the banking sector, promising efficiency gains while posing significant risks necessitating careful management.
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The Eternal Pursuit of Truth Machines
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From 13th-century visions to today’s AI, humanity’s quest to automate truth continues to evolve and fascinate.
