Category: Data Science
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The Future of Work With AI: Beyond Hype and Fear
AI is increasingly treated as a universal tool, with its implications on the shape of future workplaces being substantial. As labor shortage looms due to an aging population and reduced immigration, AI could amplify productivity without increasing unemployment. Professionals are actively embracing AI technologies. Research shows employers see AI as a productivity boost rather than…
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Attention to LLM Architectures: An Intelligent ML Engineering Guide
The article presents an informed view on different architectures used in modern language models (LLMs), including Encoder-Decoder, Encoder-Only, and Decoder-Only models. It defines their functions and specific uses. While the less complex Decoder-Only models like GPT have achieved excellent results, the article suggests that the choice of model should depend on the end application and…
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Not-So-Large Language Models: Good Data Overthrows the Goliath
In this article, we will see how Language Models (LM) can focus on better data and training strategies rather than just brute size to achieve LLM-like results (sometimes even better) and how people are already doing it successfully and democratically.
GennSev 