I recently came across a research publication titled The Shift from Models to Compound AI Systems on the Berkeley Artificial Intelligence Research (BAIR) Lab's website. Being on a time crunch I wanted a quick gist of the blog post without getting into the nitty-gritties. To make my work easier, I leveraged the capabilities of Google's Gemini 1.5 Pro to help me in this process of summarization. Once I got a rudimentary idea of the topic, I dived deep into it, and undoubtedly BAIR has done a great job with this πŸ‘πŸ»

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Last year, I co-authored a book chapter titled Do Not β€˜Fake It Till You Make It’! for Springer Nature Group's Deep Learning for Social Media Data Analytics book series πŸ“• The publication walks through a comparative study of Deep Learning models to approach the tasks of identifying phony information, verifying the validity of various claims and facts, catching fake content, and so on. It eloquently analyzes the definition of Fake News specifically clickbait, hoax, satire, propaganda, hyperpartisan, and deepfakes in the world of social media, the various forms it can take, what causes its spread, and what are the rudimentary signs of such fake news. It further discusses the limitations of the detection algorithms with insights into the fairness, interpretability, and accountability along with providing pointers for the readers regarding emerging trends in this domain.

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I just read this very interesting article -- it talks about the life of human (especially women) data annotators based in small towns and villages in India. They are the lifeline behind the most unglamorous part of the AI pipeline: data annotation, or labeling. I also learnt that India is one of the world’s largest markets for data annotation labor!

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Volumes of crude data are available at our fingertips today, and the latest concept of a #DataLake helps store any type or volume of data as-is, process it in real-time or batch mode, and analyze it at scale πŸ€½πŸ§΅πŸ‘‡

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Machine Learning models are as good as the data they consume🍴Data impacts performance, fairness, robustness & scalability of #ML Systems. If not taken care of, it leads to a TON of tech debt over time in a corporate setting, downstream effects of which are termed as DATA CASCADES 🌊 πŸ§΅πŸ‘‡πŸ»

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I have been professionally working as a Machine Learning Engineer since more than 2 years now and also, recently co-authored a book titled β€œSculpting Data for ML: The first act of Machine Learning”. My past few experience have taught me that data does not get its due limelight in #MachineLearning as compared to complex model architecture. Keeping up with 'more data beats clever algorithms, but better data beats more data', here are top 5 tips for polishing the dataset to effectively solve #ML problems πŸ€–πŸ‘‡πŸ»

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