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How to Work Smarter, Not Harder, with Artificial Intelligence

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As artificial intelligence (AI) continues to transform industries—from healthcare and finance to entertainment and education—the demand for professionals who understand its inner workings is skyrocketing. Yet, navigating the world of AI can feel overwhelming, with its complex algorithms, vast datasets, and ever-evolving tools.

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How I Automated My Machine Learning Workflow with Just 10 Lines of Python

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Identify top-performing algorithms based on accuracy, F1 score, or RMSE. August 20, 2024 29 min read Back To Basics, Part Uno: Linear Regression and Cost Function Data Science An illustrated guide on essential machine learning concepts Shreya Rao February 3, 2023 6 min read YouTube X LinkedIn Threads Bluesky Your home for data science and Al.

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Reinforcement Learning from Human Feedback, Explained Simply

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In this article, we will talk about RLHF — a fundamental algorithm implemented at the core of ChatGPT that surpasses the limits of human annotations for LLMs. Loss function used in the RLHF algorithm. The incredible performance of ChatGPT led to the rapid development of other powerful LLMs.

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Challenges of Artificial Intelligence – Understanding the Roadblocks in AI Adoption

Pickl AI

Summary: Artificial Intelligence faces significant challenges in 2025, such as data quality, privacy concerns, algorithmic bias, lack of transparency, and talent shortages. Algorithmic bias threatens fairness and requires ongoing mitigation efforts. Lack of transparency reduces trust and accountability in AI systems.

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Italy slaps OpenAI with a €15M fine over GDPR breach in ChatGPT

Dataconomy

The fine, stemming from a 2023 investigation, emphasizes the seriousness of data privacy compliance under EU regulations. OpenAI’s previous failure to report a security breach in March 2023 also contributed to this decision.

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Cool Site Shows Exactly Which Books Zuckerberg's Minions Illegally Downloaded to Train Meta's AI

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For all the revolutionary change artificial intelligence promises, it also makes lofty demands. The large language models (LLMs) that underpin products like OpenAI's ChatGPT, for instance, need to devour enormous datasets of written words to fine tune an algorithm to follow the rules of language.

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Lessons Learned After 6.5 Years Of Machine Learning

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Coding ML algorithms, debugging obscure data issues, crafting a hypothesis — it all demands deep work. The world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals. What does is the ability to focus deeply.