In this episode of Data Science at Home, we dive into the hidden costs of AI’s rapid growth — specifically, its massive energy consumption. With tools like ChatGPT reaching 200 million weekly active users, the environmental impact of AI is becoming impossible to ignore. Each query, every training session, and every breakthrough come with a price in kilowatt-hours, raising questions about AI’s sustainability.   Join us, as we uncovers the staggering figures behind AI's energy demands and explores practical solutions for the future. From efficiency-focused algorithms and specialized hardware to decentralized learning, this episode examines how we can balance AI’s advancements with our planet's limits. Discover what steps we can take to harness the power of AI responsibly!   Check our new YouTube channel at https://www.youtube.com/@DataScienceatHome   Chapters 00:00 - Intro 01:25 - Findings on Summary Statics 05:15 - Energy Required To Querry On GPT 07:20 - Energy Efficiency In BlockChain 10:41 - Efficicy Focused Algorithm 14:02 - Hardware Optimization 17:31 - Decentralized Learning 18:38 - Edge Computing with Local Inference 19:46 - Distributed Architectures 21:46 - Outro     #AIandEnergy #AIEnergyConsumption #SustainableAI #AIandEnvironment #DataScience #EfficientAI #DecentralizedLearning #GreenTech #EnergyEfficiency #MachineLearning #FutureOfAI #EcoFriendlyAI #FrancescoFrag #DataScienceAtHome #ResponsibleAI #EnvironmentalImpact