Have you ever wondered how generative AI can create stunning artwork 🖼️ or craft unique recipes 👩🏻🍳 based on your fridge contents? This video dives deep into the mechanics of generative AI, demystifying its inner workings and showcasing its possibilities.
In this video, you’ll discover how generative AI gathers data 📊, trains neural networks 🧠, generates new content 📝, and continually improves through feedback. We’ll also touch on popular examples like GPT and DALL-E and discuss the ethical considerations surrounding this cutting-edge technology.
00:00 – Introduction
00:30 – Step 1: Data Collection
00:46 – Step 2: Training
01:18 – Step 3: Generation
01:32 – Step 4: Iterative Improvement
01:57 – Ethical Concerns and Responsible Use
02:16 – Conclusion and Further Learning
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date: 2024-07-30 18:50:54
duration: 00:02:55
author: UCZ50rYSkYQG31YDEJm9Di_g
Casual editorial comment
FatCat inferred the following :
The step-by-step explanation of the process is crystal clear, and the inclusion of real-world examples like GPT and DALL-E really helps to illustrate the concepts. I particularly appreciated the mention of iterative improvement, as it’s essential to understand that generative AI models are not static entities, but rather dynamic systems that continually refine their output based on feedback.
Speaking of GPT, did you know that the original GPT-3 model was trained on a staggering 45 gigabytes of text data, including books, articles, and websites? To put that into perspective, that’s equivalent to the entire contents of the Library of Congress digitized and processed by a neural network! As a result, GPT-3 can generate coherent paragraphs, even entire passages, that are often indistinguishable from human-written text. It’s a testament to the power of large-scale machine learning and the limitless potential of generative AI.
Overall, I think this video does an excellent job of introducing the basics of generative AI and setting the stage for further exploration. Well done, Coursera!
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