Название: Converging Minds: The Creative Potential of Collaborative AI
Автор: Aleksandra Przegalinska, Tamilla Triantoro
Издательство: CRC Press
Год: 2024
Страниц: 171
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This groundbreaking book explores the power of Collaborative AI in amplifying human creativity and expertise. Written by two seasoned experts in data analytics, Artificial Intelligence (AI), and Machine Learning, the book offers a comprehensive overview of the creative process behind AI-powered content generation. It takes the reader through a unique collaborative process between human authors and various AI-based topic experts, created, prompted, and fine-tuned by the authors.

This book features a comprehensive list of prompts that readers can use to create their own ChatGPT-powered topic experts. By following these expertly crafted prompts, individuals and businesses alike can harness the power of AI, tailoring it to their specific needs and fostering a fruitful collaboration between humans and machines. With real-world use cases and deep insights into the foundations of Generative AI, the book showcases how humans and machines can work together to achieve better business outcomes and tackle complex challenges. Social and ethical implications of collaborative AI are covered and how it may impact the future of work and employment. Through reading the book, readers will gain a deep understanding of the latest advancements in AI and how they can shape our world.

Generative AI represents a category of supervised, unsupervised, and Deep Learning models designed to generate new data that resembles an existing data set. While discriminative models aim to classify or differentiate between different kinds of data, generative models mainly create. They produce outcomes that extend beyond analyzing or categorizing data, venturing into the area of creation and simulation. The term “Generative AI” refers to ML models designed to generate new data that resembles a given dataset that can contain numerical textual or audiovisual data. These models learn the underlying patterns and distributions from the data during training and can produce novel outputs such as text, images, or music during inference. Popular examples include GANs, VAEs, and some types of RNNs.

Converging Minds: The Creative Potential of Collaborative AI is essential reading for anyone interested in the transformative potential of AI-powered content generation and human-AI collaboration. It will appeal to data scientists, Machine Learning architects, prompt engineers, general computer scientists, and engineers in the fields of Generative AI and Deep Learning.








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