Foundations of Generative AI

Ken Huang (), Yang Wang () and Xiaochen Zhang ()
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Ken Huang: DistributedApps.ai
Yang Wang: Hong Kong University of Science and Technology
Xiaochen Zhang: FinTech4Good

Chapter Chapter 1 in Generative AI Security, 2024, pp 3-30 from Springer

Abstract: Abstract This chapter offers an introduction to the field of Generative AI (GenAI), providing critical foundational knowledge on neural networks, deep learning, advanced architectures, and recent innovations propelling this domain. It delineates GenAI as a branch of AI focused on creating novel, coherent content, distinguishing it from discriminative models. Tracing the origins of GenAI, the chapter elucidates the concepts of neural networks, unraveling their components like input layers, hidden layers, and output layers. Backpropagation, which facilitates training through gradient computation, is explained in detail. The chapter progresses to explore deep learning, attributed to increases in compute power and data availability. Techniques like convolutional and recurrent neural networks, which enable feature learning, are highlighted. Advanced architectures like transformers and diffusion models, based on attention mechanisms and reversed diffusion processes, respectively, are analyzed as cutting-edge innovations. The chapter concludes with promising new developments like Hinton’s Forward-Forward algorithm, Meta’s I-JEPA model, privacy-preserving federated learning, and integration of reasoning agents, painting an exciting outlook for the future. Overall, the chapter provides a layered knowledge base, spanning history, techniques, architectures, and innovations in GenAI. With its comprehensive yet accessible approach, it aims to equip readers with a holistic understanding of the foundations propelling GenAI.

Keywords: GenAI; Neural networks; GPT; LLM; Deep learning; Backpropagation; Transformers; Diffusion models; Agents; Federated learning; Overfitting; Computer vision; Natural language processing (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-031-54252-7_1

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DOI: 10.1007/978-3-031-54252-7_1

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