{"id":484,"date":"2024-10-12T00:30:51","date_gmt":"2024-10-12T00:30:51","guid":{"rendered":"https:\/\/pressbooks.ulib.csuohio.edu\/projectmanagement2ndedition\/?post_type=chapter&p=484"},"modified":"2025-01-19T22:01:41","modified_gmt":"2025-01-19T22:01:41","slug":"14-1-generative-ai-genai","status":"publish","type":"chapter","link":"https:\/\/pressbooks.ulib.csuohio.edu\/projectmanagement2ndedition\/chapter\/14-1-generative-ai-genai\/","title":{"rendered":"14.1 Generative AI (GenAI)"},"content":{"raw":"
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\r\n\r\nIn November 2022, OpenAI made a groundbreaking announcement with the release of ChatGPT, a generative AI model that surprised the world with its remarkably human-like conversational abilities, showcasing an unprecedented level of coherence and versatility in generating text across various topics.[footnote]Retrieved from https:\/\/openai.com\/index\/chatgpt\/<\/a>[\/footnote]. ChatGPT gained one million users five days after launching, while it took 2.5 months for Instagram, 5 months for Spotify, 10 months for Facebook, and 3.5 years for Netflix to reach the same number of users[footnote]Retrieved from https:\/\/www.statista.com\/chart\/29174\/time-to-one-million-users\/<\/a>[\/footnote]. ChatGPT hit 100 million monthly active users in January 2023 and more than 200 million people weekly in August 2024[footnote].Retrieved from https:\/\/www.theverge.com\/2024\/8\/29\/24231685\/openai-chatgpt-200-million-weekly-users<\/a>[\/footnote].\r\n\r\nUnlike earlier versions of artificial intelligence tools, ChatGPT quickly captured public attention for its ability to generate coherent, human-like text responses across various topics. Built on the foundation of the GPT-3.5 architecture (Generative Pre-trained Transformer[footnote]Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, \u0141., & Polosukhin, I. (2017). Attention is all you need. In 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. https:\/\/arxiv.org\/abs\/1706.03762[\/footnote]), this tool demonstrated a significant leap in natural language processing by offering new possibilities in communication, content creation, and automated tasks. Its free web interface offered accessibility to the general public. Therefore, it allowed millions of users to explore the potential of AI to be integrated into daily life and professional workflows. This launch showcased the maturity of generative AI and sparked widespread conversations about its applications, ethical implications, and future developments.\r\n\r\nThe Future of Jobs Report 2023 by the World Economic Forum indicated that some 75% of companies are set to have adopted AI technologies by 2027, while 80% plan to accelerate automation during this period[footnote].Retrieved from https:\/\/www.weforum.org\/agenda\/2023\/08\/ai-artificial-intelligence-changing-the-future-of-work-jobs\/<\/a>[\/footnote]. This rapid technological shift is expected to significantly transform job roles, with a growing demand for skills in AI, data analysis, and digital tools, while also displacing more routine, manual tasks. As companies embrace these technologies, reskilling and upskilling initiatives will become critical to preparing the workforce for these changes.\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n

14.1.1 Generative AI (GenAI)<\/h2>\r\nGenAI<\/strong> refers to a subset of artificial intelligence (AI) technologies that generate new content\u2014text, images, music, code, and other forms of data\u2014by learning from existing patterns and examples. Unlike traditional AI models (i.e., classification, regression, and clustering) that primarily classify, predict, or analyze data, GenAI models can create novel outputs that mimic the data they were trained on[footnote]Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.[\/footnote].\r\n\r\nGenAI is powered by advanced machine learning techniques, particularly neural networks like transformers<\/em>, which enable these systems to process large datasets and produce original content based on patterns learned during training[footnote]Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, \u0141., & Polosukhin, I. (2017). Attention is all you need. In 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. https:\/\/arxiv.org\/abs\/1706.03762[\/footnote].\r\n\r\nMajor types of GenAI models are:\r\n
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    Large Language Models (LLMs)<\/strong><\/h3>\r\n