Responsible Generative AI
ABSTRACT
The world of AI is undergoing a major transformation with the advent of the new Foundation Models . Foundation Models are , no doubt , very powerful with amazing generative capabilities to produce original lookalikes in various disciplines like literature , art , software , science , scholastic work , and other creative areas . However , the power unleashed by Generative AI technology has triggered several concerns about its limitations and impact on human society and surrounding ecosystems .
Governments , social scientists , technologists including many heads of AI organizations are expressing concerns about the potential negative impacts of Generative AI and the need to regulate this emerging technology . This paper presents detailed investigation and assessments of the concerns centered around Generative AI and discusses the guard rails that need to be put in place for effective and safe use of this technology . Specific topics that are covered include concerns of bias , accuracy and non-transparency of Generative AI Models as well as how this technology impacts labor , IP / copyrights , privacy , education , basic human cognitive skills , and the carbon footprint .
1 INTRODUCTION
The world of AI models is undergoing a significant transformation with the advent of the newly developed Foundation Models . Foundation Models are very powerful with impressive generative capabilities to produce original look-alikes in various disciplines like literature , art , science , scholastic work , and other creative areas . Governments , social scientists , technologists , and even the heads of AI organizations are expressing concerns about the potential misuse of the power unleashed by Foundation Models and Generative AI .
The impacts are in different facets – labor market , IP and copyrights , plagiarism , education , basic human cognitive skills , of course , the carbon footprint . Our aim in this paper is to address these impacts and concerns , whilst remaining hopeful and optimistic about the potential benefits of this technology .
The new AI models 1 that provide generative capabilities are trained on massive data – texts , images , audio , and video and structured data in tables and files . These deeply trained models are not only good at recognizing patterns but can also generate / synthesize new outputs based on what they are trained on . This leads to the capability to compose answers , create summaries , and generate new images , audios , and videos .
While the deep learning transformer models had shown similar capabilities earlier , they reached a threshold limit when trained on massive amount of information , leading to humungous models ,
1 https :// arxiv . org / pdf / 2306.17170 . pdf Journal of Innovation 15