Artificial Intelligence and the Sustainable Development Goals: GPT-3`s Reflections on the Society Domain

Abstract

Artificial Intelligence (AI) experienced significant advancements in recent years, and its impact is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential impact of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather infor-mation on GPT-3`s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We plan subsequent research for effects on ecologic and economic domains of SDGs. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

Use of AI-Based GPTs in Experimental, Empirical, and Exploratory Research Methods

“…The use of AI-based Generative Pre-trained Transformers (GPTs) in exploratory research is an innovative approach that can significantly enhance the initial stages of inquiry when researchers seek to gain insights, generate hypotheses, and explore uncharted territory [21][22][23][24][25][26][27][28][29][30]. Table 3 depicts a detailed explanation of how AI-based GPTs can be effectively employed in exploratory research:…”

The Rise of GPT-3: Implications for Natural Language Processing and Beyond

“…The model consists of multiple layers of self-attention and feedforward neural networks. The self-attention layers allow the model to attend to different parts of the input text, while the feedforward layers apply non-linear transformations to the output of the self-attention layers [11].…”

“…Conversational agents and chatbots are another important application of GPT-3 in natural language processing. Here's a brief overview of each [11]:…”