Editorial: GPT revolutionizing AI applications: empowering future digital transformation

A digest of Robin Qiu's editorial on GPT, large language models, and the future of digital transformation

Robin Qiu· Digital Transformation and Society· 2023· DOI 10.1108/dts-05-2023-066

The core problem

The editorial opens by defining GPT as a generative pre-trained transformer, a type of large language model (LLM) trained using neural networks on vast datasets primarily derived from the Internet. LLMs leverage deep learning to produce human-like text and perform natural language processing tasks such as answering questions, summarizing texts, and generating or debugging code. The author attributes the current global AI wave to ChatGPT, whose engine was trained by OpenAI on billions of texts from books, articles, and web pages. The release of GPT-4 further enhanced capabilities by accepting longer text and images as input, generating richer responses. The editorial positions GPT as a transformative technology that has captured the attention of end users, educators, engineers, scientists, entrepreneurs, investors, and policymakers alike.

Innovation

The editorial reports several key observations: - GPT and similar AI models have generated a viral sensation, becoming a hot topic across diverse stakeholders. - The overwhelming attention has led to the emergence of many ChatGPT variants globally, some of which may be imposters driven by capitalism or marketing propaganda. - AI policymakers and educators express deep concern about negative outcomes such as disinformation, academic cheating, deepfakes, bias, ethical implications, and political propaganda. - Despite these concerns, the majority of GPT-related AI products are transforming the world positively, improving productivity, convenience, and daily life assistance. - The current state is somewhat chaotic due to a lack of AI regulations and risk management protocols. - AI will continue to evolve, and future outputs could be drastically beyond our imagination. - The world is witnessing the dawn of a new digitalized era, with potential changes comparable to the arrivals of electricity, personal computers, the Internet, and smartphones.

The editorial opens by defining GPT as a generative pre-trained transformer, a type of large language model (LLM) trained using neural networks on vast datasets primarily derived from the Internet. LLMs leverage deep learning to produce human-like text and perform natural language processing tasks such as answering questions, summarizing texts, and generating or debugging code. The author attributes the current global AI wave to ChatGPT, whose engine was trained by OpenAI on billions of texts from books, articles, and web pages. The release of GPT-4 further enhanced capabilities by accepting longer text and images as input, generating richer responses. The editorial positions GPT as a transformative technology that has captured the attention of end users, educators, engineers, scientists, entrepreneurs, investors, and policymakers alike.

As an editorial, this work does not employ a formal research methodology. Instead, it synthesizes observations and reflections on the rapid evolution and impact of GPT and generative AI. The author draws on publicly known developments, such as the release of ChatGPT and GPT-4 by OpenAI, and the proliferation of ChatGPT variants worldwide. The editorial's approach is discursive, highlighting both the positive transformations and the risks associated with GPT. It calls for more research on how GPT can revolutionize AI applications to empower future digital transformations. The methodology is thus qualitative and opinion-based, aiming to stimulate discussion and guide future inquiry rather than present empirical findings.

Why it matters

The author analyzes the dual nature of GPT's impact. On one hand, GPT empowers applications to perform a wide range of tasks, from text summarization and humanlike conversation to music and essay composition, poetry, and story writing. These capabilities drive significant improvements in productivity and convenience. On the other hand, the same capabilities enable fake news generation, deepfakes, and other malicious uses. The editorial argues that the current chaos stems from missing regulatory frameworks and risk management protocols. It calls for safe protocols and regulations to ensure that AI risks remain manageable. The author emphasizes that AI will continue to evolve, and its future output may exceed our imagination. The discussion concludes by urging more research on how GPT and similar technologies can revolutionize AI applications to positively empower future digital transformations in serving industry, government, public, society, and humanity at large. To illustrate the architecture of a GPT-based application, consider the following Mermaid diagram: Mathematically, the core of GPT is the transformer architecture, which uses self-attention to weigh the importance of different words in a sequence. The attention mechanism can be expressed as: Attention (Q, K, V) = softmax (\frac{Q K ^{T}}{d _{k}}) V where Q, K, and V are the query, key, and value matrices, and d_{k} is the dimension of the key vectors. This allows the model to capture long-range dependencies and generate coherent text.

Who should read this

CS practitioners and researchers

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