The Recent History of Generative AI
As interest in generative AI and large language models (LLMs) continues to grow, I’d like to offer a brief update on how generative AI has progressed and how it has been applied to research publishing processes since ChatGPT was released. This update addresses business, application, technology, and ethical aspects of generative AI, as well as some personal observations I hope will foster discussion and stimulate further consideration of generative AI tools.
Despite the hyperbole and the hyperbolic doomsayers, there is a need for a sober reflection on the implications of ChatGPT in scholarly writing. The first step is having a good understanding of the concepts and terminology being thrown around. This piece, which Scholarly Kitchen published in July 2023, provides an excellent foundation for this understanding and discussion. This recommended reading for research offices, graduate research schools, libraries and digital research teams.
A note on AI terminology
You may have seen a variety of AI terms in media coverage, such as large language models (LLMs), generative AI, AI-generated content (AIGC), artificial narrow intelligence (ANI), and artificial general intelligence (AGI). Before we start, a brief explanation of these concepts and how they are related to each other:
BUSINESS AND STRATEGY
ChatGPT is the fastest-growing app in history. Within two months, ChatGPT reached 100 million users, compared to the 42 months it took for WhatsApp to reach the same milestone. This rapid growth is especially remarkable given that ChatGPT reportedly blocks a huge number of Asian accounts due to possible batch account registration, API abuse, and technical issues caused by excessive traffic.
What does this mean for businesses? By revolutionizing app accessibility and problem-solving, ChatGPT threatens not only Google Search and Amazon but also the Apple Store, Google Play Store, and Amazon Skill Store. Users can simply set goals, then allow the AI to choose the appropriate services or apps, execute tasks, and aggregate the results.