Generative AI: Future of AI

Generative AI, like ChatGPT, is reshaping various sectors, sparking both excitement and concern reminiscent of the early internet era. Key questions emerge: mitigating bias, navigating copyright issues, redefining job roles, addressing misinformation risks, managing development costs, and grappling with doomerism in policymaking. Despite the lack of a "killer app," generative AI's impact is profound, urging society to confront its ethical, social, and economic implications.

Generative artificial intelligence, like ChatGPT and Bard, is rapidly being adopted across various sectors, including government, healthcare, and education. Despite ethical concerns and differing opinions within higher education, its integration is becoming inevitable, prompting institutions to adapt and develop policies and practices to prepare students for a future where AI plays a significant role in both learning and work environments.

Yokosuka City in Japan's Kanagawa prefecture is implementing ChatGPT to assist with administrative tasks, citing the nationwide population decline as a driving factor behind the decision. With ChatGPT handling routine administrative work, employees can focus on more essential tasks, aiming to improve workflow and efficiency within the government, addressing challenges posed by the shrinking population.

Generative Artificial Intelligence (AI) is more likely to augment than destroy jobs, according to a global study by the International Labour Organization (ILO), revealing that while clerical work faces high exposure to automation, most jobs and industries are only partially exposed, with potential impacts on job quality rather than quantity, necessitating the implementation of policies supporting a fair and consultative transition to manage the socioeconomic effects of Generative AI.

The article discusses the transformative impact of artificial intelligence (AI) in healthcare, particularly focusing on large language models (LLMs) like BERT, GPT, and LaMDA, which have shown exponential growth and potential in improving diagnostic accuracy, treatment planning, and patient outcomes. The article emphasizes that AI, particularly LLMs, is not designed to replace doctors but to collaborate with them, offering tools such as digital scribes and conversation summarization tools to enhance efficiency and decision-making. The key message is that successful adoption of AI in healthcare organizations requires a multidisciplinary approach, involving doctors, administrators, patients, and others, to ensure ethical, inclusive, and responsible practices while addressing legal, privacy, and security concerns.

The article explores scenarios for the impact of generative AI on work, considering both job augmentation and displacement. It emphasizes the importance of policy interventions, such as industrial strategies, to shape the direction of AI adoption, support displaced workers, and ensure the benefits of automation are widely shared. It proposes ringfencing certain tasks from automation to preserve human involvement and stimulate the growth of jobs less exposed to AI.

McKinsey's latest report on Generative AI highlights the need for upskilling and training in the face of AI's impact on the workforce, emphasizing the technology's role in augmenting rather than eliminating jobs, with a focus on the importance of individual and organizational adaptability.