Innovation & Technology

Understand Generative AI with INSEAD3 min read

April 24, 2024 3 min read


Understand Generative AI with INSEAD3 min read

Reading Time: 3 minutesReading Time: 3 minutes

Generative AI, as explained by Anton Ovchinnikov in the Welcome to INSEAD Explains video series, involves machine learning models trained to produce new data that mirrors their training data. This marks a departure from traditional models aimed at specific predictions. It means that AI differs from algorithmic models by integrating the ability to combine human-like data processing, acquired through machine learning.

This functionality is similar to the living human intellect, which also formulates thoughts in the moment based on everything read and learned throughout life. AI is akin to accessing big data without being confined to specific requested forecasts. Yes, this makes it suitable for more general tasks, but it also enables the generation of more varied and multidimensional outcomes.

In our recent article Symbiosis between humans and AI, you can find how this synergy between human and machine learning happens. Moreover, there is an explanation about the technical side of the issue. So, Generative AI is a collaborative, interactive process in which humans and machines continuously benefit from and contribute to each other’s growth.

ChatGPT exemplifies such foundation models, showcasing their capability to perform general tasks effectively. These insights emerge from INSEAD’s focus on GenAI, exploring its core, potential, and avenues for value creation, underscored by rigorous academic insight.

Unique AI unicorns

In 2023, the AI sector witnessed the emergence of 22 new unicorns despite a 39% year-over-year decline, outperforming other industries like fintech, digital health, and retail tech by significant margins. Particularly, generative AI companies soared to unicorn status notably faster, reaching $1 billion valuations in just over 3 years, half the time of other startups. This trend underscores the rapid growth and potential within the generative AI space.

AI in education: For or against?

In the realm of education, AI’s potential is gradually being recognized. Canva’s study, titled AI in Education, surveyed 1,000 U.S. teachers to understand their views on integrating AI into their work. The findings revealed that while a vast majority of teachers are curious about AI educational tools, a significant portion lacks substantial experience with the technology. Despite this, teachers acknowledge AI’s positive impact on student productivity, creativity, and particularly its promise for students with different learning needs, highlighting AI’s role in personalized education. However, a significant 93% expressed uncertainty about where to begin, highlighting a knowledge gap.

To conclude, generative AI is rapidly transforming various sectors, including education, where its integration promises to enhance personalized learning and support diverse student needs. A considerable gap in practical knowledge and experience with AI technologies underscores the need for comprehensive educational programs and resources that equip teachers to effectively harness AI’s capabilities. Addressing this knowledge gap is essential for maximizing AI’s benefits in education and beyond.

Anton Ovchinnikov, a Professor of Management Analytics at the Smith School of Business in Kingston, Canada.

Anton’s research spans theoretical aspects like behavioral operations, revenue management, and environmental sustainability, as well as applied data-driven applications across various sectors. Before his current roles, he taught at the University of Virginia and the University of Toronto, and he continues to serve as a Visiting Professor of Decision Sciences at INSEAD. Ovchinnikov’s extensive teaching and professional experience, coupled with a PhD in operations management, underline his expertise in shaping the discourse on generative AI and sustainability in business practices.