What is generative AI and how does it differ from traditional AI?

As artificial intelligence continues to revolutionize industries, understanding the distinction between generative AI and traditional AI has become crucial for businesses looking to harness AI’s power. With the global AI market projected to reach $1.3 trillion by 2032, event planners and decision-makers need to grasp these two approaches to stay competitive.

To shed light on the question “What is generative AI and how does it differ from traditional AI?”, we’ve invited two industry experts to share their insights. Their diverse perspectives offer valuable guidance for navigating the rapidly evolving AI landscape.

Our contributors highlight key differences in capabilities, applications, and potential impact. While traditional AI excels at specific tasks and data analysis, generative AI pushes boundaries with its ability to create new content and adapt to complex scenarios. As we explore their explanations, we’ll uncover how these distinctions shape real-world applications and the future of AI technology.

Let’s begin with our first expert, Dror Gill, a renowned AI specialist with extensive experience in both traditional and generative AI applications.

Dror Gill

Dror Gill, a Generative AI Evangelist and winner of the 2021 Technology and Engineering Emmy® Award, explains the difference between generative AI and traditional AI: “Traditional AI is mostly focused on analyzing data, but generative AI is focused on creating content.

Generative AI creates content based on user prompts, including text, images, music, video clips, and even biological data like proteins and DNA sequences. It learns patterns of human creation and imitates them. For example, an AI image generator trained on millions of images can create new, original images by combining learned patterns. Gill illustrates this with a vivid example: “If you ask it to create an image of a spaceship in the style of Salvador Dali, even though Salvador Dali never painted spaceships, it’ll combine the colourful patterns it learned from Dali’s paintings together with the shape of a spaceship, which it learned from spaceship photos.”[1]

The key distinction lies in the output: traditional AI analyzes and categorizes existing data, while generative AI produces new, original content. This capability makes generative AI particularly valuable for creative tasks and content generation across various industries.

As we explore this topic further, additional expert perspectives will provide deeper insights into the applications and implications of generative AI for businesses and organizations. Event planners and decision-makers should consider how generative AI tools could enhance their content creation processes and boost creativity in their projects.

Amit Joshi

Amit Joshi, a professor at IMD with extensive expertise in marketing management, digital media, and data analytics, offers a unique perspective on generative AI and its distinction from traditional AI.

What is generative AI and how does it differ from traditional AI?
Generative AI is a new technology that understands context and can create or generate new content, unlike traditional AI which primarily focuses on prediction and specific tasks.

Joshi emphasizes that generative AI represents a significant leap forward in artificial intelligence capabilities. While many organizations have been using AI for years, he points out that generative AI is truly different and potentially a general-purpose AI technology. This contrasts with traditional machine learning, which has been widely used for the past 10-20 years but lacks the contextual understanding and creative capabilities of generative AI.

One of the key insights Joshi brings is the ability of generative AI to understand and work with context. This contextual awareness allows the technology to not only predict outcomes but also create new content, visuals, and images. This capability sets generative AI apart from traditional AI systems, which are typically designed for specific tasks and lack the flexibility to generate novel outputs.

Joshi’s perspective complements previous speakers by highlighting the potential impact of generative AI on various industries. As an expert who has interacted with corporate clients across telecom, media, manufacturing, and other sectors, he recognizes the broad applicability of this technology. His insights suggest that businesses should be prepared to adapt to and leverage generative AI’s capabilities, as it represents a significant shift in the AI landscape.