Although still an emerging area of interest in the consumer goods industry, generative artificial intelligence (AI) has made a prominent stance as tech with enormous potential and various applications, garnering a lot of attention, but also collecting several questions, concerns, and doubts.
To gain a better understanding of how generative AI might transform various fields across the industry, we’ve tapped into expert voices and research from Coresight, Microsoft, and Gartner.
While the consensus clearly points toward significant advancements in the area in the years to come, sources also agree that the way forward must be met with caution and small, strategic steps.
“Generative AI can result in biased and insensitive content based on the data it got trained on, as it needs a large quantity of data to train its AI models, which can damage the brand’s reputation,” warns Subroto Mukherjee, Microsoft’s strategy lead for cross industry solutions, metaverse, Web 3.0, NFT, AI, OpenAI, ChatGPT, gaming, and commerce.
Editor’s Note: Subroto Mukherjee’s contributions are his own viewpoints based on his personal experience, research, and learnings and do not necessarily reflect the opinions of his company.
What Is Generative AI?
Early iterations of artificial intelligence have been around since the 1950s. What then are the differences between general AI and generative AI?
Primarily, says John Harmon, senior analyst at Coresight Research, generative AI tech like ChatGPT uses deep learning neural nets that mimic brain cells to generate the best text from the inputs.
Meanwhile, other AI-powered tech like machine learning takes in data sets to identify relationships among data and make predictions.
“Many of these are quantitative forecasts/predictions, and the AI element ensures that the models evolve over time to stay accurate,” says Harmon.