Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities. The speed at which generative AI technology is developing isn’t making this task any easier.
This article is a collaborative effort by Michael Chui, Eric Hazan, Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, and Rodney Zemmel, representing views from QuantumBlack, AI by McKinsey McKinsey Digital the McKinsey Technology Council the McKinsey Global Institute and McKinsey’s Growth, Marketing & Sales Practice. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. Generative AI applications such as ChatGPT, GitHub Copilot, Stable Diffusion, and others have captured the imagination of people around the world in a way AlphaGo did not, thanks to their broad utility-almost anyone can use them to communicate and create-and preternatural ability to have a conversation with a user. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness. As a result, its progress has been almost imperceptible. Special Report The economic potential of generative AI: The next productivity frontier (68 pages)ĪI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers.
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