"The technology evolved from early neural networks like the perceptron in 1957 and chatbots such as ELIZA in 1961. High-quality generation became viable after Generative Adversarial Networks emerged in 2014, followed by transformer-based large language models that combine billions of parameters to produce coherent, contextually relevant output. McKinsey estimates generative AI could add $2.6 to $4.4 trillion to the global economy."
"Organizations using the technology report 10 to 15 percent savings in research and development expenses, while software teams automate 20 to 45 percent of engineering tasks. Customer service improvements prove especially compelling. Gartner predicts that by 2026, 50 percent of customer-service organizations will adopt generative AI, potentially reducing contact-center labor costs by $80 billion. Early adopters like Klarna demonstrate this potential, with their AI agent handling the workload of 700 human agents across 23 markets."
Generative AI uses machine-learning and deep-learning algorithms to produce original text, images, code, and multimedia by learning patterns from large datasets. The technology evolved from early neural networks like the perceptron and chatbots such as ELIZA, with high-quality outputs enabled by Generative Adversarial Networks in 2014 and transformer-based large language models that use billions of parameters. Generative AI promises large economic impact, with McKinsey estimating $2.6–$4.4 trillion in value. Organizations report 10–15% R&D savings and automation of 20–45% of engineering tasks. Customer-service adoption is growing, with forecasts of 50% adoption by 2026 and examples like Klarna replacing many human agents. Enterprises apply generative AI to support, content, software, process, and product design.
Read at ClickUp
Unable to calculate read time
Collection
[
|
...
]