AI has gradually invaded our lives, from the technology powering our smartphones to self-driving car capabilities to the tools shops employ to surprise and please customers. As a result, its advancement has been nearly undetectable. Clear milestones, such as when AlphaGo, a DeepMind AI-based program, defeated a world champion Go player in 2016, were hailed but rapidly disappeared from public attention.
ChatGPT, GitHub, Copilot, Stable Diffusion, and other generative AI applications have captured the imagination of people all over the world in a way that AlphaGo did not, owing to their broad utility—almost anyone can use them to communicate and create—and preternatural ability to converse with a user. The most recent generative AI programs can handle everyday tasks, including data reorganization and classification. However, their capacity to write prose, produce music, and create digital art has gained attention and persuaded consumers and households to experiment on their own, which has garnered headlines. As a result, a broader collection of stakeholders is struggling with the impact of generative AI on business and society, but with no context to help them make sense of it.
The rate at which generative AI technology is evolving is making this work more difficult. ChatGPT was available in November 2022. Four months later, OpenAI published GPT-4, a new large language model (LLM) with significantly improved capabilities. Similarly, by May 2023, Anthropic’s generative AI, Claude, could analyze 100,000 tokens of text every minute, equivalent to about 75,000 words per minute—the length of an average novel—up from about 9,000 tokens when it debuted in March 2023. Google also revealed many new generative AI-powered features in May 2023, including Search Generative Experience and a new LLM dubbed PaLM 2 that will power its Bard chatbot, among other Google products.
To comprehend what lies ahead, one must first understand the breakthroughs that have permitted the growth of generative AI, which have been decades in the making. Generative AI is defined as applications that are typically generated utilizing foundation models. These models include massive artificial neural networks inspired by the human brain’s billions of neurons. Deep learning, which refers to the numerous deep layers of neural networks, includes foundation models. Many recent developments in AI have been powered by deep learning, but the foundation models powering generative AI applications constitute a step-change evolution within deep learning. Unlike earlier deep learning models, they can process vast and diverse volumes of unstructured data and execute several tasks.
Over a wide range of modalities, including images, video, music, and computer code, foundation models have enabled new capabilities and dramatically improved current ones. AI trained on these models may perform various activities, including classifying, editing, summarising, answering queries, and drafting new content.
We are all at the start of a journey to comprehend generative AI’s power, scope, and capacities. It implies that generative AI is set to transform jobs and improve performance in areas such as sales and marketing, customer operations, and software development. It can potentially release trillions of dollars in value across industries ranging from finance to life sciences.