Generative AI (GenAI) burst upon the market just 18 months ago. Because it had such a significant impact, reaching so many people in such a short time, it’s tempting to view it as the first and only iteration of AI.
But it’s almost three-quarters of a century since the first AI concepts were described and technologies developed.
Courtesy of the International Standards Organisation (ISO), here are some key events and milestones in the history of AI:
- 1950: Alan Turing publishes the paper “Computing Machinery and Intelligence”, in which he proposes the Turing Test as a way of assessing whether or not a computer counts as intelligent.
- 1956: A small group of scientists gather for the Dartmouth Summer Research Project on Artificial Intelligence, which is regarded as the birth of this field of research.
- 1966-1974: This is conventionally known as the “First AI Winter”, a period marked by reduced funding and progress in AI research due to failure to live up to early hype and expectations.
- 1997: Deep Blue, an IBM chess computer, defeats world champion Garry Kasparov in a highly publicised chess match, demonstrating the fabulous potential of AI systems. In the same year, speech recognition software developed by Dragon Systems is implemented on Windows.
- 2011: In a televised Jeopardy! contest, IBM’s Watson Deep QA computer defeats two of the quiz shows’ all-time champions, showcasing the ability of AI systems to understand natural language.
- 2012: The “deep learning” approach, inspired by the human brain, revolutionises many AI applications, ushering in the current AI boom.
- 2016: Developed by a Google subsidiary, the computer program AlphaGo captures the world’s attention when it defeats legendary Go player Lee Sedol. The ancient board game “Go” is one of the most complex ever created.
- 2017 to date: Rapid advancements in computer vision, natural language processing, robotics and autonomous systems are driven by progress in deep learning and increased computational power.
- 2023: The rise of large language models, such as GPT-3 and its successors, demonstrates the potential of AI systems to generate human-like text, answer questions and assist with a wide range of tasks.
- 2024: New breakthroughs in multimodal AI allow systems to process and integrate various types of data (text, images, audio and video) for more comprehensive and intelligent solutions. AI-powered digital assistants are now capable of engaging in natural, contextual conversations as well as assisting with a wide variety of tasks.