This is a selective history of AI, focusing on the different available tools.

1940s–1950s: Foundations of AI

• 1950: Alan Turing publishes ‘Computing Machinery and Intelligence’ introducing the concept of the Turing Test to assess machine intelligence.​ 
• 1956: The term ‘Artificial Intelligence’ is coined during the Dartmouth Conference, marking the formal beginning of AI research.​

1960s: Early AI programs and robotics 

• 1966: Joseph Weizenbaum created ELIZA, which is largely known as the first chatbot, capable of engaging in conversations with humans.
• 1969: SRI International creates SHAKEY, the first general-purpose mobile robot capable of reasoning about its environment.

1970s–1980s: AI winters and expert systems 

• 1970s: AI experiences its first ‘winter’, a period of reduced funding and interest due to unmet expectations.​ 
• 1973: James Lighthill released the report ‘Artificial Intelligence: A General Survey’ which caused the British government to significantly reduce support for AI research.
• 1980s: Ernst Dickmanns invented the first self-driving car in 1986.
• The development of expert systems, which mimic the decision-making abilities of human experts, leads to renewed interest and investment in AI.​ 

1990s–2000's: Resurgence and notable achievements 

• 1996: IBM's Deep Blue defeats world chess champion Garry Kasparov, showcasing AI's potential in complex problem-solving.​
• 2002: iRobot launches Roomba, the first mass-produced domestic robot cleaner with an AI-powered navigation system.

2010s: Deep learning and AI integration 

• 2011: IBM's ‘Watson’ wins the quiz show ‘Jeopardy!’, demonstrating advanced natural language processing and information retrieval capabilities.​
• 2011: Apple launched Siri, the first virtual assistant integrated into a smartphone. Siri used natural language processing (NLP) and speech recognition to respond to user queries, set reminders, send texts, and more. Its release brought conversational AI into millions of homes, sparking mainstream adoption of AI-powered assistants (Aron, 2011). Siri laid the groundwork for user-facing AI by combining machine learning with a human-centric interface.
• 2012: AI startup DeepMind develops a deep neural network that can recognise cats in YouTube videos. 
• 2014: Generative Adversarial Networks (GANs) are introduced, enabling the generation of realistic images and data.​ 
• 2014: Amazon released Alexa with its Echo smart speaker in 2014, marking a significant leap in AI’s integration into the domestic sphere. Unlike Siri, Alexa was designed as a voice-first smart home hub, capable of controlling lights, thermostats, and music, answering factual questions, and ordering products. Alexa used cloud-based AI, keyword spotting, and continuous learning to adapt to user preferences. It also spurred a wave of AI competition from Google (Google Assistant), Microsoft (Cortana), and Samsung (Bixby) (Kinsella, 2019).
• 2016: DeepMind's AlphaGo defeats a professional Go player, a feat previously thought to be years away due to the game's complexity.​ 

2020's: Generative AI and widespread adoption 

• 2020: Open AI releases GPT-3, a language model capable of generating human-like text, marking a significant leap in natural language processing.​ 
• 2022: Advancements in AI lead to applications in art, music, and content creation, with tools like DALL·E generating images from textual descriptions.​ 
• 2024: The Nobel Prize in Physics honours pioneers in artificial neural networks, acknowledging AI's profound impact on science and society.

In simple terms, generative AI can write like a human, draw like an artist, or respond like an expert, depending on the task and the tool used. GenAI then analyses patterns and structures within the data to produce outputs which closely resemble human-generated work.

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