The Era of AI - but where did it begin?

When we think of Artificial Intelligence (AI), we probably could think about a vast amount of things. It could perhaps be Siri reading back your emails whilst you commute to work, ChatGPT giving some clever answers on complex questions or could even be C-3PO from Star Wars which certainly could be true for those who grew up in the 80s and 90s, as this perhaps would have been the first real exposure to what AI even is. AI has grown significantly over the past 12 months in terms of bringing everyday real-world solutions to life, we have even seen further developments from Microsoft on the newly announced "Microsoft Fabric".

As keen historians as well as technologists, this still prompted the team further curiosity to do some research on the history of AI, and where the idea was born from. So here goes.

The inception of AI can be traced back to the 1950s, when researchers began exploring the idea of creating machines that could mimic human intelligence. The term "Artificial Intelligence" was coined by computer scientist John McCarthy in 1956, during the Dartmouth Conference. The objective was to develop machines capable of tasks that typically required human intelligence, such as problem-solving and pattern recognition. Find below a handy timeline of events all the way up to the 2020s.

1950s:

• 1950: British mathematician and computer scientist Alan Turing proposes the concept of a "universal machine" and poses the question, "Can machines think?"
• 1956: The Dartmouth Conference, organised by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, marks the birth of AI as a field of study.

1960s:

• Early 1960s: Researchers focus on building expert systems and symbolic processing, exploring rule-based approaches to AI.
• 1965: Joseph Weizenbaum develops ELIZA, a computer program simulating a conversation with a human, demonstrating the potential of natural language processing.

1970s:

• Late 1970s: Expert systems gain prominence, with notable examples like MYCIN, an AI system for diagnosing blood infections, and DENDRAL, which analysed chemical compounds.

1980s:

• 1980: The first AI winter begins, characterised by reduced funding and waning interest due to unrealised expectations and limited progress in AI technologies.
• 1986: The emergence of connectionist models and neural networks revives interest in AI research.

1990s:

• Late 1990s: Machine Learning approaches gain traction, with advancements in algorithms such as Support Vector Machines and the introduction of probabilistic graphical models.

2000s:

• 2006: Geoffrey Hinton and his team introduce Deep Learning techniques, revitalising neural networks and enabling breakthroughs in speech recognition and computer vision.
• 2009: IBM's Watson defeats human champions in the quiz show Jeopardy!, showcasing the potential of AI in natural language processing and knowledge representation.

2010s:

• 2011: IBM's Watson competes in medical diagnosis, demonstrating AI's potential in healthcare.
• 2012: Deep Learning achieves significant success, with a deep neural network winning the ImageNet Large Scale Visual Recognition Challenge.
• 2014: Google's DeepMind develops AlphaGo, an AI program that defeats world champion Go player Lee Sedol, showcasing AI's capabilities in complex decision-making and strategy.

2020s:

• 2020: AI becomes increasingly integrated into everyday life, with advancements in voice assistants, autonomous vehicles, recommendation systems, and natural language processing. Ethical considerations surrounding AI, such as bias and privacy, gain prominence.
• 2022: the birth of ChatGPT - an advanced language model developed by OpenAI.
• Early 2023: Microsoft's significant multi-billion pound investment into OpenAI, sending shockwaves through the technology sector and world on its new partnership that looks to further fuel Microsoft's efforts as a leader.
• Present: Microsoft Fabric - a new revolutionary all-in-one analytics solution for enterprises that covers everything from data movement to data science, Real-Time Analytics, and business intelligence. It offers a comprehensive suite of services, including data lake, data engineering, and data integration, all in one place.

It's safe to say that the last year alone has perhaps changed the future for all of us as we enter into a new era of technology, but it's always good to remember where it all came from and then ponder over what the next "big" thing would be, and if we'd be part of it again. It therefore only seems fitting to end this blog with the following quote:

""Here's to the crazy ones, the misfits, the rebels, the troublemakers, the round pegs in the square holes ... the ones who see things differently -- they're not fond of rules, and they have no respect for the status quo. ... You can quote them, disagree with them, glorify or vilify them, but the only thing you can't do is ignore them because they change things. ... They push the human race forward, and while some may see them as the crazy ones, we see genius,?because the people who are crazy enough to think that they can change the world, are the ones who do." - Steve Jobs