Artificial Intelligence (AI) and Machine Learning (ML) are transforming the world, from powering self-driving cars to revolutionising healthcare diagnostics. But who can claim the title of the “Father of AI and Machine Learning”? The question is tricky since fields are built on contributions made by so many brilliant minds. In the UK, where AI innovation flourishes in hubs such as London, Cambridge, and Oxford, it becomes rather interesting and essential to appreciate the evolution of these technologies. This blog sets out to discover the forebears of AI and ML, highlighting key figures and their legacies.

The Birth of AI: Alan Turing's Visionary Ideas

No one can discuss the origins of AI without writing about Alan Turing, a British mathematician and computer scientist who became a legend because of his activity in Bletchley Park during World War II. Turing was born in 1912 in London and his work in AI is considered the pioneer of AI, despite the fact that he did not live long enough to see the coinage of the term Artificial Intelligence.

Recently, the 1950 article Computing Machinery and Intelligence as written by Turing has introduced the much-hyped Turing Test as a benchmark of test criteria to determine whether a machine has the ability to demonstrate human-like intelligence. He asked, Can machines gear up?-- a revolutionary point during the period when computers were bulky calculators. The theoretical foundations of computation, the Universal Turing Machine, and the principles of computation as addressed by Turing furnished the theoretical basis of programmable machines, the foundation of modern AI.

For the UK, the legacy of Turing is deeply felt. His work did not only contribute towards winning the war but also motivated other institutions such as the University of Manchester, where he was involved in the early forms of computers. But like Turing who was tragically persecuted because of his sexuality, we are reminded how then early pioneers had a problem in their society. Although Turing did not directly create ML algorithms, his idea of the machines that think like humans deserves to be called the “Father of AI.”

The Dartmouth Conference and John McCarthy's Coining of AI:

The Artificial Intelligence term was officially named at the Dartmouth Conference in the United States in the year 1956 organised by a US computer scientist known as John McCarthy. Known as the Father of AI, McCarthy suggested that it is possible to create a machine with human-like intelligence (i.e. the ability to learn, to reason, and to solve problems). His LISP programming language, which continues to be used in AI-research applications, gave early AI applications their tools.

The future that McCarthy was envisioning was grand: AI was viewed as having the ability to solve complex tasks such as playing chess, to natural language understanding. It was his work that paved the way to symbolic AI, which involves systems replicating intelligence through predefined terms. In the case of UK, the impact of McCarthy is felt globally in the AI research community of the world; as well as the working relationship with British universities, such as the University of Edinburgh, a frontrunner in AI research studies.

However, McCarthy’s focus on symbolic AI diverged from modern ML, which relies on data-driven approaches. Does this make him the definitive “Father of AI”? His role in naming and shaping the field is undeniable, but ML’s story involves other key players.

Machine Learning's Roots: Arthur Samuel and Beyond

Whereas AI is a broad concept that includes such concepts as reasoning and perception, ML, which is a subset of AI, deals with such systems as learning systems that learn based on data. Another American to be credited with pioneering ML is Arthur Samuel. Samuel later in 1959 came up with a checkers-playing program that learnt to play better by beating itself back, an early form of reinforcement learning. He gave “machine learning”, as being the study that would become able to provide computers the skill to learn without being fed by an explicit program.

Samuel’s work resonates with UK researchers, as similar ideas were explored at places like Cambridge, where early neural network research took root. Even his checkers program, which by present-day standards could be called primitive, proved that machines could modify and learn, which has become one of the fundamental principles of ML. As far as a UK audience is concerned, the contributions made by Samuel underscore the universality to the evolution of AI, which is similar to that of the British computing history.

The Neural Network Revolution: Frank Rosenblatt and Geoffrey Hinton

The development of Machine Learning, in its current form, is thanks in no small part to neural networks that mimic the way humans learn. In 1958, the Perceptron, a basic model of a neural network that is capable of learning to classify data, was proposed by Frank Rosenblatt, an American psychologist. Albeit modest, Perceptron prepared the way to present-day deep learning.

Leap to the 21st century and Geoffrey Hinton, a British-Canadian scientist, takes the stage. Hinton, commonly known as the Godfather of Deep Learning, has revolutionised neural networks in the 1980s and 2000s. Through the invention of backpropagation (with David Rumelhart and Ronald Williams), neural networks were able to learn intricate patterns, leading to today applications of AI, such as image recognition and natural language processing.

The reference point to UK, the case of Hinton, is personal. He was born in Wimbledon and educated at Cambridge, subsequently immigrated to Canada, but retaining contact with the UK AI community. This was highlighted by his 2018 Turing Award, jointly with Yann LeCun and Yoshua Bengio. The work by Hinton at Google and the University of Toronto has impacted on UK start-ups and research centres such as DeepMind, which Google bought in 2014. Supposing Martin Luther had a father, Hinton with his transformational works, becomes one of the front-line nomination candidates.

Other Pioneers and the Collaborative Spirits:

The search for the best person to label as the Father of AI and ML fails to acknowledge the cross-collaborative nature of the field. Major contributors included individuals such as Marvin Minsky who co-organised the Dartmouth Conference and made contributions to the theory of AI and Donald Hebb, the author of a 1949 book, whose ideas about neural networks were the seed of the field.

Early pioneers of ML thought also contributed to the UK in researchers such as Donald Michie, the originator of the AI department in the University of Edinburgh.

Global collaboration also impacts on the development of the field. These early developments paved the way to modern day UK institutions such as Oxford and the Imperial College London that remain at the forefront of AI innovation. It has been seen in the cooperative enthusiasm such as DeepMinds, AlphaGo project that utilised the neural networks and reinforcement learning to overcome human masters.

Why No Single “Father”?

It is difficult to assign such positions as father of AI and ML, since the field is cross-discipline and step by step. Turing gave the theoretical fire, McCarthy named and formulated AI, Samuel formulated ML, and Hinton redisigned it with deep learning. None of them made the discoveries alone, and it is their work that is interwoven with millions of others (statisticians, engineers, etc.).

To a UK audience, this history is a challenge to revel in the achievements of homegrown heroes such as Turing and Hinton without forgetting the international nature of AI. This legacy is continued through the flourishing AI industry ecosystem in the UK with government projects, such as the AI Sector Deal, and AI research and testing facilities in London and Cambridge.

Looking Forward: AI’s Future in the UK

With AI and ML still developing, the UK is in a good position to take the lead. Firms such as DeepMind and Graphcore are leading the way in ethical AI, healthcare, and climate tech alongside universities. Nevertheless, issues such as misinformation (such as existent in AI-generated deepfakes) and the ethical consideration warrants responsible innovation.

To test your growing interest in AI, there are plenty of sources. Easy introduction points are online courses with UK universities, such as FutureLearn or the AI programmes of the University of Oxford. Innovation can also be fuelled by engaging with people in events such as London Tech Week.

So, who is the Father of AI and Machine Learning? Alan Turing’s visionary ideas, John McCarthy’s formalisation of AI, Arthur Samuel’s ML foundations, and Geoffrey Hinton’s deep learning breakthroughs all stake a claim. Yet, the true story is one of collaboration, with each pioneer building on the last. For the UK, this legacy is a reminder of its pivotal role in shaping AI’s past and future. As we navigate this transformative era, let’s honour these pioneers by fostering ethical, innovative AI that benefits all.