How ML evolved from early concepts to the deep learning era.
The history of Machine Learning is a fascinating journey of ideas, algorithms, and computational advancements. Its roots can betraced back to the 18th century with Bayes' Theorem and Legendre's least squares method, which laid the mathematical groundwork. However, the modern era of ML began in the 1950s, coinciding with the dawn of computing. Alan Turing's 1950 paper 'Computing Machinery and Intelligence' proposed the famous Turing Test, sparking the conversation about machine intelligence. In 1952, Arthur Samuel developed a checkers-playing program that could learn from its mistakes, one of the first demonstrations of self-learning. The term 'Machine Learning' was coined by him in 1959. The 1960s saw the development of the Perceptron by Frank Rosenblatt, a precursor to modern neural networks. However, the field entered a period known as the 'AI winter' in the 1970s and 80s due to limited computational power and unmet expectations. The resurgence began in the 1990s with the rise of statistical learning methods like Support Vector Machines (SVMs). The real explosion happened in the 2010s with the 'deep learning' boom, driven by three key factors: the availability of massive datasets (Big Data), the development of powerful GPUs for parallel processing, and breakthroughs in neural network architectures, such as AlexNet's victory in the 2012 ImageNet competition. This marked a new era where ML began solving complex problems with superhuman accuracy.