Although it seems that the history of ML is recent and dates from the middle of the 2010s with the rise of Big Data and the improvement of computing power.
ML has existed since the beginning of the 80s–90s, the OCR (Optical Character Recognition) technology, until then based on simple pattern matching, becomes accessible to the public (software such as OmniPage, ABBYY FineReader). At that time, algorithms begin to integrate statistical machine learning (Bayesian classifiers, k-NN, SVM) to better recognise the varied fonts.
At that time, neural networks already existed (Perceptron, backpropagation). Geoffrey Hinton and others (Yann LeCun, Bengio) worked on the subject, but the results remained limited because of the lack of data and computing power.
Then in 2006, Geoffrey Hinton and his students brought neural networks back to the forefront by training a dataset of handwritten digits to predict other examples with an accuracy around 98–99%, but the real “shock” was that it worked with deeper networks, whereas before it was thought impossible to train them properly. Renaissance of Deep Learning and mainstream interest.
In 2012: real “mainstream” shift with AlexNet (Krizhevsky, Sutskever, Hinton). Thanks to GPUs and a large dataset (ImageNet), the network smashed performances in computer vision. It is from there that industry began to adopt deep learning massively.
2014–2018: explosion of applications with GANs, neural machine translation, voice assistants(Siri), then arrival of transformers (2017) which today dominate NLP and generative AI.
Software such as Omnipage used OCR to digitise paper documents. This began with pattern matching in the 1980s and then ended up adopting ML in the 1990s: statistical classifiers (k-NN, simple neural networks, probabilistic models) to recognise the varied fonts.
Speech recognition in the 1990s (dictation, call centres, customer service, voice commands in high-end cars).
Spam filtering (late 1990s). Deployed massively in email services (Hotmail, Yahoo Mail, later Gmail from 2004) and has replaced rule-based systems that were difficult to maintain.
Recommendation systems (late 1990s): Amazon (1998) for product recommendation.
Credit card fraud detection → banks such as Visa/Mastercard already used simple neural networks and logistic regression.
Falcon Fraud Manager from HNC Software (launched in 1993), used by Visa and Mastercard → it analysed transactions in real time with a neural network to detect anomalies.
You want to sell your car but you don’t know which price is the best: you have to evaluate the market before.