The Evolution of Machine Learning: A Look Through The Years

Machine Learning has a rich and extensive history.  

First conceptualized by Arthur Samuel at IBM while developing a program for playing checkers, Machine learning has since come a long way. It was one of the most promising avenues of achieving Artificial Intelligence until the late 1970s. Then, with ground-breaking advancements in electronics &s software engineering and the rise of the digital age, machine learning came into its own.

Since its first conceptualization, machine learning has evolved, expanded, and taken the world by storm. This article looks at the phenomenal rise of machine learning through the years.

What Exactly Is Machine Learning?

How It Evolved Through The Years?

Machine learning is all about building software models that can learn from data. Machine learning systems can learn autonomously and heuristically without being explicitly programmed.

It is all about analyzing data, as, unlike traditional computer systems, there are no hard-coded instructions in machine learning. For example, the chatbots in an online information technology assignment help service, Grammarly’s AI-powered grammar checker, Netflix’s movie recommender system- all have ML running in the background.

The basic idea of machine learning involves computer systems becoming more & more knowledgeable as it learns & accrues information from consecutive experiences—the more & better the data, the better the performance of an ML system.

In The Beginning

Machine learning had already a rich history long before it became a prominent force of change. Alan Turing was a crucial figure in the development of the domain. Dr Turing investigated how mathematical logic and decision theory can enable machines to solve problems intuitively. Alan Turing was an iconic pioneer in the field of Artificial Intelligence.

Turing was one of the first experts to visualize the human brain as a digital computing machine. He assumed that the human cortex underwent training and learning throughout a person’s life, like what current machine learning experiences.

Gaining Traction

In 1957, Frank Rosenblatt combined Hebb’s model of brain cell interaction with Arthur Samuels’ studies to develop the first perceptron. Perceptron is the most basic artificial neural network modelled after the neurons in the human nervous system.

However, the basic perceptron performed poorly during specific problem scenarios despite a promising beginning. Neural network research stalled after this and resurged much later in the 90s.

In 1967 came the nearest neighbour algorithm. A fundamental element of pattern recognition systems today, it was a significant leap forward for machine learning. Besides pattern recognition, the k-nearest neighbour algorithms are widely used for mapping routes and finding the most efficient route in a region.

Multilayered Neural Networks: The Next Step

The discovery of multilayers opened a new avenue in neural network research. Adding multiple layers to a perceptron model led to a massive boost in computation and accuracy. The feedforward and backpropagation neural networks were two ground-breaking inventions and the first iteration of modern artificial neural networks & deep learning.

Rise of Random Forests, SVMs and the Machine Learning Resurgence

In 1995, scientist Tin Kam Ho published a paper on random decision forests, a potent algorithm that became a tour de force in the world of ML.

The same year, Corrina Cortes, Google’s current head of research, and Vladimir Vapnik, another pioneering mind in AI, published a paper on support vector machines. 

Support vector machines remain one of the most powerful methods in supervised learning, used for classification, regression, outlier detection, etc.

German computer scientists Sepp Hochreiter and Jurgen Schmidhuber developed a paper on long short-term memory networks recurrent networks, a ground-breaking invention in ML and artificial neural networks.  

Today, LSTMs have a wide variety of applications. For example, they can learn order dependence while predicting sequences, machine translation and recognize human speeches.

In 1998, the Modified National Institute of Standards and Technology developed a database comprising a mix of handwritten digits. The database has become a benchmark for handwriting recognition models and training image processing systems.

Deep Learning, NLP, Facial & Speech Recognition….

The 21st century witnessed a phenomenal acceleration in ML research and development.

One key thing to note here is that, in the 1970s & 80s, machine learning & AI took separate paths. ML became a standalone field of research, which focuses on tackling real-world problems using probability theory & statistics methods. While ML, a subset of AI, followed a data-centric approach and mines vast volumes of data for insightful information & gaining knowledge, traditional AI research in the 70s focused on the knowledge-based expert systems approach.

Nevertheless, the rise of Information Technology, an exponential increase in the amount of digital data, the birth of the Internet, and the rise of the global tech giants, Facebook, Apple, Amazon, Netflix & Google, saw a massive resurgence in machine learning. As a result, businesses began investing heavily in AI and machine learning research, and applications bore amazingly successful results.

As technology became more powerful, so did improvements in the field of ML.

  • In 2006, Netflix was one of the first technology companies that incited further development through competition.
  • 2012 saw advancements in the world of image processing and computer vision. Stanford University developed ImageNet, an extensive visual database, and AlexNet, a Convolutional Neural Network that integrated deep learning.
  • 2014 was the birth year of the Attention Mechanism, a technique that improved the performance of neural networks drastically. This invention was a breakthrough in machine translation and a cornerstone in deep learning.
  • In 2015, we saw the introduction of the great concept of GANs. The aim was to train two networks and generate realistic & plausible data samples, such as images of human faces or handwritten digits. 

2015 was also when ResNet was presented as the next generation of Convolutional Neural Networks. The ResNet concept consisted of a profound architecture that included shortcut connections inspired by earlier gated architectures.

  • The recent years saw machine learning evolve from traditional models to neural networks. This ushered in the age of deep learning and massive proliferation in the fields of natural language processing & computer vision.

Natural Language Processing

In 2017, natural language processing and computer vision got the Transformer, a deep learning model that employed the attention mechanism and was deemed particularly useful for language comprehension. Google’s BERT (Bidirectional Encoder Representations from Transformers) was the next stage in deep learning evolution with its ability to deal with large language datasets.

By 2020, machine learning was picking up pace in NLP. We witnessed the creation of OpenAI’s GPT-3 take the industry by storm. GPT-3 is a state-of-the-art autoregressive language model that can generate and produce computer codes, poetry, and other kinds of content that are similar to those written by humans. 

  • The most popular deep learning technique employed is speech and facial recognition using a powerful LSTM model. Facebook developed DeepFace, an algorithm that recognizes individuals in photographs as accurately as humans.

Today, machine learning and deep learning have become transformative figures in technology. Adopted and implemented across almost all business sectors, the evolution of machine learning is far from over. Academic & research institutions, tech giants and the world’s brightest innovators are taking ML research to newer heights.

Key Takeaway:

From automated cars & human-like assistants to controlling plasma in experimental fusion reactors & discovering Earth-like exoplanets, machine learning is helping humanity tackle the challenges of the coming times with conviction. A day may come when Machine Learning Intelligence surpasses Human Intelligence in every conceivable way!

Author-Bio: Cark Plunkett is a machine learning engineer with a global tech giant. He currently resides in London, designs & maintains AI-powered web services, and teaches part-time at, leading assignment help and dissertation writer service.

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