By Andrew Bisset.
2 November 2020 (2 days ago)
Machine Learning has been one of the hottesttech topics in recent years. An intriguing branch of Artificial Intelligence, Machine Learning is all around us, quietly reshaping the modern world. Like Google recommending the relevant links in our searches for information, Machine Learning unveils and showcases the power of data in a new way. Working on the development of computer programs that can access data and perform tasks automatically through predictions and detections, Machine Learning enables computer systems to learn and improve from experience simultaneously. In other words, the more data we feed to machines, the faster the machine learning algorithms learn, thus improving the delivered results.
Let’s begin by answering the fundamental question, what is Machine Learning? Simply put, it is a tool for turning information into feasible knowledge. There has been an explosion of data over the past 50 years. This mass of data is pointless unless we scrutinise it and find the patterns hidden within. Machine learning techniques are applied to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be utilized to predict future events and perform all kinds of intricate decision making. At a high level, Machine Learning is the ability to adapt to new data independently and through iterations. In essence, applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results.
Looking back to the past, the first case of neural networks was in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper about neurons and how they work. They were determined to make a model of this using an electrical circuit, and thus the neural network was born. In 1950, the famous English mathematician, Alan Turing developed the well-known “Turing Test” to ascertain if a machine exhibits intelligent behaviour equal or identical to a human. In 1952, the data scientist Arthur Lee taught an IBM computer program to not only learn the game of checkers but to improve every time it played. 1982 was the year in which neural networks piqued people’s curiosity, when John Hopfield suggested creating a network which had bidirectional lines, similar to how neurons actually work. In 1997, the IBM computer Deep Blueshocked the world by beating the world chess champion then, Garry Kasparov.
Since the start of the 21st century, many businesses have realised that machine learning will increase calculation potential. In order to stay ahead of the competition, they invested heavily in the research of machine learning. Now the corona pandemic has propelled the application of machine learning algorithms in investigation and forecasting. In particular, machine learning models are utilized to recognize collective behaviour together with the prediction of the expected spread of COVID-19 across the society by employing the real-time data from the Johns Hopkins dashboard.
In concurrent, advanced mathematical models are selected based on machine learning for a computational process to predict the spread of the virus, for instance: Support Vector Regression (SVR), Polynomial Regression (PR), Artificial Neural Network (ANN) and Recurrent Neural Networks (RNN) using Long Short-Term Memory (LSTM) cells. These are performed using the python library to predict the total number of confirmed, recovered, and death cases thoroughly. This prediction will permit specific tasks based on transmission growth, including expansion of lockdown phase, executing sanitation plans and distributing daily supplies. Using the method of Polynomial Regression (PR), a minimum Root Mean Square Error (RMSE) amount over other methods is generated in projecting the COVID-19 transmission. If the spread resembles the prognosticated trend of the PR model, it would lead to considerable loss of lives as it presents the astounding growth of transmission globally.To counter this, we can only hope that everyone abides by the lockdown regulation provided by the government to reduce the transmission of the virus.
The future of Machine Learning is limitless. In only a few years, machine learning will become an integral part of almost every software application. Engineers will even embed these capabilities directly into our devices. Streaming services will know what to recommend precisely before you even make a choice. Besides that, Natural Language Processing (NLP) is training computers to understand the context and meaning of sentences. Computers will likely get very good at communicating with humans. Other applications such as Self-driving car, robots and digital personal assistants will mature with the help of Machine Learning.