Machine learning is a powerful form of artificial intelligence that people benefit from in their day-to-day lives, sometimes without being aware of it. This subfield of artificial intelligence is broadly defined as the capability of a machine to imitate intelligent human behaviour, including learning patterns, through the use of data and algorithms.
Machine learning: Overview
Machine learning makes technology more advanced by making systems and machines perform complex tasks in a way similar to humans’ problem-solving techniques.
Statistical learning and optimisation methods that allow computers to analyse datasets and identify patterns are the basis of machine learning. When combined with data mining, which is the process of uncovering patterns, anomalies and correlations within large data sets, machine learning can be used to identify historic trends and develop models to predict possible future outcomes.
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Speech and image recognition, Google Translate, Google Maps, self-driving cars, targeted advertisement recommendations, auto-friend tagging suggestions, gaming, and Chatbot, or online customer support, are some examples of technologies which incorporate machine learning.
The field of data science is rapidly growing, and therefore, machine learning is important to improve the efficiency and accuracy of data mining projects.
Businesses can use machine learning to improve decision-making.
Some frameworks used to accelerate machine learning algorithms are TensorFlow and PyTorch. TensorFlow is an end-to-end machine learning platform used to build models. PyTorch is an open-source machine learning library used to develop and train deep learning models based on neural networks.
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Neural networks are a series of algorithms which mimic the way biological neurons work, in order to identify relationships in vast data sets. Deep learning is a subset of machine learning and a neural network with three or more layers. Deep learning simulates the behaviour of the human brain in order to allow the neural networks to learn from large amounts of data.
How Machine Learning Augments Artificial Intelligence
In simple terms, machine learning helps computers learn by making them mimic the behaviour of the human brain, according to the Massachusetts Institute of Technology (MIT). This is how machine learning augments artificial intelligence.
According to a statement by MIT Management Sloan School, Professor Thomas W Malone said that in just the last five or ten years, machine learning has become a critical, and arguably the most important way most parts of AI are done. He added that most of the current advances in AI have involved machine learning.
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While it is not important for everyone to know the technical details of machine learning, one must understand what it is used for, and how it can be utilised for the betterment of the world.
According to the University of California, Berkeley, machine learning, and data mining, which itself is a component of machine learning, are crucial tools used by many companies and researchers for two main reasons: processing a huge scale of data and obtaining unexpected findings.
Companies need to deal with massive volumes and varieties of data that must be processed, and hence, the processing power needs to be highly efficient. This is where machine learning comes to the rescue. Through machine learning, models can be programmed to process data on their own, identify patterns, and determine conclusions.
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A machine learning algorithm updates autonomously. Therefore, with each run, the analytical accuracy of the machine learning algorithm improves.
Since the learning mechanism occurs without human intervention, often unexpected findings are uncovered, without the machine being programmed to do so.
Many people are confused between the terms ‘machine learning’ and ‘artificial intelligence’, and often use the terms interchangeably.
While AI refers to any of the software and processes designed to mimic the way humans think and process information and includes robotics and machine learning itself, machine learning specifically refers to teaching devices and systems to learn information and behave like the human brain, without manual human interference.
Machine Learning: History And Evolution
American computer scientist Arthur Samuel, who worked in International Business Machines (IBM), coined the term “machine learning” in the 1950s. A pioneer in the field of artificial intelligence, he defined machine learning as “the field of study that gives computers the ability to learn without explicitly being programmed,” according to MIT.
Samuel wrote the first learning programme for IBM in 1952. The programme involved a game of checkers. In 1962, Robert Nealey, a self-proclaimed checkers master, played the game on an IBM 7094 computer. Nealey lost to the computer, according to IBM.
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While the feat seems trivial compared to what can be done today, it is considered a major milestone in the field of artificial intelligence. This is because it was an instance of a machine-learning algorithm beating the performance of a human being.
In 1957, American physiologist Frank Rosenblatt designed the first neural network. In 1981, Gerald DeJong introduced explanation-based learning.
In the 1990s, the focus of machine learning shifted from a knowledge-based approach to one driven by data. The 1990s were critical years for the evolution of machine learning because scientists started creating computer programmes that could not only analyse large data sets but also learn in the process.
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In the 2000s, unsupervised learning, or learning without manual human interference, became widespread. This eventually led to the arrival of deep learning.
Another instance of a machine learning algorithm beating the performance of a human being was Russian chess grandmaster Garry Kasparov’s defeat at the hands of IBM supercomputer Deep Blue in 1997.
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In 2016, the Google DeepMind AI programme Alphago beat a person called Lee Sedol in ‘Go’, a game with a tremendously large space of possibilities in gameplay.
Google, IBM, Microsoft and Amazon have unveiled several machine-learning platforms over the past few years.
Machine Learning: How It Works
Machine learning can be divided into three components: decision process, error function and updating or optimisation process.
Decision process: According to the University of California, Berkeley, a decision process is a sequence of calculations or steps that collects the data and “guesses” what kind of pattern one’s algorithm is intending to find.
Error function: This component of machine learning measures how good the guess was by comparing it to known examples, or quantifying how bad the miss was, in case the decision process could not make the correct guess.
Updating or optimisation process: In this component of machine learning, the algorithm looks at the miss and then updates how the decision process arrives at the final decision, in order to ensure that no mistakes are made, or the miss is not as bad as the previous one.
The components of machine learning can be understood through the example of a movie recommendation system. If one is building such a system, they can provide information about themselves and their watch history as input. The accurate output would be suggesting the movies the person would enjoy.
The inputs provided by the person to the machine learning algorithm include movies they watched, high-rated movies, science-fiction, horror and thriller movies, and films starring certain actors.
The algorithm will find the parameters and assign weights to them. If the algorithm makes the correct guesses during the decision process, the weights used by it stay the same. However, if it guesses the wrong movies, the weights that led to the wrong decision will be turned down, so that the algorithm does not repeat the mistake.
Machine Learning: Applications
Financial services, healthcare companies, transportation firms, pharmaceutical companies, and governments themselves use machine learning.
Google Translate was trained to “learn” multiple languages through machine learning.
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Speech recognition, computer vision, which enables computers to extract useful information from digital images or videos, recommendation engines, which are mostly used by online retailers to make relevant product recommendations to customers during the checkout process, and online customer service, in which chatbots are replacing human agents, are some common applications of machine learning.
Machine Learning: What Is The Future?
Machine learning algorithms are not only used by governments and businesses, but also in scientific research.
Using deep learning algorithms, scientists can detect subtle patterns in the genetic structure of any organism, and develop medical treatments using the findings.
In the future, machine learning can identify diseases more effectively, fight cybercriminals, and find treatments for illnesses, among others.