Blog: The Rise of Large Scale Machine Learning
For many years, Machine Learning and Artificial Learning was more of a concept, an innovative vision for many technology professionals. Today, artificial intelligence is heavily embedded in the digital transformation of major industries and businesses worldwide.
Machine Learning is a current system of Artificial Intelligence that refers to the concept that we should provide machines with access to data to allow machines to learn for themselves. Industry analysts highlight two historic breakthroughs that led to the emergence of large-scale machine learning as the driver for accelerating the rate of progression within Artificial Intelligence. The first breakthrough was the creation of the term ‘machine learning’ by Arthur Samuel back in 1959, explaining that rather than teaching computers everything that they possibly require to understand about the world and its processes, it could be possible to teach them to actually learn for themselves. The second significant breakthrough was the emergence of the internet and the considerable rate of increase in generation and storage of digital information available for analysis.
Engineers soon began to realise that rather than teach machines how to do everything, it would be more efficient to design them to think like humans, connect them to the internet and provide them with access to all the information available worldwide.
Today, AI is everywhere, integrated with many parts of our daily lives, particularly within the digital environment. The development of Machine Learning within an industry is spurring discussions about the future of various industries and how it seems we are progressing further towards a large-scale process of automation.
A vital part of digital progression is measuring a range of data sources and analysing and optimising the data to improve overall productivity. This process is accelerating the process of automation within a range of industries.
Artificial Intelligence enables healthcare professionals to be equipped with valuable data-driven insights that provide more than just the standard information generally provided within patient care. Electronic medical records (EMRs) and Concept Processing are both powered by artificial neural networks (ANNs) that when utilised, provides a data system that enables decisions on medical diagnoses, powered by AI and Machine Learning. AI is used for a range of roles within the healthcare industry. AI plays an equally important role in creating computer-aided interpretations of medical images, developing advanced drugs for a range of varying levels of illnesses and providing online consultations.
Industry analysts highlight that AI and automation have had the biggest impact on the banking and financial industry to date, with machine learning and data analytics being vital areas within this sector. AI and analytics have become an essential part of financial technology (fintech) industry, playing a role in capital market trading, portfolio management and personal finance.
In algorithmic trading, financial traders will utilise detailed AI systems which can measure millions of transactions in a single day with no human involvement. These types of AI systems and being utilised more and more by fintech trading businesses to increase their overall trading volumes. Similarly, in the area of portfolio management, robotics are working alongside human analysts to implement detailed analytics and manage certain processes without human intervention.
Automation is being viewed as a potentially advantageous role in manufacturing by replacing humans in certain, hazardous job roles. Machine Learning, along with Cognitive Computing is enabling companies to completely automate repetitive jobs, enhancing employee productivity by applying people to more creative and analytic jobs duties that do require human involvement. Data and algorithms are enabling robots to carry out duties that tend to be repetitive or could lead to errors if there was a human fault. Automation has enhanced the speed of sales and supply chain processes across various industries.
Asia currently has the largest market for the installation of industrial robotics, with China installing nearly 140,000 industrial units last year. Outside of Asia, The United States has one of the largest markets standing at just over 30,000. For many nations, making a transition into an automated-focus industrial environment requires significant changes to the ways many nations industries operate. Whilst these changes are considerable, many countries are attracted to the benefits of efficiency, higher productivity, and the creation of various revenue streams produced by automation.
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