Are there Industries Influenced by AI & Machine Learning in 2017?

Mr. Sayantam Day, Sr. Director, Engineering, 3Pillar Global

A renewed interest in machine learning is emerging now, having similar factors that have been instrumental in the recent popularity of data mining and Bayesian analysis. This sees growing volumes and assortments of available data, optimized computational processing, many domains and affordable data storage.

Modern technology has influenced civilization in every turn of a century. Humans have always signified progress with their relentless endeavors to make life simpler than before. Thanks to the new technology, the 20^th and 21st centuries have seen a number of advancements that have revolutionized our daily lives. Envisioning a life without some of these facilitators has become a difficult task due to our reliance on them and one such advancement is machine learning.

Machine learning is a branch of artificial intelligence that aims to simulate intelligent abilities of humans by machines. It is a set of algorithms that trains a predictive model by learning from the information hidden inside an existing dataset.The trained model is then validated and tested to discover the accuracy levels, which result in a smart predictive model. The model is built in such a way that it gets smarter over a period of time.

With the emergence of new computing technologies, machine learning today is not even a reflection of the past. While it was born from pattern identification and the basic theory that computers can learn to perform specific tasks without the use of external programs, researchers specializing in artificial intelligence wanted to see the extent to which computer’s intelligence could be augmented. The iterative aspect of machine learning is essential to the success of the technology, as models are exposed to new data on a daily basis and are able to independently mould itself around it. This works through learning from previous computations to produce reliable, repeatable decisions and results. It’s a science that we have often come across – and one that’s gaining fresh momentum.

A renewed interest in machine learning is emerging now, having similar factors that have been instrumental in the recent popularity of data mining and Bayesian analysis. This sees growing volumes and assortments of available data, optimized computational processing, many domains and affordable data storage.

Machine learning has had a consistently huge impact in many domains and functions, sometimes without us really being aware that machine learning is at work. There are a multitude of use cases in real life where machine learning has worked very well, such as:

Financial services

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Banks and other businesses in the financial industry incorporate machine learning technology for two crucial purposes: to cull out important insights in data and to detect/prevent early signs of fraud. The insights can classify investment opportunities, helping investors know the ideal moment to trade. Machine learning, along with data mining, can also single out clients with high-risk profiles and use cyber surveillance to pinpoint warning signs of fraud.

Government

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Government functions such as public safety and utilities have a centralized need for machine learning owing to their multiple sources of data that need to be mined for insights. Machine learning can also assist in fraud detection and minimize identity theft.

Healthcare         

Image source: Google Images

Machine learning is a fast-growing enabler in the health care industry, owing to the initiation of wearable devices and sensors which use data to assess a patient's health in real time. This technology can also facilitate medical experts in collating data to identify trends and red flags that lead to improved diagnoses and treatment, as well as provide preventative medical care.

Marketing and sales

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E-commerce portals are using machine learning to analyze consumers’ purchase history and promote other items that they’ll be interested in. The ability to encapsulate data, analyze it and use it to personalize the shopping experience (or implement a marketing campaign) is the future driver of progress in retail.

Transportation

Image source: Google Images

Data analysis and model-based aspects of machine learning are important tools for delivery companies and those that operate in the fields of public and private transportation. Today, analyzing data to identify patterns and trends form the crux of machine learning in the transportation industry, which relies on developing efficient routes and foreseeing potential problems to increase productivity.

Currently, the size of Machine Learning as a Service Market is estimated to grow from $613.4 million in 2016 to $3.76 billion USD by 2021, at a Compound Annual Growth Rate (CAGR) of 43.7% from 2016 to 2021. Such data paints a heartening picture for machine learning to play a very real part in growing various sections of the market and to facilitate the next step in the evolution of technology-powered businesses and products.