Posted On Oct 16, 2022,

There are many fields in which we use machine learning. This blog will tell you some futuristic applications of machine learning.

Artificial Intelligence and Machine Learning are among the hottest technologies in trend right now. The global ML market size is valued at $21.17 billion in 2022 and is expected to reach $209.91 billion by 2029, growing at a CAGR of 38.8% during the forecast period, according to Fortune Business Insights.

Machine Learning is a subfield of Artificial Intelligence. From predicting the spread of the COVID-19 virus to supporting cutting-edge cancer research, AI & ML can disrupt and transform every single segment of society. Naturally, it is hard to imagine a future without machine learning in our lives today. 

The Exciting Future of Machine Learning

Machine Learning is not only offering tremendous growth opportunities but also disrupting long-standing industries. Machine Learning is easily one of humanity’s best allies by enabling businesses to make more informed decisions, helping developers look at problems in innovative ways, and offering insights round the clock with inhuman speeds and accuracy. 

In a survey conducted by PWC in 2021, 86% of individuals said that Machine Learning and Artificial Intelligence are now a mainstream part of their company. Over 50% of them reported acceleration of adoption plans for this technology after the impact of the COVID-19 pandemic on businesses worldwide.

How Machine Learning is Becoming the Technology of the Future

Machine Learning tools offer powerful capabilities to several industrial sectors. Some important ones are listed below:

Fast processing of large databases: Have you heard of ‘Big Data? It refers to the humongous amounts of information that is being generated and accessed by virtually every organization in the world in today’s world. The data flow around us is so massive that it cannot be handled with conventional tools. How are we supposed to analyze and make sense of it then? The answer is Machine Learning. It automates the entire process of generating, storing, retrieving, and analyzing data. ML models successfully extract meaningful insights from the data at high speeds and work through huge structured or unstructured data volumes. 

Round-the-clock operation: Machine Learning algorithms are robots that do your work without ever tiring or needing sleep to recharge, so they can keep performing their tasks and grinding 24 hours a day, seven days a week, 365 days a year.

ML plays a massive role in automating business operations with tools such as MLOps and AutoML. This eliminates many routine tasks in processes like network monitoring, database management, data integration, etc., and helps businesses to focus on more specialized tasks.

Ability to improve and learn: Most ML algorithms are designed to improve performance as they comb through more data. For example, Recommendation Engines are designed to recommend ‘products’ to users by predicting what’s most likely to be of interest to users. Netflix, YouTube, Tinder, and Amazon are all examples of how recommender systems are used. In fact, according to Forbes, 75% of Netflix users choose movies/shows recommended to them by the company’s machine learning algorithms.

Widespread applications: AI and ML solutions find endlessly versatile applications in several domains. This is why the Machine Learning space is growing by leaps and bounds compared to any other technology. Let’s check out a few of its popular applications below:

Industries Being Disrupted by Machine Learning

1. Healthcare

Machine Learning in Healthcare has been a considerable boost to its market growth in the last decade. It has contributed significantly to better predictions and treatments. It is expected to grow exponentially during the forecast period of 2022-2029. Its special applications in healthcare include:

a) Disease Prediction:

The advent of ML enables the analysis of a broader range of variables based on patient demographics, health records, and other sources, which can lead to accurate results in disease prediction.

b) Drug Discovery:

ML models are used to speed up the drug testing time and predict the impact and possible side effects of a drug, thereby accelerating the process of launching a drug into the market.

c) Medical Imaging Diagnosis:

ML image diagnostic tools for image analysis combined with deep learning and computer vision techniques have proved to be revolutionary in healthcare.

d. Electronic Health Records (EHRs):

Machine learning techniques, such as natural language processing (NLP) and image processing, help streamline data collection and convert data into a standard format. This can lead to enhancement in identifying clinical patterns and assist with better predictions.

2. FinTech

Banking and Finance are one of the most target industries for machine learning. The ability of ML algorithms to learn and predict from ever-expanding data enables FinTech providers to recognize new business opportunities and formulate coherent strategies. Its special applications in fintech include:

a) Fraud Detection:

ML automates the evaluation of enormous amounts of transactional data in real-time. Moreover, ML techniques can detect unusual or suspicious activity based on fraudulent historical data. ML can prevent threats in banking, payments, and insurance.

b) Loan Automation:

ML can provide banks and lenders with better insights into a customer’s ability to repay the loan. Huge amounts of data can be processed, and the aggregated data points can be compared with thousands of other customers to generate an accurate risk score using ML.

c) Regulatory Compliance:

The category of Regulatory Technology (RegTech) benefits from ML greatly. ML algorithms can monitor transactional data to identify anomalies and ensure that customer transactions comply with regulatory requirements.

3. Retail

ML and predictive analytics help retail business segments identify target markets and analyze purchasing patterns, customer behavior, social media interactions, etc. Companies can determine the best ways to approach customers, prevent business losses, and add value to their business. ML’s notable applications in retail include:

a) Customer Segmentation:

Predictive ML approaches use real-time data for customer segmentation based on common characteristics. Dividing customers into categories helps companies identify the target market for the goods or services a business offers.

b) Quality Assurance:

ML can provide insights into possible issues and trends before they begin to affect the company.

3. Churn Prediction and Prevention:

Retaining customers is always easier than acquiring new customers through expensive marketing campaigns. ML helps prevent customer churn and prevent revenue loss by identifying the traits of dissatisfaction among the existing clients through the data.

4. IT & Telecommunications

The telecom industry is currently the largest sector in machine learning – holding 27% of the total market share. They have leveraged ML to improve the customer experience, improve equipment maintenance, and significantly reduce operational costs. Its notable applications include:

a) Network Operation Monitoring and Optimization

ML leverages advanced automation in network operations to help optimize network architecture and improve control and management. This is done by identifying possible network-related issues and fixing them to optimize reliability.

b) Predictive Maintenance of equipment

ML uses data-driven techniques to monitor the equipment condition and predict failure based on previously analyzed patterns. This enables telecom companies to fix issues with their equipment and devices proactively.

Key Industry Players in Machine Learning

The market leaders in machine learning introduce new capabilities in their product offerings to strengthen their positions in the ML market. Companies such as Microsoft, Oracle, Amazon, and IBM are the key industry players leveraging Artificial Intelligence and Machine Learning to empower their businesses and innovate responsibly. For example,

Oracle launched the Oracle Cloud Data Science Platform in February 2020 to assist businesses in collaboratively training, managing, and deploying ML models to improve their predictive accuracy in data science programs.

Microsoft has launched an open database for transportation, health, population and safety, and others to help improve the performance of ML models using its publicly available datasets. 

Conclusion

The range of potential Machine Learning use cases is surprisingly broad. The success of businesses is apparent by the fact that they are embarking on the journey of digital transformation by relying on the ability of ML to automate processes and develop solutions. With reliable insights and automated expertise, there may be no limit to what Machine Learning can help us achieve in the near future.

Original Article : What Is The Future Of Machine Learning?