The Rise of Machine Learning
Artificial Intelligence is the future. There is no doubt that Machine Learning and/or Artificial Intelligence has increasingly gained more popularity in recent times. Sometimes, these buzzwords – Artificial Intelligence and Machine Learning are often used interchangeably. Though much interrelated, they are not the same thing.
Artificial Intelligence (AI) is a broader concept. It is a branch of computer science which deals with intelligent and smart devices. These machines are capable of performing tasks which are representative of human behavior and intelligence.
Machine Learning (ML) is simply a way of achieving AI. It is a field which gives computers, software and devices the ability to perform via cognition. These machines have the capability to learn without being explicitly programmed.
An approach to implement Artificial Intelligence
In the earlier days, we tried to replicate human behavior by programming the computers with a lot of logical rules and algorithms, eventually thinking that if we have enough of these rules, we could end up having a computer with “mimicked” human behavior. Then people realized that it is not possible to teach computers everything about the world, rather it is important to make them learn on their own. It is this blend of algorithms, statistics and learning from data which is called Machine Learning.
Artificial Intelligence is the required brain needed to build smart machines, while Machine Learning provides potential solutions for getting it implemented.
As Big Data is the current trend in the tech industry, Machine Learning has gained immense popularity as it has the ability to predict or suggest based on large amount of input data. But to get the maximum benefit, businesses should know which business process algorithm to select and pair so as to build models and train the machine. Some of the most common and key ML algorithms are:
- Random forests
- Decision trees
- Boosting – Gradient and XG boosting models
- Neural networks
- Deep learning
However, along with knowing the algorithms, businesses should also, learn to pair them with right processes. Few of them include:
- Data exploration
- Data quality and its management
- Comparison between different ML models and picking the best performer
- Automated predictions on outcomes and generating levels of confidence
Most of the industries have already started using Machine Learning to gain meaningful insights from large amount of data they already had. With this, they are able to gain a competitive edge and work more efficiently.
Some of the industries using machine learning applications are:
- Predictive Maintenance – Use of Machine Learning can predict when maintenance should be performed and thereby, prevents unexpected equipment failures. The underlying principle is to perform maintenance at a scheduled point in time when the maintenance activity is most cost-effective and before the equipment loses performance within a threshold.
- Conditional Monitoring – Manufacturing systems and processes are becoming increasingly complex, driving decision-making in ‘process control’ a necessity. With condition monitoring, maintenance and other actions can be scheduled beforehand to prevent failure and avoid consequences. Using ML, predictions can be made in advance for those conditions which shorten a normal lifespan of an activity.
- Process Optimization – When optimizing a process, it is important to maximize one or more of the process specifications while keeping other constraints same as before. The goal is to minimize the cost and maximize the efficiency.
- Demand Forecasting – It is easier to predict the future demand for a product or a service on the basis of past events and current trends using ML algorithms. This can be applied to inventory management, production planning or in assessing the future capacity requirements.
Banking and Financial Services
- Fraud Detection – Machine learning algorithms are able to detect and recognize thousands of patterns on a user’s purchasing journey and thus, is more useful in preventing fraud detection. Automated fraud screening systems powered by machine learning can help businesses in reducing fraud.
- Risk Analytics – Predictions about risk scores for individual customers can be made with certainty using ML algorithms. It can also help to accelerate and streamline risk processes to reduce costs from credit losses and manage operational risk.
- Credit Worthiness Evaluation – When a business applies for a loan, the lender must evaluate whether the business can reliably repay the loan principal and interest. Lenders commonly use measures of profitability and leverage to assess credit risk. Machine learning contributes significantly to modeling these applications.
- Customer Segmentation – There are different algorithms available which can help in segmenting the customers based on their purchasing habits. It helps to identify the likelihood of future purchases.
- Market Segmentation and Targeting – With the help of large datasets available, it is now possible for marketers to improve targeting, response rates, and overall marketing ROI by studying and analyzing the consumer spending habits and purchasing behavior.
- Product Recommendation – E-Commerce websites are an excellent example in this category. Based on customer purchases, it is possible to predict and recommend similar products or product accessories. In this way, it provides a personalized experience to each customer.
- Inventory Planning – Rapid changes in business requirements and the complexity of factors influencing demand are making it difficult to accurately model the causes of demand variation. Machine learning can help companies overcome this challenge. They help in forecasting the demands and thus, in planning the inventory effectively.
Energy and Utilities
- Smart Grid Management – It is now, possible to connect sensors, smart meters, and various soft wares to forecast the consumption and monitor assets to improve efficiency. Machine Learning helps in building an energy grid system with smart solutions which helps in reducing outrages.
- Power Usage Analytics – Applying machine learning algorithms to large datasets available for utilities data can help in uncovering consumer consumption patterns, managing energy constraints and, detecting and preventing fraud.
- Energy Utilization and Optimization – Significant energy savings can be achieved in many manufacturing industries through process integration. Energy utilization can be monitored and optimized based on consumption patterns and seasonal variations.
Travel and Hospitality
- Dynamic Pricing – Without the actual need to manually define complex pricing rules, it is now possible to fix the prices dynamically. Thanks to machine learning by which the prices get optimized every time whenever a user is performing any action on it. It keeps on learning itself and predicts whether to display the original price or the discounted price.
- Aircraft Scheduling – Many airlines use different techniques to create robust and reusable predictive models to provide a holistic view of operations and enhance business value. Using ML algorithms, it is possible to predict the flight delays, monitor, and improve flight operations.
- Traffic Patterns and Congestion Management – Advanced machine learning algorithms can be augmented with real-time data visualization to predict the traffic patterns so as to manage congestion. It is now possible to identify the relationships and gain meaningful insights to take appropriate actions to improve the traffic flow and minimize delays.
The Way Forward
It is essential for organizations to automate the business processes by accurately analyzing huge amount of data to identify trends and patterns. With the help of the ML experts, you are able to get help in identifying opportunities which can be profitable for your business in the future.