8 problems that can be easily solved by Machine Learning
Machine Learning and Artificial Intelligence have gained prominence in the recent years with Google, Microsoft Azure and Amazon coming up with their Cloud Machine Learning platforms. But surprisingly we have been experiencing machine learning without knowing it. The most primary use cases are Image tagging by Facebook and ‘Spam’ detection by email providers. Now Facebook automatically tags uploaded images using face (image) recognition technique and Gmail recognizes the pattern or selected words to filter spam messages. Let’s take a look at some of the important business problems solved by machine learning.
Problems solved by Machine Learning
1. Manual data entry
Inaccuracy and duplication of data are major business problems for an organization wanting to automate its processes. Machines learning (ML) algorithms and predictive modelling algorithms can significantly improve the situation. ML programs use the discovered data to improve the process as more calculations are made. Thus machines can learn to perform time-intensive documentation and data entry tasks. Also, knowledge workers can now spend more time on higher-value problem-solving tasks. Arria, an AI based firm has developed a natural language processing technology which scans texts and determines the relationship between concepts to write reports.
2. Detecting Spam
Spam detection is the earliest problem solved by ML. Four years ago, email service providers used pre-existing rule-based techniques to remove spam. But now the spam filters create new rules themselves using ML. Thanks to ‘neural networks’ in its spam filters, Google now boasts of 0.1 percent of spam rate. Brain-like “neural networks” in its spam filters can learn to recognize junk mail and phishing messages by analyzing rules across an enormous collection of computers. In addition to spam detection, social media websites are using ML as a way to identify and filter abuse.
3. Product recommendation
Unsupervised learning enables a product based recommendation system. Given a purchase history for a customer and a large inventory of products, ML models can identify those products in which that customer will be interested and likely to purchase. The algorithm identifies hidden pattern among items and focuses on grouping similar products into clusters. A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases. E-Commerce businesses such as Amazon has this capability. Unsupervised learning along with location detail is used by Facebook to recommend users to connect with others users.
Amazon product recommendation using Machine Learning
4. Medical Diagnosis
Machine Learning in the medical field will improve patient’s health with minimum costs. Use cases of ML are making near perfect diagnoses, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient. Adoption of ML is happening at a rapid pace despite many hurdles, which can be overcome by practitioners and consultants who know the legal, technical, and medical obstacles.
5. Customer segmentation and Lifetime value prediction
Customer segmentation, churn prediction and customer lifetime value (LTV) prediction are the main challenges faced by any marketer. Businesses have a huge amount of marketing relevant data from various sources such as email campaign, website visitors and lead data. Using data mining and machine learning, an accurate prediction for individual marketing offers and incentives can be achieved. Using ML, savvy marketers can eliminate guesswork involved in data-driven marketing. For example, given the pattern of behavior by a user during a trial period and the past behaviors of all users, identifying chances of conversion to paid version can be predicted. A model of this decision problem would allow a program to trigger customer interventions to persuade the customer to convert early or better engage in the trial.
6. Financial analysis
Due to large volume of data, quantitative nature and accurate historical data, machine learning can be used in financial analysis. Present use cases of ML in finance includes algorithmic trading, portfolio management, fraud detection and loan underwriting. According to Ernst and Young report on ‘The future of underwriting’ – Machine learning will enable continual assessments of data for detection and analysis of anomalies and nuances to improve the precision of models and rules. And machines will replace a large no. of underwriting positions. Future applications of ML in finance include chatbots and conversational interfaces for customer service, security and sentiment analysis.
Manufacturing industry can use artificial intelligence (AI) and ML to discover meaningful patterns in factory data. Corrective and preventive maintenance practices are costly and inefficient. Whereas predictive maintenance minimizes the risk of unexpected failures and reduces the amount of unnecessary preventive maintenance activities.
Corrective, Preventive and Predictive Maintenance
For predictive maintenance, ML architecture can be built which consists of historical device data, flexible analysis environment, workflow visualization tool and operations feedback loop. Azure ML platform provides an example of simulated aircraft engine run-to-failure events to demonstrate the predictive maintenance modeling process.The asset is assumed to have a progressing degradation pattern. This pattern is reflected in asset’s sensor measurement. In order to predict future failures, ML algorithm learns the relationship between sensor value and changes in sensor values to historical failures.
8. Image recognition (Computer Vision)
Computer vision produces numerical or symbolic information from images and high-dimensional data. It involves machine learning, data mining, database knowledge discovery and pattern recognition. Potential business uses of image recognition technology are found in healthcare, automobiles – driverless cars, marketing campaigns, etc. Baidu has developed a prototype of DuLight for visually impaired which incorporates computer vision technology to capture surrounding and narrate the interpretation through an earpiece. Image recognition based marketing campaigns such as Makeup Genius by L’Oreal drive social sharing and user engagement.
Image Recognition problem solved by ML (Reference – https://goo.gl/4Bo23X)
Most of the above use cases are based on an industry-specific problem which may be difficult to replicate for your industry. This customization requires highly qualified data scientists or ML consultants. The machine learning platforms will no doubt speed up the analysis part, helping businesses detect risks and deliver better service. But the quality of data is the main stumbling block for many enterprises. Thus apart from knowledge of ML algorithms, businesses need to structure the data before using ML data models.
If you’re ready to learn more about how Machine Learning can be applied to your business we’d love to talk to you. You can find out more at Big Data and Analytics page.