Advances in the computing technologies have rapidly evolved ML (Machine Learning). Today, cloud computing, continuously growing massive datasets, and cloud data storage has spiked the global interest in machine learning applications. We can now make informed business decisions, create analytical models, unravel the hidden trends and patterns from the data, without explicitly programming the computers. Thanks to the models equipped with advanced machine learning algorithms. Computers can now learn ‘on their own’ and return great results.
You might want to know about some real-life applications of ML! Well, we have compiled a list of twenty-one applications of machine learning across five industries. These are the use-cases of ML where it has rooted itself deeply, into practical, real-world applications. Fasten your seatbelts, let’s take a ride!
The US$9.59 trillion global healthcare market is witnessing revolutionary applications being developed through machine learning. From remote health diagnostics to surgical assistance, ML is opening up a whole new sector for precise, accessible and superfast healthcare services.
Here are the applications of ML in the healthcare industry.
#1. Medical image diagnosis and interpretation
Machine learning and deep learning has enabled remarkable breakthroughs in ‘computer vision.’ Simply put, computer vision is higher-level image processing where the input is an image and output is the interpretation of that image. Diagnosis of ailments through medical imaging is an active example of ML use-case in this industry. Enormous amounts of continually generated medical-data have enabled deep learning models to predict and diagnose the conditions of a patient. According to IBM researchers, 90 percent of all the medical data comprises of images. Hence, it makes sense to develop systems capable of interpreting the images by learning through examples.
Microsoft’s InnerEye, Enlitic, and IBM’s Watson Health are examples of some image diagnostic tools employing ML and AI. Google’s DeepMind Health is another recent example, where ML technology is being developed to diagnose and treat ‘macular degeneration of eyes’ during the old age.
#2. Healthcare data collection and predictive analytics
Healthcare technology companies are busy collecting and ‘harvesting’ the healthcare data through mobile devices and interactive apps. This aggregated health data will enable real-time treatment changes and identification of hidden patterns in it. Combine this data pool with ML, and we get a much-needed application. For example, Apple’s ‘ResearchKit’ is applying machine learning for facial recognition by monitoring its users regularly, to tackle Parkinson’s disease and Asperger’s syndrome.
Similarly, IBM has partnered with Medtronic, for real-time data analysis of diabetes and insulin through ML. The objective in this use-case is to aggregate consumer data and provide opportunities to the researchers to ‘spot’ unique medical cases and address ‘stubborn diseases.’
#3. Predicting epidemic outbreaks
Machine learning and deep learning are known to process massive amounts of data and predict the most likely outcome based on the discovery of hidden patterns. Predicting the epidemic outbreaks is one such advanced application of ML in the medical sector. Combining the data collected from satellites, real-time social media activities, historical information from the web, geography, demography and medical sources, artificial neural networks can predict the disease outbreaks.
Such support vector machine models take into account various data points like rainfall, temperature & number of positive cases within a geography to predict the most likely time for a disease to spread widely. For example, ‘HealthMap’ uses machine learning classification & visualization to provide real-time alerts on epidemic outbreaks in any country.
#4. Surgical assistance
Yes! The ‘Da Vinci robot’ you saw stitching a grape back together on the internet is the real deal. Robot surgical assistants are becoming a reality of today. Such advanced systems utilize computer vision aided by machine learning to identify objects and calculate point-to-point distance. For example, identifying a piece of graft-skin for skin transplant. ML is also employed for stabilizing the robotic limbs and their movements.
Surgical assistants have wide applications in surgical tasks. These can range from performing surgeries in tight spaces, reducing tremors and vibrations of the limbs and even guiding the human surgeon. ML holds a bright future in this niche.
#5. Precision medicine and ML
Machine learning models are being explicitly employed in the precision medicines. It involves identifying the mechanisms of ‘multifactorial’ diseases (Not having a single genetic cause) and finding the optimum alternative paths for their treatment. Diseases such as heart diseases, type 2 diabetes, and obesity can be efficiently addressed by deep learning ML models.
In precision medicine, unsupervised learning aids in identifying patterns in data while supervised learning is used to make predictions on it. Not only this, but ML is also being applied for new drug discoveries. For example, they can screen drug compounds and predict their success rate based on various biological and genetic factors. Such a use-case will help healthcare industry in optimizing the clinical-trial procedures and return more efficient results.
E-commerce has made it easier than ever for the people to consume goods and services. The prices, delivery, and quality of products are always under optimization to ensure customer satisfaction and beat competition. Let us see how machine learning is exploiting huge amount of data generated online every day.
Here are the applications of ML in the e-commerce industry.
#6. Personalized customer targeting
Machine learning is proving to be an effective tool in providing a tailor-made purchase experience to the customers. While a real-life salesperson is always present to analyze a customer’s requirements, doubts, mood, hesitation, and behavior to close a sale, same seems very tedious while selling online. This gap is bridged by the machine learning models integrated to the e-commerce sites. Once a prospect visits any such site, the ML engines spool up. They track data points like name, age, gender, geolocation, interests, and history of products bought, etc. to ‘know’ and accordingly personalize a customer’s buying preferences. The magic? This knowledge is then used to ‘laser target’ the same customer and similar profiles across a location to boost the sales. For example, ‘Target’ uses similar ML algorithms for accurate personalized customer targeting.
#7. Price optimization
In a world where prices for a similar product/service can be compared with just a few clicks, guessed sales prices don’t seem to work. Hundreds of thousands of online stores have millions of products to offer. A lack of research on the selling price can leave the sales-conversion rates into dust. How do you think it is solved? Yes! Machine learning does it. ML models vary the ‘sales’ prices and discounts by calculating numerous factors at once. Machine learning factors in the elements like competitor’s prices, time, location, demand, customer profile and supply ability to optimize the sales price of multiple goods and services. The idea here is to use ML for ‘dynamic’ pricing to increase the sales-conversion rate while providing the best value to the customers.
#8. Product recommendation
One characteristic of E-commerce sites that we witness almost every day is their ‘product recommendation’ feature. As soon as we start browsing the array of products or make a purchase, similar and relevant products are recommended to us at the exit. It is achieved by ML algorithms that are constantly learning an individual customer’s tastes. And this is highly effective! Amazon gets 35 percent of its sales through the ML recommendation engine. Though, it takes a lot of processing power for a model to correlate shopping behavior and product sales.
While algorithms written by a human for such a task will be highly limiting and tedious, ML allows a model to iterate and quantify the ‘buying trends’ repetitively. To recommend ‘what the customers did not know they wanted’ is a sure shot boost for the sales numbers.
#9. Supply-demand forecast
In today’s dynamic and uncertain environment, businesses depend on carefully managing their supply-demand chain. Predicting or forecasting it seems like a feasible option, which is possible with machine learning. Companies today are optimizing their supply-demand chain management processes using ML applications. For example improving the demand planning, inventory management and automated root cause analyses of the sales cycles. ML algorithms are also being explicitly used for forecasting the sales numbers based on the patterns found in the historical sales data. This use-case of ML has enabled enterprises to shorten their ‘factory-home’ cycle and save on costs at various stages of a sale.
#10. Customer support and servicing
Resolving customer queries at a high volume can be a daunting task. Especially for the e-commerce companies that struggle to keep the customer satisfaction high. Through text-recognition and natural language processing, chatbots are now understanding the customer issues and even resolving them. ML-based artificial intelligence bots like WorkFlow can engage and converse with the online customers and resolve any issue they have, like, say a transaction. There is a huge upside to this ability of the AIs. To name some, they don’t sleep (24x7 service), they are super fast (no waiting for the customers), they build long-term relations, and they personalize the customer experience. ML, therefore, becomes a superpower for the companies to handle the customer issues quickly and efficiently.
Humans have made useful things since the ancient times. They have sold them to those who needed them and valued them. This has continued till today, only certain aspects of the manufacturing process have evolved beyond recognition. And so, machine learning finds its authority in the manufacturing sector as well.
Here are the applications of ML in the manufacturing sector.
#11. Quality control
On an assembly line where a product is being manufactured and rapidly moving, detecting flaws and quality of finish becomes critical. Of course, if human eyes were only that fast. But ML aided computer vision comes to our rescue. Simply put, an ML model can learn from a set of examples to classify the ‘fine’ products from the flawed ones. These ML models use ‘semi-supervised anomaly detection’ algorithms to learn and execute this task. Manufacturers of high precision mechanical parts, automobiles, soft-drinks, solar panels, processed food products, etc. are already employing this technology to refine their products quality.
#12. Increasing production and lowering material costs
Machine learning can improve the ‘yield rates’ at the machine line and plant level by running predictive analytics. Companies like General Electric are already exploiting this fact. The results can be moving in this case. Integrating ML model to analyze the production-material cost have shown to boost the production by twenty percent and lowering the material cost by four percent. If numbers never impressed you, try using an ML algorithm on an assembly line!
#13. Predictive maintenance
One incredible application of machine learning in the manufacturing industry is predictive maintenance (PdM). It includes a set of techniques to monitor the conditions of ‘in-service’ machinery and equipment to predict when the maintenance should be scheduled. Modern smart manufacturing systems are interconnected with every other machine assembly in a factory via ‘Internet of Things’ (IoT) platforms. This makes it easier to execute predictive maintenance of crucial equipment.
Also, ‘adaptive maintenance’ is another term closely associated with PdM. With adaptive maintenance, unexpected downtimes and material waste can be significantly reduced without halting the manufacturing process.
#14. On-demand manufacturing
In solving the complexity of manufacturing highly-customized products at a large scale, machine learning steals the show. The machines can be ‘trained’ to understand specific design and finish requirements from a customer and manufacture it in the said quantity. For example, manufacturing an engine spare-part that is no longer used in small quantities of hundreds. Any minor change in the order will be tackled by intelligent machines only. This step will enable ‘subscription’ based business model for those manufacturers struggling with high costs and lack of demand. ‘On-demand’ manufacturing will give rise to ‘demand-driven production’ on a large scale and create new economies.
Banking and finance industry
And how can the most valued finance industry not make the use of advances in ML? The US$100+ trillion global market is betting on the sweet promises AI and ML are making them for the future.
Here are the applications of ML in the banking and finance industry that are already in force.
#15. Risk management
Banks and various financial institutions rely on their risk-management capability to make safe investments, and not lose them. For example, loan lending institutions can verify real-time creditworthiness of an applicant based on latest market trends. In such a case, the loan will be disbursed to the right profile person. Also, finance institutions can track the successful investors operating from various accounts to make an informed decision about investing. Machine learning can compliment investment managers and their decisions, allowing them to focus more on productive tasks.
#16. Fraud detection and prevention
Banks and financial institutions are highly accountable for protecting their client’s wealth and their core assets. However, cybercriminals or hackers have always made it tough for them to maintain an airtight security. So, now financial sector is thinking one-step ahead to outrun the hackers and online frauds. Machine learning is the answer. By verifying each transaction with an account’s history repetitively at each step, ML can keep the fraud transactions at bay.
Machine learning also detects ‘red-flag’ activities and possible fraud associated with them. Like huge cash withdrawals, foreign-purchases, etc. can raise the machine’s suspicion. It can then delay the user action until a human authority takes a decision. The algorithm becomes smarter with every move the users make and every decision it makes. Similarly, ML is also being employed in Banking network security to keep its valuable data secure and encrypted.
Machine learning has a fundamental feature of making predictions from historical data. This applies well to the marketing activities too! A machine learning model can make forecasts on marketing campaigns strategy and its effective outcome. This is achieved by analyzing mobile apps data, web activity of the users and previous ad campaign results. Marketing executives in the financial sector are already benefiting from this technology. Their skills and growth are being complimented through the integration of machine learning.
We cannot imagine searching for something online and getting wrong results. Search engines like Google have spoilt us! You are reading this post because an algorithm drove you here (This is what you were looking for, right?). Machine learning is the DNA of present-day search engines and social media platforms. Let us see what essential tasks ML accomplishes here.
Here are the applications of ML in search engines.
#18. Query interpretation and classification
Search engines manage to return accurate results from the internet via ‘query interpretation’ and its classification. A query is the input information by a user to get relevant information. The ML engine starts with the query classification. For example, decoding whether a query is transactional, directional or informational. Then come the spelling and synonym suggestions for expanding the possible results. The third step involves sorting ‘disambiguation.’ For example, whether the user entered ‘plane’ as in-plane surface, or ‘plane’ the airplane? Then various other crucial parameters are evaluated by the ML for returning precise results. Take any search engine as an example.
#19. Search ranking
Machine learning ranks the information on the internet according to a classification system. ML sorts the data as per the contextual ranking, primary ranking, initial retrieval, and personalized ranking. Since the introduction of ML in ranking the web content, SEO rules have changed dramatically. The search results are more precise now and display the content in order of decreasing relevance. A similar approach is followed by the social media platforms to direct relevant data-feed to the users.
#20. Url interpretation
It is of utmost importance for the search engines to correctly interpret the Url/online document to place it on the web for easy retrieval. First, ML algorithm detects the nature of the page, i.e., whether it is a forum, blog, e-commerce website, etc. Then it is checked for being spam or not. After that comes the quality check. High-quality content URLs are separated from the low quality ‘junk’ URLs. The Url is then scanned for the possible elemental relationship with the other pages by information included in them. For example, relations between places, people, any information on a topic, etc.
#21. Generating search features
These include the information or ‘features’ that are not directly related to the search query, but somehow relevant to it. Think of it as the ‘recommendation engine’ from a site like Amazon, where you are recommended products based on your search and purchases. The ML engine generates relevant features such as sitelinks, relevant searches (example, suggestions in Google) and other quantitative information.
Twenty-one real-world applications of machine learning might have wiped away your doubts about its capabilities. You just went through some ‘live-in-action’ applications of the ML in five major industries.
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