If you stay au courant with technology trends, you probably bump into Machine Learning in every other article. Whether it’s voice assistants or autonomous vehicles, ML software has made it into every industry.
Thus, you’ve likely used Machine Learning for online shopping (Amazon recommendations) or for sending a meme to your friend online (Facebook Messenger). Let alone a variety of other daily uses like face unlock GPS navigation services, or Google searches.
With that said, let’s delve into scenarios that involve the use of ML in our lives and businesses. Most likely, you didn’t even realize it was Machine Learning that powers these applications.
A Few Words on Machine Learning
If we shift our gaze from daily applications to the corporate realm, Machine Learning applications introduce companies to unprecedented use of Big Data. Thus, AI technologies usher in new ways of understanding their customers’ behavior, satisfaction, and/or loyalty. In particular, you can locate patterns and anomalies that were buried deep down just a few years ago.
From a technical standpoint, ML-powered algorithms process input data and employ statistical analysis to foretell the outcome.
Based on the data type and the desired outcome, one of four learning models yields the result:
- Supervised – uses labeled data;
- Unsupervised – uses unlabeled data;
- Semisupervised – uses the combination of the two;
- Reinforced learning – you train ML models to make a sequence of decisions.
Specialists can also apply one or a combination of algorithmic methods within each of these models. It all depends on the data sets used and the intended results.
Machine learning algorithms are primarily designed to classify objects, find patterns, predict outcomes, and make informed decisions. Algorithms can be used one at a time or combined to achieve the highest possible accuracy when dealing with complex and more unpredictable data.
Now, let’s go over the most eminent examples of ML applications today.
Best Machine Learning Applications in 2021
Machine Learning in Healthcare – Medical Imaging, Drug Discovery, Anomaly Detection
Intelligent healthcare unfolds our top list of ML applications.
Healthcare organizations have been quick to adopt their AI know-how in the fight to curb this pandemic. Thus, Kara, a product by Saykara, has become the first physician voice-enabled assistant that automates medical charting. Touted by experts as “Alexa for doctors,” this voice recognition software is used for auto-completing clinical notes. The healthcare industry has recognized its potential during a huge influx of patients during the pandemic and for ordinary clinic visits.
Medical imaging is another embodiment of intelligent technologies and computer vision.
Traditionally, medical imaging is an umbrella term that includes various radiological imaging techniques such as X-ray radiography. With the help of AI, radiologists and pathologists recognize abnormalities faster and more accurately. New methods are capable of detecting complex patterns in imaging data and yielding quantitative evaluation of radiographic characteristics.
Other benefits of AI image recognition include:
- Minimized human error and more precise diagnostics
- Well-recorded monitoring of a patient’s recovery
- Automatic diagnosis report generation
Besides hospitals, the pharma industry is also tapping into the immense potential of AI for drug discovery. Intelligent systems facilitate the lengthy timelines and processes associated with discovering and launching drugs all the way onto the market.
Overall, Machine Learning can potentially increase the accuracy of treatment protocols and health outcomes due to algorithmic processes. It can also quickly process vast amounts of medical records to perform diagnostics and treatment.
Machine Learning in Social Media – Suggesting Friends, Auto-tagging
Machine learning in social media is a two-way street. On the one hand, cutting-edge technologies make social platforms more engaging and user-friendly. On the other hand, it helps companies dig deeper into the trends.
Friend recommendation is an ML-powered algorithm that suggests friends to the users based on mutual friends. In this case, link prediction can be used to anticipate the likelihood of a future connection between two users in a given network.
Face recognition is another ML application that recognizes and tags our friends right after we upload the picture on social media. Although it seems simple on the surface, in reality, auto-tagging is a complex back-end process.
The recognition technology breaks down facial features into several data points to obtain results with the precision of a plastic surgeon. It analyzes the parameters such as the distance between the nose and lips, the width and length of the lip, etc.
This process takes a few seconds and gives us the luxury of auto-tagging on Facebook and other social media platforms.
Now let’s see how companies can benefit from Machine Learning in their social media campaigns:
- Profound social media analysis to establish a deep understanding of their target audience;
- Sentiment analysis to identify and extract customer opinion on products;
- Highlight posts that can be valuable to the audience;
- Image recognition for social media marketing;
- Chatbots on social media to automate customer service.
In 2017, the market for AI-amplified social media marketing stood at $465.2mn. According to a Markets and Markets report, that number will be higher by 2023, amounting to $2.1 billion.
Hence, it’s hardly surprising that Machine Learning is now widely used as a powerful social monitoring tool that delivers insights from a brand’s social media profiles and audience.
Machine Learning in Finance – Fraud Detection, Focused Account Holder Targeting
The overwhelming success of Machine Learning as a data processing technique has jilted banking into the adoption of AI-based technologies.
Thus, predictive analytics-based software underpins effective fraud detection and prevention in credit card usage, accounting, insurance, etc. Cognitive computing technologies also allow for anomaly analysis across multiple channels involved in payment processing.
The key points in fraud detection include:
- Data loading – large volumes of data are fed into the model to train it;
- Feature extraction – to pull information from threads involved in a transaction process;
- Algorithm training – to train the algorithm for more accuracy;
- Model creation – the final stage where ML algorithms are ready.
Ever wondered how banks target customers who yield the highest ROI? It’s called focused account holder targeting.
Machine Learning algorithms help to identify accounts that require granular sales and marketing efforts in order to turn them into lifelong customers. Once customers are segmented, banks refine this taxonomy by looking into the spending habits and capacity of their customers. Those insights form the basis of targeted offerings and ads.
Machine Learning in Retail – Dynamic Pricing, Inventory Management
AI-powered capabilities have opened up a wealth of opportunities for the retail industry. There has never been such an insane amount of customer insights for grabs and such a variety of prediction tools. Today, retailers can easily anticipate the demand on a given day, saving money and time.
A well-trained ML algorithm can even adjust prices in real-time to drive more sales and offer optimal pricing policies. This allows retailers to set product prices in response to real-time supply and demand. The technique is known as dynamic pricing or surge pricing. Without Machine Learning, this retail practice becomes tedious since manual efforts fall short of analyzing this amount of information.
ML software has also given a fresh coat of paint to inventory management. Thus, two leading American retailers are using ML-powered robots to automate inventory management. Back in 2016, Lowe’s debuted its LoweBot in 11 stores throughout the San Francisco Bay Area. These drones generate real-time data to scan inventory and detect patterns in product or price discrepancies.
Other Machine Learning applications involve:
- Sales forecast and demand management;
- Customer segmentation;
- Optimization of marketing and advertising efforts;
- Customer interaction using virtual assistants and chatbots;
- Targeted recommendations using recommendation systems;
- Text and image recognition used for invoices, packing lists, bills, etc.
Machine Learning in Travel – Travelling Chatbots, Recommender Systems
Before the pandemic hit, we saw impressive growth in digital travel sales, partly thanks to the advancements in Machine Learning. Although the travel industry has seen better times than 2021, AI algorithms are still widely used there.
- Chatbots.
Virtual travel agents are just a sliver of the ML might in the travel industry. They assist travelers in booking, selecting seats, and providing relevant information on traffic conditions, train schedules, and delays. For companies, chatbots are interactive services that rely on natural language processing and maintain a high level of customer service.
- Recommendation engines.
Recommender systems can also be a part of virtual agent software or present a stand-alone algorithm used on apps and websites. ML algorithms analyze user data and suggest new destinations that might interest users. In COVID-19 times, users could choose a non-restricted alternative destination instead of their favorite ones. Technically, the Travel Recommendation API relies on the idea of destination similarity.
Other ML use cases in traveling include sales and cost optimization, flight price generation engines, and user experience management.
The Final Word
Machine Learning has permeated multiple industries. From retail to banking to traveling, its impact is yet to kick into full gear. And as the companies are vying for the market, we’ll certainly see more trailblazing ML applications soon.
Therefore, if you consider seizing intelligent technologies, there has never been a better time for investments.
