As Artificial Intelligence (AI) takes over the world, there is one branch that you must know more about: Machine learning (ML).
This branch of artificial intelligence, which is further divided into supervised and unsupervised learning, allows computers to learn and improve from gathering and analyzing data. It’s a tool that improves performance over time and is evidence of the idea that the world is one step closer to innovations that continue to amaze!
Importance of Machine Learning in Today’s World
Machine learning transforms business operations, from healthcare to finance, and manufacturing to transportation. Data is king, and ML models analyze vast amounts of data to become more efficient and improve outcomes.
Source: CSO
Let’s examine 12 real-world machine learning applications across different industries. By highlighting the various ways machine learning is used, there is much to learn about this technology and discover the opportunities it presents for your business.
Pro tip: Looking to implement machine learning models for your business? Connect with the Data Pilot experts today and gain that missing competitive edge.
Healthcare
1. Disease Diagnosis
What’s more trustworthy when it comes to diagnoses? A machine with tremendous amounts of data related to a particular ailment or a human who will depend on memory to analyze and diagnose? Given technology’s evolution, the machine has a far lower chance of making a mistake than a human.
For example, machine learning algorithms can analyze thousands of medical images and identify patterns plus anomalies, making it easier to detect malignant cells with higher accuracy.
Computer scientists at Stanford University created an artificially intelligent diagnosis algorithm for skin cancer, which was trained to visually diagnose potential cancer by analyzing images of skin lesions. They began by creating a database of nearly 130,000 skin disease images to train their algorithm. These images represented various skin conditions, including cancerous lesions. The algorithm’s performance was evaluated against 21 board-certified dermatologists and tested on skin lesions that represented the most common and deadliest skin cancers. The algorithm’s diagnostic accuracy was on par with that of dermatologists: It matched the performance of human experts in identifying skin lesions associated with cancer. By combining visual processing with deep learning, the researchers achieved impressive results in skin cancer diagnosis.
Learn more: (https://med.stanford.edu/news/all-news/2024/04/ai-skin-diagnosis.html)
2. Personalized Medicine
Most of the population is used to taking generic medical treatments, irrespective of body type, gender, or age. By considering individual differences, genetic makeup, and medical history, machine learning can develop personalized medicine formulas for each human that would be a game changer in the industry.
Finance
3. Risk Assessment
Ponzi schemes are not just financially detrimental, but they take an emotional toll as well. Machine learning algorithms can identify fraudulent transactions, assess credit risks, and predict insurance claims by analyzing large amounts of data to detect anomalies. Financial institutions can utilize this to protect their customers’ assets and prevent financial crimes. PayPal, a leading online payment platform, employs machine learning to enhance its fraud detection capabilities.
PayPal leverages machine learning algorithms to assess in real-time whether an individual is a legitimate customer or not. They analyze various factors, including device information, email addresses, IP addresses, phone numbers, transaction patterns, and user behavior. By doing so, they can quickly identify suspicious activities and potential fraud attempts. They also check for proxies that might be used to hide an individual’s true location. Their machine learning models even determine the best retry strategy for transactions based on factors such as the card used, issuer, merchant, transaction-level parameters, processor, acquirer combination, and the day and time of retry.
PayPal employs supervised learning models, which are trained using historical data tagged as either “good” or “bad.” These models run predictive analyses to detect fraudulent patterns. PayPal’s two-sided network, with data from over 432 million active global accounts, provides a rich source of transaction and risk data. This shared intelligence contributes to making their machine learning algorithms even stronger in fraud detection.
Learn more: (https://www.paypal.com/us/brc/article/payment-fraud-detection-machine-learning)
4. Stock Market Prediction
The stock market gets everyone’s emotions running high! With machine learning algorithms, future price movements can be identified. Though this can generate profits, it can provide insight into market trends and informed investment decisions. No one wants the stock market to crash; these algorithms can foresee such an event and give enough time to prevent it.
Marketing
5. Customer Segmentation
Personalization is critical to getting a customer to buy from businesses. Imagine marketing campaigns tailored to each segment of your customer base. Machine learning models can help create targeted marketing campaigns. This will lead to increased customer segmentation and retention with vast amounts of data analyzed.
Amazon employs machine learning algorithms to segment its customers based on their purchase history and browsing behavior. This segmentation allows marketers to tailor their efforts to specific subgroups of their audience, resulting in more effective marketing messages. By analyzing user behavior (purchases, views, ratings), Amazon can suggest products that align with a customer’s interests. Amazon Personalize is a fully managed machine learning service that requires no prior ML knowledge. It offers ready-to-use models for segment creation and product recommendations. This translates to more relevant offers, higher conversion rates, and more profits.
6. Recommendation Engines
35% of Amazon’s revenue is generated by its recommendation engine.
Everyone has a unique shopping pattern or style. This can be similar, but not identical to another person. Recommendation engines can suggest products specific to customer taste, increasing the probability of sales going through.
Source: Google
7. Sentiment Analysis
Sentiment Analysis can provide the exact customer sentiment when businesses are about to launch a new brand or introduce a new product. This gives insights into how the brand or product will be received upon its release. Based on the data, companies can then ideally time the release of their product, guaranteeing maximum sales.
Manufacturing
8. Quality Control
Businesses spend a lot of money on quality control. It’s essential to provide consistent quality, may it be a bakery item or a tool. The most important reason customers repeat purchases is the consistent product quality. ML can easily detect product defects owing to its large database of images and anomalies.
Data Pilot helped a global fashion brand automate the quality inspection of garments using ML based computer vision. The manual process required the defects found in finished garments to be logged manually in a mobile application. The computer vision model identified the type of defect and highlighted the problem area on the garment. Detection of the defect became automatic where the existing fields were auto populated based on output from the model. The results reduced the logging time by half and subsequently improved product quality.
Pro tip: Ready to supercharge your business? Get a quote for custom Machine Learning services today!
9. Predictive Maintenance
No one wants their business to suffer for a few days because of an urgent maintenance requirement. This is always the case with machinery; it surprises when repairs are needed. Machine learning can predict machine maintenance requirements by analyzing sensor data and identifying patterns that might indicate impending failures. It would be a great step for businesses to implement this, save time and maximize efficiency.
Rolls-Royce, a renowned manufacturer of aircraft engines, employs machine learning algorithms to enhance predictive maintenance (PM) for its engines. PM strategies are based on real-time data, allowing for the diagnosis of impending failures and the prognosis of machine health. In the aviation industry, where safety is paramount due to high costs and potential danger to human life, PM is crucial. Traditional maintenance strategies (e.g., “fix it when it breaks”) are inadequate for life-critical systems like aircraft engines. Rolls-Royce combines flight data with engine performance and maintenance data, which is then used by ML algorithms to optimize flight routes. This optimization reduces fuel burn and minimizes engine degradation, leading to cost savings and improved efficiency.
Learn more: (https://d3.harvard.edu/platform-rctom/submission/rolls-royce-optimising-jet-engine-maintenance-with-machine-learning/)
10. Supply Chain Optimization
Companies like Uber and Careem optimize their supply chain using ML models. After feeding in multiple parameters like distance, traffic, time of day, destination and route, etc., they changed parameters for vehicle selection, which previously were just based on distance. After analyzing and improving customer experience, machine learning has optimized its supply chain too.
Transportation
11. Autonomous Driving
Machine learning algorithms can help autonomous vehicles navigate complex environments and make real-time decisions. Autonomous vehicles can make predictions and act based on their surroundings by analyzing sensor data and identifying patterns. This allows for safer, more efficient driving and can lead to the widespread adoption of self-driving cars.
Tesla, a trailblazer in electric cars and autonomous driving, relies heavily on ML algorithms to power its Autopilot software. Tesla’s Autopilot is an advanced driver-assistance system (ADAS) that aims to achieve full self-driving capability. Tesla vehicles collect vast amounts of data from sensors, including cameras, radar, and ultrasonics. The data is assembled into a unified representation of the world around the car. This representation includes information about lanes, road boundaries, curbs, crosswalks, and other critical features. Machine learning algorithms process this diverse data to recognize patterns, objects, and potential hazards. Based on the processed data, the algorithms generate driving commands that allow the car to follow the road, adhere to traffic laws, and navigate safely.
Learn more: (https://www.enterpriseai.news/2023/03/08/how-tesla-uses-and-improves-its-ai-for-autonomous-driving/)
12. Traffic Prediction
Google Maps already uses machine learning algorithms to analyze traffic patterns and predict congestion, allowing for more efficient routing and reduced travel times. This helps reduce traffic-related stress and improve the overall driving experience. This can further be used to automate traffic lights to give more preference to lanes that continuously develop longer lines.
Data Pilot Take
Machine learning is rapidly transforming numerous industries, and from a business perspective, it unlocks a treasure trove of possibilities. In healthcare, it aids in early disease detection, personalized medicine, and drug discovery, potentially saving lives and reducing costs. Finance leverages machine learning for fraud detection, personalized investment strategies, and optimized risk management, protecting institutions and boosting profits. Marketing utilizes machine learning for targeted advertising campaigns, generating leads, and understanding customer behavior, leading to increased sales and improved customer loyalty. Manufacturing benefits from machine learning through predictive maintenance, optimizing production lines, and improving quality control, resulting in minimized downtime and higher efficiency. Even transportation utilizes machine learning for autonomous vehicles and traffic prediction, leading to safer roads and improved logistics. In essence, machine learning empowers businesses to make data-driven decisions, optimize operations, personalize experiences, and ultimately achieve significant cost reductions, revenue growth, and a competitive edge. As vast data sets become even more readily available, machine learning’s influence will continue to expand, shaping the future of business success.
Ready to Harness the Power of Machine Learning for Your Business?
At Data Pilot, we specialize in helping businesses leverage machine learning to derive the maximum value out of the data they have. Contact us today for a free consultation to explore how ML can drive your business forward.