Machine learning (ML) is probably the final invention humans need to take care of. With its outrageous ability to learn from data and predict outcomes, ML may become a solution that drives innovation across industries without human intervention. While this rather fictional future is still far away, today ML is widely applied across enterprises to automate dull and repetitive tasks, as well as to enable businesses to deliver added value to end customers.
In fact, according to Statista findings, more than half of businesses deploy AI/ML to reinvent the customer experience. The trend is industry-wide, with ML algorithms fostering advanced solutions that allow for enhanced output and user satisfaction.
Here’s the review of the top 5 ML use cases that pushed the edge of possibility in various industries and allowed innovative business leaders to bring their enterprises to a whole new level:
Auto manufacturing experts has been adding autonomous capabilities for years, equipping vehicles with automatic emergency braking, lane departure warning, blind spot warning, adaptive cruise control, and lane centering. All these features formed the basis for self-driving cars like those from Tesla that can drive without human intervention.
The trend was later adopted by other major automotive enterprises. Equipped with a variety of sensors and cameras, self-driving vehicles can autosteer, change lanes, and accelerate and slow down, depending on the route and the behavior of other traffic participants. For example, Waymo software allows the vehicle to distinguish different traffic light signals to stop or start moving, determine if the lane is blocked, and even predict possible directions of road users, including pedestrians and cyclists.
While self-driving cars are still not fully deployed on roads, they may become the reality in the coming years. For now, they are operated with a safe driver. For example, Nissan’s ServCity car is already tested in London, with a safe driver interfering to handle unexpected scenarios like a sudden huge rock. In May, there will appear the first self-driving bus in Scotland; it will take a 14-mile route and carry up to 10,000 passengers weekly—of course, with a safe driver in place.
With ML algorithms’ ability to provide accurate predictions, they play a large role in addressing upcoming challenges in healthcare. For example, Insitro delegates the models to sift through massive biological data to reveal trends and determine possible disease subtypes. This allows enterprises to enable preventive drug production and improve the overall well-being of humankind.
Besides this, algorithms are valuable surgery assistants who can help navigate during the surgery or facilitate its planning. For instance, an ML-based app displays what is happening during implant insertion in 3D view, allowing surgeons to determine the position of the implant in the human brain in real-time and, therefore, ease the insertion. Microsoft’s Project InnerEye also uses 3D radiological images to tell healthy anatomy from tumors, which improves diagnosing and surgery planning.
Enterprises has always struggled with geo data collection because of the large scope of versatile, hard-to-comprehend data, often in hard-to-reach or extreme environments. What is more, the quality of data often was rather poor, preventing businesses from operating with complete and accurate data and, therefore, from deploying performant solutions.
ML algorithms for UAV drone data processing and LiDAR data processing allow for improved classification, including different natural terrains, remote analysis and classification through 3D models, and more accurate output through land use and topographical maps.
These and other ML initiatives allowed WTVIII Inc, alongside Intetics, to develop a predictive wildland fire map of the USA, which forecasts possible fire incidents within the coming 1-9 days and where they will spread. Besides nationwide impact, WTVIII Inc managed to build a solution with 30% reduced development costs.
Recommendation systems, which are one of the most popular ML applications among enterprises, analyze piles of user data and determine their behavior patterns and preferences to suggest goods that may interest the end consumer. The model is applied both in e-commerce and media, allowing users to easily find related items on Amazon, interesting movies and series on Netflix, and engaging media on Instagram.
As a result, brands deliver a far more personalized experience, improve customer engagement and service, and increase their sales. According to McKinsey, three-fourths of watched content on Netflix are recommendations, while more than a third of all Amazon purchases are triggered by ML-based suggestions.
Traditionally, FinTech and other financial enterprises equip their security systems with rules-based fraud detection, where a set of predefined rules is used to tell fraudulent activity from normal. However, these rules often neglect personal behavioral specifics or are easy to be tricked by hackers, resulting in fake alerts or missed attacks.
ML algorithms allow you to determine a typical behavior for every user and provoke alerts only when the activity of a specific user isn’t really correlating with their typical one. Besides this, ML can also produce new risk rules based on the collected data. This is applied by Sift, which is used by McDonald’s, Twitter, Reddit, and others.
The era of smart machines is just starting, with ML algorithms getting a certain degree of independence and a vote on decision-making. While the majority of models are still closely supervised by humans, even today they can provide more advanced solutions, facilitate daily business processes, and provide tailored customer service. In the future, the industry is likely to see more autonomous ML systems that will allow enterprises to rethink what they offer and provide greater impact. Intetics, as an experienced ML software development company, is eager to drive innovation and change across industries by launching even more intelligent machines.