As Industry 4.0 keeps revolutionizing the world we know, ML (Machine Learning) is among the top disruptive technologies for business. It keeps being featured in Gartner’s Hype Cycle for Emerging Technologies and is considered an innovation trigger that will bring an impact in 5-10 years.
The rising adoption of advanced tech drives market growth. For example, the Global ML Platforms market size is projected to have a whopping 33.6% CAGR between 2022 and 2028. The main trigger is the rising amount of data to analyze and process for enhanced decision-making and user experiences in the digital world.
Although ML is used across a variety of industries, from automotive and manufacturing to healthcare and media, it uses two main techniques:
- Supervised learning to train a model on known input and output data with annotation to predict future outcomes. It can be used for remote non-invasive patient diagnostics based on classification. For example, Symptomate is a bot that quickly assesses a patient’s symptoms, determines possible causes, and provides recommendations. A similar technique is applied to an online lens scanner: Opti software assesses a patient’s eyesight remotely and orders them new lenses without needing to visit a doctor.
- Unsupervised learning to determine hidden patterns or intrinsic structures in input data for improved decision-making. The technique is widely used for recommendation systems, in which an ML algorithm finds similarities in an unlabeled dataset, or, in this case, provides personalized recommendations based on user behavior and preferences. 35% of all Amazon purchases are based on such algorithm-powered recommendations.
However, industrial ML systems often combine both supervised and unsupervised learning, forming diversified chains. In this case, the output of one ML algorithm is an input to another. Let’s discover how ML algorithms are impacting several industries:
ML for Automotive
Smart vehicles and self-driving (SD) cars are becoming a possibility thanks to ML. Some challenges are accurate recognition of the nearby environment, including road signs, lane markings, buildings, pedestrians, and vehicles, while following traffic rules. Trained computer vision algorithms allow SDs to:
- Identify and classify the detected objects, as well as measure their direction, speed, and acceleration to respond accordingly. For example, Waymo software collects and analyzes data from radars and sensors to determine if there’s a green traffic light to start moving, as well as whether the lane is blocked.
- Predict the behavior of obstacles based on the training models to distinguish pedestrians from cyclists and predict their possible speed and direction.
Based on this data, it’s possible to implement an autopilot (similar to those by Tesla) with lane changing, autosteering, traffic-aware cruise control, and auto parking.
ML for GIS/Geospatial
Considering the large scope of geo data, it’s hard to collect and process. Businesses devote a lot of time to collecting and analyzing data, which is difficult in hard-to-reach and extreme environments. Furthermore, poor data quality, often during automatic feature extraction, and data fusion from different sensors prevent businesses from providing quality geospatial solutions. Trained ML algorithms allow for enhanced UAV drone data processing and LiDAR data processing:
- DSM and DTM extraction allow the classification of vector features of natural terrain like rivers and ridges.
- 3D models and 3D measurements allow for remote LiDAR data analyses and classification.
- Land use and topographical maps instead of photos or LiDAR data from drones allow for accurate data assessment and analyses.
Besides this, LiDAR processing can align strips and calibrate boresights, smoothen and clean the point cloud, and digitally elevate models. All this makes POI geofencing accurate and relevant to your business needs. As a result, the following solutions are possible:
- Accurate mapping with incorporated predictive models. For example, a precise fire map can be used to detect and predict potentially dangerous fire spots, in order to timely prevent the catastrophe.
- Relevant traffic alerts and navigation enable precise routing based on the number of current drivers, their routes, and historical data, thereby avoiding traffic jams. This is used not only by navigation software but also by taxi, location sharing, and delivery apps. For example, Uber implements its own DeepETA model to enable enhanced delivery and pick-up.
- Better parking solutions allow for optimized parking slots and an improved driver experience. For example, based on geo data, combined with a trained ML model taking into account weather conditions, holidays, traffic, and history data, users can identify vacant parking slots at the time of their arrival and navigate to them.
ML for Healthcare
ML, often combined with other emerging technologies like AI or AR (augmented reality), shows a potential to disrupt the healthcare sector. Solutions are often aimed at:
- Enhanced treatment and diagnosis through predictive analytics. Based on history and patient data, trained ML models can identify patterns in a patient’s cardiovascular history to predict potential heart failure.
- Better patient service based on predictive analytics and remote diagnosing. Relying on predefined data classification, patients can remotely order new lenses based on assessed eyesight parameters and get preliminary diagnosing. Furthermore, models can analyze patient records, available staff, the number of visits at specific times of the day/week/month, and the layout of emergency rooms to predict the required number of specialists and beds.
- Improved and more accurate surgeon support enabled by image recognition. Radiology image processing helps surgeons better navigate neural implant insertion. This allows for enhanced, more accurate, and faster surgeries. Also, radiological image processing using ML could make it faster for radiologists to define abnormalities and prepare reports.
ML for Digital: Use Cases for Business
Machine learning is widely used to improve user experience, increase online sales, and spot fraudulent activity across digital businesses. Here are some common implementations:
- Virtual assistants: chatbots, including voice-enabled ones, efficiently communicate with customers 24/7 to provide seamless customer support. Through keyword, text, voice, and image recognition, they can analyze text, images, and speech to maintain communication.
- Automatic friend tagging suggestions based on face detection and image recognition are often used by social media like Facebook to provide relevant suggestions of possible friends.
- Accurate translation is possible through natural language processing (POS tagging, named entity recognition, and chunking). A trained model classifies the input data, analyzes it, and provides a relevant output.
- Fraud detection is done by identifying data anomalies in data sets. This is often applied in FinTech solutions, in which trained models classify an activity as fraudulent or suspicious based on predefined patterns. Similar models are used to detect unacceptable content to ensure a safe and secure online environment.
- Price prediction and dynamic pricing are based on timing, demand, demography, initial and competitive price, and order size. This is often applied in the transportation industry. For example, taxi providers like Uber and airway companies often dynamically ramp up the prices at peak times or festive seasons.
- Customer satisfaction and retention initiatives can be carried out through personalized recommendations. A vivid example is Netflix, which collects all possible data about users, from demography to online behavior, to run a recommendation system. As a result, 75% of watched content is recommendations.
How to Create ML Algorithms That Can Be Quickly Implemented?
Although ML drives significant impact across all industries, allowing for more accurate data analysis and processing (and, therefore, improved user experience and reduced costs), models require a long time to be trained. This can be addressed through Transfer Learning – applying an existing pre-trained model to a new task. Such an approach allows you to reduce the resources and amount of labeled data required to train a new model. The approach is especially useful for supervised machine learning.
The point is generalization: only the knowledge that can be used in a different similar scenario can be transferred. For example, it’s efficient when categorizing MRI images, allowing you to quickly train X-Ray image recognition software. As a result, it’s quicker to launch a new solution that will deliver quality and accurate outputs to overcome a new business challenge.
Machine learning has already disrupted a number of industries with accurate solutions. They allow for improved data analyses and processing, which results in better user experience and reduced business costs.
With recent advancements like transfer learning, ML algorithms will take less time to be trained while offering the same level of accuracy. It shows potential for wider technology adoption and facilitated implementations.
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