Big data, data science, machine learning, and deep learning are all technical terms regularly mentioned together. The same goes for data analytics and machine learning. However, confusion quickly arises by lumping all these concepts together without indicating the differences and similarities between AI, data science, machine learning, and BI. Machine learning was defined initially in the 1950s as “a field that allows computer algorithms to learn without having to program them” (Samuel, 1959) explicitly. Another standard definition is this: “Machine Learning is the study of computer algorithms that allow computer programs to improve automatically through experience.” (Mitchell, 1997).
Both machine learning definitions may sound a bit dated and static. Still, they do justice to the self–learning nature that is so characteristic of the older and modern advanced algorithms. Algorithms change and are capable of developing themselves continuously. Machine learning is a dynamic learning process: the algorithms recognize patterns in the data.
According to one of the largest software companies in the world, machine learning (ML) is a form of artificial intelligence (AI) aimed at building systems that can learn from processed data or use data to perform better. This definition is practical, dynamic, and performance-oriented and emphasizes the learning nature but only lacks the predictive capabilities of algorithms.
Here is a list of ten popular machine-learning applications.
Recommendations
Algorithm-driven movie recommendations on Netflix, personalized purchase suggestions on Amazon, friend connections on Facebook, and professional network recommendations on LinkedIn are just a few examples of well-known applications of machine learning. Beyond entertainment and social platforms, machine learning plays a crucial role in enhancing online shopping experiences. It powers product personalization, refines contextual search results, facilitates interactions through chatbots, assists users with virtual assistants, and even contributes to creating artificially generated photo models. The widespread integration of machine learning across various digital domains underscores its versatility and impact on user engagement.
Irregularity Detection
Irregularity detection is a variant of machine learning that zeroes in on anomalies in the broadest sense. For instance, it excels at uncovering fraud or sifting through spam messages in email inboxes using anomaly detection. The process involves identifying statistical irregularities within the dataset, often called outliers.
Dynamic Pricing
Dynamic pricing, propelled by machine learning algorithms, autonomously adjusts prices based on many factors. These factors include competitors’ pricing, time of day, week or month, demand fluctuations, and available supply. Widely adopted in industries such as airlines, hotel accommodations, and tourism, dynamic pricing ensures that the value proposition aligns seamlessly with real-time market dynamics.
Predictive Maintenance
Predictive maintenance, driven by machine learning, stands out as one of the most logical and compelling applications. It is a proactive measure, preventing unplanned downtime for valuable machinery and installations across various sectors, including industry, maritime, civil engineering, energy, and oil and gas. Predictive maintenance’s precision averts unexpected disruptions and leads to substantial cost savings by ensuring that maintenance activities are carried out precisely when needed, minimizing unnecessary expenditures.
Process Mining
Process mining with machine learning involves deploying specialized algorithms on data extracted from event logs. These algorithms, including the intriguingly named alpha miner, fuzzy miner, heuristics, transition system miners, and genetic algorithms, delve into the intricacies of event data. The overarching objective is to unearth process deviations and enhance the prediction of future processes. Through simulations powered by machine learning software, process mining identifies anomalies and refines our understanding of operational workflows.
Law and Order
Law enforcement seamlessly integrates CCTV and machine learning data in public safety, tapping into sources like intelligent cameras and microphones. This synergy enables real-time crime detection, mapping crime hotspots (commonly referred to as “hotspots”), and predicting future criminal activity through predictive policing. This technological advancement extends to the automatic recognition of drivers holding a phone using deep learning algorithms, showcasing the multifaceted role of machine learning in maintaining law and order.
Traffic Congestion
Adaptive signal control, a system adept at automatically adjusting traffic lights in response to varying traffic volumes, operates on the backbone of classic machine learning algorithms. Beyond this, several municipalities are delving into innovative city concepts, experimenting with intelligent lampposts and zebra crossings that illuminate dynamically. This visionary approach integrates 5G technology, sensors, charging stations, and customizable light scenarios, exemplifying a comprehensive solution to tackle traffic congestion and create more efficient urban landscapes.
Algorithmic Decisions
Across ministries, administrative bodies, and implementing organizations, algorithms and machine learning applications play an integral role in decision-making to varying degrees. Industries grapple with the complexity of implementing legal regulations, often relying on decision rules applied to data. It is particularly evident in implementing bodies that enforce financial laws, such as the tax authorities overseeing real estate tax, WOZ determination, and motor vehicle tax. Moreover, consider the automatic registration of traffic violations (e.g., mobile phone use in vehicles) and the streamlined processing of traffic fines by the Central Judicial Collection Agency, showcasing the pervasive influence of algorithmic decisions in diverse facets of governance.
Robot Justice
Machine learning empowers the exploration of online case law, unveiling intricate patterns that could pave the way for a future where robot judges deliver fully automated sentences. Despite being a prospect on the horizon, the opacity of algorithmic operations poses challenges for litigants seeking insights. Nevertheless, ongoing experiments in the United States involve machine learning software assessing the risk of recidivism for individuals on bail, offering a glimpse into the evolving landscape of legal decision-making through technology.
Robotized Services
Municipalities embrace a dual approach, employing physical robots and chatbots to enhance citizen services. Robotic Process Automation (RPA) serves as a vital tool, alleviating the administrative burden on municipal officials, while physical robots stationed in city halls guide and assist citizens. The synergy of RPA and machine learning is poised to usher in the next wave of technological innovation, promising to streamline further and optimize the delivery of essential services to the public.
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