Feb 21, 2022
8 minutes read
According to Arthur Samuel, who popularized the term, machine learning algorithms enable computers to improve themselves by learning from data, even without being explicitly programmed. machine learning (ML) is a category of algorithms that allows software applications to receive input data and use statistical analysis to update outputs each time as new data becomes available. ML is a subfield of artificial intelligence (AI). Any technology user today knows about the benefits of machine learning.
Machine learning is used anywhere from automating monotonous daily issues to solving more complicated tasks. All kinds of industries can apply it for several benefits. You may already be a user of a device that utilizes it. For example, a wearable fitness tracker or an intelligent home assistant. But there are many more examples of usage of ML in prediction systems, image recognition, speech recognition, medical diagnoses, financial industry, and others.
For instance, facial recognition technology allows social media platforms to help users tag and share photos of friends. Optical character recognition technology converts scanned images of handwritten, typed, or printed text into machine-encoded text. ML-based applications recommend books, music, movies, and TV shows based on each user’s individual preferences. Self-navigating cars will be available on the market soon. Machine learning applications are continuously developing. It is why you have to think about specific considerations while working with AI-based technologies or analyzing the impact of machine learning processes.
The first manually operated computer system invented in the 1940s was ENIAC (Electronic Numerical Integrator and Computer). The initial idea was to build a machine to emulate human thinking and learning. In the 1950s, the first computer game helped check players improve their skills by beating them. It was a real breakthrough.
In the 1990s, with data-driven approaches, machine learning became very popular. Having large-scale data available, scientists started to build intelligent systems to analyze and learn from large amounts of data. As a highlight, IBM’s Deep Blue design beat the world champion of chess, the grand-master Garry Kasparov.
Machine learning tasks can be classified into some specific types. These ML categories are based on how learning is received or how feedback is given to the system developed. The most widely used machine learning methods are supervised, unsupervised, semi-supervised, and reinforcement learning. Of course, there are other approaches, and sometimes more than one is used by the same machine learning system. For example, topic modeling, dimensionality reduction, meta-learning, deep learning, and others. Let’s take a more detailed look at several of these methods.
Supervised learning trains algorithms based on example input and output data that humans label. Labeled data means a set of training examples, where each sample is a pair consisting of information and the desired output value. Supervised learning aims to let the algorithm “learn,” which means comparing its currently available data with the “taught” data to detect errors and change the model to improve it. Algorithms based on supervised learning use patterns to forecast label values on the data, additionally unlabeled.
For instance, an algorithm based on supervised learning models may compare images of sharks labeled as fish and pictures of oceans labeled as water. After being trained accordingly on these images, the supervised learning algorithm can recognize unlabeled shark photos as fish and unlabeled ocean photos as water.
A most popular use case of supervised learning is to predict statistically likely future events using historical data. Algorithms based on supervised learning can use tagged photos of dogs as input data to classify untagged photos of dogs. Such applications may use historical stock market information to filter out spam emails or to anticipate upcoming fluctuations. Supervised learning is one of the simplest types of machine learning.
Unsupervised learning provides the algorithm with no labeled data to find some structure in its input data. Machine learning methods based on unsupervised learning are precious because unlabeled data are more abundant than labeled data.
The general purpose of unsupervised learning is to recognize patterns hidden in a dataset. Another goal of unsupervised learning models is feature learning to classify raw data by automatically discovering the needed representations.
The unsupervised learning method is generally helpful for transactional data. For example, you have an extensive database with data of your clients and their orders, and you need to find similar attributes that can be drawn from customer profiles and their types of purchases. The task appears difficult for a human brain, but ML can help to solve it. For example, an application based on unsupervised learning methods may determine that women 25-30-years old who tend to buy certain kinds of soap are likely to be pregnant. Using this conclusion, you can target this audience to increase their purchases by starting a marketing campaign related to pregnancy and baby products.
Applications based on unsupervised learning methods can analyze complex data that is more expansive and seemingly unrelated without being told a “correct” answer. It helps to organize those data in a more meaningful way.
Software containing unsupervised learning methods is often used for anomaly detection, including fraudulent credit card purchases and recommender systems that suggest what products to buy next.
In unsupervised learning, untagged photos of dogs can be used as input data for the algorithm to find likenesses and classify dog photos together.
The labeling costs are pretty high because labeling requires qualified human experts. Because of this, there are no labels in the majority of the observations, just in some semi-supervised algorithms, which are the best choice for the future model building process. Those methods show that even though the group memberships of the unlabeled data are unknown, this data carries essential information about the group parameters.
Reinforcement Learning doesn’t need labeled data sets in opposite to supervised learning. Reinforcement Learning allows computer algorithms to continuously learn from the environment and maximize their performance by automatically determining the ideal behavior within a specific context. An algorithm based on reinforcement learning analyzes a particular setting where one goal is performed, such as game playing or driving. After navigating its problem space, the program provides feedback.
Topic modeling discovers the abstract “topics” that occur in a collection of documents. Topic modeling is a type of statistical model for a frequently used text-mining tool for finding semantic structures hidden in a text. For example, if you read a text about some specific topic, you expect that certain words will appear in the document more or less frequently. In the documents about dogs, you can find the words “dog” and “bone” more often. In the texts related to cats, words like “cat” and “meow” are more likely to appear. Some specific words will appear equally in both, such as “the” and “is”.
Multiple topics appear in each text in different proportions. For example, in a text that is 90% about dogs and 10% about cats, you’ll probably find about nine times fewer cat words than dog words. The topic modeling techniques produce “topics” clusters of similar terms. A topic model makes a mathematical framework from this intuition, based on the statistics of each word. This model allows analyzing documents and discovering what the topics might be, and each document’s balance of topics.
Face recognition tools can not only be used in social media for tagging photos, they are often developed to protect people. The facial recognition market is growing continuously because the technology has all kinds of commercial applications. From airport security to healthcare and customer authentication, face recognition is now widely adopted around the globe.
For instance, the Safety Radar developed by VITech is an ML-based software that analyses video streams from CCTV cameras and controls proper usage of Personal protective equipment in the workplace or other institutions.
The Safety Radar algorithm cheques workers on the accuracy of wearing protective items (Coat, Glasses, Glove, Mask, Helmet). When somebody doesn’t wear the PPE, the system can identify it and send an automatic notification to a safety engineer. The system analyses the CCTV stream and saves pictures documenting safety rules violations. You can review saved pictures and see the time and place documented on the picture. It improves the safety of the workplace and decreases the injury rate. The solution can be beneficial for different locations such as fabrication lines, steel, oil & gas enterprises, building sites, and other industrial environments where safety rules should be strictly followed.
Why do you need an automated solution for PPE monitoring? Of course, all this can also be performed by a safety engineer manually, but it takes a lot of time and effort. Additionally, human observation is less stable and accurate because people can not focus their attention long. They get tired and can be injured easier. When monitoring is not good enough, it can often provide injuries.Monitoring in real-time allows researchers to see each case and all details to avoid misinterpretation in case litigation or court. Real-time monitoring and automated alerts save time from controllers as well. VITech develops machine learning and data science solutions for medical organizations and engineering teams working for healthcare. Those machine learning solutions can be customized to fit customer needs.
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