Machine learning is part of artificial intelligence that provides computers with the ability to learn how to solve problems without prior explicit description of how to perform this task.
In some cases, the only information that a machine learning algorithm needs to be able to solve a task is a set of known examples. In other words, to make a computer identify bicycles, we just need to show it a number of examples of bicycles. This approach with a given labeled data set is known as supervised learning. In contrast, unsupervised machine learning doesn’t need labeled data. This group of algorithms provide the ability to reveal patterns in large datasets and predict future values within the revealed patterns, or to distinguish ordinary data from anomalies. Behind the scenes, machine learning solves the following formal problems: classification, regression, clustering, structured prediction, and reinforcement learning.
Machine learning solutions
Our R&D team is capable of developing various machine-learning solutions based on appropriate methods. There are dozens of algorithms that can learn from data, build models from an example training set or environmental feedback to make data-driven predictions or decisions. All these methods can be grouped around a few general machine-learning problems.
Classification
A classifier is a computer program that distributes given data over a set of pre-defined categories. In machine learning, an algorithm uses a training set of correctly classified examples to learn how to do this particular classification. After learning, the computer is able to identify which category a new example belongs to. Classification has many practical applications, such as pattern detection, object recognition, medical image analysis, face recognition, proteins classification, customer behavior analysis, and so on.
Cluster Analysis
A clustering algorithm analyzes a given data set to find the natural structure of the data and divide the set into meaningful groups, e.g. clusters. This type of analysis works similarly to classification, but it does not need prior training or a pre-defined list of classes. Among applications that use cluster analysis are anomaly detection, recommender systems, image segmentation, and behavior prediction.
Regression Analysis
Regression is used to analyze how target variables change when one or a set of other variables, predictors, are varied. The ultimate target of regression is to find a function of independent variables, which can be a line, a plane, or a multi-dimensional surface, depending on the complexity of the task. Regression analysis is widely used for different kinds of predictions such as customer-churn prediction, crop-yield prediction, and demand forecasting.
Structured Prediction
Structured prediction includes a set of algorithms that solve complex combinatorial optimization problems, where multiple interrelated decisions must be weighed against each other to find a globally consistent model. There are a number of important real-life problems that use structured prediction methods. For example, 3D scene reconstruction, natural language, or DNA string processing.
Reinforcement Learning
Reinforcement learning allows a machine or a software agent to find optimal behavior in a given “world.” An automated learning scheme works on the basis of reward feedback from the environment, which allows the agent to tune its own actions to maximize overall performance. Reinforcement learning methods are successfully used in economics simulations, multi-agent system explorations, genetics, robotics, as well as in solving gaming problems.
In recent years, Artificial Intelligence (AI), which includes Machine Learning (ML), has been a buzzword. It is estimated that, in 2016, tech companies have invested $12B in developing AI and 60% of those investments were ML.
Often a busy executive would wonder - What does ML and all its hype mean for my business? ML algorithms enable a computer (or other devices) to learn from its environment, to be more precise – from data. For instance, in manufacturing plants, ML algorithms are able to ...
Big data is no longer just the numbers we collect. Due to the advances in our computing abilities, it has transformed into value today. Machine Learning (ML) algorithms, along with other data analytics tools, help us convert Big Data into meaningful insights that can be used as business intelligence.
In the recent past, Data Science as a Service is an often-heard term. How does DSaaS relate to the analysis of big data or Machine learning? How do DSaaS companies benefit various businesses? This ...
Churn is undesirable. It indicates the rate of attrition or how many customers (or employees) are leaving an organization. It is estimated that, in the insurance industry in the UK churn in customers for home and auto insurance only results in a loss of £3.3B. Hence, companies focus on reducing churn. It would be easier to reduce churn if one were able to predict it in a timely manner.
A famous quote states - “If you torture the data long enough, it will confess to anything.” Data is a double-edged sword and hence, needs to be handled by the experts. In late 19th century (and even today too), statistics was the branch of science primarily used to make inferences from available data. The statisticians had the expertise and rigor to conduct the analysis and draw conclusions.
Today, manufacturing is becoming more complex, as well as more automated. The industrial Internet of Things is generating great volumes of data at incredible speed, forming foundation of big data for manufacturing industry. Our R&D team works on a number of solutions that use modern computer vision and machine-learning techniques to increase speed of manufacturing processes, improve reliability, and make forecasting models based on sophisticated data analysis.
The transportation industry covers air, roads, railroads, and ocean lines. The future of transportation is increasingly reliant on advances in computer vision and smart systems. Our team works on a number of applications in the transportation industry that employ computer vision and machine learning technologies. We are excited about the fact that our expertise helps in the development of smart transportation. Our smart solutions bring a new level of comfort and improved travel safety to ordinary people, as well as reduce costs for companies and authorities in charge of transport infrastructure.
The retail industry has learned to collect a vast amount of data: sales, prices, costs, logistical data, product-related data, consumer behavior and much more. All this is available for retailers now. The next step is to transform this data into valuable insight. Powerful tools like machine-learning technologies open up a new world that allows retailers to see beyond collected data. The ability to refine, understand and act upon key factors revolutionize how business is done and brings the whole industry to a new level.
Traditional agriculture practices entail common tasks such as soil preparation, planting, water and nutrient management, weeding, harvesting, and sorting. Today with the advent of modern technologies, most processes can be successfully automated. From a technological point of view, computer vision and machine learning play a central role. For example, smart systems can sort yield, visually examining each single fruit as a human would do, but with incredible speed. Intelligent weeding systems recognize weeds by their shape and allow their elimination chemically or mechanically. Farm vehicles equipped with a computer vision navigation system can work in the field autonomously—in other words, with no driver. In the end, all these systems enable farmers to significantly reduce costs and increase productivity at the same time.
The finance and insurance industry has been always one of the most intensively data-driven industries. With a large amount of diverse data and high demand on data analytics, the finance and insurance sectors have devoted attention to the rapidly developing area of machine learning. Today, machine learning offers the best opportunities in big data analysis. Using ML, finance institutions are able to reveal hidden dependencies and specified patterns among millions of records in a fraction of a second, detecting fraud, for example, or recognizing a trend.
Fortunately, these days we see continuous development of diagnostic and treatment tools available in hospitals and clinics. High-definition scanners allow radiologists to recognize anomalies more reliably and efficiently. However, the healthcare industry is always facing challenges. Our group works on solutions that help the whole industry take a big step forward: we work on automating the process of detecting the anomaly itself. Modern computer vision together with deep-learning models is already capable of seeing objects on radiology images and marking them out automatically. Such computer-aided diagnosis systems help doctors in analysis of medical images, increasing reliability and reducing workload.
Life sciences like other sciences heavily depend on routines such as counting, tracking, and classification. Today, many of these tasks can be successfully transferred to machine-learning and computer-vision systems. However, different studies and different objects either require adjustment of existing machine-learning models or tailor-developed solutions. BitRefine group offers tailor-made algorithms and is proud to take part in scientific research.
Machine Learning as a Service (MLaaS)
Machine learning as a service (MLaaS) refers to a number of platforms that provide all the infrastructure components and tools, helping developers to focus on solving business problem. Our engineers’ broad experience allows us to carry out complex tasks, offering clients flexible, high-performance, end-to end solutions.
