Artificial Intelligence is everywhere but, how can we develop applications that really take full profit from today’s state of the art?
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. Machine learning is a branch of artificial intelligence that allows predictions based on large amounts of data. This branch of artificial intelligence is based on pattern recognition and has the ability to gain knowledge from experience. For this reason, technology has found its place in industrial processes. Artificial intelligence is no longer a vision of the future. Today, large data centers and huge storage capacities make possible things that were thought of for years as distant concepts. Two branches of artificial intelligence, machine learning and deep learning, use the possibilities of big data to optimize processes, find new solutions and get new ideas.
From small and medium-sized businesses to large international corporations, each organization accumulates data that it can use. With the use of computer programs, this data is consolidated and evaluated to make predictions.
Machine learning recognizes features and relationships and uses algorithms to generalize them. With the help of data from production processes, registers and sensors properly analyzed, new solutions can be found and, for example, processes can be made more efficient. In addition to data, this requires a computing infrastructure that fits the processes of artificial intelligence and the workloads of machine learning. The exact tasks of machine learning systems are clearly defined: recognizing patterns and drawing conclusions from them that can be used in the future. In a smart factory, production processes are connected: machines, interfaces and components communicate with each other. Large amounts of data can be obtained to optimize manufacturing processes. In production facilities, intelligent systems identify objects on conveyor belts and are able to sort them automatically. These types of systems are also used in quality control, both in the recognition of product defects and in the case of incorrect color.
Currently, companies use different machine learning techniques in maintenance and support services. Using sensors, artificial intelligence helps to capture the energy consumption of individual machines, analyze maintenance cycles and optimize them in the next stage. Performance data indicates when a part needs to be replaced or where a defect is possible. As the amount of data increases, the system becomes better at optimizing and its predictions become more accurate. According to experts, companies that use machine learning-based artificial intelligence systems are increasing their economic performance. The biggest gains are expected in the IT and finance, telecommunications and manufacturing sectors. These are the main fields of application where TECNIO Centre EASY is currenly involved in, developing several projects at both national and international level.