Artificial intelligence in industry
Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis[1] and insight discovery.[2]
Artificial intelligence and machine learning have become key enablers to leverage data in production in recent years due to a number of different factors: More affordable sensors and the automated process of data acquisition; More powerful computation capability of computers to perform more complex tasks at a faster speed with lower cost; Faster connectivity infrastructure and more accessible cloud services for data management and computing power outsourcing.[3]
Possible applications of industrial AI and machine learning in the production domain can be divided into seven application areas:[4]
Each application area can be further divided into specific application scenarios that describe concrete AI/ML scenarios in production. While some application areas have a direct connection to production processes, others cover production adjacent fields like logistics or the factory building.[4]
An example from the application scenario Process Design & Innovation are collaborative robots. Collaborative robotic arms are able to learn the motion and path demonstrated by human operators and perform the same task.[5] Predictive and preventive maintenance through data-driven machine learning are exemplary application scenarios from the Machinery & Equipment application area.[4]
Industrial data sources[edit]
The foundation of most artificial intelligence and machine learning applications in industrial settings are comprehensive datasets from the respective fields. Those datasets act as the basis for training the employed models.[7] In other domains, like computer vision, speech recognition or language models, extensive reference datasets (e.g. ImageNet, Librispeech,[12] The People's Speech) and data scraped from the open internet[13] are frequently used for this purpose. Such datasets rarely exist in the industrial context because of high confidentiality requirements [9] and high specificity of the data. Industrial applications of artificial intelligence are therefore often faced with the problem of data availability.[9]
For these reasons, existing open datasets applicable to industrial applications, often originate from public institutions like governmental agencies or universities and data analysis competitions hosted by companies. In addition to this, data sharing platforms exist. However, most of these platforms have no industrial focus and offer limited filtering abilities regarding industrial data sources.