Artificial intelligence (AI) brings together a constellation of technologies that enable machines to understand, act and learn.
Among them, machine learning and deep learning, two essential components of AI applied to the industrial sector.
These two terms are often used interchangeably. Yet, they are quite distinct. Thus, it is important to identify and differentiate them in order to better understand their applications in Industry 4.0.
Machine learning : Definition
Machine learning is an artificial intelligence technology that allows machines to reproduce certain human abilities and learn by themselves without having been previously programmed to do so. Unlike traditional programming where the reasoning is done by engineers, machine learning uses algorithms to analyze data from different sources, learn from that data, and make informed decisions based on what it has learned. Thus, the data collected by this technology will enrich the statistical database of the machines, thus improving the accuracy of their forecasts and decisions over time.
Machine Learning applications in industry
Machine learning allows today to exploit data to solve complex problems. It is a decision support tool that is particularly well suited to classifying data and assigning values to it. For Industry 4.0, machine learning is rapidly becoming a decisive advantage. Indeed, with the huge amounts of data produced continuously and in real time, machine learning makes predictive analysis possible. The larger the data volumes and the more regularly they are updated, the more reliable the analyses are, thus allowing an increase in industrial performance.
Deep Learning : Definiton
Deep learning is a more recent technology that structures algorithms in layers to create an “artificial neural network” capable of learning and making intelligent decisions by itself. This is called bio-mimicry : it mimics the mechanism of neural networks.
This technology is used in particular in image or natural language recognition.
Deep Learning applications in industry
Deep learning, on the other hand, is a much more autonomous learning model that can automate certain tasks. In particular, it can identify defects on manufactured parts, recognize natural language elements and transform an observation on an assembly line into action. It is therefore a tool for optimizing industrial production.
However, the use of deep learning requires a significant volume of data that few companies currently have.
What are the differences between Machine Learning and Deep learning?
Deep learning and machine learning work in a similar way. However, the learning process of deep learning is much more efficient than that of standard machine learning models.
Indeed, machine learning models improve progressively but require corrections over time. For example, if an AI algorithm returns an inaccurate prediction, an engineer must intervene to make adjustments. However, with a deep learning model, an algorithm can determine by itself if a prediction is accurate or not via its own neural network.
Deep learning and machine learning are therefore two complementary concepts in Industry 4.0, full of promise for the future of the industrial sector.
Join the SURFEO team, from May 10 to 12, 2021, at the B2B Software Days virtual show “The Future of Digital Business”, to discuss in greater detail the various issues related to the digital transformation of the industrial sector in Europe and North America.
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