Machine learning is a branch of artificial intelligence that has the greatest future potential and brings the greatest benefits to the industry. According to relevant reports, the machine learning market size will reach US$96.7 billion by 2025. That would be a huge increase compared to 2018's $6.8 billion.
In the coming years, more and more companies will choose machine learning technology to improve their business.
Ten years ago, the term Industry 4.0 was coined to refer to the process of digitalization in the industrial sector. Since then, we have seen an increasing number of companies in the field working on implementing advanced technologies such as IoT, blockchain and all branches of artificial intelligence: machine learning, deep learning, cognitive intelligence, etc.
The application of technologies such as machine learning in industries helps improve productivity, manufacturing efficiency, and allows for faster, more flexible and more efficient processes.
In this direction, the EU is taking firm steps forward. In February 2020, the European Commission released the "White Paper on Artificial Intelligence". As the EU President said, the joint strategy of all EU countries aims to attract more than 20 billion euros in investment in artificial intelligence every year in the next ten years. This figure is expected to be achieved through private sector contributions and state co-financing.
Public investment will drive technological progress in Industry 4.0 and the electronics industry, the development of cloud computing technology and the implementation of smart factories.
Businesses from different industries will be able to benefit from the advantages of applying technologies such as machine learning in their industries, but most importantly, they will be part of the four strategic areas of this technology , namely Ceramics, Automotive, Installation and Energy Management and Food.
Companies in the ceramics, automotive, energy management, and food and beverage markets have already benefited To realize the advantages of artificial intelligence through machine learning algorithms.
They are implementing a technology that allows them to predict bad and erroneous behavior, optimize production processes, deeply analyze the market or demand to understand it better and thus more accurately Adapt to customer needs. All of this is achieved through different applications of machine learning.
In the ceramic field, artificial intelligence has begun to play a leading role. Machine learning algorithms are already used, especially in quality control processes. Through various algorithms, it is possible to predict the behavior of materials under extreme temperature conditions and detect anomalies and defects in ceramic tiles.
Research conducted with the help of artificial intelligence attempts to predict abnormal behavior of materials during manufacturing, making it possible to control and use components that are more resistant to resistance conditions than those currently manufactured.
On the other hand, by identifying incorrect patterns, they can detect anomalies in products early, reduce wasted material, and increase profitability.
Today, we have discovered that some companies are using this technology and using it in this industry and other fields. First, they are companies in the ceramics, porcelain and flooring industries.
In the automotive field, artificial intelligence is also increasingly used to improve industrial processes. Automotive and all related industries are using machine learning to increase their turnover. The industry is using this technology for predictive analysis of component durability and identifying anomalies and defects at an early stage.
Another application of machine learning in the automotive industry is supply chain optimization. This is a great opportunity to improve production processes for companies in the automotive industry. In this sense, they provide, among other functions, better control over the inventory levels required at different facilities.
More and more automotive companies are taking advantage of machine learning to improve their production processes.
In the field of installation and energy management, artificial intelligence is driving huge advancements through machine learning. The introduction of this technology in this field is developing smart networks or smart grids. This type of network will use machine learning technology to conduct real-time analysis to better adjust electricity supply to meet demand by identifying consumption patterns and intercept any failures or fraud that may occur throughout the supply chain.
Other advances in energy management will involve improved management and optimization of the network, door-to-door services, price optimization, forecasting growth by region, identifying consumption and demand peaks or for certain customers or cities Behavior.
The application of AI technology in urban energy management brings different advantages to individuals and enterprises. According to one study, smart grids will save citizens approximately $14 billion in energy costs by 2022. Many companies in the industry are already reaping these benefits by using advanced machine learning platforms to improve energy management in cities.
In the food sector, artificial intelligence through machine learning algorithms helps reduce costs and improve quality. It does this in all areas including the food and beverage industry and the catering industry. This allows the industry to gain many key advantages to improve its business. One of these strengths is analyzing the food market to understand consumer trends and thus adapt to the real needs of customers.
Another application of machine learning relates to improving hygiene in production plants. It can be used to detect whether a machine is dirty and needs cleaning, or to monitor and check the hygiene of all workers involved in the production chain.
Machine learning is also used in industry to optimize food and beverage supply chains. Today, many businesses in the food industry benefit from artificial intelligence, or more specifically, machine learning.
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