AI & Machine Learning
Artificial intelligence in industry – what’s going to be important? Artificial intelligence is now being actively applied in plant and mechanical engineering as well, with innovators presenting initial projects. But which topics will gain importance in the field of AI in the months ahead? We provide an overview.
Integrating AI models
A proof of concept is important, but does the model also work in real operation? The main challenge facing industry in the years ahead will be in the issue of integrating AI models into existing systems. AI out of the box is what many users would like to have. However, the reality frequently looks different and impedes AI projects in industry.
Most users are familiar with DevOps, and money is already being made with ML Ops. Service providers integrate and maintain models, checking the accuracy of the models in production activity. The term derives from the continuous development process used in the software industry.
Can an artificial intelligence build a machine?
AI and machine learning are not only a tool for the production line. One focus of AI projects in the years ahead is sure to lie in the area of Design Space Exploration – from which personnel are set to benefit in the spheres of development and design. In the end, the question will be: can an artificial intelligence build a machine?
The process of marketing, adapting and making models available to the customer again is gaining in importance. Indeed, some companies are already banking on federated learning and now "only" exchange models of machines.
Saving energy due to artificial intelligence
The coming years will see a large number of companies increasingly using algorithms to predict energy consumption in the interests of saving energy. Moreover, AI itself must become more energy-efficient.
The future of industrial applications lies in the combination of mathematics, statistics and neural networks. Not every problem from the factory needs a neural network. Many questions for data can be solved using methods of good old-fashioned AI.
Dependable AI plays a key role in the design and operation of AI systems. When engineering dependable AI systems, it is accordingly a matter of systematically guaranteeing dependability by using principles stemming from system and software engineering in the design, verification/validation and operation of the AI system and, in particular, of taking into account statutory and normative requirements relating to dependability right from the word go. To this end, it is important to understand that AI systems are developed fundamentally differently from classic software-based systems.
Making Data eligible for Balance Sheet
Data and data lakes have a value. That said, it is not yet possible for data to be recognized as assets on the balance sheet, however. There are initial approaches using software and models to capitalize and thus make data eligible for balance sheet recognition. As in the 1990s when software attained that eligibility, companies are waiting for opportunities in this direction, because capitalizable data also means that AI projects will become easier to finance.
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