Trustful AI – how to use machine learning in manufacturing business
In the modern world, trust means everything. Relationships between companies, their customers, and partners are built on trust and transparency. When a business progresses and uses new technologies such as Artificial Intelligence, it is crucial to save these elements. However, some new algorithms are not interpretable and thus involve risks in the business.
We care about our customers, that’s why we create trustful AI solutions. Using our systems, people feel calm and in control of the process. They can interpret automated decisions and understand why they were made.
We use state-of-the-art uncertainty estimation techniques, based on ensembles in deep learning and advanced statistical methods. That’s why our algorithms always can say how confident they are in the predictions. Our systems can perform most cases automatically, while the complex ones, where our model is not confident are performed by a professional human. Our algorithms can answer questions such as: “Why does this decision work?”, “Why this defect occurred?”, “Should I trust this prediction?” and so on.
Let’s see an example case, that we have done in STAI:
The customer was a company that performs automatic metal welding. This manufacture has a system that asses the quality of performed welding based on physical parameters. In most cases welding is good and nothing should be done, but in several percents of cases welding is bad, and such details should be dropped. It is very important to not sell bad detail, as it could be an important element of some system. Before our collaboration, each detail was validated by a special engineer.
This is a typical process in manufacturing that can be improved with machine learning algorithms.
Our experts performed complex analysis and development of the system:
- Scope of work
- Available data
- Development cycles:
By installing machine learning algorithms, we increased the speed of production, as for most details this procedure is now performed by a system. Moreover, as our algorithms provide uncertainty for each prediction, we calibrate our model in a way that instead of validating each detail, engineers now validate only most uncertain predictions (a percent of total production), as for the other details, with certain prediction, we have 100% accuracy. Our solution helps our client to both improve the quality of the system and use fewer human resources for the task.
This case is a good example of how machine learning can improve the quality of the system while reducing the amount of human work. And do it in a trustful way.
Understandable predictions and meaningful probabilistic models are the future of artificial intelligence. And we are ready to bring this future to your business. These systems take production to a new level of quality and efficiency.