Intelligent machines on the rise
Information is key
Coming into contact with new technologies: VR, AR & more
Classification, sentiment analysis and predictive maintenance
Digitalization made easy: The first steps are taken!
Once the Doxis4 ECM system has read and understood information, it has to decide what should happen with it. This is the job of classification. Parallel to image recognition, the system learns to differentiate between documents and information via learning sets. You could say that the machine bases its knowledge on past experiences. Different from machine learning, AI will use deep learning to train itself in the future. At the same time, classification will go well beyond document types. Based on assumptions made through classification, AI will develop classification suggestions and forecasts. This way, it will be possible to logically distribute information across input channels and to steer it to the next processing step. One of the fundamental attributes of AI is its ability to find hidden patterns or correlations in the chaos of unbelievably huge data sets and then to develop models that forecast behavior or outcomes.
Patterns also pertain to emotions. Sentiment analysis, often called sentiment detection, is applied as a subdivision of text mining to recognize positive and negative moods in text documents. It essentially tries to understand human emotion by reading texts written by humans and extracting their subjective opinion. This analytical method is interesting from an economic standpoint, because it identifies positive and negative opinions about a company and its products published in written correspondences and in the internet. This kind of knowledge can reveal, for instance, that a customer switched providers and products due to dissatisfaction. To run the most effective automated sentiment analysis, domain knowledge is required. Technology, application, user friendliness, etc. would be domains in software. The adjective "quick" would be praise for a technology; for user-friendliness, however, it has a negative connotation that might mean "quickly leads to errors".
Within business processes, digital assistants can run things, at first handling routine tasks, and, later on, making decisions in specific situations. As described earlier, decision-making is one of the biggest strengths of AI. Using past experiences, AI can make forecasts regarding upcoming business processes. With human support, AI forecasts can help determine capacity utilization and thereby enable faster processing. Automation would also mean that intellectual work no longer needs to be outsourced. Simple and complex tasks can be completed more efficiently by machines.
Proactive information management
If we need information, we need to find it. We have become accustomed to this. In fact, we spend 20 percent of our working hours looking for it, as a McKinsey study has found. This could also become a thing of the past. Instead of a search machine, AI paves the way to a finding machine; essentially, proactive information management. Predicting our needs, it gives us information in the context of our work, actions and decisions. The system knows its users, their information needs and work habits. It gives us a summary of all important information for an appointment already before it takes place; the system sees in our calendar what's up next. It suggests the right documents based on the business context, and we never have to search for a thing.
One of the most important fields of application for the industrial Internet of Things (IIoT) and Industry 4.0 is inarguably predictive maintenance and service based on predictive intelligence. Predictive maintenance utilizes algorithm-based data analyses to monitor the status of machines and plants. It identifies potential malfunctions already before damage incurs. Technicians have all relevant information for maintenance directly on their smart glasses. The glasses scan machine or assembly group codes and thereby supply precise information required for maintenance and troubleshooting. Wearables also warn and protect technicians from bodily harm, for example, through heat or radiation. They can also analyze the environment's conditions, while AI evaluates the recordings.
These kinds of use cases are still in the early stages though. Unlike in production, virtual assistants will take a while to emerge here. Human deficiency has a big advantage over AI: in contrast to machines, we humans are forgetful. If machines learn something incorrectly, it is difficult to fix this later on.
Page 1 - Intelligent machines on the rise
Page 2 - Information is key
Page 3 - Coming into contact with new technologies: VR, AR & more
Page 4 - Classification, sentiment analysis and predictive maintenance
Page 5 - Digitalization made easy: The first steps are taken!
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