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Machine learning: investment of a ton yields millions annually
IN SHORT
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One-time investment in machine learning earns this business case millions annually
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Machine learning: extra capacity with the same people
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Use of open source algorithms accelerates time to market
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Automation enables expansion into markets that were previously inaccessible or non-lucrative
Scale up or automate?
Scaling up in this market is an option, but that again brings overhead. "Added up, it's relatively a lot of work relative to the benefits," says Verbeek. "The client actually wanted the number of employees for the planning work to remain the same. So that was crying out for automation." Verbeek points his client to the existence of open source algorithms that can help recognize drawings. Based on this, it is possible to build an application suitable for the private segment the installer wants to target. This eventually resulted in a web application in which the architect or contractor uploads drawings. These are often pdf's, but a scan or photo of a physical drawing is also possible. With this approach via a web interface part of the manual work shifts to the end customer.
"The customer specifically wanted the number of employees for the planning work to remain the same. So that was crying out for automation."
Classification through machine learning algorithms
In a next step, the system classifies the drawings with machine learning algorithms. "The function for recognizing the drawing is open source and we didn't have to program anything else for it," Verbeek clarified. Doors, walls, but also kitchens, living rooms and other spaces are recognized by the algorithms with approximately 80 percent certainty. The latter is characteristic of machine learning. The algorithms are trained and capable of recognizing objects or drawings, but the judgment is never 100 percent watertight. There is always a degree of uncertainty in the outcome.
So the system makes mistakes, but the machine learning algorithms can be improved, making the classification better and better. "Employees are shown an outcome and degree of certainty. For example, 'a living room with a 70 percent chance that this is indeed a living room.' If they confirm that, then the algorithm learns from that." Because the system provides the recognition with a statement about the probability, users are able to choose the settings in such a way that they only have to look at the real doubtful cases. "For example, to all the statements made with a certainty of below 90 percent."
"Employees are shown an outcome and degree of certainty. For example, 'a living room with a 70 percent chance that this is indeed a living room.' If they confirm that, then the algorithm learns from that."
AutoCAD as the next step
Turnaround time of nine months
TIPS
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Scaling up is an interesting option but may involve overhead. In the business case, consider the option to automate for valuable time and/or cost savings.
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Explore the potential for using open source algorithms. This can be the basis for building an application that supports the ultimate goal of the business case.
Luminis
Luminis is a group of companies, headquartered in the Netherlands, that specializes in providing innovative solutions to business and government, primarily using emerging (information) technology.