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Figuring out what the user likes or dislikes and acting upon it

The today's sophisticated hardware is more and more in need of automated control. While the plugs for this type of control are available for decades already, programming behaviour itself is - generally speaking - not a trivial task.

Therefore, there is a great need for machines which are able to make inferences and make intelligent choices in an autonomous manner. Imagine for instance that the user wakes up at 7 AM, has breakfast and at about 8:20 heads towards work using their car. He/she does this every day from Monday to Friday except for vacation days and maybe public holidays.

Glas.AI (R) is able to track the user’s behaviour, make a statistic of the daily predicted morning departure time and output this time to 3'rd party users. It can also process this information internally and use it for instance for preheating the car 20 minutes before the statistical daily departure time, given the proper vehicle integration.

It can also cool down the car if the platform is given access to the vehicle's inside temperature and understands that the weather is very hot outside.

This is just one use case that can be built using Glas.AI (R), the possibilities are endless.

The GLAS.AI (R) platform is fully data driven and uses a very intuitive API for any data I/O. Moreover, internally, as a framework, developers can further develop strategies of Reasoning through the data and finally conclude possible actions to be taken. This is done by means of an intuitive scripting language.