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Sticos are developing a bot called @else (http://else.sticos.no) to help Human Resources (HR) departments be more efficient in their work. A lot of their time is consumed by reoccurring questions from their employees and managers. For years HR software have tried to tackle this problem by using a personnel manual. However, the use of it is sparse. The problem lies in accessibility and the fact that is easier to ask the question directly and get a qualified answer to your problem on the fly.
Vis hele beskrivelsen ]
Sticos develops and sells personnel manuals. All rules and answers to employees questions lies in the texts from the personnel manuals. Our customers can also write their own specific rules in the system.
An example correlation is: "An employee can have up to 10 days paid leave in order to take care of his sick kids under the age of 12. However, if the employee has more than 2 children under the age of twelve, he gets 5 days extra. A total of 15 days."
The correlations here are:
• one kid under 12 years : 10 days paid leave
• two kids under 12 years: 10 days paid leave
• three or more kids under 12 years: 15 days paid leave
The project can focus on one or more of the following aspects:
1. Grammatical analytics and statistical classification have different strength and weaknesses. Where IBM and Googles language understanding originates from those different approaches. Today Google, IBM and others uses a hybrid combination of both to understand text better. What is a proper combination of these techniques to understand a Norwegian text?
2. In order to reduce work and secure consistency we want @else to use the personnel manuals when answering questions from employees. What is the best way to achieve this?
[ Skjul beskrivelse ]
We have several systems that make it possible to ask natural language queries over Internet, by SMS or by voice over telephone about various tasks, e.g bus routes or telephone information. You can try yourself by calling +47 7352 1290, or checking http://busstuc.idi.ntnu.no
Telebuster is a prototype dialog system from year 2000, that understands conversations about travel information and can provide schedule information. The task will be to make it ready for 2020, by updating all the sub-systems to the latest technology, and by connecting it to online data repositories like GTFS to make it available with updated schedules anywhere in the world, all the time.
The existing system is built in layers with Prolog - PHP - and Java. It would be an advantage to reduce the pipeline by using only two layers.
Fluent knowledge of Norwegian and English is a huge advantage, but clever use of Google translate etc. can compensate for lack in experience.
Medical training in the form of nursing scenarios with dolls does currently not involve any communication with the patient simulator (doll). Any information regarding the simulator's state (healthy, leg hurts, arm is in pain etc.) has to be inferred or explained by others.
Laerdal Medical wants to enhance the simulation experience by automating the "Learner -- Patient" communication by a Conversational UI. Their wish is for the patient simulator to be able to answer questions regarding its own condition.
Norsk Luftambulanse AS has multiple helicopter bases throughout Norway and Denmark. For each mission a lot of data is generated. This information consists of mission-specific data like response time of the crew and who is partaking as well as patient-related data throughout the journey from pickup to delivery. This data can be structured, visualized and made sense of in a better way.
We need to design an automated system that visualizes recent and historic data from the air emergency operations according to the customer’s needs. Initially, the focus can be to compare the different helicopter bases. Before implementing this, a thorough validation of the data used would be beneficial, considering a lot the data is inputted manually.
Some helicopter base comparisons could be
• Response time (time spent to leave the base after the mission is received)
• Time to location in different types of weather
• Missions per day
There needs to be a notification system, ideally also based upon predictive analysis, to let people know if a base shows abnormal trends in certain categories. Every base has to follow a certain set of requirements in regards to for example response time, and would benefit from knowing if they are close to breaking any of these. Potentially, we could look at the different variables in conjunction with one another to see if they’re correlated in order to make better predictions.
We could also expand this by including medical data from the missions to further give meaning to the data. This would be by the request of the customer, and would increase the need for good security as the data is sensitive. We might have to implement a system where we have to take access rights into account to limit what each person has the ability to see.