Category: Education

Ms Jelena Tijanic, a master student at the University of Donja Gorica just defended her Master thesis titled “Ethics and Artificial Intelligence”. The thesis was done at the Master academic studies “Statistics” (EMOS) under supervision of her mentor prof. dr Milica Vukotic.

ABSTRACT – Artificial intelligence is ubiquitous and enables many of our daily routines – booking flights, driving without a driver, supports decision-making in governments and the private sector. Artificial intelligence technology delivers outstanding results in highly specialized areas such as cancer screening and building an inclusive environment for people with disabilities. They also help combat global problems such as climate change and world hunger, and help reduce poverty by optimizing economic aid. But technology also brings unprecedented new challenges. We see increased gender and ethnic bias, significant threats to privacy, dignity, dangers of mass surveillance, and increased use of unreliable law enforcement technologies.

The first part of the thesis presents the basic problems that the world is facing and why the development of artificial intelligence is a potential threat to the future of mankind. A new recommendation adopted by UNESCO member countries was presented. The second part of the thesis describes the basic concepts related to recommendation systems, the way they work, as well as their division. Here are some examples of where systems are used. Ethical problems that can be encountered during their development are described.Finally, a practical example of a movie recommendation system is described. The process of making the systems was described and the result analyzed.

Mr. Milutin Pavicevic just defended his Master thesis titled: “Investigation of neural network efficiency in prediction electricity prices in the day-ahead market”. The work focused on the use of artificial intelligence and exploration of various prediction models based on neural networks in order to improve prediction of electricity prices.

ABSTRACT – The power of neural networks in approximating continuous functions has led to more widespread use of this type of artificial intelligence in the field of time-series forecasting. This work examines the efficiency oftime-series prediction models when given the dataset of hourly values connected to day-ahead market of electrical energy. It presents the processing and windowing of the data to fit the prediction models, describes the specifics of the day-ahead market of electrical energy and more closely describes the way each of the used neural network models works.
The work looks at created neural network models with dense layers, convolutional neural networks (CNN) and recurrent neural networks (LSTM), and measures their performance. Testing results show their accuracy when predicting based on the dataset of hourly values of day-ahead electricity on the HUPX market, coupled with the hourly weather data, as well as the related dataset of the hourly values of electricity consumption in Montenegro.