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Copyright of Science: Future of Lithuania / Mokslas: Lietuvos Ateitis is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial intelligence depends on digital devices’ performance to perform tasks regularly, requiring human intelligence, using special software to accomplish work easier and faster, carrying out data-packed tasks, and providing useful analytics or solutions. It also requires a specialized laboratory that provides high-performance computing capabilities and a technical platform for deep machine learning. These resources will enable the artificial intelligence platform to master the machine learning techniques of using, developing, simulating, predicting models, and building ready-to-use technological solutions such as analytics platforms. In general, the artificial intelligence system manipulates and manages large amounts of training data to form correlations and patterns used in building future predictions . A limited-memory artificial intelligence system can store a limited amount of information based on the data that have been processed and dealt with previously to build knowledge by memory when combined with pre-programmed data. Consequently, one may ask how artificial intelligence applications contribute to verifying the truthfulness of the media through digital media. How do they contribute to preventing the spread of misleading and false news? This study tries to answer the following question: What methods and tools are adopted by artificial intelligence to detect fake news, especially on social media platforms and depending on artificial intelligence laboratories? This paper is framed within automation control theory and by defining the needed control tools and programs to detect fake news and verify media facts.

The paper examines the possibilities of speculating in exchangetraded funds by using artificial intelligence. The main goal of the research is to create a support system for speculative decisionmaking for investors operating in exchange-traded funds market. The research will be based on the theoretical aspects of artificial intelligence and speculation of exchange-traded funds. The support system is developed on the basis of reinforcement learning, the methods of synthesis, concretization and generalization were used to create and detail the system, as well as the methods of mathematical-statistical analysis were used to process them. Successful application of the chosen methodology in the design of the support system has resulted in positive trade results. Successful research broadens the boundaries for usage of deep reinforcement learning, and provides a basis for further development of the support system for exchange-traded funds. The support system put in place will shorten the time between the occurrence of a trading signal and the decision of the investor, which will help to reduce the loss of potential profits. Article in Lithuanian. Paramos sistema spekuliavimui biržoje prekiaujamais fondais Santrauka Darbe yra nagrinėjamos spekuliavimo biržoje prekiaujamais fondais, naudojant dirbtinį intelektą, galimybės. Pagrindinis mokslinio tyrimo tikslas – remiantis dirbtinio intelekto bei biržoje prekiaujamų fondų spekuliavimo teoriniais aspektais, sukurti spekuliavimo sprendimų priėmimo paramos sistemą investuotojams, veikiantiems biržoje prekiaujamų fondų rinkoje. Paramos sistema yra kuriama remiantis sustiprintuoju mokymusi (angl. reinforcement learning), sistemai sudaryti ir detalizuoti buvo taikyti sintezės, konkretizavimo bei apibendrinimo metodai, taip pat, panaudojus susidarytą sistemą bei gavus rezultatus, jiems apdoroti taikyti matematinės-statistinės analizės metodai. Sėkmingai pritaikius pasirinktą metodologiją, sudarant paramos sistemą, buvo gauti teigiami prekybos rezultatai. Sėkmingas tyrimas išplečia giliojo sustiprintojo mokymosi taikymo suvokimo ribas bei sudaro pagrindą tolesniam biržoje prekiaujamų fondų paramos sistemos vystymui. Sudaryta paramos sistema sutrumpins sugaištamą laiką tarp prekybos signalo atsiradimo ir investuotojo sprendimo priėmimo, o tai padės sumažinti potencialaus pelno praradimą. Reikšminiai žodžiai: paramos sistema, biržoje prekiaujami fondai, spekuliavimas, dirbtinis intelektas, finansų rinka, sustiprintasis mokymasis, mašininis mokymasis.