As they have been for decades, databases are a key component in various types of business applications. Nowadays, modern databases also include features that support the construction of applications based on artificial intelligence. The starting point of this paper is the questions of what are the typical use cases in which a database and generative artificial intelligence work together, what database features are used in these applications, and what other technologies the applications rely on. The results contribute to one of our practice-oriented research projects, in which we build an environment that supports AI experiments for participating companies. As input, we use open online learning materials provided by three database vendors. The databases we selected for review are all non-relational, representing three different genres, and are the most popular representatives of their genre. For each training program of the vendors, we will examine the structure and scope of the program, the courses on generative AI included in the program, and the use cases and technologies related to generative AI presented in them. Finally, we will prepare a summary of the use cases and technologies found.

Our ‘Information Age’ is based on Information Technology (IT), what we expect to create soon more new value that the traditional production. However, in spite of massive investments in IT the expected results are not always seen: opening a web link stalls with “Veryfying link”, “We can't check the safety of this website right now”, “You do not have right to access folder ‘My Documents’” etc. It seems that (one of) the reasons is currently prevailing style of software production - instead of correcting errors in existing software are produced a new version correcting some issues, but incorporating a lot of old ‘bad’ modules, creating an overflow of versions what is making software increasingly difficult to use.

The article focuses on one aspect of artificial intelligence – Generative AI (GenAI) – which is expected to offer significant opportunities in different areas of business, industry, and society. GenAI is the current state of the decades-long development of artificial intelligence (AI), and many companies are currently looking for ways to benefit from this market-changing technology. The use of GenAI in business practices is topical among companies and organizations, and decision-makers across the globe are considering the future potential of GenAI and large language models (LLM) for organizations and businesses. The aim of this paper is to examine the utilization of artificial intelligence in business operations and industry, emphasizing both the opportunities offered by GenAI as well as the challenges related to its usage. Additionally, the paper strives to determine whether the phenomenon is real or merely hype, as well as addressing its so-called revolutionary status. The topic is approached through a light literature review and discussion on the findings of two studies carried out in Finnish companies related to GenAI utilization.

Several signs suggest that the Intellectual Ability of our students may be decreasing, some even say that students are becoming ‘Dumb and Dumber’: the IQ is steadily declining (the reverse Flynn effect), Internet is becoming a database of enormous blobs of ‘dark knowledge’ with dubious truth value what students do not understand, but what they still should use, under enormous pressure of advertising students lack critical thinking ability and can manage only the imaginary world behind their mobile/console/VR headset screens; problems appear already in pre-university education, results of the last PISA tests were in many European countries much worse than in previous years. There are umpteen explanations/reasons provided – the Covid (which has not gone anywhere), proliferation of generative AI systems, especially their most common representative - the chatGPT, which undercut truth in Internet and has made questionable some of the cornerstones of university education – tests. The Covid seems to be eternal, chatGPT is already ‘out of bottle’ and we have to live with it and understand its inner mechanics in order to use it the best way. In the following are analyzed current problems in IT education and discussed ideas for improving the situation.

Computers, or in wider scope, information and communication technology (ICT), have had revolutionary impact on education. ICT is both merged to the education process and is supporting it. Computers and information technology are typical enabling technologies not in the focus itself, but when available, these are adopted in use. Performance of computers has grown exponentially: According to Moore’s law processing power and RAM capacity is doubled in 1.5 years. The similar growth rate relates to mass memories and data transmission speed. During the era of computing (from the middle 1940s) information technology has gradually transferred to new areas of use. In the beginning, the computers themselves were in focus. In the 1970s microelectronics has been the key to changes: first personal computers, then networking and cloud technologies, further embedded intelligence of devices, distributed computing, complex software, wide access to heterogeneous data sources etc. have brought the computer to the growing number of applications, also in the education sector. The purpose of this paper is to have a look at the concept “computers in education It has been the title of the conference track in Mipro over decades. It is widely handled in literature, journals, and other publications. These provide forum for experience transfer in the area, as well as encourage researchers to study the topic from the scientific direction. The paper is partially based on the experiences of the authors and aimed to clarify the term from a variety point of view.

We are witnessing a major achievement of machine learning - appearance of several chatbots which are able to produce human-quality conversation. Appearance of such ‘fluid conversationalists’ has aroused big interest especially among teachers, since they have already passed several exams designed for humans and have been used by students in winter fall 2022 exams. Their performance is not based on linguistic research, but is an achievement of data science. This performance cannot yet be rationally explained (it is based on interplay of billions of variables), thus in the following are used conversations with the currently most visible member of this family - the chatGPT to clear some myths around these programs.

Computers, or in wider scope, information and communication technology (ICT), have had revolutionary impact on education. ICT is both merged to the education process and is supporting it. Computers and information technology are typical enabling technologies not in the focus itself, but when available, these are adopted in use. Performance of computers has grown exponentially: According to Moore’s law processing power and RAM capacity is doubled in 1.5 years. The similar growth rate relates to mass memories and data transmission speed. During the era of computing (from the middle 1940s) information technology has gradually transferred to new areas of use. In the beginning, the computers themselves were in focus. In the 1970s microelectronics has been the key to changes: first personal computers, then networking and cloud technologies, further embedded intelligence of devices, distributed computing, complex software, wide access to heterogeneous data sources etc. have brought the computer to the growing number of applications, also in the education sector. The purpose of this paper is to have a look at the concept “computers in education It has been the title of the conference track in Mipro over decades. It is widely handled in literature, journals, and other publications. These provide forum for experience transfer in the area, as well as encourage researchers to study the topic from the scientific direction. The paper is partially based on the experiences of the authors and aimed to clarify the term from a variety point of view.

The rapid development of information and communication technology (ICT) and growing participation of students in work life has already in several decades moved ICT education into ‘clouds’, using sources of knowledge on Internet from all around the world. The COVID pandemic has increased this process, forcing universities to restrict classroom teaching and rapidly increased student’s self-study.At the same time, increase of amounts of data to be processed is constantly introducing new high-level software technologies, layers and layers of packages and libraries, deeper and more complex. This has created a new ‘top-down’ programming style: a new project is started with importing mass of libraries which have been used in earlier projects and only then is considered how to use them in order to solve the programming task. The self-studying ICT students see only tips of modern software icebergs and it is difficult for them to understand their working without face-to-face classroom communication where details of the ‘depths’ are explained.

Software Engineering (SE) university students often work part-time during their studies. In this setup the students can reform the practices of companies by transferring what they have learned to companies and correspondingly utilize what they have experienced at work in their studies. This symbiosis often continues as the students begin to work towards their thesis. The topic of the thesis relates to the problems in the company. These topics often solve a practical problem, which are not always in a perfect match with academic expectations. On the one hand the employer has certain expectations in terms of working for the company, whereas the supervising professor needs to follow the university guidelines. In this paper, we study this tension by focusing on the problems appearing in MSc thesis process in company context. We propose ways to act so that the different stakeholders -- the student, the professor, and the company -- reach the best possible results. We have analyzed the problems and their root causes. We have also taken the first steps toward anti-patterns for analysis and salvaging of the problems. The study is based on the authors’ collective supervision experience, which covers over 1000 MSc theses, with the combined supervision experience of over 100 years.

In software engineering, students easily find internships in companies while still studying. To combine their studies and employment, many of them seek to compose their final theses in an industry context, for the benefit of the employer as well as to simplify their context switching between job and studies. This can put the student between a rock and a hard place, as on one hand the employer has certain expectations in terms of working for the company, whereas the supervising professor needs to follow the university guidelines. An additional aspect worth considering is the university as an administrative home for the thesis and owner of the thesis process. In this paper, we study how the different stakeholders – the student, the supervising professor, and the company – should act for the best possible results, so that the company problem gets solved, and the results can be reported in accordance with the best academic practices. The research builds on authors’ collective supervision experience, covering more than 1000 theses (mainly master’s level) and close to a sum of hundred years. The thesis has been mainly supervised in two universities, with the clear majority executed in this setup, but there are also several exceptions where the thesis has been eventually accepted in some other university. The results are expressed in the form of anti-patterns, which consist of a definition of symptoms of a problem, its root causes, and proposals to salvage the situation in a practical fashion.

Once upon a time programming was done just writing commands of a programming language in a proper order, but currently software is created using libraries, API-s (Application Programming Interface), frameworks, Dockers, Kubernetes etc. Libraries load other libraries, API-s call other API-s and as a result seemingly short and simple programs may have amazing depth of code and complexity, what causes for programmers many problems, especially for students. For interpreted code this depth could be (approximately) measured with the ratio of the visual code vs code in libraries. It is shown that the number of LOC (Lines Of Code) in invisible code – code in libraries, modules, API-s etc. is even in small practical programs thousands-millions times greater than the number of lines in the visible code. Innovations (cloud computing, multicore CPU-s etc.) cause introduction of new libraries and modules which enable use of new possibilities in existing software ecosystem, but also introduce bigger and bigger amounts of invisible code. During the pandemic grow student's use of WWW tutorials, but abundance of code in programming examples/tutorials on WWW is sometimes unnecessary, caused by obsolete or unneeded packages and libraries and sometimes also by desire to earn on adverting (un-needed, but popular) code packages for high-paying customers. In the following are analyzed some Python 3 and JavaScript examples.

Computers were originally developed for executing complex calculations fast and effectively. The intelligence of computer was based on arithmetic capabilities. This has been the mainstream in the development of computers until now. In the middle of 1950s a new application area, Artificial Intelligence (AI), was introduced by researchers. They had interest to use computers to solve problems in the way intelligent beings do. The architecture, which supported calculations, were conquered to perform tasks associated with intelligence beings, to execute inference operations and to simulate human sense. Artificial intelligence has had several reincarnation cycles; it has reappeared in different manifestations since this research area became interesting for the researchers. All the time a lot of discussion about intelligence of these systems has been going on – are the AI based systems and robots intelligent, what is the difference of human and machine intelligence, etc. Abilities related to intelligence cover ability to acquire and apply knowledge and skills, as well as ability to learn. AI provides different manifestations to the term “intelligence”: the human intelligence is a wide variety of different types of intelligence, as well as the meaning of artificial intelligence has varied over time. In our paper we will look to this term, especially to provide means for comparing human and artificial intelligence and have a look to the learning capability related to it.

Improperly turned off sink and shower faucets, leaking toilets, and faults in pipes can cause significant expenses in increased water bills, and cause even more serious problems if water finds its way inside building structures. Today, many kinds of sensor systems can be used to send data to the internet from a multitude of environments, and the collected data can be processed with algorithms to find anomalies. This paper presents a prototype for measuring ambient room temperature and water pipe temperatures. As an example case, this paper shows how the wireless temperature measurement prototype can be used to detect water flow within the pipes. The flow detection could be used, for example, for finding faults in water applications and devices. This papers describes the basic operating principle for the prototype, the initial findings, challenges and future directions, and introduces a technical solution for executing the prototype software within an NB-IoT module without the need to utilize a separate microcontroller or small computer.

Machine Learning (ML) is a technology to make messages created by humans (text, images, speech etc.) more understandable for computers so that they could better answer humans' queries and needs when recalling this information. Here is considered the ML sub-area - Natural Language Processing (NLP) and presented examples of its methods using text corpuses created from MiproCE presentations.

Programming became more and more comfortable with development of third and fourth generation programming languages. Although the fifth generation project did not achieve its goals, the necessity for more comfortability is still challenging. This paper delineates the path towards true fifth generation programming. based on literate modelling with model suites that generalises model-driven development and conceptual-model programming. A model suite consists of a coherent collection of explicitly associated models. A model in the model suite is used for different purposes such as communication, documentation, conceptualisation, construction, analysis, design, explanation, and modernisation. The model suite can be used as a program of next generation and will be mapped to programs in host languages of fourth or third generation. So, we claim that models will become programs of true fifth generation programming.

Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. This is a big challenge for global information systems development and for their modelling. In this paper, we discuss on different aspects of conceptual modelling in global environmental context. The paper is the summary of the panel session "The Future of Conceptual Modelling" in the 29th International Conference on Information Modelling and Knowledge Bases.

Once-upon-a time computers and computations they delivered were considered ultimately deterministic. But currently we encounter random events, i.e. non-determinism in many areas of computation practice: non-determinism introduced by network latency, inherently non-deterministic computations with ‘big data’ and ‘deep learning’ where the results are probability distributions with errors, which also are probability distributions and both depend on initial selection of samples etc. For IT education one of the most disturbing sources of non-determinism and non-repeatability of previous examples comes from massive use of libraries and API-s (Application Programming Interface), which has made common the ‘top-down’ style of programming and negligence to practical issues - finiteness of computer memory and speed. Significance of the classical source of knowledge - printed hard-cover books - is diminishing, since by the time books are out of print there are already new versions of programs, new protocols, new technologies and libraries, and these new versions often do not work with old ones. The most relevant source of information has become Internet. But Internet is full of useless sources, since ‘Internet never forgets’ - together with sources describing latest program versions, libraries, technologies etc. there are still around tens of publications which use some by now already outdated program versions, libraries, technologies. Students, who are eager to perform well in the next recruiting interview are spending many evenings trying to swim in this swamp of non-deterministic mess, where most of presented examples are not repeatable. And the situation is becoming worse, since many of authors e.g. YouTube videos do not want to teach, but to earn using Google AdSense.