Human Race - Centrum Badań Interakcji Człowiek-Maszyna
In this experiment, we operated within the novel research area of Informal Green Spaces (often called green wastelands), exploring emotional well-being with the employment of portable electroencephalography (EEG) devices. The apparatus (commercial EEG Muse headband) provided an opportunity to analyze states of calmness and alertness in n = 20 participants as they visited selected Informal Green Spaces in Warsaw, Poland. The article aims to test the hypothesis that passive recreation in Informal Green Spaces (IGSs) has a positive impact on emotional well-being and that there is a connection between the intensity of states of calmness and alertness and 1. the type of green space (IGS/GS), 2. the type of scenery and 3. the type of IGS. The preliminary experiment showed that there might be no substantial distinction in the users’ levels of emotional states when considering existing typologies. On the other hand, data-driven analysis suggests that there might be a connection between the state of alertness and some characteristics of specific areas. After carrying out the multivariate analyses of variance in the repeated measurement scheme and finding significant differences between oscillations in different areas, we conclude that there might be three possible sources of lower alertness and increased calmness in some areas. These are 1. the presence of “desirable” human intervention such as paths and urban furniture, 2. a lack of “undesirable” users and signs of their presence and 3. the presence of other “desirable” users.
The construction industry is one of the sectors where accidents causing injuries among workers appear more frequently than in other industries. The safety issues are of high interest. There are special regulations introduced to protect all people engaged on a construction site. Besides the human attitude, every accident causes forced brakes in the construction process. It leads to financial losses and lowering the trademark of a certain contractor. Curing process of injured workers, as well as, investigation about for reasons of an accident generates additional cost. The development of accidents prevention then is highly desirable for several reasons. As wearable technologies become more popular and affordable, they can be applied to monitor the locations of machinery and the staff on the construction site. Based on that risky configurations can be detected, and relevant actions can be undertaken to protect working people from any injuries. Different types of wearable technologies are presented together with the discussion of their applicability in monitoring in the construction sector. Moreover, the survey is done to better recognition of health risks. Based on that the recommendations are formulated for future applications of wearable technologies as an innovative tool of risk management in construction.
Positive and normative claims that artificial intelligence (AI) will or should lead to adoption of a universal basic income policy (UBI) remain insufficiently empirically grounded to merit serious consideration. Long-term trends in individual/familial income portfolio adjustment (IPA) to business, economic, and technological change (BETC) point to continued incremental changes in the ways that individuals/families achieve life goals, not a fundamental structural break necessitating radical policy changes that may not be desirable in any event. Moreover, if AI proves a more rapid disruptor than anticipated, UBI-like payments can be made quickly, as recent bailouts and fiscal stimuli demonstrate.
Current technological developments, as well as widespread application of artificial intelligence, will doubtlessly continue to impact how people live and work. In this research, we explored synergies between human workers and AI in managerial tasks. We hypothesized that human-AI collaboration will increase productivity. In the paper, several levels of proximity between AI and humans in a work setting are distinguished. The multi-stage study, covering the exploratory phase in which we conducted a study of preferences using 10-item Likert scale, was conducted with a sample of 366 respondents. The study focused on working with different types of AI. The second and third phase of the study, in which we primarily used qualitative methods (scenario-based design combined with semi-structured interviews with six participants), focused on researching modes of collaboration between humans and virtual assistants.
Dominant opinion in the general public is that work automation will presumably hold negative societal implications, such as job loss, which often causes fear and misunderstanding. Contrarily to such an attitude, the approach we took in this paper is that people will experience rather positive effects of work automation, thanks to collaboration with artificial intelligence using virtual assistants. The quantitative experimental study was a business problem simulation. Participants were asked to perform tasks of a marketing manager in order to prepare a marketing campaign for a new product. Control group participants performed these tasks on their own, while experimental group participants did them in collaboration with a virtual chatbot-like assistant created specifically for this simulation. A total of 20 people participated in the study.
Chatbots are used frequently in business to facilitate various processes, particularly those related to customer service and personalization. In this article, we propose novel methods of tracking human-chatbot interactions and measuring chatbot performance that take into consideration ethical concerns, particularly trust. Our proposed methodology links neuroscientific methods, text mining, and machine learning. We argue that trust is the focal point of successful human-chatbot interaction and assess how trust as a relevant category is being redefined with the advent of deep learning supported chatbots. We propose a novel method of analyzing the content of messages produced in human-chatbot interactions, using the Condor Tribefinder system we developed for text mining that is based on a machine learning classification engine.
Collaboration with business
DOZ - Dbam o Zdrowie: we are currently running an ambitious project implementing machine Learning Algorithms to the company’s daily operations.
We frequently consult on implementing ML, DLand AI into business strategy. We specialize in botics and natural language processing and generation.