Negation is a part of language that humans engage in pretty much from the onset of speech. Negation appears at first glance to be harder to grasp than object or action labels, yet this thesis explores how this family of ‘concepts’ could be acquired in a meaningful way by a humanoid robot based solely on the unconstrained dialogue with a human conversation partner. The earliest forms of negation appear to be linked to the affective or motivational state of the speaker. Therefore we developed a behavioural architecture which contains a motivational system. This motivational system feeds its state simultaneously to other subsystems for the purpose of symbol-grounding but also leads to the expression of the robot’s motivational state via a facial display of emotions and motivationally congruent body behaviours. In order to achieve the grounding of negative words we will examine two different mechanisms which provide an alternative to the established grounding via ostension with or without joint attention. Two large experiments were conducted to test these two mechanisms. One of these mechanisms is so called negative intent interpretation, the other one is a combination of physical and linguistic prohibition. Both mechanisms have been described in the literature on early child language development but have never been used in human-robot-interaction for the purpose of symbol grounding. As we will show, both mechanisms may operate simultaneously and we can exclude none of them as potential ontogenetic origin of negation.

The development of new technology plays a major role in today's society and several different industries. While some technologies have more or less an impact upon the whole working sector, one of the more recent and controversial technologies is Artificial Intelligence (AI). In recent years, this technology has evolved continuously and is spreading across several different industries. As it is clear that AI is reshaping the workplace, it is relevant to examine how and to what extent it is affecting the digital design profession. Purpose The purpose of this study is to gain insight into the current state of adoption of AI within digital design, including graphic design and web design. Furthermore, to explore the effects of AI on the nature of the profession, from the perspectives of professionals in the industry. While focusing on the creative process and the development of digital products, it investigates how the industry is experiencing the effects of AI in daily tasks and workflows. Furthermore, it examines if the implementation of AI has lead to the development of new work processes, or if traditional tasks remain but are carried out with AI tools as assistance. Method The research method is qualitative. Through literature reviews and by carrying out interviews with relevant designers currently working in the industry, the appropriate data is collected and analyzed. The interviews focus on understanding the participant’s perspective on the topic, their experiences of AI and what effect it has on their work. Through the interviews, the study identifies to what extent AI is used in creative processes, and sheds light on the general feelings towards AI, including expectations and concerns. Conclusions The findings show that the overall awareness surrounding AI is rather divided. AI is already implemented in various design processes and software, whether the designer is aware of it or not. It can thus be concluded that AI has affected the nature of the digital design profession. However, the effects vary depending on the specific role and the related tasks. Most are interested in learning more about it but natural skepticism and lack of knowledge about the technology remain an obstacle for implementing more AI in companies.

Problemformulering: På vilka sätt hanteras och påverkas en organisations intressenter av implementeringen av AI-system?  Syfte: Syftet med denna studie är att utifrån en organisations intressenters uppfattning kartlägga på vilka sätt intressenterna hanteras och påverkas av implementering av AI-system. Metod: Studien har utgångspunkt i kvalitativ forskningsstrategi med en deduktiv ansats. Den genomförda studien är en fallstudie, där Länsförsäkringar har studerats. Det empiriska materialet är insamlat genom tio semistrukturerade intervjuer.  Slutsats: Med vår studie kan vi se att implementeringen av Länsförsäkringars chatbot påverkar de anställda. Den potentiella utvecklingen av AI däremot tenderar att påverka flera intressentgrupper. Vidare kan vi se i studiens resultat svårigheter att identifiera organisationens intressenter samt svårigheter att prioritera och värdera intressenter, vilket överlag överensstämmer med den framtagna teorin gällande intressentmodellen. Vi kan därför dra slutsatsen att Länsförsäkringar bör identifiera intressenter och dess påverkan av utvecklingen av AI för veta hur intressenter ska hanteras. / Research question: In what ways is an organization's stakeholders handled and affected by the implementation of AI-systems? Purpose: The purpose of this study is to map, based on the perception of an organization's stakeholders, in what ways stakeholders are handled and affected by the implementation of AI-systems.  Method: The study is based on a qualitative research strategy with a deductive approach. The completed study is a case study, where Länsförsäkringar has been studied. The empirical material is collected through ten semi-structured interviews. Conclusion: With our study, can we see that the implementation of Länsförsäkringar's chatbot affects the employees. The potential development of AI, on the other hand, tends to affect several stakeholder groups. Furthermore, we can see in the results of the study difficulties in identifying the organization's stakeholders as well as difficulties in prioritizing and evaluating stakeholders, which is generally in line with the developed theory regarding the stakeholder model. We can therefore conclude that Länsförsäkringar should identify stakeholders and their impact on the development of AI in order to know how stakeholders should be handled.

Volvo Construction Equipment (CE) are facing the challenge of vibrations in their wheel loaders that generate disruptive noise and impact the driver's experience. These vibrations have been linked to the contact surface between the crown wheel and pinion gear in the vehicles drive-axles. In response, this thesis was created to develop an Artificial Intelligence (AI) system, which can identify outliers in a dataset containing images of the contact surfaces between the crown wheel and pinion gear. However, the dataset exhibits variations in image sharpness, exposure and centering of the crown wheel, which hinders its suitability for machine vision tasks. The varying quality of the images poses the challenge of accurately extracting relevant features required to analyze the images through machine learning algorithms. This research aims to address these challenges by investigating two research questions. (1) what method can be employed to extract the Region of Interest (ROI) in images of crown wheels? And (2) which method is suitable for detection of outliers within the ROI? To find answers to these questions, a literature study was conducted leading up to the implementation of two architectures: You Only Look Once (YOLO) v5 Oriented Bounding Boxes (OBB) and a Hybrid Autoencoder (BAE). Visual evaluation of the results showed promising outcomes particularly for the extraction of ROIs, where the relevant areas were accurately identified despite the large variations in image quality. The BAE successfully identified outliers that deviated from the majority, however, the results of the model were influenced by the differences in image quality, rather than the geometrical shape of the contact patterns. These findings suggest that using the same feature extraction method on a higher-quality dataset or employing a more robust segmentation method, could increase the likelihood of identifying the contact patterns responsible for the vibrations.

OpenAI recently introduced ChatGPT, a chatbot powered by the GPT-3 family of deep learninglanguage models (LLMs). With the aid of machine learning techniques, ChatGPT has been fine-tuned to improve its capacity to respond to a diverse range of queries, and it has been describedas one of the most advanced machine learning technologies currently available. While AI israpidly advancing and being integrated into society, the comprehension of people's attitudestowards these novel technologies is not progressing at the same rate. Prior research studies andliterature have highlighted the importance of assessing user sentiment towards newly launchedAI services. Evaluating the expressed attitudes towards the recently introduced ChatGPT canprovide valuable insights into the product's potential, as well as highlighting any challenges orproblems encountered by users. This paper presents a study that examines the attitudesexpressed on the social media platforms Twitter and Reddit. For data collection, this studyutilized social media data in the form of free text obtained through the APIs of Twitter andReddit. A qualitative analysis is carried out with the aid of a sentiment analysis tool to assesslanguage and categorize text data based on their expressed attitudes. This data is presented in aquantitative summary. The findings indicate a favorable disposition among users towardsChatGPT in general but that there are areas of concern where users have conveyed sentimentsof feeling intimidated or having a negative resonance with ChatGPT's capabilities andachievements. This study contributes to the existing understanding of user attitudes towardsChatGPT and highlights the necessity for further research to delve deeper into this area.

Background: To remain competitive in Industry 4.0, B2B suppliers must develop new and increasingly advanced digital services by incorporating AI. However, although being of interest to practitioners, academic research on successful AI implementation in B2B functional domains is lacking. Consequently, academics have stressed the importance of developing comprehensive frameworks within B2B marketing to accelerate the creation of strategic roadmaps for AI implementation. Purpose: The purpose of this study is to explore how AI can be utilized to enhance digital servitization, according to the perspectives of one supplier and several of its customers. The aim is to provide a framework that can assist practitioners in implementing value-adding AI services. Method: To fulfill the exploratory purpose of this study, a qualitative single-case research design was applied. The empirical data was collected through twelve in-depth semi-structured interviews.  Utilizing an inductive approach, the data has been analyzed and interpreted through a thematic analysis. Conclusion: Incorporating a complete S-D logic mindset by implementing the AI solution based on all five axioms was found to enhance digital servitization. A model displaying various servitization activities connected to these axioms arose, emphasizing their collective impact. Additionally, suppliers may enhance digital servitization through the implementation of AI by engaging in three transformational mechanisms, namely customization, automation, and agile co-development.

Idag är artificiell intelligens inom sjukvården ett omtalat ämne. Tidigare litteratur visar att det finns en tydlig potential för AI inom sjukvården där det kan göra stor nytta. Syftet med denna studien har varit att belysa var den svenska sjukvården befinner sig idag och identifiera de begränsningar som finns och försvårar sjukvårdens arbete med AI. Studien har fokuserat på att tala med experter inom området för att få deras perspektiv på situationen.  Empiriska materialet tyder på att den svenska sjukvården inte är tillräckligt förberedd och digitalt mogen för någon större utbreddhet av AI idag. Det påpekas att det krävs en digital transformation där digitala rutiner och processer blir en större del av den svenska sjukvårdens vardag först. Det visar sig att regelverken kring AI och datahantering inte är kompatibla med de ambitioner som finns vad gäller AI inom sjukvården. Denna uppsats visar att det finns ett tydligt glapp mellan förväntningarna kring AI och det faktiska arbetet i sjukvården där optimismen inte är lika tydlig. Det krävs en tydligare nationell samordning, större digital kompetens och framför allt en större ödmjukhet inför att det tar tid att bli digitalt mogen för en sådan stor förändring som AI. / Today, AI in healthcare is a popular topic. Previous literature shows that there is a clear potential for AI in healthcare where it can be of great benefit. The purpose of this study has been to shed light on where Swedish healthcare is today and identify the limitations that exist and complicate healthcare's work with AI. The study has focused on talking to experts in the field to get their perspective on the situation. Empirical evidence suggests that Swedish healthcare is not sufficiently prepared and digitally mature for any greater prevalence of AI today. It is pointed out that a digital transformation is required where digital routines and processes become a larger part of Swedish healthcare's everyday life first. It turns out that the regulations regarding AI and data management are not compatible with the ambitions that exist with regard to AI in healthcare. This essay shows that there is a clear gap between the expectations around AI and the actual work in healthcare where the optimism is not as clear. Clearer national coordination, greater digital competence and, above all, greater humility are required before it takes time to become digitally mature for such a major change as AI.

Numerous facets of society have undergone major change as a result of the quick development of technology. Artificial Intelligence (AI) has established itself as a particularly remarkable and controversial breakthrough among the countless technical advancements, and is influencing numerous different industries. Given its transformational potential, it is critical to investigate how AI is affecting the digital design profession. The research aims to discover how the digital design profession is influenced by the adoption of AI from the perspectives of industry professionals. Thus, the research explores factors such as current knowledge and usage of AI, experience with significant changes in work practices, and attitudes towards the use of AI-tools. This study is being conducted using a qualitative research methodology. Interviews with relevant designers working in the sector and literature reviews are part of the process. Important information is gathered during these interviews, which is then analysed. The major goals of the interviews are to understand the participants' perspectives on the matter, learn about their AI experiences, and determine how AI isaffecting their work practices. The study sheds light on the overall attitudes regarding AI, encompassing expectations and concerns, by assessing the manner in which AI is used in creative processes. The research's conclusions show that different respondents have various viewpoints and awareness about AI. Regardless of designers' explicit acknowledgement, AI has already found its way into different design processes and tools. As a result, it can be said that AI has had an more or less impact on the digital design industry and certain fields of work practices. However, depending on the various roles and tasks involved, various implications apply. While the majority of professionals exhibit a strong desire to explore and utilize AI, naturally occurring scepticism and a lack of knowledge might prevent its general acceptance and adoption.

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Background: Self-driving cars, also known as automated cars are a form of vehicle that can move without a driver or human involvement to control it. They employ numerous pieces of equipment to forecast the car’s navigation, and the car’s path is determined depending on the output of these devices. There are numerous methods available to anticipate the path of self-driving cars. Pedestrian detection is critical for autonomous cars to avoid fatalities and accidents caused by self-driving cars. Objectives: In this research, we focus on the algorithms in machine learning and deep learning to detect pedestrians on the roads. Also, to calculate the most accurate algorithm that can be used in pedestrian detection in automated cars by performing a literature review to select the algorithms. Methods: The methodologies we use are literature review and experimentation, literature review can help us to find efficient algorithms for pedestrian detection in terms of accuracy, computational complexity, etc. After performing the literature review we selected the most efficient algorithms for evaluation and comparison. The second methodology includes experimentation as it evaluates these algorithms under different conditions and scenarios. Through experimentation, we can monitor the different factors that affect the algorithms. Experimentation makes it possible for us to evaluate the algorithms using various metrics such as accuracy and loss which are mainly used to provide a quantitative measure of performance. Results: Based on the literature study, we focused on pedestrian detection deep learning models such as CNN, SSD, and RPN for this thesis project. After evaluating and comparing the algorithms using performance metrics, the outcomes of the experiments demonstrated that RPN was the highest and best-performing algorithm with 95.63% accuracy & loss of 0.0068 followed by SSD with 95.29% accuracy & loss of 0.0142 and CNN with 70.84% accuracy & loss of 0.0622. Conclusions: Among the three deep learning models evaluated for pedestrian identification, the CNN, RPN, and SSD, RPN is the most efficient model with the best performance based on the metrics assessed.