This paper examines the application of artificial intelligence (AI) in the early stages of the design process, with a specific focus on using AI to develop user-centered designs during the concept phase. The paper presents a qualitative literature study that employs cloud-based tools like Notion and Lateral to organize and categorize relevant research articles. Additionally, the study continuously compares keywords to gain a comprehensive understanding of the topics covered. Its objective is to emphasize the significance of user-centered design and offer a fresh perspective on how AI can assist designers in creating designs that cater to user needs. The paper explores various AI tools and techniques, such as natural language processing (NLP) and machine learning, which can be employed to analyze user data and provide insights into user behavior. Furthermore, it underscores the importance of designers possessing a deep understanding of information architecture, user experience (UX), and interface design to fully leverage the potential of AI tools. While AI tools cannot replace human creativity and expertise, designers must be able to effectively interpret the data provided by these tools and use them to inform their design decisions. The study provides valuable insights into the integration of AI into the design process, resulting in more effective and user-centered designs. It serves as a valuable resource for designers, researchers, and individuals interested in exploring the possibilities of AI in design.

Studien genomfördes med syfte att undersöka användandet av en AI-chatbott för att underlätta registreringen i en social mediaapplikation som ska kunna användas som en resurspool för doktorander och forskare. Studien undersöker hur vi kan förenkla en user onboarding process med hjälp av ChatGPT, detta för att minska belastningen för nya användare och bidra till en väl formulerad användarprofil. Hur en profil bör utformas kan skilja sig beroende på syfte, en användarprofil kan delas in i två kategorier, personlig profil och professionell profil. Denna studie inriktar sig mot den professionella användarprofilen. För att genomföra studien använde vi oss av intervjuer och enkäter för att samla in data, denna data analyserades sedan och utvärderas tematiskt. Studien har formats utifrån de resultat som samlats in via intervjuer och enkäter, dessa resultat och tillvägagångssätt presenteras i denna rapport. Studien genomförde även en analys av tidigare forskning som berör bland annat textsammanfattning för att kunna genomföra studien utifrån en grund som är vedertagen i dagens forskning. Baserat på den data som samlats in kan studien dra slutsatsen att ChatGPT’s förmåga att sammanfatta texter är mycket god och nyttjandet av denna har goda möjligheter att underlätta en registreringsprocess i en applikation. Genom detta kan vi se att en user onboarding process kan förbättras med hjälp av denna teknik. / This study was conducted with the aim of investigating the use of an AI chatbot to facilitate registration in a social media application that can be used as a resource pool for PhD students and researchers. We are investigating how we can simplify a user onboarding process using ChatGPT, this to reduce the burden on a new user and contribute to such a well-formulated user profile. How a profile should be designed can differ depending on the purpose, a user profile can be divided into two categories, personal profile and professional profile. This study focuses on the professional user profile. To conduct the study we used interviews and questionnaires to collect data, this data is then analyzed and evaluated thematically. The study has been shaped based on the results collected via interviews and surveys, these results and approach are presented in this report. We have also carried out an analysis of previous research that concerns, among other things, text summaries in order to be able to carry out the study based on a basis that is accepted in today's research. Based on the data we collected, we conclude that ChatGPT's ability to summarize texts is very good and the use of ChatGPT enables good potential to facilitate a registration process in an application. Through this we can see that a user onboarding process can be improved with the help of this technology.

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The ADtree, a data structure useful for caching sufficient statistics, has been successfully adapted to grow lazily when memory is limited and to update sequentially with an incrementally updated dataset. However, even these modified forms of the ADtree still exhibit inefficiencies in terms of both space usage and query time, particularly on datasets with very high dimensionality and with high arity features. We propose five modifications to the ADtree, each of which can be used to improve size and query time under specific types of datasets and features. These modifications also provide an increased ability to precisely control how an ADtree is built and to tune its size given external memory or speed requirements.

While large language models like GPT-3 have achieved impressive results in the zero-, one-, and few-shot settings, they still significantly underperform on some tasks relative to the state of the art (SOTA). For many tasks it would be useful to have answer options explicitly listed out in a multiple choice format, decreasing computational cost and allowing the model to reason about the relative merits of possible answers. We argue that the reason this hasn't helped models like GPT-3 close the gap with the SOTA is that these models struggle with symbol binding - associating each answer option with a symbol that represents it. To ameliorate this situation we introduce index prompting, a way of leveraging language models trained on code to successfully answer multiple choice formatted questions. When used with the OpenAI Codex model, our method improves accuracy by about 18% on average in the few-shot setting relative to GPT-3 across 8 datasets representing 4 common NLP tasks. It also achieves a new single-model state of the art on ANLI R3, ARC (Easy), and StoryCloze, suggesting that GPT-3's latent "understanding" has been previously underestimated.

<p>When people write a review about a business, they write and rate it based on their personal experience of the business. Sentiment analysis is a natural language processing technique that determines the sentiment of text, including reviews. However, unlike computers, the personal experience of humans emphasizes their preferences and observations that they deem important while ignoring other components that may not be as important to them personally. Traditional sentiment analysis does not consider such preferences. To utilize these human preferences in sentiment analysis, this paper explores various methods of weighting aspects in an attempt to improve sentiment analysis accuracy. Two types of methods are considered. The first method applies human preference by assigning weights to aspects in calculating overall sentiment analysis. The second method uses the results of the first method to improve the accuracy of traditional supervised sentiment analysis. The results show that the methods have high accuracy when people have strong opinions, but the weights of the aspects do not significantly improve the accuracy.</p>

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The economic potential in introducing Artificial Intelligence (AI) into the Swedish Public administration is substantial and it is calculated to be approximately 140B SEK per year[5]. However, without a comprehensive AI strategy and lack of sufficient digital competence, an AI implementation becomes a struggle [9]. According to IBM Research, 120 million people around the world admitted that they may need to upskill due to automation and AI, which has aggravated during the pandemic. However, the lack of digital skills is the biggest barrier to this process. This study analyzes the challenges of an application of the AI technology, Natural Language Processing (NLP), in public procurement. Through a qualitative method with an abductive approach, 10 semi-structured interviews are conducted with experts from the public- and private sector and identify key challenges of NLP in public procurement which are the lack of digital skills, legal-, ethical- and organizational challenges of NLP in the public context and the public sector’s inability to create partnerships and use business networks. This thesis is a case study of IBM that contributes to research on AI in the public sector and aims to help fill the research gap that exists within this field. The study’s purpose is to analyze and provide better insight into public procurement of NLP to increase the use of NLP technologies within public administration in Sweden. / Den ekonomiska potentialen i att införa artificiell intelligens (AI) i den svenska offentliga förvaltningen är stor och beräknas vara cirka 140 miljarder kronor per år [5]. Men utan en omfattande AI- strategi och brist på tillräcklig digital kompetens blir ett AI- genomförande en utmaning [9]. Enligt IBM Research medgav 120 miljoner människor runt om i världen att de kan behöva kvalificera sig på grund av automatisering och AI, vilket har förvärrats under pandemin. Brister på digital kompetens är dock det största hindret för denna process. Denna studie analyserar utmaningarna med en tillämpning av AI- teknologin, Natural Language Processing (NLP), vid offentlig upphandling. Genom en kvalitativ metod som går ifrån en abduktiv forskningsansats genomförs tio semistrukturerade intervjuer med experter från den offentliga- och privata sektorn samt en identifiering av de viktigaste utmaningarna för NLP i offentlig upphandling. Dessa är bristen på digitala färdigheter, juridiska-, etiska- och organisatoriska utmaningar av NLP i det offentliga sammanhanget och den offentliga sektorns oförmåga att skapa, använda och dra fördel av partnerskap och affärsnätverk. Denna rapport är en fallstudie av IBM som bidrar till forskning om AI inom den offentliga sektorn och syftar till att fylla det forskningsgap som finns inom detta område. Studiens syfte är att analysera och ge en bättre insikt i offentlig upphandling av NLP för att öka användningen av NLP- teknologi inom offentlig förvaltning i Sverige.

Indiana University-Purdue University Indianapolis (IUPUI) / With the proliferation of computer science in recent years in modern society, the number of computer science-related employment is expanding quickly. Software engineer has been chosen as the best job for 2023 based on pay, stress level, opportunity for professional growth, and balance between work and personal life. This was decided by a rankings of different news, journals, and publications. Computer science occupations are anticipated to be in high demand not just in 2023, but also for the foreseeable future. It's not surprising that the number of computer science students at universities is growing and will continue to grow. The enormous increase in student enrolment in many subdisciplines of computers has presented some distinct issues. If computer science is to be incorporated into the K-12 curriculum, it is vital that K-12 educators are competent. But one of the biggest problems with this plan is that there aren't enough trained computer science professors. Numerous new fields and applications, for instance, are being introduced to computer science. In addition, it is difficult for schools to recruit skilled computer science instructors for a variety of reasons including low salary issue. Utilizing the K-12 teachers who are already in the schools, have a love for teaching, and consider teaching as a vocation is therefore the most effective strategy to improve or fix this issue. So, if we want teachers to quickly grasp computer science topics, we need to give them an easy way to learn about computer science. To simplify and expedite the study of computer science, we must acquaint school-treachers with the terminology associated with computer science concepts so they can know which things they need to learn according to their profile. If we want to make it easier for schoolteachers to comprehend computer science concepts, it would be ideal if we could provide them with a tree of words and phrases from which they could determine where the phrases originated and which phrases are connected to them so that they can be effectively learned. To find a good concept word or phrase, we must first identify concepts and then establish their connections or linkages. As computer science is a fast developing field, its nomenclature is also expanding at a frenetic rate. Therefore, adding all concepts and terms to the knowledge graph would be a challenging endeavor. Cre- ating a system that automatically adds all computer science domain terms to the knowledge graph would be a straightforward solution to the issue. We have identified knowledge graph use cases for the schoolteacher training program, which motivates the development of a knowledge graph. We have analyzed the knowledge graph's use case and the knowledge graph's ideal characteristics. We have designed a webbased system for adding, editing, and removing words from a knowledge graph. In addition, a term or phrase can be represented with its children list, parent list, and synonym list for enhanced comprehension. We' ve developed an automated system for extracting words and phrases that can extract computer science idea phrases from any supplied text, therefore enriching the knowledge graph. Therefore, we have designed the knowledge graph for use in teacher education so that school-teachers can educate K-12 students computer science topicses effectively.

As Natural Language Processing progresses societal and applications like OpenAI obtain more considerable popularity in society, businesses encourage the integration of NLP into their systems. Both to improve the user experience and provide users with their requested information. For case management systems, a complicated task is to provide the user with relevant documents, since customers often have large databases containing similar information. This presumes that the user needs to match the requested topic perfectly. Imagine if there was a solution to search for context, instead of formulating the perfect prompt, via established NLP models like BERT. Imagine if the system understood its content. This thesis aims to investigate how a free text index can be improved using NLP from a user perspective and implement it. Using AI to help a free text index, in this case, Apache Solr, can make it easier for users to find the specific content the users are looking for. It is interesting to see how the search can be improved with the help of NLP models and present a more relevant result for the user. NLP can improve user prompts, known as queries, and assist in indexing the information. The task is to conduct a practical investigation by configuring the free text database Apache Solr, with and without NLP support. This is investigated by learning the search models' content, letting the search models provide their relevant search results, for some user queries, and evaluating the results. The investigated search models were a string-based model, an OpenNLP model, and BERT models segmented on paragraph level and sentence level. A hybrid search model of OpenNLP and BERT, on paragraph level, was the best solution overall.