Global ETD Search

61	En Ny Era - Artificiell Intelligens inom Digital Marknadsföring Bergström Stacey, Emily, Björk, Fredrika January 2023 (has links) I slutet av år 2022 introducerades det nya AI-verktyget ChatGPT, en AI-modell som använder maskininlärning för att generera människoliknande svar i stor skala. ChatGPT:s snabba framväxt medför en ovisshet kring hur AI-verktyget kommer påverka praxis för digital marknadsföring. Denna studie utreder därför vilken roll ChatGPT kommer spela inom olika praxis för digital marknadsföring och ämnar därmed att utreda forskningsfrågan: Hur kommer ChatGPT att påverka praxis för digital marknadsföring? Den valda forskningsstrategin för denna studie är en kartläggning där ansikte-mot-ansikte kartläggning tillämpas. Detta stöds med hjälp av intervjuer som datainsamlingsmetod och vidare appliceras en tematisk analys för att analysera insamlad data. Fem marknadsföringsexperter intervjuades i denna studie och samtliga menade att ChatGPT på något vis påverkar praxis inom digital marknadsföring. Slutsatsen pekar därför mot att ChatGPT, trots dess nya upptäckt, redan börjat påverka processer inom praxis för digital marknadsföring och att det troligtvis i bredare utsträckning kommer fortsätta göra det på olika vis, genom att fortsätta inspirera, effektivisera och optimera. Vidare hade alla respondenter en positiv inställning till att se ChatGPT som ett komplement till dagens marknadsföringspraxis, dock en mer negativ inställning till att se det som ett substitut. / In late 2022, the new AI tool, ChatGPT, was introduced. It is an AI-model that uses machine learning to generate human-like responses on a large scale. The rapid rise of ChatGPT has resulted in a lack of sufficient knowledge about the effect that ChatGPT will have on digital marketing practices. Therefore, this study investigates the role of ChatGPT in different digital marketing practices and aims to address the research question: How will ChatGPT af ect digital marketing practices? The chosen research strategy for this study is a survey strategy, as well as the application of the face-to-face survey. This is supported by the data collection method interviews and then a thematic analysis is applied to analyse the collected data. Five marketing experts were interviewed in this thesis and all believed that ChatGPT will, and already has, in some way influenced digital marketing practices. The conclusion therefore points to the fact that ChatGPT, despite its recent discovery, has already begun to influence processes within the practice of digital marketing. Furthermore ChatGPT will most likely continue to enhance digital marketing in a variety of ways on a wider scale, through continuing to inspire as well as contribute with efficiency and optimisation. In addition, all respondents had a positive attitude towards seeing ChatGPT as a complement to current marketing practices, however a more negative attitude towards seeing it as a substitute. Artificial intelligence ChatGPT DALL-E 2 GPT-4 Digital marketing Search engine optimisation (SEO) Paid search marketing Online public relations Affiliate marketing Display advertising Email marketing Social media marketing Personalisation Artificiell intelligens ChatGPT DALL-E 2 GPT-4 Digital marknadsföring Search engine optimisation (SEO) Paid search marketing Online public relations Affiliate marketing Display advertising Email marketing Social media marketing Personalisering Computer Sciences Datavetenskap (datalogi)
62	Contextual short-term memory for LLM-based chatbot / Kontextuellt korttidsminne för en LLM-baserad chatbot Lauri Aleksi Törnwall, Mikael January 2023 (has links) The evolution of Language Models (LMs) has enabled building chatbot systems that are capable of human-like dialogues without the need for fine-tuning the chatbot for a specific task. LMs are stateless, which means that a LM-based chatbot does not have a recollection of the past conversation unless it is explicitly included in the input prompt. LMs have limitations in the length of the input prompt, and longer input prompts require more computational and monetary resources, so for longer conversations, it is often infeasible to include the whole conversation history in the input prompt. In this project a short-term memory module is designed and implemented to provide the chatbot context of the past conversation. We are introducing two methods, LimContext method and FullContext method, for producing an abstractive summary of the conversation history, which encompasses much of the relevant conversation history in a compact form that can then be supplied with the input prompt in a resource-effective way. To test these short-term memory implementations in practice, a user study is conducted where these two methods are introduced to 9 participants. Data is collected during the user study and each participant answers a survey after the conversation. These results are analyzed to assess the user experience of the two methods and the user experience between the two methods, and to assess the effectiveness of the prompt design for both answer generation and abstractive summarization tasks. According to the statistical analysis, the FullContext method method produced a better user experience, and this finding was in line with the user feedback. / Utvecklingen av LMs har gjort det möjligt att bygga chatbotsystem kapabla till mänskliga dialoger utan behov av att finjustera chatboten för ett specifikt uppdrag. LMs är stateless, vilket betyder att en chatbot baserad på en LM inte sparar tidigare delar av konversationen om de inte uttryckligen ingår i prompten. LMs begränsar längden av prompten, och längre prompter kräver mer beräknings- och monetära resurser. Således är det ofta omöjligt att inkludera hela konversationshistoriken i prompten. I detta projekt utarbetas och implementeras en korttidsminnesmodul, vars syfte är att tillhandahålla chatboten kontexten av den tidigare konversationen. Vi introducerar två metoder, LimContext metod och FullContext metod, för att ta fram en abstrakt sammanfattning av konversationshistoriken. Sammanfattningen omfattar mycket av det relevanta samtalet i en kompakt form, och kan sedan resurseffektivt förses med den påföljande prompten. För att testa dessa korttidsminnesimplementationer i praktiken genomförs en användarstudie där de två metoderna introduceras för 9-deltagare. Data samlas in under användarstudier. Varje deltagare svarar på en enkät efter samtalet. Resultaten analyseras för att bedöma användarupplevelsen av de två metoderna och användarupplevelsen mellan de två metoderna, och för att bedöma effektiviteten av den snabba designen för både svarsgenerering och abstrakta summeringsuppgifter. Enligt den statistiska analysen gav metoden FullContext metod en bättre användarupplevelse. Detta fynd var även i linje med användarnas feedback. Chatbot Artificial Intelligence Machine Learning Language Model Large Language Model GPT-3 Natural Language Processing Text Summarization Dialogue Summarization Prompt Design Prompt Programming Chatbot Artificiell Intelligens Maskininlärning Språkmodell Stor Språkmodell GPT-3 Naturlig Ppråkbehandling Textsammanfattning Sammanfattning av Dialog Design för Inmatningsprompt Inmatningsprompt Programmering Computer Sciences Datavetenskap (datalogi)
63	KERMIT: Knowledge Extractive and Reasoning Model usIng Transformers Hameed, Abed Alkarim, Mäntyniemi, Kevin January 2024 (has links) In the rapidly advancing field of artificial intelligence, Large Language Models (LLMs) like GPT-3, GPT-4, and Gemini have revolutionized sectors by automating complex tasks. Despite their advancements, LLMs and more noticeably smaller language models (SLMs) still face challenges, such as generating unfounded content "hallucinations." This project aims to enhance SLMs for broader accessibility without extensive computational infrastructure. By supervised fine-tuning of smaller models with new datasets, SQUAD-ei and SQUAD-GPT, the resulting model, KERMIT-7B, achieved superior performance in TYDIQA-GoldP, demonstrating improved information extraction while retaining generative quality. / Inom det snabbt växande området artificiell intelligens har stora språkmodeller (LLM) som GPT-3, GPT-4 och Gemini revolutionerat sektorer genom att automatisera komplexa uppgifter. Trots sina framsteg stårdessa modeller, framför allt mindre språkmodeller (SLMs) fortfarande inför utmaningar, till exempel attgenerera ogrundat innehåll "hallucinationer". Denna studie syftar till att förbättra SLMs för bredare till-gänglighet utan krävande infrastruktur. Genom supervised fine-tuning av mindre modeller med nya data-set, SQUAD-ei och SQUAD-GPT, uppnådde den resulterande modellen, KERMIT-7B, överlägsen pre-standa i TYDIQA-GoldP, vilket visar förbättrad informationsutvinning samtidigt som den generativa kva-liteten bibehålls. Keywords: KERMIT-7B SQUAD-ei SQUAD-GPT Artificial Intelligence (AI) Large Language Models (LLMs) Small Language Models (SLMs) Supervised Fine-tuning Information Extraction. KERMIT-7B SQUAD-ei SQUAD-GPT Artificiell intelligens (AI) stora språkmodeller (LLM) små språkmodeller (SLM) övervakad finjustering informationsutvinning.
64	Hfs Plus File System Exposition And Forensics Ware, Scott 01 January 2012 (has links) The Macintosh Hierarchical File System Plus, HFS +, or as it is commonly referred to as the Mac Operating System, OS, Extended, was introduced in 1998 with Mac OS X 8.1. HFS+ is an update to HFS, Mac OS Standard format that offers more efficient use of disk space, implements international friendly file names, future support for named forks, and facilitates booting on non-Mac OS operating systems through different partition schemes. The HFS+ file system is efficient, yet, complex. It makes use of B-trees to implement key data structures for maintaining meta-data about folders, files, and data. The implementation of what happens within HFS+ at volume format, or when folders, files, and data are created, moved, or deleted is largely a mystery to those who are not programmers. The vast majority of information on this subject is relegated to documentation in books, papers, and online content that direct the reader to C code, libraries, and include files. If one can’t interpret the complex C or Perl code implementations the opportunity to understand the workflow within HFS+ is less than adequate to develop a basic understanding of the internals and how they work. The basic concepts learned from this research will facilitate a better understanding of the HFS+ file system and journal as changes resulting from the adding and deleting files or folders are applied in a controlled, easy to follow, process. The primary tool used to examine the file system changes is a proprietary command line interface, CLI, tool called fileXray. This tool is actually a custom implementation of the HFS+ file system that has the ability to examine file system, meta-data, and data level information that iv isn’t available in other tools. We will also use Apple’s command line interface tool, Terminal, the WinHex graphical user interface, GUI, editor, The Sleuth Kit command line tools and DiffFork 1.1.9 help to document and illustrate the file system changes. The processes used to document the pristine and changed versions of the file system, with each experiment, are very similar such that the output files are identical with the exception of the actual change. Keeping the processes the same enables baseline comparisons using a diff tool like DiffFork. Side by side and line by line comparisons of the allocation, extents overflow, catalog, and attributes files will help identify where the changes occurred. The target device in this experiment is a two-gigabyte Universal Serial Bus, USB, thumb drive formatted with Global Unit Identifier, GUID, and Partition Table. Where practical, HFS+ special files and data structures will be manually parsed; documented, and illustrated. Hfs+ digital forensics filexray apple forensics mac forensics guid partition table gpt scheme hfs+ volume winhex apple data structure mac partitions apple partitions hfs+ journal hfs+ format Computer Sciences Engineering Forensic Science and Technology
65	Morgondagens kommunikation i dag : Chat GPT och den AI-faciliterade skrivprocessen Pettersson Svärd, Jakob January 2023 (has links) Den föreliggande studiens syfte är att undersöka hur kommunikatörer som skriver på svenska uppfattar att generativa AI-tjänster som Chat GPT påverkar skrivprocessen. Dessutom syftar studien till att undersöka vilka eventuella språkliga följder som AI-skrivande för med sig. Materialet bygger på semistrukturerade intervjuer med fem yrkesverksamma kommunikatörer som har testat att använda Chat GPT professionellt. Den insamlade empirin har kodats och analyserats med hjälp av metoden reflexiv tematisk analys. Resultatet visar att kommunikatörer upplever AI-faciliterat skrivande som en linjär process, där chatboten bland annat hjälper till att generera nya utkast och disponera skrivuppgifter. Resultatet visar även att det finns en möjlighet till AI-baserad responsgivning som skulle kunna utveckla kommunikatörers språkliga förmåga. Slutsatsen är att den kognitiva avlastning som AI-faciliterat skrivande innebär både kan leda till språkliga kvalitetsvinster och förbistringar, samtidigt som det finns uppenbara risker med att ”koppla bort” människan från delar av skrivprocessen. Detta måste kommunikationsbranschen vara fortsatt vaksam på. Naturligt språk Chat GPT maskininlärning AI-faciliterad skrivprocess skrivprocess skrivande skriva chatbotskrivande chatbot artificiell intelligens språk svenska kommunikation kommunikatör AI-skrivande AI skrivteori skrivforskning chatbot språkmodell General Language Studies and Linguistics
66	Graphic design, Already Intelligent? Current possibilities of generative AI applications in graphic design. Dehman, Hampus January 2023 (has links) This paper analyzes the current possible implementations and limitations of generative AI applications such as Chat-GPT, DALL-E, and Midjourney. The applications were used in a specific scenario to gauge whether it was able to effectively handle a potential request from a client, the scenario was to create a visual identity for a shoe company called WalkWise. The creations are then analyzed using Gestalt theories of perception and the machine-learning mechanisms that run these applications. To understand just how graphic designers may introduce these tools into their process, a process chart describing a typical graphic design process for a project has been created using data gathered from 8 professionals in the field who were interviewed. Using a thematic analysis, common occurring themes/activities were found and visualized in a process chart. The process was later analyzed using theories in process value analysis. Using all this information a conclusion was made that AI-generated art has several limitations that inhibit it from completely replacing human designers. These included: not understanding/generating vector graphics, not understanding objects in 3D space from less natural angles, often generating visual clichés, some potential copyright issues, and not being able to generate words. The implementation was therefore limited to a visual brainstorming tool which could aid graphic designers in quickly visualizing an idea or visualizing several different versions of one idea without having to sketch these differences, thereby making the idea-generating parts of the process more efficient. Artificial Intelligence Graphic Design AI Art Generative Artificial Intelligence Process mapping OpenAI DALL-E Chat-GPT Design Design Media Engineering Mediateknik Övrig annan teknik
67	Användning av generativ AI inom digital innovation : En kvalitativ studie ur innovatörers perspektiv / The use of generative AI in digital innovation : A qualitative study through the lens of innovators Süvari, Andreas, Wallmark, Rebecca January 2023 (has links) Påskyndat av teknik går utvecklingen snabbare än någonsin. Generativ AI har blivit tillgänglig för allmänheten. Det ger möjligheter för verksamheter att nyttja AI-teknik utan större insatser och kunskap. Detta skiftar förutsättningarna inom digital innovation. Denna nya aktör skapar gap i litteraturen, där tidigare forskning behöver omvärderas. Ett viktigt forskningsområde är hur användningen av generativ AI påverkar digital innovation. En annan aspekt är hur innovatörer kan nyttja, och förhålla sig till generativ AI inom innovationsprocessen. För att undersöka detta har en kvalitativ studie genomförts, där empiri har samlats in genom åtta intervjuer. Studien har resulterat i en tematisk modell med följande teman: Generativ AI som en kollega; Generativ AI som resurs för digital innovation; Generativ AI ökar tillgängligheten till AI-teknik; Känslor gällande generativ AI; Problematik gällande generativ AI; Spridd och differentierad syn på digital innovation. Studien visar att generativ AI kan påverka digital innovation genom de resulterande temana. Vidare relateras dessa teman till innovationsprocessen, där en modifierad processmodell för innovation har tagits fram. Då användningen av generativ AI är ett relativt nytt fenomen är det sannolikt att innovatörer framöver kommer att öka sin användning av verktyget, vilket medför att fynden från denna studie riskerar att snabbt bli utdaterade. Vidare forskning bör därför utföra liknande studier med jämna mellanrum, för att fånga upp nya erfarenheter som uppstår av den ökade användningen. / Accelerated by technology, development is progressing faster than ever. Generative AI has become accessible to the general public. It provides opportunities for businesses to leverage AI technology without significant efforts and expertise. This shifts the conditions within digital innovation. This new actor creates gaps in the literature, where previous research needs to be reevaluated. An important research area is how the use of generative AI affects digital innovation. Another aspect is how innovators can utilize and engage with generative AI in the innovation process. To investigate this, a qualitative study has been conducted, where empirical data has been collected through eight interviews. The study has resulted in a thematic model with the following themes: Generative AI as a colleague; Generative AI as resource for digital innovation; Generative AI increases accessibility to AI technology; Emotions regarding generative AI; Challenges regarding generative AI; Diverse and differentiated views on digital innovation. The study shows that generative AI can affect digital innovation through the resulting themes. Furthermore, these themes were related to the innovation process, where a modified process model for innovation has been developed. Since the use of generative AI is a relatively new phenomenon, it is likely that innovators will increase their use of the tool in the future. This may render the findings from this study quickly outdated. Further research should therefore conduct similar studies at regular intervals to capture new experiences arising from increased usage. Artificial Intelligence Generative AI Innovation Digital Innovation ChatGPT Innovation Process Innovation Agent Large Language Model. Artificiell Intelligens Generativ AI Innovation Digital Innovation Chat GPT Innovationsprocess Innovationsledare Large Language Model. Computer and Information Sciences Data- och informationsvetenskap
68	Comparative Analysis of User Satisfaction Between Keyword-based and GPT-based E-commerce Chatbots : A qualitative study utilizing user testing to compare user satisfaction based on the IKEA chatbot. Bitinas, Romas, Hassellöf, Axel January 2024 (has links) Chatbots are computer programs that interact with users utilizing natural language. Businesses benefit from using chatbots as they can provide a better and more satisfactory customer experience. This thesis investigates differences in user satisfaction with two types of e-commerce chatbots: a keyword-based chatbot and a GPT-based chatbot. The study focuses on user interactions with IKEA's chatbot "Billie" compared to a prototype GPT-based chatbot designed for similar functionalities. Using a within-subjects experimental design, participants were tasked with typical e-commerce queries, followed by interviews to gather qualitative data about each participants experience. The research aims to determine whether a chatbot based on GPT technology can offer a more intuitive, engaging and empathetic user experience, compared to traditional keyword-based chatbots in the realm of e-commerce. Findings reveal that the GPT-based chatbot generally provided more accurate and relevant responses, enhancing user satisfaction. Participants appreciated the GPT chatbot's better comprehension and ability to handle natural language, though both systems still exhibited some unnatural interactions. The keyword-based chatbot often failed to understand user intent accurately, leading to user frustration and lower satisfaction. These results suggest that integrating advanced AI technologies like GPT-based chatbots could improve user satisfaction in e-commerce settings, highlighting the potential for more human-like and effective customer service. E-commerce Chatbots User satisfaction Keyword-based chatbot GPT-based chatbot Natural language processing (NLP) Artificial intelligence (AI) Customer service User experience (UX) Human-like interaction Computer and Information Sciences Data- och informationsvetenskap
69	A Method for Automated Assessment of Large Language Model Chatbots : Exploring LLM-as-a-Judge in Educational Question-Answering Tasks Duan, Yuyao, Lundborg, Vilgot January 2024 (has links) This study introduces an automated evaluation method for large language model (LLM) based chatbots in educational settings, utilizing LLM-as-a-Judge to assess their performance. Our results demonstrate the efficacy of this approach in evaluating the accuracy of three LLM-based chatbots (Llama 3 70B, ChatGPT 4, Gemini Advanced) across two subjects: history and biology. The analysis reveals promising performance across different subjects. On a scale from 1 to 5 describing the correctness of the judge itself, the LLM judge’s average scores for correctness when evaluating each chatbot on history related questions are 3.92 (Llama 3 70B), 4.20 (ChatGPT 4), 4.51 (Gemini Advanced); for biology related questions, the average scores are 4.04 (Llama 3 70B), 4.28 (ChatGPT 4), 4.09 (Gemini Advanced). This underscores the potential of leveraging the LLM-as-a-judge strategy to evaluate the correctness of responses from other LLMs. LLM-as-a-Judge Chatbot Large language model LLM chatbot GPT-4o Llama 3 70B ChatGPT 4 Gemini Advanced Automatic evaluation Automatic evaluation parameters Education History Biology Computer Systems Datorsystem
70	Introducing Generative Artificial Intelligence in Tech Organizations : Developing and Evaluating a Proof of Concept for Data Management powered by a Retrieval Augmented Generation Model in a Large Language Model for Small and Medium-sized Enterprises in Tech / Introducering av Generativ Artificiell Intelligens i Tech Organisationer : Utveckling och utvärdering av ett Proof of Concept för datahantering förstärkt av en Retrieval Augmented Generation Model tillsammans med en Large Language Model för små och medelstora företag inom Tech Lithman, Harald, Nilsson, Anders January 2024 (has links) In recent years, generative AI has made significant strides, likely leaving an irreversible mark on contemporary society. The launch of OpenAI's ChatGPT 3.5 in 2022 manifested the greatness of the innovative technology, highlighting its performance and accessibility. This has led to a demand for implementation solutions across various industries and companies eager to leverage these new opportunities generative AI brings. This thesis explores the common operational challenges faced by a small-scale Tech Enterprise and, with these challenges identified, examines the opportunities that contemporary generative AI solutions may offer. Furthermore, the thesis investigates what type of generative technology is suitable for adoption and how it can be implemented responsibly and sustainably. The authors approach this topic through 14 interviews involving several AI researchers and the employees and executives of a small-scale Tech Enterprise, which served as a case company, combined with a literature review. The information was processed using multiple inductive thematic analyses to establish a solid foundation for the investigation, which led to the development of a Proof of Concept. The findings and conclusions of the authors emphasize the high relevance of having a clear purpose for the implementation of generative technology. Moreover, the authors predict that a sustainable and responsible implementation can create the conditions necessary for the specified small-scale company to grow. When the authors investigated potential operational challenges at the case company it was made clear that the most significant issue arose from unstructured and partially absent documentation. The conclusion reached by the authors is that a data management system powered by a Retrieval model in a LLM presents a potential path forward for significant value creation, as this solution enables data retrieval functionality from unstructured project data and also mitigates a major inherent issue with the technology, namely, hallucinations. Furthermore, in terms of implementation circumstances, both empirical and theoretical findings suggest that responsible use of generative technology requires training; hence, the authors have developed an educational framework named "KLART". Moving forward, the authors describe that sustainable implementation necessitates transparent systems, as this increases understanding, which in turn affects trust and secure use. The findings also indicate that sustainability is strongly linked to the user-friendliness of the AI service, leading the authors to emphasize the importance of HCD while developing and maintaining AI services. Finally, the authors argue for the value of automation, as it allows for continuous data and system updates that potentially can reduce maintenance. In summary, this thesis aims to contribute to an understanding of how small-scale Tech Enterprises can implement generative AI technology sustainably to enhance their competitive edge through innovation and data-driven decision-making. UX HCD HCI AI Artificial Intelligence Generative AI Implementing AI LLM GPT RAG Chatbot Prompt engineering Prompting framework Hallucinations Transparency Interaction Technologies Usability Linguistics Innovation SME Tech Enterprise Qte Growth factors Documentation Communication Knowledge retention Competence development Education Training Retrieval Model Data Data Management Database Knowledge base Maintenance Data security Data driven decision making Data analytics Social sustainability Economical sustainability Figma Trello Github OpenAI GTPs VsCode Azure CoPilot Google Bard Zapier GCS API Pipeline Parsing data Sorting data Storing data Human Computer Interaction Interaction Technologies Interaktionsteknik

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