Exploring AI Tool's Versatile Responses: An In-depth Analysis Across Different Industries and Its Performance Evaluation

AI Tool is a large language model (LLM) designed to generate human-like responses in natural language conversations. It is trained on a massive corpus of text from the internet, which allows it to leverage a broad understanding of language, general knowledge, and various domains. AI Tool can provide information, engage in conversations, assist with tasks, and even offer creative suggestions. The underlying technology behind AI Tool is a transformer neural network. Transformers excel at capturing long-range dependencies in text, making them well-suited for language-related tasks. AI Tool has 175 billion parameters, making it one of the largest and most powerful LLMs to date. This work presents an overview of AI Tool's responses on various sectors of industry. Further, the responses of AI Tool have been cross-verified with human experts in the corresponding fields. To validate the performance of AI Tool, a few explicit parameters have been considered and the evaluation has been done. This study will help the research community and other users to understand the uses of AI Tool and its interaction pattern. The results of this study show that AI Tool is able to generate human-like responses that are both informative and engaging. However, it is important to note that AI Tool can occasionally produce incorrect or nonsensical answers. It is therefore important to critically evaluate the information that AI Tool provides and to verify it from reliable sources when necessary. Overall, this study suggests that AI Tool is a promising new tool for natural language processing, and that it has the potential to be used in a wide variety of applications

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

On the use of CLIL at inclusive education

Decades ago, the European Council started talking about the exclusion danger that people who are not competent in a foreign language could suffer. This danger could be even worse if real integration of all of the population in bilingual centers is no effective. The aim of this paper is to elaborate a methodological proposal for the use of CLIL methodology attending to inclusive education in Castilla y León. Not only in acquiring a foreign language, also as a tool for learning in a second language (L2).Grado en Educación Primari

Uncovering lost potential : the shortcomings of DNBs chatbot

In 2018 DNB Bank ASA (DNB) launched their chatbot, Aino, an advanced virtual banking agent. Aino handles 55% of all the incoming chat traffic for DNBs Customer Center and is continuously being trained by AI trainers to increase the percentage of messages it can respond to. The former CEO of DNB, Rune Bjerke, stated in 2017 that by 2020, 80% of all incoming chat traffic would be handled by chatbots. However, to get closer to this target, DNBs AI trainers will have to make some priorities in the development process. The purpose of this study is to contribute to the decision-making process of which types of problems, and intents the AI trainers should prioritize to reduce DNBs costs. The data basis is conversational logs from conversations between customers of DNB and Aino, in addition to structural interviews with four DNB employees with significant knowledge of Aino. This thesis is a mixed-methods study that consists of both statistical analyses to determine group effect, structured interviews, quantitative content analysis, statistical analyses of chatlogs, as well as analysis of economical impact.I 2018 lanserte DNB Bank ASA (DNB) sin chatbot, Aino, en avansert virtuell bankagent. Aino håndterer 55% av all innkommende chat-trafikk for DNBs kundesenter og blir kontinuerlig opplært av AI-trenere for å øke prosentandelen av meldinger den kan svare på. Den tidligere konsernsjefen i DNB, Rune Bjerke, uttalte i 2017 at innen 2020 ville 80% av all innkommende chat-trafikk bli håndtert av chatbots. For å komme nærmere dette målet, vil DNBs AI-trenere imidlertid måtte gjøre noen prioriteringer i utviklingsprosessen. Hensikten med denne studien er å bidra til beslutningsprosessen for hvilke typer problemer, og intensjoner AI-trenerne bør prioritere for å redusere DNBs kostnader. Datagrunnlaget er samtalelogger fra samtaler mellom kunder av DNB og Aino, i tillegg til strukturerte intervjuer med fire DNB-ansatte med betydelig kunnskap om Aino. Denne oppgaven er et kombinasjonsstudie som består av både statistiske analyser for å bestemme gruppeeffekt, strukturerte intervjuer, kvantitativ innholdsanalyse, statistisk analyse av chatlogger, i tillegg til analyse av finansiell påvirkning.submittedVersionM-Ø

On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces

Multimodal systems have attained increased attention in recent years, which has made possible important improvements in the technologies for recognition, processing, and generation of multimodal information. However, there are still many issues related to multimodality which are not clear, for example, the principles that make it possible to resemble human-human multimodal communication. This chapter focuses on some of the most important challenges that researchers have recently envisioned for future multimodal interfaces. It also describes current efforts to develop intelligent, adaptive, proactive, portable and affective multimodal interfaces