Artificial Intelligence is quickly growing to be an influential part of our daily lives. Due to this, we believe it is important to analyze how cultural perceptions can influence how we interact and develop technology. We decided to focus on India due to its large economic stature, cultural influence, and influence on the technology industry.

Artificial Intelligence is quickly growing to be an influential part of our daily lives. Due to this, we believe it is important to analyze how cultural perceptions can influence how we interact and develop technology<br/>We decided to focus on India due to its large economic stature, cultural influence, and influence on the technology industry.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

With an increase in Artificial Intelligence applications in the world of technology, its usage has extended as far as the world of business, with small, medium, and large firms offering both products and services for consumer and business use. Through its historical development, and the devolvement of frameworks, algorithms, and basic toolkits, the application of AI in business was able to flourish. The development of multiple tools such as smart stethoscopes and conversational assistants throughout multiple industries has created a complex commercial enterprise of Artificial Intelligence for the specific genre of business and its applications today are bound to have major effects on the AI applications of tomorrow.

Coliving is a concept that has many benefits towards society and sustainability. This is due to the resources saved economically and environmentally when living with other people. Aisha Comfortable Coliving, a company based in Canada, provides a service where they help women find Coliving communities. A lack of knowledge pertaining to this service could slow down or halt the growth of Aisha ElSherbiny’s Aisha Comfortable Coliving company. This thesis was an extension of a broader project, “Web App for Aisha Comfortable Coliving Inc.,” which focused on transitioning from their current website platform into a web application. As an extension of this main project, this thesis is focused on the engine component design portion surrounding AI chatbots to determine which implementation would provide the best results for a small company in reaching their target audience and helping inform them through an interactive chatbot. The ability to present 24/7 support for Aisha Comfortable Coliving brings value to the company and the methods used in this chatbot can be reproduced in order to create similarly effective chatbots. This thesis delves into the various approaches and implementations researched to determine how to optimize the backend of a chatbot to provide speed, reliability, and expandability for companies aiming to create a chatbot for their users to interact with. It also discusses the methods used when implementing a chatbot called AishaBot using the IBM Watson Assistant’s platform that includes the development of Intents, Entities, Dialog Tree structure, and its WebHook functions. Overall, satisfaction pertaining to the designed chatbot engine within IBM Watson Assistant was discovered to be positive through user trials. Limitations have been discovered, feedback for future improvements have been noted, and lessons learned about the thoroughness of training data have been discussed.

This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in increasing their usage. The survey data suggested that chatbots could provide quick and convenient access to information and personalized recommendations, but their effectiveness for career resource searches may be limited. The second survey found that students who were more satisfied with the quality of resources from the career office were more likely to use chatbots. However, students who felt more prepared to explore their career options were less likely to use chatbots. These results suggest that the W. P. Carey Career Office could benefit from offering more and better resources to prepare students for exploring their career options and could explore the use of chatbots to enhance the quality of their resources and increase student satisfaction. Further research is needed to confirm these suggestions and explore other possible factors that may affect the use of chatbots and the satisfaction with career office resources.

This research paper explores the effects of data variance on the quality of Artificial Intelligence image generation models and the impact on a viewer's perception of the generated images. The study examines how the quality and accuracy of the images produced by these models are influenced by factors such as size, labeling, and format of the training data. The findings suggest that reducing the training dataset size can lead to a decrease in image coherence, indicating that AI models get worse as the training dataset gets smaller. Moreover, the study makes surprising discoveries regarding AI image generation models that are trained on highly varied datasets. In addition, the study involves a survey in which people were asked to rate the subjective realism of the generated images on a scale ranging from 1 to 5 as well as sorting the images into their respective classes. The findings of this study emphasize the importance of considering dataset variance and size as a critical aspect of improving image generation models as well as the implications of using AI technology in the future.