Abstract Machine learning and the development of Artificial Intelligence (AI) has grown nearly exponentially over the past few years. However, growing fears over the nature of AI and the use of user data by large companies has put an air of distrust over the ML community. This makes it hard to collect more data with better user representation is needed to train more useful and accurate models and creates a unique problem space that my project seeks to tackle. It raises the question; how might researchers improve public understanding of AI while creating more representative datasets? To this question I propose the solution, Project Efua; a user taught mobile AI application meant to bridge the gap between the developer community and the wider public
Woode, A (2021). PROJECT EFUA: A USER TRAINED, MOBILE-FIRST IMAGE CLASSIFICATION AI. Afribary. Retrieved from https://afribary.com/works/project-efua-a-user-trained-mobile-first-image-classification-ai
Woode, Ariel "PROJECT EFUA: A USER TRAINED, MOBILE-FIRST IMAGE CLASSIFICATION AI" Afribary. Afribary, 02 Apr. 2021, https://afribary.com/works/project-efua-a-user-trained-mobile-first-image-classification-ai. Accessed 06 May. 2024.
Woode, Ariel . "PROJECT EFUA: A USER TRAINED, MOBILE-FIRST IMAGE CLASSIFICATION AI". Afribary, Afribary, 02 Apr. 2021. Web. 06 May. 2024. < https://afribary.com/works/project-efua-a-user-trained-mobile-first-image-classification-ai >.
Woode, Ariel . "PROJECT EFUA: A USER TRAINED, MOBILE-FIRST IMAGE CLASSIFICATION AI" Afribary (2021). Accessed May 06, 2024. https://afribary.com/works/project-efua-a-user-trained-mobile-first-image-classification-ai