Abstract
Artificial Intelligence (AI) and Deep Learning have emerged as revolutionary technologies that have transformed various industries and are driving innovation across the globe. However, as AI and Deep Learning continue to evolve and find applications in numerous domains, they face several significant challenges and issues. One of the foremost challenges is the reliance on data, especially the quality and quantity of data. AI and Deep Learning models are heavily dependent on large datasets for training and fine-tuning. Weak annotations and biases in the data can lead to profound consequences, affecting the performance and fairness of these systems. Moreover, the availability of different sources of data to be integrated in a common AI model is crucial to let the AI model to mimic real complex systems. The talk will explore all these challenges, delve into the pressing issues related to weak annotations and biases in data, shedding light on their implications for the development and deployment of AI technologies in society, and propose some strategies to address them.