Epic AI Fails -- A List of Failed Machine Learning Projects

AI models are undoubtedly solving a lot of real world problems, be it in any field. Building a machine learning model that is genuinely accurate during real world applications and not only during training and testing is what matters. Using state-of-the-art techniques for developing models might not suffice to develop a model that is trained on irregular, biased, or unreliable data. Data shows that nearly a quarter of companies reported up to 50% of AI project failure rate. In another study, nearly 78% of AI or ML projects stall at some stage before deployment, and 81% of the process of training AI with data is more difficult than they expected.