Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing.
What type of problem is this?
Which of the following best describes the technical definition of Machine Learning?
Your team is working on an AI-enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data you realize you don't have enough data to train the model.
What's the best course of action?
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:
