Cognitive project management in AI v1.0

You’re working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs. Specifically, the images you need are of people in different poses. What is the best way to obtain this data?

You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project.
What’s the best way to approach this?

You’re creating an AI enabled chatbot that is going to access user data. What areas related to data governance do you need to make sure you’re addressing? (Choose all that apply.)

Use cognitive technologies/AI when you can’t code the rules or you can’t scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:

Your team is tasked with selecting an algorithm for a supervised learning classification project. Which algorithm might you choose?

When looking to implement AI to help break the Digital Transformation logjam, it’s important to:

You just joined a new company and they want to start their first AI project. Senior management thinks the best approach is to just buy AI from a vendor. You know that AI is something you do, not something you buy.
What is your next best course of action to address this?

Your team is planning an AI enabled chatbot project to help reduce call center load. They are currently determining if the project can get off the ground and working through the AI Go/No Go feasibility questions.
What stage of CPMAI is the team currently working on?

Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Choose all that apply.)

Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be.
What should the project manager do?

Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same.
What is most likely happening here?

Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?

In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data?
As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?