AI For Everyone - Part 2

In this module, I am excited to have you embark on this journey to learn how to build the best feasible AI solutions.

This module is designed to equip you with the knowledge and tools necessary to design AI systems that meet user needs and align with business goals.

Specifically, you will learn:

How to Design Feasible AI Solutions by

1. Understanding the principles and practices for creating practical AI solutions.

2. You will Learn to align user needs with AI capabilities and identify the data requirements to support your desired outcomes.

3. You will learn to set clear objectives and key results (OKRs) for your AI system, ensuring that it meets business goals and delivers measurable value.

4. Lastly, you will learn to Identify potential risks and challenges in AI implementation, including ethical considerations, data privacy issues, and technical limitations.

You will Learn strategies to mitigate these risks and address challenges effectively.

By the end of this module, you will have learned various methods and tools that will help you create the most feasible and robust AI solution.

In this lesson, we emphasize the importance of identifying the necessary data for realizing your significant idea. This critical step ensures that your AI solution is established on a robust foundation of pertinent and top-notch data.

When embarking on the journey of training an AI model, the initial step is to carefully determine the essential data elements that will serve as the foundation for the learning process. This critical decision sets the stage for the entire development process, as the quality and relevance of the data will significantly impact the model's performance and outcomes.

However, beyond just identifying the data elements, it is equally crucial to establish clear goals and objectives for the AI model. Defining what you want the AI to achieve during the learning process is essential for guiding its development and ensuring that it aligns with the intended purpose. These objectives can range from specific tasks or functions that the AI should perform to broader objectives related to improving efficiency, accuracy, or decision-making within a particular domain.

By setting explicit goals for the AI model, you provide a roadmap for its training and development, helping to focus efforts and resources on achieving the desired outcomes. This process also enables you to measure the success of the AI model against predefined criteria, allowing for adjustments and refinements to be made as needed.

The decision-making process between building and buying becomes pivotal at this critical juncture. The choice between these two options holds significant weight, particularly for organizations navigating varying stages of AI implementation maturity.

When contemplating whether to build or buy AI solutions, organizations must carefully assess their unique needs, capabilities, and resources. Building an AI system in-house offers the advantage of tailor-made solutions that perfectly align with specific requirements and objectives. On the other hand, purchasing AI technology from external vendors can provide quicker deployment, access to specialized expertise, and cost-effective solutions.

The module focuses on aiding in the development of functional prototypes and verifying design solutions. It delves into the rapid prototyping of AI experiences and the importance of validating models pre-production.

Activity 1: Define Objective Function for AI

Define AI Objective

Instructions

Please work as a group and gather diverse individuals to work through these exercises & worksheets.

What is exercise?

1. Design your reward function and weigh the tradeoffs between precision and recall for the user experience.

2. Define success criteria and agree on how to measure if your feature is working or not, and consider the second order effects.

Download the activity resources and try it out!