Understanding AI Scores: A Rubric for Evaluating AI Projects

Understanding AI Scores: A Rubric for Evaluating AI Projects

Table of Contents:

I. Introduction II. A brief history of hype in AI and data science III. Defining AI and the need for an AI score IV. Florian Duetto's AI score A. Perception B. Learning C. Interaction D. Complex Decision-Making V. Examples of AI Scores A. Data B. Alexa C. Hot Tub, Not Hot Dog D. Predictive Maintenance VI. Conclusion VII. FAQs

Introduction

Artificial intelligence (AI) has been a hot topic for several years, with many businesses claiming to be working on integrating AI in their operations. However, there is no universal definition of what AI is, making it difficult for companies to determine if they are actually working on an AI project or not. In this article, we will explore the need for an AI score and how it can help organizations decide if they need to aim for True AI or not.

A brief history of hype in AI and data science

The interest in AI and data science has been growing rapidly over the past few decades. A Google Trends Chart shows an uptick in AI interest starting around 2015, and it is exploding in a way that data science Never has. However, defining AI is not as straightforward as it seems. In 1964, Supreme Court Justice Potter Stewart famously said, "I know [AI] when I see it." Today, this approach may not be sufficient. We need a rubric to define a project's AI score to help organizations decide if they are working on an AI project or not.

Defining AI and the need for an AI score

Defining AI is essential in determining if a project is an AI project or not. Florian Duetto has created an AI score, Based on four different categories: Perception, learning, interaction, and complex decision-making. The AI score goes from 0 to 2, with each category scored on a 0 to 2 rubric. Perception refers to reading in flat data files and making simple judgments, whereas a two out of two score requires the ability to process audio or video data. Learning can range from using static rules to reteaching itself how to learn continually. Interaction refers to direct interaction with humans, all the way to advanced conversational AI. Finally, complex decision-making ranges from simple decisions to building complex strategies.

Florian Duetto's AI score

Florian Duetto's AI score is an excellent rubric to determine a project's AI standing. It consists of four categories: perception, learning, interaction, and complex decision-making, with each category scored on a 0 to 2 rubric. A score of two out of two in perception requires the ability to process audio or video data, while learning can range from using static rules to reteaching itself continually. Interaction ranges from direct interaction with humans to advanced conversational AI, while complex decision-making ranges from simple decisions to building complex strategies.

Examples of AI Scores

Data is an excellent example of a project that scores a two out of two in all four categories. Alexa, on the other HAND, scores a 1 in all four categories, making it a great example of a project with moderate AI capabilities. Hot Tub, Not Hot Dog, is an AI project that can recognize whether something is a hot dog or not. It scores a zero in all four categories, making it a prime example of a project that is not AI. Predictive maintenance is an example of an implementation that scores a zero or a one on the AI score but can still yield significant business gains.

Conclusion

The AI score is an excellent rubric to determine if a project is an AI project or not. It can help organizations decide if they need to be aiming for true AI or if they can benefit from working with AI technologies that score low on the AI score. AI and data science are fledgling industries, and there are still vast business gains to be made from technologies and techniques that are far from what we consider AI.

FAQs

Q: What is an AI score? A: An AI score is a rubric developed by Florian Duetto that helps organizations determine if they are working on an AI project or not. It consists of four categories: perception, learning, interaction, and complex decision-making, with each category scored on a 0 to 2 rubric.

Q: Why is an AI score important? A: Defining AI is not as straightforward as it seems, making it difficult for organizations to determine if they are working on an AI project or not. An AI score can help organizations decide if they need to be aiming for true AI or if they can benefit from working with AI technologies that score low on the AI score.

Q: What are some examples of projects with high AI scores? A: Data is an excellent example of a project that scores a two out of two in all four categories of the AI score. Alexa is an example of a project with moderate AI capabilities, scoring a 1 in all four categories.

Q: Can a project that scores low on the AI score still yield significant business gains? A: Yes, a project that scores low on the AI score, such as predictive maintenance, can still yield significant business gains. AI and data science are fledgling industries, and there are still vast business gains to be made from working with AI technologies that score low on the AI score.