The Idea is really good!

1. Artificial Intelligence

Definition: (AI) The science of making things “smart” (act like humans). A non-human program or model that can solve sophisticated tasks, such as a program that identifies diseases from radiologic images.

2. Machine Learning

Definition: The science of getting computers to do something without being programmed with explicit rules; a sub-field of AI. Software that makes useful predictions of never-before-seen data based on what it has “learned” from an existing dataset.

3. Deep Learning

Definition: Branch of Machine Learning utilizing algorithms inspired by the multi-layered structure of neurons in the brain.

4. Dataset

Definition: All the data that is used for building or testing the Machine Learning model. Sourced from a public resource or specifically collected.

5. AI Training Data

Definition: A dataset that a Machine Learning model uses to detect patterns and determine which aspects are most important during prediction.

6. Robot

Definition: A physical mechanical device that automatically interacts with its environment by sensing, planning and acting.

7. Natural Language Processing

Definition: A common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written. Such as how Siri or Alexa understand what you mean.

8. Speech Recognition

Definition: Used for determining the text representation of people speaking. Such as Siri or Alexa knowing which words you said.

9. AI Ethics

Definition: A concern with the moral behavior of humans as they design, construct, use and treat Artificial Intelligence as well as concern for the moral behavior of the AI itself.

10. AI Value Alignment

Definition: Getting an AI system to adopt the goals of human users or stakeholders even if these are hard to express exactly.

11. Implicit Bias

Definition: Automatically making an association or assumption based on existing mental models. It can affect how data is collected and classified, as well as how machine learning systems are designed and developed.

12. Confirmation Bias

Definition: A form of implicit bias. The tendency to favor information in a way that confirms one’s preexisting beliefs. Machine Learning developers may inadvertently collect or label data in ways that influence an outcome supporting their existing beliefs.

13. Data Selection Bias

Definition: Errors in conclusions when they are drawn from data that has been treated as random, but it is not random. IE: Concluding something based on survey results when certain types of users opt-out of surveys.

14. Computer Vision

Definition: (CV) A field of Artificial Intelligence concerned with providing tools for analysis and high-level understanding of image and video data.

15. False Negative

Definition: When a model, while predicting classes, incorrectly predicted the negative class. Such as the model inferred an email was not spam (negative class), but it actually was spam.

16. False Positive

Definition: When a model, while predicting classes, incorrectly predicted the positive class. Such as the model inferred an email was spam (positive class), but it was actually not spam.

17. AI Over-Reliance

Definition: The tendency of humans to put too much trust in an AI and automation systems beyond their actual capabilities.

18. Unintended AI Effects

Definition: Effects caused by an AI system that were not preteen by its developers. IE: Social polarization through “smart” social media feeds.

19. Semi-Supervised AI Learning

Definition: When training AI using a data model where some examples are known with labels, and others are unknown without labels.

20. Unsupervised AI Learning

Definition: When training AI using a data model where all examples are unlabeled and AI needs to find the structure or relationships between the data.