Visualizing AI: Essence and Noun Project introduce AI icons to diversify depictions of Artificial Intelligence
From enabling exciting new possibilities in technology to sparking debate over its potential impact on the economy and society, Artificial Intelligence (AI) is now central to conversations about the future.
Despite the overwhelming presence of AI in news headlines, the range of visual resources accessible to help designers, commentators, reporters, and researchers communicate about AI remains limited. Practitioners complain about the use of frightening images to represent AI in the media and iconography is heavily reliant on metaphors like robots and lightning bolts.
The limited range of ways to convey key concepts in AI and machine learning contributes to sensationalism of this critical topic and does little to help audiences understand how AI really works or its potential impact on our lives.
As part of our ongoing commitment to advancing the ethical use of AI in advertising, Essence partnered with Noun Project for an AI 'Iconathon' in late-Summer 2019. Noun Project is a platform dedicated to building a global visual language that unites the world, and our Iconathon was the first step in a collaborative design process intended to provide the public with a greater, more inclusive, range of ways to represent AI and machine learning.
More than 30 members of the local creative community gathered at Essence’s office in San Francisco and received briefings from subject matter experts - including representatives from the Center for Human Compatible AI - on the state of AI, the need to treat AI as an interdisciplinary and inclusive field, and the need for a broader, more inclusive set of visual references in AI. Participants then sketched ideas for new icons to communicate topics as varied and complex as AI training data, Natural Language Processing, AI Ethics, and AI bias.
Participant sketches were used as inspiration for the final collection of 20 icons, detailed below and available to download on thenounproject.com.
To the extent possible, the icons in the AI Icon set have been designed to communicate the behavior or concepts associated with each topic clearly, with limited reliance on metaphors.
For example, while an icon depicting a robot reading a book could metaphorically communicate 'Machine Learning,' it does little to visually explain what the process of Machine Learning actually is. In the AI Icon set, the Machine Learning icon depicts data being taken in by a computer or machine and then output into new data based on what the machine 'learned' from the input. The AI Icon Collection is built around around a consistent visual system, including the recurring use of a microchip element to represent computers or machines. It has also been designed to be more technical visually, ensuring it can be used effectively across a range of materials, from presentations to marketing collateral, to communicate these concepts.
About the collaboration, Sofya Polyakov, Noun Project’s co-founder and CEO said: 'As the leading resource for visual language, it’s important that we not only provide the visual representations that are currently available, but also lead the way for creating new visual definitions for cutting edge concepts and technologies, like AI, that are not yet clearly defined. Iconathons enable us to engage communities in the process of building this omnipresent language, so that we can use it to cross language and cultural barriers, and simplify communication.'
The Essence + Noun Project AI Icon Collection
1. Artificial Intelligence (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: 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: Branch of Machine Learning utilizing algorithms inspired by the multi-layered structure of neurons in the brain.
4. Dataset: 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: A dataset that a Machine Learning model uses to detect patterns and determine which aspects are most important during prediction.
6. Robot: A physical mechanical device that automatically interacts with its environment by sensing, planning and acting.
7. Natural Language Processing: 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: Used for determining the text representation of people speaking. Such as Siri or Alexa knowing which words you said.
9. AI Ethics: 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: Getting an AI system to adopt the goals of human users or stakeholders even if these are hard to express exactly.
11. Implicit Bias: 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: 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: 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 (CV): A field of Artificial Intelligence concerned with providing tools for analysis and high-level understanding of image and video data.
15. False Negative: 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: 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: The tendency of humans to put too much trust in an AI and automation systems beyond their actual capabilities.
18. Unintended AI Effects: Effects caused by an AI system that were not foreseen by its developers. e.g.: social polarization through 'smart' social media feeds.
19. Semi-Supervised AI Learning: When training AI using a data model where some examples are known with labels, and others are unknown without labels.
20. Unsupervised AI Learning: When training AI using a data model where all examples are unlabeled and AI needs to find the structure or relationships between the data.