Importance of Data Visualization in AI

Introduction 

Data visualization aids in the telling of stories by filtering data into a more understandable format, showing patterns and outliers. A good visualization conveys a narrative by reducing noise from data and emphasizing important information. It is the most important aspect for any company. The stats provided below clearly indicate the significance of AI in Data visualization.

• A high-quality infographic is read 30 times more than a plain text document.  
• Following instructions with illustrations perform 323% better than following instructions with text only. Sixty-eight percent of people believe a scientific claim is accurate and true if presented in simple words or numbers.  
• The number of respondents increases to 97% if you provide a simple graph with the claim. 

What Is Data Visualization? 

Visualization is any approach to generating visuals, diagrams, or animations to convey a message. Since the start of civilization, visual imagery has been an effective tool to transmit abstract and tangible concepts. 

Data visualization use algorithms to generate visuals from data so that people may better comprehend and react to that data. Artificial intelligence research aims to create algorithms that can “understand” and react to input in the same way humans can — if not better. Perhaps in the distant future, we will delegate so much to AI systems that we will lose interest in understanding the world for ourselves, but we are far from such a dystopia now. Despite the term, AI development is still human, and AI engineers extensively use data visualization. At the same time, AI approaches can alter how data visualization is done. 

Data Visualization for AI 

Artificial Intelligence development differs significantly from traditional software development. Although the first step is the same, which is writing code, here, the AI software you write takes some data as input and creates the software that is ultimately used. It is known as AI system training or learning; the ultimate output is sometimes referred to as a model. This two-step procedure is critical to the success of AI systems in fields such as computer vision: AI software can generate computer vision models better than humans. The result of the AI creation process, on the other hand, is sometimes referred to as a “black box” since it was not developed by a person and cannot be described by or to humans. Data visualization has been crucial to AI development since it can assist both AI engineers and consumers explain and comprehend these systems. Data visualization involves the following aspects: 

Choosing the Right Visual Encoding 

When choosing visual encoding for your data, it’s important to consider several factors. These include whether the data is qualitative or quantitative and who your audience will be. If you’re creating a graph for yourself, it might be more helpful to use a different style than when creating something for a client or boss. It’s also important to make sure that the visual encoding makes sense in relation to your task; if you’re trying to find patterns in large datasets, don’t just throw all that information into one big chart! 

Most importantly, be sure to speak with your audience about their preferences and expectations. If you have a lot of data points that need to be visualized, for example, it might be helpful to show them a mock-up of the end product before you start working on it. 

Selecting the Appropriate Chart Type 

You should select the appropriate chart type for your data and audience. For example, a line chart is best for showing trends over time, while a pie chart enables you to show how different parts relate to one another. 

Your choice of chart also depends on what message you want to convey. A bar graph is more appropriate than a pie chart in situations where there are many categories and few data points per category. On the other hand, if you want to emphasize several major aspects of an issue simultaneously and show how they relate to one another, then consider using a scatterplot or bubble plot instead. 

Design Guidelines for Improved Comprehensibility 

• Use a limited color palette. 
• Limit the number of colors in your visualizations to three or fewer. If you’re going to use more than three, make sure that they’re clearly distinguishable from each other and have distinct meanings. Avoid rainbow-like palettes and similar schemes as much as possible—they can be hard for people to interpret at first glance because there’s nothing about them that immediately indicates their function. 
• Limit chart types. 

The chart type should be appropriate for the data being displayed and the goal of your visualization (is it meant to convey information or spark discussion?). For example, don’t mix pie charts with line graphs—that would be confusing! There are many different chart types; here are some common ones: bar charts/columns, bubble diagrams, scatter plots/lines, and area graphs/maps. 

Use the Right Visualization Methods to Make Your AI Results Easier to Understand 

For your visualizations to be useful, they should be designed to make the results more comprehensible. To do this, you must first understand the purpose of each visualization and then select a method that fits that purpose. The following are some guidelines you can follow: 

• Use a visual encoding method that is most appropriate for the task at hand. If you want to show relationships between variables or highlight outliers or extremes, choose an appropriate graph type (bar chart, histogram) or use a scatter plot with overlaid kernel density estimates. 
• Every chart type has some inherent complexity. The more complex it is (for example, multidimensional scatter plots), the harder it will be for viewers unfamiliar with them to interpret their meaning. 

AI for Data Visualization 

Let’s understand the impact of AI in data visualization in the following subheadings: 

Use AI to Reduce the Need for Manual Coding 

AI can help you reduce the need for manual coding by doing it automatically. AI may eventually be able to write code independently, but that’s not where we’re at right now. Instead, AI can help you reduce the need for manual coding by doing it faster and more accurately than humans can. 

AI Tool May Make Data Visualization Faster and Easier 

The artificial intelligence that powers data visualization tools can help you focus on the important parts of your project, automate tasks and make better decisions. 

AI is still in its infancy, but it’s already making a big difference for data visualization professionals. 

With AI, there are fewer steps involved in gathering and analyzing data, so projects move faster than ever before. And since many processes are automated using AI technology, there are fewer opportunities for human error—a win for everyone involved! 

AI Tool May Pick Colors That Are More Accessible to Colorblind Users 

Color blindness is a disability affecting millions of people worldwide. It occurs when the retina has an underlying genetic mutation, which causes the affected person’s eyes to see color differently than they would if they were not colorblind. Color blindness can be mild, moderate, or severe, depending on how many colors are affected by this mutation. 

There are two main types of colorblindness: monochromacy (in which all colors appear as shades of gray) and dichromacy (in which reds and greens appear as shades of yellow or brown). Dichromats have trouble distinguishing between these two color categories, while monochromats cannot perceive any hue. There are also other types, such as anomalous trichromacy and anomalous dichromacy. However, these conditions affect much fewer people than monochromatic or dichromatic vision impairments. 

Because there are so many different kinds of color blindness, developers can’t create interfaces that work well for everyone who suffers from this condition. However, AI-powered tools can help users with some types of restricted vision experience less difficulty with tasks like selecting hotlink URLs on the Internet browser screen. 

AI Tool May Pick Colors That Are More Emotionally Evocative 

Color can strongly affect how we feel, and it can also help us remember things. We don’t always realize why some colors just seem to stick out in our minds, but they do. Some colors are naturally linked with certain feelings or ideas—think of how red makes you think of passion or danger, while blue is often associated with sadness or peace. So whether the AI program is looking at the data itself or simply its (human) creators’ perceptions of what each color means in relation to their data set and purpose, it could be able to make decisions based on these meanings. 

AI might also be able to pick colors that focus more attention on important elements within an infographic through their choice of hues and saturation levels.” 

AI Visualization Tools May Make You Seem More Creative to End-users 

The following is a list of examples in which data visualization tools with AI may make you seem more creative: 

• The tool will be making the data visualization. The tool is not you. The tool is not a human, nor does it have any creativity whatsoever. 
• You will be using the tool to make a visualization that looks like something you would have made if only you had access to the right technology and expertise, which requires far fewer resources or time. 

Conclusion 

Data visualization tools facilitate identifying trends, data patterns, and outliers. Technology and methods for visualizing data are fundamental for processing large datasets and making choices based on the results. If you want to pursue courses in AI and ML, UNext Jigsaw’s ML and AI course is a great choice.