Expectations for AI (artificial intelligence), including generative AI, are rapidly increasing. Among them, ChatGPT, which excels in natural language processing, is now one of the representative AI technologies. ChatGPT has excellent performance, of course, but one of the reasons why it has attracted so much attention is probably because it visualizes the result of text generation in the form of a "chat" output.
Whether or not we can make the most of the results of AI is greatly influenced by the performance of the AI itself, such as the structure and accuracy of the algorithm, as well as the methods and techniques used to visualize the results.
Here, while confirming the necessity of data visualization, we will introduce "2D map generation," a visualization method for AI analysis results that FRONTEO has patented in two countries, Japan and the United States. This two-dimensional map generation is a visualization that inevitably attracts unexpected discoveries - serendipity.
Data visualization is the visualization and utilization of data.
Data visualization is the visualization and transformation of data into an easily understandable form. Visualizing complex data through visual representations such as graphs and charts makes it easier to understand the data at a glance.
In today's business environment, vast amounts of data are constantly generated and accumulated, but they alone do nothing. Data only becomes meaningful when it is accurately interpreted and utilized for the next action. Data visualization, which allows you to instantly understand data, is an essential step in data utilization.
Benefits and purpose of data visualization
The purpose of data visualization is to better interpret data and use it to inform decision-making.
The advantage of data visualization is that trends, patterns, and correlations in data can be seen at a glance by visually representing them in graphs and charts. For sales data, line graphs are often used to show sales trends, bar graphs are used to compare product categories, etc. Another major benefit is that it becomes easier to notice changes in the situation from visualized data, making it easier to find solutions to problems.
Data visualization methods, points, and precautions
We will explain the main examples of data visualization methods, their points, and precautions.
Typical data visualization methods – use of graphs and color coding
- Line graph or bar graph
Graphing numerical values is something that is commonly used in business. Line graphs are suitable for expressing changes and trends in data, while bar graphs are suitable for comparing data, making it easier to visually understand trends and patterns.
- Heat map
This method expresses data using color shading, and is effective for analyzing big data. For example, color-coding maps is a commonly used method, and was often seen in reports on the infection status of the new coronavirus by prefecture.
Key points and precautions for data visualization
When it comes to data visualization, the key is to choose the most appropriate visual according to the data and purpose. After understanding the data deeply, we consider the best way to present it, taking into account comparisons and correlations depending on the purpose and use.
The key, of course, is to process accurate data appropriately. Pay close attention from the data collection stage, and be sure to avoid distorting interpretations through misleading expressions or intentional axis manipulation.
A method for visualizing AI data analysis results
Just as data visualization is important for general data, when using AI, not only the analysis performance of AI but also the step of visualizing the results is extremely important.
Normally, in AI applications for information retrieval and data analysis, search and analysis results are often displayed as a list, but other ways of expressing them are also being devised to make them easier to understand and more effective.
Example of data visualization of AI analysis results
List display
List display is the most popular format and is a method of presenting information in an ordered manner according to certain criteria. While it is simple, the problem is that the large amount of information makes it difficult to grasp the overall picture across items.
word cloud
A word cloud is a method of visually displaying words in a sentence by arranging them in sizes and colors depending on their frequency. For example, it is used to express trends on SNS, and allows you to see hot topics and keywords at a glance.
co-occurrence network
A co-occurrence network is a network that expresses the relationships between elements such as words. For example, words can be connected with lines based on the number of times they appear in a sentence or the distance between them, and the connections can be visualized in a network-like representation.
The necessity and effects of “visualization” of AI analysis data
The amount of data analyzed by AI is particularly large, and even professionals in the field cannot fully understand it as it is. Visualizing AI analysis data is essential in order to avoid missing out on the information that the data originally contains, and it can also have various effects.
Organizing and picking up complex information
Data visualization allows you to see the big picture and understand information accurately. It is also useful for organizing and picking up information such as which elements are important for a problem.
Hints for new points of view and ideas
By making it easier to notice characteristics and patterns in data through visualization, new points of view and ideas are born. The data trends that emerge can also provide hints for considering new strategies and approaches.
Facilitating organizational communication and decision-making
In order to effectively utilize data and improve the quality of an organization's decision-making, it is essential that all members are on the same page. By visualizing AI analysis data, not only engineers but also team members such as managers and sales staff can gain a common understanding, leading to constructive discussions within the team.
FRONTEO's AI "KIBIT" visualization method "2D map"
FRONTEO's in-house developed AI "KIBIT" is a type of machine learning that uses a unique algorithm different from deep learning, which is a major generative AI such as ChatGPT.
Furthermore, in terms of visualization, we have developed a unique technology that converts text information into a two-dimensional map and has obtained patents in both Japan and the United States*.
*“2D map generation device, 2D map generation method, and 2D map generation program”
(2D MAP GENERATION APPARATUS, 2D MAP GENERATION METHOD, AND 2D MAP GENERATION PROGRAM)
[Patent number] Japan: Patent No. 7116969 / United States: Press release https://www.fronteo.com/20240111
Flow of converting text information into a map using FRONTEO's unique "2D map generation"
Mapping text information begins with ``vectorization of language,'' or in other words, replacing language with numbers (combinations of numbers = vectors) in natural language processing.
First, we replace the huge amount of text information that is the source with a feature vector, which is a set of many numbers, and then compress the data to two-dimensional latitude and longitude information while preserving the relationships between the vectors (dimensional compression/dimensional reduction). . Furthermore, by plotting the data on a flat surface and visualizing it using colors, scattering, etc., a ``2D map'' is generated so that humans can visually recognize it.
![[held on January 3th!15th Economic Security Study Group] Security in anticipation of an emergency in Taiwan -Priorities of defense expenditures and special important goods that companies should know now-](https://www.fronteo.com/wp-content/uploads/2024/02/ai-data-visualization_flow_map.png#48957)
KIBIT products equipped with "2D map generation" technology
What can be achieved with 2D map generation and the effect "Serendipity"
This "2D map" plots similar information in close locations, making it easy to understand a huge amount of information at once, and making it easy to understand the composition, size, and relationships of clusters that have something in common.
Since you can visually and intuitively grasp the overall picture of the target information, its composition and characteristics, and the similarities and relationships between elements, you can gain insights that cannot be obtained from a list display. This leads to ``serendipity,'' which allows the discovery of unexpected information that would otherwise be missed by normal analysis.
"Serendipity" is a chance encounter with good fortune. 2D map generation is a method that inevitably attracts serendipity. Through this map, experts can discover useful and valuable information, gain new ideas and opportunities, and advance their exploration and research.
Data visualization makes AI even easier to use
As the scope of AI applications expands and remarkable progress is made, highly capable AI may seem useful and capable of doing anything. It is true that excellent AI can help us, but the ``last mile'' of solving problems and making decisions based on that data can only be achieved by human experts.
In order to help experts make such decisions in the shortest possible time and with the least amount of effort, FRONTEO will further improve the ease of use and usability of AI through visualization that "maps" language analysis results, and will promote social implementation. Masu.
