Generative AI: What It Is, How It Works, and Top Tools Explained

AI has been increasingly used for various purposes, including the efficient analysis of collected data. Recently, generative AI, such as ChatGPT, which produces creative outputs like text and images, has garnered significant attention.

This article covers the basics of generative AI, its differences from traditional AI, specific use cases, and important considerations when using it. We hope it will be a valuable resource for you.

What is Generative AI?

Generative AI refers to AI that can create original, creative content such as images and videos from scratch.

The main characteristic of generative AI is its ability to not only analyze input data but also produce optimal outputs based on that data.

Compared to traditional AI, it is expected to be utilized in a wider range of fields, and its high potential has led to it being called a "game-changer that fundamentally overturns the existing social structure."

Generative AI, also known as generative AI, is attracting significant social attention, with practical services like ChatGPT already emerging one after another.

Moreover, the content it can generate is incredibly broad, including music and 3D models, and it is already beginning to impact businesses and individuals.

Differences Between Generative AI and Traditional AI

You might be wondering, "How is this different from the AI we've had before?"

Here are the key differences between generative AI and traditional AI:

Traditional AI Infers Based on Past Data

One key difference is that traditional AI could only infer based on past data.

While both are IT technologies categorized as "AI (Artificial Intelligence)," their capabilities differ.

Typical uses for traditional AI include optical character recognition (OCR) and image identification.

For example, when storing PDF documents on a cloud server, AI OCR can automatically extract information to identify the documents. Or, in an automotive parts factory, it can determine whether flowing parts and products meet specifications.

As the examples above show, traditional AI's performance was limited to determining whether given data matched pre-learned answers or calculating predictions and trends based on input data.

Generative AI Enables Creative Outputs Based on Input Data

A second key difference is generative AI's ability to produce creative outputs based on input data.

Traditional AI's societal implementation was limited to "identification" and "prediction/inference."

Like in the previous examples, traditional AI could only compare read character information or image data against pre-input data (answers) to identify whether they were correct or incorrect, or to make predictions.

While traditional AI is still a remarkable technology in human history, generative AI can do everything traditional AI can do, plus create outputs based on input data.

Generative AI's greatest feature is its ability to independently compensate for highly autonomous actions like "thinking," "planning," and "creating creative outputs," which were previously considered human strengths beyond the reach of traditional AI.

Generative AI is a technological innovation that is expected to create a significant differentiation between companies and even individuals, based on whether they can utilize it or not.

Types of Generative AI

Generative AI is being leveraged in various ways, but currently, there are few generative AIs that can generate all types of content. The type of generative AI varies depending on the content you want to generate.

Here are some of the most well-known examples of generative AI:

Image Generation AI

By simply entering text that describes the image you want to create, you can generate an image that reflects that description.

Although the number of images you want to generate and the amount of text you enter can have some effect, images can be generated in a matter of seconds to a few minutes. This is expected to support a wide range of creative tasks, including designers for advertising and web, and the creation of new ideas.

Text Generation AI

AI analyzes the content of a user's questions or instructions and generates text that answers that content.

ChatGPT, which triggered the global spread of generative AI, falls under this category of text generation AI. It provides answer results as if you were asking, consulting, or instructing a person.

It can summarize and create explanatory text, as well as point out errors in program source code.

The information it references to provide answers is currently limited to the web, so it is important to note that the information may not always be accurate. However, it can be leveraged in a wide range of areas, from business to everyday life.

Design Generation AI

Based on entered text and image data, it automatically generates layouts and background color schemes.

As the word "design" suggests, this field directly affects human "sensibilities." Therefore, comparisons with the quality of human-created designs remain a challenge. However, like image generation AI, it is attracting attention for its role in supporting idea creation.

3D Model Generation AI

By entering text or images as reference data, you can generate 3D models (three-dimensional CG used in architectural design, games, movies, etc.).

3D models are already being used in various fields such as Vtuber avatars and simulations for the social implementation of autonomous driving. Previously, they needed to be created using specialized software, requiring a certain level of knowledge and experience.

However, with the emergence of generative AI, anyone can easily create 3D models. This is attracting attention not only for general use but also in industries where improving productivity is an urgent need, such as the construction industry.

Video Generation AI

This is an evolution of image generation AI. It can not only automatically generate videos from entered text and images but also reconstruct entered videos into completely different videos, or generate promotional videos for blog articles by simply entering the URL of the article.

As mentioned later, even with the same type of generative AI, the accuracy and capabilities of the content vary depending on the model (how the content is generated) used.

Voice Generation AI

This can output (read aloud) entered text as voice, or generate new voices by inputting voice data itself.

For example, "Google Cloud Text-to-Speech" is a voice conversion service that analyzes and reads entered text in seconds. It supports multiple languages, including Japanese, and uses machine learning to synthesize natural-sounding voices, rather than simply reading aloud.

Music Generation AI

You can generate music by simply entering text like "music suitable for studying," or compose music to match lyrics you come up with.

The music field is prone to copyright issues, so caution is needed. However, it has been put into practical use to the point that some overseas artists have actually used music generation AI to compose music.

Mechanisms and Models of Generative AI

With so many different types of generative AI being created, how does it actually generate content from entered text and other information?

The basic mechanism of generative AI is not that the system is really thinking like a person about the entered data. Rather, it converts the entered information and generates content according to a pre-built thought circuit (neural network).

That thought circuit is called a "model," and there are several types. Here, we will introduce some of the most representative AI models.

GAN (Generative Adversarial Networks)

GAN (Generative Adversarial Networks) is used as a model for image generation AI.

In simple terms, it is a model that learns characteristics from input data and generates pseudo-data.

GAN is a model that generates content through two conversion devices: "Generator (Generative Network)" and "Discriminator (Discriminative Network)." In that process, the accuracy is improved by having the Generator and Discriminator compete with each other, hence the term "adversarial generation."

It is easier to understand if you think of the Generator as a counterfeiter trying to draw something that looks exactly like the real thing, and the Discriminator as an appraiser who judges it.

In short, in order to create data and information that is almost identical to the real thing from the input data, content is generated by competing in an iterative manner. This makes it possible to generate high-resolution images from low-resolution images or to generate completely new images from text.

Diffusion Model

Diffusion models are also used as models for image generation AI.

It is a model called "Noise Reduction Diffusion Probabilistic Model." In other words, it is a model that gradually adds random noise to an image until it becomes completely noise, then removes the noise, and learns to minimize the difference between the noise-removed image and the original image.

Although it is the same image generation model, Diffusion is positioned as a more advanced version of GAN and can generate higher resolution images.

GPT (Generative Pre-trained Transformer)

Some of you may be familiar with this from ChatGPT. It is a high-performance language model announced by OpenAI in the United States.

You often see GPT-3 written, but the number after it can be thought of as a version (of course, the model mechanism itself may be different depending on the version).

To summarize the GPT mechanism, it is a "next-generation language model in which AI learns a large amount of text data, acquires the ability to generate sentences and understand language, and becomes able to make predictions and inferences."

It is characterized by its ability to generate natural-sounding sentences as if they were created by humans. Its high accuracy has made ChatGPT a global phenomenon, and it is expected to play an even greater role, not only in summarizing local documents and creating new ideas.

*By the way, the latest version is GPT-4.

Examples of Services Using Generative AI

Besides ChatGPT, there are many services in the world that use generative AI. Here are some representative services:

Image Generation: Stable Diffusion

https://stablediffusionweb.com/

Stable Diffusion is an image generation AI released by Stability AI in 2022.

Due to the high quality of the generated images and the speed of generation, it is the most famous service among AIs that generate images from text.

Text Generation: ChatGPT

https://openai.com/chatgpt

This is an interactive AI chat service that allows you to enjoy natural conversations as if you were talking to a human. It is one of the generative AI services that is attracting worldwide attention.

Unlike conventional chatbots, it can answer a wider range of questions than those set in advance, and it is characterized by its ability to have natural conversations with people.

Design Generation: Canva

https://www.canva.com/ja_jp/login/

Canva, which is well-known as a design tool, can generate images from text input.

In addition, it becomes personalized as you use it, and designs tailored to your personal tastes and preferences are generated.

AI is also used to make it easy to create designs, such as a feature that automatically adjusts the placement of elements when the size of a created design is changed.

3D Model Generation: Shap-E

https://github.com/openai/shap-e

This is a 3D model generation AI provided by OpenAI, which announced ChatGPT.

It can generate 3D models not only from text but also from images, and anyone can use it for free.

Video Generation: Runway Gen-3

https://research.runwayml.com/

This is a service that allows you to generate videos from text and images.

Due to the high quality of the generated content, it is one of the most watched AI services for video generation.

Voice Generation: IBM Watson Text to Speech

https://www.ibm.com/jp-ja/cloud/watson-text-to-speech

This is a service provided by IBM that can convert written text into natural-sounding speech in various languages within Watson Assistant.

It supports various languages and voices, and is characterized by the "custom voice" feature that allows you to record your own voice and model it.

Music Generation: SOUNDRAW

https://soundraw.io/ja

This is a music generation AI service that allows you to generate music that matches your creation intentions by simply selecting the mood, genre, and length.

Even people without musical knowledge can easily edit the song, such as shortening the intro or changing the position of the chorus. Why not create your favorite song while matching the song with the video?

Benefits of Using Generative AI

So far, we have talked about what generative AI is, its mechanisms, and its types. Here, we will introduce four benefits of using generative AI again.

Automation of Mechanical Tasks

The first benefit is that it can automate mechanical tasks.

This is one of the most easily imagined benefits when you hear about AI, but you will feel its benefits even more in the future.

For example, even a small task such as creating a template for a thank-you email to a business partner can be a hassle for those who send several to dozens of emails a day and want to send emails more easily.

In such cases, you can expect to significantly improve efficiency by having a text generation AI such as ChatGPT create the text, and you can allocate more resources to higher value-added tasks.

Support for Idea Creation

The second benefit is that it can play the role of supporting idea creation.

Generative AI is not just an exchange of information, but it also outputs final deliverables based on the content of instructions and input information. Therefore, by setting the information to be input in detail, it is possible to increase the variations of the output content.

For example, when creating a new web advertisement banner, you can obtain several samples by using image generation AI or design generation AI, and you can create a higher quality banner by discussing and brushing up based on the samples.

Reduced Burden of Content Creation

The third benefit is the reduced burden of content creation.

Conventionally, the person in charge had to create everything from scratch based on predetermined policies, but by using generative AI, content can be created in a matter of seconds to minutes.

For example, when brainstorming article titles and headings for owned media, you can create them more efficiently by using text generation AI.

Early Prototype Creation

The last benefit is that it enables early prototype creation.

The content itself is similar to the "reduced burden of content creation" explained earlier, but since you can create various variations of content without spending time, you can make more plausible decisions by discussing the generated content.

For example, even for ideas for developing new products that are considered difficult, if you use generative AI, you can get a quick output, and then you can realize the determination of new products → test development more quickly by examining and discussing them.

How to Use Generative AI

Content Generation Assistance

For example, when starting SNS operation as a new customer acquisition channel, you can generate icon images that can be used for SNS accounts by inputting information such as brand image and service overview by using image generation AI. It can also be applied to the generation of logo images and background materials based on the image.

Conventionally, converting image information into images (designs) requires a high level of creativity, but by using generative AI, anyone can create content at a certain level.

Text Creation

The second example of utilization is text creation.

For example, when sending a sales email for a new product or service to existing customers, you can create the email text in an instant by using text generation AI and inputting detailed product names and sales points.

It is difficult to verbalize the appeal of products and services such as sales points and turn them into easy-to-understand sentences from scratch, but simplification can be expected by using generative AI.

It can also be applied to summarizing long sentences or summarizing meeting minutes.

Construction of Chatbots with Built-in AI

Conventionally, chatbots could only respond to pre-set questions, but by incorporating text generation AI, they can now answer complex questions from users.

It is also expected to reduce the burden on in-house customer support staff.

Brainstorming Ideas

The fourth example of utilization is brainstorming ideas.

For example, when considering the sales strategy of existing products, you can catch up on ideas for more effective strategies by using a text generation and interactive AI such as ChatGPT and inputting information such as the product being sold, the existing target customer base, and current issues.

In addition, you may be able to gain insights and discoveries that you have never imagined before from the outputted ideas.

Future Predictions and Insights

Generative AI can predict demand, sales volume, and customer purchasing trends based on past data and trends, similar to traditional AI.

What is different from the past is that, as mentioned above, interactive AI such as ChatGPT can provide not only simple numerical values but also insights derived from those values.

Disadvantages and Precautions of Using Generative AI

Generative AI is highly convenient and is expected to be used more and more in society, but the fact is that there are many points to be aware of.

In addition to risks such as information leakage, guidelines for safe operation and legal interpretations are ambiguous due to rapid technological progress.

This time, we will introduce four points that you should pay particular attention to.

Pay Attention to Infringement of Rights such as Copyrights and Trademarks

The current guidelines state that "simply inputting the works of others into AI does not constitute copyright infringement," but "if the generated data is the same as or similar to the input data or existing data (copyrighted works), the use of the generated product may constitute copyright infringement of the copyrighted work."

Even if the person does not have malicious intent, be sure to check whether the content you generate infringes on rights such as copyrights and trademarks before actually using it.

The Information Entered into Generative AI May Leak to Others

The guidelines call for "not entering highly confidential information such as personal information, confidential information, and secret information."

The data entered by users is used for learning the AI model, and there is also the possibility that the corresponding server will be cyberattacked and information will be leaked.

Be careful when inputting information

The Hurdle of Differentiating from Content Generated by Others May Rise

While assisting content creation is a benefit, anyone can now create content at a certain level, so if you use the generated content as is, the content may be similar to that of others.

In addition, "creativity," which is necessary for differentiation, will be more required, so it is conceivable that designers who do not meet such requirements will be eliminated.

There is a Risk of Increasing Fake Content

Generative AI can create everything from images and news that closely resemble the real thing to websites.

For example, posting images that resemble celebrities or acquaintances on social media may result in the dissemination of information that is different from the actual situation around the world.

In addition, it is possible to generate "spoof icon images and collages," which have become a problem in recent years, which is highly likely to lead to slander of the person.

As described above, there are points to be aware of in the generated content, but it is difficult to determine the authenticity of the content itself, so one of the challenges facing society is the need to consider new criteria for making appropriate judgments.

Summary

Generative AI has great benefits that can be enjoyed by utilizing it, but there are also points to be aware of.

However, this is also true for the PCs and the Internet that we use on a daily basis, so I think that understanding and utilizing it correctly rather than keeping it away just because it is unknown will greatly contribute to the growth of companies, individuals, and even Japanese society.

In the business world, it can be a major risk to be left behind by surrounding competitors due to the "inability to use" new technologies, so I hope that you will deepen your understanding of generative AI through this article.