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Generative AI

  • Glossary

“Generative AI”

This glossary explains various keywords that will help you understand the mindset necessary for data utilization and successful DX.
This time, let's take a look at "generative AI," a technology that is currently attracting attention and is expected to have a major impact on the use of IT in business in the future.

What is generative AI?

Generative AI is a system that uses machine learning to learn the characteristics and structure of data from given training data, and can generate data with similar characteristics and structure based on instructions such as keywords.
While traditional machine learning has often been used for tasks such as discerning given data or making predictions, generative AI can generate images, text, program code, and more based on instructions, and is expected to expand the use of machine learning in applications and fields where it has not been used before.

The current reality of artificial intelligence and the reality of conventional "AI utilization"

The term generative AI comes from the fact that it handles a different type of task than traditional "AI applications." So, what exactly is traditional AI applications (machine learning applications)? To understand this, we will first provide a brief explanation of "traditional AI applications."

Artificial intelligence research has not fundamentally been an effort to achieve human-like intelligence.

There are many opinions among experts about "what is AI," and there is no set definition of the term artificial intelligence. Not only is it difficult to define AI, but the very definition of intelligence itself remains a difficult question. Meanwhile, many people may naively assume that the term "artificial intelligence" refers to efforts to achieve human-like intelligence, specifically efforts to achieve intelligence like that of Astro Boy.

In the early days of AI research, the goal was to achieve human-like intelligence. However, it soon became clear that this was extremely difficult to achieve. As a result, research and development has since focused on more realistic goals. Artificial intelligence research has come to refer to various efforts to enable computers to perform more advanced and abstract processing, going beyond simply calculating and returning results as specified in a program.

Due to this background, the initially too difficult goal of "human-like AI" is sometimes distinguished by the term "Artificial General Intelligence (AGI)." In other words, much of the research into AI has not been of that nature. In reality, what has been researched and developed in the field of AI is not what most people would consider "intelligence," but rather something that can make slightly advanced "judgments."

Machine Learning

Artificial intelligence in general is an effort to achieve some kind of intelligent processing, but there have been many different ways of achieving this. For example, there are approaches such as solving logically formulated problems, solving problems with clearly defined rules like chess, and making decisions based on a large number of given decision-making rules.

One of the most talked about technologies these days is "machine learning," which learns from given training data and is then able to make certain judgments. In particular, when artificial intelligence is a hot topic these days, most of the focus is on "machine learning" that uses "neural networks," a method that mimics the structure of neurons in the brain.

The emergence of deep learning has led to rapid adoption

Machine learning has attracted attention due to the rapid improvement in performance that came with the advent of "deep learning," a type of machine learning that uses neural networks. Until then, machine learning often did not perform well, and it was often difficult to train it from training data. However, it has begun to demonstrate high performance approaching human judgment in some tasks, such as "the task of determining what is in an image" and "the task of finding where a human face is in an image," and the use of machine learning (deep learning) in practical applications has rapidly progressed.

What makes "generative AI" different from conventional "AI utilization"

Traditionally, machine learning has been used for tasks such as "discrimination tasks," "anomaly detection tasks," and "prediction tasks." This is the ability to learn using training data in advance, receive data from outside, and make "judgments" or "predictions" based on that data.

In contrast, "generative AI" is a type of AI where the machine learning engine itself accomplishes the task of "generating new data and outputting it to the outside world." The reason for the "generative" part of the name is that "data generation" is what sets it apart from traditional machine learning.

"Generative AI" first came to attention as "image generating AI"

However, the concept of generative AI itself is not a recent invention. Ideas and implementations of machine learning to generate content have existed for a long time, but they have simply not been widely discussed until now.

I think the first time "generative AI" became a hot topic in the public eye was with "DALL·E," a generative AI that generates images, whose existence was announced in 2021 and became publicly available in 2022. Later, in 2022, "Stable Diffusion," an image-generating AI that was released as open source and made available to anyone, also became a hot topic.

When you describe the image you want as text data (called a prompt) and provide it as input, an image that matches your instructions is generated and given as output, which is a huge surprise for users, and this is why generative AI is starting to become a hot topic.

"ChatGPT" will make "generative AI" a big hit

Furthermore, in November 2022, OpenAI, the developer of DALL·E, released ChatGPT, a new tool that would bring the generative AI boom to a decisive stage. ChatGPT also allowed users to input requests (prompts) to the generative AI in text, and the AI would then respond by generating text data or source code.

This system also allows users to input their requests as text data (prompts) and output corresponding text data, and its operation is similar to that of image generation AI.

However, ChatGPT can also generate text data based on the user's input history and respond to it, providing an unprecedented user experience in which users can "interact with the computer in natural language," "give instructions in natural language and the computer will return the results they want," and "give instructions in natural language for the program they want to create and the source code will be generated." This has caused an unprecedented buzz.

The impact that generative AI can have on business

The main reason why generative AI has become such a hot topic in society is that the user experience is so shocking that people are amazed just by using it. While many people intuitively thought it was amazing and expected it to bring about major changes, it was not clear what it could be used for specifically or what changes it would bring about.

We are still feeling our way around how we can use this technology in practical business practices and produce results in the future.
*To support the efforts of companies in this situation, we also offer a "GenerativeAIImplementation Support Service."

Generative AI can handle different types of tasks than traditional machine learning, so it is expected to replace or assist tasks traditionally performed by humans in different fields. In addition to the discrimination and prediction tasks for which it has already been used, it is expected that machine learning will be able to take on tasks such as content creation, document creation, and partial replacement or assistance for programming tasks by utilizing its image and text generation capabilities.

The differences and similarities with conventional AI (machine learning)

The ability to generate images and text has long been considered a "task that seems to require creativity" and was previously thought to be something only humans could perform. Generative AI has the potential to change the traditional "relationship between humans and IT" because it performs actions that appear to achieve human creativity through IT. For this reason, the emergence of generative AI is seen as a disruptive change.

On the other hand, it is no different from traditional machine learning in that it learns the relationship between input and output through given training data, and operates and outputs based on that learning. Also, in discrimination tasks where it is already being used in practice, it has demonstrated the ability to replace humans.

For example, machine learning has long been unable to match humans in terms of character recognition ability, but now it is achieving a higher level of recognition ability than humans. This can be seen as an expansion of the scope of changes that have been occurring since the advent of deep learning.

Another disruptive change brought about by generative AI is the change in the user experience, where people can interact with computers in natural language. Until now, in most cases, not only the use of machine learning but also the use of IT, it was necessary to have programming skills, or at least to understand how to use specialized software before it could be used. The ability to operate when communicated in natural language has the potential to overturn the assumptions behind traditional IT use.

This change is sometimes likened to natural language becoming a programming language. However, to get the desired results from generative AI, the input text (prompt) may require unique ingenuity and consideration. This could ultimately be seen as requiring unique usage skills like programming, or it could be that it is more easily used by issuing instructions using regular programming.

Things to consider when using generative AI

What should you consider when considering whether you can use generative AI in your business?

What can we generate from what inputs to make it useful for our business?

When we hear the term "generative AI," we tend to imagine specific implementations that are currently popular, such as those that generate images or chat-style systems. However, generative AI itself is something that receives some kind of input and generates an output accordingly, and has much more diverse possibilities.

For example, there are already tools that generate videos and music. There are many types of data that would be useful for your company if they were automatically generated. The input side is not limited to text data either. There are already generative AI that can accept images as input, so it's not impossible to use other types of data as input.

Think about what kind of input/output generative AI could transform your business. For example, a generative AI could take a photo of a plant you want to pot and output a blueprint and manufacturing parameters for 3D printing the perfect flower pot for that plant.

[Points to consider]

  • What can be input and generated that will have an impact on our business?

Generative AI may need to be trained

Many excellent generative AIs have already been developed and released around the world, but they are general-purpose engines and do not necessarily have the capabilities we need.

To prepare a generative AI with the capabilities you need, you may need to train it from scratch or perform additional training on an existing generative AI to make adjustments. To do this, you will need training data to perform the necessary learning.

Naturally, data is required for this. It is desirable to secure the data necessary to prepare the generative AI you need, store it in a usable state, and put in place a system that can use that data for learning as needed. If you can prepare an engine with performance that differs from that of other companies, it will be easier to differentiate your business.

[Points to consider]

  • Can we achieve what we need with generative AI that is already available?
  • What data do we need to train the generative AI we need?
  • Secure and store that data
  • That data can be used to train generative AI as needed.

Where to incorporate generative AI into your company's IT systems

Currently, generative AI is often used by people typing in text (prompts). However, because it is an engine that generates output from input, it can be used in a variety of different ways, and it can also be used as part of automated processing.

Consider the flow of data in your business and think about where and how you can use generative AI, and what inputs you can use. You may also need to go through a process of trial and error to determine where and how to use it, including whether the initiative will produce business results.

[Points to consider]

  • In what areas of IT use in your company's business can generative AI be used?
  • Or, in combination with trial and error within the business itself, where can we use generative AI to achieve results, and can we create an IT environment that allows for trial and error?

Where can generative AI's human interaction capabilities be utilized?

Generative AI has the characteristic of being a new user interface that allows people to use IT using natural language. Not only does it allow people without programming skills to directly interact with and utilize IT, but it also makes it possible to provide a UI that feels like natural language, such as for use in user support.

Think about the workflow and overall IT processing of your business and consider where such a user interface would be useful. There must be some areas where human involvement is desired, or where generative AI would be effective in assisting human work.

[Points to consider]

  • When it comes to using IT in your business, where would incorporating a natural language UI be most effective?
  • Are there any areas where generative AI can assist people effectively? Are there any areas where it would be possible to make a difference if it could be used without programming skills? Are there any areas where a natural language UI would be more popular with system users?

"Connecting" technology that effectively utilizes generative AI by linking it with external parties

I think there are many people who have tried using generative AI chat and thought it was amazing, but didn't know how to use it. I hope this article has given you at least a little idea of what generative AI is and how it can be used.

However, the next problem is that even if you understand the potential for use, you may wonder, "Can we do it ourselves?" You may think that unless you have an in-house engineer with sufficient skills to understand generative AI and engage in full-scale programming and system development, you won't be able to utilize it.

Fortunately, there are methods that allow efficient development using only a GUI to flexibly combine and utilize a generative AI engine with the diverse IT systems and data within an organization.These are "connecting" technologies such as "DataSpider" and "HULFT Square," also known as "EAI," "ETL," and "iPaaS."

Can be used with GUI only

Unlike regular programming, there is no need to write code. By placing and configuring icons on the GUI, you can achieve integrated processing with a wide variety of systems and data.

Being able to develop using a GUI is also an advantage

No-code development using only a GUI may seem like a simple compromise compared to full-scale programming, but if it can be developed using only a GUI, it will become possible for on-site personnel to proactively utilize generative AI themselves.

The people who understand the business best are the people on the front lines. They can rapidly create what's needed, such as data utilization, generative AI, and cross-departmental collaboration processes, which is an advantage over a situation where they have to explain things to engineers and ask for help every time something needs to be done.

Full-scale processing can be implemented

There are many products that claim to allow development using only a GUI, but some people may have a negative impression of such products as being too simple.

It is true that things like "it's easy to make, but it can only do simple things," "when I tried to execute a full-scale process it couldn't process and crashed," or "it didn't have the high reliability or stable operating capacity to support business operations, which caused problems" tend to occur.

"DataSpider" and "HULFT Square" are easy to use, but also allow you to create processes at the same level as full-scale programming. They have the same high processing power as full-scale programming, as they are internally converted to Java and executed, and have a long history of supporting corporate IT. They combine the benefits of "GUI only" with the proven track record and full-scale capabilities for professional use.

What is necessary for a "data infrastructure" to successfully utilize data?

Of course, the ability to connect to a wide variety of data sources is necessary, and high processing power to process large amounts of data is also required to fully support actual business operations. At the same time, flexible and rapid trial and error led by the field is also essential.

Generally speaking, if you want high performance and advanced processing, the tool will tend to be difficult to program and use, while if you want ease of use in the field, the tool will tend to be easy to use but have low processing power and can only perform simple processing.

In addition, it is also desirable that the candidate has advanced access capabilities to a wide variety of data sources, especially legacy IT systems such as mainframes and non-modern data sources such as on-site Excel, as well as the ability to access the latest IT such as the cloud.

There are many methods that meet just one of these conditions, but to successfully utilize data, all of them must be met. However, there are not many methods for achieving data integration that are both usable in the field and have the high performance and reliability of a professional tool.

No need to operate in-house as it is iPaaS

DataSpider can be operated securely on a system under your own management. With HULFT Square, a cloud service (iPaaS), this "connecting" technology itself can be used as a cloud service without the need for in-house operation, eliminating the hassle of in-house implementation and system operation.

Related keywords (for further understanding)

  • EAI
    • It is a concept of "connecting" systems by data integration, and is a means of freely connecting various data and systems. It is a concept that has been used since long before the cloud era as a way to effectively utilize IT.
  • ETL
    • In the recent trend of actively working on data utilization, the majority of the work is not the data analysis itself, but rather the collection and preprocessing of data scattered around, from on-premise to cloud. This is a means to carry out such processing efficiently.
  • iPaaS
    • A cloud service that "connects" various clouds with external systems and data simply by operating on a GUI is called iPaaS.

If you are interested in our "Connecting" initiative,

If you are interested, please try out our products that solve IT system and business problems by using the concept of "connecting."

The ultimate tool for connecting data: DataSpider, data integration software

"DataSpider," data integration tool developed and sold by our company, is a "connecting" tool with a long history of success.

Unlike regular programming, development can be done using only the GUI (no code), without writing code. This means that it can be used by business personnel who have a good understanding of the business and can grasp the specific issues surrounding their company's silo structure.

There are many tools available that allow simple integration, but this tool is easy to use, even for non-programmers, as it only has a GUI, and it also has "high development productivity" and "full-scale performance that can serve as the foundation for business (professional use)." It can smoothly solve the problem of "connecting disparate systems and data," which is hindering the successful use of IT.

We offer a free trial version and hold online seminars where you can try out the software for free, so we hope you will give it a try.

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