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What is the use of blockchain technology?
What is the use of blockchain technology actually has very broad application prospects?
Blockchain technology is a core innovative database technology used by almost all cryptocurrencies. By distributing the same database copy throughout the network, it is very difficult for hackers to crack or deceive the system. Although cryptocurrency is currently one of the most popular blockchain applications, in fact, the technology has the potential to provide services for a very wide range of applications.
What is blockchain?
The core of the blockchain is a distributed digital ledger that can store any type of data, including cryptocurrency transactions, NFT ownership, and Defi smart contracts.

What is blockchain
Although any traditional database can store this kind of information, the blockchain is unique in its complete decentralization. Compared with a system maintained by a central administrator (such as an Excel spreadsheet or a bank database) in a central organization, many identical copies of the blockchain database are stored on multiple computers distributed in the network, and these individual computers are called Is the node.
How does the blockchain work?
The name “blockchain” is not a whim. The digital ledger is usually described as a “chain” composed of a single “data block”. When new data is added to the network, a new “block” is created and appended to the “chain”, which involves all nodes updating the version of their blockchain ledger to make it the same.
How to create these new blocks is the key to why the blockchain is considered to be highly secure. Before adding new blocks to the ledger, most nodes must verify and confirm the legitimacy of the new data. For cryptocurrencies, they may involve ensuring that a new transaction in a block is not fraudulent, or ensuring that the coin is not used more than once. This is different from an independent database or spreadsheet where anyone can make changes without supervision.
C. Neil Gray, the partner of Duane Morris LLP’s financial technology business unit, said: “Once a consensus is reached, the block will be added to the chain and the transaction will be recorded in the distributed ledger. The blocks are securely connected, Form a secure digital chain from the creation of the ledger to the present.”
Transactions usually use encryption technology for security protection, which means that nodes need to solve complex mathematical equations to process transactions.
Sarah Shtylman, a fintech and blockchain consultant at Perkins Coie, pointed out that “as a reward for their efforts in verifying changes to shared data, nodes usually receive a new amount of local currency in the blockchain, for example, the Bitcoin blockchain New Bitcoin on the Internet”
Blockchains are often divided into public chains and private chains. In public blockchains, anyone can participate, which means that they can read, write or audit data on the blockchain. It is worth noting that Without the authority of a control node, it is difficult to change the transactions recorded in the public blockchain.
At the same time, the private blockchain is controlled by an organization or group, and only it can decide who is invited to the system, and it has the right to modify the blockchain. In addition to being scattered on multiple nodes to increase security, this private chain is more similar to an internal data storage system.

How is the blockchain used?
Blockchain technology is used for many different purposes, from providing financial services to managing voting systems.
1. Cryptocurrency
The most common use of blockchain today is as the core of cryptocurrencies, such as Bitcoin or Ethereum. When people buy, exchange or use cryptocurrency, the transaction is recorded on the blockchain. The more people use cryptocurrency, the more widespread the blockchain will become.
Patrick Daughty, the senior partner of Foley & Lardner and head of the blockchain task force, pointed out that “due to the instability of cryptocurrencies, they have not been used in large quantities to purchase goods and services. Retail customers widely provide digital asset services, and this situation is changing.”
2. Banking
In addition to cryptocurrency, blockchain is also used to process transactions in fiat currencies such as the U.S. dollar and euro. This may be faster than sending money through a bank or other financial institution because these transactions can be verified and processed faster outside of normal office hours.
3. Asset transfer
Blockchain can also be used to record and transfer the ownership of different assets, such as the currently very popular NFT as a representative of the ownership of digital art and video.
However, blockchain can also be used to handle the ownership of real assets, such as real estate and vehicle deeds. Both parties of one party first use the blockchain to verify that one party owns the property and the other party has the money to buy it, and then they can complete and record the sale on the blockchain.
Through this process, they can transfer the property contract without manually submitting documents to update the records of the local county government, which will be updated instantly in the blockchain.
4. Smart contract
Another important direction of blockchain innovation is to automatically execute contracts, usually called “smart contracts.” Once the conditions are met, these digital contracts will automatically take effect. For example, once the buyer and seller meet all the specific parameters of the transaction, the payment for the goods can be executed immediately.
Gray pointed out: “We see the huge potential in the field of smart contracts, using blockchain technology and coding instructions to automate legal contracts.” Smart legal contracts correctly coded on distributed ledgers can minimize or eliminate the external need for a third party to verify performance.
5. Supply chain monitoring
The supply chain involves a lot of information, especially when goods are transported from one place in the world to another. With traditional data storage methods, it is difficult to find the source of the problem, such as where the supplier’s inferior goods come from. Storing this information on the blockchain will make it easier to follow and monitor the supply chain, such as IBM’s FoodTrust, which uses blockchain technology to track the entire process of food from harvest to consumption.
6. Voting
Experts are studying how to use blockchain to prevent fraud in voting. In theory, blockchain voting will allow people to submit votes that cannot be tampered with, and it can also eliminate the need for people to manually collect and verify paper votes.

Advantages of blockchain
1. Higher transaction accuracy
Because transactions in the blockchain must be verified by multiple nodes to reduce errors, if one node makes an error in the database, other nodes will see the difference and capture the error.
On the contrary, in a traditional database, if someone makes a mistake, it may be easier to pass. In addition, each asset is individually identified and tracked on the blockchain ledger, so it is impossible to pay it twice. One person overdrafts the bank account and spends a sum of money twice in the block It cannot be established in the chain field.
2. No intermediary required
Using blockchain technology, both parties in a transaction can complete the transaction without going through a third party, which saves time and costs to intermediaries such as banks.
Shtylman pointed out: “Blockchain technology has the ability to bring higher efficiency to all digital businesses, and enhance the financial capabilities of the population in areas where there are no banks or under-banked areas in the world, thereby providing power for a new generation of Internet applications.”
3. Extra safety
In theory, decentralized networks, such as blockchain, make it almost impossible for people to conduct fraudulent transactions. Forging transactions will require hacking every node and changing every ledger. Although this is not necessarily impossible, many cryptocurrency blockchain systems use PoS consensus mechanism or PoW consensus mechanism transaction verification methods, which makes it difficult to increase fraudulent transactions, and does not meet the maximum of participants. interest.
4. More effective transfer
Thanks to the round-the-clock operation of the blockchain, people can carry out financial and asset transfers more effectively, especially internationally. They do not need to wait for several days, do not need banks or government agencies to solve all problems manually.

Disadvantages of blockchain technology
1. The limit of processing transactions per second
Considering that blockchain technology relies on a larger network to approve transactions, its moving speed is limited. For example, Bitcoin can only process 4.6 transactions per second, while Visa can process 1,700 transactions per second. In addition, more and more transactions will cause network speed problems. Before that, scalability was a challenge.
2. High energy costs
Having all nodes working to verify transactions consumes more power than a single database or spreadsheet. This not only makes blockchain-based transactions more expensive but also creates a huge carbon burden on the environment.
Because of this, some industry leaders have begun to abandon certain blockchain technologies, such as Bitcoin. Elon Musk recently stated that Tesla will stop accepting Bitcoin as a means of payment, partly because he is worried about Bitcoin’s environmental damage. On May 13, 2021, Elon Musk tweeted that the energy usage trends in the past few months have been crazy.
3. Risk of asset loss
Some digital assets are protected by encryption keys, such as encrypted currencies in blockchain wallets. Users need to keep this key carefully.
Gray said: “If the owner of a digital asset loses the private cryptographic key that allows them to access the asset, there is currently no way to recover it. The asset has disappeared forever.” Because the system is decentralized, you cannot call The central institution like the bank requested a re-visit.
4. Potential illegal activities
The decentralization of blockchain adds more privacy and confidentiality, which unfortunately makes it attractive to criminals. It is more difficult to track illegal transactions on the blockchain than through bank transactions linked to names.

How to invest in blockchain?
In fact, you cannot invest in the blockchain itself, because it is just a system for storing and processing transactions. However, you can use this technology to invest in assets and companies.
Gray said: “The easiest way is to configure cryptocurrencies, such as Bitcoin, Ethereum, and other tokens running on the blockchain.”

Another option is to use this technology to invest in blockchain companies. For example, Santander Bank is experimenting with blockchain-based financial products. If you are interested in getting access to blockchain technology in your portfolio, you can buy some shares.
To take a more diversified approach, you can buy an exchange-traded fund (ETF) that invests in blockchain assets and related companies. For example, Amplify Transformational Data Sharing ETF (BLOK), which invests at least 80% of its assets in blockchain companies.
Dilemma
Despite the bright future of blockchain, it is still a niche technology. Gray believes that blockchain may be used in more situations, but it depends on future government policies. “It remains to be seen when and whether regulatory agencies such as the US Securities and Exchange Commission will act. But one thing we can be sure of is that our goal is to protect the market and investors.
Shtylman compares the current development of the blockchain to the early stages of the Internet. “It took us 15 years to see the first version of Google and more than 20 versions of Facebook. It is difficult for us to predict how far blockchain technology will develop in the next 10 or 15 years, but just like the Internet, It will significantly change the way we trade and interact in the future.”
Difficulties remain, especially in terms of transaction restrictions and energy costs, but for investors who see the potential of this technology, blockchain-based investments are worth bets.
The original report comes from David Rodeck and John Schmidt. David Rodeck is a financial writer in Delaware, specializing in investment, insurance, and optimizing retirement plans. John Schmidt is a Forbes consultant and assistant editor of “Investment and Retirement” magazine. The Chinese version is compiled and compiled by the chain market team, and the English copyright belongs to the original author. For a Chinese reprint, please contact the compiler.

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July 4, 2024
Why is the Metaverse in the medical field attracting attenction? Introduction examples and future issues
The use of the metaverse in the medical field is expanding. It is estimated that by utilizing 5G and new technologies, the growth rate of the Metaverse will be over 30% by 2030.
Therefore, in this article, we will explain why the Metaverse is attracting attention in the medical field, what the challenges are in promoting it, and examples of what has been done to date.
Metaverse in the medical field is expected to grow by more than 30% by 2030
The use of the Metaverse in the medical field is attracting attention. This is because the use of virtual space is thought to have a wide range of uses, such as enabling medical procedures that would be difficult in the real world, and medical examinations using avatars.
In 2021, the market size of Metaverse in healthcare was not very large. However, in the medical field, the metaverse is expected to grow significantly by 2030, as it is believed that it can be used in a wide variety of ways.
The use of the Metaverse in the medical field is expanding, with examples of surgeries being performed using AR smart glasses.
Why metaverse medicine is attracting attention
There are three reasons why metaverse medicine is attracting attention. Let’s look at them one by one.
Introduction can eliminate regional disparities in medical quality
In recent years, “medical disparities” have been occurring all over the world, including Japan.
For example, medical institutions are concentrated only in urban areas, making it difficult to receive advanced medical care in rural areas. According to the Ministry of Health, Labor and Welfare’s “Measures to prevent uneven distribution of doctors”, the regional uneven distribution of doctors continues, and some regions are experiencing a shortage of doctors.
Metaverse is also expected to solve these problems. This is because VR space not only allows us to connect with people around the world in real time, but also makes it easier to provide medical services to patients in remote areas. Therefore, we can expect to eliminate regional disparities in the quality of medical care.
Advances in 5G and communication technology have made real-time remote surgery possible.
In the field of digital medicine, it is expected that paper prescriptions will disappear in the next few decades and transition to digital treatment in the metaverse space.
In order to perform digital medicine using this metaverse space without any problems, an environment is required that allows for high-speed and high-capacity communication without problems.
What is expected of this is the communication technology called 5G. It has the characteristics of “high-capacity, high-speed communication,” “high reliability and low latency,” and “multiple simultaneous connections.”
Even now, it has a proven track record of being used for real-time remote control of surgical support robots.
5G, which is capable of faster speeds and higher capacity communications, is said to be able to be used in digital medicine, and is expected to become a new field of digital medicine.
Metaverse medical care has become a system that is easy for patients to consult.
In the Metaverse space, you create an “avatar” that is your alter ego and communicate with it.
Communication using this avatar is attracting attention as a system that makes it easy for patients to consult.
Kohei Yoshioka, a psychiatrist who runs Metaverse Clinic, a medical consultation community on the Metaverse, says the following.
“I feel that in the metaverse space where we use alter egos called avatars, it is easier to self-disclose because there is no need to actually communicate face-to-face.”
Currently, we are using the Metaverse space as a place for consultation rather than medical treatment, but I believe that by making it a place for communication, it will be easier for patients to seek advice.
In this way, metaverse medicine is also beneficial to patients.
Three medical fields aiming to utilize the metaverse
The three specific medical fields that aim to utilize the metaverse include:
- Medical (VR training)
- Pharmaceuticals (sharing of medical product information)
- Drug discovery (time and cost reduction)
Medical (VR training)
In the medical field, the metaverse of VR training is being utilized.
Specifically, this includes training for surgeons regarding surgical procedures and social skills training.
For example, in the United States, we prepare a real surgical scenario, create a virtual operating room in the metaverse space, and actually perform the procedure in that space.
The virtual operating room also helps in training for complex procedures. Additionally, in Japan, Jolly Good Inc. is conducting social skills training on developmental disorders on Metaverse.
We have progressed to the stage where we can promote lifestyle support and employment support for patients.
Pharmaceuticals (sharing of medical product information)
In the pharmaceutical industry , the Metaverse is being used as a place to share medical product information and support communication between doctors and patients.
Specifically, we are using virtual MR to effectively share medical product information.
For example, at Tsumura Co., Ltd., a major pharmaceutical company, AI presents content that matches the story to medical professionals in a 3D version of MR.
Additionally, Astellas Pharma Inc. has begun experimenting with using virtual MR to project patients’ skeletal information in 3D into the real world. For example, it has the benefit of making it easier for patients to understand their own health status when they see their bone density decreasing.
Drug discovery (time and cost reduction)
Drug discovery is always time-consuming and costly. Metaverse is being used to reduce the time and cost of drug discovery.
At Chugai Pharmaceutical Co., Ltd., researchers use the software “Nanomu” to discover drugs in the metaverse.
Things that could only be confirmed in two dimensions can now be confirmed in 3D models down to the atomic level, making it possible to create new ideas and test whether they are actually effective in the metaverse space, something never seen before. It provides a sense of speed.

How will the two types of telemedicine (including metaverse medicine) change with 5G?
There are two types of telemedicine. The first is medical care in which doctors and patients in remote locations use tools to communicate in real time.
The other is D to D (Doctor to Doctor), in which other doctors provide online support for the medical care of doctors in remote locations, and D to P (Doctor to Patient), in which doctors provide medical care directly to patients.
In this chapter, we will explain how these telemedicine systems will change with 5G.
D to D (Doctor to Doctor)
D to D (Doctor to Doctor) is when doctors in remote locations use online technology to support doctors who are actually providing medical care.
Real-time support is essential for doctors who are actually providing medical care. For example, when providing support to doctors providing medical care at a disaster site, every minute can mean the difference between life and death for a patient.
Utilizing 5G will enable high-speed communication, making it possible to provide support without time lag. The result is faster medical care for patients.
D to Patient (Doctor to Doctor)
D to P (Doctor to Patient) is when a doctor in a remote location actually provides medical care to a patient. Currently, online medical consultations are still being conducted, but they are limited to interviewing and almost no actual medical care is provided.
This is because patient data is exchanged when actually providing medical care. This patient data will be large in volume, so the use of 5G is essential. By utilizing 5G, data can be exchanged smoothly in real time and can be expected to be useful in actual medical care.

There are two issues that need to be resolved in promoting metaverse medicine.
Although it is certain that metaverse medicine will continue to expand in the future, there are still issues that need to be resolved. Let’s take a closer look at these two issues.
Safety issues
First, there is the issue of safety. When actually performing medical treatment, a multi-layered check system is in place, leading to peace of mind for patients.
Even when providing medical care in the metaverse, safety must be ensured just as in real life. Creating a safe environment for patients can be said to be a major challenge for metaverse medicine.
In order to solve safety issues, it is essential not only to collaborate with medical professionals, but also with businesses that provide metaverse space.
No matter how wonderful a Metaverse space is created, if there are doubts about its safety, it will be difficult to promote it.
Security issues
Metaverse space is a virtual space provided online. Therefore, addressing cyber security is essential.
If the security of the Metaverse is weak, it may be infiltrated by malicious hackers. If such hackers are allowed to infiltrate, medical practices may come to a halt, and patients’ personal information may also be leaked.
In order to solve these security issues, it is necessary not only to have a strong environmental infrastructure that does not allow intrusion from outsiders, but also to have laws in place to deal with incidents.

Five examples of using the Metaverse in medical care
In this chapter, we will introduce five examples of how the Metaverse is actually used in medical care.
Juntendo Virtual Hospital (virtual space imitating a hospital)
Juntendo University is collaborating with IBM Japan, Ltd. to construct the “Juntendo Virtual Hospital” within the Metaverse space.
The Juntendo Virtual Hospital is a place for medical professionals and patients to interact, allowing patients to experience a simulated medical procedure before coming to the hospital. The purpose is to alleviate patients’ anxiety through simulated experiences.
VR vaccine injection simulator (VR of intramuscular injection)
As the name suggests, a VR vaccine injection simulator allows you to simulate an injection in VR. Imma Create Co., Ltd. has released a service for training purposes, which allows you to intuitively learn how to give intramuscular injections in VR space. In addition, simulators have been released for training purposes when learning the procedures for vaccination against the new coronavirus, and are expected to be more effective than learning through classroom lectures.
Mediverse OCD (virtual space such as seminars)
MediVerse OCD aims to create new services by building a platform that connects medical professionals or medical professionals and companies in a metaverse space.
It can be used for a variety of purposes depending on the user, including sharing case information, gathering research knowledge from medical professionals, and holding simple study sessions and study groups.
Holoeyes (VR software for medical education)
Holoeyes is VR software for learning medical education or clinical practice.
Since the purpose is medical education, 3D information of the human body can be accurately reproduced in VR, making it possible to learn visually. Furthermore, it can also be used to simulate actual procedures. You can also communicate with other users in remote locations in real time via avatars.

Introducing 5 papers that will help you understand future metaverse medicine
In this chapter, we will introduce five papers that will be useful in deepening our knowledge of metaverse medicine, which will be promoted in the future.
Papers on cardiology
The use of the metaverse in cardiology is called the CardioVerse.
In cardiology, by utilizing this Cardioverse, it is possible to receive specialized services in 3D space even in remote locations, and the quality of medical care can be improved by integrating surgical robots and navigation systems. It has been reported that the effect of “increases
Papers on ophthalmology
In ophthalmology, there are many things related to AR and VR.
For example, “Using AR and VR to help patients notice visual abnormalities” and “Making it easier to train medical professionals in optometry and surgery.”
The use of AR and VR can help patients in remote locations decide whether to travel to the hospital for testing. Other training simulators introduced include the EyeSi simulator (VR Magic) and MicroVisTouch (Immersive Touch).
Papers on medical education
In the field of medical education, we classify the metaverse into four types and discuss how to utilize each type. The four types of medical education are ” Augmented Reality ,” “Lifelog,” “Mirror World,” and “Virtual Reality.”
Each type is discussed in the paper as follows.
- Augmented reality uses augmented reality t-shirts to explore inside the human body
- Lifelog is introducing a service that can utilize accumulated biometric information in the medical field.
- Mirror World is developing a platform that contributes to scientific research through games.
- In virtual reality, use of avatar services and platforms
Papers on gastroenterology
In gastroenterology, papers have been published on the clinical results of endoscopy training actually conducted in the metaverse space.
Specifically, this includes surgical training using endoscopic equipment in the Metaverse, treatment by senior doctors in the Metaverse, and discussion of surgical procedures by experts in the Metaverse via avatars.
Training and discussions held in the Metaverse space have led to results such as improved experience for doctors, shorter surgical times, and lower complication rates.
Papers on body transformation research cases
Body transformation is a phenomenon in which emotions, cognition, and behavior change by transforming from one’s own body to another body in VR space. Research on this body transformation is underway in Japan.
Specifically, the following cases have been reported.
- When white people use black avatars, their sense of discrimination is reduced.
- Using a child avatar leads to overestimation of object size and childish behavior.
- Being a superhero and helping others makes you more likely to act altruistically.
In other words, using an avatar with a different personality in VR space will have an impact on your body and way of thinking in the real world.
Summary
It is no exaggeration to say that the market for the use of the metaverse in medical care is growing, and there is no doubt that it will continue to develop. With the use of 5G, the impact of the Metaverse on medical care will increase. The range of applications will continue to expand in a variety of medical fields, including telemedicine and real-time communication between doctors and patients.

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July 3, 2024
Why is the Metaverse in the medical field attracting attenction? Introduction examples and future issues
The use of the metaverse in the medical field is expanding. It is estimated that by utilizing 5G and new technologies, the growth rate of the Metaverse will be over 30% by 2030.
Therefore, in this article, we will explain why the Metaverse is attracting attention in the medical field, what the challenges are in promoting it, and examples of what has been done to date.
Metaverse in the medical field is expected to grow by more than 30% by 2030
The use of the Metaverse in the medical field is attracting attention. This is because the use of virtual space is thought to have a wide range of uses, such as enabling medical procedures that would be difficult in the real world, and medical examinations using avatars.
In 2021, the market size of Metaverse in healthcare was not very large. However, in the medical field, the metaverse is expected to grow significantly by 2030, as it is believed that it can be used in a wide variety of ways.
The use of the Metaverse in the medical field is expanding, with examples of surgeries being performed using AR smart glasses.
Why metaverse medicine is attracting attention
There are three reasons why metaverse medicine is attracting attention. Let’s look at them one by one.
Introduction can eliminate regional disparities in medical quality
In recent years, “medical disparities” have been occurring all over the world, including Japan.
For example, medical institutions are concentrated only in urban areas, making it difficult to receive advanced medical care in rural areas. According to the Ministry of Health, Labor and Welfare’s “Measures to prevent uneven distribution of doctors”, the regional uneven distribution of doctors continues, and some regions are experiencing a shortage of doctors.
Metaverse is also expected to solve these problems. This is because VR space not only allows us to connect with people around the world in real time, but also makes it easier to provide medical services to patients in remote areas. Therefore, we can expect to eliminate regional disparities in the quality of medical care.
Advances in 5G and communication technology have made real-time remote surgery possible.
In the field of digital medicine, it is expected that paper prescriptions will disappear in the next few decades and transition to digital treatment in the metaverse space.
In order to perform digital medicine using this metaverse space without any problems, an environment is required that allows for high-speed and high-capacity communication without problems.
What is expected of this is the communication technology called 5G. It has the characteristics of “high-capacity, high-speed communication,” “high reliability and low latency,” and “multiple simultaneous connections.”
Even now, it has a proven track record of being used for real-time remote control of surgical support robots.
5G, which is capable of faster speeds and higher capacity communications, is said to be able to be used in digital medicine, and is expected to become a new field of digital medicine.
Metaverse medical care has become a system that is easy for patients to consult.
In the Metaverse space, you create an “avatar” that is your alter ego and communicate with it.
Communication using this avatar is attracting attention as a system that makes it easy for patients to consult.
Kohei Yoshioka, a psychiatrist who runs Metaverse Clinic, a medical consultation community on the Metaverse, says the following.
“I feel that in the metaverse space where we use alter egos called avatars, it is easier to self-disclose because there is no need to actually communicate face-to-face.”
Currently, we are using the Metaverse space as a place for consultation rather than medical treatment, but I believe that by making it a place for communication, it will be easier for patients to seek advice.
In this way, metaverse medicine is also beneficial to patients.
Three medical fields aiming to utilize the metaverse
The three specific medical fields that aim to utilize the metaverse include:
- Medical (VR training)
- Pharmaceuticals (sharing of medical product information)
- Drug discovery (time and cost reduction)
Medical (VR training)
In the medical field, the metaverse of VR training is being utilized.
Specifically, this includes training for surgeons regarding surgical procedures and social skills training.
For example, in the United States, we prepare a real surgical scenario, create a virtual operating room in the metaverse space, and actually perform the procedure in that space.
The virtual operating room also helps in training for complex procedures. Additionally, in Japan, Jolly Good Inc. is conducting social skills training on developmental disorders on Metaverse.
We have progressed to the stage where we can promote lifestyle support and employment support for patients.
Pharmaceuticals (sharing of medical product information)
In the pharmaceutical industry , the Metaverse is being used as a place to share medical product information and support communication between doctors and patients.
Specifically, we are using virtual MR to effectively share medical product information.
For example, at Tsumura Co., Ltd., a major pharmaceutical company, AI presents content that matches the story to medical professionals in a 3D version of MR.
Additionally, Astellas Pharma Inc. has begun experimenting with using virtual MR to project patients’ skeletal information in 3D into the real world. For example, it has the benefit of making it easier for patients to understand their own health status when they see their bone density decreasing.
Drug discovery (time and cost reduction)
Drug discovery is always time-consuming and costly. Metaverse is being used to reduce the time and cost of drug discovery.
At Chugai Pharmaceutical Co., Ltd., researchers use the software “Nanomu” to discover drugs in the metaverse.
Things that could only be confirmed in two dimensions can now be confirmed in 3D models down to the atomic level, making it possible to create new ideas and test whether they are actually effective in the metaverse space, something never seen before. It provides a sense of speed.

How will the two types of telemedicine (including metaverse medicine) change with 5G?
There are two types of telemedicine. The first is medical care in which doctors and patients in remote locations use tools to communicate in real time.
The other is D to D (Doctor to Doctor), in which other doctors provide online support for the medical care of doctors in remote locations, and D to P (Doctor to Patient), in which doctors provide medical care directly to patients.
In this chapter, we will explain how these telemedicine systems will change with 5G.
D to D (Doctor to Doctor)
D to D (Doctor to Doctor) is when doctors in remote locations use online technology to support doctors who are actually providing medical care.
Real-time support is essential for doctors who are actually providing medical care. For example, when providing support to doctors providing medical care at a disaster site, every minute can mean the difference between life and death for a patient.
Utilizing 5G will enable high-speed communication, making it possible to provide support without time lag. The result is faster medical care for patients.
D to Patient (Doctor to Doctor)
D to P (Doctor to Patient) is when a doctor in a remote location actually provides medical care to a patient. Currently, online medical consultations are still being conducted, but they are limited to interviewing and almost no actual medical care is provided.
This is because patient data is exchanged when actually providing medical care. This patient data will be large in volume, so the use of 5G is essential. By utilizing 5G, data can be exchanged smoothly in real time and can be expected to be useful in actual medical care.

There are two issues that need to be resolved in promoting metaverse medicine.
Although it is certain that metaverse medicine will continue to expand in the future, there are still issues that need to be resolved. Let’s take a closer look at these two issues.
Safety issues
First, there is the issue of safety. When actually performing medical treatment, a multi-layered check system is in place, leading to peace of mind for patients.
Even when providing medical care in the metaverse, safety must be ensured just as in real life. Creating a safe environment for patients can be said to be a major challenge for metaverse medicine.
In order to solve safety issues, it is essential not only to collaborate with medical professionals, but also with businesses that provide metaverse space.
No matter how wonderful a Metaverse space is created, if there are doubts about its safety, it will be difficult to promote it.
Security issues
Metaverse space is a virtual space provided online. Therefore, addressing cyber security is essential.
If the security of the Metaverse is weak, it may be infiltrated by malicious hackers. If such hackers are allowed to infiltrate, medical practices may come to a halt, and patients’ personal information may also be leaked.
In order to solve these security issues, it is necessary not only to have a strong environmental infrastructure that does not allow intrusion from outsiders, but also to have laws in place to deal with incidents.

Five examples of using the Metaverse in medical care
In this chapter, we will introduce five examples of how the Metaverse is actually used in medical care.
Juntendo Virtual Hospital (virtual space imitating a hospital)
Juntendo University is collaborating with IBM Japan, Ltd. to construct the “Juntendo Virtual Hospital” within the Metaverse space.
The Juntendo Virtual Hospital is a place for medical professionals and patients to interact, allowing patients to experience a simulated medical procedure before coming to the hospital. The purpose is to alleviate patients’ anxiety through simulated experiences.
VR vaccine injection simulator (VR of intramuscular injection)
As the name suggests, a VR vaccine injection simulator allows you to simulate an injection in VR. Imma Create Co., Ltd. has released a service for training purposes, which allows you to intuitively learn how to give intramuscular injections in VR space. In addition, simulators have been released for training purposes when learning the procedures for vaccination against the new coronavirus, and are expected to be more effective than learning through classroom lectures.
Mediverse OCD (virtual space such as seminars)
MediVerse OCD aims to create new services by building a platform that connects medical professionals or medical professionals and companies in a metaverse space.
It can be used for a variety of purposes depending on the user, including sharing case information, gathering research knowledge from medical professionals, and holding simple study sessions and study groups.
Holoeyes (VR software for medical education)
Holoeyes is VR software for learning medical education or clinical practice.
Since the purpose is medical education, 3D information of the human body can be accurately reproduced in VR, making it possible to learn visually. Furthermore, it can also be used to simulate actual procedures. You can also communicate with other users in remote locations in real time via avatars.

Introducing 5 papers that will help you understand future metaverse medicine
In this chapter, we will introduce five papers that will be useful in deepening our knowledge of metaverse medicine, which will be promoted in the future.
Papers on cardiology
The use of the metaverse in cardiology is called the CardioVerse.
In cardiology, by utilizing this Cardioverse, it is possible to receive specialized services in 3D space even in remote locations, and the quality of medical care can be improved by integrating surgical robots and navigation systems. It has been reported that the effect of “increases
Papers on ophthalmology
In ophthalmology, there are many things related to AR and VR.
For example, “Using AR and VR to help patients notice visual abnormalities” and “Making it easier to train medical professionals in optometry and surgery.”
The use of AR and VR can help patients in remote locations decide whether to travel to the hospital for testing. Other training simulators introduced include the EyeSi simulator (VR Magic) and MicroVisTouch (Immersive Touch).
Papers on medical education
In the field of medical education, we classify the metaverse into four types and discuss how to utilize each type. The four types of medical education are ” Augmented Reality ,” “Lifelog,” “Mirror World,” and “Virtual Reality.”
Each type is discussed in the paper as follows.
- Augmented reality uses augmented reality t-shirts to explore inside the human body
- Lifelog is introducing a service that can utilize accumulated biometric information in the medical field.
- Mirror World is developing a platform that contributes to scientific research through games.
- In virtual reality, use of avatar services and platforms
Papers on gastroenterology
In gastroenterology, papers have been published on the clinical results of endoscopy training actually conducted in the metaverse space.
Specifically, this includes surgical training using endoscopic equipment in the Metaverse, treatment by senior doctors in the Metaverse, and discussion of surgical procedures by experts in the Metaverse via avatars.
Training and discussions held in the Metaverse space have led to results such as improved experience for doctors, shorter surgical times, and lower complication rates.
Papers on body transformation research cases
Body transformation is a phenomenon in which emotions, cognition, and behavior change by transforming from one’s own body to another body in VR space. Research on this body transformation is underway in Japan.
Specifically, the following cases have been reported.
- When white people use black avatars, their sense of discrimination is reduced.
- Using a child avatar leads to overestimation of object size and childish behavior.
- Being a superhero and helping others makes you more likely to act altruistically.
In other words, using an avatar with a different personality in VR space will have an impact on your body and way of thinking in the real world.
Summary
It is no exaggeration to say that the market for the use of the metaverse in medical care is growing, and there is no doubt that it will continue to develop. With the use of 5G, the impact of the Metaverse on medical care will increase. The range of applications will continue to expand in a variety of medical fields, including telemedicine and real-time communication between doctors and patients.

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July 3, 2024
What is agritech, which is responsible for the future of agriculture? Explaining the background and merits of attention
There are concerns about the decline of domestic agriculture due to an aging population and a lack of successors, and agritech which combines agriculture and technology, is attracting attention as a means to solve this problem.
Agritech is being used around the world to successfully improve agricultural efficiency and increase production. In this article, we will introduce agritech and the initiatives of each company, so let’s deepen our understanding so that we can stay up to date with the times.

What is “agritech” that combines agriculture and technology?
Here, we will introduce what agritech is, which combines agriculture and technology, which is attracting attention.
Differences with smart agriculture
“Agritech” refers to technology that promotes IT in agriculture using ICT technologies such as AI, big data, and IoT , and is a coined word that combines agriculture (agriculture) and technology (technology). . Agritech is a term mainly used overseas; in Japan, it is synonymously referred to as high-tech agriculture or smart agriculture.
Domestic market size
The domestic agritech market size is expanding year by year, and according to the Fuji Keizai Group, the market size in 2018 was 69.8 billion yen, and is expected to exceed 100 billion yen in 2030.
Since it is a relatively new market, there are few barriers to entry, and not only large companies but also start-up companies and venture companies are participating, so it can be said that it is a promising industry with a bright future.
Reasons why agritech is attracting attention in Japan and social issues
One of the reasons why agritech is attracting attention in Japan is that Japan’s food self-sufficiency rate is low. Japan’s food self-sufficiency rate in 2020 was low at 37%, and it is clear that Japan relies on imports from overseas. As a result, there is no guarantee that we will be able to continue to secure a stable supply of food, as we are greatly affected by the situation and trade relations in importing countries.
There are also challenges in the environment surrounding domestic agriculture. The aging of farmers and the lack of successors are also a problem, but if techniques are not passed on, there is a possibility that Japan’s agricultural technology will continue to decline. As a result, the introduction of agritech in Japan has begun little by little.
Agritech’s main initiatives
We will introduce the main initiatives related to agritech, which is attracting attention not only in Japan but also around the world.
Spraying pesticides by drone
By using small agricultural drones to spray pesticides from the sky, you can improve the efficiency of agricultural work. Previously, pesticides were manually sprayed using machines, but by operating a drone and automatically spraying pesticides, it is now possible to spread pesticides over a wide area in a short period of time. At the startup company BioC ar bon Engineering, one person operated six drones at the same time and successfully planted mangroves in Myanmar.
Monitoring and management using IoT devices
Using IoT devices, it is possible to monitor and manage livestock and crops.
For example, by monitoring the daily behavior of livestock using AI cameras, it is possible to notice minute changes in physical condition and provide the necessary care in a timely manner. Similarly, for agricultural products, there is a function that automatically notifies you of the harvest time based on the growth status, and a function that links environmental information such as temperature, humidity, and sunlight hours with harvest results, and proposes the optimal growing method according to the situation. It can also be applied to other functions.
Unified supply chain management using blockchain
Through quality control using blockchain technology, we can centralize supply chain management and provide safe and secure agricultural products. Today’s supply chains are becoming more diverse and complex, and ensuring traceability to prove the safety of agricultural products is an issue.
Therefore, blockchain technology used for crypto assets such as Bitcoin is attracting attention. In blockchain technology, each block is linked by a chain, making it difficult to tamper with the data, making it possible to prove the transparency of agricultural products.
Big data analysis
By performing big data analysis using AI, it is possible to quantify the invisible know-how of experts. This makes it possible to grasp the harvest timing based on the growth status of crops and to take appropriate measures against pests, which can be used to pass down techniques to successors.
Advantages of introducing agritech
The introduction of agritech has the potential to transform agriculture, which until now had a strong analog image, into a high-tech industry, and to greatly improve the way people work. Here, we will introduce the benefits of introducing agritech.
Improving agricultural efficiency and increasing production
One of the benefits of introducing agritech is improving the efficiency of agricultural work and increasing production. The reason is that with the introduction of agritech, agricultural tasks that were previously done manually, such as sowing seeds, spraying pesticides, and harvesting, can be performed by drones and robots, making it possible to perform a wide range of tasks in a short period of time.
Furthermore, by using the free time gained from this process to improve varieties, it has the potential to lead to the creation of new varieties.
Facilitation of new entry through digitalization
By introducing agritech and visualizing the know-how of experienced workers as data using AI, it is possible to create an environment that is easy for new entrants, such as young people who want to start farming, to enter the industry.
The know-how of experts is often passed on orally or through actual experience, and there is no guarantee that the know-how will be accurately transmitted. Furthermore, as the population continues to age, it is unlikely that people will be able to continue working until they have inherited all of their skills, so it is important to reduce the barriers to new entry by converting know-how into data using AI. .
Reduction of working hours and burden
One of the reasons why the number of farmers is decreasing is that there is an established image that farming is difficult due to various factors such as physical strength and weather. Therefore, by using agritech to have drones and robots perform agricultural work on behalf of farmers, it is possible to reduce labor hours and reduce the burden. One of the appeals of this is that you can use the free time you get to work on a side job or use it on your days off, giving you freedom in how you work.
Realizing new ways of working
Agriculture has an established image of hard labor, long hours, no days off, and demanding farm work, but by introducing agritech, it is possible to realize new ways of working that have never existed before.
Using IoT, it is possible to remotely monitor crops, and it is also possible to know the growth status of crops and the timing of harvest even when you are away from home. If this happens, you won’t have to visit the site every day.
Disadvantages of introducing agritech
So far, we have introduced the benefits of introducing Agritech, but when considering implementing Agritech, it is important to understand the disadvantages as well.
Initial cost is high
One of the disadvantages is the high initial cost of installing drones, robots, and monitoring systems.
However, if you take a long-term view, you can expect significant improvements in work efficiency and increased production, so considering it as an initial investment, it cannot be said to be a disadvantage. Please make a medium- to long-term plan and consider implementing it in order to receive these benefits.
Difficulties in securing and training skilled human resources
Agritech uses AI and IoT to improve agriculture, but users are also required to have knowledge and understanding of AI and IoT.
The difficulty of securing and training human resources with skills related to these ICT technologies is an issue, and agricultural machinery manufacturers and ICT vendors are working to develop products that are easy to use even without special skills.
Lack of compatibility with software, etc.
Since agritech uses relatively new technology, there is a problem with poor compatibility of software between devices.
Even if you try to link devices from different manufacturers, they may not be able to work together because the interface specifications for exchanging data are different. In the future, this problem may be resolved as standards are developed, but it will not be resolved overnight, and I think it will take some time.

Companies and organizations that utilize domestic agritech
Here we will introduce companies and organizations that utilize agritech in Japan.
Agricultural data sharing demonstration project implemented in Niigata City, Niigata Prefecture
Niigata City is working to improve the efficiency of agricultural work and change the image of agriculture by using agritech.
Implementing agritech has been a challenge, such as high initial costs and the need for special skills. For this reason, Niigata City is implementing an agricultural data sharing demonstration project. By sharing agricultural data among multiple farmers, we have succeeded in improving productivity and production volume through data-based management decisions.
“Yanmar” utilizes in-house developed agricultural machinery
Yanmar Holdings Co., Ltd. is developing its own agricultural robot called Agrobot. As of November 2022, this has not yet been realized, but our goal is to develop a robot that can automatically perform tasks tailored to the local environment.
However, there are some robots that are already in practical use. A robot tractor is an agricultural robot that can automatically turn and work automatically by operating a tablet remotely. Agrobots aiming for complete automation may also become a reality in the near future.
“Midori Cloud” provides field monitoring system
Midori Cloud uses IT to measure and record the field environment and work, and converts it into data, making it possible to accumulate experience and share know-how by visualizing agricultural work.
Farmers’ sense and experience are important in agriculture, which requires dealing with an uncertain environment. At Midori Cloud, by visualizing this using IT, we have been able to improve the efficiency of agricultural work and increase production.
AGRI SMILE’s business goal is to pass on agriculture to the next generation
With AGRI SMILE’s service, by centrally managing and evaluating cultivation data, you can utilize the feedback for your next cultivation. We are also developing a service that allows cultivation techniques to be shared as VR videos, making it possible to pass them on to the next generation.

Companies and organizations that utilize overseas agritech
Agritech is attracting attention not only in Japan but also overseas. Here we will introduce companies and organizations that utilize agritech overseas.
Growing tomatoes in seawater “Sundrop Farm (Australia)”
South Australia has dry land that stretches all the way to the coast under harsh sunlight. Sundrop Farm was created by focusing on an environment where large quantities of seawater, which is unsuitable for agriculture, can be secured.
By distilling seawater using solar energy collected using specular reflection, we succeeded in securing a large amount of fresh water. We use this fresh water to grow tomatoes.
Agrobot (Spain) develops a harvesting robot
Agrobot is a company based in Spain that develops “strawberry harvesting robots”. Using a sensor at the tip of the robot, it not only determines when to harvest strawberries based on their ripeness, but also automatically harvests the strawberries.
In-house crop management and hydroponic cultivation “Plenty (USA)”
Plenty is a company that specializes in hydroponic cultivation, and grows kale and lettuce in-house. By managing crop data and controlling the amount of water and minerals, it is possible to adjust the flavor. Another feature is that the amount of water required for cultivation is about 1/20 of that of regular outdoor cultivation, contributing to water conservation.

The future of agritech
The future of agritech, which is expanding its market size, is bright as a savior for domestic agriculture, which is facing concerns about accelerating decline in agriculture due to the aging population and lack of successors.
Efforts to utilize agritech are becoming more popular not only in Japan but also overseas, and it will not be long before we see fully automated robots doing farming in place of farmers. The industry is in a period of growth, so let’s keep an eye on future trends.

Summary
So far, we have introduced agritech. In Japan, it is often referred to as smart agriculture, and although it is a technology that is attracting a lot of attention around the world, I think that few people understand it accurately.
Through this article, let’s deepen our understanding of agritech and pay attention to trends in agritech that are likely to attract more and more attention in the future.

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July 2, 2024
What is agritech, which is responsible for the future of agriculture? Explaining the background and merits of attention
There are concerns about the decline of domestic agriculture due to an aging population and a lack of successors, and agritech which combines agriculture and technology, is attracting attention as a means to solve this problem.
Agritech is being used around the world to successfully improve agricultural efficiency and increase production. In this article, we will introduce agritech and the initiatives of each company, so let’s deepen our understanding so that we can stay up to date with the times.

What is “agritech” that combines agriculture and technology?
Here, we will introduce what agritech is, which combines agriculture and technology, which is attracting attention.
Differences with smart agriculture
“Agritech” refers to technology that promotes IT in agriculture using ICT technologies such as AI, big data, and IoT , and is a coined word that combines agriculture (agriculture) and technology (technology). . Agritech is a term mainly used overseas; in Japan, it is synonymously referred to as high-tech agriculture or smart agriculture.
Domestic market size
The domestic agritech market size is expanding year by year, and according to the Fuji Keizai Group, the market size in 2018 was 69.8 billion yen, and is expected to exceed 100 billion yen in 2030.
Since it is a relatively new market, there are few barriers to entry, and not only large companies but also start-up companies and venture companies are participating, so it can be said that it is a promising industry with a bright future.
Reasons why agritech is attracting attention in Japan and social issues
One of the reasons why agritech is attracting attention in Japan is that Japan’s food self-sufficiency rate is low. Japan’s food self-sufficiency rate in 2020 was low at 37%, and it is clear that Japan relies on imports from overseas. As a result, there is no guarantee that we will be able to continue to secure a stable supply of food, as we are greatly affected by the situation and trade relations in importing countries.
There are also challenges in the environment surrounding domestic agriculture. The aging of farmers and the lack of successors are also a problem, but if techniques are not passed on, there is a possibility that Japan’s agricultural technology will continue to decline. As a result, the introduction of agritech in Japan has begun little by little.
Agritech’s main initiatives
We will introduce the main initiatives related to agritech, which is attracting attention not only in Japan but also around the world.
Spraying pesticides by drone
By using small agricultural drones to spray pesticides from the sky, you can improve the efficiency of agricultural work. Previously, pesticides were manually sprayed using machines, but by operating a drone and automatically spraying pesticides, it is now possible to spread pesticides over a wide area in a short period of time. At the startup company BioC ar bon Engineering, one person operated six drones at the same time and successfully planted mangroves in Myanmar.
Monitoring and management using IoT devices
Using IoT devices, it is possible to monitor and manage livestock and crops.
For example, by monitoring the daily behavior of livestock using AI cameras, it is possible to notice minute changes in physical condition and provide the necessary care in a timely manner. Similarly, for agricultural products, there is a function that automatically notifies you of the harvest time based on the growth status, and a function that links environmental information such as temperature, humidity, and sunlight hours with harvest results, and proposes the optimal growing method according to the situation. It can also be applied to other functions.
Unified supply chain management using blockchain
Through quality control using blockchain technology, we can centralize supply chain management and provide safe and secure agricultural products. Today’s supply chains are becoming more diverse and complex, and ensuring traceability to prove the safety of agricultural products is an issue.
Therefore, blockchain technology used for crypto assets such as Bitcoin is attracting attention. In blockchain technology, each block is linked by a chain, making it difficult to tamper with the data, making it possible to prove the transparency of agricultural products.
Big data analysis
By performing big data analysis using AI, it is possible to quantify the invisible know-how of experts. This makes it possible to grasp the harvest timing based on the growth status of crops and to take appropriate measures against pests, which can be used to pass down techniques to successors.
Advantages of introducing agritech
The introduction of agritech has the potential to transform agriculture, which until now had a strong analog image, into a high-tech industry, and to greatly improve the way people work. Here, we will introduce the benefits of introducing agritech.
Improving agricultural efficiency and increasing production
One of the benefits of introducing agritech is improving the efficiency of agricultural work and increasing production. The reason is that with the introduction of agritech, agricultural tasks that were previously done manually, such as sowing seeds, spraying pesticides, and harvesting, can be performed by drones and robots, making it possible to perform a wide range of tasks in a short period of time.
Furthermore, by using the free time gained from this process to improve varieties, it has the potential to lead to the creation of new varieties.
Facilitation of new entry through digitalization
By introducing agritech and visualizing the know-how of experienced workers as data using AI, it is possible to create an environment that is easy for new entrants, such as young people who want to start farming, to enter the industry.
The know-how of experts is often passed on orally or through actual experience, and there is no guarantee that the know-how will be accurately transmitted. Furthermore, as the population continues to age, it is unlikely that people will be able to continue working until they have inherited all of their skills, so it is important to reduce the barriers to new entry by converting know-how into data using AI. .
Reduction of working hours and burden
One of the reasons why the number of farmers is decreasing is that there is an established image that farming is difficult due to various factors such as physical strength and weather. Therefore, by using agritech to have drones and robots perform agricultural work on behalf of farmers, it is possible to reduce labor hours and reduce the burden. One of the appeals of this is that you can use the free time you get to work on a side job or use it on your days off, giving you freedom in how you work.
Realizing new ways of working
Agriculture has an established image of hard labor, long hours, no days off, and demanding farm work, but by introducing agritech, it is possible to realize new ways of working that have never existed before.
Using IoT, it is possible to remotely monitor crops, and it is also possible to know the growth status of crops and the timing of harvest even when you are away from home. If this happens, you won’t have to visit the site every day.
Disadvantages of introducing agritech
So far, we have introduced the benefits of introducing Agritech, but when considering implementing Agritech, it is important to understand the disadvantages as well.
Initial cost is high
One of the disadvantages is the high initial cost of installing drones, robots, and monitoring systems.
However, if you take a long-term view, you can expect significant improvements in work efficiency and increased production, so considering it as an initial investment, it cannot be said to be a disadvantage. Please make a medium- to long-term plan and consider implementing it in order to receive these benefits.
Difficulties in securing and training skilled human resources
Agritech uses AI and IoT to improve agriculture, but users are also required to have knowledge and understanding of AI and IoT.
The difficulty of securing and training human resources with skills related to these ICT technologies is an issue, and agricultural machinery manufacturers and ICT vendors are working to develop products that are easy to use even without special skills.
Lack of compatibility with software, etc.
Since agritech uses relatively new technology, there is a problem with poor compatibility of software between devices.
Even if you try to link devices from different manufacturers, they may not be able to work together because the interface specifications for exchanging data are different. In the future, this problem may be resolved as standards are developed, but it will not be resolved overnight, and I think it will take some time.

Companies and organizations that utilize domestic agritech
Here we will introduce companies and organizations that utilize agritech in Japan.
Agricultural data sharing demonstration project implemented in Niigata City, Niigata Prefecture
Niigata City is working to improve the efficiency of agricultural work and change the image of agriculture by using agritech.
Implementing agritech has been a challenge, such as high initial costs and the need for special skills. For this reason, Niigata City is implementing an agricultural data sharing demonstration project. By sharing agricultural data among multiple farmers, we have succeeded in improving productivity and production volume through data-based management decisions.
“Yanmar” utilizes in-house developed agricultural machinery
Yanmar Holdings Co., Ltd. is developing its own agricultural robot called Agrobot. As of November 2022, this has not yet been realized, but our goal is to develop a robot that can automatically perform tasks tailored to the local environment.
However, there are some robots that are already in practical use. A robot tractor is an agricultural robot that can automatically turn and work automatically by operating a tablet remotely. Agrobots aiming for complete automation may also become a reality in the near future.
“Midori Cloud” provides field monitoring system
Midori Cloud uses IT to measure and record the field environment and work, and converts it into data, making it possible to accumulate experience and share know-how by visualizing agricultural work.
Farmers’ sense and experience are important in agriculture, which requires dealing with an uncertain environment. At Midori Cloud, by visualizing this using IT, we have been able to improve the efficiency of agricultural work and increase production.
AGRI SMILE’s business goal is to pass on agriculture to the next generation
With AGRI SMILE’s service, by centrally managing and evaluating cultivation data, you can utilize the feedback for your next cultivation. We are also developing a service that allows cultivation techniques to be shared as VR videos, making it possible to pass them on to the next generation.

Companies and organizations that utilize overseas agritech
Agritech is attracting attention not only in Japan but also overseas. Here we will introduce companies and organizations that utilize agritech overseas.
Growing tomatoes in seawater “Sundrop Farm (Australia)”
South Australia has dry land that stretches all the way to the coast under harsh sunlight. Sundrop Farm was created by focusing on an environment where large quantities of seawater, which is unsuitable for agriculture, can be secured.
By distilling seawater using solar energy collected using specular reflection, we succeeded in securing a large amount of fresh water. We use this fresh water to grow tomatoes.
Agrobot (Spain) develops a harvesting robot
Agrobot is a company based in Spain that develops “strawberry harvesting robots”. Using a sensor at the tip of the robot, it not only determines when to harvest strawberries based on their ripeness, but also automatically harvests the strawberries.
In-house crop management and hydroponic cultivation “Plenty (USA)”
Plenty is a company that specializes in hydroponic cultivation, and grows kale and lettuce in-house. By managing crop data and controlling the amount of water and minerals, it is possible to adjust the flavor. Another feature is that the amount of water required for cultivation is about 1/20 of that of regular outdoor cultivation, contributing to water conservation.

The future of agritech
The future of agritech, which is expanding its market size, is bright as a savior for domestic agriculture, which is facing concerns about accelerating decline in agriculture due to the aging population and lack of successors.
Efforts to utilize agritech are becoming more popular not only in Japan but also overseas, and it will not be long before we see fully automated robots doing farming in place of farmers. The industry is in a period of growth, so let’s keep an eye on future trends.

Summary
So far, we have introduced agritech. In Japan, it is often referred to as smart agriculture, and although it is a technology that is attracting a lot of attention around the world, I think that few people understand it accurately.
Through this article, let’s deepen our understanding of agritech and pay attention to trends in agritech that are likely to attract more and more attention in the future.

Follow us on Facebook for updates and exclusive content! Click here: Each Techy
July 2, 2024
What is Data Mining and Data Science? Thorough explanation of differences and outline

In recent years, “big data” has attracted a great deal of attention, and how to utilize big data in corporate activities has become an urgent issue in all industries. Therefore, in this article, we will focus on “data mining” and “data science” related to the handling of data.

About data mining and data science

First, let’s take a look at the definitions and differences between data mining and data science.
What is data mining?
Data mining is a technique for finding “knowledge” in a large amount of data by making full use of analysis methods such as statistics and AI. As the word data mining implies, it means mining useful information (data).
What is data science?
Data science is a research field for extracting meaningful data using methods in various fields such as statistics and information engineering. Data science is a collection of many research fields, and has received more attention in recent years due to the growing social needs.
Differences between data mining and data science
Data science is required to carry out all processes from data acquisition, accumulation, analysis, model construction, verification, and problem solving. Data mining, on the other hand, is primarily focused on analysis and model building within this step.

The main methods of data mining

Many of the methods used in data mining are those used in statistical analysis and are considered to be useful in data mining as well. From here, I will explain the typical methods of data mining.
Market basket
A market basket is a technique used to discover items that are often bought at the same time from retail store sales data. By visualizing products that seem to have little relevance, such as baby diapers and canned beer, but are often purchased at the same time, it helps to create an effective sales floor.
Clustering
Clustering is a method of grouping people who have similar behaviors from purchasing data and taking appropriate measures for each group. Classification based on data similarity makes it easier to launch different marketing for each group.
Logistic regression analysis
Logistic regression analysis is a statistical method that can explain and predict the probability that a value result (objective variable) will occur from several factors (explanatory variables). Since it is an analysis method that determines the “occurrence rate of a certain event,” it can be expected to be used in various business situations.
Machine learning
In some cases, data mining uses machine learning that utilizes AI. Programming languages such as “Python” and “R” are often used for data analysis by machine learning. In particular, Python has a wealth of libraries that are useful for data analysis, making it an effective language for discovering knowledge that finds rules and relationships from data.

Data mining implementation procedure

When performing data mining, it is important to take the right steps. The following describes the specific steps required to perform data mining.
Collect data
First, collect the data that suits your purpose. By collecting as much data as possible, it will be easier to find useful data.
Process and organize data
Next, we will process and organize the collected data into a form suitable for learning. If there is a lot of useless information called “noise” or irrelevant information, AI will not be able to learn correctly. Therefore, when organizing your data, you should remove noise and analyze using only the information you need.
Analyze the data
After processing and organizing the data, we will discover and group the patterns of the data using the methods such as clustering, logistic regression analysis, and market basket introduced above.
Conduct verification / evaluation
You may find some rules or relationships in the patterns and groups derived from the analysis. In such cases, apply the discovered rules and relationships to other data, verify and evaluate whether it can be said as a general theory or as a tendency.

Example of data science utilization
So how is data science actually used in the business scene? Below, we will introduce specific use cases of data science.
Retail business
In the retail industry, leveraging a customer database can help you run more effective campaigns and make effective offers to your customers. For example, linking purchase-related data such as “when”, “who”, “where”, “what you purchased”, “what other products you were interested in”, market data, customer data, etc. By aggregating, it is possible to clarify customer behavior patterns and preferences. On top of that, if you narrow down the targets that are likely to be purchased, you can come up with effective marketing measures such as coupon distribution according to customer preferences.
It is also possible to predict future trends by combining SNS posts and Web behavior data. As a result, product demand can be predicted accurately, the number of inventories to be secured can be grasped, and inventory control can be performed, which can be expected to increase sales and reduce inventory loss at the same time.
Financial industry
In the financial industry, stock price and foreign exchange forecasts can be made by combining past stock transaction data and foreign exchange data with various economic indicators occurring in the world.
Nowadays, AI predicts not only the selection of stocks but also the timing of buying and selling, and services for automatically purchasing foreign currencies have begun to emerge, and such services are expected to become more widespread in the future.
Restaurant business
In recent years, the use of data science has been promoted in the restaurant industry as well. In fact, many stores have adopted electronic payments and loyalty points cards, and it has become possible to analyze purchasing behavior and store visit history for each customer.
In addition, when sales are not expected, we can reduce costs such as food loss by optimizing ingredients and personnel. One of the merits of utilizing data science is that it becomes easier for the restaurant industry to think about measures according to sales forecasts in advance.

Skills useful for data science
Data scientists are required to solve corporate management issues by collecting and utilizing data. To achieve this, three skills, “statistical analysis skills,” “language skills,” and “IT skills,” are indispensable. Here, we will explain why each skill is necessary.
Statistical analysis skills
Data scientists are specialists in the handling and analysis of big data. Therefore, skills to analyze statistics based on the derived data are required. Be sure to acquire mathematical knowledge such as probability, statistics, calculus, and matrix.
Language skill
In the business scene, it is required to explain the analysis results in an easy-to-understand and smooth manner even for people without specialized knowledge. In particular, in recent years, the employment of foreign workers in Japan has been increasing year by year due to the effects of the declining birthrate and aging population. It can be said that a certain level of language proficiency is an indispensable skill for smooth communication with business partners and employees.
IT skills
Data scientists who handle data naturally need general knowledge of IT. “Database knowledge”, “skills for high-speed data processing”, “programming skills”, etc. are indispensable skills for carrying out business, so it is recommended to learn repeatedly.

UMWELT of TRYETING that can effectively utilize big data!
If you want to make effective use of big data accumulated in-house, why not use TRYETING’s no-code AI cloud “UMWELT”. Since it is equipped with many algorithms that are useful for data analysis, you can easily build an AI system with just a mouse operation. Another strength of UMWELT is that the period until the introduction of AI is 1/4 of the conventional one, which enables high-speed introduction, and the introduction cost is 1/10 of the conventional one, which is the lowest cost in the industry.
Summary
This time, we introduced the differences and outlines between data mining and data science, as well as specific application examples. In the modern society where the environment and methods for handling big data have developed, the technology to obtain knowledge from data is an extremely powerful weapon. By all means, please refer to this article to firmly control the data mining process and improve the prediction accuracy.

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July 1, 2024
What is Data Mining and Data Science? Thorough explanation of differences and outline

In recent years, “big data” has attracted a great deal of attention, and how to utilize big data in corporate activities has become an urgent issue in all industries. Therefore, in this article, we will focus on “data mining” and “data science” related to the handling of data.

About data mining and data science

First, let’s take a look at the definitions and differences between data mining and data science.
What is data mining?
Data mining is a technique for finding “knowledge” in a large amount of data by making full use of analysis methods such as statistics and AI. As the word data mining implies, it means mining useful information (data).
What is data science?
Data science is a research field for extracting meaningful data using methods in various fields such as statistics and information engineering. Data science is a collection of many research fields, and has received more attention in recent years due to the growing social needs.
Differences between data mining and data science
Data science is required to carry out all processes from data acquisition, accumulation, analysis, model construction, verification, and problem solving. Data mining, on the other hand, is primarily focused on analysis and model building within this step.

The main methods of data mining

Many of the methods used in data mining are those used in statistical analysis and are considered to be useful in data mining as well. From here, I will explain the typical methods of data mining.
Market basket
A market basket is a technique used to discover items that are often bought at the same time from retail store sales data. By visualizing products that seem to have little relevance, such as baby diapers and canned beer, but are often purchased at the same time, it helps to create an effective sales floor.
Clustering
Clustering is a method of grouping people who have similar behaviors from purchasing data and taking appropriate measures for each group. Classification based on data similarity makes it easier to launch different marketing for each group.
Logistic regression analysis
Logistic regression analysis is a statistical method that can explain and predict the probability that a value result (objective variable) will occur from several factors (explanatory variables). Since it is an analysis method that determines the “occurrence rate of a certain event,” it can be expected to be used in various business situations.
Machine learning
In some cases, data mining uses machine learning that utilizes AI. Programming languages such as “Python” and “R” are often used for data analysis by machine learning. In particular, Python has a wealth of libraries that are useful for data analysis, making it an effective language for discovering knowledge that finds rules and relationships from data.

Data mining implementation procedure

When performing data mining, it is important to take the right steps. The following describes the specific steps required to perform data mining.
Collect data
First, collect the data that suits your purpose. By collecting as much data as possible, it will be easier to find useful data.
Process and organize data
Next, we will process and organize the collected data into a form suitable for learning. If there is a lot of useless information called “noise” or irrelevant information, AI will not be able to learn correctly. Therefore, when organizing your data, you should remove noise and analyze using only the information you need.
Analyze the data
After processing and organizing the data, we will discover and group the patterns of the data using the methods such as clustering, logistic regression analysis, and market basket introduced above.
Conduct verification / evaluation
You may find some rules or relationships in the patterns and groups derived from the analysis. In such cases, apply the discovered rules and relationships to other data, verify and evaluate whether it can be said as a general theory or as a tendency.

Example of data science utilization
So how is data science actually used in the business scene? Below, we will introduce specific use cases of data science.
Retail business
In the retail industry, leveraging a customer database can help you run more effective campaigns and make effective offers to your customers. For example, linking purchase-related data such as “when”, “who”, “where”, “what you purchased”, “what other products you were interested in”, market data, customer data, etc. By aggregating, it is possible to clarify customer behavior patterns and preferences. On top of that, if you narrow down the targets that are likely to be purchased, you can come up with effective marketing measures such as coupon distribution according to customer preferences.
It is also possible to predict future trends by combining SNS posts and Web behavior data. As a result, product demand can be predicted accurately, the number of inventories to be secured can be grasped, and inventory control can be performed, which can be expected to increase sales and reduce inventory loss at the same time.
Financial industry
In the financial industry, stock price and foreign exchange forecasts can be made by combining past stock transaction data and foreign exchange data with various economic indicators occurring in the world.
Nowadays, AI predicts not only the selection of stocks but also the timing of buying and selling, and services for automatically purchasing foreign currencies have begun to emerge, and such services are expected to become more widespread in the future.
Restaurant business
In recent years, the use of data science has been promoted in the restaurant industry as well. In fact, many stores have adopted electronic payments and loyalty points cards, and it has become possible to analyze purchasing behavior and store visit history for each customer.
In addition, when sales are not expected, we can reduce costs such as food loss by optimizing ingredients and personnel. One of the merits of utilizing data science is that it becomes easier for the restaurant industry to think about measures according to sales forecasts in advance.

Skills useful for data science
Data scientists are required to solve corporate management issues by collecting and utilizing data. To achieve this, three skills, “statistical analysis skills,” “language skills,” and “IT skills,” are indispensable. Here, we will explain why each skill is necessary.
Statistical analysis skills
Data scientists are specialists in the handling and analysis of big data. Therefore, skills to analyze statistics based on the derived data are required. Be sure to acquire mathematical knowledge such as probability, statistics, calculus, and matrix.
Language skill
In the business scene, it is required to explain the analysis results in an easy-to-understand and smooth manner even for people without specialized knowledge. In particular, in recent years, the employment of foreign workers in Japan has been increasing year by year due to the effects of the declining birthrate and aging population. It can be said that a certain level of language proficiency is an indispensable skill for smooth communication with business partners and employees.
IT skills
Data scientists who handle data naturally need general knowledge of IT. “Database knowledge”, “skills for high-speed data processing”, “programming skills”, etc. are indispensable skills for carrying out business, so it is recommended to learn repeatedly.

UMWELT of TRYETING that can effectively utilize big data!
If you want to make effective use of big data accumulated in-house, why not use TRYETING’s no-code AI cloud “UMWELT”. Since it is equipped with many algorithms that are useful for data analysis, you can easily build an AI system with just a mouse operation. Another strength of UMWELT is that the period until the introduction of AI is 1/4 of the conventional one, which enables high-speed introduction, and the introduction cost is 1/10 of the conventional one, which is the lowest cost in the industry.
Summary
This time, we introduced the differences and outlines between data mining and data science, as well as specific application examples. In the modern society where the environment and methods for handling big data have developed, the technology to obtain knowledge from data is an extremely powerful weapon. By all means, please refer to this article to firmly control the data mining process and improve the prediction accuracy.

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July 1, 2024
What is the impact of AI on the media? Introducing examples of media AI utilization

AI has come to be used in all our daily lives and business scenes. The wave of AI innovation is also coming to the media and content industries. In this article, we will focus on “the impact of AI on the media” and explain its outline and application examples.
The influence of AI is spreading to the media

What is AI?
AI (Artificial Intelligence) is an artificial reproduction of a part of human intelligence using software. Humans make many guesses and judgments in their lives, but AI can automatically extract and judge features such as patterns and rules for making judgments.
Impact of AI on the media
In the days when there was only TV, there was competition for ratings. However, with the advent of YouTube, Netflix, and smartphones, the range of content choices for viewers has greatly expanded. Even in such video distribution services, AI that proposes works that match the tastes of each viewer is used.
Mediaization of physical stores is also progressing
Real spaces such as physical stores are also becoming media. For example, the case of purchasing an actual product online after seeing it in a store is a typical example of mediaization. With AI, it will be possible to understand the purchasing consciousness and consumption behavior of users, which will be useful for digitizing real stores.

Types of media that are greatly affected by AI

There are three main types of media that are greatly affected by AI. Here, we will explain the outline and features of each medium.

Mass media
Mass media is a “mass-oriented” communication medium that sends information to an unspecified number of people, such as tens of thousands to tens of millions of people. The mass media plays multiple roles such as news, commentary / enlightenment, education, entertainment, and advertising, and is also known for its great social influence.
Web media
Web media are websites that send out some information on the Internet. It can be accessed not only on a personal computer but also on a terminal that can connect to the Internet, such as a smartphone or tablet.
Social media
Social media refers to media that includes social elements such as information dissemination by individuals and connections between individuals. In receiving information, it can also be a sender of information at the same time, and it is a major feature of social media that “diffusion” occurs due to interaction.
Media AI utilization cases

Vendors serving media agencies are also stepping up their efforts to leverage AI. Here, we will introduce six examples of media AI utilization.

Real-time Japanese conversion system
The real-time Japanese conversion system is an AI technology mainly used in mass media. TV Asahi, which covers the Kanto area as a broadcast target area, uses AI-OCR to display telops of athlete names in overseas sports broadcasts, and realizes automatic Japanese conversion in real time.
Fully automatic real-time subtitles
Internet TV “AbemaTV” uses AI voice recognition technology to develop live broadcast programs that display AI subtitles in real time. In addition, the subtitles sent by broadcasting are saved in the log and can be linked with various recording and broadcasting servers, so in recording and rebroadcasting, subtitles can be sent out with the touch of a button.
Image recognition
AI technology is also used for image recognition. A particular topic in image recognition using AI was “SEER” announced by the Facebook research team. SEER is a self-supervised learning technique from unlabeled random image groups on the Internet. It autonomously examines the contents of the dataset and learns in the process, achieving top-level accuracy in tasks such as object detection.
SNS video collection
A lot of attention is also being paid to “Newsdeck,” a service that automatically collects images and videos of incidents, accidents, disasters, etc. from the Internet using AI and provides them to the news media with the permission of the poster. Newsdeck collects images and videos related to incidents, accidents, and disasters in real time from various SNS, and AI classifies them into items such as “earthquake,” “traffic accident,” and “fire.” As a result, the labor of the employees in charge of the survey can be reduced, leading to an improvement in labor productivity.
Recommended engine
A recommendation engine is software that identifies the right offers, products, and content to website and mobile app users, as well as customers interacting through digital channels, to personalize the customer experience. .. AI technology mainly used in web media is being introduced by major companies such as Amazon and Netflix.
Chatbot
A chatbot is a robot program that handles real-time response work on behalf of humans. AI chatbots use AI’s ability to derive correct answers based on past statistical data and provide accurate answers to customer inquiries. In the media industry, Korona-ka has regained attention as a non-contact technology for measures against denseness and leveling of congestion, and the range of utilization has expanded.
Introducing TRYETING’s AI tools

We recommend the two AI tools developed by TRYETING for corporate personnel who want to utilize AI for internal operations and promote DX conversion. Here, we will introduce the no-code AI tool “UMWELT” and the automatic shift creation AI tool “HRBEST”, the product features of each, and the merits of their introduction.

No-code AI tool “UMWELT”
The no-code AI tool “UMWELT” is a cloud service that allows you to easily introduce AI without a server by using the existing system as it is. With a large number of proven algorithms, no programming language knowledge or special environment required for AI implementation is required. You can easily build AI just by operating the mouse. In addition, the period until the introduction of AI is 1/4 of the conventional one, and high-speed introduction is realized. Another advantage is that the introduction cost is 1/10 of the conventional cost, which is the lowest level in the industry.
Shift automatic creation AI tool “HR BEST”
With the shift creation service “HR BEST” that utilizes AI, it is possible to automatically create the optimum shift by machine learning. Employees submit the “desired date and time of shift” from within the smartphone app, and the shift creator displays the submitted information on the calendar and automatically arranges it. You can also propose “members who are likely to enter the shift” after learning past shift information. The shift table creation work, which was all done manually in the past, can be greatly streamlined.
Summary
This time, we have explained the impact of AI on the media, examples of media AI utilization, and recommended AI tools. AI technology is evolving day by day, and will become indispensable for human life and corporate development in various fields in the future. By all means, please refer to this article to deepen your knowledge about AI and use AI for your own business.

Follow us on Facebook for updates and exclusive content! Click here: Each Techy
June 30, 2024
What is the impact of AI on the media? Introducing examples of media AI utilization

AI has come to be used in all our daily lives and business scenes. The wave of AI innovation is also coming to the media and content industries. In this article, we will focus on “the impact of AI on the media” and explain its outline and application examples.
The influence of AI is spreading to the media

What is AI?
AI (Artificial Intelligence) is an artificial reproduction of a part of human intelligence using software. Humans make many guesses and judgments in their lives, but AI can automatically extract and judge features such as patterns and rules for making judgments.
Impact of AI on the media
In the days when there was only TV, there was competition for ratings. However, with the advent of YouTube, Netflix, and smartphones, the range of content choices for viewers has greatly expanded. Even in such video distribution services, AI that proposes works that match the tastes of each viewer is used.
Mediaization of physical stores is also progressing
Real spaces such as physical stores are also becoming media. For example, the case of purchasing an actual product online after seeing it in a store is a typical example of mediaization. With AI, it will be possible to understand the purchasing consciousness and consumption behavior of users, which will be useful for digitizing real stores.

Types of media that are greatly affected by AI

There are three main types of media that are greatly affected by AI. Here, we will explain the outline and features of each medium.

Mass media
Mass media is a “mass-oriented” communication medium that sends information to an unspecified number of people, such as tens of thousands to tens of millions of people. The mass media plays multiple roles such as news, commentary / enlightenment, education, entertainment, and advertising, and is also known for its great social influence.
Web media
Web media are websites that send out some information on the Internet. It can be accessed not only on a personal computer but also on a terminal that can connect to the Internet, such as a smartphone or tablet.
Social media
Social media refers to media that includes social elements such as information dissemination by individuals and connections between individuals. In receiving information, it can also be a sender of information at the same time, and it is a major feature of social media that “diffusion” occurs due to interaction.
Media AI utilization cases

Vendors serving media agencies are also stepping up their efforts to leverage AI. Here, we will introduce six examples of media AI utilization.

Real-time Japanese conversion system
The real-time Japanese conversion system is an AI technology mainly used in mass media. TV Asahi, which covers the Kanto area as a broadcast target area, uses AI-OCR to display telops of athlete names in overseas sports broadcasts, and realizes automatic Japanese conversion in real time.
Fully automatic real-time subtitles
Internet TV “AbemaTV” uses AI voice recognition technology to develop live broadcast programs that display AI subtitles in real time. In addition, the subtitles sent by broadcasting are saved in the log and can be linked with various recording and broadcasting servers, so in recording and rebroadcasting, subtitles can be sent out with the touch of a button.
Image recognition
AI technology is also used for image recognition. A particular topic in image recognition using AI was “SEER” announced by the Facebook research team. SEER is a self-supervised learning technique from unlabeled random image groups on the Internet. It autonomously examines the contents of the dataset and learns in the process, achieving top-level accuracy in tasks such as object detection.
SNS video collection
A lot of attention is also being paid to “Newsdeck,” a service that automatically collects images and videos of incidents, accidents, disasters, etc. from the Internet using AI and provides them to the news media with the permission of the poster. Newsdeck collects images and videos related to incidents, accidents, and disasters in real time from various SNS, and AI classifies them into items such as “earthquake,” “traffic accident,” and “fire.” As a result, the labor of the employees in charge of the survey can be reduced, leading to an improvement in labor productivity.
Recommended engine
A recommendation engine is software that identifies the right offers, products, and content to website and mobile app users, as well as customers interacting through digital channels, to personalize the customer experience. .. AI technology mainly used in web media is being introduced by major companies such as Amazon and Netflix.
Chatbot
A chatbot is a robot program that handles real-time response work on behalf of humans. AI chatbots use AI’s ability to derive correct answers based on past statistical data and provide accurate answers to customer inquiries. In the media industry, Korona-ka has regained attention as a non-contact technology for measures against denseness and leveling of congestion, and the range of utilization has expanded.
Introducing TRYETING’s AI tools

We recommend the two AI tools developed by TRYETING for corporate personnel who want to utilize AI for internal operations and promote DX conversion. Here, we will introduce the no-code AI tool “UMWELT” and the automatic shift creation AI tool “HRBEST”, the product features of each, and the merits of their introduction.

No-code AI tool “UMWELT”
The no-code AI tool “UMWELT” is a cloud service that allows you to easily introduce AI without a server by using the existing system as it is. With a large number of proven algorithms, no programming language knowledge or special environment required for AI implementation is required. You can easily build AI just by operating the mouse. In addition, the period until the introduction of AI is 1/4 of the conventional one, and high-speed introduction is realized. Another advantage is that the introduction cost is 1/10 of the conventional cost, which is the lowest level in the industry.
Shift automatic creation AI tool “HR BEST”
With the shift creation service “HR BEST” that utilizes AI, it is possible to automatically create the optimum shift by machine learning. Employees submit the “desired date and time of shift” from within the smartphone app, and the shift creator displays the submitted information on the calendar and automatically arranges it. You can also propose “members who are likely to enter the shift” after learning past shift information. The shift table creation work, which was all done manually in the past, can be greatly streamlined.
Summary
This time, we have explained the impact of AI on the media, examples of media AI utilization, and recommended AI tools. AI technology is evolving day by day, and will become indispensable for human life and corporate development in various fields in the future. By all means, please refer to this article to deepen your knowledge about AI and use AI for your own business.

Follow us on Facebook for updates and exclusive content! Click here: Each Techy
June 30, 2024
What is the impact of AI on the media? Introducing examples of media AI utilization

AI has come to be used in all our daily lives and business scenes. The wave of AI innovation is also coming to the media and content industries. In this article, we will focus on “the impact of AI on the media” and explain its outline and application examples.
The influence of AI is spreading to the media

What is AI?
AI (Artificial Intelligence) is an artificial reproduction of a part of human intelligence using software. Humans make many guesses and judgments in their lives, but AI can automatically extract and judge features such as patterns and rules for making judgments.
Impact of AI on the media
In the days when there was only TV, there was competition for ratings. However, with the advent of YouTube, Netflix, and smartphones, the range of content choices for viewers has greatly expanded. Even in such video distribution services, AI that proposes works that match the tastes of each viewer is used.
Mediaization of physical stores is also progressing
Real spaces such as physical stores are also becoming media. For example, the case of purchasing an actual product online after seeing it in a store is a typical example of mediaization. With AI, it will be possible to understand the purchasing consciousness and consumption behavior of users, which will be useful for digitizing real stores.

Types of media that are greatly affected by AI

There are three main types of media that are greatly affected by AI. Here, we will explain the outline and features of each medium.

Mass media
Mass media is a “mass-oriented” communication medium that sends information to an unspecified number of people, such as tens of thousands to tens of millions of people. The mass media plays multiple roles such as news, commentary / enlightenment, education, entertainment, and advertising, and is also known for its great social influence.
Web media
Web media are websites that send out some information on the Internet. It can be accessed not only on a personal computer but also on a terminal that can connect to the Internet, such as a smartphone or tablet.
Social media
Social media refers to media that includes social elements such as information dissemination by individuals and connections between individuals. In receiving information, it can also be a sender of information at the same time, and it is a major feature of social media that “diffusion” occurs due to interaction.
Media AI utilization cases

Vendors serving media agencies are also stepping up their efforts to leverage AI. Here, we will introduce six examples of media AI utilization.

Real-time Japanese conversion system
The real-time Japanese conversion system is an AI technology mainly used in mass media. TV Asahi, which covers the Kanto area as a broadcast target area, uses AI-OCR to display telops of athlete names in overseas sports broadcasts, and realizes automatic Japanese conversion in real time.
Fully automatic real-time subtitles
Internet TV “AbemaTV” uses AI voice recognition technology to develop live broadcast programs that display AI subtitles in real time. In addition, the subtitles sent by broadcasting are saved in the log and can be linked with various recording and broadcasting servers, so in recording and rebroadcasting, subtitles can be sent out with the touch of a button.
Image recognition
AI technology is also used for image recognition. A particular topic in image recognition using AI was “SEER” announced by the Facebook research team. SEER is a self-supervised learning technique from unlabeled random image groups on the Internet. It autonomously examines the contents of the dataset and learns in the process, achieving top-level accuracy in tasks such as object detection.
SNS video collection
A lot of attention is also being paid to “Newsdeck,” a service that automatically collects images and videos of incidents, accidents, disasters, etc. from the Internet using AI and provides them to the news media with the permission of the poster. Newsdeck collects images and videos related to incidents, accidents, and disasters in real time from various SNS, and AI classifies them into items such as “earthquake,” “traffic accident,” and “fire.” As a result, the labor of the employees in charge of the survey can be reduced, leading to an improvement in labor productivity.
Recommended engine
A recommendation engine is software that identifies the right offers, products, and content to website and mobile app users, as well as customers interacting through digital channels, to personalize the customer experience. .. AI technology mainly used in web media is being introduced by major companies such as Amazon and Netflix.
Chatbot
A chatbot is a robot program that handles real-time response work on behalf of humans. AI chatbots use AI’s ability to derive correct answers based on past statistical data and provide accurate answers to customer inquiries. In the media industry, Korona-ka has regained attention as a non-contact technology for measures against denseness and leveling of congestion, and the range of utilization has expanded.
Introducing TRYETING’s AI tools

We recommend the two AI tools developed by TRYETING for corporate personnel who want to utilize AI for internal operations and promote DX conversion. Here, we will introduce the no-code AI tool “UMWELT” and the automatic shift creation AI tool “HRBEST”, the product features of each, and the merits of their introduction.

No-code AI tool “UMWELT”
The no-code AI tool “UMWELT” is a cloud service that allows you to easily introduce AI without a server by using the existing system as it is. With a large number of proven algorithms, no programming language knowledge or special environment required for AI implementation is required. You can easily build AI just by operating the mouse. In addition, the period until the introduction of AI is 1/4 of the conventional one, and high-speed introduction is realized. Another advantage is that the introduction cost is 1/10 of the conventional cost, which is the lowest level in the industry.
Shift automatic creation AI tool “HR BEST”
With the shift creation service “HR BEST” that utilizes AI, it is possible to automatically create the optimum shift by machine learning. Employees submit the “desired date and time of shift” from within the smartphone app, and the shift creator displays the submitted information on the calendar and automatically arranges it. You can also propose “members who are likely to enter the shift” after learning past shift information. The shift table creation work, which was all done manually in the past, can be greatly streamlined.
Summary
This time, we have explained the impact of AI on the media, examples of media AI utilization, and recommended AI tools. AI technology is evolving day by day, and will become indispensable for human life and corporate development in various fields in the future. By all means, please refer to this article to deepen your knowledge about AI and use AI for your own business.

Follow us on Facebook for updates and exclusive content! Click here: Each Techy
June 30, 2024