So we will start the Fujitsu AI strategy briefing. Thank you very much for joining today despite your busy schedule. Let me introduce today's proceedings. First, Mahajan will explain AI technological strategy, followed by Mr. Takahashi's Fujitsu Uvance's AI utilization strategy. In total, the presentation will be for about 25 minutes, followed by a 30-minute Q&A session, and we will have a photo session at the end.
We plan to end at 11:30 A.M. or so. Public and Investor Relations Division Senior Manager Kawahara is going to moderate the session. First of all, Corporate Executive Officer SEVP, CTO, CPO, Co-Head of System Platform, Mr. Vivek Mahajan is going to explain.
Good afternoon. My name's Vivek Mahajan of Fujitsu. As you may know, looking back several years, AI is a crucial existence for all of us. Last month at World Economic Forum, there was an intensive discussion over AI. Needless to say, for Fujitsu's customers, they show a high interest in AI. Today, I would like to share with you our AI strategy at Fujitsu.
First and foremost, I would like to explain about the technology strategy, which will be followed by the presentation by Mr. Takahashi about how to commercialize and turning AI into business. For many years, and several years, so to speak, we have been talking with the customers on five key technologies. Within this framework, AI is defined as the center, and Computing Network, and AI secure AI we are going to offer.
And then, in terms of converging technology, from the viewpoint of social business, how AI is going to be fully utilized by humans. This is something that we made the proposal, including sharing APIs to the customers. We would like to be friends with AI. That means, for that to be achieved, we have our own proprietary AI technology, and these technologies have been developed consistently.
We have a track record in terms of computing, for the past several decades, and it is fused with AI and technology to solve a series of issues that we face. And as you may know, or I don't know if you may know about this, we have over 7,000 track records in terms of the business cases, and we are going to leverage on them so that we can promote efforts for AI.
Well, this is going to be what we are going to pursue. We have seven territories in terms of Fujitsu Kozuchi, seven offerings. And as you can see in this slide, what we would like to do is to continue to consult with the customers, and so that we can share the common things. 40% of the customers show interest in AI, and AI vision, they also show interest in AI vision. And the rest, about 10%, showed interest in AutoML.
Needless to say, I think we are going to offer comprehensive solutions into the customers. The strength of Fujitsu, as I have already mentioned, they are threefold. The first one is a unique generative AI and trust technology. I will come back to this point later. We have a specialized generative AI technology and hallucination suppression and knowledge graph.
We have a strength in graph AI, which I would also like to briefly touch upon later. The second point is the world-class AI technology and the world's fastest computational technology. Fugaku is illustrative of that technology, and quantum computing as well. Human sensing technology is good at the causal discoveries. We have this technology fused with the world's fastest computational technology. The third point is that we offer solutions to customers, so we have a track record in this area.
And with this track record, we always invite feedback of our customers from our customers so that we can promote AI strategy. Over 30 years, we have been leading the market in this market, therefore these are the ones we are going to continue to pursue. First and foremost, this is about unique generative AI and trust technology, which I would like to briefly talk about.
Design production for creators, assisting and design pre-production is one of our programs. In this area, we have our own unique technology and generative AI. For all the creators, all the information is to leverage on unique technologies, and it is important to mix the different general generative AIs. As far as this area is concerned, we have the LLM for that purpose, and the prompts can be used to create different kinds of designs.
I think it is going to have a good impact on animation studios. The reason why we are good at this is as follows. We have this generative AI model mixture technology, as captured in this slide, as you can see in this slide, OSS and competitors IPs. We can mix several multiple generative AIs without relearning and customization.
In fact, we had joint research and project together with MIT over five years. And as a result, we have the world-top-level facial recognition technology by combining those mixture tasks. We have come up with the generative AI models in this way. So, it is not impossible for a single company to come up with the full-scale generative AI model. We are going to combine other companies' AI technologies.
And in terms of the benchmark performance, our technology is ranked at top level. We have the Japanese language LLM, which is embedded. Thanks to the knowledge technology owned by Fujitsu, you will be able to comply with the legal requirements and internal rules. Second point, this is the second strength that we boast of, that is AI by computing.
In terms of computing, as you may know, as far as Fujitsu is concerned, we started our path from processors and all the way down to Fugaku supercomputing. Recently, we have quantum computing technology. So combining them together, computing is extremely important for AI. Needless to say, we have our own proprietary graph AI, and our unique graph AI, and thanks to it, we will be able to digitize the society as a whole.
I have come up with two examples here. The first one is what we failed to achieve in the past, that is, the billion-node scale analysis, and it would develop into several billion-node level the analysis. The other thing is a streaming graph AI. We had a long time studying research on that.
Time-varying dependencies are going to be captured in the form of a graph, and by doing so, we will be able to detect dynamic changes in real time, for example, the whole city. This streaming graph AI technology is a cutting-edge technology, that is, where we have a strength in as well. And another area is about protein discovery area.
This is already introduced. We have our own proprietary AI technology and drug discovery molecular simulation technology. They are fused together. The conformational changes of the target proteins can be detected only within as short as two hours instead of one full day. It is going to have a big impact on the screening of drug discovery. That is going to be another illustrative example I would like to share with you.
The third strength is our track record. In the area, the AI is not something brand new. We have been involved over 30 years. We offered a series of solutions so that our unique AI and the competitors' AIs were introduced to our customers. And based on those track records, customers would be able to leverage on the AI technologies, including LLM.
I introduced the Kozuchi last April, and since then, over 500 inquiries reached us from our customers. There was a high appreciation of the quality of the AI technology for Australia, Asia Pacific, from around the world, not only from Japan. There were inquiries and attention. And in this slide, you can see some of the names of our customers. I think those customers are the ones who have already been implementing specific good projects together with us.
Fujitsu Kozuchi is, has one example, for vision, that is the International Federation of Gymnastics. I would like you to show you this video clip. As you may know, in the gymnastics games, judging is extremely important, and for that, there should be high-quality learning data. And that kind of a preparation of learning data should be done manually, one sheet at a time, one image at a time.
But that is going to be the process that is not going to be appropriate, and instead of several months for the preparation, our zero-annotation technology will shorten the time required to several hours. Our technology has been recognized, and this technology is going to be widely used. And I talked about AI technology strategy. Next, I would like to invite my colleague, Mr. Takahashi, to make a presentation on the business strategy incorporating Uvance, Kozuchi into Uvance's offering.
Hello, ladies and gentlemen. This is Takahashi. I would like to talk about Uvance's utilizing Kozuchi. How do we want to make it into business? First, on Uvance's, so solving social challenges, not only the DX but by looking at the five years or 10 years, we want to embody sustainable transformation. With this background, we have established Uvance's business brand. In particular, there are seven key areas, computing network, AI, data security, conversion technology.
By utilizing these technology, Uvance's business brand will be deployed together with our customers to solve social issues. As Vivek mentioned at the onset, for example, in the drug discovery area, by using computing and AI, by selecting middle molecular area, time to market can be shortened for drug discovery.
In Sustainable Manufacturing, by using PLM, we can improve efficiency, but, in addition, in order to minimize these CO2, we can visualize, but by using AI, we can minimize CO2. So I would explain by explaining some of the examples. First, now Kozuchi and Uvance's. First topic, make Uvance's offering more con make sure to use Fujitsu Kozuchi in Uvance's. For cross-industry solution, Fujitsu Kozuchi will be utilized to make it more convenient for our customers.
And by utilizing such technology, we are able to address and solve challenges and social issues of customers. The second point, so customers will be utilizing AI more, and freely they are able to combine things and utilize technology for their solutions. So customers can utilize AI. So we provide a PaaS foundation or DI Essential.
So we provide a PSI database, and at the same time, we will provide consulting services to our customers as well. The third, use AI safely and securely, which is very crucial for Fujitsu. Over the past, over 10 years, we have activated, AI ethics, addressing hallucination issues. So after thinking thoroughly, we would like to provide AI to our customers. Now, let's look at the offering.
In particular, cross-industry four examples are the one that I would like to explain now. In particular, on these four examples, we will, contribute to sustainable transformation of our customers, which I would, explain later on. There are 12, 22 offerings, where AI technology is used, and that is vertical and horizontal. There are over 20. So in total, nearly 60 Fujitsu Uvance solutions are deployed. So by incorporating Fujitsu Kozuchi, we want to provide easy-to-use, services and offerings moving forward.
Now, Decision Intelligence PaaS of Fujitsu. This is my next topic. In the area of data integration, we have Palantir, Azure, AWS, where data is integrated so that generative AI, image analysis, text information, or predictive detection, AutoML, all those things can be coupled to provide together with blockchain to service. So we provide these services comprehensively so that customers can use whatever they want to use flexibly, which will be available from March.
By so doing, we can help accelerate digitization of customers, and we inject data science to assist DX and data trends of our customers. Now, AI that can be used safely and securely, as mentioned by Vivek. So we want to detect lies of AI, that is, action against hallucination and countermeasures against disinformation to detect fake news or countermeasures against phishing is also considered. Now, let's look at the concrete examples.
There are among the 22 offerings, allow me to explain some of the offerings that we provide. So ESG Management Platform. On this, CSRD or TCFD, we will measure CO2, and at the same time, we need to report the CO2 emissions. So unstructured data is collected, first of all, and collected data will be integrated and analyzed.
That is, crucially important. In practice, you visualize and report, but that's not all. By as for ESG Management Platform, by using AI, you make recommendations. And on hotspots, you provide advice. If you lower this, you will address this area. You can release CO2. In particular, Category 11 Scope 3, how much GHG was emitted by these old products. And in order to minimize the GHG, what you should do. And by simulating in the practice and by communicating with AI, we can minimize a GHG emission.
Such platform started to be enrolled to our customers by using Fujitsu technology and data integration, AI technology. With these integration of technology, we were able to embody this service. Next example relates to supply chain. So generative AI and AutoML are utilized to build a resilient supply chain, which is a very important element.
In particular, Japan is prone to disasters, and at the time of earthquake, we want to secure supply chain, and we have to have alternative parts already to be used after the earthquake. That is very important. At the recent earthquake. So by communicating with AI, we not only grasp the profit and loss, but the over 1,000 parts redirection was made. This is the example of a large manufacturer. So mutualization or precision of PSI is very important.
So, PSI precision can be improved to reduce costs and minimize the negative impact. Please look at the actual example. Let's recap this example. By using generative AI at the time of disaster, you optimize supply chain. Next example is in retail or distribution. Allow me to talk about this a little bit. By utilizing image analysis, you analyze the behavior of our customers, which is in use.
By using image data, you forecast the customers, and heat map and hotspot are analyzed to reflect on display as well as planning of stores. This is a mainstream approach at the retailers and distributors, but what we do is beyond that. On a real-time personalized experience is to be provided to our customers. That's what we are thinking more and more in future distribution. By using dynamic pricing, necessary products are to be delivered to individual customers.
That kind of mechanism is to be provided to minimize food loss or to improve the customer experience. This is the area that we want to address. In retail and distribution, not only optimization of supply chain, but the food loss issue or improving customer experience, these are extremely important. In those areas, by using AI, recognition of behavior pattern and recommendation, can be accelerated.
Let me talk about the next example. This is a process transformation or automation of clinical trial documents by using generative AI. Clinical trial requires a large amount of money. By using generative AI, we can automate and improve efficiency. We provide Healthy Living platform as well by putting data on the cloud. EMR data and vital data are integrated to improve the convenience of customers. This is in the trial stage.
Moving forward, by using AI, we can assist diagnosis. That is the future area that we can work on, though it will be a little bit far ahead in the future. But by using AI, we hope to do this kind of service in the future. So by accelerating implementation of the AI, we want to provide Uvance's offering more conveniently or more robustly. This is our strategy. So including our customers and partners, we extend across in a cross-industry manner to address the social challenges. This is our commitment. So I hope you excited about activities. This concludes my explanation. Thank you very much for your attention.