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out a long, lengthy form of information for that to be processed much later by another human in the loop, per se, to really looking at how do we get an experiential learning and an experiential engagement where the intent is to keep everybody safe and our workplaces safe as well, and then obviously have AI be an enabler in how we get this into the system as well as processed, and that giving us the right signals for predictive intelligence. So that’s a paradigm shift that we are looking at, obviously, in today’s times, and the evolution has taken us to this stage right now. So we as Benchmark Gen Street have been around the business of digitizing environment health, safety, and transforming workplaces for the last 30 years, and we work across the world with close to 450 global…

Subscribers. and about 8 million user base using the system day in and day out for various aspects of compliance, assurance, environment, health and safety to sustainability and ESG management to obviously looking at supplier engagement and quality and security as well. So it’s a time -tested active product and the challenge for us over the last three years has been really to transform a SaaS -based system that we have today into making it AI -first. So that’s been our motto over the last three years in how we convert all of our intelligence, learning experiences into giving our customers AI -first philosophy and methodology of engaging with the platform. I think I covered some of this aspect. And in the pipe, I think we have.

I think we have already 75 different use cases in AI that we have available. but I think our next gen is about agentifying a lot of that to deliver the right value for engagement. So with that, I think I’ll invite my colleague Chandan to take a shot at helping us walk through the use cases and really talking through the value proposition of how AI is here to change the way we do stuff. So, Chandan.

Thanks, Naveen. So now we’ll walk you through, I would say, a story of someone who is working at a work site and how they really look at the different risks and hazards at the work site. So let’s say I am just walking into my work site, and this is something that I see the moment I kind of walk into a construction site. I look at this scene. and I know something is wrong here. I am not entirely sure what is wrong here. I am not sure what kind of safety rules that they are violating. But I know, I get a sense that something is wrong here. Traditionally, in a very traditional sense, what Naveen spoke about, you know, earlier what used to happen is that I am supposed to kind of, you know, go find a form, fill it either, you know, manually on a paper or, you know, find a portal, look at a form and fill it, understand what is the type of risk, the type of hazard that I want to report on.

But now let’s look at what the transformative way of looking into these hazards and risks. So I will go online. What you are looking at is one of the programs that we have. We call it observation reporting. And this is something that is used for engaging people in reporting. Reporting any kind of health and safety concerns that they have at their workplace. What a worker can or anybody for that matter they can really do here is look at let me just I think I am not connected to internet so just give me a second to connect it back but essentially what I can do here as somebody who is using this kind of platform and AI technology I can very easily scan a QR code on my phone or scan a direct photo of something that I am seeing and then look at sharing it with the agent that we have we call it Jenny AI and the moment we kind of share it with the Jenny AI the agent can really process and look at all the different health and safety related hazards that we have and kind of fill that form on my behalf.

So let me let us take an example we will look at the same photo that I was showing you earlier and let us see what that overall process will look like. So assuming I have already captured the photo here. I will locate that photo which is in my phone or my computer. And let’s see what really happens here. What you will see here is the Genie AI, which is the agent, AI agent. It will pass through the photo which is uploaded. And that’s what is happening right now. It is analyzing the image input. It is reading through the intent of the input which has been provided. And it has filled the entire form on my behalf.

I did not have to go and tell it or describe the hazard. It says that there were a couple of workers or two workers who are working at site. And they do not seem to have any fall protection equipment which is there. Now, of course, we do understand that the AI is just looking at the photo. It does not have broader context at this point in time, which is where it will also ask you, do certain things that it does not care about. So right now it is not very certain that while the people are working at height, it’s not certain of what is the height that they are working at. And it will ask you some follow -up question that you can kind of really provide.

But this is where it will help you, you know, update most part of your form, if not everything. Now let’s, you know, assume that I don’t have a photo. I am there in the site and I just want to kind of go and report something that I saw. And I do not, I am not very fluent in, let’s say, English or the corporate language that we use. So I want to do it in my own, let’s say, language. So I am going to use an example. I am going to speak or describe what I saw in Hindi. And let’s see how the agent will respond to that. So I am going to say, I am going to speak in Hindi.

And I am going to say, I am going to speak in Hindi. And I am going to say, I am going to speak in Hindi. And I am going to say, I am going to speak in Hindi. So what I did just now, I spoke in Hindi and I described that I saw, you know, a bunch of people working it. And now let’s see what’s happening here. So the AI assistant, it is analyzing the voice, what I spoke in Hindi, and it is kind of, you know, trying to put that into the form, into an structured data for me to again go back, validate, and then submit it. So what, you know, how it really helps is, let’s say I do not have the safety inspector’s lens or competences, but I still want to contribute and I want to report things.

This AI can really help you, you know, put things in. So you can get the perspective in the right structure and get the data into the system. now let’s say you know I have reported this I saw two people they were doing something which was not really safe and it was reported into the system what’s next? The next step is for us to really understand why they were doing it and that’s where the incident investigation comes into picture it is a process for the industry to look into what really happened and then understand the root cause behind it and that’s something that we do here using the other AI that I want to talk about which we call as 5Y AI analysis and 5Y is nothing but a way of looking into what exactly happened and why it really happened and we keep asking question you know as to why it happened so in this example you know two people were working at height they were not using any safety equipment then the question would be why they were doing that you know were they not trained about it or were they not really, you know, given that safety equipment, right?

So, this is how you look at all the different reasons which really contributed to that particular incident. Now, in this case, typically when we do it in a very traditional manner, what it needs is, you know, multiple people who have years of experience, they collaborate. These are the, you know, team of cross -collaboration, you know, experience. And then they look at all these reasons. But in the absence of that kind of experience, this is where, again, AI can be used as a digital co -worker. So, in this case, the AI is helping me kind of articulate what really happened here, and then it will support the entire process of conducting a YY analysis. So, the moment I click on suggest, it kind of opens up a separate form, takes into account everything that has been reported here from a context standpoint, and the moment I click on…

generate Y statement, it will give me different branches, different options, which I, as a practitioner, I as a supervisor, I can really pick at and then conduct this analysis. And this is the process that I will kind of, you know, go and repeat until I reach to that final Y as well. So this is again, like I mentioned, the idea here is that even if someone does not have that kind of experience, it can use the LLM, the large language model, which are, you know, trained on the latest datasets. And that’s something that can, you can really use to, I would say, substitute the experience part of it. Now, let’s say we have investigated this.

And now we need to also figure out what do we do to really, I would say, repeat the recurrence of similar incident, right? Two people were standing on a drum, they were doing something they were not supposed to do. We investigated it, we understood that, you know, maybe they were not trained, maybe they were not given the right kind of equipment. So, now we need to look at what should be done to really, I would say, correct that. Typically, when we talk about corrective preventive actions, there are different controls that we talk about, right? Not all controls are same. There are certain controls which are more structured, more powerful. We call them, you know, we identify them as hierarchy of controls.

So, in this example, when someone is working at height, the first type of control that someone would look at is the elimination. Is there a way we can eliminate this risk altogether? If not, can we substitute it with a less hazardous risk, right? Instead of having two people climb the height, can we do it through, you know, maybe something else? Maybe we bring in a forklift or maybe we bring in a Caesar lift and we do that activity accordingly. And then we talk about the engineering. Engineering control and then the other administrative control that we have. So many times what happens is, you know, when people are thinking about these controls, they don’t really have a very structured thinking in identifying these controls.

That’s where we have this option or this AI agent which looks into the details and then across the hierarchy of controls which should be applied, we can look at generating those different type of controls. And that’s what you are seeing here. It is giving me a very good first draft on what are the things that I should be doing for preventing the recurrence of similar observations, similar incidents here. So just to kind of recap, this is how AI can really help people in not just understanding the context of what they are seeing at the site from a risk perspective, but also look at understanding the root causes behind it and also come up with the corrective preventive.

actions without, I would say. you know, of course, it’s not again, a replacement of human, but it is a digital co worker that you can have in your pocket and which can really guide you through the entire process that we have. Now, let’s look at the other example here. I think we spoke about fall from height and the risk that you saw there is very, very evident, right? You saw two people who are standing at, you know, maybe three meter height, and there is a risk of them falling and you know, sustaining a fracture. But there are other risks when you work in industry, which are not so visible. And one of those risks is ergonomics risk, risk, right?

It depends on, you know, what kind of activity that you’re performing, right? What type of movement, the body movement, the manual material handling that you’re doing, and it creates a strain on your shoulder on your backbone, and so on, so forth. Typically when industry you know run these programs they need people who are actually trained on these guidelines some of these are called Reba Neosh guidelines and that’s where you need someone who is certified ergonomist to really look and identify those hazards. If you are a remote site if you do not have a certified or trained ergonomist this is where the AI can be really helpful and powerful. All you need to do is take a video clip of that particular activity which is being done and then you run it through this AI agent and it can really help you identify all those risk points that you have.

So in this case what you will notice here in this video is a person who is standing next to this conveyor and his job here is to pick these boxes manually and place it back on on this conveyor. So it might look you know a very very simple activity but if you keep doing this for one hour, two hours, six hours, eight hours a day there are a lot of risks that you are exposed to from your ergonomic standpoint. So if I just kind of run this video, you will notice that the Ergo AI agent is looking at all those pressure points and trying to kind of identify those risks which you are not able to identify unless you have gone through that rigorous training of being an ergonomist.

Once it is done, you can also look at converting it out and generate a quick report here. So the moment I click on summarize, it takes all those learnings, those analysis, and it is creating a ready -made output for me to kind of go and share with the relevant people. So this was an example of, I would say, ergonomics. Now, let’s also look at another example. And I think Naveen spoke about how we are kind of transitioning from having AI as a standalone functionality or feature to now looking at the AI. So if I go to my software, I’m going to go to my software, concept where the AI functionality works in the entire ecosystem and focus on autonomous action as well.

So it’s not just about the inside, but it is also about taking action on behalf of human, of course, within the certain defined guardrails that we have. The example that I’m showing you here is of a legal compliance. Typically, when you are in an industry, you need to go through multiple type of regulatory compliances that you need to report on. I’m taking one such example of a regulatory requirement from one of the steel industry and feeding this information to this particular AI. What it will do is look at consuming this entire information and it will then deconstruct it into different requirements that we have. And this is where you will see that the agent here has deconstructed it into almost 35.

Individual requirements that the industry is supposed to comply with. At a click of button, we can also take all of these requirements into a tool called compliance calendar, which is where these requirements can really be operationalized. Right. You can, of course, interact with this agent and, you know, ask specific questions or give specific, I would say, directions. Also, in this case, I’m asking you to do a quick synopsis also, as well as taking a quick way of auditing this entire activity. So this is where the single agent is kind of, you know, connected and working with multiple of the programs that you have within that defined ecosystem that we have. Now, the last piece and, you know, one of the most important piece that we wanted to share with you is all of these individuals.

Individual AI components that we saw, they tell you, they process a single record and they tell you a story about that particular record. but what if we want to understand the overall trend and the story that all of these data points together they are telling us that is where the RISC -AI comes into picture it looks at processing all of the records that you have each and every record which is logged into the system across different programs whether it is observation whether it is incident, it is kind of processed and that is where it helps you identify the patterns the trends of precursors things which can go wrong so I think again going back to the example that Naveen used of Bhopal there were of course many many precursors before that incident happened in terms of maintenance in terms of safety culture but all of them probably went unnoticed so this is where a system like RISC -AI is extremely powerful extremely helpful which really helps you see kind of trend and help you take a preventive action also The other aspect that it can also do is help you visualize the different kind of risk that you have in your organization.

So using this chart, and let me just refresh this for a second. But what you will be able to do here is use a mathematical model to assign a severity of different type of risk that you have in your organization and visualize those on a heat map. In this case, what you will see here on the x -axis, I have the volume of those different risk categories which are captured. And on the y -axis, I have the overall weightage risk score. If I just talk about some of the examples here, for example, slip and trip risk, you will see that the count, record count is on a higher end. There are almost 75 records which are tagged to this category.

But the weightage risk score is comparatively low when we look at some of the other components here. Such as fall from height, because it is taking also into account the inherent risk that you have in that particular activity. So this is how it can really generate a very powerful picture of helping you understand what are the areas that you need to kind of focus on from a prevention standpoint and also provide you a bit of predictive intelligence about what and where you should focus next, both in terms of the different part of your workplace, organizations, as well as the different kind of activities that you’re having in the system. So with that, I will now invite Naveen back on the stage to kind of, you know, Naveen, anything else that you want to add from a closing standpoint?

Thank you, Sundar. I think this is great. I think I had a few friends come up to kind of comment on the demo, which I think they could relate to a lot more, I would say, from an industry standpoint. But overall, I think as benchmark Gen Street, I think our journey this year is going to be focusing around some of the stuff that Sundar showed. which is autonomous agents being able to do a lot of the stuff, a lot of the heavy lifting that is required by individuals who were earlier wanting to engage with a platform and type in all of those details which are required. So I think with that, I think hopefully we’ve been able to do some good justice in helping you understand how AI can transform a function like safety and look out for us over the next year or so in making it a completely agentic platform.

So with that, ladies and gentlemen, thanks a lot for your time. We do have some time for questions if you do, but yeah, otherwise we have a booth back in the room when we can have a lot more personalized conversation if you’re specifically interested. So thank you. Any questions, please let us know. All right. Thank you again. Bye.

A bumblebee cannot fly but it still does. The thing is that when this statement was made in 1930, we understood very little about aeronomical designs. And by 1980s and 1990s, more research came up and we realized that okay, a bumblebee can truly fly because its wingspan and body weight support a variable flying measure. That is what design does. And AI only understands what we know of design today. not what we can create with it tomorrow. Creativity is today’s human advantage. AI can generate, AI cannot originate lived experiences. The context, the culture, the emotion, the meaning and the human intuition matter more than ever. Good design is not about a good drawing. Good design is about good solutioning.

Good design is not about beauty. Beauty, good design is about good solutions. And good solutioning needs good understanding of design. And hence, I make a very provocative, bold statement today that resumes are going to die by 2030. Because the skills that you may have learned today, I may have learned so far today, may become irrelevant. AI probably will be able to do everything faster, better and at a much lesser cost than us. So then which is that? Not one skill. that remains extremely, extremely important. Design and creativity. The workforce that we need 5 years from now, I’m not even making a bold statement by saying 10 years from now or 20 years from now, which can think across disciplines, which can collaborate with machines and not compete with machines, which can communicate visually, which can learn continuously, which can adapt to context, to cultures, which can build and shift fast and which can adapt without fear.

And hence, being human becomes your advantage, even in the age of AI. And how do you become more human towards solutions is by the essence of imagination, that is creativity and design. Good afternoon, I welcome you all to this session hosted by Code. And now I welcome my colleague Garima, who will invite all the panelists and we’ll continue the discussion. Thank you so much. My name was Magma Sree. Thank you.

And we are proud to have Dr. Shweta Chaudhary, founder and director of CodeEDU and host of this session, a leader working at the intersection of creativity, learning design and future ready education ecosystem. Thank you all for being here. And I would like to thank Dr. Shweta Chaudhary for his time .

Hello friends and thank you for being here with CODE, the Centre for Originality, Design and Expression. Why we need it? I am thankful to Umang for setting the stage that yes, the bumblebee can still fly. So what is it that in the age of AI will keep all of us the way we are, the humans? I am privileged to have with me an August panel which has been through various walks of life as a student from esteemed institutions, as workers, colleagues and administrators into institutions of high repute and public administration, as founders who have struggled and evolved built systems which have handled talent at a larger scale. So let us hear from them what it means to them that why and how the human intelligence will stay, should stay, has to stay in the age of artificial intelligence.

I would prefer to begin with Sir from Sunstone, Puneet here, Piyush here, sorry. Piyush sir, what does this word creativity, cognition and culture mean to you as a person? That’s the best way to introduce ourselves. Rest of the chat, GPT tells about us. So today he asked. He can’t tell.

I think these are all the pillars which define any human being. Because whenever we talk about Vixit Bharat, whatever we may think of, we might have things like GDP coming our way, but at the core of it, it is the human capital. So, whether it is personally us or we talk as a nation, creativity, cognition, culture would always be the key pillars. As a founder of a tech school, I can tell you that today coding is no longer a skill. It’s table stakes now. Because how you apply that, how you solution to that, that creativity works. So, I hope this will help you lead the way forward. You can ask any question, write me an essay, create me, you know.

Give me these points. But if you are not prompting the system again, you are not challenging your cognition. You are not challenging your thinking process. And thirdly, culture. I think we have a big 5 ,000 -year -old heritage to live with. There are more than 22 languages, innumerable dialects. To be able to take that along, to be able to understand those nuances is also very important and not to be ignored in this AI -led world. Therefore, no matter where this, and it is quite evident that AI is shaking up a lot of things and will shake up a lot of things. But the power of creativity, cognition and culture is here to distinguish. what is human led and what is mission led.

Perfect. So it’s not about the countries or the continents that are fighting to fight about who owns it but it’s about the human beings of those countries and continents who will be owning it. So let’s keep us intact and keep our humanness. Thank you sir for that take. Let’s have sir who comes with a background of public administration. Sir, how do you think that creativity, cognition and culture in this setup really keeps you intact or how does this value into your ecosystem?

Thank you very much. First of all, I thank CODE for organizing this beautiful session and the kind of passion they displayed in insisting upon me to come here is laudable and that’s why I am here and I would also like to tell everyone that I liked what Umang said in the beginning very much. Beautifully he presented in a very brief thing. I told Umang personally that I liked what you said. so you know i’ll tell you how i mean creativity cognition and culture why it matters i was going around this stall i also came day for yesterday for something else so i was trying to meet this this floor has a lot of government ministries so i was trying to meet trying to find out if there is some officer from any ministry i didn’t find except in ministry of skill development stall i met a lot of youngsters consultants other people so i said are you worried about ai or are you happy about ai more often than not i said we are worried sir i also across you know across the spectrum i try to meet a lot of people and talk to them engage them it’s great fun and great learning actually so i found that you know there is a lot of fear and i found that you know there is a lot of fear and i found that you know there is a lot of fear about ai and people are actually not very clear about what kind of changes ai will bring And in this kind of fear and anxiety, we should not forget that our originality, our USPs as a human being, they will matter much more than today in the world of AI.

Because AI is what the data tells the tool or the bot to give us. If the quality of data is not correct, for example, today if the data is not reliable, the results will not be reliable. So one of my friends said that, sir, AI is not like you ask a vendor to give an AI solution, he gives you and he goes. Like an IT solution used to give a software, a computer laga diya, ho gaya. It’s a continuous engagement to improve the results. Because the AI bot will improve its own results, different habits, colors of skin and languages. I think that will matter much more in the future. Thank

A very beautiful take as put up by sir. One thing that’s most resilient is us. amongst all the crowd or amongst all the stages that are set up. Something that differentiates is our originality. Yes, and that is to be kept intact. Coming to some very beautiful solution, I would say, or innovation to education comes from a university which itself is formed on a very innovative format of education. I would request, sir, to define his definition of creativity and cognition.

Yeah, thank you for this opportunity. So, as an educator, when we jump into this new term called orange economy, so the new orange, how the orange would be. So we have seen oranges, but we have not seen orange economy. So the new terminology came which defines what creative, how cognitive, and the culture immersed together to define the human being. I represent South Asian University, which is the first university in the world set up by Sark Nation. where students come from all eight countries. So people come from Nepal to my university, people come from Afghanistan to my university, people come from different destinations. We represent Asia. So within Asia, are we same by cognitive thinking? Within Asia, we are same and we are sharing same culture.

So culture to what extent? The same culture. Are we different in culture? When we say creative, Indians are more creative than the neighborhood. Neighborhoods are more creative than Indians. So when I look at this international perspective to my institution, I engage with more students and I evaluate critically what you do better. So as an educator, when I say I am in the age of AI, we are already in the immersion of the AI. People have already started AI. Students are already using AI for different tasks. So when a student comes to me, sir, I have done this work, I ask, show me the problem. I first thing I ask show me the prompt so it is not the skill that you have done assignment you have done reports it is done by GPT your skill like that you said that your skill is how you define redefine your code how you build the skill to understand the code coding is not a new thing now lot of training usually happen but what remains us creative human being how you apply your creative brain that always you can’t beat technology always assist you I believe with my personal opinion technology always assist you technology bring efficiency in you technology support you but the decision -making is always lies in the human brain are we are using the technology responsibly we are using technology ethically there are so much concern as a educator I have to train to my new human resource And I believe this new orange economy will give lot of opportunity in the time to come to the people who feel if my job is displaced, I’m sure some new job will come that you have to learn, you have to survive and you have to adapt with the change with AI time.

So that’s my perspective as an educator.

Yes, sir. Beautiful perspective where he tries to say that culture and cognition and creativity bind us as Asia. That’s a very beautiful definition and separates us from the Western world and they tell us that this is what keeps us together. And at the same time, this is what will keep us going will be the cognition. Thank you, sir. Coming to Satya, sir. Coming from an IIT as a student, getting into a technical field of learning, engineering, and then coming to administration. I mean, all these numbers and domains, or I would say background, sound very redundant and sound very boring. So how still creativity stands with you, creativity, cognition and culture keeps you going.

Thank you so much, Swetha. Thank you very much, panelists. And I would like to thank that this question is being asked. And I just want to describe the way we are sitting here in this hall, right? After every one hour, this hall is changing with the audience, speaker and everyone. And this is the standard setup. I would like to give the answer for all the three things. This is the standard setup is being provided for all the type of stakeholders, be it global, different types. The type of creativity we are putting or hosts are putting into this, you will see after every hour, this is being changed. And the kind of cognitive inputs or… discussions are being done must be different and it will creating level of the discussion and reception of all the stakeholders and in the similar manner the kind of culture both the sides be it speaker side or audience side or host side it will be literally different so my main just to answer this question this is a very subjective and with respect to AI AI is artificial intelligent it will always be artificial and human intervention human inputs humans with inputs of human creativity cognition and this culture will always surpass the best thing I would like to agree with professor Ashish that the kind of input prompt and expertise of that particular field which always surpass and that’s why I was discussing with Upade sir that everyone is scared, children are scared I say that there is no need to be scared with them, on these things in fact, their level of artificial intelligence will be very good they will be able to give their input through their input so there is nothing to worry about

so nothing to worry about friends the walls are going to remain the same we are the emotions into those walls and that is what is going to make the difference so let’s keep the emotions and the humanness intact, I am happy to say that all my panelists here strongly say that human intelligence will supersede the artificial intelligence let’s talk about the audience, anyone who feels that the artificial intelligence is going to top us and the humans are going to stay behind or all of us are on the same page let’s start with you audience, any take on this? human intelligence or artificial? what comes first? all agree to the panel yes sir please

you mean to say there will be a timeline where this human intelligence will cease to supersede it’s a time bound super time bound position basically as AI improves is there a timeline like after certain years that AI will be better than human intelligence

please say your good name please so Saurav says there is timeline to it

I think certainly there is merit to the line of argument which you are making so as AI becomes more and more intelligent our education system is continuously under stress. It is being tested. There is a stress test happening all the time. What’s happening is that the shelf life of hard skills is really diminishing. Like if I could hold on to a skill and take my whole life career with it then it came to 20 years and now it’s a matter of couple of years, three years, four years and so that the shelf life of hard skills is really shrinking. But where we need to focus is that not only making. Earlier we used to say that don’t learn but make things.

Now it’s not only about making things. You have to understand the meaning of it. You have to apply it. Rather if you can say that not only artificial intelligence but applied intelligence is where humans are going to really be there. Because I can tell my student to code. They can make a chatbot, right? But can that chatbot tell, let’s say, a farmer in MP that whether he will be able to sell it at a good price or not? So the application of it, the solutioning of it will matter. And again, the timeline, I think it’s right now not an easy answer. But that’s the direction where we can at least all go and we know that we can expand it further.

Okay, thank you. Yes, maybe that’s one of the reason why we all talk about the fear. Is that a timeline or it will be the resilience that will go forward and the humans will become smarter or the artificial intelligence will become smarter, yes? So it’s a generation that all of us are going to see and go through. So let’s keep ourselves crossed for it that we will remain the smarter ones as the panel says or the generation sitting on that side, many of the young faces don’t even want to answer. Because they are waiting and trying to tell us. that every next is smarter one.

Yeah, so actually, as I was mentioning you that I was interacting with a lot of youngsters and in the overwhelming feeling that I got from all of them, apart from fear, is that, you know, everybody is very unclear and unsure about how the AI will shape the world in the future. I mean, he said about timeline, which could be 10 years, 20 years, I don’t know how many years, even I don’t know. Nobody, none of us are sure about how things will actually unfold in the future with more and AI systems being more and more smart, data becoming more and more strong. And so there is a lingering fear among everybody in terms of what will be the impact as it unfolds because we actually don’t know.

And there are no mathematical models or something which can predict how things will unfold. But as I said, as an administrator, as a public policy person, for me today if somebody asks me a simple question that tell me what should be the purpose of having ai i would say that you know when i go to a village i find that you know there are a lot of people sitting without any work they don’t have any money in their pockets they all look for some kind of employment and they are not very well educated so they are very poorly educated in the village school you know a lot of absentism happens in government schools in the rural areas people don’t go or whatever you know i mean all of you know this so for me i mean the for example if i talk about ai in education so i should be able to use the ai bot or ai tool to examine a person’s background very quickly and find out what best i could skill i can give to him so that he or she can fend for himself or herself have a decent job And I don’t think of anything bigger than this because there is an army and army and army of people who have no job.

And that scares me more than the AI because in the future, if in such a big country, so many people will not have work, then this may lead to social imbalances and problems. So, for me, AI should be able to, and AI does it actually, because in a class of 50, 100 students, 40 students, AI, with the help of AI, we can find out about each and every boy and girl’s learning abilities, how much quickly they can learn, and then we can design programs for them, which a human being, a single teacher cannot do. So, anything which we do with hand, for example, plumbing or repairing a vehicle or doing any hardware kind of. Everything AI cannot do.

It has to be done by hands. Robots can do one day, maybe, but then. when will that time come again I don’t know so these are my takes as far as our country is concerned or South Asia is concerned because today you know we are having neighbours Nepal, Bangladesh, Sri Lanka, Pakistan in this neighbourhood we are sitting if any one of the neighbours as we have seen in the past is disturbed the country gets disturbed so in our own interest and there is too many people all around in South Asia so if people can get some kind of job as per their abilities with the help of AI that would be the best

application so friends yes that’s an important take to understand that where the human capital will go and what will be called the human capital how does you keep it sustained yes ma

so I want to know in a developed India we youngsters from 18 to 25 years for us it is very difficult to understand it is very easy to search a lot of things on YouTube search on chat GPT but what about our parents what about our kids what about the people who are like right now in the age of 2 to 3 years and they are learning now they are depending on chat GPT or my parents are afraid of chat GPT what can happen in AI how can AI be a fraud when are we going to teach them how to use it 140 crore people when will all of them be trained how to use AI

I mean I would just say how did you teach them how to use Instagram they haven’t learned yet people are still afraid so the easier it is the easier it is it’s all about intensity education has something I will talk to everyone about it education requires an intent for sure if you have an intent then maybe yesterday a teacher first your mother and then the technology is training you to become there to reach there we will take this question again with our panelists also and also discuss about human capital per se which is the human capital human capital is scaring us and when will we be able to train it and going forward how will we be able to make this human capital so sir as a professor how do you look at professors to become better with AI?

Or what’s your take on human capital of educators?

Yeah, so this is an educator’s dilemma. To what extent we support the use of AI and the more important ethical and responsible use of AI, right? So the question came, when do we have to teach people to use AI? So as an educator, when we see, do we have to start from school? Or do we have to start from college? Or when we go to higher education? So the most important foundation comes from the school. If you look at the kids now, they are more gadget friendly. They have tablets at home. They have IPTV at home. They have mobile at home. The kid has WhatsApp too. The kid operates WhatsApp on his own. The kid has his own group of school.

Teachers put topics in it. The kid asks the meta, that I want this meta, I want an article on this topic, of 100 words, and the meta scripts it and gives it. and he copied it from there, put it in a notebook and gave it to the school. This is not a challenge, actually. People will learn. People will learn by default. People will learn by training and people will learn through pressure. High work performance environment, where you will have to learn that technology. The problem in school is, how much ability we are able to use. That is cognitive thinking. Has that student used as much brain as the teacher said, this assignment has to be done.

He copied that much and put it in the meta, or put it in the GPT. He did not make the effort that we used to make at our time, when we used to search by ourselves, open the book by ourselves, make the notes by ourselves, and he is doing all the work in GPT. So the question came, to what extent cognitive skill remains strong into the market, learning is not a challenge, learning is not a challenge, learning is not a challenge, learning is not a challenge, learning is not a challenge, government of India is also taking lot of initiative through digital skilling. Kahi saare portal create kare kiye gaye hai jahaan par government khud ye chaat ye koi bhi citizen of India un portal par jaa kar apna registration in fact I did registration yesterday only to AI readiness.

Ki main AI ready kaise ho sakta hoon. Right. I know something of AI but I may not be perfect of using the AI. So that is my ability to learn fast. Government is has launched such programs through digital skilling portal jahaan par wo free mein apne citizens ko wo training dena chaati hai. Second perspective new education policy. Government is constantly trying to make over, re -look into the perspective of policy but again the challenge comes kya hamare school AI training ke liye tayyar hai. Infrastructure AI ke liye chahiye. AI lab humko chahiye. So learning, people are willing to learn. There is no resistance to learn, but how to support. And more importantly, I always emphasize ethical and responsible use of AI.

One more example. A few days ago, people started creating their image from Ghibli. Who taught them? Ghibli images were… Millions of images were created by family, housewife, homemaker, cooking, restaurant. Everyone put their DP on their WhatsApp. Everyone put their DP. At that time, we didn’t think about privacy. Where will that photo go? We didn’t think. Instead, we thought of creative images which has been created. Now people think about GPT. A carrier creature should be created. Chat GPT will create everything which you do. Who taught? We have learned it by learning through enemies. A friend told us. YouTube told us. Chat GPT taught us. Be clear. How to create? How to create? caricature. Yes. So indeed the human human capability to adapt is an important aspect.

Hi, thank you so much for this valuable inputs like government is there, universities is there. So my question is like how we are thinking about this ruler areas. Like they say already there are many universities, there are many colleges. But still after 12 candidates, first year, second year, final year candidates is not getting that much knowledge like what is going on in AI. Now people don’t even know what is to be studied in AI. It is not clear yet. How does AI work? How AI engineering, feature engineering, this data science, data analytics fields actually work? So how does this field how does this ruler and how does this ruler and urban is fine. How will it go to ruler areas?

Like I belong to a city. Murtujapur, whose name is also unknown. So there are such children who want to learn but they don’t have money. So what is the government is planning for them? Universities which are fine, everything is best. How they can shape these underprivileged candidates economically backward class.

So, yes please. That’s a very beautiful thought that comes and in a country like ours where diversity is huge we need to understand that not everyone has that take. We would also want to listen it from both our panelists sitting on both sides of me. One coming from the government and one who has developed education in a decade. Seen India growing with it. So sir, what is the systematic approach and what it has what is the process to make all of us understand that this is new but it will not stay new. When the efforts keep going continuously, we reach somewhere. So, sir, a portal like GEM, a government -y marketplace where you have been in the part of the system right from day one of inception till today, how do you really think that the people have to trust on something of a government initiative that it will reach and become everyone’s kitty?

Any person who is having a GST number, they can onboard, register on this thing. And they can participate in various products and services of the government. And in that, as madam is asking, to validate and recognize it, all the vendors, if they have any services and products, they can be onboarded and as a reliable product and source services, we properly do vendor verification and after that, we onboard its catalog. And if all those products are to be purchased by the government, services are to be purchased by the government, then tender process can be done there and can be purchased through direct market place. So I can just say that, if I am correctly pointing out taking your question ki jo ye services aur products banenge agar AI entrepreneurs jo bhi hai jo unke product aur services banenge toh wo sarkar unko sahi madhyam se leh sakti if I am able to take yes there is a system that has to start a day 2016 jain shahid JEM shuru hua hoga humne nahi socha hoga ki that we will be able to reach to this level where every smallest of the manufacturer or vendor from the rural area, the urban areas and every part of the country can become a part of a sales or a buy, sale and buy purchase platform of that scale so it takes time for sure, technology integration and adoption and also making it a part of mainstream is a process and journey let’s listen it from Piyush Piyush how do you say for such a diversified scale the question here

sso I think AI apne aap mein ek bahaat bada democratising tool hai learning ke liye hume kya chahiye hota hai? Ek self motivation chahiye hota hai aur ek koi madhyam koi medium jo hume koi padha de jo samjha de Now this part of the problem is solved by AI Right? If you have motivation and the assumption being we all know internet is everywhere now Specifically for tier 3 towns for rural areas Now anything and everything can be learned as well as anything and everything can be built Now you will see that as a trend we will see lot of solopreneurs single person small setups kyunki aap website khud buna sakte ho creatives khud buna sakte ho marketing content khud likh sakte ho everything you it is now more empowering rather so I think it is only more democratizing and more empowering for the rural India you can build things of your own you can have aspirations which earlier needed lot more things which are now possible.

I would like to give, as a parent I would like to give your answer. I have two daughters. My elder daughter is in NID Ahmedabad. And in creativity, NID Ahmedabad is quite good. And my younger daughter is in 11th class. She is a player and plays in 11th class. My elder daughter, because she didn’t have much, I asked her how you are going to deal with AI. She said, I don’t have to deal. The answer she gave me I want to tell you how the children are thinking. Our curiosity will be reduced. Professor will also be finished. She said, I don’t need to be afraid. I am studying. I will learn that first. As long as this tool is made, it will be for all of us.

I have to understand how to use that tool. And for that, until I don’t read the subject well, until I don’t understand the subject well, there is no benefit. So I don’t have to fear about what is happening. And the younger daughter, because in her college, as you said, sir, because in addition to the responsive and ethical use of AI. So once I said, how do you do notes these days? So she said, in our college, a lot of my school children use chat GPT. So I said, you don’t use it? So she said, if I use it, I won’t be able to use myself. So this was the answer from her. So in addition to what sir, professor sir has mentioned, we have to educate them for ethical and responsive use of such kind of tool, not limiting to AI.

So perfect, sir. I would like to add one answer to his question. That. Rural urban divide has always been a challenge in India, right? I remember the time of internet 2k internet launch hua it has the perceived that internet kaha tak jayega pehle shehron tak tier 1 metro then now you see the penetration of internet to aaj gaon tak internet pohucha wo infrastructure create hua government ne utna scale up kia telecom companies ko utna strong banaya ki wo us market tak pohuchka aapko aaj ek affordable rate me data de paye right aaj rural humari population hai that plays significant role in terms of economy in terms of business in terms of small business also and I am agree that education is also a fundamental right for the student who live very far areas of India AI ko bhi wo infrastructure chahiye AI ko bhi scale up karne me utna time chahiye the way internet penetrated slowly AI also in its studies in school, in course in its curriculum, in its lab it will assimilate slowly things moving we always look at where technology comes from, where technology is made and how technology diffuses technology sometimes don’t go immediately to every places, sometimes there is systematic movement of technology from one place to another place one more thing should we fear?

it is better to learn the things we should not afraid of whatever challenges us we should adapt to it better the version is the things which challenge you, it is better to look at it, adapt it and find out the new solution that how we can bypass how we can surpass and how we can head to head head to head competition with the technology right Maybe there is one question by Saurabh That with time technology will become so strong That it will surprise human beings Maybe it could be When you as a human being Feed the LLM When you as a human being You feed the LLM What could be the possible situation of a cricket shot Then the Chetji PT will tell you You have to play this shot Which is unique Because you have to feed the LLM first How LLM will know The large language model Has to understand the human What human think first Then the LLM work in the background For the answer to be the correct one The question has to be the correct one And the question has to come from the audience And the people How do we take it forward

Yes sir Namaste Thank you Thank you for a wonderful session I joined in late It was a little far from the other one So for India to become the India of the world we are talking of a 4 trillion economy 7 .3 trillion in 2030 it could easily be 10 trillion if we start trading our people we are not trading our people so if anyone of you are connected with policy makers Indians who travel outside India should suppose I travel to the UK and I work in the UK and I pay 40 % tax in the UK then 33 % of that 40 % should come back to India if I want to maintain my citizenship of India we will become 10 trillion economy just by this if Donald Trump can play everything we can also play so that’s question number 1 if anyone has any comment on that question number 2 is someone mentioned about motivation for education and the medium for motivation motivation comes and goes I think I would like to hear all of you all all of you all of you The power of confidence and does stereotyping in the education format that we have today, does it enhance confidence or does it kill confidence?

Because I meet so many students across the country. Last week I was in Hubli, I was in Belgaon, you know, I traveled to the depths of the country. And you’ll meet students who are fantabulous. People say we have Gen Z right now. I think we have Gen X, Gen Y, Gen Z, all 25 year old, depending upon the geography they are in right now. So fabulous students, but low on confidence. They are not from the city. So what does the education system do to multiply and enhance this confidence? If confidence increases, then it will do everything.

So I’ll take the second question. I think as an education system, we have to move towards a more inclusive education system. from learning to creating to applying. And when we create, when we make things, there is a different kind of a dopamine release which is there and it also gives you confidence. If I have built a working prototype, which right now, currently the education system by and large is not really supportive of creating things. It is about learning things. And today’s discussion where we were, you joined in late, is that even this is not sufficient now. Now we not only need to create, but we also need to apply it. That is it useful? Okay, this thing exists and it works.

But is it useful for someone? This is the next level because with the AI coming in, this is where we need to be. But by and large, to answer your question, I think the confidence really comes by creating and not just by learning. I think the confidence is the key. I think the confidence is the key. I think the confidence is the key. So if we have more and more creating opportunities, building opportunities for our students across the board in education, across programs, across the board, I think the confidence level, and this is right from K -12, I am not even talking only of higher education, this is right from K -12, I think more and more creating is what we need to really instill.

Thank you, Piyush. We would say that, yes, there was a time when knowledge was the reason of confidence. I know it, that’s why I am confident. But today it is that because I can build it, I can have the confidence. So friends, we are moving from the age of knowledge to the age of cognition, from the age of knowing something to the age of creating something. That is where we are here to discuss that it’s not just the artificial intelligence that is going to take us forward, but it’s all our collective cognitive ability that’s going to keep the Vixit Bharat or the India or all of us to come back to India. because yes, we were this what we are today but to get you all back to all of us we have to stay the way we are and that is something that is going to get us in that let us keep this intact the layer intact, the context the culture, the creativity of ours with that friends, I would thank my panelists for being here with us because we have to take this stage forward and the floor will be open for all of us to discuss we also have a team to have a product demo and a few of our friends to join hands together in taking creativity and cognition way forward in education so I would thank all of you for being such great listeners and be here till the time we have the next part of this session coming forward with the product demo I would thank my panelists to be here and would request for a group picture with all of us can we have a group picture so friends this is an inaugural or unveiling of one of the products that we say may I request the team also to come forward the team here may we have Ajay Rivalia sir, Viplav sir Nandaji, Garima ma ‘am Mansi the moment Vijay sir may we please have you here yes we have discussed about creativity and cognition this is going to be the tomorrow and this is something that is going to keep all of us intact so we are the torch bearers to it and we present to you a product which is going to make it better and educate all of us for creativity and cognition may I request our mentor Unkar sir to please join us Thank you, friends.

And now put your hands together for a product demo, which the AI -led education platform brings to all of us.

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Thank you. Thank you. May I please request your statement for how do you take it forward along with your current work that you are doing how will you add it up as a creativity layer to the systems

No, I think this is what would separate a graduate from a real world professional because we really need this layer beyond this, everything wrote, everything monotonous is going to be taken up you know one gentleman asked about the timeline, that question is pretty real and I think partnerships like these will really help us future proof our students, so really looking forward

Thank you sir, we also have our team from MEC Connect from across the borders with us to join hands The Next Gen Academy Thank you

So, we are proud to contribute to this academic industry partnership by bringing the design -oriented courses to our coding students. Our focus is that our students should not only learn the coding. They should have idea about the design thinking, digital thinking. They should apply all these stuffs in the product development. We want to make them entrepreneur. So, definitely this product going to help us a lot. Thank you.

Thank you, sir. We have a strong, strong education partner with us called Nimbus. Yes. Learning is already there with the academic institution. So, I saw this entire presentation was really great. One of the thing which, you know, I saw here was with learning, the accessibility should be there. So, we have solved the problem of accessibility. But with CodeEDU, I guess collaboration, providing the next -gen courses, exploring or connecting or creating network with developers. Definitely help the students to be industry ready. And, you know, really do wonders into this area.

Thank you sir, the make connect We are going to I am happy to see that the product, to be frank we are too excited as well and I as she said that we will be taking it abroad and see that we have a platform of students so we will be definitely taking it and joining hands with them on this Thank you. So thanking our partners may I request our partners Ajay Rivalia sir, Vipulov sir to please come forward for this for marking this milestone time that we have we have good education partners with us who plan to take us forward not just across the country but across the continent and make our intent stronger make our intent stronger within MOU that yes together we stand to make the education more meaningful for the Vixit Bharat to come so may we have a picture to document this All of us are overwhelmed to stand on a stage which the government has provided us with, so we want to be a part of this milestone.

Thank you. Thank you. Piyush sir, Gyan Prakash sir, please come on stage. Thank you. so with this I will conclude this session I hope everyone enjoyed this insightful and wonderful session and everyone agreed with this AI may automate ecosystem and system but creativity determines direction thank you so much everyone

So congratulations to all of you. You should be proud. And I just want to say that on behalf of the 30 ,000 AMDers worldwide, and particularly 10 ,000 in India, I just want to congratulate you and thank you for this opportunity to have this discussion. Since we are a small group, I think we’ll keep it informal. And I want to make sure that somebody please keep track of time so that I do justice to my colleagues here and the dais. The topic that I’ve been asked to talk about is sort of building AI readiness from compute to capability. In AI, in the field of AI these days, there seems to be an over -indexing of AI and GPUs. When in reality, AI is much broader.

GPU is obviously a significant part. It’s a part of the core infrastructure. But what we do at AMD is to really provide a full suite of AI capability from AI on AI PCs to core infrastructure to all the way out to the edge. And I have my colleague Tim from AMD, so we decided that we’re going to tag team. And so I’m going to focus perhaps a little bit on the sovereign side, and then Tim can focus on the enterprise side. That’s okay with you. So let’s just talk about sovereign AI in practice and exploring the motivators. So this particular slide is something that is created by the Department of Energy in the United States as part of a new initiative that was kicked off by the Trump administration called the Genesis Initiative.

and I had a role to play in terms of trying to support and address in crafting this initiative and the framing is very simple. If you look at the top line, I don’t know whether this has a pointer, it’s okay. Okay, so the top line, the white line is funding in the United States for R &D. Today, the United States spends about a trillion dollars a year in R &D. That’s just my involvement. Not all of that is government spending. It’s roughly about, say, 20 to 30 % U .S. government and the rest industry. The bottom line is what we consider research. Output efficiencies. So the problems are getting harder. it is getting more challenging and even though we are trying to tackle really important problems the sense is that throwing money is not having the same rate of return and this slide basically asks the question how do we reduce the gap and at least the thesis for the Genesis mission is that you can use AI as a way to accelerate scientific discovery the Genesis mission has three areas of importance for people who don’t know about the US Department of Energy the US Department of Energy is the nation’s largest physical science agency so it has it operates through 17 national labs and some of the earliest ones, like the one Oak Ridge National Laboratory, which I used to lead before joining AMD, came to being during the Manhattan Project.

And Manhattan Project, about 65 % of the entire funding of Manhattan Project was at Oak Ridge National Laboratory. And in addition to, you know, in fact, I think the Prime Minister mentioned that, about nuclear energy, both the destructive aspect as well as the significant outcomes that came out of that from nuclear medicine to nuclear navy to nuclear energy. These all came, you can trace back to Manhattan Project. So U .S. Department of Energy is not only responsible for energy, but it’s really a science organization. It’s got three priorities. One is just discovery science. The second is energy. And the third is national security. and national security. America has a really interesting thing, a way of keeping the nuclear arsenal away from the military in the sense that it is the U .S.

Department of Energy and not U .S. Department of Defense or Department of War that is responsible for the nuclear arsenal. And the three lab directors, Los Alamos, Livermore, and Sandia, has to certify each year that the arsenal is ready for the President of the United States. So this is a piece of the hypothesis. If you think about research, you can look at the left side. It starts with hypothesis, then you conduct experiments, get the data. And today, you take the data, use AI, machine learning, et cetera, you get analysis. What you’re trying to do is to make this much faster so that you can have science outcomes coming out. That’s it. do it in a reduced cost because you cannot throw more and more money at this problem and enhance global collaboration.

I think there is a genuine interest on the part of the U .S. that this whole premise is not just a U .S. issue. And so I think that it’s likely announcements that suggest that countries like Japan and Europe and UK and others may be part of this overall approach to drive sovereign AI for those aspects of AI deployment and scaling that is uniquely a government or state function. So as I mentioned broadly, scientific discovery, energy and national security. But if you look at the scientific discovery further to the next step, then you will see healthcare, education, skilling, all these things. Fundamentally, a government function. And this is not an easy task because if you think about how these institutions’ research is done, I mentioned large fraction of it in the private sector, a lot of it is done in academia funded by government, and then of course in national labs in the United States, India has its own set of national labs, academia, etc.

So what you need to do is take a look to see how do you integrate all this data? At least the U .S. Department of Energy operates these large, multi -billion dollar light sources, neutron sources, specialized scientific experiments. You need to be able to incorporate all these things, so you have to federate the compute and data, you have to have cloud -enabled lab operations, which is not how things are done today. Security and governance by design, especially when you’re thinking about public -private partnerships, even in the enterprise commercial scale, you want to make sure that you have secure computing, you have confidential computing, you can maintain integrity, but also if you think about national security, you have additional layer, and then you want composable standards versus infrastructure.

So this particular program was kicked out by Secretary Wright, well the President of the United States, then Secretary Wright, and the last fourth quarter of last year, and the first announcement was done with Lisa Su, our CEO, because one of the things that they wanted to do was a unique public -private partnership, and so the core infrastructure, which is currently called American Science Cloud, this program is just being stood up, is going to be run on an MI355 cluster, which is what this entire program that is aimed at driving innovation is going to be run on. And so we are really excited to be a part of this. initially US and soon an international effort to drive innovation in those areas that are uniquely government function.

I’ve had a ringside seat in computing for the last 30 years and been responsible for a lot of supercomputing deployment, a dozen or so. The last four or five of them were number one systems in the top 500, each first of a kind. This is another important thing. Innovation, if you think about AI, AI didn’t happen magically with NVIDIA or AMD. It happened because US government took the risk to invest in first of a kind systems. So we were the first to deploy 30 ,000 NVIDIA GPUs when people thought that CUDA was a four -letter word. Now everybody thinks that this is this amazing software, but change comes hard to people. And so I just want… I want you to know that…

particularly all of you who are youngsters things are going to evolve. If you think that AI is just like the Prime Minister said, it’s just the early stages. So you have to be open and you have to be part of this drive for effective, scalable and impactful AI. Then deep learning came where this mixed precision computation then generative AI and last year was really authentic AI and some of us think that this year we’re going to focus increasingly on governance. Governance does not mean regulation there is a role for regulation by governments but governance is that how do you want if you want to have agentic systems driving, accelerating innovation you want to make sure that the output has a person in the loop.

The one way to simply think about it is that if you are researchers here, if you have a professor who’s got a dozen students who are doing research you don’t let the students just go publish things. There is the professor’s responsibility, there is the peer review committee, etc. So you want that human in the loop before you can update and let this thing to drive innovation while it also allows it to do things that AI does best. So this is how we think about compute to capability, a model of national AI readiness. We want its rest on talent, talent and readiness of talent, giving people access to compute and models. Research enablement is key because you want people to operate AI in an environment where you’re questioning things and innovating all the time as opposed to assuming that what we in the industry is providing you is the only solution.

So I think… If you look at countries that are leading in AI, there is a very strong R &D and innovation foundation that is allowing you to lead because there are people who are questioning every time somebody says something. to make sure that it is validated, it’s continuing to innovate. Start up an innovation lab because you want to take these ideas and start new companies because many of these new innovation and new technologies may be led by people with new ideas and opportunities and of course ultimately enterprise and public sector adoption. We strongly believe, and again I heard Prime Minister Modi say, open ecosystem and open source, open platforms. These things, if you think about iOS and Android, India I find has a lot of penetration of Android systems because inherently open systems allows you to innovate without getting locked into vendors.

And so we at AMD have a commitment to make both our hardware infrastructure and our software infrastructure to be based on open standards so that you can innovate. Around this, any part of this infrastructure. can be part of a new startup or new company adding to that. That is also an important way for India to become part of the supply chain and the semiconductor ecosystem, because you don’t have to start with an attempt to go in for two or three nanometers. You can actually do amazing work and be part of leading -edge technology at different form factors. So I mentioned a little bit about how we think about agendic flows and AI scan work. This is simply the way you think about it.

The inner loop is an autonomous loop where AI and agendic AI does things, what it can do fast, it can operate. If you have 100 ,000 GPUs, you have 100 ,000 agents tackling this problem and it can actually go through the hypothesis -driven experiments and systems. So you can do simulation, campaign scale coordination, machine speed execution, etc. But we do not allow it to update. the outcome until a human in the loop has had the opportunity to validate to make sure that we don’t have unintended consequences. Now, how do you build this thing? So this is, if you haven’t gone to the AMD booth, I would encourage you to do. This is my only plug in this presentation. We spent a ton of money to bring this Helios rack here just so that you can have a sense of what is, not what this particular rack can do, but giving a glimpse of what is possible the next year and the year after.

So we, in 2007, myself and two of my colleagues started what is called the Exascale program. And the challenge was to deliver an Exascale system for under 20 megawatts. Because if you had just scaled the capability in 2007, it would have taken three to four gigawatts. And we knew that the government was not going to… sign, $4 billion for power, just electricity alone to run the computer. So we were motivated to drive that. And we delivered that first exascale system, Valfrontier at Utrich, for less than 20 megawatts. Everybody thought it was crazy, it cannot be done, but there are some things, when you put audacious goals, people rally around and then deliver. This particular rack, in one rack, there are 72 GPUs that will deliver 2 .9 exaflops of AI compute, which is FP4, not FP64, just to be very clear.

But for AI capability, you get 2 .9 exaflops of compute capability for 220 kilowatts. Right? That, even for somebody who’s been in this field for a long time, it’s just mind -blowing. this is where we are headed AI is the fastest adoption of any technology that humanity has introduced we’ve gone from 1 million active users to 1 billion in a matter of just a couple of years and we are headed to 5 billion users so there is a lot of opportunity to innovate in this field and all of us are going to continue to create these opportunities as Lisa said, we are entering the Yara scale so already people are thinking about the next 1000 so let me just say you can get to Zeta scale by just taking 300 of those racks and putting together and then it’s another 3x so I would say in the next 10 years maybe we would be at this 10 ,000 factor so the kind of problems that you are thinking about should not be constrained by what you can do today by the time you figure out the solution for an important problem compute will be there.

That is what we in the industry like to promise you. And I think advancing national economies, these are one of the things that people might you would be forgiven if you thought that does AMD do these things and how prevalent are our compute capabilities. I think Tim is going to tell you that our GPUs and our systems are in every hyperscaler globally and when it comes to HPC and national priority missions, AMD is the leader. If you listen to President Macron, he referenced Alice Recop, which is the first AI factory that the French government announced, the CEA announced, which is based on AMD MI430X, which is a variant of the MI450 on the right that you see outside.

I will close by saying that a shared path forward is really what we are looking for. I know India is in the early stages and we are really delighted to actually have this conversation. Thank you very much.

I’d like to invite our next speaker, Paneerselvam M, CEO of the METI Startup Hub at Ministry of Electronics and IT, Government of India. Dr. Paneerselvam M is a distinguished leader with over two decades of expertise in innovation, management, strategic growth and market development. He’s been instrumental in advancing India’s startup ecosystem and fostering impactful partnerships between the government, industry and entrepreneurs. In his

drawing insights out of this data and then comes the interface layer where most of it is going to be really driven by agents, by agentic AI and of course as Thomas mentioned there is always going to be a human in the loop perspective but as we progress this is going to change as well. So you know the two fundamental things that I want to share, one is the entire transformation in the readiness space for AI is an opportunity for you know certain change and intent needs to be very very clear and then comes the curiosity to learn about this little bit more for each business owner and then comes the implementation part of it and then start -ups have a very very critical role to facilitate this because you are coming in almost as AI natives, working with an understanding of this and you can really go out and demonstrate value.

and help implement the entire readiness, improve the readiness quotient or improve the readiness quotient for small and medium enterprises and ensure that, you know, this is a broad -based growth opportunity for businesses across the country and not limited to just a few of them, right? So there is huge potential and I think enough has been spoken. The summit itself is a proof of the kind of curiosity. We have had 267 ,000, you know, registrations, people who have registered in the last five days. Unexpected, overwhelming response to some extent that we couldn’t really handle it, right? At the same time, it gives us immense pride and excitement for the amount of curiosity and excitement for the amount of curiosity and excitement for the amount of curiosity and the youngsters in India, across India.

travel here from the length and breadth of country to understand what is AI going to be and how this is going to impact and what the opportunities are and that is itself is a fantastic starting point and and as I said you you know there’s a lot of happens this is in Indian sovereign models coming the tech the five layers the infrastructure the design you know all the layers are being worked upon in the Indian context and we are ready as a nation we are ready as a government to facilitate this this real disruptive transformative technology but the truth is it’s you know it’s also important for it has to populate into all the layers of the society beat just not just limit itself to large and large corporates but also to to small and medium enterprises and of course it is already populated with the large and medium enterprises and of course it is already populated well into the d2c to the individual users and it’s much much beyond the beyond the chat, GBTs of the world.

So with that, I think I once again take the opportunity to thank the entire team from AMD and we have had some interesting conversation and I look forward for the continued partnership with AMD and METI Startup Hub because in our perspective, corporates have a huge role to play in the success of the startups. Thank you.

Thank you. There’s a couple of things that I want you guys to think about as I go through my talk. 30th of November, 2022. The world changed. ChatGPT was launched. And I’m willing to bet that everyone in this room, myself included, what we thought we knew about AI changed. Two years ago, what we thought even after ChatGPT changed. A year ago, what we thought changed. And what I’m hoping is as you leave here, other things would have changed in the last 45 minutes of listening to these talks. Okay, so I’m going to skip through the reason why we need to go through and need compute. But I think one thing that is very, very, very important is things are moving so fast.

And things are moving in a way that we cannot predict that the only way that anybody is going to be successful is an open ecosystem. And, you know, both of these themes. And I think the other gentleman before me have alluded to this as well. and I’m going to take you through specifically around software. I mean, everything to do with AI really, I’m a hardware guy, I used to design chips, but everything today is software, right? And I was talking to one of my colleagues and I said, okay, so I’m going to India, I’m going to do all this, we’re going to go through. And I said, is it really going to be the, you know, are they going to understand it?

And he said, Tim, India is software. This is what we do. He said, you’re going to be in front of the best people in the world that are going to understand what you want to talk about. So I’m really going to focus on the software side of that. And one of the things that I wanted to do, understanding that we had our esteemed colleague from MITEI here, is we do have lots and lots of experience in this space. And one of the things that I want to highlight is some work that we did with Lumi in Finland. Now, why is this important? So within Europe, almost all the languages are Indo -European, right? If you know a little bit of Greek, if you know a little bit of Latin, if you know a little bit of one of the languages, there’s 27 countries in Europe.

so let’s call it 27 languages and then you have Finland Finland is a Uralic language nothing to do with any other language in Europe absolutely different construct, different base different absolutely everything and so what we found working with the guys in Finland is they were coming to us because they put in this Lumi supercomputer and they said okay so we have a small country in Europe, 5 million native speakers and we have to take all of this work that’s been done English, big codex Spanish, big codex, Hindi, English big codex of all of that to do your training, suddenly you have a language of 5 million people how do you get that language into your LLM model so that it becomes useful now I’m probably going to get the pronunciation really really wrong here okay but I did actually use chatGPT to look at the 22 Indian languages right so if we look at Bodo, Konkani, Dogri, Sindhi, Nepali there’s less than 5 million people that speak those languages so how do you get an Indian LLM that caters for everybody as we’ve seen from President Modi AI for all of that kind of thing and this is the kind of area that with Mighty this is where we would like to work with you guys and be able to bring some benefit of the work that we’ve been able to do now remember the first day 30th of November 2022 this machine was inaugurated so it was put together all of the systems were put together it was all brought up the chips were made years before this machine was inaugurated my birthday 13th of June 2022 6 months before ChatGPT came out so this machine with 12 ,000 GPUs that had the foresight from the Finnish government was using AMD technology to run AI before ChatGPT came out.

So a lot of people that think that a lot of the stuff from AI has come from a specific area. This again, think of our way of thinking. We were there and we have the ability. We actually did the Bloom 176 billion parameter model. It was an open model made for European languages. So again, we would love to bring this knowledge and use with the Indian ecosystem to make this successful for everybody. I’m not going to spend a lot of time on hyperscalers. They’re obviously an important part of the market. It’s where a lot of the capabilities go into. We’re there. We have tens of thousands of GPUs. We actually have, as Thomas mentioned, we have the Helios system coming here.

Please go and take a look at it. If you like Harvard, it’s an interesting piece of kit. But really the idea here is whether you’re in a hyperscaler… or whether you’re in any other area, there is an ability to have a wider ecosystem. And again, inference, so AMD specifically, it’s not really an AMD pitch, but there was an idea in the market that AMD was inference only. That dates from Q1 2024. That’s two years old. So again, we have to kind of change that thinking, right? That’s older thinking. We actually now, again, completely open source. There’s a Primus ecosystem or a Primus tool for the open source, which enables you to be able to do all of the training that you need to do for all of your Indic languages or for your use cases, which again is completely open.

Enterprise AI. This one I think is an interesting one. I know when I started going out to customers and going out to enterprise customers, the difference in customer knowledge on what AI was, was amazing. You go into one customer and they say, okay, so this is our use case and we’re seeing these kinds of sizes of matrices, so we’re doing these optimizations. And then you go into another customer and you say, what are you doing around AI? And the guy goes, oh yeah, we’re doing Gen AI. Okay, great, yeah, what are you doing with Gen AI? We’re using LLMs. Okay, great, so using LLMs, what do you think? LLMs. And they had no idea, right?

It’s just, we have to do something with AI. And that has changed over the last 18 months and chatbot was something that most people said, okay, that makes sense, I understand chatbot, we can fine tune the model, we can do an internal AI system within the company. And now we’re starting to see with the agentic workflows this entire plethora of different use cases coming through. And so how then do you take it from a research institute or people that actually get onto your accelerator, whether that’s a GPU or a TPU or an FPGA or whatever else? and get it to a stage where actually people within a corporation can use it. And so this is something that has been understood.

And again, no lock -in, open, everything here is something that can be used without having to tie you into one particular area. And actually, I’ll come on to it a little bit later as well. It’s also something that I’ve been very impressed with, with the infrastructure that MITEI have put into place. In this case, with the public -private partnership, you have GPUs, you have TPUs, you have Inferentia, you have all of the different types of accelerators available to you within the Indian ecosystem that MITEI have made available to you. I’ll come on to that a little bit later more. But again, the idea here is that whatever the ecosystem is, or whatever the compute that you’re using, you’re able to go from an area where, whether it’s in the cloud or whether it’s non -prem, you have an ability to be able to give your employees within your enterprise an ability to be able to use that AI assistant or tool.

Neo clouds so these are the kind of what we call the smaller clouds, you know, they’re not the hyperscalers they’re a little bit more nimble they are a little bit more available to doing things a little bit different a lot of these guys are doing sort of bare metal and managed Kubernetes services, but it is coming to areas where they’re becoming like APIs, token factories there’s an ability for these guys to be able to provide you with compute quickly easily and at reasonable pricing to enable you in whatever it is you’re trying to do we find these are the first movers in the market and again in the same way that we’re integrated and working with the hyperscalers, we have these relationships with the Neo clouds and actually we’re working with quite a few of the guys here in India as well to make that available for you as well, so the whole idea again here is there is that compute that’s available please go out and understand the benefits or the trade -offs between the different types of Kubernetes services that you have out there and get the right solution for you guys.

Now, I’m assuming that most people here are going to be startups. And again, startup is an interesting area, right? So you have a startup, you know what you want to do, you absolutely are laser focused on getting your MVP out there, getting in front of customers, how do you generate some value, how do you generate some revenue? Although that these days is less and less important, it seems, as people get funding even sometimes before a product. But one of the things that you guys have to be sure of is that the compute that you have and the capabilities that you have are capable for the products that you actually have to then go and put into position.

And so this is an area where we understand that proof of concept, it’s very important. And again, I was chatting with the CEO of Mighty here before, it’s something he was saying, you know, POC to PO. You know, you have to be able to make sure that you understand the technology and how you can take that to market before you can actually go and invest. So we have a couple of different ways that we can help here within the ecosystem. You could actually go on there right now, there’s the AMD Developer Cloud. You can get, I think it’s 50 or 100 hours of free compute. You want to go on, how does AMD work, you know. It’s always going to be dependent on use case and what you’re trying to do.

But there is a huge TCO advantage, which of course is important for startups. Get onto the Dev Cloud, get it working. We actually provide Docker containers, so that’s everything put into a single Docker. So you can download a Docker and run it, so you don’t have to spend your time and your energy installing all of the software, putting everything together, get everything working. We’ve done all of that for you. Take the Docker down, get your model off of Hugging Face, get your weights off of Hugging Face. Use your own model and do something else. Whatever there is that’s in there, in the open source ecosystem is there and it’s going to work. Give it a go.

Give it a play. And then of course from that we can… can take you into our accelerator cloud a little bit more sort of hands -on, making sure we understand what you’re doing, helping, guiding, and assisting you in moving yourself forward there. And then, of course, we have the relationships in with the industry, you know, try and buys, being able to get you access to the computer, being able to get you the right solution at the right kind of price. So this is something also that I really want to highlight. So day zero support of models. Now, we announced this. So Quen3 Codex came out last week, day zero support on AMD. Baidu came out with one of their paddle models this week, day zero support on AMD.

What does day zero support mean? Well, it means that it’s not the first time we’ve seen this code. It runs on AMD. It’s guaranteed. It’s optimized. you know a lot of people think that to run something in AI you need a specific GPU the whole point is with day zero support absolutely false right again with Lumi pre -chat GPT in 2022 we were building LLMs for effectively an Indic type language languages right and so the ability is there if there’s a new model coming out you want to run it you want to test that you want to see how it works for you guys then that is there and runs out of the box and you know again if we look at this line in the middle you know PyTorch if you look at the history of PyTorch you know there were lots of signatories on PyTorch to make sure that was available for everybody AMD was one of them this mainly comes out of Microsoft and Meta who did not want to be closed in to a single supplier so actually what you’re doing with PyTorch is you’re writing Python code right you’re not writing vendor specific code it’s an open ecosystem that’s the whole point right you don’t want to be tied in you know it’s gonna slice for innovation it’s going to increase So PyTorch came out and that is the basis of 99 % of all of the customers I talk to, right?

They’re all writing Python under PyTorch. JAX is then coming forward. Triton, this is a Python -like language which is specific for gem optimization. Again, if you’re getting to that area where you’re actually seeing the gem sizes that are coming through from your operations and want to do gem -level operations, then Triton enables you to do that at the compiler level. So then you can be completely agnostic of what the underlying hardware is. The ecosystem and the underlying compute becomes kind of abstracted away because Triton enables you to run on anybody. It’s just a compiler for the new architecture. If we look at these models on the bottom here, President Modi this week has announced the first 12 Indian languages.

I can’t wait to get you guys here. right, fully supported day zero support, you know just to give you an example here, DeepSeek of course when DeepSeek came out, they did some things a little bit special multi -head latent attention was new we had day zero support with DeepSeek why? Because we’re one of the main contributors to SG9 there’s not a tie -in to an inference engine here, it’s an open ecosystem so we were able to come out of the door with better TCO, better performance, better cost and full support through SG9 on that DeepSeek model which was you know, the leader of its time because of our complete commitment to the open ecosystem just to give you an idea, again you’re walking out of here in 45 minutes with changed ideas, this is what we’re going for here I did have two minutes I now have five, I don’t know who bought me extra time but I owe you a beer Okay, so really actually that’s kind of the end of the pitch here.

One thing I would say is we do have a booth here at 5 .10. I’m sorry, I’m going to do a little bit of an AMD plug at the end here. But do come by and see us. You know, we actually have some of the neoclads there. We have some model creators, vendors, some ecosystem partners there. You know, come see, come change your mind. Come see what’s available within an ecosystem with the compute that’s available for you guys. Okay, thank you.

So first of all I’m Gilles Garcia I’m French so we can talk about LLMs for French language if you want so I’m French, I’m based in France but I’m covering worldwide and I’m focusing on physical AI for the communications and robotics and industrial so we have been talking a lot about AI and most of the people are thinking AI means GPUs, big cloud and what we are seeing is a big shift, that’s another change that we are seeing, change management, so I’m the change management first but changing is we are seeing the AI moving into the edge and moving into the far edge which is industrial, robotics vehicles as well as the networks and so for that you need to have different type of beast, GPUs is one aspect of it but you need to have very profound different technology that AMD has as part of the bread portfolio that we have, these technologies need to be able to send to the market and we need to have a lot of that are able to send the data to the market and we need to have a lot of and we need to have a lot of that are able to send the data to the market and we need to have a lot of that are able to send the data to the market that are able to send the data to the market act, react in a so quick manner that there is no time to go back to the cloud for that.

And so these technologies need to be, of course, that will be inference, but need to be able to take decisions and act very safely, reliability, reliable, without having to rely on the cloud. And so that’s a new change that we’re seeing at AMD on the physical AI, which will become very, very important for us, is how do we take what we have learned in the cloud, and how do we make it available in the physical AI? Software is a big thing. Full stack, meaning hardware and software being able to deliver to the customer solutions, is what AMD is aiming for. And so our CEO, Lisa Su, was saying, it’s AI anywhere. And one size does not fit all.

Meaning that if you want to address a robot you can put a GPU into it, it will burn to hell. So you need to have a very dedicated accelerator with a high software stack, open source, that will be able to have this robot perceiving, visualize, act, touch and be able to act accordingly to what his purpose will be. At CES early January, Lisa Su brings on stage Gene01, which was the first humanoid built on AMD technology. That’s just impressive. Everything has been done by a startup in Italy to make this humanoid being able to sense, visualize, touch when somebody is touching it and when it’s touching something to act and react very rapidly without having to rely on the centralized source.

So I will not be longer than that. Physical AI is probably something that India, by the way, will have a lot of things to act into. Because GPUs are there already where physical AI is something that you will have to create. A lot of things related to medical, related to autonomous networks, autonomous cars, autonomous plants, industrial, and that’s where I think India will start, with all the startups and capability to use accelerators that are much smaller than what GPUs are, and this is all available today in the EMD portfolio. So I will stop here, encourage you to come to the EMD booth, and we can continue the discussion. Thank you.

Well, so we gave you a lot of information on AI, gave you four different accents, I think the French guy probably carries today. But my one message is that stay curious, as all of us have said, things are going to change and continue to change at a rapid pace. And, you know, people talk about so many thousands of GPUs, that will not be the main thing. and I think that’s something that we need to because you will find that we there’s a whole lot of interest in trying to provide you with even more powerful GPUs for their infrastructure while at the same time provides you very lightweight low power at the edge and so I think stay curious look from the from a start -up community point of view for a research point of view but academic point of view look for really interesting problems challenges to deliver the infrastructure that you need because ultimately this applications with where it is going to change society and life that’s all thank you very much Thank you.

Thank you. Thank you.

Mr. Ramesh Chaturvedi, Secretary of Ministry of Agriculture and Farmers Welfare. Sir, please come onto the stage. Our Honourable Chief Minister, Mr. David Rupadnavi is here. Good morning, sir, and welcome. May I also invite Mr. Johannes Jutt, Regional Vice President, World Bank, onto the stage, please. Honourable Chief Minister of Maharashtra, Mr. Devendra Fadnavis, Honourable Minister. Shri Ashish Elarji, Shri Nitesh Raneji, our distinguished guests from India and around the world. Very good morning. On behalf of the government of Maharashtra, I welcome you to the session on Using AI for Food and Climate Resilience. Agriculture is at a turning point. Climate change is making farming riskier, resources are limited and markets are changing quickly. However, there is an opportunity.

Digital tools and AI are advancing fast. Our goal is not just to use AI tools. We must build intelligence into our public systems to help everyone. For India, the change is essential. It is the key to food and nutrition security, higher farmer incomes and a stable economy. India is a country with a strong economy. India has shown that digital systems work when they are open and well -governed. Our next step is to bring AI into this framework in a responsible way. Under the leadership of Honorable Chief Minister of Maharashtra, the state has launched the Maha Agri AI Policy 2025 -2029. This policy uses AI for pharma advisory services, market information, data exchange, product traceability, innovation and research, and creating capacities of stakeholders.

We are moving beyond pilots to projects at full scale. Mahavistar is the country’s first AI -powered network and information and advisory services. Today, Mahavistar is being used by more than 2 .5 million farmers to get advisories in Marathi language, and recently the first tribal language in the country, Bili, has also been integrated into Mahavistar. AgriStrike is helping to bring AI into the market. It is helping farmers to get seamless access to various schemes and services. the Maha AgEx which is an open federated and consent driven architecture for data exchange it is helping us to bring diverse data sets together to get us a big picture. Agriculture is now a key part of India AI mission. We are proud to work with the government of India to lead this change.

I want to thank the Ministry of Electronics and Information Technology, Ministry of Agriculture Extra Foundation, the World Bank, MS Swaminathan Research Foundation, the Gates Foundation and all our partners for their support. It is now my duty to invite our Honourable Chief Minister to the stage. He will share his vision for using AI to strengthen our food systems and protect our climate. After the address of Honourable Chief Minister, we have a panel discussion with our distinguished panelists. Welcome.

A very good morning to all of you. Shri Devesh Chaturvedi ji, Rajesh Agarwal ji, Vikas Rastogi ji. Mr. Jonas Jett, Srimati Swaminathan, Shushankar Maruwada, my colleagues, Shriashi Shailar ji, Nitesh Rane ji. All the dignitaries present here, namaskar and good morning to everyone. It is my privilege to address this distinguished gathering at the India AI Impact Summit. And this important session. On AI in agriculture. We meet at a very defining moment across the world. Food systems are under strain. Climate volatility is intensifying. Water tables are falling. Soil health is deteriorating. Supply chains are fragile. And global markets are unpredictable. For countries from the global south, agriculture is not merely an economic sector. It is livelihood, social stability and national security.

India understands this very deeply. And under the visionary leadership of our Honorable Prime Minister Narendra Modi, India has placed digital public infrastructure and responsible infrastructure at the center stage of national development. The India AI mission is about using technology to deliver inclusion, transparency and scale Today, agriculture must sit at the heart of this mission Over half a billion Indians depend directly or indirectly on agriculture Yet, smallholders face fragmented information, rising input costs, climate uncertainty and limited access to credit and markets Traditional extension systems, however committed, cannot match the scale and the speed required Artificial intelligence changes this equation AI can provide hyperlocalization It can be used to predict and predict the future of agriculture It can be used to predict and predict the future of agriculture It can be used to predict and predict the future of agriculture It can be used to predict and predict the future of agriculture It can be used to predict and predict the future of agriculture It can be used to predict and predict the future of agriculture It can be used to predict and predict the future of agriculture It can be used to predict and predict the future of agriculture It can be used to predict and predict the future of agriculture credit scoring based on crop intelligence, transparent traceable supply chains, real -time market advisories.

But let me emphasize, AI is not a magic. As Honorable PM said in his inaugural session, AI must be built on trusted data, ethical governance, and public accountability. Without trust, scale will not happen. Last year, Maharashtra made a very clear and decisive strategic decision. AI in agriculture must not remain confined to demonstrations or pilots. It must reach millions. Under our Maha Agri AI policy 2025 -2029, we adopted Maha Agri AI policy 2025 -2029, we adopted a policy -led, ecosystem -driven model. built on openness and interoperability. Allow me to share what this has meant in practice. As rightly told by our Secretary Mahavistar, our AI -powered mobile platform delivers multilingual personalized advisories, market intelligence, pest alerts, and access to government services more than 2 .5 million downloads, acting as a digital friend to all these farmers.

This demonstrates one thing very clearly. Farmers are ready for AI when AI is designed for them. AI -based pest surveillance, crop sap integration is our mantra. By integrating, geospatial analytics, With post -surveillance, we have delivered early warnings to cotton -growing farmers, reducing crop vulnerability and finance risk. This is predictive governance in action. Agriculture data exchange is also one thing which is defining this step. We are building a statewide interoperable agriculture data exchange based on open standards and strong data governance. Data must empower farmers, not exploit them. Traceability digital public infrastructure in today’s global markets, the transparency is a mantra. We are unveiling a blueprint. For more information, visit www .fema .gov. a traceability DPI that will ensure end -to -end visibility across value chains enhancing food safety, export competitiveness and consumer trust and this is not proprietary.

It is being designed as a replicable public infrastructure model for India and the entire global south. In partnership with India AI Mission the government of Maharashtra, the World Bank and the Wadhwani AI, we launched a global call for AI use cases in agriculture. The resulting compendium of real world AI applications in agriculture was released in Delhi on 17th February 2026. This compendium documents successful AI deployments from Africa, Asia, Latin America and beyond. India is convening global knowledge for the benefit of the global south. As we move towards AI for Agri -2026 in Mumbai, our vision rests on four pillars. AI must be transparent, auditable and explainable. Open and interoperable digital infrastructure. Innovation cannot scale in silos.

Investment and scaling. Technology without capital remains just a theory. And inclusion and gender equity is also a mantra. Agri -2026. Is the international year of women in agriculture. AI solutions must be designed. with women farmers, not merely for them. Maharashtra today presents one of the most compelling agri -innovation ecosystems globally. 150 lakh hectares of cultivated land, diverse agro -climatic conditions, leading agriculture universities and AI research centres, a vibrant start -up ecosystem, a clear regulatory framework, and single -window facilitation for investors. We invite venture capital funds, impact investors, multilateral development banks, corporate innovation arms, and philanthropic foundations to partner with us. And in this partnership, we initiate a global partnership between Maharashtra and the United States to develop and leverage the technology to create a future for all.

Maharashtra is a partner of the International Development Fund. Maharashtra is a partner of the International Development Fund. Maharashtra is a partner of the International Development Fund. Maharashtra is a partner of the International Development Fund. Maharashtra is a partner of the International Development Fund. Maharashtra is a partner of the International Development Fund. Maharashtra is a partner of the International Development Fund. co -developing traceability DPI modules, investing in agri -tech startups, supporting digital literacy, especially among women farmers, building capacity in the rural AI ecosystems. When you invest in Maharashtra, you invest in scalable solutions for engaging economies worldwide, food security, climate resilience and AI governance are deeply connected. Countries that master AI -enabled agriculture will secure farmer incomes and strategic stability.

India has the scale, DPI and democratic governance model to demonstrate how AI can be deployed responsibly at population scale. Maharashtra is proud to be laboratory of that ambition. Friends, this satellite session is a declaration. We will move from pilots to platforms, from fragmented data to interoperable systems, from experimentation to execution, from intention to investment. The government of Maharashtra stands ready to collaborate with the government of India, with states, with global institutions, investors, researchers and farmer organizations. Let us ensure that AI becomes a force for

Thank you. Thank you, sir, for your visionary address. You always continue to inspire us to aim higher and achieve better. And under your leadership, I can assure you the Agriculture Department will rise to the challenge and serve the aspirations of more than 15 million farmers of the state of Maharashtra. Thank you so much, sir. We will now start the panel discussion in a few moments. Thank you. Thank you. Thank you. Thank you. Once again. Dr. Devesh Chaturvedi, he is the Secretary, Ministry of Agriculture and Farmer Welfare Dr. Chaturvedi leads our national effort in agriculture and farmers welfare Mr. Johannes Jett, he is the Regional Vice President, World Bank Mr. Jett brings a vital global perspective on development and finance from the World Bank Ms.

Soumya Swaminathan, she is the Chairperson of Dr. M. S. Swaminathan Research Foundation Dr. Swaminathan is a global leader in science, a champion for sustainable research and a strong advocate for mainstreaming women farmers’ role in agriculture Mr. Shankar Maruwala, he is the Co -Founder and CEO of Agestep Foundation He is a pioneer in building digital public infrastructure that empowers women farmers to develop their own agriculture and empowers people at scale and I am very proud to say that the Government of Maharashtra and Agestep Foundation together have brought out Mahavistar, which more than 2 .5 million farmers are using today to get the advisories and information that they need on a daily basis. The objective of this panel discussion is to move from vision to implementation.

Specifically, we will deliberate on how to institutionalize AI within agriculture systems at scale, how to ensure inclusion, especially of women farmers and smallholders, how to build interoperable, trustworthy and sustainable AI governance ecosystems, and how to strengthen collaboration between the center, states, global institutions, industry and academia. The session is also an important precursor to AI for Agri 2026 Global Conference, where we will continue these deliberations in greater operational depth with governments, investors, investors. innovators and development partners. AI for Agri conference is being held in Mumbai on 22nd and 23rd of February at Jio World Convention Centre. With this context, let’s begin our discussion. My first question is to Dr. Devesh Chaturvedi. Sir, under your leadership, the ministry has taken significant steps in advancing the digital agriculture mission and operationalizing the Agri -STEC framework.

You are laying a strong digital foundation for the sector. As we now look at integrating AI more systematically into agriculture, how do you envision the central state collaboration framework, specifically to ensure that AI deployments are aligned with national architecture while allowing states the flexibility to innovate based on local agro -climatic and socio -economic context? And finally, how can we institutionalize this collaboration? to achieve population scale impact while mentoring interoperability and data trust. Thank you.

A lot of questions in the same question. So what I’ll do is I’ll just first take you through the initiatives. First of all, we deeply appreciate the leadership taken by Maharashtra under obviously the leadership of a vulnerable chief minister and with the agriculture department. They have done exceptional work in digital agriculture mission by developing farmer IDs and digital crop survey. And also they launched Mahavistar as a precursor of Bharatvistar. And recently on 17th, government of India have also launched one of the first integrated AI -based system for the farmers, which is Bharatvistar, which presently is undertaking, providing services, which is work through the app. Android based app as well as through mobile telephony on weather advisories ICR based crop advisories, pest advisories market information regarding various agriculture produced, traded in the Mondays and lastly the government schemes of government of India.

Now why is this important, AI is important in agriculture? Like we did a lot of, we started with digitalization of services, different services we had DBT we had online systems of applying for various common person, applying to the common service centers or through the mobile apps but what was felt was that while we had initiated this process to ensure that the bureaucratic red tapism is removed, what we were moving towards was a sort of digital red tapism because within our ministry different schemes had different apps and they had different ways of selection and within the state also horticulture had a different database of farmers, agriculture had a different database, animal husbandry has a different database, crop insurance has a different database.

So basically a farmer who has to avail so many services was, we felt that he or she was getting lost in which app to use and which one to use. And sometimes it becomes more difficult to avail the services through online systems or to get advisories than to go to a person and say, okay, tell me how to do it. So the whole idea was that once we have this AI -based system, we have a same platform for different applications and different advisories at a click of the button or maybe just as a voice. So that is the whole idea of shifting towards AI -based solutions. So now what we have initially in the first phase in the artificial intelligence system, the Bharat Vistar or the Mahavistar of Maharashtra, is that the advisories, the crop advisories, the weather advisories, schemes information about how to apply and what is the status of that application, and also the Monday rates.

All these have been put in the one platform. You can just make a – presently it is working in English and Hindi, but in the next three to six months we’ll be taking it towards all the Bhashani -related languages. And the next step is, as we mentioned, that the states are working together with us for the digital public infrastructure. So close to 9 crore farmer IDs have been developed. So what is a farmer ID? And you must have read the statement of Honourable Finance Minister, that DPI is the new UPI. So what is the basic – this agri -stack, which is a part of DPI, is that for agriculture is that we have – each farmer has a unique farmer ID with the back end of all the crops the person has sown, what is the land available to that person, all the data with the share of the land and the crops sown and the soil health card details if the soil health has been given.

So with these basic details available on the system, it empowers the farmer through that ID to avail services because it is already approved by the relevant authorities in the government. So the person does not have to or the authorities who are giving the services are not required to cross -verify the credentials of the farmer based on the record of rights or maybe the Girdhavari or whatever it was in the different states. So every state and Maharashtra is one of the leading states here. We are working together to have a saturation of farmer IDs and crop survey. And once this is there, then this AI will further transform into a very, very tailored advisory. So a person calls or gives the farmer ID or Aadhaar.

And at the back end, we will, based on the consent, access the details of where the farmer is from, what is the crop being grown, what is soil health conditions. And very targeted advisories will be given, which will be made operational in the next three to six months. So instead of pushing data which may not be of interest of the farmers, very specific, tailored, data for that farmer will be available based on integration. of digital public infrastructure with Bharat Vistar. And the third aspect will come when we do the predictive models. And we tried that and you must have remembered in the inaugural session when Google CEO mentioned about that predictive model which we did with about 3 .8 crore farmers.

We used 100 years data of IMD and a model to predict a monsoon for the next one month and for next week. And that prediction was fairly accurate and farmers we got the feedback to farmers did take the decision to sow and to irrigate based on the predictive model which was sent. And now we will expand the predictive models to ensure that we get more advisories of the market situation, of the weather situation which will help improving the decision making of the farmers and so that they can increase their productivity, reduce the cost. So that is the whole idea of AI in agriculture. And we hope that more and more farmers will adopt it and it will be a lot and it will be a lot exactly a replacement but a sort of additional to the human, we can say, extension services, which we find is not able to reach to the farmers because of the resource constraints of each state.

The extension machinery, the KVKs, all our state extension machineries, it’s very difficult to reach each and every farmer because of the fact that we can’t have a person sitting in each village reaching to each farmer. But AI, along with digital public infrastructure, along with the mobile and internet penetration in the various rural areas, will ensure that that gap is removed and we get more and more access to the farmers on services and advisors. That is the whole idea of having center and state interoperability. But I hope I have answered most of the questions which

As you rightly mentioned. AI systems are acting like a digital friend of the farmer so they are available at any point of time through multiple channels and in a language they understand in FEDSAR with ministries assistance we were able to get access to multiple images of pest and disease and with IIT Bombay we have been able to develop models where farmers can take a picture and they can find out what pest and disease is it and then ultimately what is to be done based on the knowledge created by agriculture universities and ICR institutions so I think there is a great opportunity for us the national government has the scale and the states have their own specific skill sets and knowledge together if they combine I think we can reach out to each and everybody in the farming sector.

Thank you sir I will move on to Mr. Johannes Jutt now the regional vice president of the World Bank the World Bank has been a long standing partner to both the government of India and the government of Maharashtra we have multiple projects going on concurrently as well as we have had in past as well. And these projects have been aimed at strengthening agriculture systems, climate resilience, and institutional capacity. As we move into the era where AI technologies are evolving at unprecedented speed, how can development partnership adapt to remain agile and responsive? In particular, how can we structure programs and technical assistance model that provide just -in -time support to central and state governments, enabling them to experiment, iterate, and scale AI solutions responsibly?

to be here today. So we’re on the cusp of a major revolution in how support to farmers and agriculture is happening. I actually grew up on a farm. I worked on a farm from the ages 10 to 21. I think every hour I wasn’t in school that I was actually at home. I was working in a farm. In some ways it feels paleolithic because we didn’t have computers. We had telephones that were connected to wires and our ability to get information about what was happening around us was extremely limited. We spent a lot of time trying to find out the things that today you can find out very, very quickly using small AI for agriculture. And that’s truly revolutionarily empowering for farmers.

But to make that work for farmers, there’s a lot of things that need to go right. And I think it’s worth reflecting a little bit about on the different roles that we have. Thank you. actors in the ecosystem have, starting obviously with government. My colleague mentioned a number of these things earlier. The government’s responsibility is principally on foundations, things like the governance of AI, the interoperability, accessibility, obviously ensuring that educational programs include appropriate types of skilling in the use of digital services. This is a big challenge in countries like India, where frankly there are still people who don’t have sufficient literacy to read what comes over a basic smartphone. Ensuring that the research and extension that is provided through these small AI platforms, is credible, is trustworthy, is backed by science.

I think that’s also extremely important. Of course, farmers will find out if they aren’t. but at high expense, right? So we want to make sure that they’re not being advised to do things that are negative for them. And then also looking at the cost of service, the connectivity, what does the farmer actually need to be able to link into these different types of platforms that give information? Because, of course, we’re often also talking about farmers who have very, very few assets and who may be essentially unable to stay permanently connected or even easily connected to the Internet. They’re going to have very basic smartphones, et cetera. So the government has a lot of work to do in all of those areas.

Then you can look at what can the private sector do. Now, one thing that the government needs to do is encourage, crowd in private sector capacity and capital. But once we turn to the private sector, what is the private sector’s principle? advantage. I think that there’s a lot of creativity in the private sector. So the actual applications that are being developed are being developed by individuals in the private sector with a passion for specific sorts of issues that are constraining farmer success. And, you know, that creativity will result in a number of different applications that will be aimed, in most cases, to help farmers overcome certain hurdles that they face. And, you know, we can kind of let a thousand flowers bloom there and see what actually takes root.

And it’s amazing what you start to see. Just yesterday, I was learning about an application in Morocco developed by a tomato farmer who was able to give advice about how much water tomato plants need simply by taking a picture. of the current tomato plant. Take a picture and it tells you how much water you actually need to give this plant, which obviously in a water -stressed environment is vital, vital information. And then, you know, there are roles for institutions like my own, the World Bank Group, which can help to provide some of the financing that helps develop these applications, and also the foundational backbone for artificial intelligence. And we can also play a role at the advisory end, where we are helping to truth -test, if you like, the information that’s coming through different applications that are coming out of the AI sandbox in different contexts to make sure that it’s actually providing information that’s useful to the end beneficiary and enhancing from a productivity perspective at the farm level.

Thanks.

I think you have rightly pointed out the role of innovation and research and what we see is we require high quality robust data to actually build upon that and as Honourable Chief Minister mentioned, MahaEGX is one step in that direction wherein we bring diverse data sets and make them accessible to researchers, academic institutions, departments and also start -ups and many of these start -ups we will see they are showcasing their innovations in AI for Agri conference in Mumbai. So we request all of you to please come there and see for themselves what kind of excitement they have and what kind of solutions are envisaged. I have one supplementary question to you. How do you see a platform such as AI Impact Summit as well as AI for Agri global conference contributing to deeper global collaboration and south -south knowledge exchange in this domain?

Thank you for that additional question. I mean, obviously, India is in a great position to lead the development of AI, particularly for developing countries where there are still significant challenges helping poor people to escape poverty permanently. India has demonstrated digital innovation for a long period of time already. It’s got an enormous population with a huge variety. The challenges of bringing farmer -appropriate data to the farmer’s fingertips in India are – I was going to say India is a microcosm of the rest of the world. It’s hardly a microcosm. It’s so huge. But because you have so many languages, so many different regions, so many different types of crops, and the starting conditions at the farm level are so incredibly varied, figuring out how to make AI at the farm level work, in India will automatically have a large number of spillover learnings for other countries around the world.

and because India after China and the United States is the country in the world that is best positioned actually to push all of this work forward and because it is itself a developing country, it’s very, very clear that it will have a central role to play in South -South learning for those reasons.

Thank you so much. I move on to Dr. Swaminathan. Dr. Swaminathan, your father, Professor M .S. Swaminathan, played a historic role in shaping India’s agriculture transformation during the Green Revolution, ensuring food security at a critical juncture in our history. Today, as we speak of a new phase of transformation driven by AI, we are again at an inflection point. You have consistently championed science -based policy, sustainability and the empowerment of women farmers. With 2026 being recognized internationally, as the year of women farmers, how can we ensure that AI -led agriculture transformation strengthens women’s agency? knowledge access and climate resilience and what institutional safeguards and design principles must be embed today so that this new technological revolution becomes equitable farmer centric and grounded in scientific integrity

Thank you very much for that question Vikasji not only is this year the international year of women farmer but we know that agriculture itself is increasingly being feminized with many men actually leaving farming to the women and migrating out to the cities for other opportunities so it is really essential to put women at the center of all that we are discussing and I think the chief minister today gave us a wonderful vision of what can be the future provided of course like you said that there are the guardrails there are the institutions there are the safeguards and the design principles that we think about from the very beginning so my father professor MS Parminathan used to say that the green revolution was not only about the seeds, of course the seeds played a very big role you know the high yielding varieties but it was about the entire ecosystem and the institutions that were developed at that time which included the outreach you know later on the Krishi Vigyan Kendras of course were developed but also the access to credit, the water, the fertilizers, the education, the empowerment and ultimately became a success because farmers realized the potential of it and took it on.

So what he used to say is that you know every technology, no technology is pro -poor or pro -rich or pro -woman or against women, it’s how we use that technology so it’s really like you said the inflection point today is how do we use this very powerful technology that’s come to us. So I think there are a few points here, you know, to make sure particularly that women farmers are not left behind. The first important fact is that women in India, the minority of them who have their name on the land document, so mostly it is in the man’s name, and Deveshji was telling me today that this is improving and that the latest census shows that perhaps at least a quarter of the properties are also in the name of women, either jointly or, but that still means that, you know, three -fourths of them don’t have.

And a system that operates basically on publicly available data will then leave out those whose data sets are not available. So I think it would be really important at the early stages itself to think about how women’s data can be incorporated because the algorithms are fed by the data we have. And so all of these advisories may be very suitable for a man who’s operating a tractor on a farm, but not at all relevant for a woman who’s still working with outdated instruments and trying to, you know, till her land. And particularly when we look at more remote areas, tribal areas, where women do a lot of the agriculture like millets, for example. Mostly it is women who grow millets.

And there’s still a lot of mechanization which is absent completely. It is all still very much done using traditional methods and tools, and it involves a lot of drudgery. So I would say that, you know, one of the benchmarks that I would look at is, is it reducing the drudgery and the workload on women farmers? Is AI helping to do that? So I think we also need to look at certain indicators for success. And you mentioned science. I mean, I’m a medical researcher, and the way that we evaluate products is by doing clinical trials, by examining the data and the evidence. And then recommending it for wider use. so again a note of caution would be to as we roll it out we need innovation certainly we also need to do the evaluation looking at inherent biases looking at who’s being excluded looking at are there unanticipated risks or side effects that we didn’t know about but most of all it’s this inclusion I think we don’t want those who are already left behind to be further left out so I think the ongoing research and data collection and feedback loops and most importantly having the voices of those for whom we are developing all these I think in the room I don’t think we have any farmers or women farmers so we are all discussing from what we know but if you are the farmer like you were saying working there and you know the constraints under which you are working so I think the women farmers and farmers in general must have a role they must be part of these committees that evaluate or make recommendations or make suggestions on improvement it has to be an iterative process I think any technology is as good as the application for which it’s developed I’ll give you one example of an app that the MS Farminathan Research Foundation developed for fisher women.

We had very successful app for fisher men called the Fisher Friendly Mobile App that won the UN Tech for Nature award last year. But fisher women were as usual left out and so the Women Connect app actually gives them on a tablet information that they need to sell because once the fishermen have come back from seeds, the women who have to do all of the post harvest and the same is true for crops or fruits or vegetables as well. So that connection to the market, of course information about pests and pathogens and when to buy what and what inputs to use but also being able to organize themselves. And I think women, there are many FPOs now and FPCs and SHGs made of women farmers, empowering them and giving them the knowledge and tools.

And the last thing I would say is we still need humans in the loop. I don’t think we should think that completely making everything run by machines is going to solve our problems. I think it’s risky there. And in a country like India, we also need employment. And so we should think of, and I don’t know how many of you have seen this film called Humans in the Loop. But it’s a tribal woman from Jharkhand who actually raises questions about the algorithm. It’s a very thought -provoking film. So I think Humans in the Loop is going to be important. We have our Krishis, Sakhis and so on. We need to empower them with these. So I think AI and all these digital tools, if they’re used in addition to the traditional knowledge and wisdom that people have and augment it and give them at the right time, at the right place, the knowledge they need, I think we can go a very long way.

Thank you.

Thank you, madam. You have rightly pointed out the need to be more sensitive and while developing systems for inclusivity. and to ensure that for whom they are being developed and they are in the loop and they are being consulted. In fact, the feedback mechanism that we have developed in Mahavista takes care of those requirements. I’m also very happy to share that Government of Maharashtra and Dr. M .S. Swaminathan Research Foundation are working together on some of these issues in terms of how to bring women’s right in farming at the center stage. How do we create bio -happiness using our universities and educational systems? And what kind of nutritional security we must look for? Because we have food security, but it’s the nutritional security that we must aspire for.

So we are happy to have support and assistance from MSSRF in that direction. My final question is to Mr. Shankar Maruwada. Mr. Shankar, ECSTEP has played a role. A foundational role in shaping India’s DPI landscape through open source platforms such as Sunbird. which has powered large -scale systems like Diksha, Mahavistar and Open Network Initiative built on backend protocol. These efforts have demonstrated how open standards and interoperable architecture can enable population -scale transformation that we are already seeing today. As we now enter the era of AI -driven public systems, how should we think about standardizing AI -based ecosystem in a similar spirit? How can we bring DPI into AI? And what architecture and governance principles are required to ensure interoperability, trust and sustainability in AI deployments across sectors such as agriculture?

Again, a whole lot of questions, but let me make my best attempt to answer those. More than 100 years ago, the world faced what was known as a Malthusian crisis, where Malthus, the economist, predicted that if we continue to grow, in the same way we’ll run out of land, we’ll run out of soil. We were a billion and a half then. We are 8 billion. Most of us may not even have heard of the Malthusian crisis. What happened? Someone called Haber and someone called Bosch created a miracle. Haber synthesized ammonia using high pressure and temperature and Bosch put it into an industrial process. That phenomena is now historically known as pulling bread out of air. It took a lot of effort and as Soumya said, creation of a massive ecosystem.

Germany, which pioneered this, lost that race to US. Because US did a better job of diffusing the technology safely to the farmers. They created the discipline of agriculture engineering. They created institutions like the Fertilizer Development Center. They helped. technology demonstrations to farmers to show them how synthetic ammonia could be used. By the way, 50 % of the nitrogen in our body comes from synthetic nitrate ammonia. That’s a fact. So we owe a lot to Haber and Bosch. China then took it on in 80s by buying 10 big plants from Kellogg, training 300 million farmers, showing them how to use synthetic fertilizers. And they went on to be the global leaders in agriculture. India is at a point where if we learn the lessons from such past things, our green revolution, our DPI experience, we are at a pivotal point where the equivalent of pulling bread out of thin air is pulling intelligence from the earth and providing it to the farmer.

this is again not science fiction Mahavistar, the pioneer along with Bharatvistar have taken the first steps to this so when a Mahavistar was designed to build off what Swami has said, it was designed with inclusion in mind inclusion diversity was not an afterthought because to solve for not just Maharashtra’s problems for India’s scale and diversity we need to think of the last person, the most discriminated in the remotest part of India and design systems that work for them we call that DPI now let me give you a specific example of this in Bharatvistar right from the beginning the design specs was we need an illiterate farmer to build off John’s point about digital literacy with a feature phone, not a smartphone, to be able to talk in his or her native language and native dialect.

Marathi itself has many dialects, right? Talk on the phone, like the way she is comfortable talking to another person. Ask the question, have a conversation, get a bunch of answers. That process took us the better part of nine months. Why? Because it’s not just AI. It’s data. It’s processes. It’s training the farm extension workers. It is having trust on will this work? What about the costing? Will I blow up my entire stage budget on a model, right? Do I have autonomy? Can I switch models out, in and out? These are very, very difficult questions. It took us in partnership with a whole lot of people. I mean, government of Maharashtra led the effort, but IndiAI mission, Bhashini, IIT Madras, IIIT Hyderabad, World Bank, Google many other providers everybody chipped in the little part of the solution now here is the best part because we all collaboratively invested in figuring out a solution there that solution could be deployed in Bharat Vistar with more confidence easily again the same challenges that secretary Chaturvedi talked about do we have the data he used a very nice phrase digital red tapism our data is in different formats what matters is the intent of the government of India which triggered the process which allowed Bharat Vistar to be launched the day before it’s a start data will get better, the systems will get better, usage will improve that will generate more data and then over time years the ecosystem will be built this we know from our experience what makes this happen what is that secret sauce the design principles it is the same as DPI what worked for DPI we are taking those same principles one open interoperable systems think networks and not just portals and platforms and siloed and fragmented systems what’s the best example of this the railways in India we have such a vast landscape but the rails are common every state can decide what it wants to move private public defense farming the Indian railways is just providing a backbone that allows everyone to do this there was a time when we had different rail gauges right now that sounds so silly but there was a time like that But India is showing that we don’t have to repeat those early mistakes in digital also.

By creating interoperable networks based on open protocols like Beacon, by collaborating with each other, one of us is bringing in data, somebody is bringing in technology, somebody is bringing in policy, somebody is bringing in research. These collaborative open networks and with the launch of Bharat Vistar puts India in a very unique and responsible position. Unique because we have these open rails, we have the experience of DPI. Responsible because it is a start. Unlike the technologies of the past where you perfect the technology and then deploy AI, you deploy something minimum to start and then evolution, models get better, data gets better, usage gets better. And then it gets better and better over time. that is the unique junction we are in in India what will that mean?

when I -CAR plugs into this network with its weather and pricing data that network makes it available to any state that wishes to turn on the supply from I -CAR when a private sector comes out with a very innovative app let’s say the tomato example that John talked about any state can say I like that I think I will have that made available to my farmers for the farmers they anyway trust the state they can go to the same app and now see this also there if the tomato app person wants they can go directly to each farmer very very expensive so Shared Rails allows us to spread innovation diffuse it very quickly through society keeping in mind both inclusion and inclusion and inclusion and rewarding innovation because innovation has to be rewarded.

And I want to end with a very simple analogy. When Edmund Hillary climbed Mount Everest, he made a lot of people believe it is possible. When Mahavistar was launched, it made the country believe that it is possible to make AI serve the farmer. And to that extent, the responsibility that Mahavistar, Maharashtra government and government of India has is to create these pathways for the rest of the country for the other states. At XTEP Foundation, Nandan Elekani, we made a declaration two days ago. We would like to see a world by 2030 where there are 100 such diffusion pathways, each created by a different set of people in different sectors, in different countries and continents, but each inspiring.

different AI pathways to safe impact at scale. And it’s a very exciting vision. It’s a very collaborative vision. If you all get together, we can also create miracles in our own lifetime. Thank you.

With that profound thought, we’ll conclude today’s panel discussion. I thank all the panelists. They have really opened a new vision in front of all of us. And we’ll invite all of you to AI for Agree conference in Mumbai on 22nd. Thank you so much. We don’t have question actually. Time for question. The next session is about to start.

Technology Security and Data Privacy Officer at Vodafone India Limited, with over 20 years of experience in cyber security domain and governance structure. Rounding off the panel, we welcome Mr. S .T. Abbas, Senior DDG and Head TEC, also holding additional charge CMDTCIL, with over 35 years of experience in telecom standards, certifications, spectrum management and network regulation. I would request all the panelists to please come forward for a quick photograph Thank you, sirs. Please take your seats. Let’s engage deeply on how to balance information. Innovation with privacy and trust. I now hand over to Dr. Tangirala Ji to begin the session. Thank you.

Chairman, member, Mr. Mitter, distinguished delegates, my fellow panelists, I welcome everyone to this second session. The clock is already ticking, so I will be brief in my opening remarks because I come between the audience and the distinguished panelists, which I don’t intend to do. The session title is Building Customer Trust Through AI -Driven Operations. The importance of trust was highlighted, among others, by Mr. Shantigram Jagannath as well, when he was speaking about AI through telecom networks and the at -scale problems that we could try and solve. Thank you. Now, while customers may not interact with AI models directly, they are affected by the outcomes of the decisions. And therefore, you know, whether it’s outage management, service continuity, grievance handling, you know, while efficiencies may improve, the responsibility for decision integrity ultimately remains with the telecom service providers.

And clear and proactive communication with the customers would become very important. And that is where, you know, there are impactful applications of AI in telecoms, in spam and fraud prevention, which a person had mentioned in his opening remarks about how 2 .1 million numbers were disconnected using AI -based tracking. But the challenge is also that we need to reduce this spam. while minimizing false positives, avoiding customer inconvenience, and fully respecting privacy and regulatory requirements. So that is always a big concern. Then, of course, this whole issue of the human in the loop or human in the mix. We need this automation to have an element of human control that is so that the system does not run away with its own decisions.

So we have, for all these issues and more, we have eminent speakers here, both from the service providers, from the R &D, and as well as from the standard -setting body of DOT. I will request each of them to give their thoughts, and then maybe a few of you… Both of them have presentations to make. I’ll request them to keep it to about five minutes or so, so that we have time for further discussion. Thank you.

And the reason for it is in the scam economy, regulation cannot move as fast as scammers. Scammers are not bound by geography. They’re not bound by laws. They’re very technically capable and they’re very well funded. They have all the things that mobile operators would like to have. I think it’s important to understand that we have to focus on stimulating innovation. At GSMA, about 12 months ago, we formed a coalition called Cross -Sector Any Scam Task Force. It involves more than 39 organisations from 17 countries, including the social media platforms, so Meta, Google, TikTok, AWS. And the aim was to drive or identify and prioritise initiatives and activities that we could do as an industry to help combat. Now, one of those activities was let’s gather what the industry is doing.

Now, in just the last couple of months, across Asia Pacific, we’ve gathered case studies of more than 40 instances where operators without regulation have developed, implemented, and used successfully some sort of strategy or service to combat scam. And I think that’s an indication, along with GSMA’s globally working with people like Virginia Tech and with our foundry, with our proof of concept around data sharing, is the industry is focused on this. And the danger, of course, of implementing service -based rules is they restrict innovation in the future. And so we really need to focus on outcomes when it comes to regulation. And I think we all universally subscribe to the fact we need to combat scam. We need to work together.

And it’s not just the people in this room. We need to collaborate and work across the ecosystem. to make that possible. I think those principles actually also apply in the broader sort of sense of the term is how do we grow 5G, how do we make 5G meaningful to the whole economy, to all users. It’s about stimulating that ecosystem and making sure that they are using 5G and 4G and mobile broadband into meaningful solutions for the population. And the important thing also for India is India is rising not just economically but also in its position in the telecom world and the GSMA sort of global ecosystem is India is a real telecom superpower and it’s on the rise.

And that means actually it cannot just be worried about its domestic situation. actually it has to embrace that statesman role to be a global leader. And so actually considering cross -border, how does India play its role in a global ecosystem are critical to actually the sustainability and growth of the global ecosystem of which India’s vision is dependent on. It cannot exist alone. And I think it’s important that when we focus on innovation and solving things like scam, it is as part of a global community. It’s not just a national community. And so the actions we take, the innovations we look to stimulate have to be part of that global solution. Thank you.

That was thought -provoking, some of the things that you said about collaborative innovation or innovation through collaboration. We will come to that in a bit when we go for the questions. So, with that now may I request Dr. Rajkumar Upadhyay, CEO of CDOT for his presentation and opening remarks.

Respected Chairman, Mr. Lahoti, Mr. Mittal, Mr. Tanglura fellow panelists, industry leaders, policy makers experts, ladies and gentlemen thank you for inviting me here I think in the previous session there was talk about how do you optimize your network how do you self heal your network how do you make correction in the network so I’m not going to talk about that even though we also as India we have developed our own 4G and 5G we used because we were the late comers so we used quite a bit of AI in terms of predicting the faults because a lot of logs are generated by various systems so I’m not going to talk about that I’m going to talk about where is the PPT?

where is the PPT? So I’m going to talk about some use cases which we have developed during last few years. We are CDOT. We were established in 1984, and we had the legacy of developing the rural telecommunication. We work in primarily three to four areas, the mobile wireless, cyber security, information security that is done by quantum. Quantum AI is a horizontal thing and advanced telecom applications. But I will focus on these are our product line, and all of these products actually use AI because AI is so pervasive. Without AI, you cannot function. So all these product lines, whether it is mobile, whether it is cyber security, information security, disaster management applications, are using AI in a big way.

So one of the key application, key product what we have developed is Fraud Pro. What it does, it actually detects the fraudulent connections in the system. I think you may be aware with the cases of Jamtara, Mewat and all these sim factories running. And these sim factories were destroyed by this particular software. What it does, it groups all the images of the same person because if you go and buy 500 sims using same Aadhaar card or same driving license, this is what was happening. So it detects that and it not only matches the images, it also matches the demographics, name, father, name. And sees that whether names and photos are same but names are different. So using, I will come to the number.

I think some number was described in the beginning that how many connections were disconnected using this software. So this is deduplication and finding the, and in fact we developed for telecom, it is being used now, going to be used in driving license, passports, income tax, deduplication, Manrega deduplications. The second one, I think this, it mentions the AI. AI analysis, 86%. 7 crore mobile numbers. and it was very well used even to, you know, find out dead bodies in the Balasore train accident. The first use case of this particular platform was to identify the dead bodies. The second one is financial risk indicator. I think you would have seen in newspapers RBI has mandated the banks to use financial risk indicator.

What it does, if A is transferring money to B, so B, the credentials of B are checked with the platform which we have developed, which we call digital intelligence platform. And the platform returns a figure that this is risky number, is medium risk or low risk. If it is a high risk number, the bank will not happen, let that transition happen. And it has saved a lot of fraud cases currently. And all the banks actually are using this FRI, which is able to tell that the B number where the money is going is a, you know, dangerous number or a well -identified fraudulent number, the money is stopped. The next is the Chakshu. Chakshu is again crowdsourcing platform.

wherein if you get a fraudulent call or a promotional call or any kind of call or fake KYC or faking as police, you can report and using crowdsourcing, we are able to disconnect the number and we are able to take. And this is again using our Sanchar Sati app. Just to bring to the notice of the audience, rarely a government app has a download of 18 million plus. This is 18 million plus downloads are there. The hits in the website of Sanchar Sati are 25 crore. Very rarely you see that. So this shows the popularity of how customers are protected using the AI -based platform. This is again AI -based platform. This Tapcoff and CIR here, I don’t know how many of you have used.

I would request those who have not used, please use it. In Sanchar Sati, you go, you give your mobile number, it will tell you all the connections under your name. using fuzzy logic, fuzzy AI and fuzzy logic. It doesn’t ask you any other detail. And that number also we ask because we want to verify that it is you and the OTP sent. Otherwise, no other details are asked. Just by the detail, we are able to find out how many numbers are there. And just to bring to your notice, 70 lakh connections have been disconnected using this. People have themselves disconnected their numbers because it also tells you, this is not my number, disconnect it. This was a big problem for us.

When we blocked the SIMs in the country, these guys went outside. And they started pumping calls using Indian numbers. It’s spoof call. This technology is available. I can get call from my own number in using this. We were getting 15 million calls per day, 15 million. And now you see, this was a very complex system because when the call hits at the gateway, the system has to decide within millisecond that this call to be let it go through or block it. it has to be decision has to be millisecond and it has to be zero error because no actual call should be blocked and today we are able to do our because the rigorous testing happened with all the operators today we are able to we have totally neutralize this of course they have found another way they are they have taken sims in places like Cambodia Indonesia Myanmar from there they’re calling so again the AI based system is alerting that these are the numbers of that country and we are alerting the governments of that country AI based security solution because cyber is another major area for all of us and somebody was mentioning the AI will do cyber attacks and it’s true we see in our system AI attacking the systems earlier it was human and now it is fully AI so you have to use AI to counter it so the cyber security solution today what we provide is fully AI based so that it can coordinate between various particular solutions disaster management we have used AI you may be aware that India has deployed ITU CAP based disaster management system as well as 3GPP cell broadcast based disaster management system which is implemented across India we use AI here because what happens like IMD is giving me a warning on rain CWC is giving me a warning on flood weather report is coming so we federate all these inputs and using AI you have less than 2 minutes so this I won’t go through NMS of course we use AI to see that how the when the network is likely to be down and this is actually implemented in Bharatnet 1 and 2 it tells you that this is likely to fail this router is misbehaving or this node is misbehaving so that was my last slide so in nutshell what I am saying that the a lot of AI applications are needed for the customer side to protect the customers and we have made India has made a good progress in terms of reducing the frauds reducing the fraudulent connections therefore safeguarding the customers and we will be very happy to take these technologies to any part of the world given it is implemented at India scale thank you so much

thank you Dr. Rupajay that was very interesting the flavor of the kind of R &D that has been done and the apps that have been developed we now move to someone on the panel Mr. Matan Babu Kashi Lingam who is representing the service providers and you carry a lot of burden of customer expectations on your shoulders so do tell us about your thoughts on the topic of today’s thank you thank you

So a few things that we have done as a service provider, majority of the topics are touched upon from fraud and cyber security. I’m trying to say the role of AI in terms of establishing trust, our entire whole ecosystem, which is telecom ecosystem, relies primarily on the customer trust. So to ensure that we have given trust journey for our customers and in the means of adopting AI, there are various core secure pillars that we have followed through. Any AI adoption should have the reasonability, reliability, trust, privacy should come to deliver that. So as a TSP, when we have embarked on the journey of AI, that’s the first and foremost core element that we have taken into consideration.

We are one of the TSPs in the country who have taken the journey of privacy since past five plus years now. We are completely certified on PIMS ISO 27701. We are the only TSP in the country who have governed privacy by design and have certified ourselves against that as well. So that is to only ensure that the trust is given back to the customer. Now I will come back on the journey of AI adoption. First thing that has happened is consumerization of AI. AI has been part and parcel of our life since all of us learned about Siri, Alexa. Day to day home we have been living with AI for many, many years now. So the consumerization of AI happened many, many years back.

What happened in enterprises, there came the pressure of adoption of AI in enterprise. In that, the first and foremost thing that we did is we took as applied in consumer. Let us try and adopt it in AI. Enterprises as well. well. Obviously, it has its own benefit. The benefit being it shares quick win, right? So you get to see a first yearly win that you are able to see by deploying AI in your setup. So how enterprises embarked on that journey is you pick and choose one department, one function, one key problem that you are faced with, deploy AI in it, and you see results. So we saw all of these examples. Fraud. It’s a serious problem for the entire country as a whole.

What can we do? Can we leverage AI? And AI is capable of giving me million eyes and million hands in name of a single human operating that, right? So the power of AI came to aid. We are able to today identify fraud. Sir also briefly touched upon cyber security. So as national critical infrastructures as what we are as TSPs, today we are pressed with serious amount of attacks. So India apparently in the past one year has hosted as many mega significant events. whether it is G20, whether it is Mahakum, then there is situational geopolitical tensions that we went by and now we are hosting AI summit. So national critical infrastructures like TSPs are also faced with increased volume of cyber attacks increased volume if I tell would be not 10 times it would be in as many count that I could multiply with that is a quantum of increased cyber attacks that we are seeing now in the cyber field we are also limited with the number of professionals we have.

So the power of AI not just for the attackers as defenders as well we have started leveraging how can we leverage the power of AI to combat them. So we have, so those are quick wins right? Network operations with the advent of 5G we wanted self operating self healing networks. So in various smaller smaller areas where AI can be embarked that we can realize a very very quick business value, enterprises started adopting That’s the first part that we wanted the quick win, we saw the quick win. The challenge that came with that is we started seeing them in piecemeal approach. The data that we were working upon was almost similar. You gather this intelligence information from the same network elements and nodes.

But you started to look upon through different lenses. All that I need to do is to look through different lens. But I started creating individual siloed repository of data. So if you look at corporates today that have embarked AI, you will see as many isolated silos of data created for them. Because each of them want their lens. And for every lens, they didn’t see the data through the lens. They created a total isolation of the data. Second thing that happened is mammoth amount of infrastructure. Anybody who touches AI today talks about GPUs, humongous. Power that is required to run, etc., etc. That again at enterprise. Thank you. it is siloed data, siloed infrastructure that has been taken into account.

So the journey that we are today in is we had the quick wins, we have taken the first few steps, but we are re -looking at from a different standpoint as we see currently. So we have stepped back. Is there data deduplication that can be done today? In lieu of 20, 30 silos that I have created, do I want to create one single repository of this data? Thereby the secure element also becomes easier. If in silo I have to secure everywhere, bring them in one area, I have the ability to secure them well. Can I leverage a common platform infrastructure, which is the AI infrastructure that is required to put the data and then do these work?

We are doing that. So you can still leverage a comprehensive LLMs, individual businesses in variety of functions, I have taken their own purpose -built LLMs, right? Because… You will have a HR function. The provider for HR is a specific, say SAP, would be primarily driven for HR. And surrounding systems which are talking AI would have built on top of it. There will be a self -healing network. The network provider builds an AI -driven system. So there we are now stepping back to see can we build a comprehensive central LLM, which will still deliver the purpose that are required for looking at. So at V, the premise is core infrastructure, put data in comprehensive, expose them through interconnected enterprise API architecture, thereby businesses and users do not have to talk to the data directly.

They talk through the enterprise model, touch the AI infrastructure, and go and reach back the data for various reasons. So it could be to service my service provider. It could be to service my customers. It could be my customer support. Thank you. bridging them. That’s a platform journey that we are doing that. So this consolidation, like I told, privacy is by design. We are able to do the DPDP compliance inclusive, which is minimize the data. Data in one area, we are able to minimize them as appropriate. That’s what I wanted to share with. Thank you.

Thank you. Fascinating. Now we come to Mr. Abbas, who is the senior DDG from TEC standard setting. He’s promised that he will make a different presentation. So over to you, Mr. Abbas.

the name of application what are the technology used what is the purpose like that then what was the impact or harm information with the what was the incident like physical harm environmental property psychological so these things also forms part of the 30 key fields in which the input is to be given for the schema and then some of the information which is to be masked later on so those related to the name of the submitter email and other things related to submitter information which will be redacted later on similarly the taxonomy as earlier I told that it will classify the incident into different categories depending upon the incident type whether it is a subcategory as network description service quality outage or it is a security beach or AI mismanagement or then affected system whether core is affected whether the radio access network is affected whether the edge is affected or IOT components or physical so which part of the network is affected or any application which is related to user is affected and then what is the incident severity whether it is critical high moderate or low so that also will be recorded and cause of failure if it is known to the user otherwise the deployer or the service provider has to enter this what was the cause of the failure so basically this database will give input to the service provider also that they themselves can examine it they can analyze it and then realign their AI related application so that these incidents don’t recur in future so it’s a gradual auto development of their own AI system which will be then error free and gives the best results output so for this is only this standard has been made but it is not going to give any mitigation mechanism or something.

This is to be decided by the deployer who has deployed those AI application and it is not mandatory. Just as a beginning, when the new computer system came, initially there was not much thing but when the incident started then computer emergency response team was proposed and it started working on collecting the data related to the computer incident. So similarly since the AI has already begun, so we should have this mechanism in place so that we can have the AI incident reporting database also available. Thank you so much.

Thank you so much Mr. Abbas. Presenting arguably and congratulations arguably world’s first time that such a standard has been put out. So since we are fresh off with you, I will start with a question for you about what you have just now presented. you said it is not mandatory it is voluntary of course we will see where that journey goes as you said about certain coming in after the computers but can you tell us a little bit more about what value it offers to the telecom service providers if they voluntarily adopt this standard

so telecom service providers they have already started using the AI application in their network optimization network and services to the users orchestration of resources so many things already the AI application has started so if any incident which gives an unintended outcome if it is recorded and reported then it will be in the best interest of the service provider that those incidents are analyzed and then rectified for so that it is not occurring in the future so in this way it is a can be best utilized by the service provider and since the structure of the schema and taxonomy both are given so it will be a same structure compilation of data which every service provider is doing so that will give benefit to the regulator and policy makers to how to go about the AI policy because of those input which we get from those incidents.

So therefore, Mr. Kashi Lingam, would you think it offers a voluntary adoption of this standard offers any benefit to you from the side of a service provider?

I think like sir rightly mentioned about incident recording has been not a new phenomenon for at least people who have been in the IT industry. Recording cyber specific incident additionally has also started happening. However, we have tied back to the same ITIL framework that has been there historically followed. So enhanced AI is yet another tool which is landing up creating possibly an outcome. The outcome could be erroneous. It could be an event, incident, bias could be one of the situation that are arising. So as TSPs, individually while we have started doing this internally as we have adopted the journey of AI for us, these are recorded events. But one manner that it helps and supports in the framework as TEC has put across is, yes, it can be streamlined in a manner that the rest of the populace, if they have to refer by, can also be referred.

Because today there are no standalone companies, right? Every company is in the area. They are in the area of digital and IT. They are only doing. work in their own function. If you ask a bank, bank has to tell that I am an IT company in the service of doing banking. That is how it is changed. So IT plays a crucial role and AI will be a supporting arm in that. So this record keeping will make the ability for us to scale our AI and models as appropriately. With the advent that India wants to, and we have already announced three LLMs coming our way already, homegrown, home developed here, a platform like this will possibly help us manage and then refine our models well.

So you mentioned how enterprises are becoming digital first. And you also spoke in your initial remarks about AI for enterprises. So how do you look at this controlling costs of, you know, costs the infra part you did deal with, but how about the costs? Of AI for enterprises? Any thoughts on that?

Currently, it is still a significant amount that is being incurred upon. So the cost optimization, a larger chunk of cost optimization comes from the infrastructure as a whole. So about 80 -90 % of the cost to AI goes primarily on the infra in itself, both in store and as well in compute. The rest obviously comes in the skills. So today, while we definitely showcase the world that we have humongous talents that are getting built in the AI area, for an enterprise still to have these skilled engineers to build upon AI is still an adaptable work -in -progress area. So I think in the journey, we are now looking at AI to come in the aid of AI.

So we were in conversation with one of the AI -driven companies yesterday, and the way he highlighted back to us, telling that earlier… the total employee base was 10 ,000. Now there is a refinement and optimization by incorporating AI and thereby there is reduction in employee base. But then if we look at the people who are operating in AI, which was 30 now has gone to 3 ,000. So you cut down here and increase over there. So we were trying to tell them that the true power of AI is actually in making sure that AI is not touched with people, human. So reduction in human by upskilling that as appropriately is an important element for us to do. Thank you.

I’ll come to you, Dr. Upadhyay. You did, you know, I know I cut you off or sort of gave you a time pause there. Could you tell us a little bit more about what you were doing, what you’re doing with respect to disaster management, the application that you spoke about?

Disaster, yeah. Yeah. So disaster management, as you know, earlier, how did you? It used to happen. Suppose there is a cyclone in Odisha. A mail will go from IMD to chief secretary. Chief secretary will write to district collector. District collector would, in his best way, try to send the cyclone exactly to come. And we used to have thousands of lives lost, property lost. Today, using AI and the sensors, the system what we have done, this is one unified platform where all the alert generating agencies, IMD, CWC, FRI, DGSE, so all alert generating agencies are connected through APIs, auto. All the telecom operators are connected. All the alert dissemination agencies like SDMAs in the states are connected.

So it is all powerful one system. Now there is an alarm, a sensor alarm comes that there is a cyclone likely to, or rain likely. This is automatically read by the system. It prepares the message and finds out what is the geo -targeted area. Because earlier the problem was, they will put these kind of threats but nothing will happen. So people will take next time very casually. But today it is a geo -targeted system. It will alert only to the people who are in that belt. Suppose there is a cyclone hitting Gopalpur in Odisha it will only alert the people who are likely to be affected much before. And it will tell you also you need to evacuate you need to evacuate.

If you need to evacuate what is the arrangement by the government or you need to stay indoors. So all that happens and it was actually presented in parliament. The death in for example I am taking the case of Odisha where thousands of people died in 99. The death is zero. So what happened that after that we implemented because India is a large country and sometime a large population is to be alerted in some other cases. That time SMS gets delayed. You know SMS is a sequential process. SMS is sent by SMSCs. There was a new technology called cell broadcast where you don’t see the messages common. You don’t send through SMS you just broadcast it. So we developed a technology called Cell Broadcast And it was recently used in Cyclone Montha And how do you use AI?

Because now I am getting Inputs from various agencies I federate it My system federates all these information Using AI, builds one Particular message, finds out what is The right area where it is likely to hit And sends only to those people And the beauty of this system is, earlier there was a system Of group SMS, they will find people who are staying there Now even if you are a foreigner You are available at that particular time there It will pick your number and it will Give you the message. So tsunami Is coming, so We don’t know, people may be from here And there at the beach. So this has A very good, and in fact we have Published a paper in ITU, ITU has taken This as a report So this is going Forward, we feel that this particular System will meet The requirement of early warning for all By UN by 2027 And we are already talking to many countries And soon this solution will be Deployed in few countries which is Thank you.

Thank you.

In fact, in your presentation, you also spoke about fraud pro and so on. But I will, in the interest of time, I’ll move to Mr. Gorman about this fraud and scam. Now, you did in your opening remarks talk about the importance of collaboration across sectors. Also, the opportunities of, you know, of engendering innovation through collaboration for controlling or combating scams. Could you elaborate a bit upon that?

Sure. Thanks. Thanks for your question. I think this builds on actually the last couple of comments. I mean, what we’re talking about here is sharing data between multi -parties through standardized interfaces and then using AI or something or other to produce a good outcome. And all these things are innovations. They’re on the leading edge of something. If I start with the first thing, data sharing through standardized interfaces. standard interfaces, you know, GSMA has open gateway APIs program and that is contributing to providing data points which can be used in assessing risk for transactions. There’s other data that can be shared that could help address scams earlier in the cycle. Example, there’s lots of other data points to do and that’s the proof of concept that GSMA is working on in Southeast Asia, sharing data.

The challenge with doing that is you’re at the borders of regulatory compliance. You’re talking about private information or personal information or maybe not. There’s sometimes debate. But to be effective, you’re talking about being able to measure the risk on a particular individual user by sharing information across multiple parties. That requires some regulatory support, sandboxes or other activities to help find, to develop the innovation that finds the solutions that help combat scams. I think one of the things we need to focus on in industry is how do we create that nurturing environment that permits exploration of data sharing in a privacy -enhanced way. There’s lots of nice new technologies that have the impact while complying with the regulations and the privacy we want to maintain.

But ultimately, from a mobile operator point of view, I would say there’s four pillars in this combating scam thing. The one is the network, making sure the network cannot be manipulated in favor of the scammers. And that’s by CLI spoofing, all that sort of stuff. Let’s cut that out. If you introduce AI, there’s other things you can do on top. The second is what can mobile operators expose to the ecosystem so that the ecosystem can measure and respond to risk? Open gateway APIs is one thing. The POC I talked about before is another. And there may be other things. The third is what can mobile operators provide as services to their customers? in the same way the physical environment you can provide hard hats and things like that there’s things you can help customers and they can choose to acquire or choose to use them of their own choice to help protect them online and the fourth thing is digital skills digital skills historically we’ve considered is a destination in actual fact we now know we’re never going to hit that final point skills are going to continue to adopt and to adapt and it’s critical that we focus on all four pillars and that from a regulatory point of view and ecosystem point of view we’re collaborating so that the data can flow we can try and test things and we overcome the prejudice that may be stopping innovation because there’s an expectation that you can’t do these things so it requires policy makers regulatory to sponsor to nurture these things I mean I can guarantee I work into 90 % of mobile operators in Asia Pacific and I start a sentence with I want to suggest we use consumer data for I won’t get past halfway through the sentence they’ll say nope you can’t do that But in actual fact, if we want to be successful, no single entity, especially no mobile operator, has all the information.

I mean, if a mobile operator arbitrarily starts turning off SIM cards because they think maybe that traffic looks a bit dubious, I mean, you’ve only got to look at the Optus outage in Australia where three or four people died because they couldn’t call emergency services. You don’t want to be taking that action. It requires collaboration, regulatory support and policy support.

Yeah, thank you. Thank you. Network, ecosystem, hard hats and upskilling. I think that’s a good way to end the discussion here on the panel. But we have time for one question. Yes, Mr. Jha. We have less than two minutes.

Thank you. Very quick, very brief. The question from Mr. Julian Gorman. as we have said we are under attack may we attack anytime. We have also said that we should align with the global trends in order to combat these fraud and all those things. You have heard our panelists who are the icons in their field of manufacturing and standardization and PSPs. Could you suggest two steps that global leaders should take to align the world with themselves and two steps that India should take to align with the world. Thank you.

I mean two steps globally. So the proof of concept we are trying to do in Southeast Asia is actually prove that data can be shared domestically but across borders also in a safe secure way and has impact on controlling scams. One thing we need to remember with scams all we are doing by taking action against scams is increasing the cost of the business case and if we increase the cost of the business case here then another area becomes more favorable and that could be just different types of scams or it could be different locations and so that leads to the what do we need to do globally. We need to act across borders. We need to act as a collective global community.

GSMA has a program called United Against Scams there will be a lot of things about that in Barcelona but India is obviously taking great action domestically or taking steps domestically sharing that knowledge across borders and being able to share that data across borders is important and so I would leave it at those two points

Thank you, it also gives us pause for thought, maybe as regulators we also need to look at collaborating efforts across regulators because there are again sectoral issues that we need to do and so with that we are now at the end of the session, I would request the audience to give a big round of applause to my panelists who have given us very good insight into the topic at hand and thank you so much

Thank you moderator sir and all our distinguished panelists for such a vibrant discussion on usage of responsible AI the standards, the repository, various government app for enhancing consumer experience. Your insights will greatly benefit the overall digital ecosystem. Now I would request Dr. M .P. Tangirala to present mementos to our distinguished speakers as a token of appreciation. First to Mr. Julian Gorman. To Dr. Rajkumar Upadhaya. To Mr. Mathan Babu. To Mr. S .T. Abbas. Now I invite Sri A .K. Jha, Principal Advisor, TRAI to present a memento to the moderator of this session, Dr. M .P. Tangirala as a token of appreciation for moderating such a productive session Thank you so much, sir Now I take this opportunity to invite all the speakers for a group photograph I once again would request Chairman, sir, M .P.

Tangirala, Secretary, sir and all the Principal Advisors to please join the session speakers of this panel for a group photograph Thank you give a huge round of applause to all the panelists for joining us. Thank you. Thank you. Thank you. Thank you.