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India’s AI stack, bridging government vision with enterprise needs. My name is Amanraj Khanna. I’m a partner and managing director for India at the Asia Group. Also my privilege to be a moderator here today. I have to say the energy here is still so palpable, even after five days of this. So it’s absolutely brilliant to be here with you all. We’ve had a truly fascinating moment in India’s AI ambitions. Some massive policy announcements. We just had Pax Silica announced this morning. Very exciting indeed. Significant infrastructure investments. Brad Smith, if you heard him yesterday, Microsoft announced $50 billion in the global south alone. After $20 billion, that has been announced for India. Google, as you know, $15 billion. And growing enterprise adoption.

Anthropic announced its partnership with Infosys, Tata, OpenAI. I’m sure you’re all watching this. But announcement is one thing. Deployment and then achieving scale quite enough. So that’s why this panel matters. Today’s conversation brings two critical perspectives to this fundamental question. What does it take to translate that vision into adoption and scale? One of my two distinguished speakers brings unparalleled insight into infrastructure being built through national R &D institutions. The other sees the reality of what India’s largest enterprises actually need when they deploy AI. So we are here to have an honest conversation about these two tracks. Where do they connect and perhaps where they don’t. So with that let me introduce my distinguished panelists. First I have to my immediate left Mr.

Vivek Kanneja. Vivek is the executive director of the Center for Development of Advanced Computing, CDAC as it’s known. And CDAC has built the Parham supercomputing series providing AI compute infrastructure for government departments and national missions. It conducts cutting -edge research into high -performance computing and cybersecurity. and also trains thousands of engineers annually in advanced computing and AI. Vivek, of course, has held multiple senior leadership positions within CDAC and has guided national initiatives in these critical areas. Welcome, Vivek. I also have to my far left, Nitin Bajaj. Nitin is Director of Sales for Conglomerate Accounts at Intel India. He leads Intel’s engagement with India’s largest enterprises on their digital transformation and AI adoption journeys. He has over 28 years of global experience in sales and technology leadership and orchestrates a broad partner ecosystem.

These include system integrators, ISVs, cloud providers to deliver Intel -based solutions spanning cloud, AI, HPC, 5G, edge, and end -user computing. So in summary, he sees firsthand what drives enterprise infrastructure decisions and what actually prevents companies from moving AI from pilot to true scale. So with those introductions, let’s get right into it. Vivek, why don’t I start with you? So here’s my first question Vivek. CDAC has built param supercomputing series and AI compute infrastructure. What compute capabilities does CDAC actually provide today? Who uses them? For what workloads? And what are the key constraints that you see operating within?

Okay, thanks. So as you know, CDAC is a scientific society under the Ministry of Electronics and IT. And one of the mandates that we have is to build supercomputing capacity in the country. This is the mandate which is given to us under the National Supercomputing Mission where we have developed a series of supercomputers under the brand name PARAM. We started in late 80s, starting with the PARAM 8000. And now we have the PARAM series of supercomputers which are installed with our own software. About 48 petaflops of supercomputers are installed in the country. And we have been able to build some of these supercomputers And we have been able to build some of these supercomputers and we have been able to build some of these supercomputers and we have been able to build some of these supercomputers and we have been able to build some of these supercomputers of overall performance connected over the National Knowledge Network or the NKN as it is called.

This capacity is going to be augmented to about 100 pitaflops by the end of this year with 60 installations. Most of these installations are today being used by researchers. About 15 ,000 researchers fire jobs across these machines on the National NKN. A lot of it is being also used by MSMEs. For example, we have opened Paramuthkarsh which is housed in our city at Bangalore Centre for use by start -ups and MSMEs. The kind of applications that run here include drug discovery, bioinformatics, we have protein folding. We have molecular modeling. You have weather prediction. So I mean almost all of these are being used. number crunching problems, oil exploration, finite element modeling, computational fluid dynamic problems. So all such problems are being run across these clusters by researchers.

We also have a lot of expertise in various domains which we have developed in -house over a period of years where we are hand -holding a lot of these agencies, a lot of government agencies are working with us on this.

Thanks so much, Vivek. Lots of threads to pull on there, but for a moment, let me go to Nitin. Nitin, you work with some of India’s largest enterprises, some of our national champions. So when these enterprises commit to AI, which you must have seen increasingly so, what are some of the actual barriers that prevent them to move from pilot projects to some of those production -scale deployments that everyone envisions, especially at events like this?

Thank you. First of all, thank you for inviting me. It’s a privilege to be here and talking in front of such an esteemed crowd. So basically, I think I’ll kind of break this into two, three pieces. One, be it Indian enterprises or be it global enterprises, I think everybody is grappling with the same sort of problem statement. And everybody is trying to find those use cases which are very, very pertinent for their own enterprises. And some certain enterprises are into manufacturing domain, some are there in the retail side of it. And so the typical use cases that they have could be around smart manufacturing, smart retail, or specific generic use cases where they want to do a lot of document search.

They have to have fine -tuned search on the policies, the TNCs and things of that sort. So essentially, the way you see it, I see it in twofold. One, They are looking at the speed, and everybody is trying to figure out the best ROI. So I think the biggest gap today is what to use, whether to use it on -prem or whether to go on cloud, use open APIs which are available to them. Then once those use cases are ready, as you said, from pilot to production, what is the final cost of that deployment? And then the third angle that comes in is whether to kind of centralize all of this or to take it to the edge.

So there is no single answer. And when you think of all the ecosystem providers that are there today, be it from silicon to ISVs to system integrators, everybody has a pocket of expertise in their own sense. But today there is no single formula. And the entire. AI journey is changing so rapidly models are being dropped at a speed of light and then the entire from silicon to OS to everything else, the whole ecosystem is changing so fast that even the enterprises are trying to figure out what is the best deployment model for them, what is the best ROI that they can get out of it but in the midst of all of this, in pockets, lot of these enterprises are trying to see what are the specific use cases that they can bring to fore that can bring some incremental benefit to whatever operations that they are running in their organization.

Now I can give multiple examples to it for example in a manufacturing domain, there could be surveillance kind of use cases multi -modal use cases, there could be use cases around how to look at inventory and then how to look at complete digital twin dark factory kind of a scenario in case of retail it is all about say it could be around preventing the thefts, the pilferation that happens or doing customer analytics. And then, as I said, a lot of document search kind of examples are going on. But in most of those cases, the whole decision -making is between edge versus on -prem, cloud versus sovereign data centers, what kind of models they should use, and how to find out the right ROI, which is where we feel frugal AI is what we kind of propose to the industry.

And I’ll talk more about that maybe later. But what is the best deployment model that can really help an enterprise scale at a level, at a cost point, which is really making sense to them?

Let me ask you a very quick follow -on question on that. Do you see that Indian enterprises are increasingly sophisticated in making these choices? And, of course, Intel works globally, right? So how does it compare in terms of its maturity, its sophistication, compared to the other markets where Intel also operates?

Clearly, in terms of use cases, I think we have the edge. But again, the veracity of data. it’s a big problem second i would say that these enterprises when you think of it a year back almost everybody wanted to buy gpus and a lot of these enterprises have bought systems which are powerful enough to run all kinds of use cases but still they are in that pilot phase because again because of that roi factor so things are maturing of course enterprises are becoming smarter from llms we are now looking at slms so they are trying to kind of figure out the right silicon where they have to kind of land their workloads on so as things are emerging i think things are getting better but yeah it’s still some time before you see those live deployments coming out

for the benefit of large larger audience sorry for interjecting i think just just to add to add to what he just said we are seeing a lot of places where people are not being able to come out of the POCs. I think one of the major reasons that, at least personally I have seen, is that people are very happy with the POCs. They can train them on curated data sets. But once it actually goes and hits real -life situations where the data needs to be cleaned up, it’s not clean, you have no proper experience in actual deployments of the MLOps, you have done it in a canned manner, then suddenly the reality hits that, no, it’s not that simple.

And as you talked about the ROI, then you have to make those choices, whether I should have an on -prem this thing, do I really need a GPU for my problem? Or can I work across multiple VMs on a simple, simple IT infrastructure? So those are the choices that hopefully in the next coming… years, intelligent choices will be made. People, we will start to see more MLOps, engineers coming out and deploying these things at scale because POCs is fine, but the real revenue will come only once you actually deploy it at scale.

Understood. Thank you so much for those perspectives. Unlike yourselves, you know, you’re both technologists. I’m a little bit in the policy space. I’m in the fringes of this. I work on tech policy, and I haven’t had a single conversation here with, you know, a foreign investor which hasn’t talked about sovereignty or dependency. So I’m going to bring my next question. I’ll bring you over to my area because I’d love to pick both your brains on this. So Vivek, let me talk to you about this first, right? So India talks about sovereignty, but SEDAC still relies on global technology, right, whether that’s chips, systems, you know, software stacks. So realistically, what can India… India built domestically versus what will we always need to source globally, right?

and where should we focus our capability? So question to you as someone who’s really on the cutting edge.

Okay, so let me answer that both technically and politically correct way. See, when you talk of sovereignty, let’s see what does it really mean. Do you want to be completely independent in the entire vertical, right from silicon up to the application? Is that really possible, right? Let’s say I need to design a GPU. It’s a good aspirational goal. But do I have the wherewithal today to do the entire thing in -house? Probably not. I don’t have the IPs. We can start, definitely. We don’t even have today a fab that can give me a 3 nanometer or below production capabilities and package capabilities. So I think a more pragmatic approach is to have maybe the silicon coming from outside and everything.

Everything above that should be under my control. So you should be able to control all your… critical choke points. So that’s the model that India AI has taken. For example, they have created a farm of GPUs which are available under the control freely or at a reasonable cost for the developers. The models which are being built on top of that are under your control. How those models are going to be orchestrated is under your control. The application that will use those models is under your control. So for me, that is the sovereignty where you are getting the maximum ROI. Should I really be competing against an H100 or a B200 from NVIDIA? Probably not in the short term.

But yes, as an aspirational goal, just to let you know, CEDAC is designing its own GPGPU based on RISC -V. We will probably have something by the end of 2029 -30. But then, till that time, we really need to have a lot of this entire stack under sovereign control. maybe using chips from outside, whether it is NVIDIA, whether it is Sapphire Rapid or Granite Rapid from Intel, or from AMD. But everything above that should be, all my critical choke points should be under my control.

So I have to compliment you. That was a very candid and pragmatic answer. So compliments to you. Nitin, can I reframe that question a little bit for you? So, you know, there’s, as you know, in the same vein, there’s a significant policy focus on data sovereignty and data localization as well, right? So when your enterprise customers are making AI infrastructure decisions, how much do these factor into those choices? And how do they compare with cost and performance considerations as well? So has this calculus shifted with some of the policy developments that we’ve seen over the past couple of years in AI?

Well, I would say not really. So it all depends on the industry and the market. So it all depends on the industry that enterprise is in. for a banking industry, for healthcare industry, data sovereignty is very, very important. For a manufacturing industry or for any other, say, retail or any other industry, they are trying to build use cases on the cloud because that’s where they feel that they can build those use cases very, very quickly. They can simply call the APIs available there and then they see that the performance is much better and they get better accuracy. So it’s a mix of both. Now, then comes again, even in a manufacturing environment, as I was calling out, in an OT environment, a lot of these manufacturing firms would want their data to reside within their perimeter because it is so, so close to them.

Then again, when it is coming to deployment side of it, they’re again looking at an edge deployment which becomes within the perimeter. So it’s a mix of both. Now, finally, again to the point that I was making in the beginning anything that has to scale the price becomes a key driver so it is about okay I have a model that I have kind of fine tuned on the cloud now when I have to take it to deployment can I use something locally which is available today with me without making a lot of investment and then can I scale it which is where when Intel is talking about it we basically kind of focus on frugal AI today the Intel core and ultra core CPUs they have a GPU, NPU and a CPU all combined in a single processor which allows you enough capability to run maybe a 7 or 8 billion parameter model so the typical requirements that you will have on the edge are very well suited on this CPU itself and when it comes to a data center side the Xeon 6 processors today are able to run a 20 billion parameter very easily.

They can go up to 80 billion parameters depending on the specific use case. So do I need a GPU in every instance is the first question that we are trying to ask. And today everybody has a CPU in their environment. So can they reutilize that? And can they test it out? Can they look at performance levels? One example that I can kind of discuss with my customers is if you’re looking at, say, a prompt -based engine where you need to do a document search, typically a human eye can read about 10 prompts in a second if somebody is a fast reader. Now if a processor can give you 15 to 20 prompts, is this good enough as a performance for you or do you want 200 prompts in that particular second?

Maybe 15, 20 prompts is good enough. So that’s where the cost versus performance comes in. That’s where one has to be very, very… calculated in terms of what is their end use that they are looking at and what would best suffice that particular usage so again it’s a mix of uh this uh localized uh data versus what can go on to cloud and once you look at uh the scale of it again you have to look at the cost because when you’re scaling it on the cloud the cost may be very very different from what you can get on the on -prem side of it i’m not a kind of proponent of on -prem versus cloud for me i think both of them bring their advantages but what i’m trying to say is the customer has to really look at what is their end use and at what cost they want to

understood so we need to diversify our approaches and have a product to mission fit which is absolutely critical think of it in the past everybody used to think only mainframes can solve the problem today the applications are not only running are no more running mainframe, they came on to CPU level things and then today we are looking at micro services. So everything has kind of evolved over time and same things are happening on the AI side of it today. Understood. So one quick question that’s on everyone’s mind and to both of you. From each of your respective perspectives, whether that’s government R &D infrastructure or enterprise deployment, is talent and the capability gap still a critical choke point from each of your perspectives?

So Vivek, why don’t I start with you on this?

It is. It’s unfortunate but yes it is. I mean we do find that we have a set of very bright engineers coming out but most of them are trained in good theoretical understanding of what machine learning is. They are good at mathematics, basic understanding but when it comes to actual deployments on field. I think that’s where we are lacking. Maybe we need to have a serious look at our curriculum in the colleges to see how do I train large models, how do I deploy large models using MLOps, because today, as I said, most of these kids are working on curated data sets. They are working on some standard test cases and test and validation cases.

But when it comes to real life, life is not that rosy. I mean, you have data which is missing, you have data which is skewed, you have data which needs to be cleaned, I have real time constraints, I have other security considerations. So those are not a part of the curriculum. So that’s where I think some capstone projects where you are able to handle beta scale data needs to be put in place. Theory is fine, but still there’s a lot to learn on the practical side.

I’ll give a different perspective. The way I look at India versus all other countries. in other countries it’s an aging population for us it is a booming population at this point in time which with average age of 13 to 25 or so which is kind of exposed to AI so maybe today we may see some sort of gap in terms of AI capabilities but 2 years down the line or 4 years down the line I think that we bridged very very quickly so we have that benefit of demography here in the short run of course as an individual myself I am also learning from the kids today how AI can be deployed so gaps will be there but I think this is a learning curve for everybody

Thanks Nitin I want to get to just a couple more questions and then we’ll try and be very quick one I’ve been wanting to ask you and one that I get asked often is the energy and sustainability question you know super computing uses huge amounts of energy can have societal impacts right CDACs super computing whether the you know large data centers that Intel perhaps you know works with So how do we think about energy and sustainability implications, especially in the Indian context? So Vivek, perhaps, with you.

So from my perspective, I look at it to be addressed at two levels. One is something that we have been doing. Coming from a VLSI background, I can tell you that there are today’s standard techniques which are being used in all associate designs which are power -aware designs. So you have multiple power islands, you have clock tree gating that happens, you switch off those cores which are not being used. So that’s from a design perspective. But when it comes to the platform design, there are smart choices which are being made. For example, if I talk about CDAC solutions, today we are using liquid cooling as well as water cooling in a ratio of almost 70 to 30.

And we are slowly moving the entire thing to a pure liquid pool thing. There are other advanced techniques which use only air cooling. So, ultimately what is your PUE will actually determine. So, typically we see a PUE of around 1 .2 or so as compared to a conventional water -cooled thing which is about 1 .4, 1 .5. There are definitely green norms which are being proposed. I think the question here to be asked and we need to do this kind of a benchmarking across all the models is what is the energy that I spend per token for training or for inferencing. I think there should be critical benchmark. We need to seriously look at how I can optimize my models to be more power aware.

Can I have compressed models which take less energy? So, yes, energy is one of the critical factors and especially hyperscalers would need huge amount of power. I mean, there is this joke that we keep on saying that *** But along with the hyperscaler, you also need to have a small power plant which needs to be designed together with it.

Thank you. Nitin, to you.

I’ll have two, three points here. One, from manufacturing point of view, Intel is utilizing the latest technologies like ribbon fed and power via, which improves the power efficiency by 15%. So these are the latest technologies available. Second, I would say that we are running our own data centers. Which are running at a PUE of 1 .06, which is the most efficient power data center PUE that you could see. So there’s a white paper on intel .com. I would appreciate if those interested can look at it. Third, I would say, again, power is a problem. Or is the first and the foremost ingredient to running those data centers. So one has to be, again, very cautious of what kind of models you are running and then where it is landing.

So if you. If you’re able to be, if you’re more judicious in terms of our selection. then of course we can save power in some ways.

So one final question. I realize that we’re time’s up. One question, let’s put this in a quick sentence if you can. When we assess India’s progress over the next three, five years, what does success look like to each of you? So maybe a sentence.

Success for me would be AI being actually deployed in a lot of workflows and making the life much simpler and enjoyable for us.

Thank you.

For me it is like when we were like about 150 in terms of data usage. Today we are number one in terms of data usage. When it comes to increasing the generic intelligence of people, so today we are using media for media and for entertainment, all the data that we are consuming. If we can consume the data for improving or improving the general intelligence of public. that will make a large scale impact on the society and India at large. So in two or three years, if a Sabziwala can figure out how they can kind of up -level their state, that would be the best way. And then all the Indic models and all the other use cases that are coming out should be able to support those.

And then there will be a mass -scale deployment of AI across the board.

Thanks so much, Nitin. And I see that time’s up. I wish we could pick your brain further. This has been a truly fascinating conversation. And thank you for being so candid with all your responses. Thank you. So please join me in thanking both Vivek and Nitin. Thank you. Thank you. I would now like to invite Sangeeta Reddy, Joint Managing Director, Apollo Hospitals, to give her remarks. Thank you.

Good evening everyone it is indeed a pleasure to be here and thank you to the NDIA mission and the NDIA summit for acknowledging this as an important topic to include within the panel discussions scheduled during the NDIA summit I also would like to thank the panel for the summit, so maybe we should just close with a bang, and I see Bhanu sitting with five women, so good luck to them. But I am here on behalf of the Central Square Foundation. Central Square Foundation is a philanthropy working in school education in India, and one of the focus areas for us is edtech. And now that edtech is getting powered with AI, it becomes very important for us to ensure that we impart AI literacy to every student, parent, and child in India, so they are able to become active contributors to AI rather than just being passive recipients.

And that is the fundamental premise of this discussion. CSF has been working on a program, a large -scale program on AI literacy called AI Summit, and this is the first program with a curriculum for AI literacy that has been built in partnership with the Wadwan, the Wadwani School of AI, which is also represented on this panel. So with that I will hand over to Bhanu to take the proceedings forward. Thank you.

Thank you Gauri. Good evening everybody. Like Gauri said we are in the last few panels of the summit and I have the distinguished privilege of moderating four stalwart women in the education space. I don’t think there’s any other, all other panels were the other way around where there were a lot of men and very few women. So it’s my privilege to moderate all of you. I will try to keep it as collogical as possible but you can also trouble yourself between with questions and stuff. So today’s dialogue is really about building futures through AI literacy for India and the Global South. AI is already in our hands. It’s in our phones, it is in our homes, it is in our classrooms whether we like it or not.

The conversation today is really about what features, futures do we want to build of the… tool which is coming into our hands, right? I think the discussion about should AI be in the classroom or should AI be a learning tool is already passed. It is going to be there and it is for us to figure out how to use it productively, right? So with that as a backdrop, we will move forward into the conversation and quickly talk about each one of you here. Shabana, senior project scientist from the Vardhani School of Data Science and AI, IIT Madras. Ramya, we all know Ramya, CENTA and a lot of other things as well before that. Tanushree, co -founder and CEO of Transform Schools and Chitra, founder of Chrysalis, right?

I would actually start the conversation with Shabana putting you on the spot, right? So your journey really in computer science really started with the trigger with your 11th grade computer science teacher. part in you in the classroom, right? In a rural school in the backwaters of Kerala, right? Nice place to be there. But from that time to now the role which you play as a post -PhD fellow in IT Madras, working in the ed tech space with all the things you do in AI, both on the usage side, design side, and the regulatory side, I would really like to ask you what, according to you, are the transformative bets AI can bring in

Okay. So if you want to analyze the transformative bets, the major transformation that AI can bring into the classroom, I feel that we should look at the impact that AI has on the key stakeholders in education, namely the students, the teachers, parents, and the education bodies and organizations at large. So if you look at learners, I think the biggest value add is in terms of personalization, right? Because research has already shown that one -on -one human tutoring results in better learning gains as compared to a traditional classroom -based learning. But we know that in India and also across the global south, the student -teacher ratios are quite large, which actually prevents personalization from happening. So this was the case before, but with AI in picture, this can change, and in fact this is changing.

Because AI can help students with personalized learning in terms of customized lessons that is tailored according to the student’s specific conceptual gaps or misconceptions. Or AI can also suggest tailored learning pathways. It can also provide individualized assessments and also tailored feedback pertaining to the performance of the students in the assessments. So that way AI can play a big role in adding quality to the learning process of the students, which definitely helps in bettering. So this was the case before, but with AI in picture, So this was the case before, but with AI in picture, this can change, and in fact this is changing. this can change, and in fact this is changing. this can change, and in fact this is changing.

Now coming to teachers, again, so we always say that, you know, AI is not going to replace teachers, but it is going to be a better assistant. Its role is going to be an assistant, which can help in better productivity, teaching productivity, as well as the quality of pedagogy is what it brings onto the table. So, for example, teachers can use AI -based tools to create better lesson plans, to run diagnostic tests that can help inform the misconceptions or learning gaps for students in the class and tailor instructions based on that. It can help in, you know, kind of producing required kind of assessments and also even evaluating them. So that way, the value add is in terms of improved productivity and also informed pedagogy.

And so another significant transformation that AI brings is that it lowers the barrier to quality education, especially for the underserved classes. So with the voice -based capabilities and the multilingual. capabilities of AI. Now, quality learning content is available in regional languages to learners, which in fact, you know, not only affects the teachers and learners, but it also affects the parents, in the sense that now parents can have a more engaged contribution to their child’s learning. So that way, it opens a lot of opportunities. And now coming to educational bodies or organizations at large, so we know that data is collected across multiple points. Including assessments, attendance systems, and program implementations, etc. Now with AI, it is possible to collect all of these data points, combine and analyze them to give actionable insights, such as, you know, identifying risks of dropouts, identifying high risk students, perform better resource planning, etc.

So that way, AI is already transforming education in a big way. And it is going to be, it has a lot of potential to, you know, play a greater role in education as a whole. now so this kind of emphasizes the need for AI literacy because you know AI is already a part of classroom and all the stakeholders are kind of already interacting with AI so there is the need for the for everybody right not just the students or teachers but even parents and generally for people to have a better understanding of the AI concepts how these tools work in order to use them responsibly

thank you so I hear three things you said stakeholders student teacher and maybe even the parent like a teacher and the second thing you said is that getting AI to the last child in our population which is equity and accessibility the third thing which you said is for all the use cases which you talked about to be utilized there has to be AI education in the form of where it is right that brings us to the stakeholders, right? So Tanushree, you across the last seven plus years have impacted over 30 million students, right? So my question to you is, from the government school systems point of view, and when you go on the ground to students, right, especially the government schools primarily, right?

How are they responding to AI in and around them in the classrooms, be it at homes or wherever they’re interacting? What are you seeing on the ground?

Thanks. Thanks for that question. And thank you for inviting transfer schools on this panel. So I think in past seven years, with our experience of working with the government schools, and as an organization, we work on improving learning and life outcomes. But with the program, which is AI Samarth, we have seen one emerging thing, that a child is able to learn and learn. And that is, we have seen that a child is able to learn and learn. And that is, we have seen that a child is able to learn and learn. And that is, we have seen that a child is able to learn and learn. And that is, we have seen that a child and learn.

And that is, we have seen that a child is able to learn and learn. And that is, we have seen that a child is able to learn and learn. And that is, we have seen that a child is able to learn and learn. And that is, we have seen that a child is able to learn and learn. And that is, we have seen that a child is able to learn and learn. And that is, we have seen that a child is able to learn and learn. And that is, we have And I have two great examples to share from our home state, which is Odisha. It’s from the class nine. They’re both from the class nine grades.

One, there’s a girl whose name is Shraddha. And so she, when we asked her that, you know, how’s her experience in a class where she’s getting these lessons from AI, Samarth. So she said that I used to use AI tool as something, you know, just in a fun way. But now I’m able to cross check my difficult subjects or the topics. And if I’m still facing any difficulties, I’m able to go back to my teachers and cross check again. So she’s using AI as a companion as well. So what I see here is that curiosity is converging. Converted into confidence. That is one big thing. There’s another child whose name is Poonam. she said that for us when we saw AI around us everybody was the AI was a buzzword you know we were everybody was talking about it but it was for all entertainment because we were saying that okay you can you can gamify it you can make a cartoon movie about it all of that but when I see AI now is that I can get my project ideas from it I can actually you know do my preparation from it so and it is very useful to see that because they are the first generation learners in a government school so it’s it’s a great I would say pleasure to see that happening in a government school and I mean AI with AI Samarth we are reaching the end of the world and we are reaching the end of the world and we are reaching the end of the world and we are reaching the end of the world and we are reaching the end of the world and we are reaching the end of the world and we are reaching the end of the world and we are talking about point talking about point talking about point nine million students in all and what we nine million students in all and what we nine million students in all and what we have seen as a pattern overall is three things.

One, which I mentioned earlier, is curiosity converting into confidence. Second, that AI is able to support learning, sorry, language, improving into languages. And second, reducing the learning gaps students have. And third is, which is the most important thing which AI Samarth does, is that teaching them about the ethics, the biases, and therefore it’s a responsible engagement. So that’s the

Thanks, Tanushree. It’s lovely that you talk about the story of Shraddha and Poonam.

Thank you.

the journey from curiosity to confidence. And along the journey, being able to use digital tools in the form of AI and exercise their agency as a learner and not lose it.

That’s correct.

So that’s a beautiful story. And I think, you know, 0 .9 million Shraddhas and Poonams.

Yes.

Thank you. And onward, upward and onward. And more than that, right? So that moves me to another dimension, right? So, Chitra, you’ve been, you founded Chrysalis over 25 years back. And you founded it out of the pain of being a mother of two daughters and their disillusionment in the school system at that point in time.

My disillusionment.

Your disillusionment. They were too young to be disillusioned. And the interesting connection here is that you actually started off as a full stack ICT curriculum embed provider from grade 1 to grade 12 for CBN. and you have seen that whole wave, right? And we still have over 40 % of our classrooms which still don’t have computer labs. And now you’re at the start of another wave with AI, right? So I think you’re looking at the second wave in your own journey, Chrysalis, you and your team and other people working with you, right? So my question to you is how are teachers in the government schools and the low -fee private schools in particular, not the middle and upper, but the low -fee private schools, today, you know, transacting around integrating AI into their life, work, and fun?

Yeah, thank you, Bhanu. That is a very important question to be thinking about. And I can speak from a pre -AI Samarth and a post -AI Samarth point of view. So very grateful to CSF and everybody who’s, you know, really pushed us into this wave with a very… Very, very… purposeful and meaningful work, and it’s giving us a lot of insights into this community of teachers and students. So if you look at preparedness when we entered, and I think we are now close to 200 ,000 students that we will be, you know, who would have been exposed to AI literacy, we are talking about the preparedness being, I look at preparedness from two points of view. One is awareness about AI, the skill sets, or whatever it means to handle a tool.

And the other is, I think, the sentiment and mindset that every teacher or every student has, right? So from an awareness point of view, again, the spectrum is, I don’t even, I just hear this buzzword, and I don’t even want to go near it on one side. And on the other side, yeah, I’m dabbling with it all the time on my WhatsApp. I know it. And then there’s always this confusion between the thin line that AI and, you know, an algorithmic. provides. So I think AI Samad has really empowered them on certain skill sets that has gotten them to understand what is AI and how I can be putting that to use. That’s from a skill set point of view.

But for me, I think more interesting is the sentiment and mindset point of view. If you look at that, it’s a spectrum between hope and fear. And hope leading to over, you know, stimulation, right? And fear, resistance and not really getting there. So I think that’s a very important spectrum. And I think a program like AI literacy and Samad kind of brings in an equilibrium. I think that’s very important because, and I completely, you know, I can relate to what Tanushree was saying, we see so many children and so many teachers who are now us, hey, you know what? I thought everything was bad about AI. Like I was so fearful. I thought it’s going to replace me as a teacher.

I now understand that if I hold the agency and I know what is what, and it’s not rocket science. I think that’s the first demystifying thing, right? I mean, it’s not anything that I cannot be conversant with. And we are talking about government teachers, affordable private school teachers who have found a confidence like she was mentioning. And that has made them approach AI in a very purposeful way, right? They are integrating it. I think the way we built the curriculum, thanks to the Wadwani school and CSF, and we’ve also made some meaningful contribution to that curriculum and content development. We are seeing that the use cases we build in, there is a very, very high relevance to what the teacher is doing, which is very important.

It’s not just literacy about what is what. but how it can be purposefully used by the teacher. So that’s a big winner for us. The second, when it comes to sentiments, we’ve heard children, actually children and teachers in particular, tell us that, you know, I had a negative approach and today I walk in with so much positivity. I know this can be engaging. This can be purposefully done. And even that over, I don’t know what to call it, but I think over -utilization, you know, where they would just generate lesson plans because somebody asked the coordinator or the, you know, educational officer asked them. They just go and dabble with the LLM, CHAP, GPTA and then churn out lesson plans, not even knowing what to validate, how to validate.

And that has also been kind of equalized. So I think it’s a beautiful, in my opinion, an equalizer. And of course, over a period of time, it will become a leveler. I look at it from that point of view. And there’s much more. work to be done, but I think it’s a beautiful start where there are so many positive stories in the field that’s giving us a lot of hope. I think even for players like us, you know, organizations like us, I wouldn’t say we were not in that spectrum between hope and fear. It’s not only about the teachers. We are all in this phase of, you know, is it going to be the thing tomorrow or is it going to be, you know, what is our position in this world?

And I think this is kind of now getting to some equilibrium. That’s what I would say.

What I hear you say is that, you know, tens of thousands of teachers are finding their balance in the classroom with confidence and agency, right? And I think that’s a good state to be in. And we have a lot more work to do. India is a big country and Global South is even bigger, right? That brings me to Tanushree. Sorry, Ramya. So Ramya, your journey really started off with the time you spent in McKinsey and you are actually kind of bootstrap the education practice for this region in McKinsey. And then from that horizontal view, you actually became a falcon and then you dove into teachers and the problem of teachers and there comes CENTA, right?

So over the past few years, like 11 years in CENTA, from across 100 countries, 100 plus countries, teachers have engaged with you. And now you’re at a place where you’re seeing AI come into the mix, right? So I would ask you to kind of be that falcon, fly back out of India and look at the global south and kind of talk about what are you seeing, what are you hearing from teachers in AI, not just from India and what you’re doing with AI but also in other countries where there isn’t any such thing happening.

I mean like in a remote part of Jharkhand we’ve had a teacher saying that I was worried that my students are getting into this world faster than I am and now with this literacy I’m able to catch up with my students which is a very nice thing of her to say but those are some of the perspectives to start with I don’t know if I answered because I was not sure what I was supposed to do as a falcon coming out

Thank you, thank you Ramya I think good conversations, three things to pick right, one is the stakeholders being touched and their journey from curiosity to confidence for some actors and then for a lot of other actors finding the balance between the hope and fear spectrum, right, so I would like to kind of take the conversation a little bit down into the implementation and design of all of what we’re working, right, I mean that’s where the rubber really meets the road right, so I would go back to Dr. Shabana and Shabana you have been a part of the curriculum design and the curriculum review and the endless debates of how should we help a rural school student in Orissa understand computer vision and what metaphor would we use that and those are fascinating conversations we had in those times.

But now looking back at the journey and looking back at what we’re hearing from the field, not just ASM, but there are a lot of other literacy programs happening. But I would really like you to kind of, you know, focus a little bit on moving towards what is it that you would like to see in the design, which kind of ensures ethical judgment and critical thinking among all the stakeholders, the child as well as the teacher.

Dr. Shabana Bhattacharya Okay. So first, I think we should start with the child. Dr. Shabana Bhattacharya Yes. so when we look at the available AI literacy curriculums around the world we see that many of them deal with foundational AI literacy along with AI skilling but AI skilling is more about how to practically use AI which is more role or context dependent so for example AI skill for a software engineer could be different from what AI skilling would be for a teacher but AI literacy refers to the foundational set of AI concepts that is kind of universal that is something that everybody should know about so as a part of the AI Samad curriculum so we collaborated together the Badwani school and CSF collaborated together to come up with a wonderful curriculum which I feel is like very grounded and it kind of focuses on the key AI foundational concepts that everybody should be aware of and later you could build on top of it to do further skilling which is more tailored towards each role So to talk about the AI Samarth curriculum, we have four key pillars.

The first being understanding what is AI and what are the applications of AI, like looking at, you know, the everyday applications and identifying the AI component, because we all interact with AI in some form or the other without realizing that, you know, it is a form of intelligence that we are interacting with. So understanding that is very important. Understanding what is AI is important. And, you know, also, you know, kind of having an awareness about some of the key technical aspects, like what is data? What is the role of data in training an AI system? And, you know, about vision, about NLP. So these are the basic AI concepts, the technical concepts that, you know, students and teachers and universally everybody should be aware of.

And then once we have an idea about the software. The role of data in training AI systems, that naturally leads us to understand about the societal impacts, the environmental impacts, et cetera. So, for example, so we. we know about the issues of bias and fairness in AI systems, right? So we know that, okay, an AI system is trained based on the data. So depending upon the kind of data that was used to train the systems, we can have, you know, these issues. And these arise whenever the systems are used in practice. So the third pillar is definitely about the societal impact, the environmental impacts, what happens when we require the computations at scale. And finally, about how to practically interact with these systems, right?

How to write effective prompts, because we all interact with, almost all of us interact with generative AI tools such as the chat GPT, right? So then how to actually frame an effective prompt to get the desired result. So that is also very important. So I feel that these kind of cover the basic fundamental AI concepts that is universal, that everybody should be aware of,

So just one more. One minute on. how do we in all of this safeguard critical thinking

okay so coming to critical thinking so so when we know that an AI system is trained using data we know that you know the kind of research that we would get on a query would depend on the kind of data that the system has seen so which would make a student or a teacher or any user you know be vigilant about using the AI the the results from the AI tools directly so we have to be careful about double -checking cross -checking with valid sources and not trusting the result you know directly and also coming to the critical thinking part it is more about you know not over relying on AI tools and using your judgment to kind of validate the results to verify like I said with verified sources whether the results are you know valid or not so which is also important

I think just correcting all the results is a very important thing to do and I think on that there was also a lot of exercise which was done across the curriculum to sprinkle various good ways of using AI where we basically kind of said it’s not about asking the AI for the answer but actually doing your own exercise and submitting your answer and then asking AI to review and improve which is that critical thinking loop which we reinforced across multiple places. So I’d move forward a little bit to Chitra now. So Chitra, I would like to take you down into a teacher training scenario where your team has trained a lot of teachers on AI literacy via the curriculum and then they were cascading forward, right?

So at that level, right, what is it that you and your team have done in terms of building confidence for the teachers who are in the training program to carry that back into their classrooms and deliver it to the children?

I think we… always believed that teacher training is more about building an emotionally safe environment whatever topic we are training teachers on and with AI the hope and fear spectrum, this becomes even more relevant. So I think both in the curriculum the way we’ve done it and in the way we facilitate and train the teachers

Namaste Krishnan ji Pleasure to have you here We welcome Shri Krishnan ji from MIT Thank you for joining us today sir

Chitra So I think we definitely need to look at how the confidence is built. In a light hearted way I also want to say a lot of teachers have now started saying we are becoming more polite and classroom Any other questions? idea why? It’s nothing to do with the training but to do with LLMs. Because chat GPT always tells them, hey, you’ve asked a brilliant question. So that is becoming contagious. And the teachers are today like, you know, we’ve seen that happen. So that is why I wanted to bring that was there is a lot of role modeling that happens when there is a cascading, right? And I’m simply saying an LLM cascading also is bringing that politeness.

I hope we don’t become artificially polite, but then I’m hoping that some of these things rubs off in the language of teachers. So I think that hope and confidence is what trainers are modeling. And that I think is being carried on by the teachers when they do the cascading to those students. And one other thing in the literacy, I mean the Samarth model, I feel is the capacity we are building in the teachers is actually strong because there is the responsibility of the teacher to go back and train the teacher. And see, they are all teachers. You know, when we analyze why teachers resist training, it’s because they think that they are the deliverers and then to receive becomes difficult for us.

So I think I love this AI Samarth model, the literacy model we have built, thanks to CSF and, you know, the whole think tank there, is that yes, I’m going to learn and some of us are actually going to translate it to the students. And so that rub off is really, really working well. So I think the preparedness or I would say the teachers are very gently handling this whole thing because for them it is a new topic. It’s not like I’ve done math teaching for 25 years and I’ve found all my children getting into IIT and all of that. This is a new topic, this is a new skill, and they are very, very careful when they are doing it.

So there’s a lot of deep respect that they have in terms of translating this into their own training, Banu. So I think in many ways this has been an eye -opener into how usual teacher training and how this AI literacy teacher training is bringing very new insights into how teacher training can happen. So I think that’s very, very critical.

Thank you, Chitra. We would now request Shri Krishnanji to kind of address us, Secretary Mighty.

I’m sorry to literally photobomb this session. It was not my intention. Ram is a very old friend. And when she asked if I could stop by, I just thought I would. Primarily, more than anything else, to thank all of you for participating. in probably a historic summit, one that finally democratized AI. In that sense, I think what we have managed to do is to actually bring people into the room. And more than even people, we have managed to bring people’s concerns into the room. And I think it’s very fitting that we are talking about education today, which is so critical in every sense of the word. Partly because I think if we have to achieve anything we need to achieve, we need people to do it and people who have the skills and who have the abilities and who have the competencies to actually do what needs to be done with technology and make sure that it works for them and make sure also that they’re able to participate and they’re able to actually take advantage of it.

The other part of it is, of course, while we are making them capable, to what extent can we use technology? I think it’s nobody’s case that we have enough teachers or teachers’ assistants or we have the kind of technology that we need. We need the kind of resources. we want for education. If technology helps us to multiply that rather than substitute it and if technology helps us to actually enable our teachers to deliver better, nothing like it. And I think those are the kind of applications that we seriously need to look for. The Expo is replete with those examples. Incidentally it’s open tomorrow and it’s extended up to 8pm today so if you haven’t gone and taken a look at it, let me make a pitch.

Please do go take a look. I think a huge number of social applications of what technology can do and how it can work for people. I think all of that is something which would interest many of you but more than anything else I’m not an expert on education and I would be the last one to suggest that we should, I mean this is a silver bullet and it will solve all the problems. It may, it may not. But I think we need to sort of experiment. with it but more than anything else I think one thing I do know that students of all ages and students in all disciplines need to be aware of the potential of the technology and see what it can do for them and I that in itself I think would be significant the government of India has already made a policy call that they would teach about AI from class three onwards and I’m sure that many states would also do the same thing and that would form part of the curriculum and likewise in practically every institute or every university I think AI needs to be taught across all disciplines I mean not necessarily only to the computer science people in the IITs but to just about everybody because again the jobs in AI are not just of the guys who build the models I mean those are the nerdy PhDs there are probably about 300 of them across the world who’ll do it the rest of us are not going to do that the rest of us are not going to be Sam Altman or Peria Amadei or Dennis none of those I mean, all of them we saw yesterday.

The rest of us are going to be more journeymen, more people who will actually figure out ways in which this technology will affect us so that people need to know, people need to understand, even in an area like art history, what is it that the technology can do to their own discipline and how they can sort of leverage it. So if the Central Scale Foundation and educationists and everybody focuses on this area and is able to actually train the next generation to do this, train the next generation to figure out what jobs can work for them, that’s going to make all the difference. I or none of us are in an age group where, I mean, Rom and I are probably about, we are contemporaries, so we are too gray and too old now for this to make a huge difference for us.

But this is a technology for the next generation, and they should not be lost in this. And that is something which, I think we need to keep a sense of what is going on. to the message and the whole message of this summit is inclusion and bringing everyone in and we have to also ensure through the education process that no one is left behind so thank you very much for having me and i wish you all the best again a plug for the expo try and take a look and thank you all for joining us here today thanks thank you you you

and then we’re going to dive right into my immediate left. I have Alex Reed -Gibbons, who is the CEO of the Center for Democracy and Technology, one of the leading advocacy organizations in the world, working on civil rights, civil liberties all around the world. She’s based in D .C. To her immediate left is Amanda Craig. Amanda is the General Manager for Responsible AI Policy at Microsoft. To Amanda’s left, we have John Edwards. John Edwards is known to many. He is the Information Commissioner of the United Kingdom. And to John’s left, we have Denise Wong, who is the Deputy Commissioner of the PDPC in Singapore, the Privacy and Data Protection Commission. Welcome to our panelists. So we have two regulators, an industry representative and a civil society representative.

And I come from the IAPP. If you don’t know the IAPP, we are a global professional association, a not -for -profit but also policy, and we’re neutral. We’re not just a company. an advocacy or a lobbying body, we bring together the people who do the work. Many of them are in the room right now who do the very hard work of data protection and AI governance all around the world. All right, let’s jump in. The title of the session reflects trust as an engine for growth. Let’s think about that just for a minute. Just a few short years ago, I think it was two and a half, maybe three years ago, this event started in Bletchley Park in England.

And in that iteration of the event, it was named the AI Safety Summit. Right around that time, the EUAI Act was being negotiated. It soon passed after that. But a lot has changed in that two or three years. This event is the AI Impact Summit. The event last year in Paris was the AI Action Summit. More recently, we have seen, the not yet fully implemented EUA. AI Act become subject to an omnibus package where some of the expectations of that original act are being dialed back a little bit. And we’ve seen broad critique of regulatory structures, trust and safety structures that might inhibit growth and innovation in AI. There clearly is a deregulatory mood in the air.

In fact, I think it’s notable that there has not been much discussion of law or regulatory initiatives that might create guardrails to help guide the adoption of AI. So clearly, we’re in an odd moment, and an odd moment for this panel. But as I walked around the campus of this event, this enormous campus, I noted something that was, I think, quite significant. Just about every second banner or poster, just about every large printout, printed word in the show floor, somewhere had trust. safety or privacy as part of the messaging. In fact, the sutras, and we’ll talk about them as we go through the session, the principles announced by the Indian government largely around trust and safety.

And so what gives? What’s the dichotomy here? At one moment we are saying it’s a deregulatory mode, we step back. Well, at the same time, we are actively embracing and discussing trust and safety, risk management, protecting consumers, citizens, human beings as they engage with AI. So do we care or not? Are we actually in a deregulatory moment, or have we just gotten quiet about the need for guardrails and trust and safety in these systems? I would say for business, risk exists regardless of whether there’s a law in place or not, and so businesses have an imperative to respond. I’m going to tell a very, very quick story, and that is that in 1891, when electricity was first being brought into the White House in the United States, then President Benjamin Harrison and his wife, Caroline, were actually terrified of flipping the light switch.

And so they hired the electrician from the Edison Company, a man named Ike Hoover, who went on to become the chief usher of the White House. They hired him to flip the light switch. I think the message of this story is that we won’t use it if we don’t trust it. And so as AI is being pulled through the walls of our world, as it’s creating light and switches and tools for us to use, I think we need to ensure that we’re comfortable flipping those switches. And that is the topic of our panel today. So let’s jump in. And our first question is going to be about just the moment that we find ourselves in.

And I’m going to start with Alex. why are trust and safety important to innovation? And maybe speak to this dichotomy that I’ve highlighted. Why is it in this moment that we can’t talk about regulation, but everywhere it seems we’re talking about trust and safety?

Yeah, first of all, thank you for convening us, and it’s a pleasure to be here. I think you really hit the nail on the head in your introduction, which is when we think about the long -term success and sustainability of AI, and that is business sustainability for the companies, as well as societal sustainability for all of us. The secret is not just an acceleration, the biggest, fastest, most capable model. The real story is one of adoption, and that has been the overwhelming theme of the summit this year. And for people to adopt this technology, they need to trust it. And that’s trust in multiple different facets, right? Is the tool fit for purpose? Does it work in your language?

Is it appropriate for your culture? Will it protect your privacy? Is your data going to be secured? What is the quality of the information that is grounding that model and those outputs? And I think people are really waking up to this, and they’re demanding more. This is both as individual users and then, of course, for enterprise customers, too, who themselves are saying, we’re on the front lines thinking about how to integrate AI into our business operations. We’re the ones who will likely be sued if this goes wrong. So this is where trust really is the fuel of innovation because it is what’s going to be the economic driver of these tools being adopted. And the other thing that I would add is what we see is not only that trust is important for innovation in the abstract, but this is also where responsible, thoughtful regulation can be fuel for innovation as well.

Because the same way that we want to be able to drive cars without all of us being experts in how a motor works, Product liability and good laws around the creation of these tools help make sure they outsource some of that work for us so that we don’t all have to be doing the individual labor of deciding whether we can trust. So many times people will create this false framing of regulation versus innovation, as opposed to thoughtful being regulation being the fuel that actually allows us to sell, buy, and use these tools.

Excellent. Fascinating. John, I’m going to jump to you, and Amanda, I will come right back. But I’m going to jump to you. The U .K. doesn’t have an AI law in place. It has lots of laws that will apply to AI. I think data protection and the GDPR Act in the U .K. is a great example of that. But talk to us a little bit about regulating in the absence of an AI law. What does that look like in the U .K.? And do you see organizations exhibiting behavior that demonstrates that they’re focused on the ideas that Alex suggested, that trust and safety matter regardless of the relationship? What is the regulatory structure that sits over them?

Yeah, absolutely. Absolutely. Absolutely.

There it is.

No, very much so. I mean, the data protection laws apply across the board wherever technology touches personal data. So we have a de facto regulatory regime under the UK GDPR. Coming back to your comment about trust, it’s so important, and there is a role for regulation actually in assisting businesses because businesses are trying to deliver that trust proposition to consumers. But by what metric? Right. And that’s, I think, where regulation can provide a common standard. So, you know, we require, it’s a regulatory tool, that you have to do data protection by design. You have to do data protection impact assessment. You know, we expect. Privacy by design. We expect. respect risk assessment. So all of these things are regulatory requirements, but they are also tools that help intermediate between businesses and the consumers to demonstrate that there is a basis for trust.

And an organization like the ICO is there for both sides to see, well, there’s someone actually overseeing that. And that’s a role that we do discharge. Judge, to your point about the absence of prescriptive regulation in the UK on AI, we don’t see that particularly as a deficit. I mean, I think there’s a lot of policy work going on in areas where policymakers and regulators do need to step in. That’s ongoing, and I won’t comment on that. But, you know, there are ongoing issues about the distribution of proceeds from the use of creative materials and the like. That carries on. But… In the absence of an explicit rule, it’s incumbent on my office to deliver safety and confidence and metrics for industry and to deliver certainty over what can be seen as an uncertain law.

So we’ve gone out and said, well, here’s how we see the technology -neutral general principles of the GDPR apply when you train a model, for example. We see, for example, the EU AI Act in Article 10 talks about the need for fairness. Well, we’ve been able to articulate those obligations by way of guidance, linking it back already to the GDPR principles. So, you know, there’s a mapping. I don’t think at the moment for the available applications of artificial intelligence technologies that there is a lacuna. It’s there. With the GDPR. And we are there to provide. confidence and certainty about how you apply that, how you improve your products with it, and how by doing so you engender that trust that you described at the outset.

Excellent. Okay, so Amanda, tell us, do you agree that there’s not a need for additional rails, traffic indicators in AI? Is John right that the existing regulatory structure is really providing enough guidance or is it the case that Microsoft is using internal principles, frameworks, standards that you might adopt to build programs and services that you think meet the expectations of trust and safety of the marketplace?

Thank you. From a Microsoft perspective, we are focused on implementing our responsible AI governance program and see opportunity for lots of different governance models that governments could pursue in terms of implementing existing regulation, developing additional regulation that complements that existing regulation. I think the through line for us, the bottom line, is very much what Alex started us off with, that we do very much see, we’ve seen through multiple generations of technology, we’re not going to have adoption, we’re not going to have use of this technology without trust. And we need to have governance programs at technology companies. We need to have governance efforts by governments that are ensuring that we have an evolving conversation about trust.

Because if I pull the thread on the analogy you started us with, like how do you flip on a light switch and that can be scary when you’ve not done it before, I think the other thing that is very challenging, true about this technology, is that it is also very dynamic. It is evolving very quickly. quickly. And people might even be scared that, like, they won’t know where to find the light switch next week. And that brings a whole different set of challenges. And so that requires not just confidence in how you are able to sort of trust the technology today, but also that there’s trust in a governance process that will continue to iterate and evolve alongside the technology.

Excellent. Denise, help us then here. I know Singapore has released guidelines, standards around AI. Tell us about the Singaporean experience in thinking about regulating trust and safety in AI.

Thanks so much, Trevor. And thank you to the IAPP for putting this together and for having us. Maybe I’ll answer that question by linking some of the concepts that we’ve talked about. And that sort of underpins our philosophy. Trust and safety is the outcome that we want. You know, we want to create the necessary conditions for the society. to thrive for the public and the enterprises to use the technology with confidence. So AI for that public good. To do that, we need governance. We need a framework of thinking about how we can govern the technology, and we’ve been doing this for all sorts of technology. AI is but one. Regulations are a mechanism, a type of governance mechanism that you use when the necessary and correct conditions exist.

And so that map of that concept informs how we think about our governance approach. So on issues that are very clear, where there are clear harms, we have stepped in to regulate. An example of this is elections regulations that we put in place where we prohibited the use of AI deepfakes to represent candidates. It was time -limited. It was for the period of elections, but we stepped in and put a law in place for that. We also have laws for AI creating online harms, as well as AI in scam situations. So that is the part where we regulate for clear and present harms. For the rest of it, a lot of it we leave to sectoral regulations where there’s already a web of existing regulations, and on specific issues as well.

John and I and many of us are in the data protection field where, as John has said, there are already existing laws that can be tacked on, updated, reviewed in order to deal with this new technology that has come about. So where we have done AI governance frameworks and tools that you’ve mentioned is where we’ve seen a need to create some sort of horizontal principles and platforms to think about the sector agnostic general issues on transparency, on what model governance for corporates could look like. We haven’t seen the need to regulate that horizontal layer just yet, but certainly a need to articulate some of these principles. And that also allows us to create more certainty for the market, to give them some direction that actually this can be a market -driven assurance system that has demand, has supply, and has what we’ll call them proto -standards, early days of standards about what good looks like.

So that’s the work that we’ve been doing and trying to create and simplify. We have a seed and an assurance ecosystem that sits, I would say, adjacent and complementary to regulations where they’re needed.

Fantastic. Please, please.

So just to comment on that, one area that I think is proving very important, and people are discovering this across jurisdictions, is even where existing laws apply, there is a problem where AI systems make it hard to know whether or not those laws are being broken. So this is where that transparency layer you were articulating really becomes important.

Give us an example.

Yeah, and I’m going to make it U .S.-centric just because it’s the one that’s top of mind, so forgive the bias here. So in the U .S., we have equal employment laws. It is against the law to discriminate in the course of hiring. So in theory, a piece of software that perpetuates discrimination against particular candidates, for example, not considering the resumes of people over a certain age, is violating an existing law. So people will say we don’t need any further regulation. We’re done. The problem is you can tell in a human run system where it was just a bad apple in the HR department, it’s been historically easier to prove that case. Now, when it’s AI powered software making that decision, it is really hard as a worker who’s just put in a resume and never got an answer back to know if something was going wrong.

If you actually get up your courage and file a case, it is really hard to prove your case if there is discrimination. And so without some type of disclosure regime that requires transparency in these high risk scenarios, high impact scenarios, to have transparency and disclosure about the system that is being used, impact assessments to make sure that discrimination isn’t happening, you actually don’t get the remedy that people really need under existing law. And so that’s where I think this horizontal piece can complement the sector specific vertical laws in a light touch way, but actually gives meaning to the laws on the books.

So I think that’s a great example of the harm trigger that Denise described, that we identify a clear harm and that may be a place where additional regulatory structure might be helpful. I think we heard pretty significant consensus across our panel. Trust and safety is good. That’s good that we’re there. That’s a great consensus to achieve. And not complete consensus on the idea that additional regulation is needed yet. With the exception perhaps of a few scenarios in which we can identify high risk or harm. Let’s go to our audience for a second. Help us describe the relationship between innovation and regulation in AI. If you think it’s a great relationship, thumbs up. If you think it’s a bad relationship, thumbs down.

If you think it’s complicated, make it complicated. What do we think? Oh, I see a lot of content. What does our panel think? I think it’s a good relationship between innovation and appropriate regulation. Fascinating. We have a very strongly opinioned audience here. That’s great. Let’s talk about regulation again and dive in just a bit deeper. I think one of the things that’s tremendously challenging is prescriptive regulation, trying to understand harms that might occur before technology is fully adopted broadly in the marketplace. I’m a veteran of the privacy world going back to 1995, 1996. And in the late 1990s, we were talking extensively about cookies and how do we regulate cookies and the privacy issues associated with cookies.

Guess what? We’re still talking about cookies often. And I know for many of the privacy and data protection, they’re nodding already. They’re crying a little bit because it’s so, so painful to implement. implement many of the cookie banners and cookie consent mechanisms that we have. And I’m not entirely sure, we might get John to admit this even, that, you know, those cookie banners are actually driving the outcomes that we hope for. We identified the biggest and worst harm or concern and dedicated resources appropriately to that. Amanda, I’m going to jump right to you. Talk to us a little bit about identifying those harms. Alex gave us one, which is perhaps AI reviewing HR submissions, resumes, CVs, and language in those CVs may actually create results that were not intended, that create bias, that, you know, in a human -driven system would be easier to find, in an AI -driven system just much, much harder to find.

That’s a great example. How do we identify those prescriptive harms, those harms that we’re not quite sure about yet, that may emerge? Do we do it through principles, through ethics, through what?

I think all of the above to some extent. Part of why we start with principles in our governance program is I think it’s helpful to orient towards what do we care about, right, as we then try to build a program that realizes those outcomes. I think we also can look at existing law that reflects where there are harms, like in the employment context, where people could be mistreated or treated unfairly that we know we care about. And there’s been a lot of effort and regulation to define high risk, high impact. At Microsoft, we have something called the sensitive uses sort of scenarios where, you know, we have three categories where technology could have like an impact on someone’s life opportunity or consequential life impact of something like employment or education opportunities, for example, or how someone’s treated under the law otherwise, all sort of fit in that context.

We have the second big category of harm that we have defined as around sort of the risk for psychological or physical harm. So think about vulnerable populations there. Think about the use of AI in critical infrastructure. And then the third category is the use of AI that impacts human rights. So, you know, we have our way of defining what is really high impact. You know, a lot of governments, again, have taken different routes. I think the other thing that we’ve seen is the kind of emergence of a conversation around sort of technology itself that poses specific high risks. For example, highly capable models that have a whole other set of risks that are the risks that are being defined.

And that’s one thing that I just want to draw out as we think about this and drawing upon what I feel like, you know, and I didn’t grow up in the privacy world, I grew up in the cybersecurity world. And one of the things that I think a lot about as we work on, you know, defining these harms and figuring out what to do about them, that we can learn from the kind of… decades of work on cybersecurity is the challenge of thinking about how to address risk across the supply chain. And I think it’s a slightly different conversation in AI than it has been traditionally in security with software and cloud technologies. But there is like a common principle or approach that I think we should really look at closely, which is, you know, we are oftentimes in the context of AI thinking about risk and harm where the technology is actually used, right?

And then what’s difficult is figuring out what do we do across the whole supply chain to manage that risk and have that be cohesive. And one of the things that in the cybersecurity context, we know what the risk or harm, it’s much simpler. It’s security risk, that we care about. But we have the same challenge in terms of like, how do we manage that risk across the supply chain? And one of the challenges over decades of work in the cybersecurity context is… Instead of wanting to… put emphasis on one part of the supply chain or the other at any given moment instead of, like, really dealing with the really hard governance challenge that it is everything at once.

And so I think when we, you know, think about the complexity of defining harms in the AI space, that’s important work to do. And also, in the context of managing risk for any of those harms being realized, we also need to think really hard about looking across the whole supply chain at once. Even though it’s hard from a governance perspective, that’s going to be most important for managing the risk ultimately.

Fantastic. And I misspoke. It’s prospective, not prescriptive regulation. But John and Denise, maybe talk to us a little bit about that. And let me frame it for you both. And Denise, we’ll have you start. Clearly, with data protection regulation, we have had the GDPR now for over seven years. And the effect of that stands out. And I think it’s important to think about that. And I think it’s important to on the global policy environment has been enormous. We now have over 120 countries that have privacy laws in place. Many, many, many of them have genealogical lines that point back to the GDPR. And yet we haven’t seen that in AI yet. The EU AI Act has not taken off around the world.

We don’t see a Brussels effect happening on AI. Is it because the challenge of identifying harm, the challenge of prospectively trying to identify what might

You always ask me the tough questions. I think, first of all, the harms question, because I think that’s relevant to the regulation question that you’re asking. I think the starting point must be that every country has a unique context. And it’s the job of the government to figure out how to do that. And I think that’s the challenge of prospectively trying to identify what’s harmful, what’s harmful. harmful to their society. I think there’s going to be a huge amount of overlap, but at the end of the day, what’s harmful in one context that’s harmful in India may not be the same as what’s harmful in the US. And the cultural context matters. That said, I think there’s actually increasing consensus, I feel, about what harms or archetypes of harms there are vis -a -vis AI.

And we see that, for example, the International AI Safety Report is starting to anchor some of this taxonomy and sort of buckets and archetypes of harm, and we also see that beginning to happen at Iceland, for example. Those conversations are happening. How does that link to prescriptive regulation or legislation? I think that if the harms are still being coalesced and formed, it’s quite difficult to be very prescriptive about how you deal with those harms, because that, by definition, is sort of changing and still coalescing. It’s still quite nascent. That’s not to say… we should step back. I think we just probably need a slightly more agile way of thinking about that broader concept of governance.

So in the social media context in Singapore, we did it via codes of practice. So we have a broad sort of umbrella legislation that creates a legislative frame for which these codes of practice apply. But the codes of practice can be updated more easily. Same thing, actually, with our data protection law, the PDPA, which is structured quite differently from the GDPR. Our PDPA is actually very not prescriptive. It’s outcome driven. It’s fairly broad. But most of the guidance that PDPC provides, and these are for compliance, is done in advisory guidelines. So I think there are regulatory mechanisms you can use that are less prescriptive than primary legislation. And that gives you enough levers. It’s tools in a toolkit, basically, to be able to deal with the harms and with the problems that the society is facing.

Excellent.

To dispute you a little bit on the lack of a Brussels effect, I will say, I mean, going actually back to Denise’s point, so not only is there some harmonization happening around the scoping of the harms, I think that certainly is happening, but also on potential points of intervention. So, for example, one of the key elements of the EUAI Act is looking at high -risk scenarios and having their remitigations in place. We have similar laws under consideration in multiple states in the United States, one in the books already in Colorado. They would never say it is a copycat. It came from its own origins. But it is lawmakers thinking what is an appropriate right -scale intervention to that particular risk.

You can look at the recent transparency laws that were passed in California and New York, very similar discussions to the Code of Practice for General Purpose AI models that came out under the EU practice. You can look at the EUAI Act’s provision for regulatory sandboxes and this notion that we want small and medium -sized enterprises, and others to be able to innovate and get a little bit of forgiveness or wiggle room under the laws as they figure out how the regulations apply. That law just got passed in Utah. So there are these glimmers where we are seeing… smart solutions to specific problems and people learning from each other.

I think in the absence of that umbrella AI standard, that interaction with fellow regulators across disciplines and domains becomes really important. Or I will ask you, does it become really important?

Yeah, it is. It’s hugely important that we coordinate. You know, these are new challenges that we’re all facing. On the GROK issue, obviously, it’s under investigation, so I won’t be able to say too much about it. But, you know, we’re interested in what, you know, how models are trained, what data they’re trained on, what output filters are included, what kind of safety mechanisms. I’m interested in what kind of ingestion there is of data when it’s used at that level. But there’s some complexity in that case as well because, you know, you’ve got users using a tool that’s amplified. It’s amplified by social media. I don’t know whether the same functionality is available in any other image generation tool that just hasn’t got the same media because it’s not amplified by a social media platform.

And but, you know, very early on, I think I was back home in New Zealand, actually, on about the 5th of January and started to see this. And I messaged back to the office and said, what are we doing? What’s Ofcom doing? How are we connecting to our international colleagues? And that’s so important. And so we’ve, you know, we’ve messaged into GPA. We’ve coordinated very closely with Ofcom. And, you know, we have to cope with the fact that regulation is a little bit fragmented. So Ofcom is responsible for administering the Online Safety Act in the UK. Now, that is legislation that seeks to regulate the kinds of harmful content that can be delivered to a child’s device, for example.

Right. I see this thing. Is that regulated by online safety? If so, it’s Ofcom. How did that get to me? Well, that depends on how the underlying data was processed. That becomes an ICO, you know, GDPR issue. So we need to be working very, very closely, and we are. But also with the crock issue, one of the very early things we did was to reach out to our colleagues in the GPA, the Global Privacy Assembly, and say, who else is looking at this? Let’s make sure that we’re not sort of treading on each other’s toes, or at least that we’re sharing information, that we’ve got the same ideas, that we think the same way. And that can be tremendously powerful, whether or not you can point to a regulation that that app or that platform is clearly in breach of.

To describe a set of expectations about harm mitigation across a coordinated group of global regulators, I think can be quite powerful. And, you know, just to see how, you know, the alternative for some of these platforms is not necessarily being investigated and trained by the ICO. So it’s like what I noticed the first day that I was here when I went to flip TikTok on and saw this is not available in this country. So if the offering in a particular jurisdiction does not meet the standards and norms of that jurisdiction, these organizations need to understand that they can be switched off, that they are not actually all powerful.

I just have the image of the U .K. Information Commissioner doom -scrolling TikTok in my head now. Let’s do a quick round, and please do keep your answers short, but innovation is not limited to technology, is not limited to business practices. It’s also very powerful in the… The privacy -enhancing, safety -enhancing tools that we use inside organizations. It’s in regulatory structures. Denise has mentioned regulatory sandboxes, or maybe it was Alex, but we’ve heard regulatory sandboxes mentioned. What is the one innovative idea in trust and safety that you think holds real promise? And I’ll let you do one sentence to explain it, but this is a speed round. So we’ll start with Amanda and then work down and come back to Alice.

One sentence. Okay. Is that my sentence? I think about provenance tools as an area of innovation. Again, this is calling upon my cybersecurity background, but I think, you know, something like agentic AI is an area where there’s a lot of interest -concerned governance momentum. And one of the challenges is being able to look at something that is fundamentally not just like one technology. It’s technology. It’s a bunch of very dynamic components, models, platform tools, services, applications all working together. And while that feels like a really new, hard challenge, we actually can draw upon what we know of software to actually be a set of dynamic components as well. And one of the ways that we’ve figured out how to govern that or working towards figuring out how to govern it is with software -built materials, something that really allows you to have the ability to track those dynamic components.

And I think that’s something we can apply to agents.

So it increases transparency. It tells you, you know, which algorithm or which system this might have come from. It helps with accountability broadly. Yeah. Excellent. John, what’s the most promising trust and safety innovation that we have?

Well, you challenged us with one word. So I’m going to go with agency. Agency. And I think it’s, for me, it’s a word that, you know, so much of our world is dominated by consent, which is, I won’t say broken, but it’s under strain as a useful concept. Agency, I think, has capacity to recognize that, the objective is to maintain and to restore and maintain an individual’s agency as it uses any product. And that’s consent. It’s actually making sure that provenance is delivered, for example. You can’t have agency if you don’t know the origin of the data that is delivering this agentic miracle to you. It gives you tools at the other end. And consent is always conceived of as a front -end authorizing concept.

But agency says, okay, I’ve done that now. Where’s my delete everything button? Or my I don’t want to do this anymore button. So I think if developers can be thinking about how they deliver the best possible service in a way that restores and maintains the agency of the consumer, I think that will go a long way to addressing some of the problems that we’re seeing. And I think

Fantastic. I had a lot of… Professor years ago now who described burden -shifting wrenches in the law. And I think consent is a burden -shifting wrench that moved much of the burden to the data subject, to the individual. Agency, it sounds to me, is an idea to move back to those who might be accountable and have them have fiduciary or stewardship responsibilities for that person. Denise?

I would pick privacy -enhancing technology. I think it’s an interesting technological way to deal with at least one part of the problem, which is how do we secure the data, how do we make sure that the personal information is well protected. And it’s advancing so quickly. So two years ago, we were looking at federated learning for training of AI models, and no one could figure it out. I think it’s actually being done in production now. So there is – I’m a lawyer, so I can say this. Sometimes the law cannot solve the problem. But actually, maybe another technology can.

Fantastic. Alex?

Well -staffed, empowered, independent regulatory bodies that can help represent the public interest. Wow. And because in some countries those are under attack right now where that is not available, well -resourced, technically informed, independent civil society that can play that role in the interim.

Fantastic. Yeah, the importance of having watchdogs, yeah, entities that are watching and observing, commenting, enforcing, really powerful. So there are four great innovations, provenance, agency, privacy -enhancing technologies, and well -funded regulators or civil society. Well done. I think that is a great start. Let’s do another audience poll. How many of you here in this audience are responsible for AI or AI governance, AI ethics, AI safety inside your organization? Hands up. It’s almost the whole room. Keep your hand up if you’re also responsible for something else in addition to AI or it’s just AI. It’s more. I think it is a pretty complete overlap, almost a complete overlap. There’s at least a significant percentage that were responsible for more than one thing, and one of those things was AI.

I think that’s an example of the complexity that we see inside organizations today. John described the coordination necessary between Ofcom and the ICO in the Grok investigation, which is ongoing, because there was not a single place where regulatory authority existed to address that concern. This is a really complex environment. The number of harms or issues span from children’s safety to intellectual property. From bias and algorithmic discrimination all the way through deepfakes and other things. Alex, how do we… How do we put that all into a pot and make it something meaningful?

Well, what if you can’t put it all into a pot? A pot is a common denominator in all of those things, but AI is a tool that touches everything. So I really do think you actually need a nuanced approach that looks at a particular risk, what those mitigations are for that risk, and then goes from there. The privacy considerations when you are sharing your most intimate concerns and questions about the world with a chat bot is very different than these questions about deep fakes and fraud and impersonation. It’s just you need to have a different legal regime. I think some of the common elements that run through, one is that transparency and rigorous approaches to risk mitigation really matter, and that can either be through regulation or through principles and best practices with meaning and standardization and watchdogs reading those disclosures.

And the second is this burden of the user. So when Trevor introduced me, we described my org. organization. We represent users’ rights around the world. I am all for user empowerment. And also, we cannot put the burden solely on users to navigate this moment. Indeed. And that is the major lesson of the cookie example you were saying before. We didn’t misdiagnose the harm. We misdiagnosed the remedy, which was the burden on individual users when we don’t actually have market choice, nor the time or mental energy to just read a whole bunch of disclosures and act alone. And so solutions that acknowledge the harm are tailored, but also take that burden off individual users. So you’re empowering users, but not burdening them or leaving them to essentially defend themselves unprotected.

We have to think about that.

Okay. Sadly, we are at the end of our time, but we have one more pop question for all of you, and we’re going to let this be our close. We have gone through the AI Impact Summit, the AI Action Summit, the AI Safety Summit. Five years from now. What is the AI summit going to be called? What’s the word that’s going to be in the middle there? So this is a one -word answer again. What’s it going to be? I know it’s a tough question. So, Denise, I’ll start with you because you’re able to handle the toughest questions. Ah, the AI Trust Summit. Okay, John?

Nostalgia.

Nostalgia.

Thriving.

Thriving, AI Thriving Summit. Okay.

I’m going to cheat. For the people, by the people. It’s more words. They’re so strange.

Some of the people. It’s hilarious. To get on a poster. Here’s what I know. I know that there is incredibly hard work that needs to be done to bring trust and safety to this ridiculously powerful technology that I think, as Sundar Pichai says, will be more profound than electricity. That hard work happens every single day inside organizations. I’m going to cheat. I’m going to cheat. I’m going to cheat. I’m going to cheat. I’m going to cheat. I’m going to cheat. that are implementing these tools inside civil society, that are watching and guiding that behavior inside regulatory offices that are navigating to ensure that marketplaces around the world, that the digital economy gets this right. I feel better because people like this are doing that work every day, and I hope you’ll join me in thanking them.

Thank you very much. Thank you so very much. Thank you. Thank you. Well done. Well done. You were fantastic as expected. So what is the I am and I fly to London for magic.

And we are delighted to note that many of the activities that were outlined in the MOU have already kick -started. So I think to that extent, it’s fantastic that Dr. Padhya is here today and will share with us his ideas on where an Indo -German collaboration in innovation and AI can have a value proposition for both countries. I’m also delighted to have my own colleague, Dr. Thomas Kuhn, who has come all the way from Germany from the Fraunhofer Institute of Experimental Software. He will be sharing some of our experiences and competencies in AI, particularly with focus on workplace changing, which is, of course, one of the biggest topics we have today, but also manufacturing, agriculture, and health.

And I think one of the key topics that Fraunhofer can present to the Indian audience is data space. We have created a secure data space in the cloud for many years where challenges remain. Industries are brought in and discussed. So you will hear a lot from him. I’m particularly delighted that four Bangalorean CEOs are here today. We have with us Sindhu Gangadharan, who needs no introduction, I think. Anyone knows that, and who doesn’t know is coming under a stone. So we also have Anshuman Eversi for CTO of Mercedes -Benz Research and Development Center in Bangladesh. And we have Dattatri Salagame from Robert Bosch Software Solutions. And, of course, Prashant, he’s sitting at the back, Doris Rami from Averior, which was earlier Continental.

So as you can see, a power -packed industry captain session, which will follow after the panel. And last but not the least, we have Murali Nair from Bretelsmann Stiftung, which is a think tank, which has been producing products. A lot of knowledge papers around India and Germany, and actually positioning India as a positive partner for Germany long before Germany even started looking at us so seriously. So thank you for that, Murali. I’m standing here in front of you today as the head of Raunafa in India for the last 18 years extremely proud that Raunafa is one of the largest applied research ecosystems in the world we have 76 institutes in Germany we are present in more than 80 countries we produce two patents for every working day we are also the inventors of the LP3 white LEDs and many such inventions but what really singles us out in this innovation corridor is the fact that we take research from lab to the market in the shortest period of time so this is an area where India really needs support and we are delighted that we were one of the first movers coming into India 18 years ago and even today there are very few international R &D organizations that are active in India you We have been earning more than 70 million euros in the last 10 years from research contracts with Indian industry, government and research organizations.

And we have an absolutely amazing network of partners in India, including organizations like CDOT, but also with Indian industry, which is really gearing up for innovation. So I think we came in at the right time and took advantage of this, you know, innovation trajectory that has picked up in India. And it’s exciting to see what’s unfolding before us. I have to say that today what we are bringing together is really a thought -provoking session. We only have 15 minutes, so I would like to get on with business immediately. And we hope that some of the thoughts that we are leaving behind with you today will resonate and will actually trigger a long -term dialogue and engagement so that we can take the Indo -German innovation collaboration to greater heights.

I would now like to invite Mr. Georg Enzweiler. To kindly deliver his special address.

Good morning, ladies and gentlemen. I don’t know whether it’s maybe that we are just as crazy as you are sometimes that we are big fans of Karnova here in India. It’s always delightful to be invited by you here in India. You do amazing work here in the country. I wish we’d have several Anandi ears here in the country to multiply your efforts. So thank you very much for the invitation. And I’m impressed by the distinguished panel of speakers today and of panelists here. Thank you. All of the panelists and speakers here today are drivers of technology, and so it makes sense that you are also at the forefront of innovative AI technology. From increasing crop revenue, improving diagnostics, or minimizing errors in manufacturing, AI undoubtedly has huge potential for social and economic good.

AI is predicted to contribute between $5 and $15 trillion to the global GDP by 2030. But there are also questions, of course. How can we ensure that this growth is inclusive? How do we minimize negative effects for people and the planet? What kind of effect would this have on labor markets, for example? After all, the motto of this summit is actually welfare for all. India, for one, has ambitious goals to create massive computing infrastructure in big parts powered by green energy. Germany is investing in so -called AI lighthouses, which foster AI innovations for climate and environmental protection. Since 2020, Germany has funded over 60 projects leveraging AI for sustainability. The topics range from wildfire prevention and renewable energy to biodiversity monitoring.

And circular economy. that government, academia, and industry work hand -in -hand. By promoting research and development, creating a clear regulatory environment, and investing in the training of skilled workers, we can unlock a broader potential for AI. India shows huge potential here. It accounts for 15 % of the global AI talent pool and has the highest AI skill penetration rate. In terms of research and development in AI, India ranks third in the world after the U .S. and China. India and Germany, with their longstanding scientific partnership, are natural partners in creating solutions that are sustainable and inclusive for all. Hence, only two days left. Ministers Vaishnaw and Wildberger launched the India -Germany IA Pact, a new partnership focused on implementation -driven collaboration across government, industry, research, skill development, and innovation.

It will include aspects as AI for industry and manufacturing, talent, skills, and mobility, joint research, innovation, and infrastructure, and overall AI for social good. So I very much look forward to the discussion of today’s session, that it can give fruit for thought on this most relevant topic. Thank you again for the invitation, and I wish all of us an interesting discussion on, many of these pertinent topics. Thank you.

Thank you for your kind words, Mr. Ensweiler. We now have the pleasure of hearing Dr. Thomas Kuhn, who heads the Division of Embedded Systems in Fraunhofer IESE. As you all know, we talk artificial intelligence. In Fraunhofer, we call it augmented intelligence, which means that human intelligence is still at the core of what we’re talking in terms of AI. It’s a pleasure to bring Dr. Thomas Kuhn to India. The floor is all yours.

Yes, thank you for inviting me. Yeah, what is Fraunhofer? doing in the field of AI. You all know AI is on one hand large language models, mostly driven by the US. We have huge open source models coming from China. So what is Fraunhofer contributing? And our goal is to bridge industry and academia to support the industry in creating new products and bringing ideas into practice. And I’ve chosen here a few highlights on what we can do as Fraunhofer. So for example, when you speak about AI, the question is, how reliable is the result of AI? Do you get concrete, do you get reliable information? That’s one topic, trustworthiness. How can we achieve trustworthy responses? That’s something that Fraunhofer is researching on.

For sustainable growth, it’s also very important to preserve knowledge that is available in the company, especially if people retire. Small and medium -sized enterprises have exactly this problem that knowledge is leaving, and you cannot get this knowledge back because the new employees simply don’t have this knowledge anymore. So how can you preserve knowledge? We call this industrial AI, so bringing AI models into practice, creating specific, specialized AI models based on company data, based on sensitive data, not personal data of persons, but sensitive data for business, sensitive data for companies, so that they can support these companies with these needs that the AI can provide. For example, it creates a colleague that is an expert for a specific device and that can help.

humans to work more efficiently with this device. And last but not least, as my colleague already did introduce, data spaces are a key technology for that because for AI, we need to be able to train AI and therefore we need a reliable access to data and a way to share data, but based on rules. And that’s what data spaces are meant for. With Raumhofer, we have the ability to instantiate data spaces that scale up to 10 ,000 transactions per second, and that’s quite a lot. So Raumhofer is structured into alliances, and the most important one is the big data and the artificial intelligence. Alliances, so more than 30 institutes that team up with AI, in creating AI strategies, each institute with its own field of competence, but all of us support best practices, studies, expert opinions, and we support all companies in creating new solutions.

And as you can see, we have a lot of fields that our institutes are working on, life sciences, healthcare when it comes to diagnostics, to personalized medication, logistics and mobility for optimizing supply chains, making them more resilient, production and industry for that all, very important. We also need to keep production in Europe to remain resilient, so it must be economic to produce in Europe. Energy and environment. Renewable energies. Smart goods. self -managing grids. That’s very important topics also for AI and big data, business and finance, security, to make sure that our data is well protected. I cannot go into detail for all of these topics, so we’ve collected a few samples on web we are doing from embedded AI, image analysis, collaborative transport, cancer diagnostics, swarm intelligence, of course also defense topics.

I cannot go too much into detail here but AI has a huge impact and why the ways regarding general purpose LLMs is not our ways. The applications that can be created with AI, the transformation of industry that is very well our knowledge. to bring AI to use. for industry. And therefore also to use for all, and then we are back at the topic of this event here, welfare for all and happiness for all, because every industrial revolution in the end did yield much more wealthiness for everybody. I have brought some highlights, and I promise I won’t go into details here, no worries. So the virtual colleague is one thing that is important when people leave companies.

We have expert knowledge, we have experience, and this is very hard to preserve. So just imagine a virtual colleague that follows the people in the field, that learns from them, and that keeps the knowledge in the company once this person leaves, so that the company knowledge is retained. That’s one project. One project that Fraunhofer is doing in the field of AI. to support industry and also smaller medium -sized industries. Trustworthiness, that’s very important. We all know that AI is not perfect, and probably some of you have already tweaked the AI maybe a little bit on purpose to see, okay, how far can I go? With that, AI basically is stupid. It has a lot of knowledge, but it has no understanding of that.

Trustworthy AI means we observe, for example, the field of use for a specific AI model and track whether we are leaving this field. We have an uncertainty wrapper that gives you, with each response of the AI, an uncertainty value that says, okay, how trustworthy is this particular response? And that’s very important when we want to apply AI also in safety -relevant environments, for medical. for medical diagnostics, for image recognition, and also in traffic. That’s very important then to know how safe is the assumption of the AI here. Moderated AI training is very important when we want to work together. For example, a robot that can grab something that every human can do, grab into a box and take something out.

For a robot, that’s a very big challenge. We can train robots to do that, but it’s much more effective when we can do a federated training where everybody can contribute data, and in the end we receive one model, but one model where you cannot derive any sensitive data back because nobody wants to share their sequence. So that’s where data stays. That’s where data stays. That’s what data spaces are meant for. We share data based on rules. We can provide data just for training of AI. Everybody will see the raw data for that. We just use it for training of AI. And here, Fraunhofer is researching both government policies and efficient implementations of cross -company data spaces.

So that’s my pitch, my motivation. I hope I was able to give you some ideas, some insights in what we can do with AI besides of open AI. I think Sam Altman is not here, so I can’t say that. They’re doing great things, but there are also great things that can be done in addition to that. Thank you.

Thank you very much, Thomas. This was just an appetizer. As you know, we have a lot of time. We have very limited time, but anybody who’s interested can visit our stand, where we’ve also demonstrated some of the use cases in AI and agriculture, and also AI and medicine, and AI in manufacturing. You’re welcome to visit us at the German Pavilion. I would now like to invite the CEO of Center for Development of Telematics to share a few thoughts on where India and Germany can work together. Thank you so much.

Thank you so much, Anandi. Thank you, Anandi. Quite pervasive, it is being applied to almost all the sectors. And where it is not applied, it will be applied sooner or later. because it underpins the competitiveness, productivity, and societal resilience. And for India and Germany together, Germany being the precision engineering expert, you know, for years, and India bringing the scale, I think there is a very good way that India and Germany work together and align how the AI is taken forward in a very responsible, ethical way, so that not only it helps us economic progress, but also meets the social good challenges. As we know, India’s AI system is quite vibrant, ranking third globally for its competitiveness and developer activities.

Our AI mission is already in progress. We are investing more than $2 billion. We have given to all the startups 38 ,000 GPUs, and this will be further increased. It could bring $1 .7 trillion value to India’s economy by 2035. And the tech sector particularly is productive to contribute $280 billion revenue this year itself. Germany’s AI market is also rapidly expanding, driven by strong industrial integration, particularly in manufacturing and substantial public -private investment. Germany’s AI market is expected to be nearly 30 billion euros by 2030. I wrote that there are more than 600 startups in Germany, and we also have a lot of startups. I think together, how the startup ecosystem could work together for the benefit of not only both the countries for bringing global good to this sector.

So there will be a fair win -and -win situation for India and Germany, partnership between two great countries given our diplomatic relations, given the, as I said, the precision in engineering which Germany is famous, and the scale what we bring in India. I think there are many areas where we can contribute, but I thought I will focus on two, three. One, I think it was mentioned in smart manufacturing. Germany’s leadership in industry 4 .0 and India’s expanding manufacturing. I think this government has invested heavily on the manufacturing under various schemes like PLI and DLI schemes, and it is going to go forward. And therefore, how do we use AI in smart manufacturing? We would like to work with Germany in terms of developing smart manufacturing standards, cross -border industrial data flows and safeguards, enable energy efficiency.

And since we arrived late in this manufacturing space, a lot of our manufacturing plants have actually started using AI. In fact, recently there was a science evenistic wherein the Tata’s, how they are going to bring down the cost. using AI in manufacturing. The second point I think Anandi brought up for agriculture, I think Parnasar is already working in India for agriculture, and agriculture being one of the key partners for economy, the service sector and the economy, agriculture sector, I think it also makes us a good partner for how do we improve the productivity, the yield, the income for farmers, the efficiency gain, productivity gain, and that will be another area we can work together. I would also like to say that, you know, given, especially given the economic progress we are making, and we see a lot of challenges coming from the cyber security area.

We get millions of attacks in our country, millions of attacks. and I’m not sure what is happening in that part. So, therefore, AI in cybersecurity is very, very important, which we use to some extent and we would like to learn from Germany, whatever, and we would like to share these, what we are doing, how we are using AI to, you know, come to a pattern because the kind of data flow, for example, in India, we receive data rate at 10 terabyte per second. 10 terabyte per second is the data rate. And how do you process this data in real time and be able to tell that, that how and where the attacks are happening? As far as the CDOT is concerned, we work in various areas.

I don’t know, some of you may be aware. I would like to place it on record that India made its own 4G and 5G, and it was launched by Honorable Prime Minister in September 25. And this was a journey we took alone. For 6G, we would like this journey to be together with the world because we were actually not. We never developed 1G, 2G, 3G. Suddenly, our Honorable Prime Minister said, you know, 4G and 5G, and we did in two, three years, and it is launched today. Thank you. close to 170 ,000 base stations are radiating, generating a data rate of 5 petabyte. And we’ll go further. So 5G, 6G is one area. The second area is quantum because as all we know, many of the leading economies have put a sunset date to the current level of encryptions which will be broken because of the quantum computers.

And therefore, there is a need to work on quantum together. We, particularly in CBOX, we work only in quantum communication. In fact, I am the chair of the National Quantum Communication Hub along with IIT Madras. We fund a lot of research into quantum to start up. So this could be another area where we could work together. The third thing is I’m not aware what kind of frauds happen in Germany or happen or don’t happen. In India, there were, you know, there were a series of frauds, cyber fraud calls or this type of calls. So we developed the system at India scale and are successful today in blockage. For example, I’m just giving example of we used to get 1 million regular international calls, and we were getting 15 million spoofed calls, 15 million spoofed calls per day.

And at my gateway, in 5 milliseconds, I have to decide whether this call is a real call or spoof call. Today we have deployed the system in India, which in 5 milliseconds tells you this is a spoof call, drop it, and this is a real call, send it. So at India scale. We have developed a platform called SanchalSathi .gov .in. I don’t know how many of you are aware. This is, again, a digital intelligence platform. One side is exposed to the customers, the citizens, and in back it is connected to all kinds of stakeholders. What used to happen is, as Telecom, I have declared one, let me first check the time. So, yeah, I think it’s happening.

So one is that, you know, in telecom I have said this number is turbulent. Bank doesn’t know. Police doesn’t know. So what we did, we integrated all of the stakeholders in one single platform, which we called digital intelligence platform. Once I declared this number to be a turbulent number, for any reason, it is flagged in bank, police, everywhere, so that everybody takes care. Today, when the actual financial transition happened between A to B, there is a module which we have developed called FRI, financial risk indicator. Before the money is transferred, the bank digs through the database and says, this money is going to be, is it safe? So my system tells me, no, it is not safe.

This guy is not a safe guy. So the transition is stopped. So we have done the fraud management at scale. We would like to work with Germany to further enhance it and how to use AI into this. And going forward, I would only say my time is over. AI should not widen inequalities. It should strengthen inclusion. Productivity and resilience. Let us ensure AI becomes a pillar of sustainable economic growth and social good. As Anandi said, we already have an MOU with Fraunhofer, which we are working together. I would be very happy that, you know, if this partnership goes, must go on. And we together are going forward, given the geopolitical changes happening, we’re going forward together.

We will go and develop the technologies together for the wider social good. Thank you so much.

Thank you, Dr. Upadhyay. I can only underscore that it has been an absolute privilege working with you. And it is not something where German technology is being given to CDOT, but we are actually co -creating. There are India -specific challenges for which teams from CDOT, who are extremely qualified and they have amazing infrastructure, I would really appreciate at some point, Dr. Upadhyay, some of our German delegates, maybe not this time, but next time, come and see the kind of work you’re doing. Because I think that brings a different level of understanding inside and trust. Without wasting any time, I’m sure all of you are waiting to listen to Professor Dr. Sinemus. We had her yesterday, and I must tell you that it’s really amazing to see, first of all, a woman as a head of innovation and digitalization in Germany.

to get the leg between research and then bringing it really to society and to economy growth. And at the end of the day, I’m coming out of the biotechnology area, and I think we really have lost a lot of chances in coming from the research really bringing to economic growth. And I think AI has the opportunity, and as well quantum, as you mentioned, to do this. And, Your Excellencies and gentlemen, ladies, it is a great honor for me to deliver a special address. I never have given a special address, and try to do this in the German National Technology Forum. I’m coming out of a region where we are working together. struggle together with Fraunhofer.

Even in my hometown, we have two, and one of this is the Fraunhofer Cyber Security, where we have the digital hub of cybersecurity as well. So we built a startup ecosystem in the area on the interface of cybersecurity and digital and AI. So many of this could be an anchor point afterwards. As Minister for Digitalization and Innovation, I very much welcome the opportunity to discuss how we can turn AI into concrete public value in manufacturing, in agriculture, in healthcare, and through trustworthy AI, as Thomas Kuhn has pointed it out. The AI Summit, all is about AI and we see how dynamic the process is going on. AI is already transforming how we produce, how we heal, how we grow food, and how we govern.

And the key question therefore is not if AI will shape our society. The question is how. And whether economic development and social growth move forward together rather than separate directions. And looking at the dynamic AI is going forward, we really have to be careful that we balance these economic development and the social part. And I think this is something which has to do with trust or trust as well. In my region developed a clear strategy. And our digital strategy and AI strategy are based. On a simple but a helpful demanding principle. That is. technology must serve people, not the other way around. Our AI -made Innocent Agenda combines innovation with responsibility. And we want AI that strengthens competitiveness, but also social coherence, fairness, and trust.

And this is the way we invest not only in research and infrastructure, but also in practical projects that deliver tangible benefits for citizens and business. So we have a funding program. It’s over $60 billion. And we invest in people who have a vision in how AI can benefit humans. So we invest in 170 startups till now, and I want to give you some examples so that you get an idea. And I start with healthcare, because healthcare is one of the most promising fields for social good. We support innovative projects that would be very difficult to realize without our public backing. And we fund, for instance, one project, it’s called DEMO, and DEMO, it’s not DEMO, it’s DEMO, and it’s a small group, three people who are working on PhD at the Technical University of Darmstadt by Alma Mater, and I was happy to give them 1 .8 million euro, because they are developing a load -bearing roboting wheelchair.

A session to a walking robot. that can safely and autonomously overcome barriers such as stairs or uneven ground using advanced robotics and AI. This project will help for the people with mobility impairments. That is no science fiction. It is a difference between dependence and independence participation on everyday life. And I think that is really a good example of how we invest our funds. Another example is RISCA, Risk Certification in Cardiology Using AI. The goal is to build a clinic decision support system that analyzes patterns in ECG recordings with AI and detects cardiovascular diseases at an early stage. so you can go through prevention. That are two of a lot of AI and healthcare startups we are giving our funds in.

And a second field I would like to highlight is agriculture. Even for India, agriculture is a very important area and a rich and crucial pillar of economic development and food security. Again, we support a project and there is only one of a lot on the early detection of plant diseases. Using automated analysis of satellite data, so we even connected with the space, AI models can recognize signs of plant stress and disease at every early stage of growth circles. So, you can really reduce pesticides and analyze before. So, you can really reduce pesticides and analyze before. So, you can really reduce pesticides and analyze before. AI can, at the end of the day, make agriculture more sustainable and more productive at the same time, and I think this is something where we have to go through.

Another example in our strategic agenda in the future of AI is that we set up an AI innovation lab at Hessian AI, co -funded by my ministry, and we provide a high -performance computing environment specifically for AI applications, along with advisory services for science, business, and public administration. So this is a particular focus in the startup and SME. We want to enable smaller players to develop innovation AI solutions, and we want to be, at the end of the day, in a position that trustworthy AI is not a slogan but a practical challenge. And this is why we established an AI quality and testing hub in Hess, a public -private company that develops methods and tools to test.

AI systems. So at the end of the day, the aim is to translate the idea of trustworthy AI into testable criteria and practical producers that business can use real deployments. So you can say that is trust for AI and we give it as trustworthy criteria. To the end of the day, I think, looking on the discussions we have had in the last days, that the inner German dimension is particularly important. India has shown with initiatives like other UPI and IndiaStack how digital public infrastructure can scale and enable innovation at massive levels. Thank you. Germany can bring expertise in regulation, not too much, data protection. and quality assurance. Together we can build bridges between scale and safeguard, between innovation and rights, between economy, development, and social good.

Because we are thinking grounded on the same values, democratic values. And let me close with a conviction that guides our work. AI will not automatically lead to better outcomes. It depends on the choices we make, what we fund, how we regulate, which ecosystems we build, and whom we include. So let us proceed on our democratic values and go forward with a union, German collaboration in AI. Thank you.

Thank you so much, Dr. Sinemus. It was really a thought -provoking and a very concrete talk on what we can do with AI. There is the scope for the German collaboration. Without much ado, let me take forward what we all on the topics that you have mentioned by inviting the four CEOs to please come on stage. Can I request Atatürk, Sinemus, Anshuman and Prashant to kindly come up? We have a few minutes to really get into the topics. And I would like to just start by very quickly saying that In the last few days, I’ve attended quite a few of the sessions here, and there are so many nomenclatures for AI. It’s been defined as a transformational technology that comes once in several years.

People give examples of the steam engine and of the Internet revelation and Industry 4 .0, which dramatically changed the way we live and work. We are now riding an AI transformation, but complete with anxiety, with speculations, with admiration, but also with excitement. So let me take the audience through to what makes a CEO stay up at night. And let me start with Pratatvi. Vosh has been a pioneer in India. You know, you came here a century ago, and you’ve stayed on, resilient to the Indian market, which has not always been very easy. And you’ve been a pioneer in India. You cover a lot of base, signing across manufacturing, software, medical, and many more sectors. How does the vision board of Bosch lead in terms of the AI development and deployment?

Maybe the first question not keeps me awake in the night. The fact that you are 100 -year -old and, you know, if you are 100 -year -old, the effort that you make changes also higher. So that keeps me awake at night. As Bosch, I think we are working on two sides of it. So one side, you know, if you see we are deploying AI into the market. You know, if you see autonomous driving, AI cockpit, or AI in healthcare. So these are our products which go into the market. So fundamentally, we are, you know, navigating. We are navigating a new business model, new behavior of consumer, and developing new products for that. on the other side now we are using AI to disrupt long held beliefs of how we build software how we do engineering so this is my say while we are huge excitement to position our AI centric products in the market I would be dishonest if I say we don’t have anxiety how to get over the paradigm shift of engineering and software development using AI so these are the two edges at which we are operating now thank you so much

Sindhu let me come to you you are the world’s leader in enterprise application software one time I remember you telling me that 97 % of companies worldwide use SAP it is already embedded in your workflows even before people are talking and wondering how to go about it but how do you see you have a responsibility because you have a captive client base How do you see these innovative technologies being rolled out to companies while they still have anxieties about their business? And how do you go about assuaging those fears and building in a model that works on layers that are already set and, like he said, very difficult to move right now in retrospect?

Well, first of all, Anandi, it’s great to be here. It’s an absolute honor to be amongst this very distinguished panelist and you and all the previous speakers who have been saved. Yeah, I think, like Zazadeh said, we live in times which are changing, particularly in technology, right? I mean, literally every day you have to open up and see what is the new LLM that’s out there, right? So that’s the way it’s been and which we are now in the open. So in that sense, this is… The assurance, when we say, when we are talking about the world’s largest enterprise application, the failure from SAP, it’s a lot of… The cost that our customers place on us, many of them sitting right here at those tables, are too hard.

And so then when we say 87 % of the world’s business partners, such as an SAP system, it’s a matter of lack of responsibility and a lack of trust, which we continue to take forward, given the 1226 industries and across 12 portfolios that we have, right? But at the same time, given the pace at which technology is changing, our responsibility also from an AI perspective is embedding AI across the core of our business processes. So if you take a process like a lead to cash or a total risk force management or a design to upgrade, strategic sourcing or procurement, this is where we show our customers that we’re not helping you to make your best, we’re doing the most compliant and the most ethical and the most responsible manner, right?

And so I want to make me say, to make sure and protect the choice. Customers also have the choice to run in a secure manner, in a compliant manner, in an ethical manner, and in a responsible manner, and which means also translating from several of our speakers. You need to be able to explain to this decision that you are making or asking the user to make, giving that explainability, the transparency, the fairness of that decision -making, the auditability, right? And that becomes even more important because today when we are talking about urgentity, we are talking about autonomous workflows, we are talking about decision -making. Yes, the human is in the room, but part of the workflow is completely autonomous, right?

And so that’s a lot of the work that’s keeping us busy at night and mornings, I would say, and really making sure that when we say customers can run at their best, in a compliant, in a secure manner, in a responsible manner? How do we make sure that they are to go through the research?

Tim, what is the holdup and failure for customers to run in a safe manner? Whether you’re up at night or not, you’re certainly on your toes. The only person who’s been up at night for several months now, and I can personally vouch for this, Krishan, Continental is now a Romeo. It has not only changed in name, but also in terms of its core processes, some of its business sectors. So innovation is not only in the name, it’s also what you do. What is a Romeo now up to and what is happening?

First of all, thank you, Amandeep and Neera. I’m sure all the panelists agree. I think in India, we have moved from support apps to center of excellence. I think in Aumavio, we’re focusing on two things. One is certainty of allocation for our customers, for the audience, innovation for the stakeholders. And the second was customer -centric innovation. Just to let you know, one year I think we’ve been able to gain efficiency improvement in R &D in excess of 20%. How did we do this? Actually, there are cool things. I think we do with one strength and core technology, be it in the plants in terms of how do we enhance the quality using air, be it in terms of, you know, any product that’s done in products, there’s always a lot of false calls, which requires a lot of efforts for you to redo that.

So that’s where the implementation of AI in terms of enhancing the quality. And the second is certainly government development, I think using the GitHub, what I was talking about, where this work is coding and also for developing different test scenarios and a test situation. So these are the things that we are really focusing on. One, as I said, how do we really enhance the velocity in engineering and R &D? We’re using this automated technology, and also we do something called ReckNet. In automotive, the biggest challenge today with so much of complexity of technology is coming with the requirements, because always you leave one or two, which is very, very important. Like how do you really enhance this in terms of the ReckNet?

And second is the attention of the agent, which is used in the enterprise in the simple function like finance, controlling, or market analytics. So this helps us in giving a very good summary for the leadership to take decision based on the data. And third is certainly on the product innovations. And we have a place here in the Jammu and Pakistan, where we have a display, which is dollar code here in India, one of the autonomous mobility, which is called Enhanced Light Fusion. We have been able to, with the use of AI, enhance only the… the camera vision for easier detection and the second is something called e -travel companion where you seamlessly interact with the vehicle and the third is like our driver will seamlessly access with the car.

These are the three which was developed innovations which is displayed in the German stand as well. So in summary, I think we continue to drive because of what we call our increase to seamless integration into the operation.

Thank you so much Prashant. Let me go to Mercedes. Mercedes -Benz is a North Star in innovation. I mean you’ve been around several decades. It’s also been a very competitive environment in manufacturing which a lot of people say that Germany has lost its edge. But it’s an old school field. So how do you bring in AI into the second kind of operations, which is reflected in your standardized operations and very, I said, old school procedures? Is it challenging to get AI into it now?

Good morning, everybody, and thank you for having me here. Yes, Mercedes has been one of the most important companies and we plan to remain virtual. We’ve been doing this for many, many years. AI as a technology is not a challenge. We don’t see any technology as a challenge. And we have been using it. I think if I’m not wrong, Mercedes -Benz was the first automotive company to bring AI into the cars in 2019 itself. So if you’re driving cars from 2019, you must have experience AI. It’s a challenge. It’s a challenge. Today our focus is monthly forward works. For example, we want to offer technology to our customers. That means you drive the car and you experience a lot in the car, how the car behaves, whether you direct the car, etc.

And whether the parts are behaving in the seat. And the second part that we also are planning to do is tighten our operations using AI. We want to bring operational efficiency using AI. And many things have been mentioned. One such thing we are trying to implement. Now it can bring benefit to all of us. So, we have technology. It’s very exciting. Because sometimes you feel that it’s making so many things evident. I think that’s now and then has been the same thing and same impact so we are looking further to going ahead with using it.

And ,of course, NBRDI in Bangalore is playing a very big role into this process?

Yes. The 2019 application was largely developed NBRDI that was sent to by our AI experts back then.

It was so good we have another 5 minutes and I’d like to use this for a rapid round talking about India so we’ve always heard about the big combination of manufacturing excellence in Germany, engineering, scale and speed in India Let me ask Dr. Tri, is this heightened German interest in India, is it offensive or defensive?

I would say it was long overdue. I think given the capabilities of the two countries and the common interests that we share, for reasons unknown, we play it very low, while many others play it significantly high. So I think now we can collaborate on many dimensions, not just the precision engineering of Germany, which more than that. Because the pragmatic approach Germany brings in to solve problems from the first principles is what is required now. Because we are in a phase where we have to cut the norms of AI and to marry the substance of AI. So for this, I think we, I mean, we all know in this room… Thank you very much. So it might be a moment generally, you know, how we can remain together.

So it’s an opportunity that I see. Thank you.

That’s very diplomatically put. Sindhu, I come to you. You have just inaugurated a huge campus and your ambitions of taking more than 14 ,000, 15 ,000 people. The controversial question that I’d like to put to you is they say that the cost of the charge is four is to one. That is, for one German, you can get four Indians in it. Not only one. Not even one. Srinu, I will write answers. No, of course. The value of the charge is just the opposite, right? So we are looking at a demographic dividend in India, but there is a huge aspect of inclusion, productivity, skilling, reskilling. How does that play into your vision for India?

You started off by talking about the campus, right? And we were talking only about the campus. What’s the charge point of view? I don’t know. I don’t know. I don’t know. I don’t know. I don’t know. campus, right? So that, and I think we just had Chancellor Max coming to campus as well, and looking at India as a constant detachment, it’s clear of my hands. I don’t see it in any of these conversations. Let’s not be, today when we look at, and we mentioned GitHub for pilot, and if I just look at CloudCode, the latest SOPAs that we have, the speed at which you can do things is of course changing, but what it means is experts can focus on high -value SOPAs, right, and high -value domain use cases, and this is the beauty of the pace at which innovation is being, and this is also why companies like SAP or anybody else here in SOPA are invested in India, because they want to build a system of countries and research question is always the right.

So we really want to bring this and the pace at which our people here in India are able to take forward, understand the context, drive forward things, drive that leadership, creation of IC, it’s the change we’re talking about.

Thank you, Sindhu. I have actually two more provocative questions for my other two, but due to the paucity of time, we’ll have to take a break. Because we still have Morley talking about his study, but I think the fact is clear that the opportunity you have in India has been an opportunity like never before. It’s an influential point. It is an opportunity for cooperation and working together in a very, like Mr. said, with shared values, a very clear agenda. And I thank you for being here and for sharing your thoughts. And can we really quickly come and talk about your study, please? because we just have two minutes and we have to close so quickly come Thank you so much.