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I direct my first question to Minister Rosiński. Minister, Poland has been implemented and shaping digital governance and also investing in sustainability and resilience of national systems. What are lessons learned and what lessons are the most relevant when we talk about implementation of AI in national systems? Maybe the other one. Yeah.

Thank you very much. Thank you. Thank you. like energy sector, water price, health care. That is the main point of our day. Critical infrastructure, I think it’s the crucial point in every country. We cannot imagine how can we run the business if we have… We have no energy, no water, and our data is not enough protected. And we support also local government. We create local… through cyber security. And that is connected with digital skills, especially hygiene with this area. And cyber security is linked with AI, with trustworthy AI. That is the also important thing if we use AI, especially national LLMs, and we can use it for the security of our business. And if we use AI, we can also use it for the security of our business.

And if we use AI, we can also use it for the security of our business. And how can we train the national data? That’s why in Poland we’ve built also Polish LLMs. And how can we train the national data? That’s why in Poland we’ve built also Polish LLMs. is Bielik, which is one public LLM, and the second one is Bielik that is the first one Plan, the second is Bielik that is with cooperation with academia, with private sector, and we support also. That can allow also be competitive for Polish business. That’s whole, if we see this whole ecosystem and we can also exchange our ideas and show our knowledge with other countries. That is the way, the proper way.

to be safe and to use trustworthy AI.

Thank you very much, Minister, for using beautiful examples of language model from Poland and their role in Polish ecosystem regarding both public sector and private sector and for framing AI as a matter of public responsibility and resilience. And now let’s move at the international level and have a look at the global dimension. And I would like to ask a question to Atsuko Okuda. How can global standards ensure interoperability and resilience of AI systems across regions?

Thank you very much. First of all, good afternoon to all of you. And I would like to thank the audience. organizer for inviting ITU, International Telecommunication Union. And as some of you may know, ITU is the oldest UN agency specialized for digital technology. And we have standardization work, including on the topic of AI. Now, what does AI standards do for all of us? Number one, it will enhance interoperability, which means that if a system or solution is developed in India that can talk to the system, as His Excellency mentioned, in Poland and vice versa, and that will lower the investment cost, that will increase the efficiency. So what are those standards that could be useful because of the interoperability, and especially within the country as well as within the region or globally?

So one concrete standard… Oh, by the way, just to give you the magnitude, ITU has over 200 already approved AI standards, and 200 more are in the pipeline. So in total, we have about 500 standards in place as well as in the pipeline. So you can see there are many different standards which are available for everyone. So what are those standards? Number one, for the interoperability, we believe that data, the interface, and protocol are critical. For example, we have a shared data format that we can all use. Otherwise, how can I share my data with you with a different data format? Two, standardized API so that system -to -system communication will be smooth. And three, of course, communication protocol.

Now, because based on these standards, we have more, how can I say, comprehensive standards. Thank you. For example, AI for network automation, multimedia AI processing, standards as well as machine -to -machine data sharing, the frameworks, for example. And second, we also have a harmonized terminology, vocabulary, and reference architectures. Because when I talk to, it’s not only you, but with anyone, some aspect of AI, how do we know that we understand the same thing? So this taxonomy, vocabulary, and the reference architecture is critical for interoperability and for us to be able to develop and exchange data or develop the algorithm together. So we have our AI model. Life cycle definition, so I know what you are referring to, and you know what I’m referring to.

Three, we have a context. Performance and testing are related. so that we can test, validate, and we have also conformance specifics that we use as a standard to validate that what you are sharing is what I can validate. So this, I hope, the standards are useful for enhancing the interoperability as well as to enhance the collaboration within the country as well as across the regions. Thank you.

Thank you very much. Standards are a very important pillar of building trust. Another is inclusive governance. Changatai, how does multi -stakeholder cooperation translate into real public trust in AI governance?

Thank you very much and thank you very much for the invitation and I’d like also to thank the organisers Millennium and Poland of course for inviting me now for your question, for any process I think, inclusivity breeds legitimacy and thereby trust, so if you have all the stakeholders who are affected by whatever policy that is, so you must have government, civil society, the technical community and the private sector all talking to each other and giving their point of views from their perspectives, I think then you can result in policies that have a greater buy -in so once people are involved in the process they’re more likely to adopt that process and secondly the transparency of the process also matters people need to know how these decisions came about and also what was the decided and this can be done with open consultations public comment periods and accessible documentation that builds confidence.

This is basically the same model that has built the internet what it is now. You have the public comment period etc and then these are adopted The IGF as well shows that this works The Internet Governance Forum is a multi -stakeholder dialogue and within our framework we discuss AI governance as well and a lot of other things misinformation, disinformation etc and this approach can anchor AI governance in legitimacy Trust as well is built locally so these discussions should not just be happening at a global level and then trickle down Local communities should be able to contribute in some manner and this process should be a cycle. So the feedback loop should be down but also up.

So there’s a resonance going on there. And then I think lastly, accountability mechanisms is also very, very important. So a multi -stakeholder corporation without clear accountability methods, people will not trust it because they need to know if they have an issue, where can they go and express that concern and that it will be dealt with in some manner or function. Thank you.

Thank you very much. I couldn’t agree more. Trust is also built locally and that’s why I would like to direct my next question to Odes. How can community -driven digital ecosystems can contribute to building trust to AI locally?

Thank you. Good afternoon, everyone. I say that modestly and saying thank you for your attention. Thank you for the invitation to join this panel. to give context to community participation, both at the innovation level and at the policy level, I would like to start with where Chinetai just finished, which is that community is a big stakeholder and a big participant in the multi -stakeholder framework. If you think about deploying AI solutions, especially for public services, then you realize that the inclusivity is what builds trust. The ability to deploy AI and to be consumed by every citizen is at the core of the trust between the users and the providers of the services. So taking into account that community, making sure that it’s included.

I’ll give an example. If you think about linguistic diversity that is there in many of the communities, in many of the countries of this world, you realize that if you build such a product, or an AI solution and it’s in language that only 20%, 50 % of the population understands, then the trust is broken between the provider, which is the public sector, and that part of the population, which is the citizens. The second part is that in the innovation cycle as well, we’ve seen on and on AI being deployed, but it doesn’t reflect the realities of certain communities, and that’s both, you can think about it linguistically, you can think about it contextually, you can think about it in different forms and shapes, it takes in different domains.

So the participation of the community into that, in ensuring that the innovation and the policy level align with the needs and the realities of those particular communities are very important. To finish off, I think that communities or cities and communities, and the citizens are also a big part of that. on how AI systems are improved because once you deploy such a system and you don’t have a feedback loop, then you realize that those particular technologies only work for some time and the adoption goes down after some time. So I think those three things are very key in building trust. First, inclusivity, part of it. Second, the participation in the innovations as well. And lastly, the feedback mechanism for how those services are being consumed, are being used and what can be improved.

Thank you very much. Trust also can influence economic confidence and cross -border collaboration. That’s why I would like to direct my next question to JJ. Does regulatory… Alignment. directly influence international trade? What is your perspective and observation? If you could share experience from in the Polish Chamber of Commerce.

Well, I will just share the experience from the perspective of Poland in EU and India. Normally, all are saying a lot of regularities always, you know, dishearten the business and the investments. But I think in this particular case, if it comes to the AI, I think we need a guidebook because without that, everything can go haywire. So if you look at the regulation with the EU AI Act, which has been implemented in 2026, I think in a way it makes a kind of issue for the investors. But on the other hand, if you take it, if you have the clear guidelines, it’s always very good in the lieu of the India, EU FTA that the Indian companies will be ready.

for deployment of the AI algorithms and other things within Europe. Now, let’s take the example also how EU, even businesses are saying that, well, the regulations are very tough, the compliance is very tough, but EU is also doing from their own side to make it easier for the businesses. I can use the example here from 2025, where in France there are 10 AI companies from India, which are actually part of the accelerator program, and EU is also ready to give a sandbox solution for all the regulations. So in all my perspective is you need a kind of control, especially on the generative AI, and you need some kind of control on the AI. So the rulebook which EU has given, it will be like, you know, I would say it’s a playbook for all the AI companies involved, and I think that India should be involved.

India should take the advantage of that because if… If they are already prepared to adhere to the rules, then I think the entry will be easier for the companies. So I definitely support the regulation because in this particular matter of AI, we need regulation. Because if you see the other countries, I will not take the names. One is using for policing its own people, and second is using it only for making the money. So yes, it’s good, but with sense.

Thank you very much. In our discussion, we have also three representatives of the private sector who know practical aspects very well because they have to deal with all these challenges on a daily basis. So I would like to start with Mariusz Kura. Mariusz, how do you scale AI solutions across regions while managing regulatory divergence?

Thank you, Lidia, and good afternoon, everyone. Good afternoon. Distributed software development. for the international IT companies is not new. We have started practicing this a millennium, 10 years back, when we, together, we were opening the office, the delivery center in Pune, Maharashtra, here in India. And simple practice to scale up and be fast is to have exactly the global offices, and like our development team, can build some solution, let’s say, in one day and deploy it, and the next day, business in Europe can verify if it’s working as it was expected. If not, then our development team in India can fix it even on the same day. So that’s the one way how we’ve been scaling up so far.

But the challenge nowadays is exactly how to scale up and follow all the regulations, and how to work for the different regions, for the different countries, where we have exactly, like for the public sector, a lot of rules. And hopefully… Hopefully from ITU we have as well two more hundred certifications. So, yeah, the way how we can standardize it, standardizations. So, AI engineers and AI solution providers in India need to learn and need to be compliant with all those standards. And it’s very difficult nowadays because it’s so fast. It’s changing almost like every week. And how to exactly follow that? At Bilenium, recently we have developed as well one dedicated solution, which is the AI compliance suite.

And this tool is quite complex. It’s not only covering the governments and compliance area, but as well as helping the organizations to use the right AI tools. Nowadays the enterprises they are using in a while, Edita will be talking about the Copilot, but there are plenty of the different tools used in the enterprises. And our solution is helping the organizations to navigate the users to the right solution. And what does it mean, the right solution? For example, it could be as well from the cost -effective perspective. Like, for example, should we use this and utilize the tokens from that provider? Or maybe another provider is having the better license practice and policy offering. So, that’s, I believe, what can help, yeah, kind of that solutions for the IT solution providers.

Thank you.

Thank you very much for a beautiful example how AI can help manage AI. And now let’s have a look at infrastructure. And I have a question to Pramod from an infrastructure standpoint. What does trusted AI require? On the ground, what does it require? in terms of data sovereignty, in terms of secure compute and resilient digital backbone.

God afternoon, everyone. Pleasure to be here. So when AI starts moving, getting adopted into public services, critical national security deployments, the trust moves not just on the models, but moves from the models and data to the underlying foundation. When I say foundation, where is the model running? What compute is it tuning? Is it running on? Do you control the data? Is there, you know, what jurisdiction? Do you control the data? Do you control the data? Do you control the data? Do you control the data? Do you control the data? Do you control the data? Do you control the data? Do you control the data? There is, you know, the security components around it. So all in all, you know, there are three questions that one needs to ask before you say that you fully trust AI, right?

The first question is on the control. The second one is, you know, can you tell me what happened, right? The AI system, will you be able to explain what happened across each of these layers? And third one is, is it up? So the control part is like we just discussed, you know, control, not just in the data. Data sovereignty just doesn’t mean that, you know, data space is local. But what we’ve seen from our customers asking, you know, is there any other jurisdictional law that can, you know, override saying, hey, I need full visibility of the data, of that infrastructure, you know, auditability and so on and so forth. So I think that’s, do you have the keys?

Is a key question one needs to ask. The second one. is on the explainability, on the visibility, and not just on the model monitoring, whether I am getting accurate data, but overall on data, who accessed, what is the governance around it, what happened in the network. So across all the foundation, if you don’t have full visibility, you will not be able to explain why a system took a decision, right? Because now we are talking about critical infrastructure. The decision it takes can impact the impact could be disastrous. The third one is, again, resilience. So the resilience, by resilience, we mean can AI stay up? Let’s say if it is in healthcare, in a remote tier city, a hospital deploys an AI to diagnose the system.

A patient walking in at 2 a .m. on a Sunday morning, you know, it, the system needs to be out. It needs to be resilient like any other financial system, but here the implications are huge. So AI is moving from being just a software service to AI as a foundation where all of these elements need to come together before anyone can say I fully trust. I think that’s the

Thank you very much. And it is common knowledge that technology are widely diffused and used only when they are trusted. And sometimes human factor is important barrier in AI adoption. That’s why I would like to ask Edita, who works with users a lot, what determines whether AI is truly adopted by teams?

Excellent question. Thank you so much for that. Good afternoon, everybody. Thank you for all the comments. So we’ve been talking about infrastructure, about security, cybersecurity, about the legal aspects of AI. However, we should remember that deployment is technology, but the users, they want to change. We want to change the way how they are acting with AI. From the practical perspective, because I’m responsible for driving adoption in the past, it was the topic of the modern work. Now we have AI, and we should remember that majority of users of AI are end users. They are not people who are taking part in conferences like this one. They are not that fluent with technology, but in the same time, we expect from them to be fluent and to change the way how they act.

How they work. So from my experience, it’s extremely important to communicate in the right way in a simple word. in simple words and simple examples how AI can be the powerful tool. Not because of the features, because we all know that features are not driving anything, nor business, nor processes, nor business scenarios, whatever we have in our minds. And in AI, everybody can use AI in a different way. This is the biggest challenge from the change management perspective as well, because we can have the best technology, the best model, but if the users, they don’t know how to use it, if they don’t know where it leads to, it’s hard to expect that we’re going to succeed on scale.

Thank you very much, and thank you to all of you for sharing your views in the first round of questions. In the second round, we will turn from strategy to implementation, and I will ask all of you, for a very short reflection from this level. And Minister, what is the most… complex operational challenge governments face when deploying AI in public services what is your view

Shortly, of course, what JJ mentioned about I talked about this, uh, that this, um, a very important for also Polish perspective and how can also see that perspective other other countries, except EU. That is the other it’s important that how can we train the data, how can we use the data, and how it will be the future or generative AI? That we have to use, of course wisely. It is a very important the final goal, and how it will be used. Especially for public sector and especially for for our for our citizens if we look in in in that way, that will be good for for everyone. And of course um implementation of of ai in public sector and of course when use also this data private private companies that is important to see how can we also fight against deep fakes, and the false information thank you

Thank you very much. Atsuko where do you see the big implementation gap today? Is it standards, lack of standards, skills, governance, what it is?

Thank you for this very important question. I believe that there is perhaps awareness challenge as well as the capacity challenge, because I think that this whole discussion on standards came as a surprise to many of the participants. Actually, this is not the first session I’m talking about standards. This is actually third during the summit. But I am not sure, unless you are the standardization person, you don’t normally think about, okay, there are building blocks available, right, that I can start building something based on the building blocks. So we are trying to promote the importance of standardization and using the standards so that you don’t have to. Thank you. I believe we need a lot of different capacities, the capacity to articulate the issue.

What is it that you or we want to address? Sometimes AI may or may not be the answer. Some other technologies may be able to help you better. So I believe this articulation is a huge maybe opportunity and challenge as well. After you articulate, how do you plan, how do you translate that articulated issue into an operational project and initiative? I believe it’s another layer of a capacity challenge. So I can see that there are many countries, companies, agencies who want to take advantage of the AI, but I hope that this discussion is helpful. To concretize those steps moving forward. Thank you,

Thank you very much. my next question will be directed to our technical experts Pramod and Mariusz and the question is in real AI projects what most often slows down implementation.

first definitely not technology because I think we’ve seen technology is always almost ahead very true over the last couple of years the advancement that have happened so despite advanced technology being available despite GPUs being available the platforms being available we still don’t see too many monetizable AI use cases and and that’s that’s a big problem Everybody is trying to figure out where my ROI is, what is that use case. And that again boils down to few key aspects. One is the biggest friction is on data. So we’ve seen, especially in India, we’ve seen many, many, many pilots. And almost 80 % of those pilots don’t make it to production. And the key reason is on the data.

Data is siloed, data is not ready for AI scale. There’s no governance built around data. And that’s why POCs, you use a good set of data and you show value. But then when it comes to production, most of the times they don’t have enough data to get the value out of it. The second, again, AI cuts across. In an organization, AI cuts across. It cuts across many functions. There is the technology. It cuts across multiple functions. team is saying, you know, we are ready with this, but then there is legal aspects, there is an IT guy sitting, you know, I cannot allow you to do this, and so forth. So that alignment is not thought through, right, and that also again slows down the adoption.

So I think these are the primary, and then again, you know, the trust factor comes in, the third part is, how much do you really trust AI to do, you know, do you see the how much risk comfort do you have, is there a human afterthought required for every decision it makes, so I think that organizations need to choose that balance on or choose the best use case where, you know, it’s balanced without requiring too much of human intervention, can I deploy this? Those are the key factors that we see, especially in India, that are slowing down the adoption.

It seems that whatever we are discussing, infrastructure or other challenges, human factor is always at the end and behind everything. Mariusz, is your experience similar or do you have different observations?

I totally do agree with Pramod. It’s not us technology who is slowing down it. Maybe sometimes, but it’s many times on the business side and especially for the medium -sized enterprises. If they don’t know if they can work with some solutions or if they don’t know if they can take the solutions, for example, from India, they will step back and they will go to the more trusted local providers. So I believe that the standards that we are talking, it will help us a lot. So that’s my practice.

Okay. Edyta, what… What is the most common… human barrier from your view?

Thank you for this question. So first of all, we talk again about humans, the most important factor in the same time the biggest challenge and the biggest opportunity. From my perspective, I think that while talking with users, because today I’m a user voice, I can hear very often that people, they are reflecting what’s going to be next if I’m going to be replaced by AI. What’s in it for me? And we also need to find the message as organization, no matter if public or a private sector, how to communicate all of those changes that are coming. Another topic I’m facing while talking with the users, they basically don’t know what to expect next because as we have noticed that AI is another revolution and the revolutions are getting one after another very shortly.

And when the users, they can hear, okay, I should be more productive. I don’t want to be more productive anymore, right? I don’t want to do faster meetings. I don’t want to do faster notes, right? It’s nice. But in the same time, my brain and the number of different impulses I’m getting from outside is simply too high. Our brains are not capable to manage that in the right way. We’re closer to depression and we know in which direction it goes. So how we are communicating AI as a part of the tool is extremely important. So be careful what are you talking to your users. Don’t tell them that they will be more productive. But maybe the quality of their work is going to be better.

Maybe they don’t have to repeat the same tasks every day, but we must be very, very careful what kind of wording we’re using in regards AI adoption. Thank you.

Thank you, thank you very much. My next question will be to Chengetai because he looks at these challenges from the global perspective and has access to data from all regions. What, in your view, what would be the most important practical step to strengthen public trust in AI deployment?

Thank you very much for that question. And by the way, I totally agree with you. I think the first one is quite obvious. Inclusive participation in AI decision -making. So ensuring that the affected communities or the affected individuals are not affected by AI. And I think that’s a really important step. And I think that’s the most important step. And I think that’s the most important step. And I think that’s the most important step. And I think that’s the most important step. And I think that’s the most important step. into how the systems operate and before they are deployed, so not after the fact. We shouldn’t be fixing things after the fact, but we should go on an input before the deployment.

The second one is independent oversight, so establishing review bodies that include civil society and the technical experts, so not just the regulators and industry, but a 360 approach to it. Thank you

Thank you very much. We are approaching the end of our session, so I would like to ask Odes for a quick comment. What ensures AI remains inclusive in real world implementation?

There are a few key factors to look through when you talk about inclusivity. I think the first is to look who it is meant for and to ensure that they are accounted for. And this can happen in different forms. For example, when you look at data sets that power AI models, most of the time they tend to come from, let’s say, the global north, meaning that they won’t be very contextually aware when they’re deployed in the global south. So there’s that need to contextualize the AI system being developed to ensure that they really respond to the users that are meant for. I think the second part of ensuring inclusivity is also ensuring the local value creation.

I think we’ve seen too often imputation of AI systems, but not… the understanding of how especially small nations can participate in building and deploying AI for their interests. So I think those two things are very, very critical. And the other part is also, I guess, the linguistic perspective that I mentioned before, looking at the linguistic diversity that exists around the globe and ensuring that people are able to consume that particular technology being developed. I think when we often think about AI and how it’s deployed, we tend to look at the first 20 % of the market, but the rest 80 % also needs to be accounted for. So, yeah.

Thank you very much. Last question, and I will ask JJ for a very brief one sentence answer. What creates long -term confidence? cross -border AI investments from your perspective?

Well, you know, I think I can simply say it’s a mix of everything. The involvement from the right people, I would rather say the people who are on the top, who are taking the serious decision investments, because that’s very important. And the people who are involved, they should know what they want it for, because AI deployment is a big thing, but you should know what you want to solve with it. So that’s very important.

Thank you very much. Its time to wrapwrap up our discussion.

For our next very special, I would like to call upon three very special young innovation champions on the stage and share their experience. We have with us Srinidhi Bagla, Jai Vardhan and Adhiraj. Please come on the stage and share your journey. Thank you.

Hello, my name is Adhiraj Chauhan. And I’m a high school student of 11th grade. And I’m the founder and CEO of Delta AI Revolution, Delta standing for change. The reason my company is called Delta AI Revolution is because I’m a very, very passionate entrepreneur who believes in the intersection of solving societal issues with modern day technology. So I firstly like to extend my heartiest thanks to the Atil Innovation Mission. It is in their Atil Innovation Tinkering Lab, which I started my project and created my first MVP. Also to Intel for providing support. It’s important mentorship and to my very own school who’s provided. We support and been there with me every step. So my journey started when I realized that amongst the youth in our country, mental health is an epidemic.

And despite a lot of efforts because of a large population, the ratio of psychiatrists to people is one psychiatrist for 100 ,000 people. So my startup is a mental health support platform. It is an AI -driven platform training different therapy techniques ready to cater up to more than 100 disorders. We provide our platform to different psychiatrists firms such as Dr. Mora Psychiatric Clinic. And we are also in talks with the Delhi Psychiatrist Association. We provide our platform to them which they can provide to their clients. We’ve touched over almost 20 clients right now. We’re shifting to a B2C model. I’d also like to thank the Ministry of Electronics and IT who has provided me funding. And again, I’d like to thank Agile Innovation Mission and Intel for providing me this opportunity as a young innovation leader and a young entrepreneur.

Thank you so much.

Thank you. Shreenidhi please come on stage and share your experience.

Hello everyone myself Shreenidhi from BG’s National Public School Bangalore. I’m very grateful for everyone who’s been part of organizing the summit for giving us this wonderful opportunity of being here and presenting our project. It gives us confidence to build something new and gives us confidence that people believe in the youth today and innovation just doesn’t depend on age it depends on intent. So my project is basically charades named after a game which most of us might be knowing dumb charades where where the players are supposed to explain a movie or a song name without using speech and only hand. I decided to name my project charades because this is because the game is similar to something similar to what we try to help.

It is a glove that converts sign language to speech and speech to Braille trying to help the deafblind community. Right now we have developed our models over thousands of images using machine learning deep learning and all of this was possible only because of the boot camps from Tinkerpreneur Challenge, the mentorship programs from Atul Tinkerpreneur and Intel, Neeti Aayog, the mentoring sessions held by the summit organizers and we’re really thankful for everyone who has been part of this summit. Yeah that is everything I would like to say right now. Thank you.

We have our next next innovator and I don’t want to introduce him he’ll introduce himself and it’s going to be a very surprising and his journey is very surprising and let me call him on stage

thank you ma ‘am and hello everyone I am Jaywardhan Tyagi so if I’m a bit clean I just recently got appeared on Shark Tank India where I secured funding from Sir Raman Gupta founder of Boat Lifestyle and a founder fellowship from Sir Ritesh Agarwal who is the founder of Oyer Rooms so yeah to start with like let’s describe myself on broader spectrum I am an engineer I’m a student and I am a reader so so so broader AI in healthcare has evolved structurally over the recent decade. Like, if I had to describe radiology AI in 2016, it would be like a metal detector at an airport. But today it’s like a full airport security system with a CT scanner, with behavioral analytics and security cameras and, you know, all.

So, we have seen amazing benchmarks, especially from University of Florida recently, this year and the previous year’s end. And we have seen great progress in medical vision language models. But the question that matters isn’t how well these models perform on these curated benchmarks. It is, will they maintain this performance when the distribution shift is introduced? So, the distribution shift is like some edge cases, which are not so substantial. Like, if we talk about radiology, an input from a newly installed MRI, with a different contrast that can be considered uh considered as a distribution shift actually vision language models today uh are very poor on handling those distribution shifts they hallucinate a lot so basically uh the problem isn’t the architecture itself but it’s the thinking that okay a single model has the power to understand like every part of every dynamic of human health which is of course possible and you know but this is less than a technical necessity and more like uh you know obsession with scaling so yeah so basically um what we have derived it’s it’s almost like thinking a transcription model which doesn’t take audio as an input but takes video frames and just just try to determine what the person is saying from those videos it’s possible but inefficient so what’s the solution there The solution is a system or a framework that reasons across modalities and refers to previous conclusions, contradicts them, and finally describes them all in an understandable manner rather than a clinical report.

So, yeah, it turns out I’m working on the same thing. So, yeah, so before I describe Neuropex EIS technology, as it appeared on Shaktang, it’s good to first clarify what it is not. So it’s not a classifier for, you know, narrow disease prediction tasks. It’s not a standalone VLM with a reporting layer attached, and it’s not an orchestration on a GPT. So now let’s, like, discuss what it is really. So we have two pipelines. One is for radiology, and one is for dermatology. A radiology pipeline has dyno plus clip plus retrieval augmented vision language models, which actually… are able to understand multiple sequences of MRIs and can read the x -rays as well and can describe them in real time using clinical language.

It’s still in the active development when it comes to structuring those findings, but yeah, it’s still in the game. So the older radiology pipeline, which near the shark tank time, that was like a segmentation model, which took in 3D MRI files and just segmented the three tissues, CSF, gray matter, and white matter tissues in the brain. So what happens is when you have those tissue segmentations and you have those proportions, you can actually risk for a wide variety of neurological disorders. That was all of the radiology pipeline. I plan to actually show the demo as well, but we have time constraint. Yeah. So. Let’s talk about deep down then. Deeddom has a visual language model that’s trained on Demoscopy, Clinical and Histopathology Datasets and So you first describe your problem Vocally and then you answer A clarification question And then it just generates a report And it’s live out there, you can just sign up on the Neuropexia Site.

So let’s cover up It seems no less than a mission And this mission aligns with India’s goals of Leveraging technology for an outcome Driven impact. And yeah, it turns out We’ll be working on it So yeah, thank

Thank you so much I would now like to invite our mission director Atal Innovation Mission to have a few words And address the audience

Thank you Thank you Thank you Thanks Arunima Such a pleasure seeing you all here So many partners You know it’s amazing Were you guys listening to what they were saying These kids It’s unbelievable You know I just finished A session, this was on the future of work And I was coming, there were four of us And I was telling them the biggest challenge For us will be The first is I asked them to raise hands Of how many people have been laid off There was only one And I told them He’s the only person ready for the next 10 years It’s very important And you know the problem We are trying to solve on mental health That is going to be the biggest challenge Going forward The disruption is so immense That the ability To re -skill and re -do ourselves Is going to be so high And it’s going to be generational So I think people who have just gone into the workforce And at least for the next 10 odd years Otherwise which are going to face the brunt of it.

And that’s where things like this are going to be critical. But what I was saying there is, and which is going to happen here, in the next 96 hours, Sarah, you and I will celebrate our 10th year of the journey. But more importantly, we will also celebrate the 10th birthday of the Atal Innovation Mission. And just imagine, it is a 10 -year -old, which is today the world’s largest grassroots innovation mission. It’s unbelievable. And this is where you’re seeing what is happening. See the results. These are the ones which are now just going to take on that new India. And that is what I was saying there, that the big challenge is not going to be creating jobs, because just now, we are looking for 1 million jobs a month, right?

So far. Now we will have 12 and 13 and 14 -year -olds ready to take on tasks. We are fossilized completely. And the point here remains that that is where I say two points. The biggest delta multiplier of AI, the benefactor of this is India. The biggest benefactor of AI as a delta multiplier is India. I’ll tell you why. 1 .4 billion will be 1 .6 by 2060. 1 .6 billion people completely empowered. And starting from a low income to shoot up to be one of the biggest economies of the planet. You see the delta? We finally have a delta. We have a tool which is going to make that happen. thing is, for some of us, ma ‘am, we might actually see it happen in our own lifetime.

It is going to be so fast. It is so rapid. And the biggest benefit there which comes is two things about India, which are our biggest strengths. Think about it. The first is the ability to work in an unstructured environment without a playbook. You showed it. The way worst example in human history which happened, the biggest calamity was COVID, the pandemic. There was no playbook. You did not know what to do with it. You emerged as the strongest economy within COVID. You did it. It was unstructured. No one in the world had a playbook. The biggest strength of all of you, and we look up to you as the future which you are, and you’re going to be creating.

the superpower of the world, the biggest strength of India is getting a job done, regardless of the resources available. Ask an Indian, he will get the job done. And Sarah, that is what is the strength of this Jagannath. Guys, today it is about you. Really fantastic. And you know, we are so lucky we have our old partners with us, who started with us right away. Thank you, ma ‘am. Thank you, right from the beginning. You, Sarah. Thank you for walking this journey with us. It’s a long way to go. We have a lot to do. And we are all with you and behind you. And actually looking forward and looking up to all of you.

So thank you for making us proud. Very well done. And your presentation? remarkable. Thank you. Thank you very much.

Thank you so much, sir. Now we have with us a very special guest, Mrs. Sarah Kemp. She is the Vice President International Government Affairs, Intel. I would request,

Good afternoon. It’s not very often in your life that you get an opportunity to make such a difference. And so I want to start by saying thank you, because this journey of 10 years has been life -changing to all of us. And I want to start also by asking all of our technologists, to start… our future technologists, to stand up so that we can properly thank you. So all of the future technologists in the audience, I see you all with your – Stand up. Thank you. You are inspirational, and you are what gives me hope for the future. When I read the headlines and I get a little pressed, I take out my Changemaker brochure that has all of your projects in it, and I think, wow, there is hope for the future.

And I would also echo, I think India’s superpower is absolutely its people, and it is what’s going to make a difference. And I also want to say that I am so grateful to the Indian government for their support. For how they have – teed up and how they are framing AI. At this summit, not only is this summit making history because it is the first summit in the global south and it’s going to lead the global south and India is going to lead that, but what I’m really excited about is the heart and the human that India has put at the center of AI and making sure that the AI is to help people first and foremost.

And so to our future technologists, we put on you a great responsibility because with great talent comes great responsibility. You are looking at the future and you are looking at the future and you are looking at leading us forward. You have the ability to make the society you want, to make us a better version of ourselves by using AI. for good. And I just want to say I am very excited because I have great ambitions for all of you. But with that, I do want to just thank all the partners and look forward to another 10 years. And before we know it, we’ll be there. And I just want to say again, thank you. On behalf of Intel, it has been an incredible honor to be able to be a small player in this.

So, thank you.

Thank you so much, ma ‘am. That was really inspiring. I would also like to mention that, you know, we have top 50 students present, you know, AI tinkerpreneurs present with us. And they were shortlisted by Intel and Atal Innovation Mission by rigorous evaluation. And they were trained and, you know, mentor session was done. So, I would request the dignitaries on the stage to unveil the tinkerpreneur compendium. ma ‘am sir can you please unveil the tinkerpreneur compendium yes we can also have the three young innovation champions jaywardhan srinidhi adhiraj to come can we also have hufeza salim yes on the count of three you can open the ribbon three I see very less energy, you know. Thank you.

Thank you so much. We actually have… So, you know, as our mission director just said that this is the 10th year of Atal Innovation Mission, I mean, like, we, everyone here should be very excited about it because something that you’re seeing right now is being seen only by you. Nobody here has witnessed… the logo of 10 years of Atal Innovation Mission. What they are holding in their hands is the logo of 10 years of Atal Innovation Mission. Can we have a huge round of applause from the crowd? We are also going to play a video. We are also going to play a video. Thank you so much, sir, for joining us. Okay, let’s move on to our next session.

We have a very special address by Mr. Ojasthi Babbar. Can you please come on stage and

identify whether each one of the AI innovations which are happening all across are actually worth backing or not. Otherwise, it’s all noise, all hype, and we try to stay distant from them as such. But having said that, this is the framework for our evaluation. What exactly do we do? Once somebody passes on this, off with this captive network framework, how exactly do we help? How exactly does one incubate, accelerate, and invest? And what kind of value addition do we bring in while we have spoken about them bringing in that kind of value? The first one is rapid validation, if you move on to the next slide. The incubator, the accelerator, and as an investor, we help in rapid validation of these ideas.

The earlier side, though, but we can probably lock on to this slide as well. So we help you stress test that particular feasibility. We help you stress test whether your particular solution would actually work in the real work or not. By bringing in the right corporate client, by bringing in the right pilot partners as such and making sure that the rap… So at the incubator and at accelerator, we have a philosophy. We say we need to fail fast, but we need to fail forward. We need to learn quickly, iterate quickly, and move fast. The second one is, of course, of the controlled pilots that we bring in through our corporate partners. We have a corporate adoption program which we utilize wherein a lot of corporate partners plug into the incubator to give in problem statements which are solved by different entrepreneurs at each one of the different levels.

So that is one of the other programs that we have. Post that, there’s a litmus test that we do and that we help out with is by making sure that there is the right revenue model associated with each one of the startups that actually present and that are actually incubated as such. And here in terms of… These revenue models, we help them optimize the inference cost. I think we’re short of time so the essence is to ensure that we make sure that there’s enough revenue which is coming in, the revenue model is right and tight and that can move forward and get to a global scale level as such and of course the last one being making sure that once you’re growing you would be in need of capital and that capital comes in with the right partners, the right strategic investors and the other stakeholders as such.

Stakeholders like Atal Innovation Mission, like Intel would probably play a very important role when you’re scaling up from 0 to 11. So moving forward that’s the last slide that we have that is actually the gist of our entire AI thesis as such. We believe that any AI innovations which would actually thrive in an Indian ecosystem would have domain depth they would have the right proprietary data which they would utilize to create a mode, a barrier to entry as such and given multiple returns and relevant returns as such and of course having infrastructure railroads like that we have in the country as such if they can utilize the distribution access that we have I think we have a winning equation right in our hands for all AI innovations all across.

In the interest of time I’ll just stop up there. Thank you.

Thank you so much sir. I would now request Ms. Sara to please felicitate Mr. Rojasvi. Can you please come on stage sir? Can we have a round of applause? Can we also have Adhiraj and Jaywardhan to come on stage? We would like to honor you with something for being such good innovation champions. Sara ma ‘am if you could do the honors. Thank you so much. We now have our next speaker. He is the founder, co -founder and CEO of Hooper AI, Mr. Gaurav Dagongar. Can we have a huge round

Since I know we’re pressed for time, I’ll get going right away. I must say I was extremely happy today to come here to get a chance to talk about Hooper. But I think what’s made me really happy is sitting right in between Jayavadar and Srinidhi. I don’t think I’ve felt that energized. I’ve felt energized in a long, long time. Since we are a music technology company, let’s do this a little differently. How many of you recognize this tune? You get it, right? Thank you. Had to. This song released 50 years ago, more than 50 years ago, composed by R .D. Burman, written by Anand Bakshi, sung by the great Kishore Kumar. In 2016, and the reason I had to bring this up is I happened to make a cover version of this song that became really popular.

A few years later, a mint brand launched in India using this cover version as their audio campaign. And as I checked last week, over 100 startups have still used this in the last three months. To promote their product or their brand. Now the question is, have they got a license? Did Anand Bakshi get paid? Did R .D. Burman get paid? A little selfish, did I get paid? A lot of youngsters here who will make covers or who will make originals in the future need to ask this question. And that’s what we do. I’m Gaurav Dagaonkar. I’m the co -founder and CEO of Hooper. And I’ve made my passion my profession. I graduated from IIM Ahmedabad and became a music director.

So for a long time, I made music for films. I’ve had the fortune of having folks like Arijit Singh, Sonu Nigam, Shreya Ghoshal sing my songs. But after 10 years in the music industry, what I felt was, one, India loves its music. Whether it’s our films, whether it’s TV, whether it’s ads, all the deals, 6 million reels we consume daily, they run on music. And yet, when it comes to music rights and music licensing, there seems to be no knowledge. That’s an opaque space. I bet if there’s any entrepreneur in this room, who is using a Bollywood song, do you know how many licenses you need? The better question is, I don’t think, did you even know you needed a license in order to use it, right?

And that’s what we’re solving. Before we built Hooper, India did not have a single platform, even one, that could actually license music. It gives me great pride to say that Hooper is India’s first native, homegrown music licensing platform. And of course, we are a part of the Atal Innovation Mission ecosystem, so that makes me extremely happy. In a nutshell, we are a marketplace, where on one side, the largest labels, the largest artists come and list their songs. So you have folks like Yash Raj Films, Universal Music, even people like A .R. Rahman, next week we’ll get Hanuman Kind, listing their songs. And on the other side, it’s basically brands who come and like it. the music.

Over the last couple of years, we now have over 3 lakh of India’s biggest influencers and 220 brands that are licensing music from us. And it works in a very, very simple manner where the song gets uploaded on the platform, a brand discovers it, licenses it, and the royalty or the revenue goes to the artist. Beneath all of this is our AI infrastructure layer. And it works in a really cool manner. First, when a song comes in, we process that raw audio. We use a multimodal AI there to create different tags such as mood. Is this a song? Is the song a happy song, a sad song? Will it go for a fashion brand or a sports brand?

We also use LLMs to understand brands and try to create some kind of a fingerprint for every brand. And then we try and match the two. What music would work, say, for Baskin and Robbins? What music would work for a Dairy Day? What music would work for a Baskin and Robbins? What music would work for a Baskin and Robbins? mantra and so on that’s essentially what we have done and now it gets exciting because we’ve legally licensed music from authors and composers we can now build on top of it what if say for Mahendra Thar I want to create a hip -hop remix and I want to do it legally and ethically so that the artist gets paid and I think that is where I would love to invite many of you who probably have music as a passion and would want to build on top of the Hooper stack that’s a bit on our AI layer I love doing you know I love my job because on one side we’ve got the largest creators using the platform be it folks like Ranveer Brar, Ashish Vidyarthi, Sadhguru, the Chief Minister of Maharashtra Mr.

Devendra Fadnavis’s YouTube channel uses Hooper and I hope that this year we also get a chance to soundtrack our Honorable Prime Minister’s social media content and videos and apart from that we also have brands large brands like Himalaya, Myntra, Mariko as well as startups that use the platform I’ll just take half a minute to play you a short audio visual that will give you a glimpse of what Hooper has done in the Indian soundtracking ecosystem If the visual doesn’t load I believe it might be better I just want to sing a song That’s so good, that is nice It’s pigeon India Oh Thank you. Thank you. the AIM ecosystem in trying to ensure that India tells better stories, tells them legally, ethically and responsibly.

Thank you so much.

Thank you so much, sir. So we would like to felicitate you if Saramam could do the honors again. He played some music. At least we can clap. So today we have with us top 50 AI thinkpreneurs. You know, these are the people who got selected from about 3 ,500 applications and they are here today from each and every corner of the world representing at the AI Impact Summit. Before I call them, to stage, to give them certificates. I would like to request Ms. Dipali Upadhyaya, our program lead, Ms. Sufeza Salim, Mr. Sumit, our admin and finance head, to please felicitate Ms. Sara Kemp, who is, you know, a huge partner. Intel has been supporting us in training, mentoring, and the selection process of these top tinkerpreneurs.

Thank you so much, ma ‘am. Thank you. give a chair. Everybody please give a chair. These are our star students and you know yeah so Shubham is here to you know felicitate them.

Yeah so from DAV Centenary Schools do we have? Yeah please come forward and then from Infant Jesus School Infant Jesus yeah and ML Khanna the mentors, the teachers as well as the students. Come forward please for a quick photograph. Just come forward please. Yeah Take your certificates and stand You just hold the certificates and take a picture and then why don’t you also come Come come come Come in come in Come in come in Come in come in Come in come in Come in come in Then we have Vidyashil Pagadmi, Radiant International School, Lakeford School and KVIISC. Please come forward quickly. Vidyashil Pagadmi, Radiant, Lakeford and KVIISC. Silver Oaks, Silver Oaks, JSS Matriculation. You can also come forward please.

Join them, join them please. Go ahead. them. Yes, please. The next lot can come. Yes, please. Yes. Somalwar School, Father Eggnall, Murarji Desai, please come forward. Come forward quickly. Yes, please. We can move to the next lot. Yes. Next lot, please, quickly. Murarji Desai, Silver Oak, Yes, please. Please come forward. Yes, after this, all those schools who are left can come over, the students as well as the mentors. All those schools after this who are left can come forward, the students as well as the mentors. That would be the last camera shot for the day. So whoever is left, please come forward. There is a session scheduled after this. So please, whoever is left, come forward.

The students and the mentors. Quickly settle down please The last lot is here Thank you Ma ‘am you can Just settle down Just settle down this room Thank you ma ‘am Thank you Thank you Thank you ma ‘am Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you Thank you

Thank you. Thank you very much. We have very little time, so I want to first of all introduce Fred. Fred Werner is the Chief of Strategic Engagement Department at ITU Welcome Fred to give the opening remarks

Hello Let me start with a question What if the last thing that humans ever invent is invention itself Now what do I mean by this? We had, if you’re familiar with Roman Jampolsky He’s a leading AI safety expert And I met him in New York at the UNGA last fall And he said, Fred, what is AI for good? I said, well, what do you mean? He said, well, is it for good or for good? Well, what do you mean? And he said, well, for good as in beneficial, as in good Or as in for good, forever I said, hmm, good point And he said, what if AI is the last thing that humans ever invent?

Now, you might agree or disagree with that statement, but it’s not hard to imagine a future where most future inventions will either be invented by an AI or with the help of an AI. And if that is the case, then I think we do need to make sure that AI, if it’s going to be for good, is indeed for good. So my name’s Fred Werner from the ITU. It’s the United Nations Specialized Agency for Digital Technologies, and we’re also the organizers of AI for Good with 50 -plus UN sister agencies. Now, AI for Good was created in 2017, and if you think about that, that’s basically an eternity in terms of AI years, looking at how fast it’s been developing.

And back then, it was really all about the fear and the promise and the hype of AI. Most solutions existed in fancy PowerPoint slides, but there wasn’t a whole lot of substance. But that changed rather quickly. In 2023, we saw the advent of generative AI. Last year, the unofficial theme of the summit was the rise of the AI agents. And now we’re looking at a world where you’re basically entering a zero -click world where agents are not waiting for our prompts. They’re actually acting on our behalf. And in addition, you have the physical embodiment of AI in the form of robotics, embodied AI, brain -computer interfaces, and we’re even looking at space AI computing now. Now, so I think we’re safe to say there’s no shortage of high -potential AI use cases that can be used to help solve global challenges.

Anything from affordable healthcare to education for all, food security, disaster response, the use cases are definitely there. So what is the goal of AI for Good? Well, simply put, it’s to unlock AI’s potential to serve humanity. And how do we do this? Well, first of all, we can’t do this alone. Nobody can. That’s why we have AI. We have 50 UN sister agencies as partners of AI for Good, contributing knowledge, sharing expertise, helping to drive our standards work. building cooperation around AI governance and we’re very privileged to have here the two co -chairs and facilitators of the UN AI Global Dialogue who will be doing the closing remarks. Now, I could talk about AI for Good for days but to save us some time, I just want to show you a little video so you can actually see AI for Good in action from our last summit.

If we could please play the video. I have a joke that I always say for these occasions. AI is easy, AV is difficult. Actually, we don’t need to see the video. Oh, ah. Is it going to happen? Yes. But now we need sound. Since there’s no sound, that’s lovely, Geneva. Ah, that’s good. We are more than the AI generation. We are the generation that is determined, ladies and gentlemen, determined to shape AI for good. So no matter how fast technology moves, let us never stop putting AI at the service of all people and our planet. If you want an AI literate society, meaning resilient and ready for the future, we need to integrate these new tools into schools, curricula.

Let’s build a future where AI advances progress for all humanity. A shared digital future that is again inclusive, equitable, prosperous and sustainable for all. It is no coincidence that this era of profound innovation has prompted many to reflect on what it means to be human and on humanity’s role in the world. AI must help bring us closer, not to divide us apart. That’s one of the foundational promises of AI for good. We all now have, I think, a much greater level of awareness around AI, and we all need to shift into that as fast as possible because this technology is moving so fast. Ladies and gentlemen, this was a real… fast -track operation that we did, which we call the International AI Standards Exchange Database.

in your domain or industry that require this type of trigger. And we have just started the last step right from the general division. Let’s go! I think it’s fair to say that AI for Good is indeed more than a summit. It’s a movement, it’s a global community, and it would be nothing without you, the participants. 3, 2, 1! Thanks for watching. I’m not sure who that last guy was. Now, I think one of our… I think people often misunderstand that AI for Good, it’s known as a summit that takes place each year in Geneva. But it’s actually a year -long activity. We have online events almost every day of the week, all year long. And we’re organized around three pillars.

Solutions, skills, and standards. And if you look at the solutions pillar, we have machine learning challenges, we have startup pitching competitions, all types of activities to identify real practical applications of AI that you can use here and today. And on the topic of Edge AI, we had a build -a -thon on Edge AI just a few weeks ago here in India. And we also had machine learning challenges on tiny ML, tiny machine learning devices. And when we’re looking at skills, we launched the AI Skills Coalition. And a big piece of that is going to be creating basically machine learning environment sandboxes where we can do training and mentoring for governments to upskill their constituencies on the use of AI using the data from our machine learning challenges.

So it’s not hypothetical. It’s using real data for real solutions. And the last piece, of course, the bread and butter of ITU, is standards. And we have over 400 AI standards published or in development covering a whole suite of topics. But more specifically related to the session, we have a standards work on future networks, basically 5G, 6G and beyond, and a pre -standardization effort on AI native networks. So basically, these are examples of AI for good in action. And the theme of this session is actually edge AI in action in the global south. And I’m very much looking forward to the discussion. And thank you for your time and attention.

Thank you so much, Fred. Now, we have the keynotes coming. Thank you. First of all, let me call Professor Lal. Brijesh is my great friend as well as colleague. He was the Bharti School Chairman, but also right now he is currently looking at edge AI research. Our touch points with ITU are many, where he has hosted AI for Good Challenges, WTSA Hackathon. He was a judge, as well as Kaleidoscope. He is very active. Thank you very much, Vijay Singh, for coming, and over to you.

So it’s been a while. Thank you, Vishnuji, for having me. I’ve been participating in these AI for Good activities, so there’s been a lot happening, not just these talks that you have, but also something on the ground. Hackathon is an example of that, with participation from all over the globe. So today I’m going to talk about some of the work that’s happening here at IT Delhi, where we’re trying to leverage the edge. And the other thing that I’m going to run through very quickly, is TSI and its role in edge. So because we’re focusing here on accelerating development across the global south, so I’m going to pick up those two examples today. Right. So what we’re trying to say is that you have lots and lots of edge agents that will now act simultaneously and in coordination.

So the reason why edge is becoming more and more important is this converge of communication, compute and control. And this convergence is now quite real. And because this convergence is real, it is enabled at least in today’s technology only by a strong edge control specifically for tasks in the area of haptics. As I will show in the next slide, require you to not miss or make mistakes because some of them are catastrophic. And for that reason, a strong development in the area of edge is important. The other reason why looking at edge is important from the perspective of Global South is that. While it might not be easy to have foundation models that solve all the problems of the world, at least.

to an extent context has become increasingly important in modern times. People want to provide solutions which are very very specific to the task at hand and context can be best leveraged or used if there is a strong edge capability that is present. So in that light it is important that the global south focuses on building its strength in the area of edge. This slide here talks about some of the work that we are doing with respect to haptics. Haptics as you know is this sense of touch primarily consists of two aspects. One is kinesthetics which is the pressure that we feel and the second is tactile or texture which is the quality of surface that we you know the fine grained texture of the surface that we are able to measure using our skin.

So the thing with this kind of a modality is that while it seems to be almost abstract it is quite pervasive. It is all around us the temperature. including you know the hardness the softness or the way people meet each other greet each other you know all of that is very very important we just don’t you know it’s not overt but it’s important nonetheless so we sort of take it for granted however it is very very important and therefore it needs to be looked at a little carefully now the challenge with haptics is that while as we move from speech to video people did talk about bandwidth and they did talk about latency and there were quality of experience measures that evolved with haptics it goes to the next level because if you have unsynced and delayed haptic inputs or feedback then it becomes quite confounding and it confuses the person and it sometimes can be quite disconcerting so for this reason it is extremely important that the haptics data that you receive is accurate and received on time.

So for this it becomes extremely important that there is a strong capability that is present at the edge. Now here at IIT we are trying to implement it using two ways. One is what we term as split control where we have tried to move from having solutions deployed only in the cloud and the endpoint. We try to put in significant amount of capability on the edge itself. The other aspect that we are looking at carefully is trying to convert signals which are haptic in form to signals which give you the intent rather than actual measurements of pressure as what haptics is to machines. So these two things are primarily handled at the edge. The first one is quite clear.

Let me just say a few words about the second. So when we talk about intent in today’s world whenever you look at a haptic solution it is sort of locked in right from the operator to the end point where you have some kind of manipulation, dexterous manipulation of the environment around the device. However it’s very very hard for devices of different different manufacturers to interoperate and this happens because it is very very tightly coupled to all the signals that are generated and the form factor of the devices it’s not as simple as pick up any camera and the image that you get you can show it on any display so for that reason the idea is to convert those signals into intent send the intent to the other side and the edge on the other side makes sense of the intent and converts into into a signal that the other or the far point can then use to do whatever works needed so these are the two things that we sort of look at with with reasonable amount of interest at iit delhi and we continue to contribute to standards primarily in the area of msc and quality of experience where multi -modality is involved right now this is the edge foundation network.

I’ll skip this in interest of time because I do have a couple of slides that I want to walk you through because there’s some work that’s also being done by TSDSI which is our SDO here in India and they have in conjunction with ITU doing quite a lot of interesting work which is edge centric. So let me talk about few of those. So there are a few technical reports that have come out of late. There’s a stock of dynamic AI ML models for self -sustainable V2X applications. So V2X applications is being looked at carefully. There’s also work in the area of security aspects and advanced and AI enhanced passive digital twinning initiatives. So we have some technical reports that have happened in this area.

There’s also developing of standards work that’s happening. There’s architectural support for tactile applications that I just spoke about. There’s talk of 6G AI architecture for RAN and also AI native scalable reference. Architectures. I think maybe we’ll talk about quality of experience in the next slide but that’s another thing we’re looking at. We’re also carrying out technical studies in all of these areas in interest of time. You’ll have the slides you can go through them when you find the time. This is the other thing that they wanted me to bring to light to this audience. Just a couple of minutes. So the global standard forums that are of interest to the audience here people who look at edge carefully.

There’s ITUR IMT 2030 framework for included ubiquitous intelligence for overarching design and then there’s ITUT related standards CGPP standards and of course the M2M. So all of these standards are of interest to the audience here and people trying to do research in this area and besides this TSTSI has been trying to be inclusive by holding these flagship conferences annual ones so that more and more people get insight into what is happening. With that I’ll close because we’re really short of time here. Vishnu ji back to you

Thank you Bajeshji Thank you for bringing out the Indian research in the topic and bringing out the 8GI framework also it’s very less time let me invite Ranjitha Ranjitha obtained her PhD from IIC her current research involves causal inference, survival analysis and Bayesian neural networks over to you Ranjitha

Yeah so something that he also missed, I actually do work in federated learning and many other learning paradigms so let me just start, so mine is going to be a technical talk where I’ll tell you the motivation for using federated learning, especially the role of federated learning in telecom networks and why really are people discussing about this the motivation is of course data explosion there’s exponential growth of in mobile data data traffic and you have all these diverse services that are there in 6G EMBB, URLC, I’m sure this audience is well aware of this then there are bottlenecks in these legacy networks which actually motivated moving more towards edge centric architectures. The goal is of course I think this is something very important that most of the standards are looking at.

Predictive zero touch automation closed loop wireless control and this loop closure latency requirement about less than 10 milliseconds for mission critical optimization. And this is exactly where federated learning comes in as a key enabler of privacy preserving and distributed intelligence. So all of this is captured in the AI native network concept where now AI is no longer a peripheral layer but it’s actually coming into the RAN. So this is enabled by what is called as this ORAN alliance particularly the RIC or the RAN intelligence controller and this is how the whole sub 10 millisecond latency requirement is fulfilled. Something that is not very clear here is So why do you really require edge intelligence, right? So to make it even faster and achieve the sub 10 milliseconds, you actually have to bring in inference and training to the edge rather than taking data to the cloud, right?

So that’s where the whole paradigm shifted and this argument about edge intelligence or edge native intelligence came in. And especially something called as MEC or multi -axis edge computing also was introduced. So this brought in a huge architectural change. That is, now we have the core network talking to RAN and then RAN talking to the UEs. And this is where the whole, you know, the UEs basically now have the intelligence along with the MEC controllers. So federated learning. Upon all these things, one very important aspect. that’s how we relate to AI for good is that of privacy right so think of the use case of traffic prediction where there is you know there’s a need for loads and loads of data but you know this data consists of raw user logs location history or I mean if you share it with the with the centralized controller it’s just privacy violation so the solution is to now bring code to the data and not take data to the code right so that’s the that’s where federated learning comes in the intelligence now or the training happens at the edge and only certain metadata is given to the cloud so what is this what is its implication in telecom so there’s impact on privacy that’s exactly where it’s supposed to make the impact and then of course there’s impact on latency and bandwidth so personalization of AI models is possible in real time large -scale training can still happen in the core network but the personalization of smaller models for you know for the edge and there’s impact on bandwidth because i no longer need to send data to the server and of course there’s a huge impact on architecture because you saw there that it becomes a hierarchical style of an architecture where core network is at the top and user ues are at the bottom okay so i just wanted to introduce quickly introduce two use cases in fact left it’s in fact a use case from uh from france it was this is for um a traffic prediction in fact uh predicting certain traffic spikes when they had a football match and uh this scenario is where you need to allocate dynamically uh resources for this particular stadium event so here what’s happening is each of these ues or base stations are picking up uh the traffic in their local area sharing it with the core network sharing it with a mec controller and then the core network is able to say you know how to really route the traffic so that you know there’s less congestion the other one is v2x so this is again for uh sharing road conditions or you know accident information and other things it’s very easy to see why uh fl may be useful here each cars can talk to its own edge server and then go to the cloud server where the global model is trained so this this sort of envisages how federated learning has become a very important technology so last but not the least i come from i have i’m the pi of the lab which is called as intellicom lab at triple it delhi uh we have a collaboration with it delhi uh for this entire work

Thank you, Ranjitha, for the excellent talk. We had an introduction at least for federated running and also the framework that architecture that she explained is really interesting. Last time when ITU colleagues were here, we had visited the lab. If you haven’t done that, please talk to her. It’s a very exciting research which they do. And we also have great collaboration with BAPI and colleagues in IIT Delhi. Thank you, Ranjitha, for coming. we have a panel now we have approximately 20 minutes maybe for the panel let’s kick off the panel can I invite Fred to moderate the panel and can I invite the panelists Mala Alagan and Sakshi to please take the seats Fred to kick off, thank you very much over to you Fred

thank you so I’m looking forward to this panel where we can aim to demystify Edge AI a little bit and explore the practical use cases and AI strategies but first I’ll introduce the panel so the first panelist her name is Mala she has a full name but she personally asked me to just call her Mala and I wish all panelists would do that it’s much easier that way so Mala is currently a technologist at the Center of Excellence Wired and Wireless Technologies at Art Park sorry, Art Park so Mala is currently a technologist Prior to this, she was a postdoctoral research at the Teikian Group at Technical University Berlin. She’s also involved in 6G initiatives such as AI RAN for efficient resource allocation and millimeter wave communications.

And she also has been a visiting researcher at UC Davis and TU Berlin. Welcome. Our next panelist is Alagan Mahalingam, founder, CEO, chief software architect of RootCode. Alagan is the founder of RootCode, and in his early 20s, he worked as a researcher at international research organizations such as the Geoinformatics Center at the Asian Institute of Technology, Thailand, also the University of Tokyo, Japan, where he worked on satellite communications and solar panel optimization algorithms. Alagan. Mahalingam was also awarded the special title of ICT Entrepreneur of the Year at the National ICT Awards in 2021. and also the Young Entrepreneur of the Year in 2024. And he’s also the envoy for the government of Estonia e -residency. So I see a lot of Estonia connections here today.

Last but not least, we have Shaxi Gupta. So she’s the Global Government Affairs responsible for Qualcomm. She’s a tech policy professional in AI and emerging technology policy analysis, market research, and stakeholder engagement. So if we could have a please warm welcome for the panel. So first question is for Mala. As an AI -enabled XR applications, and they’re split between private 5G and on -premise public 5G, could you please give us some examples of XR applications in different scenarios, and what are the trends? And what are the trade -offs in scalability, security, and security?

They get the immersive experience in their own preferred regional languages. And one other application that we have done is the XR -assisted medical emergency care. Here the focus is on the, to provide timely medical response to the patient who was suffering with a cardiac arrest and so on. And an SOS alert would be sent from the, by the bystanders from the life circuit exact to the first responders. And the medical experts and the ambulance through 5G connectivity. Once the first responder gets the alert, he arrives at the scene with XR glasses and IOT wearables. and also the AED kit. So, and while giving the CPR, the IOT patient vitals would be displayed, augmented onto the real -time video.

And the real -time video would also be sent to the medical expert. And the medical expert will guide whether to continue the CPR or it could be the AED and so on. So, the timely response will save multiple lives. So, in this case, we have used public 5G network. But for the XR -assisted facility tour, we have used private 5G network. So, the private 5G network is mainly to have on -premise HCI applications. And this would bring the core next to, the data generation. And then we can also… do real -time decision -making for industry 5 .0 applications. And going forward, we would like to have some of our applications to be in the open source and have it in the best place, like ITUs, AI for good, right?

So then the international community can access this open source AI models and they can fine -tune the models and they can do the rigorous testing before it is bringing it to a real -world deployment. That is what I’m looking forward for this.

Yeah, thanks so much, Mala. And I think this really is a good example of AI for good in action. And I think, to your point, these solutions don’t happen by magic. There’s a lot of difficult problems. There’s a lot of problems to solve. And by putting these solutions in the AI for good, good sandbox that might lead to future standards which could make them replicable and then you could have that adoption at scale. So I’ll just go to the next panelist, Alagan. Given your rich experience in developing AI solutions for partners in different geographies, can you please give us some examples of edge AI deployment in real world scenarios, their impacts, the nuances you see in AI strategies on edge AI in the different regions?

Because from your bio you’ve been involved in many different parts of the world. Thanks.

I started RootCode 11 years back because I was in love with building AI solutions as a college student and then now 11 years later the technology that we have built is used by more than 92 million people across 27 countries including many European governments like the government of Estonia, Portugal and many others. We chose to build edge AI in many cases. One, the obvious one, to bring technology to under -connected spaces and also to increase speed in many cases and sovereignty. And the most interesting project that we have done recently, let me tell that story, a couple of years back, Portugal realized that their farmers, especially the small -scale farmers, didn’t get enough access to advisory and intelligence to grow their crops.

And things have been changing because climate change and unpredictability in growing crops, a lot of people were leaving farming. And so we built a solution from a hardware, a software product and also an AI model. The hardware goes into the soil, so you understand the soil nutrition and you take pictures with the mobile app and we can process the pictures to understand is there a problem with the plant, right? And we built and it worked out fantastically well. And then I tried to bring that to Sri Lanka. I grew up in Sri Lanka and to date a big part of our development team is in Colombo, in Sri Lanka, more than 120 people. And so we went into one of these villages in the middle of the mountains of Sri Lanka, Nubar earlier and I was super fascinated and when we tried to deploy this, we realized they don’t have reliable connectivity in some corners of the villages and our solution was worthless.

And that’s where we started bringing in Edge. So we brought in a new version, we had a Raspberry Pi and we started testing models like GemR, and also we did our own convolutional networks like 2D things to figure out like where do you optimize? You don’t want to use LLM for everything, right? And by the end of it, we managed to bring the same value that the software gave to connected users. to people who didn’t even have internet in some part of Sri Lanka. And that reminded me how much edge is needed, especially in the global south. And yesterday I was at a dinner talking to some of the development finance colleagues from DZ. And somebody was talking about why don’t we put computes on the wheels in a tuk -tuk?

So imagine like we can’t process too many things on a small device of Raspberry Pi. What if you get a tuk -tuk coming to your village every other day or once a week with a data center built in, with Wi -Fi LAN, so farmers can connect and do the processing. Smaller banks, smaller institutions can do. And I was like, yeah. So this week has been super fascinating. And sometimes when we think about edge, we think it’s needed only in places that are not really connected, like rural parts. We have built this, we have built a beautiful solution that’s used in America. If you think America is well connected, you should take a road trip. When you go out of the city, you realize some parts are very disconnected.

And we built, for one of our clients, we built a solution that helps rural patients who are at high risk with remote patient monitoring. And then, yeah, EDGE works all around the world, not just in the South. If I, when I think about all my learnings, because there are so many learnings building EDGE for multiple geographies, multiple customers, multiple communities. If I were to single out, I would single out the fact that when you are trying to do something in the EDGE, you shouldn’t try to think of the model and go find a solution. But instead think of the task and then work backwards on how do you build and distill or fine tune a smaller model.

And that runs on the EDGE because in the EDGE, you can’t do everything, right? if you are building an AI assistant for farmers, you don’t want the AI to be able to tell why two of the famous CEOs didn’t want to hold hands. I mean, that doesn’t matter. You want it to answer about plants and agriculture. So the heavier the model is, it becomes impossible to deploy. So we work on multiple technologies to one, quantize or prune the models in a way that creates a smaller version that does exactly what’s supposed to happen. And I think the global south needs to grow with this AI transformation of the world because infrastructure takes decades, but the next few years is going to change the way we live.

And that’s why we are here. So I’m excited

Yeah, thanks a lot. And I think what you’re saying here has almost been the theme of this week where you don’t need the biggest AI or the biggest large language model. And I think if you look at the example of India, where they’ve managed to enable… billions to have a digital ID to enable financial inclusion, financial payments with the public interest at heart and with relatively low -tech solutions, you can indeed bring AI to the edge in cases that make a lot of sense. So thanks for that. Sapsky, question for you. In your experience with Europe, the intersection of technology, innovation, and AI strategies, what do you think are the metrics to evaluate the usage of edge AI such as availability and capability of hardware at the edge and also the connectivity, privacy, and data issues that you see in your line of work?

Thank you, Fred. And let me start by saying it’s an absolute pleasure to be sitting with fellow panelists and speakers who have preceded me who are deploying edge AI and are doing research on edge AI. At Qualcomm, we are very focused on edge AI and think that that’s going to be the future of how not just Global South, but globally, we’re going to be using AI. So we… Um… And I really relate to what you said about the way to think about deployment of AI is actually to think backwards about what is the use case that you’re trying to solve. And then you think about what is the best architecture that you want to use.

Is it just cloud? Is it on -prem? Is it an edge cloud? Or is it on -device AI? So we have to think about it from a distributed architecture point of view when we think about the use cases that we have here in the Global South. And I do want to mention one important distinction here, which was touched upon earlier also, is that when we think about AI, there’s a training part of it, and then there’s an inferencing part of it. Inferencing is where you’re thinking processing is actually happening. So while training can continue to happen on the cloud, a lot of it, a lot of the inferencing, as we’re seeing, is moving towards the edge.

Now, in terms of availability, if I want to talk about it, I think we’re increasing. We’re increasingly seeing, and Qualcomm is deploying this at the edge of… So, you know, AI being available at the edge, not from, you know, the very basic thing that we all use every day is your smartphones. That we have on -device capabilities coming onto smartphones with 10 billion parameters models already running on -device. So that means that you do not need to be connected. If you’re in flight mode, you do not need to be connected on internet and you can still use AI. So that’s amazing in my point of view. We also have it coming to actually cars. So Qualcomm has developed that technology where you can now use HCI onto the, or it’s actually in development.

We have demos at the Qualcomm booth, which I’ll come to later, but which you can, so HCI is coming to the cars as well. And it’s increasingly coming to IoT devices and your smart glasses as well. So in terms of availability, I think we are seeing increasingly that it’s coming to all types of devices. That are connected to internet now. Now, why is AGI relevant? And some of my panelists have already touched on it. I think latency, security, privacy, personalization, low cost, low power are all very important factors for why AGI becomes important for Global South. We may not have access to as much power. We may not have access to as much water as needed.

But with AGI, we don’t have to worry about that. Apart from that, I do want to touch on one thing. That is Qualcomm, one of the things that we have is a program called Tech for Good, wherein we partner and work with startups and small businesses around the world. We invest in them. We mentor them. They use Qualcomm hardware to develop solutions at the edge. In fact, I do want to encourage that in Hall 4 at our Qualcomm booth, we have some of these startups who are displaying this technology. One of the examples is actually from India. It’s called Raksa Health. They’ve actually built an on -device AI healthcare assistant where it’s for doctors and patients both, where the doctors can take down symptoms and provide solutions for their patients and for patients to actually look up their prescriptions and be able to access all their records offline and ask questions about it.

So, yeah, I think that’s how we’re seeing the transition happen. Thank you.

Thank you. Yeah, some amazing use cases. And I think this week’s coming out of Davos where the narrative was all about go, go, go, the insatiable demands for energy. There’s talk of putting data centers in space. But I think this panel also brings things a bit down to earth where, you know, you can have AI on the edge, and, of course, there’s a lot of things to solve there. when it comes to connectivity, when it comes to data compute. I think there’ll be a lot of standards development work that needs to emerge from this to make this work at scale. But I think your use cases and the way you’re approaching the problem, especially starting from the what are you trying to solve and work backwards from that, I think is very refreshing compared to all the headlines we’ve been seeing lately.

And I don’t see it either or. I see it as a big piece, a complementary piece of the puzzle. So with that, I really want to thank the panel and if we could have a round of applause for them. Thank you.

Thank you very much, Fred, for running the tight panel. Now we are coming to the closing. Thank you, panelists, insightful remarks. Yes? Okay. Can I ask a quick group photo of the panelists, please? Panelists? Yes? Thank you. Thank you very much. Thank you very much. Now we are coming to the closing. There are excellent closing remarks coming. Can I please request Her Excellency Miss Lopez, Ambassador, Permanent Representative, Permanent Mission of the Republic of El Salvador to United Nations Office and other international organizations in Geneva to please give her closing remarks.

I’m actually based in New York. Thank you. Well, good afternoon. I know that I don’t have much time, but I had just to say that this discussion was very enlightening. Thank you so much for sharing everything what you’re doing on the ground. And I guess that it was very clear to me that HAI means simply using an AI closer to where things happen. That means closer to people, closer to services, communities, rather than deepening only faraway systems. So amazing what you’re already doing. So this can be important for development because it can work better in places with limited connectivity, as we were hearing, and it can help with speed. And it can help with speed, cost, and privacy, since not everything has to be sent everywhere.

So I guess that I had to begin also with something. I am also the co -chair of the Global Dialogue on AI Governance. This is going to happen in July this year, and it’s going to be the first dialogue of its kind. So trying to also bring together what we have been hearing from member states and also other stakeholders in these months, I can tell you three specific things connecting with what we just heard today. First, that people must remain at the center. And we have heard with all these examples. And I guess that a common message that we have been hearing also in this week is that AI should be developed and used in a way that protects but also helps people.

Second, closing the gap is not a slogan. We are hearing this a lot. It requires decisive support. And I was very pleased, for instance, saying that you’ve been trying to replicate in some countries what it has in others, for instance. And I think that’s a really important thing. This information sharing, this is critical if we’re talking about closing the gaps. And the third message, the final one, is that we should avoid a world of disconnected approaches. And this also is aligned with what I was just saying, that cooperation across different national but also regional approaches, it will help us to reduce fragmentation. So, with that, I just have to tell you that we’re very looking forward to see some of you in Geneva in July, so we can hear and learn more about what AI is.

So, it’s my pleasure to give the floor to my distinguished co -chair, Ambassador Reintam Saar. He’s going to explain to you very, very shortly what the global dialogue on AI governance is. And this is really important work that we are putting a lot of effort to it. Thank you so much again for the invitation.

Thank you. yes hello hello everyone frankly i really feel humbled among real experts not to say i feel helpless so please allow me then to do a little bit of awareness raising when it comes to the first global dialogue on ai governance and maybe this way i’ll try to fit into a discussion that we’ve heard here today so three points on my side first about tasking so the tasking was to put together a distinctive identifiable un global dialogue with all the elements that are prescribed in the mandate so bringing governments and stakeholders together to exchange best practices and of course to focus on cooperation and to execute it back to back with itu ai for good summit in july in geneva produce co -chair summary.

So this is what we are going to do. So, so far we’ve engaged with member states, with stakeholders, multi -stakeholders, and from member states we’ve kind of covered three different approaches, I would say a little bit. Risks versus opportunities, state -centric approach versus multi -stakeholder approach, closing AI divide versus free market innovation. But we also were able to pick up three convergences. Practical outcomes preferred over endless theoretical discussions, alignment with existing UN processes, avoiding duplication, clear timeline formats and thematic focus to produce actionable insights. And the unified element, I would say, in these discussions is that the dialogue needs to be inclusive. and capacity building was absolutely a crucial element that is, of course, one of the most important things to a global self.

So from multi -stakeholders, what we’ve heard, the key words, so to say, were trust, transparency, no duplication, interoperability, equal access and participation for everyone, rooting the dialogue in human rights and to be of a practical value and innovative in form. So what we are going to do, we will guide the discussions, but we will not predetermine the outcome. It’s for member states, it’s for you, for stakeholders. And, of course, we will engage also with international scientific panel that was also established through the same resolution. We will rely on member states and your wisdom, we would need to collect this wisdom somehow. and this is something that we are going to do because we would need this wisdom so that the dialogue would be really inclusive we would come up on certain point with a road map to Geneva where you would see building blocks towards dialogue and whatever opportunities to engage into dialogue and of course I very much hope that all these fantastic ideas and frankly I mean chapeau to the panel because you are already making or changing life on the ground and it’s absolutely fantastic we really need this also to inform our dialogue and so that the dialogue would be also result oriented on the ground.

Thank you very much.

Thank you Monsieur Excellencies. At this point I would like to call Fred to give out the mementos if you don’t mind Fred please. and can we have the momentos for Brijeshji thank you very much Ranjitha Ranjitha please Moala can I request Nodal officer to please felicitate Fred yeah we have an event with him yeah yeah yeah yeah yeah yeah yeah yeah yeah yeah thank you very much for attending the session session is closed thank you thank you Thank you. Thank you.