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Belgrade, and Paris. CPRG brings policymakers, educators, industry, and citizens together to reimagine AI and the future of society. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you everyone for joining this session. Before starting the session, I would like to tell you about CPRG and the future of society, which is a joint initiative. The Center of Policy Research and Governance is a policy think tank that is continuously researching policy and governance issues in different fields. Two years ago, we developed a center for the study of the relationship between emerging technologies and society. Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society.

Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society. Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society. Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society. Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society. Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society. Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society. Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society.

Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society. Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society. Under Future of Society, we developed a center for the study of the relationship between emerging technologies and society. In light of this, just one year before, we have published one report, Usage of AI in Higher Education. Now, we have just launched, going to release one more report, Usage of AI in School Education. In next month, we are going again, going to launch a report, Future of Job. Future of Job. What kind of future skills, what kind of future jobs are coming?

and they are going, they are transforming we are going to launch a report on that but now, it is in next month but now the report we are going to launch that is AI in school education and to launch that, I call all my guests and Pranav ji to the stage now we have a short presentation with some salient findings from our study

So AI in school education, this is a survey report that we have conducted late last year as part of our ongoing internal activities on mapping AI usage among students in India in various sectors in India So over the past year, CPRG has now released two reports on AI adoption in education So last year we released a report on AI adoption in higher education This was the first ever survey based report in India on mapping everyday AI use among college students Today now we are launching our new report on AI adoption in school education Both studies have been conducted in Delhi where we have actually gone to students, interviewed them to understand what are they using AI for, how often they are using AI for and what are various challenges and opinion on usage of AI So firstly, if we just compare our broad findings, what we find is that AI use among school students remains relatively high, though marginally lower than what we found among college students within the same city because both studies were conducted in Delhi.

Yet what we find is that nearly 50 % of students, and these are of course, these are students from private schools in Delhi, that was our limited sample, almost 50 % of them use AI -based tools. These could be generative AI platforms or other AI tools multiple times a week. What are patterns of AI or edtech use as per academic stream? So what we’re finding is that while AI use, especially generative AI platforms such as strategy, GPT, Gemini remains relatively high. What this is also leading to is also, leading to some sort of a challenge to traditional methods of learning and edtech platforms that have become extremely prominent and widely used over the past few years. then what are students using AI for so apart from asking how often are students using AI we also try to delve into what are they using AI for and what we find in our study is that AI use is essentially concentrated for generally searching for new academy for academic information while studying or writing assistance and this of course varies across streams because some students may be more into more engaged in practice solving question solving and yeah use depends on depends on usage but however what we find is that among science students for instance while there’s high AI usage for learning concepts there is very limited usage for structured tasks like calculations or calculations or solving questions because that is where various AI platforms still have relatively low accuracy now what is perceived helpfulness of AI for school examinations and entrances and here what we interestingly what we find are a few things one there is relatively high perceived helpfulness of AI platforms for both studying for school exams and entrance exams while especially for entrance exams, students who are in the science team are more likely to prepare for entrance exams are still more dependent on offline classes or edtech platforms.

Yet the level at which we are seeing perceived AI helpfulness, it means that there is an emerging challenge that is coming to edtech platforms through free usage of generative AI platforms. AI support in learning and performance. So how do students rate AI -based platforms or AI -based tools in terms of their actual impact? And what we find is that apart from, of course, learning complex topics, improving their time management, there is a substantial proportion of students who are actually attributing improvement in their academic performance to use of AI platforms. At the same time, students report issues with accuracy and challenges in AI use. One of the major challenges with respect to AI use is that a significant proportion of students regularly encounter AI hallucination or are able to identify that they are getting incorrect information.

Then secondly, as I mentioned, when it comes to accuracy for logical or numerical subjects, there is relatively lower reported accuracy. Again, this is something that various platforms are still working on in terms of trying to improve their performance and accuracy. Next is apart from their overall planning and understanding overall AI uses, we also try to compare AI platforms and their performance with other tools. So what we did was we asked students, number one, is our AI performing? Are AI platforms better than YouTube? or ICT -based learning, and there what we find is that there’s still overwhelming support for YouTube, video, or ICT -based learning tools. Secondly, there’s a whole question of adaptive learning and AI addressing individual needs.

Here, there is an overwhelming evaluation by students that while AI tools might be helpful, they are not necessarily providing solutions that are specific to their needs. And this, of course, might be because of the nature of AI tools that students are using, which is in most cases free models of generative AI platforms as opposed to specific AI tools that are actually able to undertake adaptive learning. And then finally, we tried to ask about AI versus human interaction. So the idea of AI tutors or AI -based learning tools replacing in -person teaching, there again, there’s an overwhelming support, there’s essentially overwhelming support for the idea that students still prefer AI -based learning tools. So there’s an overwhelming support for the idea that students still prefer traditional human interaction.

based learning. So what we’re finding in our study is that while AI is definitely emerging as AI use is definitely increasing significantly among students, it is still considered as a supplementary tool as opposed to a main as opposed to a replacement or substitute for traditional teaching. So these were some of the findings we have more detailed findings in our report and at the end I would just like to thank our team that worked on this report. I would like to thank Nitin, Mehta and Ms. Suchitra Tripathi for their guidance and oversee of this research and I would like to thank our team members Gauri, Shreya, Anupriya, Rashi, Mika and Shugal for their active involvement and participation in the study.

Thank you so much.

Thank you Pranavji for the presentation. Today as a panelist now we have Professor KK Agarwal sir, President South Asian University We have Professor Pankaj Roda, sir, Chairperson of National Council of Teacher Education. Suresh Yadav, sir, Executive Director, Commonwealth Secretariat. Andrao B. Patil, sir, Assistant Secretary, Higher Education. And we have Aditi Nanda, Director, Education and Industry, Intel. And, Agrawal, sir, you have seen, you know, the transformation during IT movement. And if I can align correctly, at that time you had developed Interpress University. And maybe because at that time IT was also in boom and you were in the process to develop a new institution. So, you have seen the transformation. So, when you are developing an institution, you must be having in mind how IT is going to challenge those, you know, kind of traditional or conservative approach of, you know, institutions.

Now again you are the president of South Asian University, it’s one of the iconic institutions in India. And again you are facing new challenge from the AI. So how you are finding this AI is different from the past IT. Because in your lifetime you have seen two movements, first IT, now AI. And at the same time you are developing two new institutions. Because before you, Sao was not in that position. But now Sao is leading. So how you are finding?

Thank you Ramananthi for the question. Yes, in a way when I was asked to develop the very first university in Delhi, Indraprastha University. And it was a challenge because it was the first university in the country. and your very right IT movement was also in the offing it probably happened by coincidence that the vice chancellor which is me which was appointed at that time belonged to the discipline of IT. This was probably never a calculation but it happened for the good of the country and the university I believe because you could get two in one kind of person to develop so we made sure that right from beginning IT is, that was the time when if you remember I saw the students in Delhi incidentally I think this was the first university in Delhi for the students after Delhi University who was an affiliated university so I was seeing the students go to the Delhi University colleges, they are not satisfied with the employment and in the evening they go to a tech company and do a course there so I was there for the course and they were very happy Now that was very disturbing to me Why the students should feel Not very satisfied at the end of the formal school Or formal college And then try to do that So my first thing was Let’s combine the two So our curriculum itself should integrate both If the students have a job in IT sector Why should we not realize this And make sure that every subject is more IT saving And so on and so forth Now when I am here The challenge obviously as you say is AI AI is fortunately being adopted by the youngsters even faster Which was expected IT was also adopted by them faster than the elders AI is being adopted much faster than elders Which is a good sign Only thing which one has to see is As I said in the whole process of using AI AI Let’s make sure it supplements our creativity.

It does not give us a shortcut to creativity and thereby reduce our creativity powers. That is a challenge which we have to face in academics. Short of that, it’s a good opportunity for all of us.

Sweshar, while working with President Mukherjee, you have introduced a lot of technological tools, and a lot of innovation, not only in the finance industry, but as an advisor of the President, you have introduced a lot of educational innovation as well. I think that was before the time of 2014 and 2015. After COVID -19, the educational system has been changed, and it is getting changed very fast. How, you know, you will analyze and how we’ll assess this kind of change, and what will you suggest, you know, to educational institution and to the head of the institution to, you know, kind of to address those challenges posed by AI and other emerging technology?

Thank you very much, and first of all, a big congratulations on this fantastic report, which talks about the AI in school education and also your previous reports, which talks about AI, and I think it’s a very good documentation to understand where we stand as a society, as a country, as an institution in the emerging landscape. COVID changed, Ramananji, drastically the way the world look at the various way of doing the things. I mean, going to the office was normal. Now, not going to the office. office is normal. So there is a fundamental shift. It’s very difficult to get the people back to office and the argument is that if I can do my job better while sitting in my home, why do you want me to come to the office?

So these are the fundamental shifts which we have witnessed post -COVID. And then if you look at the artificial intelligence, it’s a paradigm shift. It’s not only 180 degree, it’s a 360 degree shift. We don’t know which direction and what direction we are going. Any organization, any society, any institution which is not live and kicking to this new emerging reality will be fossilized. Remember, we have in 180 controlling the almost one -third GDP of the world. And it was not the country which was leading, it was the institutions. It was the institutions of that time. which were producing the skill which can produce the goods and services and the material which can dominate the world. So it was the role of the institutions.

Of course, the government has now tried to recreate Nalanda, which is coming out very well. So the point I’m trying to emphasize is that the role of educational institutions is of paramount importance. No institutions can dominate the world. No country can dominate the world unless the institutions dominate the world. If you look today, the U .S. is dominating the world not because of the military power, but because of the higher education system. If you look at China, the Chinese universities are coming on the top. The number of research in the field of computer science, AI, machine learning, computer vision is dwarfing the research being done in the United States now. So that’s the level of the shift.

So when I’m talking about, in your topic, reimagining the education system and education system in the United States, India, I’m not talking of today, I’m talking of India of 2050, India of 2100. And one thing I keep saying that India, a lot of people say it’s a $5 trillion economy, they’re very happy that we are the third largest in PPP, fourth largest in the other term, but I’m not happy. Because India as of now of 1 .5 billion people, if you look at the European standard of GDP per capita, we should be more than 70 trillion. If you look at the American standards of GDP, we should be more than 150 trillion, more than the size of the world economy. So that is the level, that is the where we have to think that what kind of institutions we need, what kind of infrastructure we need, what kind of history we need.

Is it the degree, the undergrad degree, master’s degree, PhD’s degree, I got all the degrees. I studied in India from IIT, Indian School of Business, I studied in US, UK, Germany, India, India, India, India, India, India, India, India, India, India, India, India, Sweden, everywhere I have just to educate myself that how the things are different. What are the fundamental differences? So that is something which we have to realize and not do the reforms. This is not the time for doing the reforms in the higher education system. It’s like reimagining. You see what we reimagine India in terms of digital India, we are getting the dividend. We are a country which is entirely on different level generating billions of transactions on the digital UPI system which was unheard.

So similarly we need a higher education system, we need a general education system which can give an exponential bump to India’s story and that’s not going to be the normal system. It’s going to be something very, very different and that is going to be based on the foundation of the technologies. We have been talking that this is the first time in the history of India though it has been tried several times in the past to link the north and south. Language barriers always existed. But AI dismantles the barrier. I was in my village. We set up AI lab. We set up AI shop. And my message to villagers, you can speak in your Bhojpuri to U .S., to Russia, to Japan.

So that is the first time a fundamental shift in connectivity is happening around the world. And India being a young nation, a country of young people, almost 44 million students in the higher education ecosystem, almost running parallel to China, we have that power and potential to change. And the moment we are able to use this technology, I’m sure that we will realize the potential. So I say in terms of potential, I say I am number one economy. India is number one economy, not third or fourth. So that’s the mindset. Because I have to reach to my potential. And I will reach the potential only when I know my potential, what is expected. So there is a huge responsibility of the Indians of the present generation, not only for themselves, but the Indians of 2100, Indians of 2050.

And if we are not able to capitalize, this AI boom will be left behind. If you see the geopolitics around the world, we say it’s a new war and all, but it’s the technology war, it’s the AI war. Countries are understanding that those who will dominate AI, they will dominate the world for the next century. So we have to love it. We have no option as a nation. And the education system, which is one of the biggest in the world, will have a very catalytic role in realizing that dream of India

Pankaj sir, as a head and dean, you have changed the curriculum of University of Delhi. You have also… Well, you know… you know introduce lot of skill -based course during your time and make it you know outcome oriented but the ai challenge is new uh you know and now as a chairperson of nct you also seeing the lot of diversity among the institutions from the jhabua to delhi and you know it’s a multi -layer system and as you know chairperson of nct how will you introduce kind of you know ensure institutions they can respond in the same manner to the challenge of ai because there are a lot of diversity in india and there is a lot of diversity you know about having those kind of resources because ai also need a lot of resources not in only in financial term but in the term of technology and kind of having electricity and other thing so how do you how will you ensure?

on the same topic. So AI can assist. AI cannot be a master. It is an assistant. If we use it for ethical reasoning, if we use it for creativity, collaboration, adaptability, I see teachers will increasingly function as mentors and learning designers, not learning followers, and ethical guides and facilitators of inquiry in a classroom situation, as well as in writing textbooks and developing curriculum. AI -based output demands AI supervision. AI supervision, I mean, AI cannot be left free to design any curriculum. We need to supervise it. When I say, we all know the difference between governance and leadership. Governance, I call, like, governance means compliance manager. If whatever is coming to you, you are implementing it.

You know? whether it is a college, university or any other organization. And if you are an academic leader, then you make a change in that compliance. Compliance will take place because governance is essential. But at the same time, you bring change according to the needs of your institution, needs of your students, needs of your financial resources, etc. Similarly, in education, we must not become AI followers. We should become AI leaders for the time. Yesterday, Honorable Prime Minister said we have tremendous potential to become AI leaders for the world. In those lines, as NCT Chairman, we have brought two new programs, NPST, National Professional Standards for Teachers, and NMM, National Mentoring Mission. Both are designed on a digital platform, on a digital world.

And AI is helping us analyzing people’s queries, their questions, their anxiety, and helping them. to identify right mentor for them. And mentor -mentee is always a guru -shishya context which is very meaningful and useful. I’ll close this remark by saying now we are moving away from treating technology as one of workshop. Rather than we should shift towards multi -semester AI spine. AI is spine of entire education system nowadays. And our new program ITEP have multiple context of AI based technology. We must transit from product only evaluation to process rich evidence of learning. That is more meaningful. In 2012 CBSC brought continuous comprehensive evaluation. Now AI is helping us to go for process rich evidence in learning.

Risk landscape is there. Bias, heliconations are there. But uneven access to technology is also a challenge that should be taken into consideration. My last closing remark is AI plus education can take us towards VIXIT BHAGAL 2047. AI is not a choice. It is a part of our life and providing us multiple new methods of research, new methods of industrial internship, but education which is providing culture, language and humanistic approach, both need to work hand in hand for better future for VIXIT BHAGAL 2047. Thank you.

Patil sir, as an Adjacent Secretary, School Education, you embedded technology and through technology you have been in our track not only Nipun but other platforms. Thank you. The focus of the government on learning outcomes has improved a lot. Now you are in higher education. And higher education is a very diverse sector. And at the same time, in contrast to school education, in higher education, you have more controlling power than a single person. School education is subject to some time in contract list. So that’s why. What is your vision now to transform those higher education institutions in the age of AI? Because the challenge of AI is constantly coming. Not only for the students, but as well as administrators as well.

And at that time, what are you planning? How will you address those issues?

Thank you, sir. Thank you so much for giving me the opportunity. I would like to ask a few of the… I think I’m seeing a lot of students here. Can somebody tell me how much time telephone took to reach to 5 crores? How much subscriber are users? Yes. any guesses 30 as a good guess anybody else quickly 50 years okay good some more yes yes somebody sitting right up the stable 75 years yes so it took five you grow people go the telephone my light we took 75 years it took 38 years to reach this radio took 38 years to reach to 5 crore people our charge EPT any guesses Gemini took for 60 days to do is to the 5 crore people whereas charge a pity to 40 days to restore to 5 or people so this is the I think there is a quantum jump or whatever you see It is a huge jump.

And with this, it is a big challenge for the educationists in both school and higher education. I can just read some figures for many of you that in world, we are having around, say, mobile users. In the world, there are 749 crore people, whereas in India, 120 crore people. Internet, 600 crore people. They are using it in India, it is 100 crore. In Google world, 580 people, 580 crore people are using Google, whereas in India, it is 80 crore. And charge APT, world, it is 80 crore. This is last month’s data, not this month. So around 7 crore people, they are using charge APT in India and 1 crore in Gemini. So around, maybe by this time, 10 crore people will be using charge APT in Gemini here.

Now the challenges, what are coming up, I will come to that. I am not pessimistic at all. But if you see. In the education ecosystem, Suresh sir also has told. and other speakers have just told. This is very important to see what is the cohort. Around 25 crore children are in the school education and 4 .6 crore students are in the higher education. So around 30 crore we can say. Now 15 lakh schools are there in India. And right now if you see the infrastructure around 4 crore, sorry 4 lakh schools only having the computers. ICT labs and tablets and other things. So it is a huge challenge to take the AI revolution to last mile. We are aware, as I told you I worked in school education, now in higher education.

So we are having integrated approach and we are working on that. But we need your help. Second one if you see in school education, around 1 crore teachers are there right now. And most of them are women. So which is really good change is happening there. But how many of you are in school education? many are AI savvy or AI literate, we are working on that and NCD chairman sir has already told on that, Pankaj sir has told on that. Now coming to the different digital divide, Delhi schools if you say and the remote area schools, the tribal areas or as you can see, madam is also from Bangalore, I last week went there, there is huge development so the cities, the way they are catching up AI is huge, humongous progress is there but rural area and other places it is a big challenge.

Central schools like KVS, NVS they are doing really good in catching up with AI, using the AI technologies, even CBS is coming with AI curriculum, whereas in the report also I have seen like Andhra, Assam, Tamil Nadu and few other states are using the AI curriculum and AI tools for implementation in the education system, whereas other states are using AI. to catch up. So there is little bit divide in this and it will take time for India to catch up. But yes all of us are now agreed that yes AI is not going anywhere. AI has to be used. AI is useful and same time AI is not enough. We should treat AI as a machine not as a human being which is very very important.

AI if you started taking as a human being then it will be problem. It will be huge mental stress on the students and other users also. So we are aware of this. That is why school education has taken very wise decision to introduce AI curriculum in third grade. It is not to teach the AI. It is to teach what is AI. What are the uses of AI and whether it is good or bad. So children should know about it which is very very important. So coming generation, coming of generation new generation, next generation must Learn AI because it is very, very useful. Yesterday, as Pankaj sir has told, the Prime Minister has told that AI, India has to become hub of AI.

And yesterday evening, yesterday full day, we had the meeting with Spain universities. Today, again, we are having the meeting with the Spain universities. Like that, a lot of meetings are going on, MOAs are happening. You may be knowing that IIT Madras has developed one tool where Dr. Kamakoti has spoken. It has spoken in Tamil and it has been translated in 11 languages of India. As Suresh sir was also telling that when you speak in Bhojpuri, it can get translated in others. So there is huge potential. I have seen from Siksha Lokam, they have shown me that again in Bihar, the villagers, the women, they are talking about dropouts. Why I got dropout? Why my daughter is getting dropout?

What are the issues? They are talking in the local language. And AI is actually summarizing. They are translating in English and various other languages. So they are talking and with that there is no typing, nothing else. It is getting summarized, classified and as an administrator we can take decisions. So AI is a boon if we are using it very properly and AI will become a ban if it is misused or unethically used. As sir was asking me for the challenges in AI, yes there are many challenges. What we are doing right now is updating the curriculum, we are doing educational governance which is coming up. But many IITs they brought AI schools in their campuses.

They are having MOUs with Google, Microsoft and various other places. Wadwani Foundation has also started one AI school in one of the IITs. A lot of investment is going on. We have already started AI CO in education and IIT Madras is hosting that. A lot of work going in that. Sarvam is also. He is also helping us in. those initiatives. But yes, there is parity, there is disparity. We need to sort out those issues. And AI is not only for the STEM that we understood and we are implementing that way. Everybody has to understand what is AI and how we can take it forward. As Suresh has told about economy, I think we both have worked previously in Ministry of Education and Ministry of Finance together.

I got his guidance there. So the way he has told, you can see it is, now we are talking about reimagining the education. So whatever you imagine, what is your vision, you are going to achieve that. So we should not limit our vision. I think 140 crore population and plus it is coming up. It is required to have really big vision, but same time necessity skills. Skills are required. And one of the reports suggests that if one year of schooling is happening, the 24 percent, there is output increase in the labor output actually. Labor can the output will increase by 24%. And in India we are having these certain issues. If you see what labor force is giving the output in US, what is given in South Africa and what is given in India, there is really we need to think about it.

So year of schooling is very very important. We are having challenges of dropouts also. Luckily Vidyasa Mishra Kendras and other tools we are using to trace the dropouts and bring them in the mainstreaming. You can also see around 5 crore children are dropped out. And various state governments are working on that to bring it down. So European Union few countries may be having this population of 5 crore. So challenges in India are more, much more but as Madam was also asking me what will be the impact of AI summit. I think it will be huge impact on us. Next two years we can see what will happen. the way India is going to change as again I can say one last example and come back when I was working in banking department people said there is something called payment through the mobiles and when I was discussing with our CMDs of the banks those days they were CMDs now it is MDs and they told me that no it is not going to work here and South Africa started there Airtel itself started it there and 2016 when DMO has come we can see the huge impact and now NPCI we can see the way it is happening around 50 % of digital transactions are happening from India world’s transactions there is huge change I think another two years we can see there is huge change in AI adaptability and using it but one caution is that AI has to be used as a tool it has to be used ethically and it has to be used for the humanity that is what I can say thank you so much and we are getting prepared for that sir as IITs are far better IIMs are far better whereas central universities are catching up with this EI and we are trying to help with them.

Thank you sir.

Thank you sir. I think that as you have brought everyone on one platform in school education similarly, the same way, in higher education institution, the same way, the scale and maybe the other institution’s scale will increase. We have also Aditi Nanda, Director of Education and Industry. Aditi, in India’s digital journey, I should say that whatever we have seen, lot of transformation in the last 20 years, there has been a lot of importance of the private sector. With the government institution and the education institution, Kali humne ek dekha ki Sharvamaiyai ne apna ek language model launch kiya. Aur usko kaafi hua. To Intel India ke educational journey se kaafi associate raha hai. As a part of the industry, how do you see as an opportunity and challenge?

Not for the only industry, but for the education sector as well. Thank you, Dr.

Namanan, and thank you for having me here. It’s been very interesting and it’s been a pleasure for me to listen to all the other panelists here. Got to learn quite a lot. And congratulations on the report. So very interesting and very pertinent point that you raise, that the industry also needs to work with different players, not just with the government, but also academia. and create a change. So I have a very interesting job. I work with the ecosystem and industry. And in that, I get to work with different startups, get to know different ISVs, and really see the innovation that’s happening. And some of these innovations are interesting to see because they are cutting edge.

They are coming from India, for India, and then they go for the world. Like you just mentioned, sir, Patil sir was just talking about, you know, the digital payment. And I think you were mentioning M -Pesa from an Airtel perspective. So how we have taken, you know, the UPI and other things, and we are taking this to the world. It’s a very proud moment. But it starts with an idea. And it starts with something that needs to be nurtured by everyone. If we have, and that’s what the AI Summit, it’s a great moment for all of us. We’ve put ourselves on the world map. We’ve shown the world that we can do great. And here is where the technology innovation is happening.

And from an Intel perspective, We work not just very closely with higher ed but also K -12 and of late we’ve been working with some start -ups to come up with solutions which impact the students at large. So I was talking to somebody the other day and I think Sreshtha was talking about, you know, Bhojpuri getting translated. So I was talking to somebody and said, why are learning outcomes in the Indian tier 2, tier 3 and rural areas not as great? You know, the response came ki bache ko maths or physics nahi samajh mein ata, yeh problem nahi hai. Bache ko English nahi samajh mein ata, yeh problem hai. Kyunki hamara teaching medium o bache ke language mein nahi hai.

And what we are doing today in terms of making sure that the content reaches everybody in the language that they understand. I think that is going to be a game changer. And that is coming from AI and AI is coming from a combination of people. Folks like all of us in the room coming together and saying, okay, let’s make something that will have an impact at population at large. so those are things and you know I was talking to you just before this, he said India mein aisa nahi hai ki people don’t want to buy technology they are not afraid of technology but the problem is and how many of us as parents will always say laptop nahi, bachcha ko laptop nahi dana, bachcha bigar jayega but why are we not seeing the value, why are we not seeing why are we not seeing that a creation device like a laptop or something that is more than a consumption device, where is the value creation in that, can we have AI courses, courses starting from class 3 onwards, going up to higher ed and we have in fact worked, my colleague of mine has worked very closely with CBSC to create a curriculum which has gone into schools right and we worked, Intel has worked together and helped put that together we have a program called Unnati for higher ed and now we are bringing in these courses which are AI for Future Workforce under that umbrella, which has courses like AI and manufacturing.

And we have put this out in Gujarat Technical University, and recently we had somebody come in from there. This girl was the first time, first generation to go to a college. She went through this program, and in this program we also had internship. So she had interned with a startup, sorry, with an industry in Surat that was doing basically textile manufacturing. And she created a project on defect detection using AI. So a kid from a rural area going to college for the first time as the first generation going to college, being so confident about what she had created because it was being used in an industry, and she could see the impact. I mean, those are the stories and those are the things that make you feel like you want to work in this.

The rewards are huge. I think that is what is needed, and Intel is doing, obviously, a great job. All those… bringing these things together and all the programs that we have, whether it’s Unnati, whether it’s Future for Workforce, whether it’s, you know, the stuff that we do in the K -12 space. We’ve got an ISV, a startup that we work with, which is helping teachers become, you know, AI -enabled. So creating, and there is, and it’s all running locally. The content doesn’t even need to go into the cloud. We have solutions running on AI PC, which is what Intel is now bringing to the market. And I would invite you all to please come visit our booth at, of course, AI Summit, because that’s what has brought us all here.

And we’ll show you some of the really cool use cases and demos where voice -to -voice gets translated on the device. So you don’t even need to connect to the internet. You don’t even need to connect to the cloud. Everything is happening on the device. The content is there. And I think I heard hallucination is one problem. That is… what you also, you know, in the report identified. What if the content sits locally on the device itself? So you’re only looking at class 9 science. So when a child asks about a question, maybe they’re just wanting to know how do I get into NEET and JEE, the answer’s coming from there. And it’s coming from a language, coming in a language that the child understands.

So what if that happens? And that exists today. We’ve worked on it. So think of it as a 24 -7 tutor. And one more thing, you know, I don’t know how many of you will relate to this, but at least I used to. When the teacher’s teaching, sab samaj mein aajata tha. But jab ghar jaake wohi concept padhu, toh ye kya hua? Ye kaha se gaayab ho gaya? Toh jab aisa hota hai, and if you’re an introverted child, who do you go and ask? And how do you create that, say, space of asking? You can have tuition teachers, you can have personalizers. But if there is a bot, that is not judging this child. And is saying, hey, come here, I’ll teach you in the language you understand.

and you know as a parent that this is all happening on the PC it is all safeguarded there is lesser chance of hallucination that is what we are working towards and I will finish with because there are all esteemed panelists I think I should finish with a quote Arthur C. Clarke said technology done right is like magic and if we bring that magic of technology plus AI to all kids in India I think we have done our job that’s what

thank you Aditi I think we have few minutes more and we can have just you know a quick round intervention just on the issue when we just try to reimagine institutions what are the two things that we can do in future of institutions and what are the two things that we can do in future of institutions We want to see or we do. Sir, if I may ask, what do you want to see in the future of higher education? What do you want to see?

Anamanand Ji, in the field of higher education, what are you talking about reimagining AI? I think, as Rohrat Ji said, we designed the entire curriculum on the dashboard. We have to make youth the part of the dashboard. The power of AI, which we have established in the national education policy, is that we have to do student -based education. Every classroom will have the same level of students. We have to force the assumption of massification of education. We have an opportunity to come out of this. And to lose this opportunity is a crime. It is a world crime. we shall have to come back to this individualization of education just taking advantage of my little longer journey in education Mr.

Patil said the schools may be penetration I just like to remind him when first time the computers were sent to the schools one had master complained to me sir government has given the computers so costly that was the stage from where we have come a long way and now we have reached a critical mass the journey is not going to stop the journey is going to be accelerated what we call the avalanche effect in physics that avalanche effect has come and to prevent it from being arrested this is our responsibility youth will take it forward individual responsibility which I am talking about and an international perspective he goes to the class the first day he says how many ties of size 10 cm by 10 cm I will need to fill a room of 1 m into 1 m in fact it is such a simple question everybody should answer it nobody raised a hand he was frustrated where have I come to teach if this is the level and I was told it is a good class very frustrated finally a girl raised hand said ok at least somebody she said yes come on we will work it together he says sir everything is fine but firstly tell us what is a tie see in that African area the tiles were never used.

They were used for round rooms with round floors and square tiles or rectangular tiles were not in the dictionary. And on that basis, we declare all that class failed in mathematics. That is what we are doing today with the help of simple tests. So we have to find out what is the ground level situation and then go ahead on that to test the ingenuity of that. Lastly, we have not to teach the subjects. We have to teach the students. And therefore, for each student, what can we do? Again, I say, AI is an opportunity, great opportunity. We are talking about reimagining higher education in this summit. And my request with all the persuasion is let the youth assert themselves that we need these subjects to be taught for our degree.

And technology enables us to do that. We will have to do that. That’s my call on this. Thank you.

Thank you, sir. Suresh sir, in the same manner, when you reimagine institutions and you are heading up, you know, you’re a part of a global body, what kind of future and what kind of, you know, I will say two or three things you want to see in the future, you know, futuristic education institution.

India has millions and trillions of problems in each and every corner. You pick up one problem, solve it. You get your degree and go. You don’t need to pass all the examination. So that’s the fundamental shift India needs. If we want to go back to what I said in the beginning, that we want to be a nation where skill, capability drives the economy, not the other way around. So that’s the second. The third one you see, the 12th education system, the higher education system, the primary education systems works in silos. We have to find and technology allow it to do it to interconnect the entire systems. And in the U .S., the higher education and the high school systems are very well connected in the part of ecosystem.

The moment we do that, we will have a thriving higher education, thriving education system, and pushing India into a very high growth trajectory. to realize the dream which I talk about, our number one nations, not by 2050, 2070, but very soon. Thank you.

Thank you, sir. Pankaj sir, as a chairperson of NCT, when you reimagine a teacher education institution or think about how a teacher education institution will be in the future, what are the two or three features that come to your mind that you think a future teacher education center should have?

Yes, as a regulator for teacher education, now Vixit Bharat Adhishthan is coming, where it has been proposed to go with AI -oriented regulator. That regulator is not supposed to have a lot of human working for it, but 70 to 80 % assessment will be done through AI. So, AI is going to play a an important role, not only as a regulator, but also as a norms and standards developer for the nation, for academic programs also and for teachers also. I think the responsibility to promote research ethics among young people is very, very critical at the moment. Somebody is writing a letter to his wife and asking AI to give me a letter. So this is ridiculous. It cannot give you emotion into that, personalized flavor to that.

So research ethics, when you are doing any research for any class level, then we need to think of assessment devices, evaluation and assessment, which is lacking behind. We are developing content through AI, but we are not doing assessment through AI. This year, CBSC is trying to assess class 12 answer script through technology, but those would be only scanned documents. We’ll check by teacher. from their own remote place. But that is the beginning of bringing technology into assessment. And my last point would be, Indian knowledge, Indian languages, we must start working very, very hard on this. Because if we actually want to pass on Indian tradition to the next generation, AI can become an important tool for that.

If we take AI out of Western knowledge, if we promote it in Indian knowledge, Indian context, Indian languages, then we will really help the next generation. And as the Prime Minister said, we have two AIs, Aspired India and Artificial Intelligence. So we must take both of them to optimum use. Thank you.

Thank you, sir. Patil sir, from the ministry perspective, how you visualize future universities, and what kind of change you want to bring higher education institutions? which we want to build for the future.

Again, same thing that Sir has told that it should be integrated. School and higher education, I would like to say that few universities have agreed to reach out to 100 schools. In Pune, there is a university called COEP. So they are telling that every day one school will come, visit, see their libraries, see their laboratories, meet their teachers. The teachers will go to the schools, they will interact. Because many of them are not knowing what is the present school. And what I was in the school and today’s school, there is huge change. Really huge change is there. So that has to be seen and it should be integrated. One more point that NEP says there is innate talent among the students.

So students should understand that and work on it, on your skills and meaningfully contribute to the economy which is very, very important. So once 140 crore population of India started contributing to the economy means above the income tax. level I am telling that pre -income tax level so minimum 5 lakhs or 6 lakhs it is going to be huge change here third point is brick mortar schools are going universities are going that is already we are seeing this huge change but same time teachers cannot be removed actually the teachers mentors facilitators has to be there and even we are requested even Intel we had last time meeting also with the companies to be mentor actually you should also tell kids enough is enough one hour up you are playing with the games or you are using this thing so stop it there which is really required so ethical use is very very important yes we need to create a platform where all of the people can come that is what EI COE in education happening with Madras IIT where schools and higher educations are coming together higher institutions are coming together private players also coming together so I think I recently seen one startup in IIT Delhi where they don’t like this hotel rooms and all that.

So he not want any hotel rooms at all, like that. These startups don’t have any classrooms, they don’t have any infrastructure at all. But they teach in medical education actually with this permission from the regulator. Paramedician basically are working it. Youngsters are here, lot of youngsters are here. Friends, their annual turnover is 200 crore in just last two years. They are telling another one year will reach 400 crore. So I think there is huge opportunity for all of us. We should work on it. Thank you so much.

Thank you, sir. Aditi, your comment on future of institution.

Sure, sir. I think everybody has done a great job. Job of articulating that. If we do this, everything will be done, I think. That is what I think.

Thank you everyone for joining us and thank you for our eminent panel to put light on reimagining the institutions and I think that what we are thinking about how the future institutions will be when we start thinking it will start to grow and thank you everyone

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.

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.

These are consequential challenges that have impacts in people’s lives on a day -to -day basis. And our session is going to address how global standards, collaborative public -private solutions, and rights -based approaches can enable responsible AI with meaningful real -world impact. And we know that these things don’t just happen on their own. It takes deliberation. It takes thought. It takes engagement to make sure that the products and approaches that we’re using in the AI field avoid some of the pitfalls that may be associated with them. And the companies are going to share some of the good practices that they’re engaging in about how that works in the real world. And we know if they don’t engage in that way, that the risks are there and very much present.

And so looking at how we can put in place practical safeguards that ensure that AI works for people, not only in advanced economies or for the dominant platforms, but for the people that we’re trying to deliver these benefits for. Responsible and effective AI governance and clarity of rules for both companies and government. and alignment around the global norms will help us to get to that point. Companies, of course, have a responsibility to respect human rights and address the risk to people stemming from their products. And human rights due diligence, of course, is one of the process -based ways and a pragmatic way to weave this into corporate operations. But, of course, governments are the ones that also have a responsibility here, too, to create a level playing field, and we talk a lot about that.

We want the incentives for companies to be there so that the ones that are engaging responsibly are actually rewarded for that responsible engagement as well. Our BTEC project at OHCHR is aimed at how do we make this conversation happen. So through convenings like this one, through engaging with companies, pulling out their good practices and letting all of you hear about them and encouraging others to do the same is what that project is really about. And we are really looking at and working with, of course, how to use tools like the UN Guidelines. We’re working with the United Nations, the United Nations, and the United Nations to try to get the best out of the work that we’re doing.

And we’re working with the United Nations to try to get the best out of the work that we’re doing. So we’re working with the United Nations to try to get the best out of the work that we’re doing. And we’re working with the United Nations to try to get the best out of the work that we’re doing. And we’re working with the United Nations to try to get the best out of the work that we’re doing. And we’re working with the United Nations to try to get the best out of the work that we’re doing. UNESCO’s AI recommendations on ethics and figuring out how we weave those into the decisions and work that’s being done now.

And as I said, bringing this conversation to this summit where there is truly a global and multi -stakeholder effort is happening to really look at AI innovation and deployment has been incredibly important. So without further ado on that front, I want to hand over to my colleague and co -sponsor here, Tim, over to you.

Thanks, Peggy. And good morning, everyone. Ambassador Reintesma from Estonia and co -chair of the AI Dialogue that the United Nations is holding. Of course, Peggy and dear panelists, it’s really wonderful to be here with you today to be part of this conversation on responsible practices and industry standards. And as we all know now where AI is moving, you know, from something we discuss in theory to really something that is shaping the decisions in real time and real institutions. and of course for real people. I’d like to thank particularly the Office of the High Commission for inviting us to join in on this and for working with us on organising this event. It’s been a pleasure.

At UNESCO we often return to a simple idea that trust is not something technology earns through ambition alone but really it is earned through design choices, through safeguards and accountability and that’s why the recommendation on the ethics of AI we believe is so important because it does give the world a shared foundation, a first step on how AI could be built and used in ways that protect people’s rights, that promote fairness and support inclusion. So we’ve been translating this global agreement and framework into local realities and through what we call the RAMS, the Readiness Assessment Methodology Reports which we’ve now launched in around over 80 countries and just two days we did India’s readiness assessment report.

And these assessments provide a kind of clear -eyed look at how regional landscapes can evolve, inviting us to move beyond theory and towards this responsible human -centred deployment of AI we hear about. And so by grounding innovation in these evidence -based diagnostics, we hope to ensure that progress remains aligned with those shared values. But, of course, a recommendation only matters if it can be applied by people who are actually catering, creating and using AI. And so that’s the purpose of the initiative I’m going to introduce today, and I’m very happy to say that UNESCO, in partnership with LG AI Research, is developing a global massive open online course, or a MOOC, as more commonly known, on the ethics of artificial intelligence.

And the course will be delivered on Coursera with a very clear goal, to make AI ethics learning accessible to a wide global audience, and to make a practical… for day -to -day work. And so, as I mentioned earlier, the key idea behind the MOOC is ethics by design. And so in simple terms, that we don’t wait until something goes wrong to ask these ethical questions. We should build these questions into the process from the beginning. And the course will help learners think through issues like fairness, transparency, safety, accountability and inclusion at the stage when decisions are still being made rather than after systems have already been deployed. The course, of course, is really going to be focusing on practical tools so that we can offer clear ways of thinking and working that can be used in everyday settings.

So it’ll help learners, for example, recognise common risks early, ask better questions during development, document the decisions made responsibly and think through the impact of AI systems on different groups of people. we’re moving beyond a one size fits all approach and we’ve done this by collaborating with experts from over 10 countries and 5 continents with some of the leading minds from the University of Oxford and the Alan Turing Institute and this global group, this global coalition is really vital because AI of course doesn’t operate in a vacuum it’s shaped by languages, it’s shaped by cultural norms and institutional capacities and of where it is developed and deployed so by integrating these diverse perspectives we’re trying to move from the theory again to the live reality so ultimately this MOOC is a capacity building effort with a simple purpose to help more people around the world build and use AI in ways that are responsible, inclusive and worthy of public trust we look forward of course to this continued collaboration with governments, with industry, with academia and civil society as we try to move forward we take it forward and we hope many of you will engage with the course when it launches, not only as learners, but also as partners in building a stronger culture of ethical innovation across the world.

Thank you very much.

Thanks, Tim Curtis, UNESCO. We’re all looking forward to it. Now we have anticipation. We’re very fortunate to have an addition to our program today with Ambassador Tomsar, the permanent representative of Estonia, who’s one of the co -facilitators for the Global Dialogue on AI that will be launched in July, and a big responsibility. And he’s here to tell us a little bit about where it’s heading and how you all can contribute. Please, Ambassador.

Thank you very much. Good morning. I don’t know, is it morning? Yeah, maybe. So after three days here in India, I think that I lost time. I’m not track of understanding. Is it morning or evening? But thank you, UNESCO and Office of the High Commissioner for Human Rights. for convening this really important discussion, and of course to all our hosts here in India. And I also thank partners who contributed to this work. Today I’ll speak on behalf of two co -chairs of United Nations Global Dialogue on AI Governance, and two co -chairs are from Salvador and Estonia. The first Global Dialogue on AI Governance was mandated by all member states through a General Assembly resolution adopted in August 25.

So this is a member states driven process. It belongs to every country, to all member states. And its task is very practical, while its scope is multilateral. So this… The aim is, you know, to come together. It is a platform where governments and stakeholders exchange best practices and experiences, and this, we believe, can strengthen international cooperation on AI governance and ensure human -centric AI supports sustainable development and reduce, indeed, digital divides that are already there. So we’ve engaged with member states and different stakeholders about their priorities, and let me bring to your attention four points from these priorities. So first, they want safe, secure, and trustworthy AI systems, and the trust here, of course, is an absolute key word.

Second, they want to close capacity gaps. Many developing countries need infrastructure, skills, and compute to participate fully in AI economy, and inclusivity and equal access are essential here. Third, they want governance approaches that can work across borders. and be practical. So fragmentation raises the cost and weakens trust. So interoperability is absolutely key. And fourth, and that is, I think, quite actual here, they want AI anchored in human rights and international law. And this includes protecting vulnerable groups, addressing bias and discrimination, and ensuring oversight and accountability. Now, we know human rights are not optional. They are part of a mandate agreed by member states. And today’s focus on responsible practices and industry standards responds directly to these priorities.

And standards turn principles into action. They shape risk management, they clarify accountability, they guide human oversight, and they give companies and regulators tools they can apply in real systems. so let me say that the global dialogue will not and I guess it cannot impose one single model we will listen we will identify common ground we will build on existing initiatives ethics of AI was mentioned here and it’s of course one of them we’ll avoid or try to avoid duplication and we will focus on practical value so I encourage you to bring your experience into this process share what works share what doesn’t work help us identify approaches that can scale across regions and level of capacity and in best case if we succeed and failure is not an option safety and trust will be visible in how systems are designed deployed and governed they will be reflected in real safeguards and in benefits that reach more people and this is very important for us thank you So I thank you and wish you a productive day, practical exchanges that move our common work forward.

And with this, I give it over to the real experts and panel. Thank you very much.

Thank you, Ambassador. Wonderful to have you with us, and I think we’re all looking forward. We’re looking forward to having all of you join us in Geneva in July. So with that introduction by the three of us, we’re really, as the Ambassador said, going to turn it over to those who can really inform us about how this work is happening and I hope inspire us to both give support and emphasis, amplification to the work that you’re doing and bring more into the fold around responsible business conduct. So with that introduction, I’d really like to start, Ankit Bose, with you from NASSCOM. We had a great conversation yesterday. I’d love for you to inform our audience that NASSCOM represents the leading Indian tech industries and we want to hear more about your work and what you’re doing to encourage companies and help them to ensure a responsible work environment.

Thank you.

Thank you so much for having me here. And it’s my pleasure here to address the audience. So NASCOM has been there for almost four decades plus, right? We have been helping the tech industry in the country to shape and change the whole agenda for the country. I think that’s what we have been doing, specifically on responsible AI interests, right? I think the mission for NASCOM started in 2021. So we started with a gap that, you know, we were seeing a lot of AI was getting developed. But again, I think we found that there was some missing element that was the responsible, the trust, the human element. I think that that is how the mission started. From that point in time, our main core objective has been to develop open assets, right?

Build capacity, build, you know, adoption, right? And I think help all the different components in the ecosystem, right? Right from the government to the startup, the SMEs, right? All of them. So we have been trying to help them. And we’re trying to help them go up the ladder. and then really become aware, I think, not only the gloomy side of AI, but also the bright side if they adopt responsible AI governance practices right at the early. They can have a big upside. I think that’s what we have been doing.

Can I ask, Ankit, how does this work? You mentioned that full range of companies that are involved, and one of the topics we spoke a bit about yesterday is the difficulties sometimes when you have big companies, we have some of them represented here, but also startups and small and medium enterprises. How do you differentiate? How do you make sure that we’re engaging across that very differentiated group of industry?

Yeah, so I think if I take it, I think there’s the big techs, right? Then there are the services companies. Then there are the middle -sized, small, and startup. I think all five of them have different sort of engagement, right? The big tech, I think, are playing at the front foot, right? The services companies have to follow their contracts, right? The bigger services companies. The medium tier companies, they are really trying to understand how they grow their AI base at the same time build, you know, that services or product using right governance principles. But again, I think the bigger support is needed from the, you know, the smaller startup, right? Because they are really, really fighting for day to day, right?

I think, and believe me, I think a startup founder has to first build a business, a tech, a team, right? And also get funding and apparently focus on, you know, a lot of things around governance, right? I think in that whole journey that we have seen, they are putting it at a second or probably the side burner, which is something which we see is a complete no -no. If you do that, you know, when you’re building a product, right, you might miss when you’re scaling. I think that’s what we are.

Great. Thanks very much. I think we’re going to turn to the scale side of it now with Alex, you’re next in line. So, Alex Walden, you’ve been working on these issues within Google, and I think one of the insights that I’ve learned from you over the time we’ve known each other is really how complex it is to bring to product teams and those that are on the technologist side some of these issues of responsible business conduct and human rights, and give us the benefit of your wisdom about how that works and how we can do it better.

Thanks for the question, and I love that you said that because I do see a very important part of my role as making sure that the stakeholders that we work with understand how things are working within companies because that helps us be better and you be better advocates for helping us improve. So, anyway, but to your question, because I know I’m going to be fast, I think where it really starts for us is from the values perspective. Obviously, we’re a company that’s founded on values around freedom of expression and privacy and bringing the benefit of our technology to everyone, and so that is where… That’s where it begins. But obviously, we have things like… Like, ultimately, it’s the sort of governance inside of the company that is what permeates throughout the 180 ,000 people that work at Google to ensure that we are being responsible in the way that we’re developing AI.

So as a baseline for us, responsibility and thinking about what responsibility means has to start with human rights, and then we can build from there. So we have a corporate policy that says that we have a commitment to respect the UN guiding principles on business and human rights. And we’ve also built on that with things like our AI principles that reinforce sort of more of an operational way in which we can manifest those values in all of the teams that are working to develop the various models or applications of the models in, say, Google Cloud or YouTube or Search. Just to maybe hone in a little bit on the types of standards that we’re using, because I think that’s important because there’s so much work being done in our ecosystem.

We use the UN guiding principles. We use the work happening. We use the work happening at the OECD, the work at UNESCO. engagement with our peers in industry through the BTEC project, through global network initiative, and this is just a few, but all of the guidance that comes out of those places and the dialogue that happens there helps us ultimately inform how things are working inside the company. And then just one layer down, then I’ll stop. I think having programs and processes like training and dedicated teams, ultimately that’s how you operationalize this through getting a product to market. And so I can say more, but I think those are kind of the big picture structures for what’s required for a company to do this at scale.

So, you know, I’m not going to let you off the hook quite that easily. So we know that this isn’t always easy, though, right, that there are obstacles to really convincing people it’s worth the time. I’ve been in the room where hand -wringing is described as the, you know, no more hand -wringing about safety. We’re going to, we need to just move forward, and I’m sure there are pressures. that you face as the lead for human rights within this company trying to get your message heard. Tell me a bit about how you’ve been able to sometimes surmount some of those necessary challenges that you might see from that different perspective on whether or not these are hurdles or supports for the company to do its mission more effectively.

Well, I mean, I think in general we have sort of corporations are incentivized to put products on market that are safe and that are trusted by our consumers. People know Google best through Google Search or Gmail, the varieties of consumer -facing ways they’re engaging with our products. And so we do have an inherent sort of market business reason to put out products that people trust and deliver good outcomes. And so we have to have processes inside that… that make that real. And so what we do is we have model requirements just at the most granular level. Before any product goes to market, there are model requirements. And so those teams are focused on ensuring that they’re validating the data and doing testing and doing evaluations.

And that’s at the model level. And then at the application layer, we have requirements for teams to be, again, doing testing, additional evaluations, setting additional guardrails, and focusing on what mitigations are going to be put in place for, again, things like Gemini before that gets launched. And then we have to have executives review these things. So before anything goes to market, leadership needs to understand what the risks are and how we’re mitigating them and have a plan in place to address that. So that is an important part of the process for us. And then last, we have post -launch monitoring, because obviously we can do all the testing in the world, but once you’ve launched a product, you have to be able to do all the testing.

there may be novel or new or residual risks that arise. And so we have to have a process for continuing to monitor that, understanding it, getting feedback, improving and improving

Great. That’s super helpful, Alex, to understand that multilayered approach that needs to happen within companies, including, I think, that executive level that you mentioned. I mean, the signals from on top will actually inspire all of those other levels to do what we’re hoping they’ll do. And we have another example with us of some of these practices. I want to turn to Hector Duroir, who’s the director of responsible AI public policy at Microsoft. And we want to hear more about what you’re doing to embed responsible policy practices within Microsoft’s approach.

Thank you very much, Peggy, and thanks for having Microsoft here. So, yeah, I want to start with the inception of our responsible AI approach, which was in 2018. And at that stage, you didn’t have codes, directives, regulations. Frameworks guiding our approach. We’re nearly starting from a blank page. And we didn’t talk about foundational models or frontier models at that stage. It was all about specific AI system and applications, such as facial recognition, for instance, which was very popular. So we forged our AI principles around priorities such as privacy, such as reliability, inclusion, fairness, safety, security. And these high -level principles, the whole challenge was to translate them into practice afterwards. And so it’s really on this basis that we forged the Office of Responsible AI when we created it in 2019, around these principles, which then became our RAI standard, guiding all our actions across our different programs.

One of the programs that I want to reference here is our Sensitive Use Case program. So it’s a team within the Office of Responsible AI that is in charge of doing some triage, challenging basically sensitive use case coming from our different markets. on AI systems and models that could actually violate these principles that I was referencing. And so this team analyzed these use cases and then when it occurs that it’s necessary, bring them to our ITER committee, which is our AI ethics committee. And it involved Microsoft Board, both at the CTO level and the present level. And I think the board inclusion is very important in this kind of internal risk management framework. And so this work has been informed during the past years by many interesting developments.

So the OECD AI principles, obviously, but also the UNESCO recommendation on AI ethics. And I think all these principled approach that evolve or refine nuanced with AI capabilities are actually so important and very useful signals for us to refine our own AI governance program within Microsoft.

Hector, you’ve talked a little bit about how you look at it from an internal perspective. But we wanted to hear a bit of how you look externally, like what are the drivers between how you engage. across the sector and with the government side as well.

Yeah, and I think we always navigate this very interesting interplay between best practices and international norms and regulatory standards. And a very good example here is the line of voluntary commitments that have been signed across the AI summits. And so if you look at Letchley Park in the UK or the South Korea summit that happened afterwards, it really helped us, as Alex was referencing, to ground our model testing approach, especially against public safety and national security risks. So when we talk about cybersecurity, for instance, or loss of control, or CBRN risks, that really grounded some very solid testing approach with some concrete operational triggers, concrete high -risk domain that we’re monitoring at the model level.

So that was one. The OECD hyper -reporting framework, which came out of the Hiroshima AI process, is another very good tool that I was involved in and I want to reference here. It was launched along the lines of the Paris AI Summit. And actually, it’s a very good way to understand how risk management transparency works in practice and how real -world deployment experience and transparency experience can guide upstream developments. And so it’s this kind of feedback loop that it creates that’s very interesting. And because we’re in Delhi, just to reference the voluntary commitments that were signed yesterday, I think that’s another very, very, very good and positive approach that the Indian government have been taking, especially on one of the commitments which basically encourages company to forge multilingual capabilities approach.

So basically build better evaluations against safety risk, not only in English norms, but beyond English norms. And I think that speaks about, you know, our principle of inclusion. That’s so important, and I’m very happy that they initiated this work.

I have to say, one of the contrasts I’ve been making when I look at what’s been talked about here in Delhi as opposed to… prior summits is that issue of inclusion. And the language issue, I think, is so underrepresented in some of the conversations we have. So it’s wonderful that you’ve given that a shout out. We’re very fortunate, Yuchil, to have you with us as well. Yuchil Kim, who is the vice president at LG for AI research. So we’d really like to hear more about how you’re engaging with these global technical and policy standards. We talked about the UN Guiding Principles on Business and Human Rights, the UNESCO recommendation on AI ethics, and, of course, the MOOC that’s being worked on.

So give us a sense of how these frameworks are being engaged with by LG.

So the essential of MOOC is for the practitioner The practitioner usually is struggling with the same question How do I actually apply this in my day -to -day work? So we are focusing on the bridge in the gap So we provide the best standard risk So we get a lot of risk that Timothy mentioned So we also contribute to our own experience So I previously mentioned about our process And we made also our AI -powered data compliance system And also I will mention soon We have an annual report about AI ethics activities So I hope the MOOC can be a good practice for everyone It will launch in this half So last one is I want to talk about transparency So we have a lot of activities about the AI Responsible AI Inclusive AI So we published our annual accountability report on AI So yesterday we released the third edition.

So here are some some track of that. I will spread out after our session. So please refer my documents.

Well yeah. Wonderful. No I think it’s super interesting to understand both how you’ve been looking at that learning process within the company but also how that more global approach working with UNESCO is going to be very helpful and I think it’s one of those areas where we all know so much more needs needs to happen. But we’ve we’ve heard the the company perspective here and we’re very fortunate to have with us from the World Benchmarking Alliance, Namit Agarwal. And Namit, you know, I think one of the things that we’ve talked about is how we incentivize the race to the top amongst all of these actors in this space. And you’re going to, I hope, give us some some insights based on the the work that the World Benchmarking Alliance is doing about how capital and investment can be used to make sure that innovation is being approached in a responsible way.

Over to you, Nami.

Thanks for having me here. And I’m not representing investors, but we do work with several stakeholders, including investors, civil society, governments, and companies. So we are a nonprofit, and we try to strengthen accountability of the world’s most influential companies so that their impact on people and planet can be sustainable. We also assess the world’s most influential tech companies on whether they are advancing a trustworthy, rights -respecting, and inclusive digital future using standards such as the UN Guiding Principles, but also others that were mentioned by my fellow panelists here. Our role is to provide comparable, credible, and standardized data that our stakeholders can use because of the challenges that we face. Because it’s an ecosystem approach, so how can they work together in doing that?

So capital can definitely incentivize innovation and responsibility, but capital alone cannot do that. We published our latest assessments of 2 ,000 companies at Davos last month, and particularly from the tech side, what we found is close to 40 % of the companies have disclosures on AI principles, but just above 10 % meet the expectations, global expectations on the governance aspect of it, and none of these 200 companies that we assess disclose their reports on human rights impact assessment. And I think that clearly shows that while there is a lot of intent, some work is happening, but governance and accountability are not really there, so we need a lot of work to happen there. And we believe responsible innovation requires incentives for long -term risk management, clear expectations that are tied to capital.

So I think that the way we’re going to do this is to look at the market, and then we’re going to look at the market as a whole, and then we’re going to look at the market as a whole, and then we’re going to look at the market as a whole, and then we’re going to look at the market as a whole, and then we’re going to look at the market as a whole, and then we’re going to look at the market as a whole, and then we’re going to look at the market as a whole, and then we’re going to look at the market as a whole, and consequences for weak governance because it has to be consequential for companies to move in that direction.

And I think that is where investors have a very catalytic role to play. We convene a coalition of investors and civil society organizations

Now, I mean, I think it’s so interesting that we work in a sector that is incredibly based on data, but yet we don’t necessarily bring it into this conversation in the ways that we need to, and that idea of both incentivizing the right practices and leverage within companies, but also, you know, too many conversations sort of focus on the tech industry as a whole and sort of group everybody together as if they’re all engaging in the same way. And so the work that you’re doing really helps us to understand those nuances more. Could you go a bit deeper and look a bit at some of the examples and concrete suggestions coming out of your work as to how to push that discussion forward more?

Absolutely. So I think the first thing is engagement and dialogue, and I think that is a very important way. And we have been fortunate to have good engagement with both Google and Microsoft on this panel. but again it’s important to build on engagement because it’s a continuous process it’s important for investors to engage with some of the leaders but also engage with companies who are fence -sitters to bring them along faster the laggard will definitely catch up and come on board but for investors and if you want to for capital and finance to incentivize responsible innovation, responsible AI there are three things that we believe investors should definitely do first is on AI governance and board oversight investors should ask whether there is clear board level responsibility on AI risk whether executive incentives are aligned with long term human rights risk mitigation and whether governance applies across the full AI value chain second is on implementation at product and business model level and we heard some examples just now investors need to move beyond policy statements and ask companies how ethical principles are translated into product level strategy How high -risk use cases are identified and whether there are internal mechanisms and controls to, you know, identify harms as they emerge.

And third is robust human rights impact assessments and asking whether companies conduct AI -specific impact assessments. Are they publishing meaningful summaries? Are mitigation measures integrated into product cycles? And I think this is an area where we have seen a lot of gaps.

Great. Thanks, Namek. I wonder if we could actually take that one step further and get some input from the other members of the panel on what that looks like in practice. Because, of course, this is a panel that’s focused sort of on the company perspective. I think we have some of our real partners here on the civil society side. And as much as they understand that that conversation needs to happen, I think they sometimes find it difficult to be able to make sure that the way those risks are assessed really looks like bring in the voices, bring in the experiences of people, and particularly people in different contexts and different environments in which the companies are being, their products are being rolled out.

So those issues of stakeholder engagement, access, dialogue with the civil society side, it would be great to hear a little bit more about some of the lessons that you’ve all learned there. And I see you shaking your head. Please tell us from the NASSCOM perspective how you look at it.

Well, I think from an enterprise lens, right, I think when they are trying to implement responsible AI or trustworthy AI, right, I think the biggest issue is there are different groups internally, right, the tech group, the business group, the legal risk group, right, the finance group, right. And then all of them are working in silos, what we feel like, because the business want the best, you know, for the business, the tech wants to put the best technology, right. The risk is very, you know. Right. conservative, right? And finance always has upper limit on what they want to spend. So that’s what issue. I think what helps is if all of them build a collaboration which can be taken use case by use case.

I mean, the high impact use case can have more investment, more focus versus a low risk, right? I think that’s the first thing. The second thing is I think what from NASCOM what we are seeing, there’s a lot of frameworks which are getting developed, right? Every country, every place you go, there’s a new framework, right? But from the framework heavy or the concept heavy to action is not happening. I think that’s a big gap, right? So if a technologist is trying to implement responsible governance, right? A developer is trying to implement, right? He will be lost in the framework. He doesn’t know what’s actionable. I think what he should do. So I think that’s one big need.

I think that’s what we are also driving. We are trying to drive a multi, you know, multi -different organization -led approach where we have all different sizes of organizations, where we come together and start discussing, collaborating, implementing, right? I think that’s the second nugget. I think these are two points. I know time is up.

No, that’s great. I mean, I think it shows that that collaborative effort is going to be super important rather than a siloed approach for so many practical reasons as well, that companies can only respond to so many different frameworks. And what they need to do is have the simple guidance and support that they need to actually implement at this stage. Headquarter, do you want to say quick comments from the company side about how you’re facing those challenges?

Yeah, two very quick examples on how we involve civil society and academia in this process. So our work really sits at the intersection of policy, research, and engineering groups. And to inform product development with our responsible AI principles, we regularly publish some internal policies. And it’s an iterative process with our research teams, with our product teams. And as part of this process, we actually include academics who have a specific REST domain expertise or think tanks and civil society organizations which have been thinking very deep about the deployment of 1AI system, 1AI model in certain contexts. And so that really informs the product that we do from the inception. And I think the second example that was raised is the big topic and the governance challenge that we face is the importance of refining AI evaluations.

That’s the constant thing. And in India, for instance, we’ve been working with some NGOs around a project named Samishka to build some community -led benchmarks, which is basically a safety tool that we include afterwards in the system construction, to really get data sets that are grounded in a community with a specific… cultural aspects, specific contextual aspects, because if you just translate safety tools from English to another language, you lose all the context for which this safety tool has been built. And so that’s another example of really an area where we need more cooperation between civil society and governments and companies. It’s really how do we build these safety tools beyond English norms such as an India.

That’s great, and it takes work to do that, and the more we can spread, you’ve done some of the work, you know how to do some of it, and diffuse it amongst other companies that could learn from it. That’s part of what we’re trying to do with BTEC, but I think there’s a lot more to be done. Yuchil, do you want to come in?

Yes, I agree with his comment. The safety, we should work together. So that’s the reason why we make our annual report, because sharing our best practice and also sharing our struggles, what we had a struggle of, that we think that’s a very important thing. So this is my colleague mentioned about it. There’s an African proverb that said, if we want to go fast, go alone. If we want to go far, go together. So building a trustworthy and safe ecosystem is not a sprint. So it’s a long journey, so we can go together.

It’s a long journey with a lot of sprints happening day to day, as far as I can tell. Some of them here at the summit, but over to you, Allie.

So much sprinting. I think maybe just to pick up specifically on the stakeholder engagement piece. So a few things. One, I think it’s important for companies to have a programmatic approach to stakeholder engagement, so we need to have ways in which we’re regularly engaging with stakeholders in general, not just on a specific product question. But so I would say first a programmatic approach, and then second, something that is more ad hoc. So when we need to consult specifically on a product, we need to have a sort of process and way to do that. The other thing is we have programs internally like trusted tester programs where we are working with third -party organizations to make sure that they have early access or pre -launch access to models or to a product in order to test it so that we can identify potential risks or errors ahead of time and address them before we launch a product.

And then last, just to highlight something that we do is similar to others, our research team called Impact Lab, which is part of the overall human rights programmatic work at the company, engages directly with communities in doing research to inform how we are improving our products and what we’re developing. So that work is also happening through the research team specifically. And they recently launched something called the Amplify Initiative, which is an open -source app. This is specifically on language inclusion that allows… members of the public and communities to engage in the fine -tuning work around our language models. So specifically that there is a lot of, there’s a wealth of information and expertise out there that we should all be benefiting from, that we can benefit from, and it’s open source so we can also share it with others in industry.

That’s great to hear, and I’m sure more needs to be done on that front, but the amplification effect is so crucial. Look, we could probably go on talking all day, but I see the clock is ticking down. Now, fortunately, rather than us try to draw the conclusions from this, we’ve welcomed in another speaker to give us some concluding remarks to pull some of these pieces together. I’m very happy to invite Parvati Adani from Sero Amarchan Mangaldas to help us think through some of these issues. Please.

Thank you for that. I think it’s partly easy and partly tough because there was a lot to understand, but I think we can go back from this. conversation. But firstly, thank you. You’ve held the conversation beautifully. And thank you to UNESCO, United Nations, NASSCOM, and everybody in this room who brought their knowledge, their conscience to this conversation. Actually, I just want to talk a little bit about a conversation with a machine. As we were thinking about this topic, and we were engaging on this issue, I wanted to share something that I might, I feel resonates with a lot of what you’ve talked about. I did something in preparation, I decided to ask the tools that we’re talking about over here, a question that we avoid asking ourselves.

Do you, I’m talking to the tool, do you have ethical limits? Do you understand the difference between what it can do and what it should do? And I’m going to quote verbatim. On conscious, at a conscious level, the answer is I don’t know. And neither does anybody. Nobody else. The gap is a philosophical, uncomfortable position where think of me as having no home inside. I have no continuous thread of existence and I cannot verify about myself what you have asked me. I don’t have any consequences to bear. Now, what came back, though unexpectedly thoughtful, showed us about restraints, values and what it appears to have internalized. It acknowledged the difference between instruction and conscience, a lot of what we’ve talked about today.

And so, I think when we talk about this, we said human rights are not optional. We cannot ignore the impact on people and planet. we have to make incentives for good governance so when a tool cannot understand this for itself I think we have to do the job what we have chosen in India and when we are having this conversation this location is not ceremonial, it’s very deliberate that we have thought about innovation over restraint and we have to think about that being the right choice we allow innovation to be in a safe place without feeling the weight of the regulation and I think we have a lot to learn from all of you who have been doing this for so long the privacy, the safety, the impact on children and vulnerable groups the question is whether the people that we are talking about are going to be the subjects of the transformation or just its audience or just the object An AI system that cannot understand a language or a Hindi woman speaking legal questions is serving a narrow slice of what it calls a universal solution.

So any framework for safe and trusted AI that does not express and understand informality, language, and gender is not incomplete by accident. It’s incomplete by design. I think the ideas of an interoperable, flexible system is a forward -looking and an inclusive one. I think a lot of what you mentioned, Alex, about governance inside the company, it’s wonderful what you’re talking about. And I think the voluntary commitments that have been reflected in this summit is also fantastic. So now we come to the harder work. The ambition is real. The infrastructure exists. But ensuring that… We don’t leave with just good intentions and good ideas, but action. Thank you.

Thank you, Praveen and Dhani. It’s wonderful to hear those perspectives. We’re coming to the close of this session. Just a few parting words to all of you. I think we’ve done enough in this short conversation to really give a sense of how complex some of these issues are, the dynamics within companies and externally and then globally across different geographies, the challenges that are faced. But the reality is that all of us have a responsibility to engage on these, and we each have different roles. We’ve heard a bit about what some of the companies are doing. We’ve heard a little bit about how we can challenge them and incentivize the actions that they take in the space.

There are good practices, but they’re not universally applied. They’re not available to some companies. There are companies that may want to engage in this, and we can help them to do this. NASSCOM and I have been… We’ve been discussing a little bit about how we make, simplify things, bring in more into the fold of this conversation. And, of course, we’re here in an environment where we have governments that are looking at what do we need to do to create responsible business practices and incentivize them as well. So I hope everybody walks out of the room thinking, what can I do to continue this conversation? How can I differentiate between companies that are thinking about these issues in a way that will deliver for myself, for my children, for my future the ways that we want to see?

AI innovation will work if there’s trust and if the companies that are delivering it actually invest in delivering products that will really give those values, that will inform and give us human dignity going forward in the future. So thank you all so much for joining us. Thank you for fitting this into your schedule today, and enjoy the rest of the summit. Thank you. Thank you. Thank you. Thank you. Thank you.