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00:00networks to generate new information.
00:03We don't get gibberish.
00:05We don't get gibberish.
00:07Especially under conditions of extreme events,
00:12climate change events, hazards and so on,
00:15we want to make sure that it's not sort of,
00:18because it's an unknown event,
00:20it doesn't sort of start manufacturing new information.
00:23So it's based on existing information.
00:25And when it doesn't know, it will say it doesn't know.
00:28There's limited information.
00:29But there are also monitoring and feedback mechanisms
00:32where we have human agents.
00:34So computational agents and human agents
00:36will sort of use the information together.
00:39So in a pilot case that we are planning to do,
00:41we want to sort of try and sort of test this framework
00:45because we want to make sure that there is
00:47repeated robust results coming out.
00:50It is AI.
00:51And then when users interface with it,
00:54they will use generative AI tools
00:56because that will help with natural language processing
00:59and things like that,
01:00so that people can use, ask questions in ordinary...
01:03No, are not in a position to do.
01:06Historical knowledge, everything you are putting there.
01:09Yeah.
01:10AI tools you are putting here.
01:13Real-time data feedback you are getting from...
01:17The field.
01:18The field.
01:19Yeah.
01:20Through data feedback collection, through manual,
01:25then reporting challenges, reporting requirements,
01:28geotagged local conditions, everything you are doing.
01:31Yeah.
01:32Even through cell phone.
01:35Yes.
01:36Photos, all these things.
01:37Yes.
01:38Then you can capture everything
01:40and at the same time you can give the outcome
01:44or write a solution for the problem.
01:48That is a hope, sir.
01:49That is a hope.
01:50But we want to proceed very cautiously
01:52because there is so much of an ability
01:55to sort of move too fast with generative AI tools.
01:58Right now you can go to chartGPT,
02:00you can go to various other LLM tools
02:02and ask questions about Andhra Pradesh natural farming.
02:06That we will ask,
02:07but only problem is here,
02:10real data, dynamic data, if you feed,
02:15then find a solution through AI.
02:18Yes, that is something that we are definitely...
02:21That it will help.
02:22Yeah.
02:23That is where I am thinking.
02:24Yes.
02:25What are all the tools available for us,
02:27if you can apply.
02:28Yes.
02:29Then solution will be
02:30or accurate solution,
02:36nearer to 95%, 90% accuracy.
02:39Then it is a game changer.
02:41Yes.
02:42Especially if there are,
02:43in the pilot phase if we have human agents
02:45who are sort of verifying that.
02:47Yeah, pilot project, both you can do always.
02:50Ultimately human angle will be subjective.
02:57Mission and accuracy will be more objective.
03:00Yes.
03:01That is the stage you have to reach.
03:03This is Harsha from Producers Trust USA, sir.
03:06Yes, sir.
03:07They are also meeting us in Davos.
03:08In Davos.
03:09Salesforce is one of the world's largest IP.
03:12Harsha, can you...
03:13Yes, sir.
03:14We are Producers Trust, sir.
03:15We developed a new data technology platform called LRM, sir.
03:18Based on Salesforce's CRM existing platforms.
03:20The idea, to put it in simple words,
03:22it is like to create a version of RTGS for agriculture, sir.
03:26So when created,
03:27the model would look somewhat like this, sir.
03:29Existing supply chain is very simple, sir.
03:31We have something like a producer,
03:33a trader, wholesaler, retailer.
03:35But if you can reimagine supply chain
03:37and think of it more dynamically, sir.
03:39And this is a technology platform
03:41which helps data intelligence
03:44and data coordination between all the actors.
03:46So we know where things are missing,
03:48what is the actual need of a brand,
03:50what is the actual produce of the consumer, producer.
03:53And then we can connect.
03:55And then we know where to invest,
03:56where to develop infrastructure,
03:58what are the critical missing gaps, sir.
04:00We'll have more precision.
04:01And one interesting area is carbon credits, sir.
04:03Just like in energy,
04:04we're talking about PM Suryagar,
04:06where the consumer can gain money by producing energy
04:11or by the root of solar.
04:12We can similarly give revenue through carbon credits
04:15by growing green cover on the farm.
04:18Because, sir, just in modular,
04:20we have this research for this green cover.
04:22That will lead to carbon credits.
04:24But to get that, we need data.
04:25So this is like creating an ecosystem.
04:27If you create an ecosystem,
04:29then carbon credits, money will come?
04:31Yes, sir. The idea is to do that, sir.
04:33No, earlier they used to give.
04:36Now they are going differently.
04:38But what is your assessment today?
04:40We are actually doing a pilot, sir,
04:42with the agriculture department, POC for...
04:44No, no, no.
04:45What I'm saying,
04:46government, overall global experiences,
04:48are you confident to get carbon credits?
04:51As of today,
04:53originally thermal plants,
04:55people used to buy carbon credits.
04:59Then they will produce...