WEBVTT - Saving Crops with AI 0:00:03.000 --> 0:00:05.200 When a lot of us think of farming, it reminds 0:00:05.280 --> 0:00:08.039 us of simpler times, and perhaps it feels like one 0:00:08.039 --> 0:00:11.119 of the remaining industries exempt from the influences of the 0:00:11.160 --> 0:00:15.120 modern tech world. But imagine a world where the success 0:00:15.160 --> 0:00:18.880 of your family's farm crop yield is access to AI tools. 0:00:19.480 --> 0:00:21.960 There's so much labor and effort that goes into maintaining 0:00:21.960 --> 0:00:25.479 a farm, especially when farmers have to anticipate unpredictable weather 0:00:25.520 --> 0:00:29.920 patterns and unprecedented seasons brought on by climate change. Plants, 0:00:30.200 --> 0:00:33.600 like humans, are living things, with millions of tiny organisms 0:00:33.680 --> 0:00:37.519 both attacking and assisting their life cycle. Some threats to 0:00:37.560 --> 0:00:39.800 crop life are smaller than the human eye can see, 0:00:39.960 --> 0:00:42.760 and when not addressed, the results can be disastrous to 0:00:42.840 --> 0:00:46.080 local economies. But what if AI could solve the problem. 0:00:46.280 --> 0:00:49.440 Giving eyes and access to where farmers cannot reach. AI 0:00:49.560 --> 0:00:52.879 protects crops and the economy from the threat of microbial pests, 0:00:53.000 --> 0:01:00.560 resulting in a more prosperous tomorrow. Hey there, I'm gram 0:01:00.640 --> 0:01:05.200 Class and this is technically speaking an Intel podcast. The 0:01:05.240 --> 0:01:08.440 show is dedicated to highlighting the way technology is revolutionizing 0:01:08.480 --> 0:01:12.280 the way we live, work, and move. In every episode, 0:01:12.319 --> 0:01:15.520 we'll connect with innovators in areas like artificial intelligence to 0:01:15.600 --> 0:01:19.679 better understand the human centered technology they've developed. There has 0:01:19.680 --> 0:01:23.759 always been a disconnect between nature and technology. However, today 0:01:23.840 --> 0:01:25.760 there's a lot of science and technology at the core 0:01:25.800 --> 0:01:29.120 of modern farming, and we're not talking about GMOs. One 0:01:29.120 --> 0:01:32.880 of the biggest issues in agriculture is environmental threats. Farmers 0:01:32.880 --> 0:01:36.280 struggle with protecting crops from diseases and pests without using 0:01:36.360 --> 0:01:40.720 tools that could adversely affect consumers. AI has been instrumental 0:01:40.760 --> 0:01:44.959 in helping farmers detect pests before infestations occur and result 0:01:45.040 --> 0:01:48.520 in huge crop loss. But before we get into exactly 0:01:48.520 --> 0:01:51.800 how it all works, I want to introduce our guests. 0:01:53.280 --> 0:01:57.080 In twenty seventeen, Rishi kish amitt Nayak's family farm in 0:01:57.080 --> 0:02:00.520 India so ninety percent crop loss due to pest in infestation. 0:02:01.440 --> 0:02:06.320 After partnering with a fellow student, Niharika Haridas, the two Megatronics, 0:02:06.440 --> 0:02:10.239 Robotics and automation engineering students found a way to use 0:02:10.280 --> 0:02:13.280 AI to develop a method that could detect crop pests 0:02:13.320 --> 0:02:17.040 through thermal imaging. This system, called kishan No, has been 0:02:17.040 --> 0:02:21.960 proven effective and very affordable to local farmers. Rishikish America 0:02:22.040 --> 0:02:22.839 thanks for being here. 0:02:23.040 --> 0:02:25.040 Such a pleasure to be here, Graham, thank you for 0:02:25.040 --> 0:02:25.600 the invitation. 0:02:26.000 --> 0:02:30.440 We're also joined by Shwita Karuna, intel's Director of Government 0:02:30.520 --> 0:02:34.600 Partnerships and Initiatives for Japan and the Asia Pacific. Sharita 0:02:34.600 --> 0:02:37.720 has over twenty three years of experience creating trusted government 0:02:37.720 --> 0:02:42.320 relationships and fostering government programs that encouraged the implementation of 0:02:42.440 --> 0:02:46.200 modern science into the workforce PLUSH. She was instrumental in 0:02:46.240 --> 0:02:51.359 helping kishan No grow as a farming tactic across the region. Welcome, Shwrita. 0:02:51.400 --> 0:02:53.280 Thank you, Graham, such a pleasure being here. 0:02:56.080 --> 0:02:58.840 So let's start at the beginning a very interesting story 0:02:58.880 --> 0:03:01.400 around rishikish Can you tell a little bit about the 0:03:01.440 --> 0:03:05.000 problem that your family and other farmers experienced back in 0:03:05.040 --> 0:03:06.119 twenty seventeen. 0:03:07.120 --> 0:03:10.280 In India particularly, it's an agricultural country, so more than 0:03:10.320 --> 0:03:12.960 seventy percent of the people do agriculture as their own 0:03:13.000 --> 0:03:17.720 major occupation. In twenty seventeen, my father's grandfather was completely 0:03:17.720 --> 0:03:22.440 invested into agricultural farming, and during that time, in Orisa particularly, 0:03:22.480 --> 0:03:25.239 there was a plant best attack that couldn't be identified 0:03:25.280 --> 0:03:28.000 for a longer period of time, and that resulted in 0:03:28.080 --> 0:03:31.040 a lot of crop losses and hectares of land was 0:03:31.080 --> 0:03:35.000 just lost because of an unidentified pist. Personally, we saw 0:03:35.000 --> 0:03:37.600 a lot of farmer suicides in our own village, and 0:03:37.640 --> 0:03:41.000 that was the major reason when I thought, Okay, I 0:03:41.160 --> 0:03:43.040 do have a background of engineering, I do have a 0:03:43.040 --> 0:03:46.520 background of robotics, so why not to create something for 0:03:46.760 --> 0:03:50.320 our own farmers. And being part of that family where 0:03:50.320 --> 0:03:53.520 we do farming in our parental site, I was just 0:03:53.880 --> 0:03:55.960 touched with that fact that I need to do something 0:03:56.000 --> 0:03:57.200 for the farmers. 0:03:58.520 --> 0:04:02.400 In Rishikishi's village alone, there were four farmers who took 0:04:02.440 --> 0:04:06.280 their lives as a result of the devastated crop, and 0:04:06.320 --> 0:04:09.119 his family saw a ninety percent crop loss that year. 0:04:10.400 --> 0:04:14.400 The infestation was so devastating to their livelihood his family 0:04:14.440 --> 0:04:17.880 considered leaving farming all together. And to make matters worse, 0:04:18.320 --> 0:04:23.520 the problem was difficult to identify and trace. Before we 0:04:23.560 --> 0:04:26.680 get into the actual details of how you solved it 0:04:26.760 --> 0:04:29.600 in Arika, how did you get involved in the project. 0:04:30.279 --> 0:04:34.279 I decided to pursue mecatronics and automation at Viatchene out 0:04:34.279 --> 0:04:37.480 of a sheer passion for robotics as a twelfth grader. 0:04:37.760 --> 0:04:40.440 So I came across the work that many companies like 0:04:40.480 --> 0:04:44.120 Boston Dynamics were doing at that point and exactly right 0:04:44.160 --> 0:04:47.280 the spot pro vote of course, and I was just 0:04:47.680 --> 0:04:50.400 enthralled with potential that it helped, Like it was like, 0:04:50.480 --> 0:04:52.520 oh my, what this could change humanity? 0:04:52.920 --> 0:04:54.200 And I was like, I need. 0:04:54.080 --> 0:04:56.120 To do something in this space. I wanted to help 0:04:56.160 --> 0:04:59.200 people with this new technology. And that's how I went 0:04:59.240 --> 0:05:02.400 to Aitchen and that's where I'm Metri Shikish and we 0:05:02.480 --> 0:05:05.400 started talking and we were talking about this project and 0:05:05.440 --> 0:05:07.560 I was like, you know, that's that's amazing that we'll 0:05:07.680 --> 0:05:10.720 let me contribute to it as well, and that's how 0:05:10.760 --> 0:05:14.560 we started collaborating on the project and then we participated 0:05:14.680 --> 0:05:18.480 in the Inaugril Intelliet Global Impact Festival and the rest 0:05:18.560 --> 0:05:21.800 is history. We had a wonderful time and you know, 0:05:21.839 --> 0:05:24.280 the support that we have gotten from Intel for it 0:05:24.320 --> 0:05:26.960 as well has been phenomenal and that's the reason that 0:05:27.040 --> 0:05:29.400 Kishano is at the place where it is right now. 0:05:29.800 --> 0:05:32.800 Excellent. So now as the I guess the sixty four 0:05:32.800 --> 0:05:36.039 thousand dollars question is how does the kishan No work. 0:05:37.160 --> 0:05:41.440 Kishano basically taps into saturate based thermal imagery. These images 0:05:41.520 --> 0:05:45.320 can detect temperature variations and crops which often indicate Streuss 0:05:45.680 --> 0:05:49.480 disease or pestal activity. For instance, areas affected by certain 0:05:49.520 --> 0:05:52.960 pest or microbol infestations may exhibit different thermal patterns compared 0:05:52.960 --> 0:05:56.560 to healthy areas. We collect images from Sentinel two and 0:05:56.640 --> 0:06:00.240 lands At eight satellites. Those satellite images are then sys 0:06:00.440 --> 0:06:03.960 to get index mapping out likes, for example, vegetative indexes 0:06:03.960 --> 0:06:07.800 and moisture indexes through a software called QGIS, so it 0:06:07.880 --> 0:06:11.000 basically gives us the values for those vegetative indexes and 0:06:11.000 --> 0:06:16.160 moisture indexes, and these gathered thermal imageries processed using AA algorithms, 0:06:16.560 --> 0:06:19.480 where we've processed the images first into the open Veno 0:06:19.560 --> 0:06:23.200 platform and we get a d blood image for better 0:06:23.240 --> 0:06:26.520 accuracy of training of the models. Then these algorithms are 0:06:26.560 --> 0:06:30.479 trained to recognize patterns or animalies that correspond to microbilan 0:06:30.520 --> 0:06:33.960 pest outbreaks. Over time, has more data is collected and analyzed, 0:06:34.000 --> 0:06:36.640 the AA model becomes more accurate and efficient in its 0:06:36.680 --> 0:06:40.479 prediction and leveraging the power of machine learning. Once a 0:06:40.520 --> 0:06:43.240 potential threat is identified in the system, the systems can 0:06:43.279 --> 0:06:46.320 send alerts or recommendations to the farmers in the local 0:06:46.440 --> 0:06:50.200 administrative levels, where we also design the physical device apart 0:06:50.240 --> 0:06:53.920 from the AA algorithm to get a confirmatory test that 0:06:53.960 --> 0:06:57.279 there is a pest or plant disease outbreak. This actually 0:06:57.320 --> 0:07:00.279 includes information about the type of threat, it's severe, and 0:07:00.360 --> 0:07:01.800 recommendation algorithms. 0:07:02.240 --> 0:07:03.560 This proactive approach. 0:07:03.240 --> 0:07:06.919 Helps farmers to take actions before the problem becomes widespread 0:07:07.240 --> 0:07:09.880 and saving both time and resources. 0:07:10.600 --> 0:07:12.720 I'd like to talk about Intel open Veno a little 0:07:12.720 --> 0:07:16.120 bit so quickly, just to inform our audience. INTE open 0:07:16.200 --> 0:07:19.640 Vino is a cross platform toolkit developed by Intel that 0:07:19.680 --> 0:07:24.120 deploys deep learning models on visual data sets, helping computers 0:07:24.160 --> 0:07:27.840 better recognize and process images to inform decision making. But 0:07:27.920 --> 0:07:30.760 I'm curious as someone who's just as interested in what 0:07:30.920 --> 0:07:34.000 didn't work as opposed to what ultimately does. Why did 0:07:34.080 --> 0:07:36.280 you decide to use Intel open Veno. Were there are 0:07:36.320 --> 0:07:37.600 other methods you tried first? 0:07:38.600 --> 0:07:41.040 So we did try a lot of techniques, and we 0:07:41.160 --> 0:07:45.320 found that open Veno worked perfectly with our project, especially 0:07:45.320 --> 0:07:48.119 with the goal that we were trying to achieve. So 0:07:48.640 --> 0:07:50.640 we saw that the hardware requirements as well as the 0:07:50.640 --> 0:07:54.720 software requirements did completely match. Also, we had mentorship from 0:07:54.720 --> 0:07:58.000 Intel and we were able to properly and in a 0:07:58.000 --> 0:08:01.760 better way adapt to those systems to our project, and 0:08:01.800 --> 0:08:03.040 that's the reason which it was open. 0:08:03.120 --> 0:08:06.520 We know, we actually tried to degler images through some 0:08:06.600 --> 0:08:11.280 deep learning algorithms, but those algorithms was actually not satisfying 0:08:11.280 --> 0:08:14.880 the accuracy that we actually wanted, so open Veno just 0:08:14.880 --> 0:08:16.640 suited out the case perfectly. 0:08:17.480 --> 0:08:19.760 One thing I'm interested in is the pests that were 0:08:20.040 --> 0:08:22.440 being detected. Am I right in saying that it had 0:08:22.480 --> 0:08:23.960 a unique therm signature? 0:08:24.240 --> 0:08:24.640 Yeah? 0:08:24.680 --> 0:08:26.280 And how did you discover that? 0:08:27.000 --> 0:08:30.120 In twenty seventeen, Once we identified the problem, we actually 0:08:30.160 --> 0:08:33.080 tried to create a physical device through a thermal camera 0:08:33.120 --> 0:08:36.680 set up and microprocesses. We were rotating that device among 0:08:36.679 --> 0:08:40.200 the periphery of the crop fields to understand what exactly 0:08:40.240 --> 0:08:43.079 the thermal traces are in the leaf of the crop plants. 0:08:43.440 --> 0:08:47.040 And once we understood what are the thermal signatures for 0:08:47.120 --> 0:08:49.520 different crop plants, we understood there is a concept that 0:08:49.600 --> 0:08:52.520 whenever there is a pathogen or a plant disease, there 0:08:52.559 --> 0:08:56.480 is a certain increase in the leaf temperature. And if 0:08:56.480 --> 0:09:00.520 we identify that leaf temperature increases in the particular or 0:09:00.559 --> 0:09:04.319 in a particular duration of time, we can actually significantly 0:09:04.360 --> 0:09:06.160 say that there is a best attack or a plant 0:09:06.200 --> 0:09:09.640 disease in the crop area. Once we had the theory, 0:09:10.000 --> 0:09:13.800 we tried to incorporate that similar formula in the vegetative 0:09:13.800 --> 0:09:16.400 index of the satellite setup. So in twenty nineteen we 0:09:16.440 --> 0:09:19.079 had the physical setup, we tried the same literature to 0:09:19.160 --> 0:09:20.559 understand it to the satellites. 0:09:22.520 --> 0:09:26.160 Hearing Rishikish and Aharika elaborate on how they design their 0:09:26.240 --> 0:09:29.840 imaging tool reminded me of my own experience attempting to 0:09:29.880 --> 0:09:32.840 develop systems to work remotely in the jungles of Africa. 0:09:33.720 --> 0:09:36.760 It's not an easy feat though, as there's no real 0:09:36.800 --> 0:09:40.120 infrastructure for these sorts of products, especially when they are 0:09:40.120 --> 0:09:44.520 limited by internet access and availability in the area. Hearing 0:09:44.559 --> 0:09:47.880 how much progress these two had made with their program, 0:09:48.640 --> 0:09:51.240 maybe wonder about the challenges that went into making this 0:09:51.360 --> 0:09:57.120 tool available in the rural farmlands of India. 0:09:57.200 --> 0:10:00.400 There has always been a digital divide in India, as 0:10:00.440 --> 0:10:03.040 we can see, but now it's been narrowing and that's 0:10:03.080 --> 0:10:05.280 a very good news for all of us, and that 0:10:05.320 --> 0:10:09.880 infrastructure is also becoming better. There's also research that India 0:10:09.880 --> 0:10:12.320 has one of the cheapest internet out there in the world, 0:10:12.880 --> 0:10:15.760 so I mean, it's being adapted and we are glad 0:10:15.800 --> 0:10:17.800 that it is. But when we were working on it, 0:10:17.840 --> 0:10:21.720 we did face a lot of infrastructure issues regarding internet 0:10:21.760 --> 0:10:26.720 services as well and internet connectivity exactly, and sort of 0:10:26.800 --> 0:10:30.080 having that satellite imagery. Gaining access to the satellite imagery 0:10:30.160 --> 0:10:32.960 was very difficult for us because that area wasn't mapped. 0:10:33.160 --> 0:10:36.160 Remote areas aren't usually mapped with that much precision as 0:10:36.200 --> 0:10:38.800 that of let's say, an urban area, so we did 0:10:38.840 --> 0:10:41.760 have some issues with that, but then we did try 0:10:41.760 --> 0:10:44.800 our best to solve those and gain satellite images from 0:10:44.840 --> 0:10:45.880 the areas that we neated. 0:10:46.880 --> 0:10:49.640 Farmers in the villages particularly, they were quite a bit 0:10:49.679 --> 0:10:52.840 skeptical to try this out, and the farms because in 0:10:52.840 --> 0:10:56.000 India particularly didn't back that time, we didn't have that 0:10:56.120 --> 0:11:00.520 much of agritechnology tools or products, and going as a 0:11:00.559 --> 0:11:04.920 youngster something around in class ninth or tenth and trying 0:11:04.920 --> 0:11:07.679 out as some different new projects or new census in 0:11:07.720 --> 0:11:11.439 the field, they were quite a bit skeptical. So managing 0:11:11.480 --> 0:11:13.360 that side of that, Okay, we are doing something good, 0:11:13.480 --> 0:11:16.360 we are doing something better for your own crops, we 0:11:16.400 --> 0:11:19.240 are doing something for the best of the society. Convincing 0:11:19.280 --> 0:11:21.880 them was one of the very huge challenge over there 0:11:21.920 --> 0:11:22.480 in India. 0:11:23.120 --> 0:11:25.800 What kind of data or training processes were involved in 0:11:26.200 --> 0:11:31.000 training the model to recognize microbiopests in the crops. 0:11:31.480 --> 0:11:33.760 Initially it was only deep learning algorithms. 0:11:33.880 --> 0:11:36.000 Further on, when we had a lot of thermal praise 0:11:36.080 --> 0:11:38.000 data and we had did the d blood images, we 0:11:38.000 --> 0:11:40.600 were just focused on the CNN models to train the data. 0:11:41.160 --> 0:11:43.400 And it hadn't given a good accuracy of for around 0:11:43.440 --> 0:11:47.360 ninety points something percentage, so it was a pretty good 0:11:47.400 --> 0:11:50.880 accurate to start with for a particular set of crops. 0:11:51.360 --> 0:11:54.680 You said, CNN, could you just define what that is please? 0:11:54.920 --> 0:11:56.520 Conventional neural network. 0:11:56.600 --> 0:12:00.680 Okay, And that's just another AI technique to for learning. 0:12:01.000 --> 0:12:03.760 Yeah, yes, a machine learning okay, okay. 0:12:04.440 --> 0:12:08.199 And you just mentioned about the accuracy that you achieved. 0:12:08.280 --> 0:12:10.760 Would you say that's typical for the Intel Open Veno 0:12:11.000 --> 0:12:13.320 platform to get that sort of result. 0:12:14.160 --> 0:12:16.800 The accuracy is for the total accuracy of the model 0:12:16.840 --> 0:12:20.000 for a particular set of crops, for example, tomatoes and wheat. 0:12:20.559 --> 0:12:22.720 For those two crops we had an accuracy fround ninety 0:12:22.720 --> 0:12:26.080 point two eight percentage, and for other crops it's still 0:12:26.080 --> 0:12:29.160 in the process of getting more accurate and all. So 0:12:29.280 --> 0:12:31.960 for these two crops, overly, it was the accuracy that 0:12:31.960 --> 0:12:33.400 we measured out and. 0:12:33.400 --> 0:12:36.439 In terms of the Intel Open Veno technology, can you 0:12:36.480 --> 0:12:40.160 think of anything any other farming use cases beyond pest 0:12:40.200 --> 0:12:41.520 management and crop protection. 0:12:42.160 --> 0:12:45.240 Currently, we were trying to work on crop genome analysis 0:12:45.760 --> 0:12:48.280 where we were actually trying to understand because of the 0:12:48.280 --> 0:12:51.240 climate change to the new variants of crops are needed 0:12:51.280 --> 0:12:54.240 to adapt to the new climatic conditions. So we were 0:12:54.240 --> 0:12:57.280 trying to understand how exactly we can use machine learning 0:12:57.320 --> 0:13:01.880 algorithms to create new genomes in the crops the microbiology 0:13:01.920 --> 0:13:02.360 side of it. 0:13:02.760 --> 0:13:05.680 So yeah, that's one area that I was completely focused 0:13:05.720 --> 0:13:07.640 on in this past recent days. 0:13:08.280 --> 0:13:10.079 I would like to add on to that, and as 0:13:10.480 --> 0:13:14.520 Education mentioned, convolutional neural network model that we used, it 0:13:14.679 --> 0:13:18.000 was at that point not something that was used by 0:13:18.080 --> 0:13:21.000 the AI community, but then we now see a lot 0:13:21.040 --> 0:13:23.080 of use cases for that and that's something that we 0:13:23.120 --> 0:13:25.959 are very glad about. And also some of the use 0:13:26.000 --> 0:13:28.040 cases that I have at least found as an AI 0:13:28.240 --> 0:13:32.120 enthusiast that models like these could have is in real 0:13:32.160 --> 0:13:35.400 time data, especially as the climatic change has become a 0:13:35.440 --> 0:13:38.240 huge issue. It is something that can help a lot 0:13:38.280 --> 0:13:40.719 of farmers with when there is excessive rains or when 0:13:40.720 --> 0:13:43.160 there is no rain at all, to predict these through 0:13:43.200 --> 0:13:47.160 AIML technologies. And I believe that the limit is boundless 0:13:47.320 --> 0:13:50.880 when it comes to AI technologies. Right we are seeing 0:13:51.200 --> 0:13:54.240 a start of a new era of AI, and I 0:13:54.280 --> 0:13:57.240 am very glad to see how I was being used 0:13:57.320 --> 0:13:59.960 by lots of companies, and we also hope to go 0:14:00.040 --> 0:14:04.120 contribute to that, and I hope for a very bright future. 0:14:06.280 --> 0:14:08.880 AI has been the focus of a lot of discourse 0:14:09.080 --> 0:14:12.199 over the last couple of decades. While many of us 0:14:12.240 --> 0:14:15.880 experience it as virtual assistance in our phones and computers, 0:14:16.440 --> 0:14:20.160 AI has been giving us listening, watching, and reading recommendations 0:14:20.200 --> 0:14:23.400 for years and we continue to see it evolve and 0:14:23.520 --> 0:14:28.480 even create content like images and written stories. But that's 0:14:28.520 --> 0:14:31.920 all just the beginning. AI has so much potential to 0:14:31.960 --> 0:14:35.840 positively impact the way we work and live. It can 0:14:35.840 --> 0:14:39.359 be used to detect new variants and threats in agriculture 0:14:39.480 --> 0:14:43.320 brought on by climate change conditions. The Intel Open Vino 0:14:43.440 --> 0:14:47.680 technology played an essential role in this, providing higher accuracy 0:14:47.800 --> 0:14:51.840 for detection. I'd just like to switch a little bit 0:14:51.880 --> 0:14:54.760 to the agribusiness side of things. And maybe I can 0:14:54.800 --> 0:14:57.880 get Shuita to comment on this in terms of the 0:14:58.000 --> 0:15:01.640 Intel Open Vino and its app cation here for pest detection. 0:15:02.240 --> 0:15:05.720 Do you see it complementing other pest control methods in 0:15:05.800 --> 0:15:10.240 agriculture and does it have the potential to replace pesticides 0:15:10.280 --> 0:15:13.720 and insecticides and farming replace. 0:15:13.480 --> 0:15:16.480 Is a little on the harsher terms. 0:15:16.520 --> 0:15:18.680 What I would actually look at it is AI and 0:15:18.720 --> 0:15:22.960 agricultures really helping farmers make data driven decisions, optimize crop 0:15:23.040 --> 0:15:27.600 yields conserved resources like water and energy. The challenge here 0:15:27.800 --> 0:15:30.280 is not just the solution part of it is also 0:15:30.360 --> 0:15:35.920 kind of encouraging next generation technologists student innovators to come together, 0:15:36.400 --> 0:15:40.520 look at the local problems like what Neharikan risikation have done, 0:15:40.760 --> 0:15:43.360 and then create a solution using all the skills they've 0:15:43.440 --> 0:15:46.000 learned as part of their formal education as well as 0:15:46.040 --> 0:15:48.400 as part of being a part of Intel programs the 0:15:48.440 --> 0:15:53.800 Interdigital Rediness Program portfolio, come together and democratize AI skills 0:15:53.800 --> 0:15:57.280 in a way which gets a common person a farmer, 0:15:57.360 --> 0:16:02.840 to understand trust and emergingology like artificial intelligence and hopefully 0:16:02.880 --> 0:16:06.440 become comfortable in applying it to solve the daily problems 0:16:06.480 --> 0:16:08.440 they would be facing as part of their community. 0:16:09.160 --> 0:16:12.320 I love that term democratization of technology, and I think 0:16:12.400 --> 0:16:15.560 that's ultimately what technology does is get it more accessible 0:16:15.760 --> 0:16:18.480 and cheaper to get it to the far regions of 0:16:19.160 --> 0:16:21.720 the world. I'd just like to expand a little bit more, 0:16:21.760 --> 0:16:24.000 maybe if you could explain some of the programs that 0:16:24.080 --> 0:16:29.000 are available through inter Open VENO to help farmers or 0:16:29.520 --> 0:16:33.200 businesses with limited resources to get access to this sort 0:16:33.240 --> 0:16:34.720 of technology and expertise. 0:16:35.600 --> 0:16:37.800 I'll just take a step back here, right because we 0:16:37.920 --> 0:16:41.240 keep talking about increasing digitization, which today a lot of 0:16:41.280 --> 0:16:44.720 governments and countries are going towards. But what it really 0:16:44.760 --> 0:16:48.280 means is when we focus on increased digitization, we also 0:16:48.400 --> 0:16:51.520 need to invest more in building the digital readiness of people, 0:16:52.120 --> 0:16:55.360 specifically in terms of emerging in critical technologies like AI 0:16:55.760 --> 0:16:59.000 or what you spoke about, like the usage of open Wino. 0:16:59.040 --> 0:17:01.800 How do we get person to understand how they can 0:17:01.960 --> 0:17:05.000 utilize the technology like open we know to be able 0:17:05.040 --> 0:17:08.399 to solve their local problem and create indigender solutions. So 0:17:08.520 --> 0:17:11.440 all this kind of comes together through a whole program 0:17:11.480 --> 0:17:14.200 portfolio which we have which is called the Intel Digital 0:17:14.280 --> 0:17:19.040 Readiness Programs, which is driven in collaboration with government, academia, 0:17:19.160 --> 0:17:25.040 civil society, and the industry and focuses around building shared value, 0:17:25.280 --> 0:17:31.240 shared responsibility so that we can really demystify democratize these 0:17:31.240 --> 0:17:34.520 superpowers which we keep talking about, like artificial intelligence for 0:17:34.600 --> 0:17:38.800 a very broader and a diverse audience for young budding 0:17:38.840 --> 0:17:42.879 technologists like Neiharika Ushikish but also for those who are 0:17:42.880 --> 0:17:44.960 going to be consuming the technology at the other end 0:17:44.960 --> 0:17:48.800 of the spectrum. The programs are a lot, they're many. 0:17:48.960 --> 0:17:51.960 They range from you know, programs like AI for Citizens, 0:17:51.960 --> 0:17:54.720 which talks about getting a citizen to understand how to 0:17:54.800 --> 0:17:58.359 navigate his or her life in an AI driven world. 0:17:58.640 --> 0:18:01.840 AI for Youth, which really allows us to empower youth 0:18:01.920 --> 0:18:05.080 with not just the technical skills associated with AI, but 0:18:05.119 --> 0:18:08.760 also the social skills in a very inclusive manner. And 0:18:08.800 --> 0:18:11.520 then we have AI for Future Workforce, which is for 0:18:11.840 --> 0:18:15.800 vocational community college students, engineering students, which really helps them 0:18:15.840 --> 0:18:19.520 to understand how to be prepare themselves for becoming a 0:18:19.560 --> 0:18:22.920 part of the future workforce. So a huge spectrum, lots 0:18:22.920 --> 0:18:24.960 of programs, but the one which is very special to 0:18:25.040 --> 0:18:27.280 all three of us in this case, and I'm sure 0:18:27.480 --> 0:18:30.280 Education Aherka would agree with that is our EI Global 0:18:30.320 --> 0:18:34.520 Impact Festival, because this is where we work with all 0:18:34.520 --> 0:18:37.920 these student innovators. We get them together and we get 0:18:37.920 --> 0:18:42.480 them to celebrate their AI innovations with a huge passage 0:18:42.520 --> 0:18:45.840 which does not just allow them to showcase what they've built, 0:18:45.840 --> 0:18:48.400 but also helps them hone their skills by getting mentored 0:18:48.400 --> 0:18:51.640 by Intel technologists because at the end of the day, 0:18:51.920 --> 0:18:54.720 these young students are the next generation technologists, so we 0:18:54.760 --> 0:18:58.080 want to make sure we work for them to support 0:18:58.119 --> 0:18:59.680 and build an AI ready generation. 0:19:00.680 --> 0:19:03.879 Platforms like these have been really instrumental and I have 0:19:04.000 --> 0:19:08.640 seen the impact on ground that they make in supporting technologists, 0:19:08.680 --> 0:19:11.800 young technologists like us, and we have always been very 0:19:11.840 --> 0:19:15.479 grateful for the opportunities and mentorship as well that Intel 0:19:15.520 --> 0:19:18.720 has provided. And that's something that we wish that every 0:19:19.040 --> 0:19:22.240 budding technologist in India and all over the globe can 0:19:22.280 --> 0:19:26.680 at least experience, because mentorship and guidance is an important 0:19:26.720 --> 0:19:30.800 pillar of one's journey and having someone who can teach 0:19:30.840 --> 0:19:33.720 you more about AI, how to use AI, and how 0:19:33.760 --> 0:19:37.119 to benefit from AI, especially with the immense potential it 0:19:37.200 --> 0:19:39.200 has that is life changing. 0:19:42.040 --> 0:19:45.600 You're listening to technically speaking, an Intel podcast will be 0:19:45.680 --> 0:19:58.160 right back. Welcome back to technically speaking an Intel podcast 0:20:03.160 --> 0:20:06.760 shweeta last episode of this podcast, we talked with Reachhuvu, 0:20:06.920 --> 0:20:12.000 one of your colleagues, about the ethics and responsibility of AIM, 0:20:12.040 --> 0:20:15.359 wondering if we could get your thoughts on how you're 0:20:15.400 --> 0:20:20.159 working with governments and industry leaders around AI and trying 0:20:20.160 --> 0:20:24.439 to help them navigate some of the ethics and responsibilities 0:20:24.480 --> 0:20:25.800 around AI development. 0:20:26.560 --> 0:20:29.160 That's a very interesting question for us, right because when 0:20:29.200 --> 0:20:31.800 we speak about digital reddiness or how do we build 0:20:31.840 --> 0:20:35.320 digital readiness, we look at three pillars. Largely, one is, 0:20:35.640 --> 0:20:40.000 of course learning the skills of emerging technologies like AI, 0:20:40.160 --> 0:20:44.919 but more importantly, getting to understand and trust those skills, 0:20:44.960 --> 0:20:47.639 So getting to understand not just what the advantages are, 0:20:47.720 --> 0:20:50.640 but also what the pitfalls are. Getting to understand which 0:20:50.680 --> 0:20:53.880 situation should we apply the emerging technology in and which 0:20:53.920 --> 0:20:57.720 ones we should abstain from. So our programs, in fact, 0:20:57.760 --> 0:21:00.879 inculcate a lot of discussions around these there is, which 0:21:01.480 --> 0:21:04.760 range from the ethics piece of it, which range from 0:21:04.840 --> 0:21:06.800 how how do we make it more inclusive, how do 0:21:06.840 --> 0:21:10.479 we make it more diverse? And so much so that 0:21:10.560 --> 0:21:13.000 if you kind of package it all together, it comes 0:21:13.040 --> 0:21:16.119 under the larger umbrella of responsible AI. So how do 0:21:16.200 --> 0:21:20.359 we really encourage not just youth, but every citizen, which 0:21:20.359 --> 0:21:23.159 includes the governments who we collaborate with and partner with 0:21:23.560 --> 0:21:27.400 to understand what is the responsible use of these superpowers 0:21:27.480 --> 0:21:32.439 like AI to gain broader socioeconomic benefits for everybody. 0:21:32.720 --> 0:21:35.679 As a youth igffellow. That is exactly what I focus 0:21:35.760 --> 0:21:40.160 on Internet governance right and how AI governance works and 0:21:40.200 --> 0:21:43.800 how AI can be regulated. But then what about AI innovation? 0:21:44.240 --> 0:21:47.600 It shouldn't be regulated or stifled due to laws coming 0:21:47.640 --> 0:21:51.520 into place that can have that effect where people continuate 0:21:51.680 --> 0:21:55.320 and they can't contribute to new technologies, so that there's 0:21:55.359 --> 0:21:58.159 a delicate balance between them, and that is what I 0:21:58.240 --> 0:22:01.320 also do look into. And the whole area of how 0:22:01.440 --> 0:22:04.760 becoming emerging technology is like even robotics which has a 0:22:04.840 --> 0:22:09.520 huge artificient intelligence ethics background out there, So how do 0:22:09.560 --> 0:22:12.919 we harness this without harming humanity? And that is something 0:22:12.960 --> 0:22:16.560 that I believe all stakeholders, including the youth of our 0:22:16.560 --> 0:22:19.760 country or the globe, should be focusing on because there 0:22:20.160 --> 0:22:22.760 also tends to be the whole bias of youth not 0:22:23.240 --> 0:22:26.520 being given a voice when it comes to these emerging technologies. 0:22:26.560 --> 0:22:29.199 But I believe if they do understand what it is 0:22:29.240 --> 0:22:32.920 about and what potential risks it has and what potential 0:22:32.960 --> 0:22:35.520 benefits it has, that gives them the knowledge to use 0:22:35.560 --> 0:22:37.320 it responsibly and ethically. 0:22:39.359 --> 0:22:43.520 Using AI can be as complicated as Niharika has pointed out, 0:22:44.200 --> 0:22:47.119 but the tool she and Wishikish have been able to 0:22:47.119 --> 0:22:50.639 create from this place of innovation and AI have changed 0:22:50.640 --> 0:22:52.760