1 00:00:00,120 --> 00:00:05,360 Embrace AI because especially genital AI is bringing so much 2 00:00:05,360 --> 00:00:08,119 intelligence. And you know, I think I quoted 3 00:00:08,119 --> 00:00:11,800 our CEO that said, you know, AI is not going to take your job, 4 00:00:11,800 --> 00:00:15,560 but someone using AI could take your job. 5 00:00:26,840 --> 00:00:29,160 Hello and welcome to the Retail podcast. 6 00:00:29,480 --> 00:00:35,680 Now I'm it's really graced because Azita Martine, who is 7 00:00:35,680 --> 00:00:39,680 the lead or president or vice president, Vice President, I 8 00:00:39,680 --> 00:00:44,200 always keep promoting people. The CEO of NVIDIA directors, the 9 00:00:44,200 --> 00:00:49,480 vice president of retail, global retail for AI and NVIDIA was the 10 00:00:49,480 --> 00:00:53,440 keynote kick off speaker at NRF on day one. 11 00:00:53,440 --> 00:00:56,000 And as some of you know, I've already given you my thoughts on 12 00:00:56,000 --> 00:00:59,320 where we're going with AI. Basically, I position the fact 13 00:00:59,320 --> 00:01:01,560 that last year was about the use case. 14 00:01:01,560 --> 00:01:05,239 This year we're hearing about AI transformation and Satya has 15 00:01:05,239 --> 00:01:08,680 already spoken about how the SAS models are going away and 16 00:01:08,680 --> 00:01:11,320 there's an AI layer that's coming on top. 17 00:01:11,320 --> 00:01:14,280 But hey, you're not here to listen to me, we're here to 18 00:01:14,920 --> 00:01:18,760 listen to you. So if you don't mind taking us 19 00:01:18,760 --> 00:01:22,040 through your because at your keynote, you sort of set the 20 00:01:22,040 --> 00:01:26,600 scene about what's happening in the world of AI and the industry 21 00:01:27,520 --> 00:01:29,360 pretty much from a global context. 22 00:01:29,360 --> 00:01:32,360 And then you sort of board it to what it means to NVIDIA. 23 00:01:32,360 --> 00:01:35,000 So I was just wondering if you don't mind sharing that with the 24 00:01:35,000 --> 00:01:39,000 audience? Generative AI is now at a stage 25 00:01:39,000 --> 00:01:42,080 where it's moving from pilots to become real. 26 00:01:42,160 --> 00:01:46,120 And the best way I would describe it is it's it's almost 27 00:01:46,120 --> 00:01:50,640 like an AI factory that takes your data, which is like the raw 28 00:01:50,920 --> 00:01:55,720 material in that factory, and it's generating digital 29 00:01:55,720 --> 00:01:58,400 intelligence in the form of AI agents. 30 00:01:58,400 --> 00:02:03,080 And these AI agents are making your employees more productive. 31 00:02:03,080 --> 00:02:08,960 They're delivering intelligence that just enables your company 32 00:02:08,960 --> 00:02:13,360 to be much more productive. So now what customers are 33 00:02:13,640 --> 00:02:17,160 companies are doing is fine tuning these models with their 34 00:02:17,160 --> 00:02:22,720 data, whether it's PDFs, documents, customer data and 35 00:02:22,800 --> 00:02:28,360 images and videos and be able to create these digital agents that 36 00:02:29,040 --> 00:02:32,200 are helping your employees become more productive. 37 00:02:32,200 --> 00:02:35,880 And so an example would be shopping advisor, which we're 38 00:02:35,880 --> 00:02:37,640 going to show to you a little bit later. 39 00:02:37,960 --> 00:02:42,000 And this shopping assistant is almost like replicating the 40 00:02:42,000 --> 00:02:45,560 knowledge of all your best sales associate and having that 41 00:02:45,560 --> 00:02:50,080 available 24 by 7 at scale on your e-commerce side to help 42 00:02:50,080 --> 00:02:53,400 your customers find exactly what they're looking for. 43 00:02:53,520 --> 00:02:56,000 What's been top of mind for the retailers that are coming to 44 00:02:56,000 --> 00:02:57,880 talk to you? You know what? 45 00:02:58,160 --> 00:03:03,000 What are they saying as like their top of mind for 2025? 46 00:03:03,080 --> 00:03:06,120 You know, it ranges from everywhere where you know you 47 00:03:06,120 --> 00:03:10,640 have early adopters and you also have companies that it's like it 48 00:03:10,640 --> 00:03:14,320 has to be proven by 5 other companies or 10 other companies 49 00:03:14,640 --> 00:03:18,120 before I would do it. And I would certainly say that 50 00:03:18,800 --> 00:03:23,000 this is so much more real than e-commerce was in the year 2000. 51 00:03:23,000 --> 00:03:26,560 And the companies that are going to adopt it early are going to 52 00:03:26,760 --> 00:03:30,680 absolutely have a competitive advantage over the ones that are 53 00:03:31,080 --> 00:03:32,800 going to be the laggards. Absolutely. 54 00:03:33,120 --> 00:03:38,280 And so, and the people that came to to to see some of our 55 00:03:38,280 --> 00:03:43,000 technology during NRAS, I think range from, you know, 56 00:03:43,000 --> 00:03:47,680 commitments from the very top that, you know, we want to, you 57 00:03:47,680 --> 00:03:52,880 know, in the stores, prevent shrink or have a, you know, more 58 00:03:52,880 --> 00:03:56,480 optimised layout to make it easier for customers to find 59 00:03:56,480 --> 00:03:59,760 what they're looking for and be able to check out faster. 60 00:04:00,000 --> 00:04:04,200 And that's all computer vision technology all the way to how 61 00:04:04,200 --> 00:04:08,760 can you help us improve throughput in our distribution 62 00:04:08,760 --> 00:04:12,680 centres and how to make fulfilment a lot faster in our 63 00:04:12,680 --> 00:04:17,079 fulfilment centres all the way to, you know, what are the top 64 00:04:17,079 --> 00:04:21,760 generated AI use cases that are going to bring business value 65 00:04:21,760 --> 00:04:26,800 to, to our to the companies. And one of the areas that I 66 00:04:26,800 --> 00:04:31,040 highly recommend, even though 20%, less than 20% of retail 67 00:04:31,600 --> 00:04:36,360 revenue comes from e-commerce, I think generative AI has 68 00:04:36,480 --> 00:04:40,920 completely reinvented e-commerce and there was an opportunity to 69 00:04:40,920 --> 00:04:44,920 leverage it to improve search, to improve to have these 70 00:04:44,920 --> 00:04:49,120 shopping assistance on the website and to even create 71 00:04:49,680 --> 00:04:54,560 advertisement that retailers can make revenue from by selling it 72 00:04:54,760 --> 00:04:58,000 to to the consumer packaged good companies. 73 00:04:58,000 --> 00:05:02,600 So I think there's a lot of opportunity to increase revenue 74 00:05:02,600 --> 00:05:06,160 leveraging generative AI on e-commerce and mobile sites. 75 00:05:06,160 --> 00:05:07,120 So. It sounds like there's a real 76 00:05:07,120 --> 00:05:11,640 mix of generate new revenue for retail media, help the customers 77 00:05:11,640 --> 00:05:15,480 have a better experience through the automation and bringing, and 78 00:05:15,480 --> 00:05:18,200 you said this earlier on, bringing all of that knowledge 79 00:05:18,200 --> 00:05:22,680 of your sales to the customers. In terms of Nvidia's focus in 80 00:05:22,680 --> 00:05:27,280 the retail space, I'm curious about where you see it going on. 81 00:05:27,280 --> 00:05:29,960 NVIDIA, most companies know as far GPU. 82 00:05:29,960 --> 00:05:35,960 So our GPU is, is what companies like Open AI perplexity, Meta 83 00:05:35,960 --> 00:05:40,640 are using to train these real large language models and so 84 00:05:40,640 --> 00:05:42,720 forth. But, and there is actually an 85 00:05:42,720 --> 00:05:46,880 accelerated computing company. And what that means is not only 86 00:05:46,880 --> 00:05:52,040 our hardware, which involves our GPUs, our networking, even our 87 00:05:52,240 --> 00:05:57,320 own CPUs, but on top of it, we have a whole set of acceleration 88 00:05:57,320 --> 00:06:00,960 libraries and our application frameworks. 89 00:06:01,240 --> 00:06:04,080 And basically those acceleration library and application 90 00:06:04,080 --> 00:06:09,680 frameworks are the acceleration software that developers use to 91 00:06:09,680 --> 00:06:14,960 build these AI application and to build their, to build and to 92 00:06:14,960 --> 00:06:18,680 deploy, right. And so we're really a platform 93 00:06:18,680 --> 00:06:24,080 for building AI applications. And the blueprint that our CEO 94 00:06:24,080 --> 00:06:29,960 talked about doing his CES Kenu is basically step by step a 95 00:06:29,960 --> 00:06:35,160 workflow of how do you build a particular Gen AI application 96 00:06:35,160 --> 00:06:38,440 using our platform with open source models. 97 00:06:39,320 --> 00:06:43,280 So it's got step by step instructions. 98 00:06:44,080 --> 00:06:48,840 You know, how do you do fine tuning, how do you use RAC, How 99 00:06:48,840 --> 00:06:53,080 do you do the different components of building a general 100 00:06:53,080 --> 00:06:56,040 application? And it even includes the code. 101 00:06:56,360 --> 00:07:00,200 And so that's what helps developers build these 102 00:07:00,200 --> 00:07:05,640 applications a lot faster and ensure that their performance 103 00:07:05,640 --> 00:07:08,680 from an inference perspective, which is the responsiveness of 104 00:07:08,680 --> 00:07:12,640 the generative AI application is the fastest and the most cost 105 00:07:12,640 --> 00:07:14,520 efficient. So when you look to the future, 106 00:07:14,520 --> 00:07:19,960 when you look to the next two or three years, what fee do you see 107 00:07:19,960 --> 00:07:22,240 in from what you've taken from the show? 108 00:07:22,600 --> 00:07:26,520 What do you think will be the the sort of winning focus areas 109 00:07:26,520 --> 00:07:29,680 in AI? Is it in the business? 110 00:07:29,680 --> 00:07:32,320 Is it in front of house? Is it back? 111 00:07:32,360 --> 00:07:34,200 I'm just curious on on your opinion. 112 00:07:34,280 --> 00:07:38,480 I mean, I would say it's almost everywhere, but an area that's 113 00:07:38,480 --> 00:07:41,720 benefiting the most from AI is supply chain. 114 00:07:41,920 --> 00:07:44,760 Got you. And there NVIDIA is really 115 00:07:44,760 --> 00:07:48,160 pioneering two things. One is called physics AI. 116 00:07:48,520 --> 00:07:52,440 And physics AI is AI that understands physics. 117 00:07:52,440 --> 00:07:58,960 It understands dimensions, volume and so forth, but more 118 00:07:58,960 --> 00:08:05,680 importantly, it understands how people and objects behave in a 119 00:08:05,680 --> 00:08:09,760 digital trend of a real space. So it allows you to create a 120 00:08:09,760 --> 00:08:14,000 physically accurate digital representation of a store or 121 00:08:14,000 --> 00:08:19,000 distribution centre, simulate different layouts and be able to 122 00:08:19,240 --> 00:08:23,080 predict accurately how people and objects behave. 123 00:08:23,320 --> 00:08:27,080 And so that's being used for optimising throughput and 124 00:08:27,080 --> 00:08:29,600 distribution centres in your filming centres. 125 00:08:29,600 --> 00:08:34,240 It's used for optimising layout of stores for higher revenue. 126 00:08:34,679 --> 00:08:38,559 And so that's physics AI. Second thing that I talked about 127 00:08:38,559 --> 00:08:44,000 was physical AI. And physical AI is basically AI 128 00:08:44,000 --> 00:08:48,040 for the physical world. And it's a combination of 129 00:08:48,960 --> 00:08:54,440 computer vision, which is driven by smart cameras, robots and 130 00:08:54,600 --> 00:08:58,160 models that understand physics. So it's a combination of our 131 00:08:58,160 --> 00:09:03,760 Omniverse simulation software, our robotics technology, and our 132 00:09:03,760 --> 00:09:07,800 computer vision technology. And it's all used to be able to 133 00:09:07,800 --> 00:09:11,280 train robots in a simulation environment. 134 00:09:11,560 --> 00:09:15,960 And the reality of it is that that in the physical world, you 135 00:09:15,960 --> 00:09:20,760 cannot even estimate how many situations can come up. 136 00:09:21,080 --> 00:09:25,400 But in the, in the, in the digital world, you can actually 137 00:09:25,400 --> 00:09:29,840 simulate thousands of scenarios and be able to train those 138 00:09:29,840 --> 00:09:35,400 robots to behave a certain way during different situations. 139 00:09:35,400 --> 00:09:39,960 So I think we showed a video during the keynote that showed 140 00:09:39,960 --> 00:09:43,800 that an incident happened in one of the aisles and a bunch of a 141 00:09:43,800 --> 00:09:48,680 boxes fell. And so there what what was 142 00:09:48,680 --> 00:09:53,480 happening is the smart cameras were providing perception. 143 00:09:53,800 --> 00:09:57,000 So it's like you're looking down, the AI is looking down to 144 00:09:57,000 --> 00:10:00,320 see what is going on. And then you have an agentic 145 00:10:00,320 --> 00:10:04,880 agent that is now going to has seen this and it's instructing 146 00:10:04,880 --> 00:10:08,920 another agent to reroute the forklift that was coming to go 147 00:10:08,920 --> 00:10:13,480 through a different aisle in order to ensure that, you know, 148 00:10:13,480 --> 00:10:16,400 no safety situation happens, right. 149 00:10:16,600 --> 00:10:22,200 So that's an example of physical AI, which is for the physical 150 00:10:22,200 --> 00:10:25,560 world. And I think that's a good way to 151 00:10:25,560 --> 00:10:28,480 describe this. NVIDIA actually announced our 152 00:10:28,480 --> 00:10:35,280 Cosmo Nematron foundation model, and that's basically a 153 00:10:35,360 --> 00:10:39,400 foundation model that has been trained with billions of hours 154 00:10:39,400 --> 00:10:42,200 of video. Yeah, and it is to the physical 155 00:10:42,200 --> 00:10:46,600 world what tragedy BT has been to text and language. 156 00:10:46,640 --> 00:10:49,080 Yeah, I've been in technology all my life and I'm like 157 00:10:49,160 --> 00:10:52,040 struggling to keep up because my brain is exploding. 158 00:10:52,040 --> 00:10:55,240 Where what NVIDIA have announced and where you're taking it. 159 00:10:55,240 --> 00:10:59,480 If you could go back in time and tell a younger you to come into 160 00:10:59,480 --> 00:11:02,280 this world, I think you've mentioned your career. 161 00:11:03,040 --> 00:11:04,960 Yeah, it was an aerospace. Engineer, that was. 162 00:11:04,960 --> 00:11:06,960 It you were an aerospace engineer and then you sort of 163 00:11:07,160 --> 00:11:09,840 shifted to NVIDIA and you you weren't really meant to end up 164 00:11:09,840 --> 00:11:13,000 in rhythm. But anyway, my question being if 165 00:11:13,000 --> 00:11:15,760 it's OK to ask you about empowerment in terms of I'm 166 00:11:15,760 --> 00:11:19,240 looking at my career, what would be some sort of words of wisdom 167 00:11:19,240 --> 00:11:21,560 that you would if you could go back to a younger self? 168 00:11:21,560 --> 00:11:27,160 I think that really generative AI has democratised AI in many 169 00:11:27,160 --> 00:11:29,920 ways. So I want to encourage, maybe 170 00:11:29,920 --> 00:11:35,320 you don't need to code, but you can still use generative AI to 171 00:11:35,320 --> 00:11:39,120 become so much smarter. So I want to encourage the 172 00:11:39,120 --> 00:11:45,400 younger generation to embrace AI because especially generative AI 173 00:11:45,680 --> 00:11:47,760 is bringing so much intelligence. 174 00:11:47,760 --> 00:11:52,440 And you know, I think I quoted our CEO that said, you know, AI 175 00:11:52,440 --> 00:11:57,800 is not going to take your job, that someone using AI could take 176 00:11:57,800 --> 00:12:00,440 your job. And so I want the younger 177 00:12:00,440 --> 00:12:05,000 generation to embrace AI and use it to, to be a lot more 178 00:12:05,000 --> 00:12:07,200 productive. At the end of the day, I would 179 00:12:07,200 --> 00:12:10,960 say even if you're in science, you got to love what you do. 180 00:12:11,280 --> 00:12:16,120 So my first recommendation is really look around you, look 181 00:12:16,120 --> 00:12:18,920 inside, really understand what your passion is. 182 00:12:19,120 --> 00:12:22,760 Learn as much as you can about what's happening in that area 183 00:12:22,760 --> 00:12:26,560 that you're interested in. Choose the top companies in that 184 00:12:26,880 --> 00:12:31,520 and aim high, aim big. Don't let your insecurity or 185 00:12:31,520 --> 00:12:36,800 your fear hold you back and get out of your comfort zone. 186 00:12:37,320 --> 00:12:41,400 Volunteer for roles that maybe you don't need 100% of the 187 00:12:41,400 --> 00:12:45,120 qualification, but you know, if you even read 60% of it, I'm 188 00:12:45,120 --> 00:12:48,640 sure you're smart enough to learn the rest of it and 189 00:12:48,640 --> 00:12:50,800 surround yourself by super smart people. 190 00:12:50,800 --> 00:12:54,480 Because the more you get out of your comfort zone, the more 191 00:12:54,480 --> 00:12:56,800 you're going to grow and the more you're going to learn. 192 00:12:57,000 --> 00:12:59,360 I love it. And then what a beautiful way to 193 00:12:59,400 --> 00:13:01,560 end the interview. Thank you so much for giving me 194 00:13:01,560 --> 00:13:02,640 your time. Thank you. 195 00:13:02,680 --> 00:13:05,920 Thanks for having me. Hi Alex, I'm Cynthia Contours. 196 00:13:05,920 --> 00:13:09,560 I'm the Director of AI for retail CPG at NVIDIA as is it 197 00:13:09,560 --> 00:13:12,400 was talking to you about our AI blueprint for retail shopping 198 00:13:12,400 --> 00:13:13,960 assistance. Yeah, I'm here to show you a 199 00:13:13,960 --> 00:13:16,320 little bit about it and and answer some questions. 200 00:13:16,600 --> 00:13:20,200 So yeah, we're demonstrating the AI assistant for a number of 201 00:13:20,200 --> 00:13:24,280 different categories. 1 is fashion, the other is furniture. 202 00:13:24,360 --> 00:13:26,360 Two really interesting categories, but also I can talk 203 00:13:26,360 --> 00:13:28,880 to you about grocery as another example. 204 00:13:29,280 --> 00:13:31,760 So let me go ahead and start with, let me go ahead and start 205 00:13:31,760 --> 00:13:33,680 with fashion, which is as an example. 206 00:13:33,960 --> 00:13:36,080 Now one of the things that consumers have really been 207 00:13:36,080 --> 00:13:39,760 struggled, struggling with is it had to change their behaviour to 208 00:13:39,760 --> 00:13:41,440 an e-commerce site. I need to think about what are 209 00:13:41,440 --> 00:13:44,880 the keywords I need to search. And the reality is that's not 210 00:13:44,880 --> 00:13:47,840 the way we work, you know, as you mean this, you know, what 211 00:13:47,840 --> 00:13:50,160 are the upcoming fashion trends for this spring? 212 00:13:50,320 --> 00:13:53,640 Hey, do you have any purses or shoes to go with this skirt? 213 00:13:53,640 --> 00:13:56,120 That's really the way we work. So I'm going to do an example 214 00:13:56,120 --> 00:14:00,000 here of I have an upcoming event that I'm going to a lunch and 215 00:14:00,000 --> 00:14:02,560 I'd like a recommendation for a dress or skirt so. 216 00:14:20,720 --> 00:14:23,320 OK, Alex, see, as you can see, I asked for a dress and skirt 217 00:14:23,320 --> 00:14:26,640 appropriate for an outdoor event and it brought back a dress and 218 00:14:26,640 --> 00:14:28,040 skirt. But since it gave me a skirt, it 219 00:14:28,040 --> 00:14:30,200 also gave me some blouse ideas as well. 220 00:14:30,200 --> 00:14:33,160 Now typically in a search, you come back with just the products 221 00:14:33,440 --> 00:14:36,960 and then I need to start digging in, double click on the product, 222 00:14:37,120 --> 00:14:40,800 go and see more information, and then come back out, double click 223 00:14:40,800 --> 00:14:42,560 again. So then the beautiful thing with 224 00:14:42,560 --> 00:14:45,480 the shopping assistance is I can now just ask questions. 225 00:14:45,920 --> 00:14:48,720 And so with that, let me go ahead and show you how that 226 00:14:48,720 --> 00:14:55,160 works. So I like the skirt, but of 227 00:14:55,160 --> 00:14:59,000 course the natural question is does it require dry cleaning? 228 00:14:59,000 --> 00:15:16,040 So typos and all. 229 00:15:17,320 --> 00:15:20,000 It's AI. It understands. 230 00:15:20,000 --> 00:15:23,440 So here we've got some great results coming back. 231 00:15:24,080 --> 00:15:26,560 I didn't have to go in and look and hunt for that information is 232 00:15:26,560 --> 00:15:29,320 able to go and search for that information, bring it back makes 233 00:15:29,320 --> 00:15:32,120 it, it transforms it from my interactive with the e-commerce 234 00:15:32,120 --> 00:15:33,920 site too. I'm working with your stylist or 235 00:15:33,920 --> 00:15:38,160 your store associate just really naturally the next piece that's 236 00:15:38,160 --> 00:15:42,480 great is then being able to ask multiple questions, all right? 237 00:15:42,680 --> 00:15:45,760 Or being able to say, oh, I saw these pair of shoes online. 238 00:15:45,760 --> 00:15:47,880 Do you have something like that? So let me go ahead and show you 239 00:15:47,880 --> 00:15:50,880 what that looks like. It's really an image search. 240 00:15:50,880 --> 00:15:52,640 So let me go ahead and transition and show you what 241 00:15:52,640 --> 00:15:55,000 that might look like. So I saw these pair of shoes 242 00:15:55,000 --> 00:15:57,680 online. They were really fabulous. 243 00:15:58,080 --> 00:16:01,160 And I want to see if you have something similar for me because 244 00:16:01,160 --> 00:16:02,440 of course they were a little expensive. 245 00:16:02,840 --> 00:16:09,720 So do you have any shoes like these? 246 00:16:12,600 --> 00:16:16,080 And so in the background, we're going and saying, OK, what are 247 00:16:16,080 --> 00:16:19,400 the attributes of this image? Let me see what other products 248 00:16:19,400 --> 00:16:21,320 in the catalogue are similar to this? 249 00:16:21,480 --> 00:16:23,880 It could be red shoes, it could be strappy shoes. 250 00:16:23,880 --> 00:16:25,520 It could be shoes with embellishments. 251 00:16:25,760 --> 00:16:27,720 And in fact, you can see that's what we've got. 252 00:16:27,720 --> 00:16:29,960 We have one pair of shoes. We've got something strappy. 253 00:16:30,560 --> 00:16:34,520 Now this this shoe has an ankle component, has embellishment 254 00:16:34,520 --> 00:16:36,800 components. So I have ankle components and 255 00:16:36,800 --> 00:16:38,520 embellishment components as well. 256 00:16:38,520 --> 00:16:42,080 So it's not a really nice job of finding similar things that also 257 00:16:42,520 --> 00:16:45,800 go with that skirt, right? I can then say, OK, I like these 258 00:16:45,800 --> 00:16:47,240 shoes, but how high are the heels? 259 00:16:47,240 --> 00:16:49,440 What's the price? What's the comfort level? 260 00:16:49,560 --> 00:16:51,840 What are how do people review them and rate them? 261 00:16:51,840 --> 00:16:54,840 So all of those in all that information, I can just ask 262 00:16:54,840 --> 00:16:57,760 naturally, the next piece that I probably would like to show is 263 00:16:57,760 --> 00:16:59,960 the ability for integration, because this is a nice 264 00:16:59,960 --> 00:17:01,720 experience. But the next piece is, well, I 265 00:17:01,720 --> 00:17:04,440 want to be able to add to carts, right? 266 00:17:04,440 --> 00:17:07,760 So it's really simple the. E-commerce website. 267 00:17:08,839 --> 00:17:12,319 This is an e-commerce website. Website that I would have and so 268 00:17:12,319 --> 00:17:16,160 the images there are they catalogue images I as the 269 00:17:16,160 --> 00:17:18,599 retailer provides. These are catalogue images that 270 00:17:18,599 --> 00:17:20,160 are in your retail catalogue right now. 271 00:17:20,160 --> 00:17:23,240 So we're keeping it very simple. I can do an example though of 272 00:17:23,240 --> 00:17:26,280 visualisation, right? And I'm happy to show that. 273 00:17:26,280 --> 00:17:28,280 And I'm doing that in a furniture example. 274 00:17:29,080 --> 00:17:32,040 So the other piece is being able to search for multiple things at 275 00:17:32,040 --> 00:17:34,120 once, right? You can go ahead and search for 276 00:17:34,280 --> 00:17:36,240 I want purses and shoes to go with a skirt. 277 00:17:36,240 --> 00:17:39,400 Do you have any tops? And to go with this, Kirk, all 278 00:17:39,400 --> 00:17:42,400 of that is just very natural. So Azita may have mentioned a 279 00:17:42,400 --> 00:17:45,320 furniture demo where you're visualising furniture in your 280 00:17:45,320 --> 00:17:47,960 room because furniture is obviously considered purchase. 281 00:17:48,240 --> 00:17:51,200 There's challenges to oh, I like it, but how's it going to look 282 00:17:51,200 --> 00:17:53,480 in my room? The price point in that is 283 00:17:53,600 --> 00:17:56,520 lower, right? But I'm not sure about it if 284 00:17:56,520 --> 00:17:58,840 it's going to work and being able to play and actually 285 00:17:58,840 --> 00:18:00,120 visualise it. So let's go ahead and let's 286 00:18:00,120 --> 00:18:03,960 transition to that next. OK, so as a consumer, I used 287 00:18:03,960 --> 00:18:06,960 your mobile app and I scanned my room to create a 3D room. 288 00:18:06,960 --> 00:18:09,240 So we're going to go ahead and ask to pull that information up. 289 00:18:10,120 --> 00:18:21,510 So I'm going to go on site. So what it's doing is it's 290 00:18:21,510 --> 00:18:23,870 saying, OK, this is Cynthia, she's got a living room. 291 00:18:24,360 --> 00:18:26,960 Let's take that 3D skin of the living room and we're putting it 292 00:18:26,960 --> 00:18:30,840 into Omniverse and VS 3D collaboration platform. 293 00:18:31,040 --> 00:18:33,720 So this is a, this is a 3D scan of my room. 294 00:18:33,720 --> 00:18:35,840 This is not a 2D photo of my room. 295 00:18:35,840 --> 00:18:38,600 Yeah, right. And so I have objects in here. 296 00:18:38,600 --> 00:18:42,800 I have a couch, I have a table, I have a chair, for example. 297 00:18:43,200 --> 00:18:46,400 And so the first thing is obviously I don't have a case of 298 00:18:46,400 --> 00:18:48,520 luck, right? I want to, I want to now bring 299 00:18:48,600 --> 00:18:53,680 change my room and make a case. So do you have any modern 300 00:18:53,680 --> 00:18:56,400 couches? Let's start there. 301 00:18:58,040 --> 00:19:00,800 And very similar to the fashion example, it's going to go back. 302 00:19:00,800 --> 00:19:03,280 It's going to bring back some recommendations for me. 303 00:19:03,480 --> 00:19:06,280 It's going to give me information about those couches. 304 00:19:06,280 --> 00:19:09,040 And so here we have 3 couches, right? 305 00:19:09,440 --> 00:19:11,440 I've got something that's very architectural. 306 00:19:11,440 --> 00:19:12,880 I have something that's a little softer. 307 00:19:12,880 --> 00:19:15,680 I like that one better. And then I've got a curve couch 308 00:19:15,800 --> 00:19:17,680 that's interesting, but I'm not sure about it. 309 00:19:17,760 --> 00:19:21,760 But the one I like that modern sectional is on the high end for 310 00:19:21,760 --> 00:19:23,720 me in price. The one that I don't like, but 311 00:19:23,720 --> 00:19:25,760 it's outside of my price range. And then there's the curb caps. 312 00:19:25,760 --> 00:19:27,280 That's much more affordable for me. 313 00:19:27,280 --> 00:19:28,640 So let me go ahead and explore it. 314 00:19:28,640 --> 00:19:31,720 I might not necessarily explore it or be deterred that, you 315 00:19:31,720 --> 00:19:34,080 know, because of price points at this point. 316 00:19:34,080 --> 00:19:41,120 So let me go ahead and just explore replacing my couch with 317 00:19:41,800 --> 00:19:45,920 the curb couch. Now notice I'm not giving the 318 00:19:45,920 --> 00:19:48,440 product name or anything like that, right? 319 00:19:48,480 --> 00:19:52,440 The generatory I, the agent is understanding it and then it's 320 00:19:52,440 --> 00:19:54,640 going ahead and giving command back a tape. 321 00:19:55,080 --> 00:19:58,520 I've got a database right, of the 3D assets for my furniture. 322 00:19:59,000 --> 00:20:02,000 So I'm replacing the council was in there with this 3D asset of 323 00:20:02,000 --> 00:20:03,360 furniture. So I can see it. 324 00:20:03,360 --> 00:20:05,600 What is it doing? Oh, now it's really transformed 325 00:20:05,600 --> 00:20:06,920 my room. It's making it more curved 326 00:20:06,920 --> 00:20:09,280 instead of squared, but it's still not crazy about it. 327 00:20:09,280 --> 00:20:11,440 It was right. We can do better, right? 328 00:20:11,520 --> 00:20:12,920 We can do better. So let me try that other. 329 00:20:13,000 --> 00:20:16,000 There's a couch that's kind of on the high end, but let me let 330 00:20:16,000 --> 00:20:26,360 me try that instead, right? So here's something that that I 331 00:20:26,360 --> 00:20:29,360 may not have explored it because of price points, right. 332 00:20:31,960 --> 00:20:33,400 And here we go. Oh, I like that. 333 00:20:33,560 --> 00:20:37,720 Yeah, OK, I really like that. But I am going to need something 334 00:20:37,720 --> 00:20:40,680 round in the room. And I did see a table that I 335 00:20:40,680 --> 00:20:43,560 liked again, you know, I'm using examples outside of my price 336 00:20:43,560 --> 00:20:46,880 range or I just want to find something like this fast. 337 00:20:46,920 --> 00:20:51,600 OK, OK, so I've gone ahead and I've I've uploaded the image. 338 00:20:51,920 --> 00:20:58,440 Do you have a table like this? No, I'm not asking coffee table 339 00:20:58,440 --> 00:21:01,920 like this nesting coffee table like this marble top coffee 340 00:21:01,920 --> 00:21:03,720 table like this. So I think as you may have 341 00:21:03,720 --> 00:21:07,080 talked to you about about the ability to search on images and 342 00:21:07,080 --> 00:21:10,120 not only did it give me the coffee table, but it also gave 343 00:21:10,120 --> 00:21:12,960 me some recommendations for chairs right as a part of this. 344 00:21:13,200 --> 00:21:15,960 So love it. It came really close to what I'm 345 00:21:15,960 --> 00:21:24,480 looking for so. And there we go. 346 00:21:24,600 --> 00:21:26,400 All right, I love it. That gives me some of the 347 00:21:26,400 --> 00:21:28,680 roundness, you know, for the room. 348 00:21:29,960 --> 00:21:31,280 And you can imagine going further. 349 00:21:31,280 --> 00:21:33,520 So let me go ahead. I think I talked about doing a 350 00:21:33,520 --> 00:21:35,840 multiple query search. Let me show you what that was 351 00:21:35,840 --> 00:21:37,840 like. Because now that I got my couch 352 00:21:37,840 --> 00:21:41,200 and my table, right, maybe I want floor lamps, right? 353 00:21:41,200 --> 00:21:43,520 And alternative chairs or something of that nature, right? 354 00:21:56,200 --> 00:21:58,560 Now, more than likely in this scenario, it's going to give me 355 00:21:58,560 --> 00:22:01,680 similar chairs back, but let's see what it let's see what it 356 00:22:01,680 --> 00:22:05,960 does. OK, I came back with three floor 357 00:22:05,960 --> 00:22:09,840 lamps as different examples that would work in this environment, 358 00:22:10,640 --> 00:22:13,400 as well as one chair that it really highly recommended. 359 00:22:14,560 --> 00:22:16,440 I'm not so crazy about the floor lamps. 360 00:22:17,320 --> 00:22:19,760 I'm not so sure about the chair, but let me go ahead and explore 361 00:22:19,760 --> 00:22:22,520 it, right. This is something that's you're 362 00:22:22,600 --> 00:22:25,240 putting control in the hands of the consumer to explore and 363 00:22:25,240 --> 00:22:28,600 visualise, to then say, oh, yeah, I do love it, or yeah, 364 00:22:28,600 --> 00:22:30,480 that didn't work out. So I hope this is giving you a 365 00:22:30,480 --> 00:22:34,480 sense of just the power of generative AI and AI agents for 366 00:22:34,480 --> 00:22:37,480 retail shopping assistance. So I'm sure the next question 367 00:22:37,480 --> 00:22:38,720 is, you know, how do I find out more? 368 00:22:38,720 --> 00:22:42,520 So you come to nvidia.com and there's a full page on retail 369 00:22:42,520 --> 00:22:45,440 shopping assistance. So we create an AI blueprint 370 00:22:45,760 --> 00:22:50,360 that includes an AI agent. It includes Nemo components for 371 00:22:50,360 --> 00:22:56,120 guardrails, a pointer to Llama 3.370, BA large language model, 372 00:22:56,120 --> 00:22:59,920 being able to rank, being able to understand imagery, a an 373 00:22:59,920 --> 00:23:01,800 accelerated database, vector database. 374 00:23:02,400 --> 00:23:05,600 All of this is available in the blueprint for people to explore. 375 00:23:05,600 --> 00:23:08,840 Try it out, Import your own product, catalogue information 376 00:23:08,840 --> 00:23:11,760 and imagery, play with it, Start to change out components that 377 00:23:11,760 --> 00:23:14,240 make sense for you, and then ultimately get to the point 378 00:23:14,240 --> 00:23:16,120 where you might want to deploy it for yourself. 379 00:23:16,800 --> 00:23:19,000 Where do people? Start a good question. 380 00:23:19,040 --> 00:23:21,440 The place to start is sign up to be notified. 381 00:23:21,440 --> 00:23:25,840 We have an upcoming package, Rev launchable blueprint, so it'll 382 00:23:25,840 --> 00:23:28,160 be available for your teams to click a button. 383 00:23:28,160 --> 00:23:32,000 It'll be unpacked and put on a clouds environment for them to 384 00:23:32,000 --> 00:23:34,360 start starting with immediately. They don't need to know how to 385 00:23:34,800 --> 00:23:36,840 download and install it. We're making it as simple as 386 00:23:36,840 --> 00:23:39,040 possible for for your teams to get started. 387 00:23:39,840 --> 00:23:42,160 You can also work with a number of our partners as well.