WEBVTT 1 00:00:00.080 --> 00:00:04.639 Atop a Falcon nine booster is set to lift off from Launch Complex thirty nine 2 00:00:04.679 --> 00:00:09.000 a the Kennedy Space Center at ten fifty three pm Eastern. I'm Chris Karragio, 3 00:00:09.119 --> 00:00:16.559 NBC News Radio, NBC News on CACAA Lomolinda, sponsored by Teamsters Local 4 00:00:16.640 --> 00:00:21.920 nineteen thirty two, Protecting the Future of Working Families Teamsters nineteen thirty two dot 5 00:00:22.039 --> 00:00:34.079 org. The information economy has a rid. The world is teeming with innovation 6 00:00:34.240 --> 00:00:40.000 as new business models reinvent every industry industry. Inside Analysis is your source of 7 00:00:40.119 --> 00:00:44.600 information and insight about how to make the most of this exciting new era. 8 00:00:44.840 --> 00:00:50.560 Learn more at inside analysis dot comsideanalysis dot com, and now here's your host, 9 00:00:51.039 --> 00:01:03.159 Eric Kavanaugh. Indeed, ladies and gentlemen, welcome to the future. 10 00:01:03.320 --> 00:01:07.840 It's careening toward us all the time, but it seems like it's coming faster 11 00:01:07.000 --> 00:01:11.680 these days than ever before. I'm not sure exactly why that's true. They 12 00:01:11.719 --> 00:01:14.079 told me when I got older, time I go buy faster, so maybe 13 00:01:14.079 --> 00:01:19.400 that's what's happening. But I could swear this Jenai stuff has just put everything 14 00:01:19.400 --> 00:01:22.959 at light speed, certainly for the enterprise, but also for small business, 15 00:01:22.959 --> 00:01:26.680 for anyone who's played around with this stuff. It is a leap into the 16 00:01:26.680 --> 00:01:32.040 future. And as the futurist William Gibson once said, the future is here 17 00:01:32.079 --> 00:01:37.120 already, it's just not evenly distributed. And I love that expression because it 18 00:01:37.159 --> 00:01:42.040 really speaks to the pockets of innovation that you see around the world and helps 19 00:01:42.079 --> 00:01:48.159 you understand that new technologies and new methods do take time to percolate through society, 20 00:01:48.200 --> 00:01:53.840 to permeate to various circles in the business world or in the consumer world, 21 00:01:53.840 --> 00:01:57.599 for example. And this generative AI stuff affects darn near everything, and 22 00:01:57.640 --> 00:02:02.719 it's really open the eyes of businesses worldwide. I mean the numbers we're hearing 23 00:02:02.760 --> 00:02:06.799 are just off the charts in terms of the number of people using this stuff 24 00:02:06.840 --> 00:02:12.039 already. I mean, hundreds of millions of people are using this technology already 25 00:02:12.159 --> 00:02:16.000 today and it's more or less brand spanking new. So it was twenty twenty 26 00:02:16.000 --> 00:02:21.280 two that it was released in general chat GBT three I believe are three point 27 00:02:21.319 --> 00:02:24.080 five. We're up to like four point five now. Of course Google has 28 00:02:24.199 --> 00:02:30.319 barred now it's called Gemini, anthropic has clawed there or other. There's Mistral 29 00:02:30.479 --> 00:02:34.599 that has the interesting approach. They're taking with smaller models. But the bottom 30 00:02:34.599 --> 00:02:37.759 line is that Jennai is everywhere, and today we're going to dive in with 31 00:02:37.800 --> 00:02:43.879 two awesome experts. We've got Baoja Bui from FPT. It's a Vietnamese company, 32 00:02:44.159 --> 00:02:46.759 amazing company. I went to Vietnam last year to check out their headquarters 33 00:02:46.759 --> 00:02:52.319 and everything they do and was blown away. Really impressive company, very hard 34 00:02:52.360 --> 00:02:55.039 working, very diligent, very focused. They get stuff done. They're not 35 00:02:55.039 --> 00:03:00.599 worried about the flash and anything silly. They're just focused on getting the work 36 00:03:00.680 --> 00:03:04.439 done and building cool technologies. So we have Baoha to talk about that, 37 00:03:04.719 --> 00:03:09.080 and Jim Wilt as well is a fractional CTO and distinguished engineer. And these 38 00:03:09.120 --> 00:03:13.800 folks are really sparked. So in studio of audience, live studio audience, 39 00:03:13.800 --> 00:03:15.960 don't be shy. Asked them the hard questions on a first to say my 40 00:03:16.120 --> 00:03:20.120 take on what Jenai is, and I'll throw it over to Booja and over 41 00:03:20.199 --> 00:03:24.240 to Jim as well. Really, these Jenai engines are predictive engines. That's 42 00:03:24.240 --> 00:03:28.919 what they really boil down to. They are predicting either the kind of image 43 00:03:29.000 --> 00:03:31.240 they think you want based on your prompt or the kind of text you want 44 00:03:31.319 --> 00:03:36.599 based upon your prompt. Now, they were trained on massive amounts of data 45 00:03:36.639 --> 00:03:40.159 on the interwebs, not on everything we learned a couple months ago and one 46 00:03:40.199 --> 00:03:45.919 of our first shows of the year that even the folks at open Ai realized 47 00:03:45.919 --> 00:03:49.000 early in the game they can't just train this on any data that's out there. 48 00:03:49.000 --> 00:03:52.199 They have to focus on certain data sets to get the end result they 49 00:03:52.240 --> 00:03:54.680 want, which is an engine that will reflect back the kind of text that 50 00:03:54.719 --> 00:03:59.159 you want. And it's very powerful. Folks. If you haven't used this 51 00:03:59.159 --> 00:04:01.319 stuff, you need to start playing around with it. And you first start, 52 00:04:01.479 --> 00:04:04.000 then you understand what it does. Then you can kind of get digged 53 00:04:04.000 --> 00:04:06.560 deeper into the weeds to figure out how to use it. But they're basically 54 00:04:06.599 --> 00:04:10.199 predictivens, is what they really are when you get right down to it. 55 00:04:10.439 --> 00:04:14.960 So at that Boha, tell us a bit about yourself and what you see. 56 00:04:15.000 --> 00:04:16.680 What is Jenny and I to you, and what should people use it 57 00:04:16.680 --> 00:04:23.560 for? Yeah, well, thank you Eric for that comments that we are 58 00:04:23.600 --> 00:04:29.560 not flashy. I want to be flashy, but we just we techy people 59 00:04:29.800 --> 00:04:33.160 just can't be flashy, So it's a skill I have to up myself for. 60 00:04:34.160 --> 00:04:39.959 But that said, though, so bo. I've been in IT and 61 00:04:40.040 --> 00:04:45.439 technologies for almost thirty years now, or even more than that. I stop 62 00:04:45.519 --> 00:04:53.959 counting the moment we hit above twenty my so been with IT technology. I've 63 00:04:54.000 --> 00:04:59.639 been CIOs, I've been vps, I've been on the application development side, 64 00:05:00.079 --> 00:05:05.439 been on products. I've been but since twenty fifteen, No, wait, 65 00:05:05.519 --> 00:05:12.680 twenty ten. I you know, I got introduced into data management, and 66 00:05:13.480 --> 00:05:17.879 you know the journey starts there. I got passionate about just making informations out 67 00:05:17.920 --> 00:05:21.639 of data. You know, how do I get that information into the hand 68 00:05:21.759 --> 00:05:29.639 of the users, the executives, the people who's driving trucks, not needing 69 00:05:29.720 --> 00:05:33.480 to know when I have to deliver a certain product to a certain customers of 70 00:05:33.600 --> 00:05:39.360 patients. I was in healthcare for ten years at that point. Sale my 71 00:05:39.600 --> 00:05:44.920 whole the introduction to data, I was with the HIE. You know, 72 00:05:45.000 --> 00:05:49.319 if you still remember those terms, you know, the data for healthcare was 73 00:05:49.360 --> 00:05:55.399 a big thing back in two thousand and five ten. So that's my introduction 74 00:05:55.519 --> 00:05:59.560 to data. Now I've been with FPT, I've been their customers, I've 75 00:05:59.600 --> 00:06:05.519 been working with them, and I really appreciate your confirmations that you know, 76 00:06:05.560 --> 00:06:13.920 they are hardworking, very smart as a customer. I was constantly amazed by 77 00:06:14.000 --> 00:06:18.439 the level of professionalisms that I got from Amputy. So not a plot, 78 00:06:18.480 --> 00:06:26.040 I'm just kind of recounting my journey here. What is jen Ai? You 79 00:06:26.120 --> 00:06:30.439 know, Jenei? Like you said, it starts out to be or aio, 80 00:06:30.560 --> 00:06:33.279 all right, It starts out to be predictive. You have data, 81 00:06:33.360 --> 00:06:39.360 you have the ability to use code to really mind that data and then generate 82 00:06:39.399 --> 00:06:44.560 information based on that data or predict based on the patterns that came from that 83 00:06:44.680 --> 00:06:50.600 data. And then it gets into the ability to really now using patterns to 84 00:06:50.680 --> 00:06:57.879 start producing new material. So that's what Jenei is for me. And where 85 00:06:57.920 --> 00:07:01.839 are we going with jen Ai? It's everywhere, it's you know, Jim. 86 00:07:01.879 --> 00:07:06.480 With Jim and I talked a lot about jen Ai and the growth of 87 00:07:06.600 --> 00:07:12.439 jen Ai and Ai overall. But Jim, it took fifteen years for cloud 88 00:07:12.480 --> 00:07:15.920 to be adopted. I still, you know a few years ago there was 89 00:07:15.959 --> 00:07:23.120 a company that's still not looking at cloud. But jen Ai in two years 90 00:07:23.480 --> 00:07:27.959 we got widespread adoptions. I talked to my son. My son is doing 91 00:07:28.000 --> 00:07:31.199 a co op at a farmer company, and I asked him, so, 92 00:07:31.279 --> 00:07:34.879 what are you doing over you know, your bread time oh, he's using 93 00:07:34.959 --> 00:07:45.120 jen Ai to do some optimizations in their lapwork RNA recognitions RNA pattern So you 94 00:07:45.160 --> 00:07:49.920 know, it goes from a corporate level to students. You know how many 95 00:07:50.199 --> 00:07:58.199 the professors trying to prevent their student from generating ai jen ai for papers. 96 00:07:58.399 --> 00:08:01.399 But is that not a bad thing because at the end of the day, 97 00:08:01.160 --> 00:08:07.360 is why reciting information that you can get from Google just put it in your 98 00:08:07.360 --> 00:08:13.639 own words? Is that you know, so again permeate every aspect of our 99 00:08:13.680 --> 00:08:20.439 life nowadays. So we all have to get onto that bandwagon. That's a 100 00:08:20.439 --> 00:08:22.279 great way to put it on. Like the way you said that, where 101 00:08:22.319 --> 00:08:28.199 are we going with Jenny? I everywhere? I'll throw it over to Jim 102 00:08:28.279 --> 00:08:31.839 to comment on that. I have to say, I mean I watched the 103 00:08:31.839 --> 00:08:35.679 whole big data craze take off fifteen seventeen years ago. Let's say that was 104 00:08:35.720 --> 00:08:41.120 the biggest thing I'd ever seen. This is like five to ten times bigger 105 00:08:41.159 --> 00:08:43.639 than that in terms of the number of people getting in, in terms of 106 00:08:43.679 --> 00:08:46.879 the impact, in terms of the number of people and impacts, it's just 107 00:08:46.879 --> 00:08:50.039 off the charts. But what do you think, Jim, Yeah, it's 108 00:08:50.120 --> 00:08:54.600 interesting. You both have really covered a lot of the basics that we wanted 109 00:08:54.639 --> 00:08:58.960 to, you know, get out there in terms of the history and where 110 00:08:58.960 --> 00:09:05.080 it's come. Baha's correct, and looking at adoption of the PC was you 111 00:09:05.080 --> 00:09:09.840 know, about ten years, fifteen years to get to fifty million users. 112 00:09:09.879 --> 00:09:13.919 That took you know, about ten years to fifteen years for cloud adoption to 113 00:09:15.000 --> 00:09:20.720 really land. And when we look at machine learning and generator of AI, 114 00:09:20.840 --> 00:09:24.879 machine learning has been around for ten years plus, it's been around. There 115 00:09:24.879 --> 00:09:28.840 are people that have been working in the space for over twenty thirty years with 116 00:09:28.960 --> 00:09:33.240 neural networks and so forth. But the big difference with generative AI is is 117 00:09:33.399 --> 00:09:37.919 it's really applicable to the common person. Anybody can get into a prompt situation 118 00:09:37.039 --> 00:09:41.480 to some learn some prompt engineering, if you will, and start getting results 119 00:09:41.519 --> 00:09:46.679 that are just massively interesting. And I think that, you know, the 120 00:09:46.720 --> 00:09:52.240 statistic that hit me that Baja mentioned is actually it was two months generative AI 121 00:09:52.399 --> 00:09:58.120 has reached one hundred million users. That's twice what it took these other technologies 122 00:09:58.120 --> 00:10:03.159 what ten you know, fifteen years to achieve. You know, the Internet 123 00:10:03.200 --> 00:10:07.000 took five years to achieve fifty million users. It's already surpassed the airnet's adoption 124 00:10:07.120 --> 00:10:13.519 in just two months. What's also exciting about Generator of AI is that we're 125 00:10:13.559 --> 00:10:20.120 traditional AI is using what's traditionally called supervised learning. Baja mentioned it's and you 126 00:10:20.200 --> 00:10:24.440 did too, Eric. It's mentioned that it's based on a lot more data, 127 00:10:24.519 --> 00:10:28.399 and that's called unsupervised learning. What that allows it to do is to 128 00:10:28.480 --> 00:10:31.720 create, when I'm going to say, patterned recognitions and results that they're out 129 00:10:31.720 --> 00:10:37.200 of our view. So it looks like it's thinking, if you will, 130 00:10:37.279 --> 00:10:39.879 on its own, but it's working with such massive data. It's it's really 131 00:10:39.919 --> 00:10:45.200 really exciting to see that it can find patterns that we normally wouldn't find ourselves. 132 00:10:45.440 --> 00:10:48.840 And I think that's one of the things that makes it that aha moment 133 00:10:48.879 --> 00:10:54.320 that oh this is different. It's accessible, and it produces results that are 134 00:10:54.639 --> 00:11:01.840 pretty transformational because it's putting together patterns that we expect to see that are rallied. 135 00:11:03.120 --> 00:11:05.919 Yeah, that's a great way to put it. And maybe Booja, 136 00:11:05.960 --> 00:11:09.679 I'll throw this one over to you. What I've seen with AI and other 137 00:11:09.720 --> 00:11:13.240 capacities too, and now of course with gen AI is that almost invariably it 138 00:11:13.360 --> 00:11:20.840 surfaces something that I did not expect, and that's very interesting because typically any 139 00:11:20.879 --> 00:11:26.720 AI algorithm is just a recipe that is designed to address a particular type of 140 00:11:26.799 --> 00:11:31.519 situation. Let's say a particular function, a particular array, or something that 141 00:11:31.559 --> 00:11:37.200 happens in the world. You're trying to predict what happens or optimize what happens. 142 00:11:37.679 --> 00:11:41.000 And you would think that we know ourselves well enough to not be surprised 143 00:11:41.000 --> 00:11:43.399 by our own behavior, but that's just not the case. Like and so 144 00:11:43.559 --> 00:11:48.440 every time I use one of these engines, it throws up something I did 145 00:11:48.440 --> 00:11:52.720 not expect, and that's good news because that's it triggers the creative process, 146 00:11:52.080 --> 00:11:56.759 and it makes us think more. What do you think? I think I 147 00:11:56.799 --> 00:12:03.759 absolutely agree with you. I think that we, as great as our human 148 00:12:03.799 --> 00:12:09.360 brains are, we have limited capacity or you know, how fast we can 149 00:12:09.440 --> 00:12:13.320 generate, oh, how much we can retrieve. So it's not I don't 150 00:12:13.320 --> 00:12:20.639 think that it's a it's a I think it's more of a retrieval, you 151 00:12:20.679 --> 00:12:26.159 know ability, whereas the machines it's gonna take back. It's gonna take everything 152 00:12:26.200 --> 00:12:28.919 that it knows and give it back to you, and it reminds you of 153 00:12:30.080 --> 00:12:33.799 things that hey, I knew this, but I didn't remember. And that's 154 00:12:33.879 --> 00:12:41.399 where the benefits or the efficiency gains in using generative AI is so great. 155 00:12:41.440 --> 00:12:48.399 It's because it really takes away your the need for us to remember everything that 156 00:12:48.440 --> 00:12:52.559 you have to you know, that's why we're saying the old day, but 157 00:12:52.720 --> 00:12:58.559 it's really is still there. You know, you have document template because people 158 00:12:58.600 --> 00:13:05.840 cannot remember everything every single time, so you need template to repeat to save 159 00:13:05.919 --> 00:13:09.960 time. You don't have to recreate things all the time. Generative AI is 160 00:13:11.080 --> 00:13:16.240 able to really do that retrieval of your template and give it all back to 161 00:13:16.320 --> 00:13:22.279 you very timely. But you know, retrieval and knowing what to retrieve and 162 00:13:22.320 --> 00:13:26.080 when to retrieve is also important because yeah, as much as we like to 163 00:13:26.200 --> 00:13:31.279 get everything back, you don't always want everything that JENEI give back to you, 164 00:13:31.480 --> 00:13:35.279 at least quite yet. Well, and that's a comment I had made 165 00:13:35.360 --> 00:13:39.679 before our show. I was talking with Mark Palmer, who was a real 166 00:13:39.919 --> 00:13:43.399 smart executive in the tech space, and he mentioned that he read some research 167 00:13:43.440 --> 00:13:48.000 recently that showed something like eighty percent of people who are using jen ai right 168 00:13:48.039 --> 00:13:52.279 now are just taking whole hog, whatever it comes out of that engine and 169 00:13:52.360 --> 00:13:54.559 using it. That has a very bad idea right about how you want to 170 00:13:54.759 --> 00:13:58.679 always vet that stuff Oh. Absolutely, it will give you good ideas, 171 00:13:58.759 --> 00:14:01.519 but you have to be careful because number one, it does hallucinate, so 172 00:14:01.559 --> 00:14:05.399 it does make stuff up. It is generative by nature, so it generates 173 00:14:05.440 --> 00:14:09.399 new things based upon old things. Right about what it's seen. All it's 174 00:14:09.399 --> 00:14:13.519 doing is reflecting back patterns of what it's been trained on. But you have 175 00:14:13.559 --> 00:14:18.399 to be very careful about vetting that stuff, right huh. Oh, absolutely, 176 00:14:18.559 --> 00:14:24.039 you have to because it's trained on informations out there, you know, 177 00:14:24.159 --> 00:14:31.279 especially some of the open source gen ai open ai for example, it's being 178 00:14:31.399 --> 00:14:37.720 trained on the Internet, and not everything on the internet is usable. Just 179 00:14:37.799 --> 00:14:43.279 be careful. Not everything is accurate, right, Not everything is accurate and 180 00:14:43.320 --> 00:14:48.840 even when so that's kind of going into some of the rest that when we 181 00:14:48.840 --> 00:14:50.399 well, you know, what we have to do in order for us to 182 00:14:50.440 --> 00:14:56.879 be ready for gen AI is some of those vetting the under the training of 183 00:14:58.000 --> 00:15:03.799 your resources to make sure they know how to use it safely and effectively. 184 00:15:05.039 --> 00:15:07.679 Yeah, and that's the curation process, right. So we keep hearing about 185 00:15:09.039 --> 00:15:13.320 RAG models, retrieval, augmented generation, which, if I understand it, 186 00:15:13.679 --> 00:15:18.759 really what you're doing is you are trying to use your own trusted, governed 187 00:15:18.840 --> 00:15:22.080 data as an anchor, and then you want to be able to spin up 188 00:15:22.200 --> 00:15:28.320 text using the LM as a text generative capacity if you will. But you 189 00:15:28.360 --> 00:15:31.960 wanted to use your facts that you give it, and those facts are posted 190 00:15:31.000 --> 00:15:35.440 as embeddings either in the model itself, which some people do, or in 191 00:15:35.519 --> 00:15:41.639 a vector database which is then adjacent to the model. And people understand the 192 00:15:41.039 --> 00:15:46.159 whole concept of persisting something in the database and then recalling something from the database. 193 00:15:46.480 --> 00:15:48.960 But here we've added a third element, which is the language model to 194 00:15:50.200 --> 00:15:54.200 generate stuff. But still the same concept applies. Right, We're persisting something 195 00:15:54.240 --> 00:15:58.720 in a data store, and then we're leveraging that along with the generative capability 196 00:15:58.720 --> 00:16:03.000 of the large language model to create fresh new text that is ideally accurate with 197 00:16:03.039 --> 00:16:07.799 what our corporate data says, right bouh In a way, yes, so 198 00:16:07.120 --> 00:16:12.840 you know we you know, if you're using your data alone and retrieval and 199 00:16:12.879 --> 00:16:19.879 then to supplement it with the large language to to make the interactions easier or 200 00:16:19.919 --> 00:16:26.039 to generate it's in text and it's limited. But the ability of gen AI 201 00:16:26.200 --> 00:16:30.879 to you know, so large language model gives you the ability to read the 202 00:16:30.960 --> 00:16:37.120 data that in a database just not possible you know pdf my You know some 203 00:16:37.240 --> 00:16:42.879 of the older companies that I have worked with, the Banks Insurance Company, 204 00:16:44.240 --> 00:16:49.200 they have one hundred years of the documents that's probably still in some kind of 205 00:16:49.360 --> 00:16:56.919 fish or images. Large language model nowadays give us that capability to really retrieve 206 00:16:56.960 --> 00:17:03.840 that information. And you said either ambed or vector base it, so that 207 00:17:03.000 --> 00:17:07.000 now you can retrieve it. So that's the advantage of some of the newer 208 00:17:07.160 --> 00:17:14.240 capability. And then based on that, then it gives the ability for us 209 00:17:14.279 --> 00:17:22.079 to interact with it using natural language. So that kind of expand the resource 210 00:17:22.160 --> 00:17:27.400 pool of who can rechieve that kind of information versus before all the tech people 211 00:17:27.480 --> 00:17:32.640 have to write some kind of query. Right now you have to you know, 212 00:17:32.720 --> 00:17:37.680 a much larger resource who can comp for those right And see, this 213 00:17:37.759 --> 00:17:41.279 is I mean, you just hit on one of the biggest transformations that we're 214 00:17:41.279 --> 00:17:44.519 seeing right now. And I heard it from my buddy Loo Simon with a 215 00:17:44.559 --> 00:17:47.920 company called up Tom here in Pittsburgh. He's the one who first said it 216 00:17:47.920 --> 00:17:51.000 to me. And this is so big and we'll talk about this after the 217 00:17:51.039 --> 00:17:52.799 break, but this is a seriously big deal. We used to always have 218 00:17:52.839 --> 00:17:56.920 to learn how to speak in computer language in order to talk to computers. 219 00:17:57.039 --> 00:18:00.480 So we had to learn to speak SEQL to struct query language to talk to 220 00:18:00.599 --> 00:18:07.240 databases. We had to learn Python to talk to various Python apps or that's 221 00:18:07.319 --> 00:18:08.839 used for risk management, lots of different stuff. Honestly, you had to 222 00:18:08.880 --> 00:18:14.519 learn cobol in order to talk to a mainframe for example. Now you can 223 00:18:14.519 --> 00:18:18.160 talk in English words to the computer systems. That is a huge deal. 224 00:18:18.160 --> 00:18:30.400 We'll be right back here listening to Inside Analysis. Respect, welcome back to 225 00:18:30.599 --> 00:18:37.519 Inside Analysis. Here's your host, Eric Tabanac. Take us all right, 226 00:18:37.640 --> 00:18:41.319 folks, take us to the future and see if we're talking about jen Ai. 227 00:18:41.759 --> 00:18:45.119 It is futuristic. It's amazing, folks. Let's be honest, play 228 00:18:45.119 --> 00:18:48.640 around with this stuff. It's going to blow your mind. A friend of 229 00:18:48.680 --> 00:18:52.319 mine turned me on to mid Journey, which is through uh discord. I 230 00:18:52.319 --> 00:18:56.160 guess it's a discord channel, and oh my goodness, it's crazy. The 231 00:18:56.200 --> 00:19:00.440 amount of artwork and the sophistication of the art that you can create with this 232 00:19:00.480 --> 00:19:04.839 stuff. It's just wild. So things are changing rapidly. I remember hearing 233 00:19:04.920 --> 00:19:10.720 who is saying, whoever wins this war wins the world. Now again, 234 00:19:10.759 --> 00:19:14.279 there are use cases for this stuff. There are dangerous sides of it, 235 00:19:14.319 --> 00:19:15.400 deep fakes, things of this nature. We're going to talk about some of 236 00:19:15.440 --> 00:19:18.960 that, but this segment I wanted to get deeper into are you ready and 237 00:19:19.039 --> 00:19:22.319 how do you know? So? How do you know if you're ready to 238 00:19:22.400 --> 00:19:26.119 leverage this kind of technology? Organizational readiness? As we said we picked up 239 00:19:26.200 --> 00:19:30.039 last segment. We're talking about how you can now talk to computers in English 240 00:19:30.240 --> 00:19:33.000 words, in prompts right as supposed to having to learn SQL or Cobo all 241 00:19:33.039 --> 00:19:36.799 these other languages. And you can also use it to tell you what code 242 00:19:36.839 --> 00:19:37.920 does. You can feed it code and say what does this code do? 243 00:19:38.240 --> 00:19:41.400 And it'll tell you. It won't be always completely accurate, but it'll be 244 00:19:41.400 --> 00:19:45.119 pretty darn close. And I've heard from enough people now to know that it 245 00:19:45.160 --> 00:19:48.799 gets pretty close. It gets to about eighty percent in terms of coding of 246 00:19:48.839 --> 00:19:52.559 writing what you want, and then you just fine tuned to get your last 247 00:19:52.559 --> 00:19:56.759 twenty percent. But Jim Wilde throw it over to you. In terms of 248 00:19:57.200 --> 00:20:00.799 organizational readiness, well, I mean that the most clever thing I heard in 249 00:20:00.839 --> 00:20:07.000 our pre call last week was to start your first use case internally, so 250 00:20:07.119 --> 00:20:11.400 nothing that faces your customers or your prospects, your partners, but do some 251 00:20:11.519 --> 00:20:15.160 project that faces just your internal staff to learn about these things. Because it's 252 00:20:15.200 --> 00:20:18.160 like a new pair of skis or something with jet engines on them. You 253 00:20:18.200 --> 00:20:21.640 have to figure out how to ride them. But what do you think about 254 00:20:21.640 --> 00:20:25.839 all that, Jim, Yeah, that's a good way of putting an analogy. 255 00:20:25.880 --> 00:20:30.839 It's like seize jet engines. You wouldn't go out on the slopes with 256 00:20:30.960 --> 00:20:36.079 other people around if you have jet engines and your skis. I think one 257 00:20:36.119 --> 00:20:44.240 of the interesting aspects is there's fear, uncertainty, deception sometimes and organizations. 258 00:20:44.240 --> 00:20:49.839 When you're looking at the adoption of jenator AI, where we're excited about it 259 00:20:49.319 --> 00:20:53.839 and public in general is excited about it, a lot of organizations have, 260 00:20:55.039 --> 00:21:00.400 when I'm going to say, a healthy fear, which is in most situations 261 00:21:00.400 --> 00:21:06.400 pretty healthy. Right when it came to PCs or the internet or cloud, 262 00:21:07.039 --> 00:21:10.839 it was good to come in it kind of slow. It's not going to 263 00:21:10.880 --> 00:21:17.240 work this time because you really are not prepared for the adoption that it has 264 00:21:17.279 --> 00:21:19.680 gotten in the general public. And so if you have a policy in your 265 00:21:19.759 --> 00:21:26.319 organization not to allow people to use generative AI, be aware they're using generative 266 00:21:26.359 --> 00:21:30.519 AI no matter what it's on their phone, and I know that we want 267 00:21:30.599 --> 00:21:36.000 to protect corporate assets, corporate secrets. Find that's great. But I think 268 00:21:36.039 --> 00:21:38.559 if you go about the adoption we talked about, I think you're going to 269 00:21:38.599 --> 00:21:42.279 find you can ease your way into it and get what I'm going to say, 270 00:21:42.680 --> 00:21:48.759 a lot of those fears dismantled and a lot of the excitement generated because 271 00:21:48.759 --> 00:21:52.799 you're doing it with internal uses, internal purposes. You mentioned about code analysis, 272 00:21:53.319 --> 00:21:56.960 I think it's a code companion. Every developer should have, you know, 273 00:21:57.000 --> 00:22:00.720 a time to their hip, because it will help them find errors that 274 00:22:00.759 --> 00:22:06.480 they're putting in their code. So fast. You don't let it write the 275 00:22:06.519 --> 00:22:10.759 code for you outright. What you do is it's your companion. It's going 276 00:22:10.799 --> 00:22:12.640 to find your blind spots. It's going to find where you made a mistake. 277 00:22:14.119 --> 00:22:19.039 I wrote an example of this using multi threadic code, and I put 278 00:22:19.039 --> 00:22:23.000 an air on it in purpose and it was amazing. Not only could the 279 00:22:23.039 --> 00:22:26.759 generative AI find the air, which none of my colleagues could, by the 280 00:22:26.759 --> 00:22:30.960 way, it also fixed the air, which all of my colleagues could do, 281 00:22:32.440 --> 00:22:36.640 but it fixed it in a very concise and correct way. Now I'm 282 00:22:36.640 --> 00:22:41.160 in charge of the code therefore I'm the one responsible accountable. I should never 283 00:22:41.279 --> 00:22:45.279 just let it fix the code and put it into production. That's just silliness. 284 00:22:45.480 --> 00:22:48.279 But if you do think about it, there's a lot of value it 285 00:22:48.319 --> 00:22:55.720 can be used internal to organizations that makes the organization execute much more operationally efficient. 286 00:22:56.160 --> 00:23:00.880 And that's something Bohan I have been doing with other organizations is coaching them 287 00:23:00.119 --> 00:23:07.960 on ways to enhance their internal efficiencies using generative AI. Yes, everybody wants 288 00:23:08.000 --> 00:23:11.359 to be the first one out there, and there are some amazing products that 289 00:23:11.440 --> 00:23:17.519 are coming out as products from organizations, but if you're new to it, 290 00:23:17.680 --> 00:23:21.160 you really need just to kind of frold it inside. One of the retail 291 00:23:21.279 --> 00:23:27.720 organizations I highly regard as highly innovative has taken and create a degenerative AI tool 292 00:23:27.799 --> 00:23:33.119 and given it to fifty thousand of its employees and they don't know what they're 293 00:23:33.119 --> 00:23:34.960 going to do with it. They just want to learn what they're going to 294 00:23:36.039 --> 00:23:37.920 learn from it and what they're going to do from it, and that's that's 295 00:23:37.920 --> 00:23:41.759 a great attitude. You give them a safe tool that's not going to put, 296 00:23:41.799 --> 00:23:45.720 if you will, your corporate data in danger, and you see how 297 00:23:45.759 --> 00:23:48.240 they're using it, you monitor, you see how they're using it, and 298 00:23:48.279 --> 00:23:52.359 you find out what they insights, you know what blind spots they're finding, 299 00:23:52.359 --> 00:23:55.440 what got you moments, they're getting those those things you said, you you 300 00:23:55.440 --> 00:23:57.839 know, Oh my gosh, how did it come up with this? Yes, 301 00:23:59.000 --> 00:24:02.480 exactly. Everybody should grabbing that. And I'll kind of pass a lot 302 00:24:02.519 --> 00:24:04.640 about how because I think she can probably talk a little bit to the concept 303 00:24:04.680 --> 00:24:12.839 of encouraging operational efficiency in organizations first in terms of learning what you can do 304 00:24:12.880 --> 00:24:18.279 with the tool before you go crazy putting it out there for everybody to see. 305 00:24:18.640 --> 00:24:22.400 Well, there's just so many things that we can gain efficiency, right 306 00:24:22.480 --> 00:24:26.440 you know. So Jem and I been working together on a few of them. 307 00:24:26.880 --> 00:24:33.960 One is to lavish AI for sound analysis, you know, to look 308 00:24:34.000 --> 00:24:41.880 at what can we do to use sound to detect issues in machines and predictive 309 00:24:41.039 --> 00:24:48.519 maintenance or just assist in the diagnosis of it. There's others, uh use 310 00:24:48.640 --> 00:24:57.400 where we have products already out there using visions too to detect issues on say, 311 00:24:57.680 --> 00:25:00.960 you know, winterbine, if that's a crack, how do you know? 312 00:25:02.680 --> 00:25:10.640 You know, so monitoring using vision AI. We're also working on code 313 00:25:10.680 --> 00:25:15.160 assists, like Jim was talking about, every developers nowadays really need to leverash 314 00:25:15.720 --> 00:25:22.599 ai jen ai to help them just make you know, as a double person, 315 00:25:22.799 --> 00:25:25.839 Jim was, I don't know, Eric, have you ever worked with 316 00:25:25.920 --> 00:25:29.920 any kind of coding. But at the same time, you know how tedious 317 00:25:30.000 --> 00:25:33.160 it was to sit there and type all these commands. If there's something that 318 00:25:33.240 --> 00:25:38.720 really accelerates that coding for you so that you can focus on the big concept, 319 00:25:40.000 --> 00:25:47.880 that's where optimizing the efficiency of every aspect of the organizations will be wonderful. 320 00:25:48.200 --> 00:25:52.400 So that's that's where we are seeing. You know, organization really should 321 00:25:52.400 --> 00:25:56.880 adopt look at what are the tedious things that nobody really want to do or 322 00:25:56.960 --> 00:26:00.359 they want to do but they wish it could be fast. That's where we 323 00:26:00.440 --> 00:26:06.240 apply AI to help. But how do you get the organizations ready as well? 324 00:26:06.319 --> 00:26:08.440 Right? What are the things that let's just say, if you create 325 00:26:08.480 --> 00:26:15.480 the tool to set, I don't know, predict something within your organizations or 326 00:26:15.640 --> 00:26:19.319 to generate some content within the organization. First of all, you need to 327 00:26:19.359 --> 00:26:25.599 make sure that your model is chain on information that you want your employees to 328 00:26:25.759 --> 00:26:30.759 use and don't just train it onto the internet because who knows what's out there. 329 00:26:30.240 --> 00:26:36.359 So you know, organization readiness, making sure that the organizations, the 330 00:26:36.440 --> 00:26:41.359 employees are ready and understand what are some safe AI practice should be. And 331 00:26:41.400 --> 00:26:48.400 then also from a technology standpoint, setting up the tool that can deliver that 332 00:26:48.640 --> 00:26:53.240 safe products. You know, I came from data. Data governance is my 333 00:26:53.440 --> 00:26:57.759 preach of the day these days, you know, every single time, get 334 00:26:57.759 --> 00:27:03.480 your data governance in place, get your data clans, get your data in 335 00:27:03.680 --> 00:27:07.319 a place where your model can get to so that it can be trained. 336 00:27:07.359 --> 00:27:14.880 Because if it takes your data scientists pages to get to the data, it 337 00:27:14.960 --> 00:27:18.839 really doesn't get your organization ahead of the game. That's and that's a really 338 00:27:18.839 --> 00:27:22.079 good point that you just made. And so I'm going to throw out a 339 00:27:22.119 --> 00:27:26.839 thought here and maybe a throw it over to Jim first, and then baoha. 340 00:27:26.240 --> 00:27:30.559 You can ask it what am I doing wrong? You can ask it 341 00:27:30.599 --> 00:27:33.400 all sorts of questions and feed it a document. So one of the I 342 00:27:33.400 --> 00:27:40.759 think relatively underappreciated and incredibly powerful use cases or functions of these tools is summarization. 343 00:27:41.279 --> 00:27:44.359 So you can take a very long document, let's say a thousand page 344 00:27:44.400 --> 00:27:48.279 documents, feed it in and it's almost like a dynamically generated cliffs notes. 345 00:27:48.599 --> 00:27:52.920 When you're in high school and college of these cliffs notes. Right, It's 346 00:27:52.920 --> 00:27:59.200 like Boom's like for you. It check out all the filler words and it 347 00:27:59.440 --> 00:28:03.920 come dons for you and get you going so fast. You know, Jim 348 00:28:03.720 --> 00:28:12.000 and I were just experimenting what last week we point a model to some users 349 00:28:12.000 --> 00:28:18.480 document user guide that we found on the internet and then feed the engine that 350 00:28:18.519 --> 00:28:22.960 we create a bunch of code and TELP to interpret it for us. It 351 00:28:23.039 --> 00:28:27.119 did it just like that. Jim got the engine all set up in what 352 00:28:27.200 --> 00:28:33.160 fifteen minutes fifty minutes to get the first setback, and then you know it 353 00:28:33.200 --> 00:28:38.240 takes a little bit more training to get to some more eligible result, but 354 00:28:38.440 --> 00:28:42.119 less than a day. Yeah, that's that's crazy. And I was talking 355 00:28:42.160 --> 00:28:48.599 to some folks from Matillion, a software company MDM style company. They had 356 00:28:48.599 --> 00:28:51.839 a use case where they said, look, let's just try to throw it 357 00:28:51.839 --> 00:28:55.480 at chat GPT and just see what it says. Just and some people were 358 00:28:55.480 --> 00:28:56.839 skeptical, like, just give it a try, and so they did, 359 00:28:57.160 --> 00:29:02.440 and their minds were blown how accurately it came back. And so the point 360 00:29:02.519 --> 00:29:07.279 being, think about the whole side of discovery. When you're doing data analysis. 361 00:29:07.480 --> 00:29:11.319 The discovery side is so important because you're trying to find the unknown, 362 00:29:11.440 --> 00:29:12.799 right. I mean, it's one thing if you're trying to find a known 363 00:29:14.319 --> 00:29:17.000 like with a query to the database, give me what have already persisted, 364 00:29:17.000 --> 00:29:19.799 But if you're trying to look for patterns that have not yet been identified, 365 00:29:21.279 --> 00:29:26.759 this kind of technology is incredibly powerful because it will on a moment's notice, 366 00:29:26.720 --> 00:29:34.279 absorb tremendous amounts of data and then throw back seemingly random reflections that are very 367 00:29:34.319 --> 00:29:37.839 accurate and very compelling. So on the discovery side, I mean I kind 368 00:29:37.839 --> 00:29:41.799 of view these engines as being a new front end for almost everything in the 369 00:29:41.920 --> 00:29:45.960 enterprise. Eventually, what do you think about HAA, I agree, I 370 00:29:47.000 --> 00:29:52.440 agree the discovery. That's exactly what Jem and I were trying to figure out 371 00:29:52.039 --> 00:29:59.759 last week is how we have an obscure platform that not that many people know 372 00:30:00.039 --> 00:30:06.559 about anymore and very hard to find resource for even if you had resource. 373 00:30:06.720 --> 00:30:11.400 Some of these platforms that have been in an organization for fifteen twenty years, 374 00:30:11.440 --> 00:30:18.960 the amount of organic growth and customizations and that governance on this platform are just 375 00:30:19.680 --> 00:30:26.079 mind blown. But you know, how do you really accelerate that discovery process 376 00:30:26.119 --> 00:30:33.119 so that you really get the engineers to where they produce results versus doing these 377 00:30:33.160 --> 00:30:40.480 tedious, unvalue added activities. Honestly, so Ai, that ability is just 378 00:30:41.039 --> 00:30:45.440 it amazed me still and get me very excited. Well, and you do. 379 00:30:45.559 --> 00:30:49.000 So the guys from Matillian we're mentioning they did then need to go, 380 00:30:49.079 --> 00:30:52.279 so they figured something out about how to do it, and then they actually 381 00:30:52.319 --> 00:30:56.440 had to go do the hard work with their existing information systems to get the 382 00:30:56.480 --> 00:31:00.680 design work and the engineering done. At least they knew which way to go. 383 00:31:00.720 --> 00:31:04.200 At least they knew what's possible, Jim, I'll throw it to you. 384 00:31:04.759 --> 00:31:10.440 Being able to suss out what is genuinely possible in any given situation is 385 00:31:10.559 --> 00:31:15.079 half the battle. Then you have options that you can choose to go in 386 00:31:15.119 --> 00:31:19.200 this direction or that direction. But if you haven't effectively discerned the nature of 387 00:31:19.240 --> 00:31:23.960 the challenge or the range of possibilities, you're still in the dark, and 388 00:31:25.000 --> 00:31:29.079 you're shooting in the dark. So this discovery component of these large language models, 389 00:31:29.079 --> 00:31:33.440 I think it's very compelling for being able to understand what's happening and then 390 00:31:33.519 --> 00:31:36.920 have a really good idea of where to go next. What do you think, 391 00:31:37.000 --> 00:31:42.559 Jim, that's interesting because the discovery process can be enhanced by asking the 392 00:31:42.720 --> 00:31:48.799 generative a engine give me ten questions. Ask me ten questions as I'm going 393 00:31:48.839 --> 00:31:56.519 to discover something, and then we'll prompt you with best practices of discovery to 394 00:31:56.640 --> 00:32:04.160 feed itself information so it can start offering you better analysis. The example how 395 00:32:04.240 --> 00:32:10.839 I was talking about was so much fun because it's an obscure platform that really 396 00:32:12.359 --> 00:32:16.519 hasn't been leveraged, let's say in tens of years, and we are able 397 00:32:16.519 --> 00:32:22.680 to find an old syntax manual with two thousand pages. So doing the pag 398 00:32:22.720 --> 00:32:29.319 approach for grounding or language chaining, you feed that two thousand page manual in 399 00:32:30.160 --> 00:32:37.319 and now it's making what I'm going to say, very precise outputs that are 400 00:32:37.359 --> 00:32:42.920 specific to the rules of the language and the syntax of the language that it 401 00:32:43.000 --> 00:32:49.960 was built in. And so you can take things that are ancient in the 402 00:32:50.000 --> 00:32:53.000 computer world and make it very modern to the answers that can give with the 403 00:32:53.119 --> 00:32:59.400 very accuracies that you're seeing, and you know it could be one hundred percent 404 00:32:59.519 --> 00:33:02.279 accurate eighty percent of the time, and the times that it's not accurate, 405 00:33:04.680 --> 00:33:07.440 you're going to have to have enough knowledge to call that out. The hallucinations 406 00:33:07.440 --> 00:33:13.240 of the biases the blind spots. However, what we found is that as 407 00:33:14.000 --> 00:33:19.559 you're going through a discovery process, it's those those gotcha's. We're basically things 408 00:33:19.599 --> 00:33:22.319 I wasn't thinking about. It will think you know, have you thought about 409 00:33:22.440 --> 00:33:25.079 and it will give you some things. Have you thought about this? Have 410 00:33:25.160 --> 00:33:29.880 you thought about that? And it's like, no, actually I haven't. 411 00:33:30.160 --> 00:33:34.000 So it's a companion, right, It's kind of that person that that can 412 00:33:34.240 --> 00:33:37.319 can have a conversation with you and throw things out at you, but you're 413 00:33:37.319 --> 00:33:42.240 not going to reject them because you're asking them to do that for you. 414 00:33:42.839 --> 00:33:45.160 If that's a really good way to put it, and we'll talk about this 415 00:33:45.200 --> 00:33:49.319 in the next segment coming up here in just a moment. But using it 416 00:33:49.359 --> 00:33:53.799 as a companion, like an incredibly knowledgeable intern who will tell you whatever you 417 00:33:53.920 --> 00:33:57.880 ask it may not be right all the time. It kind of reminded me 418 00:33:57.920 --> 00:34:00.440 of the old Yogi bearra quote, Right, ninety percent of the game is 419 00:34:00.440 --> 00:34:04.440 half mental or something like that that can be completely correct. Eighty percent of 420 00:34:04.480 --> 00:34:07.480 design the clock the broken clock is correct two times a day. So well, 421 00:34:07.480 --> 00:34:08.960 don't put that to help folks, will be right back. You're listening 422 00:34:09.000 --> 00:34:22.559 to a very cool episode of Inside and Out. Welcome back to Inside Analysis. 423 00:34:22.719 --> 00:34:29.800 Here's your host, Eric Kavanaugh. All right, folks, back here 424 00:34:29.840 --> 00:34:35.000 on Inside Analysis, Rocket and Rolling today with baalja w from FPT and distinguished 425 00:34:35.039 --> 00:34:39.039 engineer and fractional CTO Jim Wilts. This guy knows a lot and a lot 426 00:34:39.079 --> 00:34:40.800 of people too. You know, there's a lot of cool people. He 427 00:34:40.840 --> 00:34:45.119 does a lot of stuff which is always fun. And we want to talk 428 00:34:45.119 --> 00:34:49.880 about pitfalls. There are always pitfalls. I think I'll just throw it out 429 00:34:49.880 --> 00:34:53.719 there. There are already these anecdotes that get thrown out. It's amazing how 430 00:34:53.760 --> 00:34:59.119 fastest stuff happens. Everyone's probably heard about the lawyer who went out and threw 431 00:34:59.199 --> 00:35:01.960 citations in and it turned out to be nonsense citations. Well, shame on 432 00:35:02.039 --> 00:35:07.000 that fool for not just taking the extra step to make sure. And this 433 00:35:07.039 --> 00:35:08.719 is where you get into the rag model concept right. In other words, 434 00:35:08.920 --> 00:35:15.400 you want to use a trusted data source like whatever sources they have for cases, 435 00:35:15.440 --> 00:35:19.760 for historical cases. And I'm telling you, folks, lawyering as an 436 00:35:19.760 --> 00:35:23.599 industry is in the crosshairs. Accounting as an industry is in the crosshairs of 437 00:35:23.639 --> 00:35:28.800 AI. All these things that we've done traditionally are absolutely positively in the crosshairs 438 00:35:29.199 --> 00:35:31.760 of AI, and as Jim was kind of suggesting eighty percent at the time, 439 00:35:31.800 --> 00:35:36.079 it'll be one hundred percent accurate. You do have to know when it's 440 00:35:36.119 --> 00:35:38.599 the twenty percent to take a hard look at stuff. So that's the biggest 441 00:35:38.679 --> 00:35:44.440 pitfall I think is don't just rush in and start throwing this stuff it generates 442 00:35:44.440 --> 00:35:47.599 out into the world. Be careful about what you make that's customer facing. 443 00:35:47.639 --> 00:35:52.880 There's several issues already, but those are probably the biggest ones. Jim Will, 444 00:35:53.000 --> 00:35:57.400 I'll throw it over to you. What are any other major pitfalls you've 445 00:35:57.400 --> 00:36:00.679 seen that people should watch out for with jen Ai. I think one of 446 00:36:00.760 --> 00:36:05.360 the things I run into and you almost have to take a pitfall as comical 447 00:36:05.800 --> 00:36:10.239 and enjoy the entertainment aspect of it. When I was writing an article, 448 00:36:10.559 --> 00:36:15.199 I was tweaking hyper parameters just to see what happens when you break it right. 449 00:36:15.840 --> 00:36:20.960 And I had a test explained quantum entanglement, and I did it with 450 00:36:21.039 --> 00:36:25.360 different temperatures and I chose one that was out of the parameter range and it 451 00:36:25.400 --> 00:36:31.480 created gibberish. I mean, my you know, one year old granddaughter could 452 00:36:31.480 --> 00:36:35.639 have done better. But it was just random characters group together. So it 453 00:36:35.679 --> 00:36:38.679 wasn't even English language. It wasn't any language at all. It was kind 454 00:36:38.719 --> 00:36:45.400 of thing. So it will break and you and part of the learning process, 455 00:36:45.440 --> 00:36:49.000 you need to know when it breaks, how to break it, buy 456 00:36:49.039 --> 00:36:52.599 it breaks, you know, and you need to be on guard for these 457 00:36:52.679 --> 00:36:57.679 kinds of things. But as I was finding things, question the answers. 458 00:36:58.119 --> 00:37:00.239 That's probably one of the things I would say in every situation, what it's 459 00:37:00.280 --> 00:37:05.719 going to be critical. You talked about adoption in another earlier segment. You 460 00:37:05.760 --> 00:37:09.000 want to start with none mission critical and work your way towards mission critical. 461 00:37:09.119 --> 00:37:14.480 You don't start with mission critical. That's the lawyer example you gave and others 462 00:37:14.480 --> 00:37:16.800 that are out there in the last week. You don't want that. Start 463 00:37:16.840 --> 00:37:21.440 with non mission critical and work your way with confidence and learning and maturity. 464 00:37:22.199 --> 00:37:25.159 I've asked times when it gives me a quote, can you give me the 465 00:37:25.199 --> 00:37:30.679 source of the quote, and it will say, oh, I'm sorry that 466 00:37:30.840 --> 00:37:32.760 I made that up. I mean literally, that's the words that we'll use. 467 00:37:34.679 --> 00:37:37.000 I'm sorry, I made that quote up. That didn't come from that 468 00:37:37.239 --> 00:37:42.719 Forrester Gardner article, or if it does refer to an article, I'll say 469 00:37:42.719 --> 00:37:45.280 Can you give me the number enumeration of the articles so I can look it 470 00:37:45.360 --> 00:37:50.119 up and it will give me a false number. So you have to drill 471 00:37:50.239 --> 00:37:55.000 down into, you know, the material before you publicly go somewhere with it. 472 00:37:55.159 --> 00:38:01.440 If it's just helping you make a decision, it can be somewhat wrong 473 00:38:01.519 --> 00:38:05.280 and you're not going to hurt anybody with it. But if it's anything you're 474 00:38:05.320 --> 00:38:09.199 going to be then promoting forward. You have to be accurate about the data 475 00:38:09.239 --> 00:38:13.320 and the results, and you could ask good questions. Can you give me 476 00:38:13.800 --> 00:38:16.079 the source for that answer? Can you give me references to where I can 477 00:38:16.119 --> 00:38:20.639 refer others to learn more about the answer. If it can't do that, 478 00:38:20.760 --> 00:38:23.920 you're probably in the hallucination zone and you want to get out of there right 479 00:38:25.320 --> 00:38:30.559 Well, you know so, Jim, you talked about making sure that you 480 00:38:30.719 --> 00:38:37.039 check the words right. And part of the pitfall of adoptions also is moving 481 00:38:37.079 --> 00:38:40.320 too fast for the organizations. I think that we really need to watch for 482 00:38:42.199 --> 00:38:46.400 is the organization ready and if not doesn't mean that you shouldn't adopt, but 483 00:38:46.440 --> 00:38:52.840 you really have to build almost like a safety net and an on ramp for 484 00:38:52.960 --> 00:38:59.079 the organizations to adopt. So meeting your organization, your user where they are, 485 00:38:59.440 --> 00:39:04.480 Making sure we understand where people are with the ability to use what we're 486 00:39:04.480 --> 00:39:09.719 going to put out that and whether or not we have provided the appropriate level 487 00:39:09.840 --> 00:39:15.199 of handholding for this kind of work is very important too. You're reminding me 488 00:39:15.239 --> 00:39:17.960 of something. I'm so glad you brought this up because you put this concept 489 00:39:19.000 --> 00:39:21.280 of an on ramp, So you want to be careful at this pace at 490 00:39:21.280 --> 00:39:23.119 which you go. You want an on ramp, and you also want an 491 00:39:23.199 --> 00:39:27.639 off ramp, right, I think you want an off ramp, And what 492 00:39:27.679 --> 00:39:30.960 I'm referring to is that, And this is going to be an ongoing question. 493 00:39:30.360 --> 00:39:36.039 Where do you actually insert the generative capability in your workflow. There's going 494 00:39:36.079 --> 00:39:37.440 to be some point at which it comes in, and you're going to want 495 00:39:37.440 --> 00:39:40.039 to know, of course where that is, and you're going to want to 496 00:39:40.039 --> 00:39:44.480 have a toggle to turn it off or on. So if you use it, 497 00:39:44.519 --> 00:39:47.079 for example, for customer service for chat bots, this is going to 498 00:39:47.079 --> 00:39:52.760 be probably one of the most common use cases is fueling a chat bot and 499 00:39:52.800 --> 00:39:59.199 feeding it enough information from past conversations to know where to go. As the 500 00:39:59.199 --> 00:40:02.000 beauty of having an existing system is that you can just take the database of 501 00:40:02.079 --> 00:40:07.760 everything your chatbot has already said. Do some analysis on when the chatbot did 502 00:40:07.840 --> 00:40:12.719 a good job and when the customer was satisfied. Okay, that's the behavior 503 00:40:12.719 --> 00:40:15.199 we want to model and mimic. But then at some point, if it 504 00:40:15.239 --> 00:40:19.679 starts going walky, you want to have the off ramp. Oh, turn 505 00:40:19.719 --> 00:40:22.559 it off like this, shut it down. It's just like an employee you 506 00:40:22.599 --> 00:40:25.519 maybe drank too much before going into work that night, and like, okay, 507 00:40:25.639 --> 00:40:29.159 you better go home, buddy, it looks like you're both well. 508 00:40:29.239 --> 00:40:32.480 That come up and comes out in any chatbot is so important. You really 509 00:40:32.519 --> 00:40:38.199 need to be able to collect the information when your chatbot start making no sense 510 00:40:38.639 --> 00:40:43.679 that you know, be able to really train that because it's a continuous training 511 00:40:43.719 --> 00:40:46.039 as well. Right, Well, that's the thing too. So my buddy 512 00:40:46.199 --> 00:40:51.480 Usama Fayad, who runs the Institute for Experiential AI, I throw us over 513 00:40:51.480 --> 00:40:54.079 at the gym. He said, the whole thing about experiential means human in 514 00:40:54.119 --> 00:40:57.679 the loop, and he makes a really good point. And there is this 515 00:40:58.000 --> 00:41:00.679 narrative in the media that oh, AI it's going to just take over the 516 00:41:00.679 --> 00:41:04.000 business and run everything. No, no, no, no, you sure 517 00:41:04.039 --> 00:41:07.039 don't ever want to do that. You want to have someone watching it all 518 00:41:07.079 --> 00:41:09.159 the time to make sure that it does what you want it to do, 519 00:41:09.280 --> 00:41:14.440 because it will move it blinding speeds, and so if you don't course correct 520 00:41:14.440 --> 00:41:16.639 in time, it's going to be with blinding speeds when careen off the tracks, 521 00:41:16.719 --> 00:41:21.480 right, Jim, Yeah, I think the chatbot example is a good 522 00:41:21.519 --> 00:41:27.199 segue into what I'm going to say augmenting. I'm going to say services and 523 00:41:27.719 --> 00:41:31.079 everything that's augmented means as a human it's in that loop. They have the 524 00:41:31.079 --> 00:41:37.599 accountability and responsibility to have the final say. So maybe what we'll see I 525 00:41:37.639 --> 00:41:42.480 would love to see in the future is opposed to just chatbots doing it randomly 526 00:41:42.800 --> 00:41:47.440 and anonymously, that it connects you directly to a person with information that's augmented 527 00:41:47.480 --> 00:41:52.239 by the conversation, so the person on the other end can understand your situation 528 00:41:52.400 --> 00:41:59.760 much faster, and then through the generative AI get you to your resolution much 529 00:42:00.039 --> 00:42:05.280 aster. But that human is going to if you will facilitate that process or 530 00:42:07.280 --> 00:42:13.000 you know in the sense that it's not completely blund But you brought up a 531 00:42:13.000 --> 00:42:19.280 good point too. There's an article I wrote where I started it with the 532 00:42:19.360 --> 00:42:22.480 tractor didn't replace the farmer, you know, and generative a. I won't 533 00:42:22.480 --> 00:42:28.599 replace your job, right, However, I am the article with the tractor 534 00:42:28.639 --> 00:42:34.519 didn't replace the farmer, but farmers with tractors did replace those without. And 535 00:42:34.679 --> 00:42:37.719 I think that's a big distinction to what you said, Eric, because it's 536 00:42:37.800 --> 00:42:43.519 very specific. The human in the loop is a necessity. The power of 537 00:42:43.559 --> 00:42:45.360 the human and the loop, if you will, in terms of accuracy and 538 00:42:45.480 --> 00:42:52.559 impact, is going to increase astronomically, and that's going to now create what 539 00:42:52.639 --> 00:42:54.679 I'm going to say, what Baja was saying earlier. Am I going to 540 00:42:54.719 --> 00:43:00.280 ban papers from being written by generator of AI? Or am I going to 541 00:43:00.320 --> 00:43:05.960 allow it to augment people who are writing papers? Different question, But that's 542 00:43:06.039 --> 00:43:09.079 kind of the thing you have to get through. Well, Jim was a 543 00:43:09.119 --> 00:43:13.480 T shirt. I have never been a T shirt. So I'm advocating for 544 00:43:13.599 --> 00:43:16.960 allow Jenny I to write paper. I think we're augmenting, and I think 545 00:43:16.960 --> 00:43:23.239 one of the cool okays augmenting. Yeah, you've got I wrote something. 546 00:43:24.840 --> 00:43:28.960 Read this and tell me what I wrote. Okay, Okay, that is 547 00:43:29.000 --> 00:43:31.320 not what I was trying to say. All right, correct it for me 548 00:43:31.360 --> 00:43:34.639 a little bit. Let it correct it and I can say Okay, that's 549 00:43:34.679 --> 00:43:37.079 good. Now I understand where I made a mistake. Now I'm going to 550 00:43:37.119 --> 00:43:40.880 continue writing from that, not take it. I'm going to take what it 551 00:43:42.039 --> 00:43:45.320 corrected and make it my voice and my intention, and then have it read 552 00:43:45.320 --> 00:43:49.159 it back to me again. So it's a it's a give and take when 553 00:43:49.199 --> 00:43:52.960 you think about it, well, it's go ahead, Eric, Yeah you 554 00:43:52.000 --> 00:43:54.760 made you brought up such a it's such a great use case. And I'll 555 00:43:54.760 --> 00:43:59.000 give an example of why, and then we've got a podcast ponus segment coming 556 00:43:59.079 --> 00:44:01.079 up here next. But think about in customer service, and this is what 557 00:44:01.239 --> 00:44:06.960 really fascinates me about these engines is how it decides to choose what it chooses, 558 00:44:07.559 --> 00:44:08.639 right, And that's why you have to try it, to learn and 559 00:44:08.679 --> 00:44:12.199 to kind of figure it out, because there is a method to the madness. 560 00:44:12.519 --> 00:44:14.800 Obviously, it's a computer system, right, I mean, it's got 561 00:44:15.360 --> 00:44:17.679 rules and instructions and things that's supposed to do. But you think about customer 562 00:44:17.760 --> 00:44:23.159 service, and historically you have a customer service center, it's going to bring 563 00:44:23.199 --> 00:44:25.960 on bringing up your system, bringing up your record, et cetera. It's 564 00:44:27.000 --> 00:44:30.719 a very slow process and it's typically from one system where there are lots of 565 00:44:30.760 --> 00:44:35.000 other systems where customers have touched the organization touch points as they call them, 566 00:44:35.480 --> 00:44:38.119 on the call center, on the website, in a store, maybe I 567 00:44:38.199 --> 00:44:43.159 wrote a letter or something. You never know. But these GENAI engines can, 568 00:44:43.320 --> 00:44:47.920 in a moment, like as you're getting to the customer service rep, 569 00:44:49.000 --> 00:44:52.000 whip up a whole bunch of stuff that's accurate and relevant and just give suggestions 570 00:44:52.000 --> 00:44:55.679 to the call center person. So now it's not this slow, painful thing. 571 00:44:55.719 --> 00:45:00.400 It's going real fast and the human can decide what to use not to 572 00:45:00.480 --> 00:45:02.320 use. That's why it's so powerful. Folks. Podcast bone a second, 573 00:45:02.400 --> 00:45:08.280 I'm coming up next. You're listening to Inside Analysis. All right, folks, 574 00:45:08.280 --> 00:45:12.760 time for the podcast bonus segment here and a fantastic inside Analysis. We've 575 00:45:12.800 --> 00:45:19.800 been talking to Boo Ho We of FPT America's and Jim Wilt fractional CTO and 576 00:45:19.880 --> 00:45:23.199 distinguished engineer, and we're talking all about jen Ai use cases, what to 577 00:45:23.239 --> 00:45:27.239 do, how to get started, and as we were getting excited about that 578 00:45:27.320 --> 00:45:30.880 last use case, maybe about how I'll throw this over to you first. 579 00:45:30.639 --> 00:45:34.360 You know, one thing I figured out early is well, wait a minute, 580 00:45:34.360 --> 00:45:36.840 if this thing can write in German and French, and Spanish. I 581 00:45:36.880 --> 00:45:40.760 bet it can write in HTML and JavaScript and C plus plus. And short 582 00:45:40.800 --> 00:45:45.920 answer is yes, it's your can and it can also absorb that kind of 583 00:45:45.960 --> 00:45:51.519 information too. So think about systems and information systems talking to each other in 584 00:45:51.559 --> 00:45:54.880 bits and bytes, and you know you get log files basically, which to 585 00:45:54.960 --> 00:45:58.360 a human if you know what the log file means, okay, now it's 586 00:45:58.400 --> 00:46:01.960 meaningful. But the aale of log files you get, like thousands and thousands 587 00:46:01.960 --> 00:46:06.039 of log files you're trying to scan through, well ninety eight percent of them 588 00:46:06.039 --> 00:46:07.840 are the same, and then the ones that are different, Ah, that's 589 00:46:07.840 --> 00:46:10.719 when something probably went wrong. You can even throw that stuff at these engines 590 00:46:10.719 --> 00:46:13.760 and say hey, I got all these log files, what the heck does 591 00:46:13.760 --> 00:46:15.920 that mean? And get back some interesting answers. What do you think boja? 592 00:46:15.960 --> 00:46:22.280 Oh absolutely, actually that was cybersecurity. I think leverage AI at least 593 00:46:22.320 --> 00:46:28.000 a few years ago. You know, there's been systems that knew exactly what 594 00:46:28.079 --> 00:46:34.079 you said. If you in a network management at all, one of the 595 00:46:34.239 --> 00:46:38.559 dreaded activity is read the log file. You know, you can monitor your 596 00:46:38.599 --> 00:46:43.519 network all day long, but if you don't analyze that lock file and read 597 00:46:43.559 --> 00:46:45.920 the log file and figuring out what it's telling you. It's quite less and 598 00:46:46.039 --> 00:46:52.880 useless, right, So that's one of the applications that had been produced, 599 00:46:53.039 --> 00:47:00.920 and it basically general use gen AI or a large language model to read the 600 00:47:00.000 --> 00:47:07.280 log file and then just detect the differences the abnormality and call out and abnormality 601 00:47:07.679 --> 00:47:15.280 and then do adjuscency analysis and to understand what kind of of abnormality it is 602 00:47:15.519 --> 00:47:21.320 and where the threat is coming from. So absolutely, well that was specific 603 00:47:21.360 --> 00:47:28.159 for cybersecurity, but you know applications application debugging, right, Oh my gosh, 604 00:47:28.639 --> 00:47:34.800 right, can you those things? Because it's brutal, I mean very 605 00:47:34.840 --> 00:47:38.679 brutal. I'll throw it over to Jim. Debugging has historically been one of 606 00:47:38.679 --> 00:47:44.280 the hardest things to do, and even with sophisticated technology. And I remember 607 00:47:44.559 --> 00:47:47.039 fifteen sixteen years ago interviewing a guy I always mentioned this. He had such 608 00:47:47.039 --> 00:47:51.400 a cool name, Zohar Gilt, that was his name. He was with 609 00:47:51.440 --> 00:47:54.639 the company called Precise and they got bought by Idea. But what they would 610 00:47:54.639 --> 00:47:57.800 do is help you, this is a long time ago. They would help 611 00:47:57.840 --> 00:48:02.079 you do monitoring of system and application dynamics and why did this break? Why 612 00:48:02.079 --> 00:48:06.320 did this go down? Even back then, you would have interesting heuristics. 613 00:48:06.400 --> 00:48:09.400 You'd see CPU usage went down on a timeframe, and the application speed went 614 00:48:09.480 --> 00:48:13.639 up, and all this stuff happened. But the human beings still had to 615 00:48:13.679 --> 00:48:15.960 parse all that together in his or her head to make sense of it, 616 00:48:16.000 --> 00:48:19.599 and then you would learn patterns of things. Oh okay, if this goes 617 00:48:19.679 --> 00:48:22.880 up and that goes down, it's usually because there's a disk failure or something. 618 00:48:22.920 --> 00:48:27.159 Well. Now, in the world of development, in part because of 619 00:48:27.239 --> 00:48:30.760 Kubernetes, but in part because of just the complexity of the cloud, we 620 00:48:30.920 --> 00:48:35.880 have containers and containerization, and we have all kinds of observability. I mean, 621 00:48:36.119 --> 00:48:37.599 there are so many tools out there. I joke about this. There's 622 00:48:37.599 --> 00:48:43.239 a company Mesmo, which just sits on top of your observability data and helps 623 00:48:43.280 --> 00:48:45.719 you marshal it and go in the right direction. So there's so much activity 624 00:48:45.880 --> 00:48:49.360 that there is a meta tool now that sits on top of it. That's 625 00:48:49.360 --> 00:48:52.960 how much observability we have. But it's still up to the developer to piece 626 00:48:52.000 --> 00:48:58.599 it all together. So this Gennai stuff can be tremendously powerful at absorbing massive 627 00:48:58.599 --> 00:49:01.920 amounts of these log files, finding some correlation between them, and at least 628 00:49:01.920 --> 00:49:05.960 getting you on the right track. What do you think, Jim, Yeah, 629 00:49:06.000 --> 00:49:12.239 there's definitely the ability to scan and look for anomalies or anything along the 630 00:49:13.119 --> 00:49:16.039 non what I'm going to say, traditional paths, because logs can be mundane 631 00:49:16.280 --> 00:49:22.000 and you're looking for that needle in the haystack can be very frustrating and to 632 00:49:22.159 --> 00:49:25.400 broker it across multiple logs. What's really you're going to be able to start 633 00:49:25.400 --> 00:49:30.480 doing with AI is pitting multiple logs against each other. It could be through 634 00:49:30.519 --> 00:49:34.280 the time stamps, there could be through events. Whatever you can do. 635 00:49:34.360 --> 00:49:37.199 You need to train it enough to understand that this log is connected to this 636 00:49:37.320 --> 00:49:43.760 log this way. Then you're going to be looking at multiple facets of the 637 00:49:43.760 --> 00:49:49.679 same problem from multiple viewpoints, so that you can have better ways of understanding 638 00:49:49.679 --> 00:49:52.199 what's going on that. The other thing is, you know with generative AI, 639 00:49:52.280 --> 00:49:55.280 you can write your test cases. It can look at your code, 640 00:49:55.280 --> 00:50:00.519 look for anomalies in your code. I think one of the things it's really 641 00:50:00.519 --> 00:50:04.119 interesting is it can read back to you what your code is doing. If 642 00:50:04.400 --> 00:50:07.920 you sometimes are trying to understand this code that you don't understand what it's doing, 643 00:50:08.239 --> 00:50:12.960 comments don't make sense. Who comments to their code anyway? Then you 644 00:50:13.000 --> 00:50:15.320 can make a correction to the code, or it can it augment you to 645 00:50:15.400 --> 00:50:19.920 making a correction to the code. Then you get another model to look at 646 00:50:19.920 --> 00:50:23.400 that change that you made and explain what it's doing. Is it still doing 647 00:50:23.440 --> 00:50:28.360 what it was said before or is it doing something different? Is that what 648 00:50:28.400 --> 00:50:30.960 you wanted for an outcome? So we're going to get to where I'm going 649 00:50:31.000 --> 00:50:36.800 to say, we're going to leverage these tools increasingly more complex, you know, 650 00:50:37.000 --> 00:50:39.679 around the way that we can solve problems that we never would have attacked 651 00:50:39.719 --> 00:50:43.360 the problems before before. I would look at one log at a time, 652 00:50:43.920 --> 00:50:49.039 or I'd get a room. I've done this and telcom where you put people, 653 00:50:49.239 --> 00:50:51.599 you know, a dozen people in a room for six weeks to look 654 00:50:51.639 --> 00:50:54.480 at all the logs to find out where did the error occur, you know, 655 00:50:54.800 --> 00:51:00.360 because the CEO was contacted by a text and you can't find it. 656 00:51:00.119 --> 00:51:06.559 With Generator AI, we can start training the models to look at things multifaceted 657 00:51:06.719 --> 00:51:08.840 so that we can actually start finding errors that we never would be able to 658 00:51:08.880 --> 00:51:13.440 find on our own before because it just has more eyes. If you will 659 00:51:13.480 --> 00:51:17.400 on the screen, then we can put go ahead baha, well and you 660 00:51:17.400 --> 00:51:22.239 know, just on top of that, you need experience engineer to be able 661 00:51:22.280 --> 00:51:28.039 to read the lock and understand the lock and really recognize the issues when you 662 00:51:28.079 --> 00:51:31.119 come across it. Now, with jen Ai, you really can take all 663 00:51:31.159 --> 00:51:37.360 of that and abstract it and make it so that the people you'll need one 664 00:51:37.519 --> 00:51:43.480 experienced engineer versus you need a roomful of engineer well to do it. That's 665 00:51:44.119 --> 00:51:47.320 such as Yeah, that's such a great way to put it, and it's 666 00:51:47.360 --> 00:51:52.119 exciting that I think we have to close on that line because that's brilliant. 667 00:51:52.159 --> 00:51:54.119 The future is here and it's exciting what folks will be talking about. Ha. 668 00:51:54.599 --> 00:52:00.320 We of FPT America's look up this company FPT. They're here. Huge. 669 00:52:00.840 --> 00:52:04.480 I mean they're a massive company. I have a wonderful origin story about 670 00:52:04.480 --> 00:52:07.639 where they came from. Very interesting stuff. And of course Jim Wilt distinguished 671 00:52:07.880 --> 00:52:13.480 engineer and fractional CTO. We do archive all these events for later listening and 672 00:52:13.599 --> 00:52:15.559 viewing. Send me An Nemei if you want to be on the show. 673 00:52:15.599 --> 00:52:17.039 Info at Inside Analysis dot com. We'll talk to you later. You've been 674 00:52:17.119 --> 00:52:25.639 listening to Inside Analysis and now the Voices of ACAA was an exciting announcement. 675 00:52:25.840 --> 00:52:30.119 Want to hear NBC News or KCAA anywhere you go. Well, now there's 676 00:52:30.119 --> 00:52:35.679 an app for that. CACAA is celebrating twenty five years and our silver anniversary 677 00:52:35.840 --> 00:52:39.360 with a brand new app. The new KCAA app is now available on your 678 00:52:39.400 --> 00:52:44.079 smart device, cell phone, in your car, or any place. Just 679 00:52:44.119 --> 00:52:47.679 search KCAA on Google Play or in the Apple Store one touch and you can 680 00:52:47.719 --> 00:52:52.960 listen on your car radio, Bluetooth device, Android Auto or Apple Car Play, 681 00:52:52.159 --> 00:52:57.440 catch the KCAA buzz in your earbuds or on the streets. Celebrating twenty 682 00:52:57.440 --> 00:53:01.360 five years of talk news and excellence with our case aac Just do it and 683 00:53:01.480 --> 00:53:08.679 download it. K c AA celebrating twenty five Yeah. Hi, this is 684 00:53:08.719 --> 00:53:13.119 Graham Smith and I'm running for County supervisor and District three. I know you're 685 00:53:13.119 --> 00:53:15.320 getting inundated with messages about the election, so i'll keep this short. If 686 00:53:15.320 --> 00:53:19.280 you want a supervisor who won't waste your tax dollars, who will be a 687 00:53:19.320 --> 00:53:22.760 watchdog over the ten billion dollar county budget, who understands the danger of relying 688 00:53:22.840 --> 00:53:27.639 solely on warehouses to keep our economy going, and who isn't taking donations from 689 00:53:27.679 --> 00:53:31.440 corporations or sitting politicians. Vote for me Graham Smith for Supervisor in District three. 690 00:53:31.559 --> 00:53:35.079 I've worked for twenty years in finance and business. I know how to 691 00:53:35.119 --> 00:53:37.880 grow our economy, create jobs, and make sure your tax dollars aren't being 692 00:53:37.880 --> 00:53:42.519 wasted. I'm not a politician. I'm just a resident of Samberdardino who's tired 693 00:53:42.519 --> 00:53:45.280 of unqualified leaders who make promises they can't keep. And I care about people, 694 00:53:45.320 --> 00:53:49.800 affordable housing, and honest and open government. We need to address our 695 00:53:49.880 --> 00:53:53.000 challenges in a realistic manner. A county supervisor needs to pay attention. So 696 00:53:53.000 --> 00:53:57.599 if you're ready for a supervisor who prefers skills over sound bites, someone who 697 00:53:57.639 --> 00:54:00.760 wants to serve the people instead of themselves, I think we need to try 698 00:54:00.800 --> 00:54:04.239 something new. Vote Smith for Supervisor. You can go to Smith for SB 699 00:54:04.400 --> 00:54:07.800 dot com s M I T H F O R SB dot com. The 700 00:54:07.840 --> 00:54:13.079 election is March fifth. Mailing your ballot today paid for by Smith for Supervisor. 701 00:54:13.159 --> 00:54:19.360 I'm Graham Smith and I approve this message. 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There's never been a better time for men 744 00:57:15.360 --> 00:57:19.119 to be whoever they want to be. Yet it's never been less clear who 745 00:57:19.159 --> 00:57:23.559 men really are. Guys Guy Radio, starring author and Guy's Guy Robert Manny, 746 00:57:23.840 --> 00:57:29.559 is coming to CACAA every Wednesday at eight pm. Whether it's relationships, 747 00:57:29.760 --> 00:57:35.079 sex, wellness for spirituality, join Robert as he interviews the experts and shares 748 00:57:35.079 --> 00:57:38.159 his insights on building a world where men and women can be at their best. 749 00:57:38.440 --> 00:57:45.960 Guys Guy Radio, Better Men, Better World NBC News Radio. I'm 750 00:57:46.039 --> 00:57:52.280 Chris Karragio. House Intelligence Chair Mike Turner says CIA Director at William Burns told 751 00:57:52.320 --> 00:57:55.119 him that a ceasefire in the Israel Lamas war is close. Speaking on CBS 752 00:57:55.119 --> 00:58:00.880 News Face the Nation, Turner said the potential deal comes after weeks of negotiations 753 00:58:00.880 --> 00:58:05.320 with international leaders. I was just briefed by the CIA director who Burns Friday. 754 00:58:05.360 --> 00:58:08.760 Personally. He is the one who is conducting the ceasefire negotiations, and 755 00:58:08.800 --> 00:58:13.239 he believes that we're close, and I think that is very, very important 756 00:58:13.280 --> 00:58:15.360 to accomplish. The Congressman said, it's important to get the deal done because 757 00:58:15.360 --> 00:58:20.480 there's hostages still being held by Hamas and there's a major need to get aid 758 00:58:20.519 --> 00:58:23.280 into the Gaza strip. This comes one day after Israel reportedly said yes to 759 00:58:23.320 --> 00:58:28.960 the framework of a potential temporary cease fire and hostage release agreement. The US 760 00:58:29.000 --> 00:58:32.079 official so the deal would involve a six week ceasefire and the release of at 761 00:58:32.159 --> 00:58:37.320 risk hostages. The Supreme Court will issue at least one decision tomorrow. The 762 00:58:37.440 --> 00:58:42.880 ruling may be the case of former President Trump's eligibility for Colorado's presidential primary ballot. 763 00:58:43.239 --> 00:58:45.960 The opinion or opinions will be posted online at ten am Eastern. This 764 00:58:46.000 --> 00:58:51.039 comes after the Colorado Republican Party asked the High Court to act before Super Tuesday 765 00:58:51.039 --> 00:58:53.840 primaries. This week, Crews and Texas are battling strong wins as they fight 766 00:58:53.880 --> 00:58:58.599 the largest and most destructive wildfire in state history. As of this morning, 767 00:58:58.639 --> 00:59:02.000 the Smokehouse Creek fire has wartched over a million acres across the Texas Panhandle and 768 00:59:02.039 --> 00:59:06.960 into Oklahoma since Monday. More than five hundred structures have been destroyed, and 769 00:59:07.000 --> 00:59:10.519 the fire is only about fifteen percent contained. Critical fire conditions are expected to 770 00:59:10.519 --> 00:59:15.039 continue today, with the National Weather Service predicting wind gusts of fifty miles an 771 00:59:15.039 --> 00:59:20.519 hour, with red flag warnings posted across the Central Plains. NASA and SpaceX 772 00:59:20.519 --> 00:59:22.639 are hoping for good weather for tonight's launch of the Crew eight mission to the 773 00:59:22.639 --> 00:59:29.360 International Space Station. Unfavorable conditions prompted officials to scrub yesterday's scheduled launch attempt. 774 00:59:29.519 --> 00:59:32.639 The SpaceX Crew Dragon Endeavor, atop a Falcon nine booster, is set to 775 00:59:32.679 --> 00:59:37.440 lift off from Launch Complex thirty nine A the Kennedy Space Center at ten fifty 776 00:59:37.480 --> 00:59:44.679 three pm Eastern. I'm Chris Karagio. NBC News Radio, NBC News on 777 00:59:44.880 --> 00:59:50.679 CACAA Lowland. Sponsored by Teamsters Local nineteen thirty two Protecting the Future of Working 778 00:59:50.719 --> 01:00:00.480 Families Teamsters nineteen thirty two dot org. The election is March fifth. Ballots 779 01:00:00.480 --> 01:00:04.199 are in the mail. Mail them in today. Now there's something to vote 780 01:00:04.239 --> 01:00:08.440 for because there's a new Marshall in town. Vote Derek Marshall for twenty third