WEBVTT 1 00:00:00.120 --> 00:00:03.960 From daylight to the evening hours. Kung Fu Panda four is claiming the top 2 00:00:04.000 --> 00:00:08.320 spot at the box office this Oscar weekend. The animated film made nineteen point 3 00:00:08.320 --> 00:00:12.119 four million dollars from opening day in preview screenings. June Part two falls to 4 00:00:12.199 --> 00:00:15.439 second and is on track to have a one hundred and fifty million dollars domestic 5 00:00:15.480 --> 00:00:18.719 total by the end of the weekend. I'm Chris Karragio, NBC News Radio, 6 00:00:21.000 --> 00:00:26.160 NBC News on KCAA Lomelada, sponsored by Teamsters Local nineteen thirty two, 7 00:00:26.280 --> 00:00:39.079 protecting the future of working Families Teamsters nineteen thirty two dot org. The information 8 00:00:39.200 --> 00:00:43.960 economy has a rod. The world is teeming with innovation as new business models 9 00:00:44.000 --> 00:00:50.520 reinvent every industry industry. Inside Analysis is your source of information and insights about 10 00:00:50.560 --> 00:00:55.320 how to make the most of this exciting new Eric learn more at inside analysis 11 00:00:55.439 --> 00:01:07.519 dot comsideanalysis dot com and now here's your host. Eric. A. Yes, 12 00:01:07.719 --> 00:01:12.079 indeed, ladies and gentlemen, welcome to the future. It's careening toward 13 00:01:12.200 --> 00:01:17.480 us all the time, but it seems like it's coming faster these days than 14 00:01:17.560 --> 00:01:19.640 ever before. I'm not sure exactly why that's true. They told me When 15 00:01:19.680 --> 00:01:23.359 I got older, time I go buy faster. So maybe that's what's happening, 16 00:01:23.560 --> 00:01:27.719 But I could swear this Jenai stuff has just put everything at light speed, 17 00:01:29.040 --> 00:01:32.439 certainly for the enterprise but also for small business. For anyone who's played 18 00:01:32.439 --> 00:01:36.319 around with this stuff, it is a leap into the future. And as 19 00:01:36.959 --> 00:01:41.680 the futurist William Gibson once said, the future is here already, it's just 20 00:01:41.760 --> 00:01:47.200 not evenly distributed. And I love that expression because it really speaks to the 21 00:01:47.239 --> 00:01:52.239 pockets of innovation that you see around the world and helps you understand that new 22 00:01:52.319 --> 00:01:57.000 technologies and new methods do take time to percolate through society, to permeate to 23 00:01:57.519 --> 00:02:01.959 various circles in the business world or in the consumer world, for example, 24 00:02:02.280 --> 00:02:07.000 and this jenerative AI stuff affects darn near everything, and it's really open the 25 00:02:07.039 --> 00:02:12.840 eyes of businesses worldwide. I mean, the numbers we're hearing are just off 26 00:02:12.879 --> 00:02:15.639 the charts in terms of the number of people using this stuff already. I 27 00:02:15.639 --> 00:02:21.759 mean, hundreds of millions of people are using this technology already today and it's 28 00:02:21.919 --> 00:02:24.840 more or less brand spanking new. So it was twenty twenty two that it 29 00:02:24.919 --> 00:02:30.479 was released in general chat GBT three I believe are three point five. We're 30 00:02:30.560 --> 00:02:34.439 up to like four point five now. Of course Google has barred now it's 31 00:02:34.439 --> 00:02:38.439 called Gemini, Anthropic has clawed there or other. There's Mistral that has the 32 00:02:39.039 --> 00:02:44.039 interesting approach they're taking with smaller models. But the bottom line is that Jenai 33 00:02:44.120 --> 00:02:46.840 is everywhere, and today we're going to dive in with two awesome experts. 34 00:02:46.840 --> 00:02:53.560 We've got Baoja Bui from FPT. It's a Vietnamese company, amazing company. 35 00:02:53.599 --> 00:02:55.919 I went to Vietnam last year to check out their headquarters and everything they do 36 00:02:57.039 --> 00:03:01.000 and was blown away. Really impressive company, very hard working, very diligent, 37 00:03:01.159 --> 00:03:06.039 very focused. They get stuff done. They're not worried about the flash 38 00:03:05.759 --> 00:03:09.199 and anything silly. They're just focused on getting the work done and building cool 39 00:03:09.240 --> 00:03:14.360 technologies. So we have Bioha to talk about that. And Jim Wilt as 40 00:03:14.360 --> 00:03:19.719 well is a fractional CTO and distinguished engineer. And these folks aren't really sparked, 41 00:03:20.080 --> 00:03:22.719 so in studio of audience, live studio audience, don't be shy, 42 00:03:22.759 --> 00:03:25.639 asked them the hard questions on a first to say my take on what Jenai 43 00:03:25.719 --> 00:03:29.479 is, and I'll throw it over to Boja and over to Jim as well. 44 00:03:30.000 --> 00:03:34.520 Really, these Jenai engines are predictive engines. That's what they really boil 45 00:03:34.639 --> 00:03:38.240 down to. They are predicting either the kind of image they think you want 46 00:03:38.280 --> 00:03:42.240 based on your prompt or the kind of text you want based upon your prompt. 47 00:03:42.439 --> 00:03:46.120 Now, they were trained on massive amounts of data on the interwebs, 48 00:03:46.360 --> 00:03:50.159 not on everything. We learned a couple months ago and one of our first 49 00:03:50.159 --> 00:03:53.840 shows of the year that even the folks at open Ai realized early in the 50 00:03:53.840 --> 00:03:57.639 game. They can't just train this on any data that's out there. They 51 00:03:57.639 --> 00:04:00.960 have to focus on certain data sets to get the end result they want, 52 00:04:00.199 --> 00:04:03.560 which is an engine that will reflect back the kind of text that you want. 53 00:04:03.879 --> 00:04:06.960 And it's very powerful. Folks. If you haven't used this stuff, 54 00:04:08.000 --> 00:04:11.120 you need to start playing around with it. And you first start, then 55 00:04:11.120 --> 00:04:13.879 you understand what it does. Then you can kind of get digged deeper into 56 00:04:13.879 --> 00:04:15.800 the weeds to figure out how to use it. But they're basically predicted vension 57 00:04:15.920 --> 00:04:18.240 is what they really are when you get right down to it. So at 58 00:04:18.240 --> 00:04:23.680 that Boha b tell us a bit about yourself and what you see, What 59 00:04:23.839 --> 00:04:26.319 is Jenny and I to you, and what should people use it for. 60 00:04:27.160 --> 00:04:31.839 Yeah, well, thank you Eric for that comments that we are not flashy. 61 00:04:32.639 --> 00:04:38.759 I want to be flashy, but we just we techy people just can't 62 00:04:38.800 --> 00:04:43.680 be flashy. So it's a skill I have to up myself for. But 63 00:04:43.839 --> 00:04:49.959 that's that though. So boha. I've been in IT and technologies for almost 64 00:04:50.000 --> 00:04:55.000 thirty years now, or even more than that. I stop counting the moment 65 00:04:55.079 --> 00:05:02.439 we hit above twenty. My so been with IT technology. I've been CIOs, 66 00:05:02.560 --> 00:05:08.720 I've been vps, I've been on the application development side, I've been 67 00:05:08.800 --> 00:05:14.920 on products. I've been but the since twenty fifteen, no, wait, 68 00:05:15.000 --> 00:05:20.160 twenty ten. I you know, I got introduced into data management, and 69 00:05:20.959 --> 00:05:27.160 you know, the journey starts there. I got passionate about just making informations 70 00:05:27.199 --> 00:05:30.839 out of data. You know, how do I get that information into the 71 00:05:30.879 --> 00:05:36.560 hand of the users, the executives, the people who's driving trucks, not 72 00:05:36.800 --> 00:05:42.920 needing to know when I have to deliver a certain product to a certain customers 73 00:05:42.959 --> 00:05:46.199 of patients. I was in healthcare for ten years at that point. Sale 74 00:05:46.759 --> 00:05:53.399 my whole the introduction to data, I was with the HIE. You know, 75 00:05:53.480 --> 00:05:58.519 if you still remember those terms, you know, the data for healthcare 76 00:05:58.759 --> 00:06:02.279 was a big thing back in two thousand and five ten. So that's my 77 00:06:02.399 --> 00:06:06.759 introduction to data. Now I've been with FPT, I've been their customers, 78 00:06:06.839 --> 00:06:15.000 I've been working with them, and I really appreciate your confirmations that you know, 79 00:06:15.079 --> 00:06:19.279 they are hardworking, very smart. As a customer, I was constantly 80 00:06:20.160 --> 00:06:27.519 amazed by the level of professionalisms that I got from FPT. So not a 81 00:06:27.639 --> 00:06:32.600 plot, I'm just kind of recounting my journey here. What is jen ai? 82 00:06:33.480 --> 00:06:36.519 You know, Jenei, Like you said, it starts out to be 83 00:06:38.279 --> 00:06:42.360 or aio, all right, It starts out to be predictive. You have 84 00:06:42.480 --> 00:06:46.120 data, you have the ability to use code to really mind that data and 85 00:06:46.160 --> 00:06:51.759 then generate information based on that data or predict based on the patterns that came 86 00:06:51.800 --> 00:06:58.560 from that data. And then it gets into the ability to really now using 87 00:06:58.680 --> 00:07:03.959 pattern to start producing new material. So that's what jen Ai is for me. 88 00:07:04.639 --> 00:07:10.360 And where are we going with jen Ai? It's everywhere, It's you 89 00:07:10.399 --> 00:07:15.000 know, Jim with Jim and I talked a lot about jen Ai and the 90 00:07:15.519 --> 00:07:19.480 growth of jen Ai and Ai overall. But Jim, it took fifteen years 91 00:07:19.480 --> 00:07:23.839 for cloud to be adopted. I still, you know, a few years 92 00:07:23.879 --> 00:07:29.800 ago there was a company that's still not looking at cloud, but jen Ai 93 00:07:30.240 --> 00:07:34.560 in two years we got widespread adoptions. I talked to my son. My 94 00:07:34.600 --> 00:07:40.519 son is doing a co op at a farmer company, and I asked him, 95 00:07:40.560 --> 00:07:43.759 so, what are you doing over you know, your bread time or 96 00:07:43.839 --> 00:07:50.959 he's using jen Ai to do some optimizations in their lapwork RNA recognitions RNA pattern 97 00:07:51.879 --> 00:07:58.879 So you know, it goes from a corporate level to students. You know 98 00:07:58.959 --> 00:08:05.160 how many the professor is trying to prevent that student from generating ai jen ai 99 00:08:05.600 --> 00:08:07.959 for papers. But is that not a bad thing? You know, because 100 00:08:07.959 --> 00:08:13.519 at the end of the day, is why reciting information that you can get 101 00:08:13.560 --> 00:08:18.279 from Google just put it in your own words? Is that you know, 102 00:08:18.399 --> 00:08:24.519 so again is permeate every aspect of our life nowadays. So we all have 103 00:08:24.600 --> 00:08:28.720 to get onto that bandwagon. That's a that's a great way to put it 104 00:08:28.759 --> 00:08:31.959 on. Like the way you said that, where are we going with Jennai 105 00:08:31.399 --> 00:08:37.519 everywhere? I'll throw it over to Jim to comment on that. I have 106 00:08:37.600 --> 00:08:41.120 to say, I mean, I watched the whole big data craze take off 107 00:08:41.240 --> 00:08:46.519 fifteen seventeen years ago. Let's say that was the biggest thing i'd ever seen. 108 00:08:46.080 --> 00:08:50.279 This is like five to ten times bigger than that in terms of the 109 00:08:50.399 --> 00:08:54.759 number of people getting in in terms of the impact, in terms of the 110 00:08:54.840 --> 00:08:56.240 number of people and impacts, it's just off the charts. But what do 111 00:08:56.279 --> 00:09:01.480 you think, Jim, Yeah, you both have really covered a lot of 112 00:09:01.519 --> 00:09:05.840 the basics that we wanted to, you know, get out there in terms 113 00:09:05.919 --> 00:09:11.279 of the history and where it's come Bau, how it's correct. And looking 114 00:09:11.320 --> 00:09:16.879 at adoption of the PC was you know, about ten years fifteen years to 115 00:09:16.879 --> 00:09:20.440 get to fifty million users. That took you know, about ten years to 116 00:09:20.480 --> 00:09:26.600 fifteen years for cloud adoption to really land. And when we look at machine 117 00:09:26.679 --> 00:09:31.080 learning and generator of AI, machine learning has been around for ten years plus, 118 00:09:31.120 --> 00:09:35.080 it's been around. There are people that have been working in the space 119 00:09:35.159 --> 00:09:39.000 for over twenty thirty years with neural networks and so forth. But the big 120 00:09:39.000 --> 00:09:43.440 difference with generative AI is is it's really applicable to the common person. Anybody 121 00:09:43.720 --> 00:09:48.879 can get into a prompt situation to some learn some prompt engineering, if you 122 00:09:48.919 --> 00:09:54.879 will, and start getting results that are just massively interesting. And I think 123 00:09:54.919 --> 00:09:58.200 that you know, the statistic that hit me that Baja mentioned is actually it 124 00:09:58.240 --> 00:10:05.840 was two months, Generative AI has reached one hundred million users. That's twice 125 00:10:05.039 --> 00:10:09.919 what it took these other technologies what ten, you know, fifteen years to 126 00:10:09.960 --> 00:10:13.919 achieve. You know, the Internet took five years to achieve fifty million users. 127 00:10:13.000 --> 00:10:18.000 It's already surpassed the airnet's adoption in just two months. What's also exciting 128 00:10:18.000 --> 00:10:26.320 about generative AI is that we're traditional AI is using what's traditionally called supervised learning. 129 00:10:26.039 --> 00:10:31.159 Baja mentioned it's and you did too, Eric. It's mentioned that it's 130 00:10:31.200 --> 00:10:35.399 based on a lot more data, and that's called unsupervised learning. What that 131 00:10:35.480 --> 00:10:39.159 allows it to do is to create what I'm going to say, patterned recognitions 132 00:10:39.159 --> 00:10:43.960 and results that they're out of our view. So it looks like it's thinking, 133 00:10:45.200 --> 00:10:46.720 if you will, on its own, but it's working with such massive 134 00:10:46.799 --> 00:10:50.919 data. It's just really really exciting to see that it can find patterns that 135 00:10:50.960 --> 00:10:54.840 we normally wouldn't find ourselves. And I think that's one of the things that 136 00:10:54.919 --> 00:11:01.960 makes it that aha moment that oh this is different. It's accessible, and 137 00:11:01.039 --> 00:11:07.720 it produces results that are pretty transformational because it's putting together patterns that we wouldn't 138 00:11:07.759 --> 00:11:11.720 expect to see that are valid. Yeah, that's a great way to put 139 00:11:11.720 --> 00:11:16.279 it. And maybe Baoja, I'll throw this one over to you. What 140 00:11:16.440 --> 00:11:20.360 I've seen with AI and other capacities too, and now of course with GENAI 141 00:11:20.919 --> 00:11:24.799 is that almost invariably it surface is something that I did not expect. And 142 00:11:24.840 --> 00:11:33.000 that's very interesting because typically any AI algorithm is just a recipe that is designed 143 00:11:33.039 --> 00:11:39.159 to address a particular type of situation. Let's say a particular function, a 144 00:11:39.200 --> 00:11:43.559 particular array, or something that happens in the world. You're trying to predict 145 00:11:43.559 --> 00:11:46.960 what happens or optimize what happens. And you would think that we know ourselves 146 00:11:48.039 --> 00:11:50.360 well enough to not be surprised by our own behavior, but that's just not 147 00:11:50.440 --> 00:11:54.279 the case. Like and so every time I use one of these engines, 148 00:11:54.559 --> 00:11:58.080 it throws up something I did not expect, and that's good news because that's 149 00:11:58.399 --> 00:12:03.200 it triggers the creative process and it makes us think more. What do you 150 00:12:03.240 --> 00:12:07.799 think? I think I absolutely agree with you. I think that we, 151 00:12:11.039 --> 00:12:16.039 as great as our human brains are, we have limited capacity or you know, 152 00:12:16.679 --> 00:12:20.960 how how fast we can generate or how much we can retrieve. So 153 00:12:20.080 --> 00:12:26.360 it's not I don't think that it's uh, it's it's a. I think 154 00:12:26.399 --> 00:12:31.480 it's more of a retrieval, you know ability, whereas the machines it's gonna 155 00:12:31.559 --> 00:12:35.080 take back. It's gonna take everything that it knows and give it back to 156 00:12:35.120 --> 00:12:39.679 you, and it reminds you of things that hey, I knew this, 157 00:12:41.360 --> 00:12:46.799 but I didn't remember. And that's where the benefits or the efficiency gains in 158 00:12:46.919 --> 00:12:52.240 using generative AI is so great. It's because it really takes away your the 159 00:12:52.360 --> 00:12:58.960 need for us to remember everything that you have to you know. That's why 160 00:12:58.399 --> 00:13:03.240 in we're saying the old day, but it really is still there. You 161 00:13:03.279 --> 00:13:09.480 know, you have document template because people cannot remember everything every single time, 162 00:13:11.200 --> 00:13:16.679 so you need template to repeat to save time. You don't have to recreate 163 00:13:16.720 --> 00:13:22.039 things all the time. Generative AI is able to really do that retrieval of 164 00:13:22.240 --> 00:13:28.080 your template and give it all back to you very timely. But you know, 165 00:13:28.279 --> 00:13:31.960 retrieval and knowing what to retrieve and when to retrieve is also important because 166 00:13:33.639 --> 00:13:37.279 yeah, as much as we like to get everything back, you don't always 167 00:13:37.320 --> 00:13:43.120 want everything that JENEI give back to you at that's quite yet. Well, 168 00:13:43.120 --> 00:13:46.679 and that's a comment I had made before our show. I was talking with 169 00:13:46.759 --> 00:13:50.159 Mark Palmer, who was a real smart executive in the tech space, and 170 00:13:50.279 --> 00:13:54.360 he mentioned that he read some research recently that showed something like eighty percent of 171 00:13:54.360 --> 00:13:58.679 people who are using jen Ai right now are just taking whole hog whatever it 172 00:13:58.720 --> 00:14:01.840 comes out of that engine and using it. That has a very bad idea, 173 00:14:01.919 --> 00:14:05.080 right, bo, How you want to always vet that stuff? Oh? 174 00:14:05.120 --> 00:14:07.960 Absolutely, it will give you good ideas, but you have to be 175 00:14:07.960 --> 00:14:11.320 careful because number one, it does hallucinate, so it does make stuff up. 176 00:14:11.360 --> 00:14:16.000 It is generative by nature, so it generates new things based upon old 177 00:14:16.000 --> 00:14:20.679 things. Right about what it's seen. All it's doing is reflecting back patterns 178 00:14:20.679 --> 00:14:24.159 of what it's been trained on. But you have to be very careful about 179 00:14:24.279 --> 00:14:30.000 vetting that stuff, right, Oh, absolutely, you have to because it's 180 00:14:30.080 --> 00:14:35.759 trained on informations out there, you know, especially some of the open source 181 00:14:35.519 --> 00:14:43.440 gen ai open Ai for example, it's being trained on the internet, and 182 00:14:43.519 --> 00:14:48.159 not everything on the internet is usable. Just be careful. Not everything is 183 00:14:48.200 --> 00:14:52.600 accurate, right, Not everything is accurate, and even when so that's kind 184 00:14:52.639 --> 00:14:56.759 of going into some of the rest that when we well, you know, 185 00:14:56.799 --> 00:15:01.039 what we have to do in order for us to be ready for gen Ai 186 00:15:01.320 --> 00:15:09.480 is some of those betting under the training of your resources to make sure they 187 00:15:09.519 --> 00:15:13.799 know how to use it safely and effectively. Yeah, and that's the curation 188 00:15:15.080 --> 00:15:18.480 process, right. So we keep hearing about RAG models, retrieval, augmented 189 00:15:18.559 --> 00:15:22.679 generation, which, if I understand it, really what you're doing is you 190 00:15:22.720 --> 00:15:28.919 are trying to use your own trusted governed data as an anchor, and then 191 00:15:30.120 --> 00:15:33.159 you want to be able to spin up text using the LM as a text 192 00:15:33.240 --> 00:15:37.320 generative capacity, if you will. But you wanted to use your facts that 193 00:15:37.399 --> 00:15:41.360 you give it, and those facts are posted as embeddings, either in the 194 00:15:41.360 --> 00:15:46.320 model itself, which some people do, or in a vector database which is 195 00:15:46.360 --> 00:15:50.840 then adjacent to the model. And that people understand the whole concept of persisting 196 00:15:50.840 --> 00:15:54.559 something in the database and then recalling something from the database. But here we've 197 00:15:54.559 --> 00:15:58.679 added a third element, which is the language model to generate stuff. But 198 00:15:58.720 --> 00:16:03.600 still the same concept applies. Right, We're persisting something in a data store, 199 00:16:03.080 --> 00:16:07.080 and then we're leveraging that along with the generative capability of the large language 200 00:16:07.080 --> 00:16:12.600 model to create fresh new text that is ideally accurate with what our corporate data 201 00:16:12.639 --> 00:16:17.080 says. Right Baha in a way, yes, so you know, we 202 00:16:17.480 --> 00:16:22.320 you know, if you're using your data alone and retrieval and then to supplement 203 00:16:22.399 --> 00:16:29.960 it with the large language to to make the interactions easier, or to generate. 204 00:16:30.200 --> 00:16:33.759 It's in text and it's limited, but the ability of gen AI to 205 00:16:34.000 --> 00:16:38.799 you know, so large language model gives you the ability to read the data 206 00:16:40.000 --> 00:16:45.360 that in the database just not possible. You know, pdf my. You 207 00:16:45.399 --> 00:16:51.919 know some of the older companies that I have worked with, the Banks Insurance 208 00:16:51.960 --> 00:16:57.519 Company, they have one hundred years of documents that's probably still in some kind 209 00:16:57.600 --> 00:17:04.880 of fish or images. Large language model nowadays give us that capability to really 210 00:17:04.960 --> 00:17:11.640 retrieve that information. And like you said, either AB or vector bates it 211 00:17:11.079 --> 00:17:15.839 so that now you can retrieve it. So that's the advantage of some of 212 00:17:15.880 --> 00:17:22.440 the newer capability. And then based on that then it gives the ability for 213 00:17:22.559 --> 00:17:29.880 us to interact with it using natural language. So that kind of expand the 214 00:17:30.079 --> 00:17:36.559 resource pool of who can rechieve that kind of information versus before all the tech 215 00:17:36.599 --> 00:17:40.960 people have to write some kind of query. Right now you have the you 216 00:17:41.000 --> 00:17:45.079 know, a much larger pool resource who can come for those right and see 217 00:17:45.079 --> 00:17:48.599 this is I mean, you just hit on one of the biggest transformations that 218 00:17:48.599 --> 00:17:52.920 we're seeing right now. And I heard it from my buddy Lose Simon with 219 00:17:52.960 --> 00:17:56.319 a company called up Tom here in Pittsburgh. He's the one who first said 220 00:17:56.319 --> 00:17:59.400 it to me. And this is so big and we'll talk about this after 221 00:17:59.400 --> 00:18:02.200 the break, but this is a seriously big deal. We used to always 222 00:18:02.200 --> 00:18:06.279 have to learn how to speak in computer language in order to talk to computers. 223 00:18:06.519 --> 00:18:08.599 So we had to learn to speak SQL, the structured query language to 224 00:18:08.680 --> 00:18:14.799 talk to databases. We had to learn Python to talk to various Python apps 225 00:18:14.960 --> 00:18:18.079 or that's used for risk management, lots of different stuff. Honestly, you 226 00:18:18.160 --> 00:18:22.200 had to learn cobol in order to talk to a mainframe for example. Now 227 00:18:22.720 --> 00:18:26.400 you can talk in English words to the computer systems. That is a huge 228 00:18:26.440 --> 00:18:37.480 deal. We'll be right back here listening to Inside Analysis. Respect. Welcome 229 00:18:37.559 --> 00:18:47.039 back to Inside Analysis. Here's your host, Eric Tabanac. All right, 230 00:18:47.119 --> 00:18:49.839 folks, take us to the future and see if we're talking about jen Ai. 231 00:18:51.279 --> 00:18:53.599 It is futuristic. It's amazing, folks. Let's be honest, play 232 00:18:53.640 --> 00:18:56.160 around with this stuff. It's going to blow your mind. A friend of 233 00:18:56.160 --> 00:19:02.000 mine turned me on to mid Journey, which is through Discord. I guess 234 00:19:02.039 --> 00:19:06.000 it's a discord channel, and oh my goodness, it's crazy. The amount 235 00:19:06.039 --> 00:19:10.319 of artwork and the sophistication of the artwork that you can create with this stuff 236 00:19:10.400 --> 00:19:14.680 is just wild. So things are changing rapidly. I remember hearing who is 237 00:19:14.720 --> 00:19:18.359 saying, whoever wins this war wins the world. Now you know again there 238 00:19:18.400 --> 00:19:22.319 are use cases for this stuff. There are dangerous sides of a deep fakes, 239 00:19:22.319 --> 00:19:25.000 things of this nature. We're going to talk about some of that, 240 00:19:25.400 --> 00:19:27.799 But this segment I wanted to get deeper into are you ready and how do 241 00:19:27.839 --> 00:19:30.279 you know? So? How do you know if you're ready to leverage this 242 00:19:30.359 --> 00:19:34.279 kind of technology organizational writiness? As we said, we've picked up last segment. 243 00:19:34.480 --> 00:19:38.160 We're talking about how you can now talk to computers in English words, 244 00:19:38.200 --> 00:19:41.839 in prompts right as supposed to having to learn SQL or COB all these other 245 00:19:41.920 --> 00:19:45.599 languages. And you can also use it to tell you what code does. 246 00:19:45.680 --> 00:19:48.119 You can feed it code and say what does this code do, and it'll 247 00:19:48.119 --> 00:19:51.440 tell you. It won't be always completely accurate, but it'll be pretty darn 248 00:19:51.480 --> 00:19:55.240 close. And I've heard from enough people now to know that it gets pretty 249 00:19:55.279 --> 00:19:57.839 close. It gets to about eighty percent in terms of coding of writing what 250 00:19:57.920 --> 00:20:02.799 you want, and then you just fine tuned to get your last twenty percent. 251 00:20:03.160 --> 00:20:07.279 But Jim Wildell throw it over to you. In terms of organizational readiness, 252 00:20:07.599 --> 00:20:10.960 well, I mean that most clever thing I heard in our pre call 253 00:20:11.200 --> 00:20:15.599 last week was to start your first use case internally. So nothing that faces 254 00:20:15.599 --> 00:20:19.960 your customers or your prospects, your partners. But do some project that faces 255 00:20:21.319 --> 00:20:25.039 just your internal staff to learn about these things. Because it's like a new 256 00:20:25.039 --> 00:20:27.200 pair of skis or something with jet engines on them. You have to figure 257 00:20:27.240 --> 00:20:30.279 out how to ride them. But what do you think about all that, 258 00:20:30.359 --> 00:20:33.640 Jim, Yeah, that's a good way of putting an analogy. It's like 259 00:20:33.759 --> 00:20:40.440 seize jet engines. You wouldn't go out on the slopes with other people around 260 00:20:40.599 --> 00:20:45.839 if you have jet engines and your skis. I think one of the interesting 261 00:20:47.000 --> 00:20:52.759 aspects is there's fear and certainty deception sometimes and organizations. When you're looking at 262 00:20:52.799 --> 00:21:00.400 the adoption of Jenator of AI, where we're excited about it, publican general's 263 00:21:00.440 --> 00:21:03.519 excited about it. A lot of organizations have what I'm going to say a 264 00:21:03.559 --> 00:21:11.519 healthy fear, which is in most situations pretty healthy. Right when it came 265 00:21:11.559 --> 00:21:17.160 to PCs or the internet or cloud, it was good to come in it 266 00:21:17.440 --> 00:21:22.079 kind of slow. It's not going to work this time because you really are 267 00:21:22.119 --> 00:21:26.680 not prepared for the adoption that it has gotten in the general public. And 268 00:21:26.759 --> 00:21:30.279 so if you have a policy in your organization not to allow people to use 269 00:21:30.359 --> 00:21:37.119 generative AI, be aware they're using generative AI no matter what it's on their 270 00:21:37.200 --> 00:21:41.279 phone. And I know that we want to protect corporate assets, corporate secrets. 271 00:21:41.440 --> 00:21:45.880 Find that's great. But I think if you go about the adoption we 272 00:21:45.039 --> 00:21:49.000 talked about, I think you're going to find you can ease your way into 273 00:21:49.119 --> 00:21:53.079 it and get what I'm going to say, a lot of those fears dismantled 274 00:21:53.240 --> 00:21:59.480 and a lot of the excitement generated because you're doing it with internal uses, 275 00:21:59.559 --> 00:22:03.359 internal purposes. You mentioned about code analysis. I think it's a code companion. 276 00:22:03.400 --> 00:22:07.920 Every developer should have a tied to their hip because it will help them 277 00:22:07.960 --> 00:22:14.319 find errors that they're putting in their code so fast. You don't let it 278 00:22:14.559 --> 00:22:18.039 write the code for you outright when you do is it's your companion. It's 279 00:22:18.079 --> 00:22:21.720 going to find your blind spots, it's going to find where you made a 280 00:22:21.759 --> 00:22:27.319 mistake. I wrote an example of this using multi threadic code, and I 281 00:22:27.359 --> 00:22:32.480 put an air on it purpose and it was amazing. Not only could the 282 00:22:32.519 --> 00:22:36.240 generative AI find the air, which none of my colleagues could, by the 283 00:22:36.240 --> 00:22:40.480 way, it also fixed the air, which all of my colleagues could do, 284 00:22:40.960 --> 00:22:45.119 but it fixed it in a very concise and correct way. Now I'm 285 00:22:45.119 --> 00:22:48.440 in charge of the code, therefore I'm the one responsible accountable. I should 286 00:22:48.440 --> 00:22:52.920 never just let it fix the code and put it into production. That's just 287 00:22:52.000 --> 00:22:56.279 silliness. But if you do think about it, there's a lot of value 288 00:22:56.680 --> 00:23:03.200 it can be used internal organizations that makes the organization execute much more operationally efficient. 289 00:23:03.680 --> 00:23:08.359 And that's something Bohan I have been doing with other organizations is coaching them 290 00:23:08.599 --> 00:23:17.480 on ways to enhance their internal efficiencies using generative AI. Yes, everybody wants 291 00:23:17.480 --> 00:23:19.880 to be the first one out there, and there are some amazing products that 292 00:23:19.920 --> 00:23:26.039 are coming out as products from organizations, but if you're new to it, 293 00:23:26.160 --> 00:23:29.680 you really need just to kind of froll it inside. One of the retail 294 00:23:29.759 --> 00:23:37.200 organizations I highly regard as highly innovative has taken and create a degenerative AI tool 295 00:23:37.279 --> 00:23:41.599 and given it to fifty thousand of its employees and they don't know what they're 296 00:23:41.599 --> 00:23:44.440 going to do with it. They just want to learn what they're going to 297 00:23:44.519 --> 00:23:47.400 learn from it and what they're going to do from it, And that's that's 298 00:23:47.400 --> 00:23:51.240 a great attitude. You give it a safe tool that's not going to put, 299 00:23:51.279 --> 00:23:53.240 if you will, your corporate data in danger, and you see how 300 00:23:53.240 --> 00:23:56.720 they're using it. You monitor, you see how they're using it, and 301 00:23:56.759 --> 00:24:00.839 you find out what they insights. You know, what spots they're finding, 302 00:24:00.839 --> 00:24:03.000 what got you moments they're getting those those things you said, you you know, 303 00:24:03.039 --> 00:24:07.359 oh my gosh, how did it come up with this? Yes, 304 00:24:07.480 --> 00:24:10.960 exactly. Everybody should be grabbing that. And I'll kind of pass a liver 305 00:24:11.039 --> 00:24:15.759 about how because I think she can probably talk about to the concept of encouraging 306 00:24:15.880 --> 00:24:22.559 operational efficiency in organizations first in terms of learning what you can do with the 307 00:24:22.599 --> 00:24:26.319 tool before you go crazy putting it out there for everybody to see. Well, 308 00:24:26.319 --> 00:24:30.079 there's just so many things that we can gain efficiency, right, you 309 00:24:30.079 --> 00:24:36.119 know, So Jim and I been working together on a few of them. 310 00:24:36.359 --> 00:24:42.720 One is to lavish ai for sound analysis, you know, to look at 311 00:24:44.079 --> 00:24:51.160 what can we do to use sound to detect issues in machines and predictive maintenance 312 00:24:51.640 --> 00:24:57.279 or just assist in the diagnosis of it. There's other use where we have 313 00:24:57.440 --> 00:25:07.400 products already out there using visions two to detect issues on say, you know, 314 00:25:07.759 --> 00:25:11.200 win turbine, If that's a crack, how do you know? You 315 00:25:11.279 --> 00:25:18.599 know? So monitoring using vision AI. We're also working on code assists. 316 00:25:19.000 --> 00:25:25.559 Like Jim was talking about, every develop person nowadays really need to leverish AI 317 00:25:25.720 --> 00:25:30.079 jen AI to help them just make you know, as a double person, 318 00:25:30.279 --> 00:25:33.359 Jim was a double I don't know, Eric, have you ever worked with 319 00:25:33.400 --> 00:25:37.400 any kind of coding, But at the same time, you know how tedious 320 00:25:37.480 --> 00:25:41.640 it was to sit there and type all these commands. If there's something that 321 00:25:41.720 --> 00:25:47.880 really accelerates that coding for you so that you can focus on the big concept, 322 00:25:48.480 --> 00:25:56.359 that's where optimizing the efficiency of every aspect of the organizations will be wonderful. 323 00:25:56.720 --> 00:26:00.880 So that's that's where we are seeing, you know, organization really should 324 00:26:00.920 --> 00:26:06.400 adopt Look at what are the tedious things that nobody really want to do or 325 00:26:06.480 --> 00:26:08.839 they want to do but they wish it could be faster. That's where we 326 00:26:08.920 --> 00:26:14.720 apply AI to help. But how do you get the organizations ready as well? 327 00:26:14.799 --> 00:26:17.920 Right? What are the things that let's just say, if you create 328 00:26:17.960 --> 00:26:23.200 the tool to set I don't know, predict something within your organizations or to 329 00:26:23.680 --> 00:26:29.000 generate some content within the organization. First of all, you need to make 330 00:26:29.000 --> 00:26:33.480 sure that your model is chained on information that you want your employees to use, 331 00:26:33.920 --> 00:26:38.240 and don't just train it onto the internet because who knows what's out there. 332 00:26:38.720 --> 00:26:44.839 So, you know, organization readiness, making sure that the organizations the 333 00:26:44.920 --> 00:26:49.839 employees are ready and understand what are some safe AI practice should be. And 334 00:26:49.880 --> 00:26:56.880 then also from a technology standpoint, setting up the tool that can deliver that 335 00:26:56.119 --> 00:27:03.200 safe products. You know, I came from data data governance is my preach 336 00:27:03.319 --> 00:27:07.400 of the day these days, you know, every single time, get your 337 00:27:07.480 --> 00:27:11.279 data governance in place, get your data clans, get your data in a 338 00:27:11.359 --> 00:27:17.119 place where your model can get to so that it can be trained. Because 339 00:27:17.160 --> 00:27:22.720 if it takes your data scientists pages to get to the data, it really 340 00:27:22.720 --> 00:27:27.559 doesn't get your organization ahead of the game. That's and that's a really good 341 00:27:27.640 --> 00:27:30.880 point that you just made. And so I'm going to throw out a thought 342 00:27:30.920 --> 00:27:34.839 here and maybe a throw it over to Jim first and then baoha, you 343 00:27:34.880 --> 00:27:38.480 can ask it what am I doing wrong? You can ask it all sorts 344 00:27:38.519 --> 00:27:42.079 of questions and feed it a document. So one of the I think relatively 345 00:27:42.240 --> 00:27:48.920 underappreciated and incredibly powerful use cases or functions of these tools is summarization. So 346 00:27:48.960 --> 00:27:52.359 you can take a very long document, let's say a thousand page document, 347 00:27:52.799 --> 00:27:57.559 feed it in and it's almost like a dynamically generated cliffs notes when you're in 348 00:27:59.319 --> 00:28:03.640 high school and these cliffs notes, right, it's like boom, that's like 349 00:28:03.839 --> 00:28:07.720 for you. It take out all the filler words and it condensed it for 350 00:28:07.880 --> 00:28:12.319 you, and it gets you going so fast. You know, Jim and 351 00:28:12.359 --> 00:28:21.960 I were just experimenting what last week we point a model to some users document 352 00:28:22.119 --> 00:28:26.960 user guide that we found on the internet, and then feed the engine that 353 00:28:26.000 --> 00:28:32.480 we create a bunch of code and tell to interpret it for us. It 354 00:28:32.519 --> 00:28:36.519 did it just like that. Jim got the engine all set up in what 355 00:28:36.720 --> 00:28:41.640 fifteen minutes fifty minutes to get the first setback, and then you know it 356 00:28:41.680 --> 00:28:47.759 takes a little bit more training to get to some more eligible results, but 357 00:28:47.920 --> 00:28:51.599 less than a day. Yeah, that's that's crazy. And I was talking 358 00:28:51.640 --> 00:28:56.119 to some folks from Matillion, a software company MDM style company. They had 359 00:28:56.119 --> 00:29:00.359 a use case where they said, look, let's just try to throw it 360 00:29:00.359 --> 00:29:03.519 at chat GPT and just see what it says. And some people were skeptical, 361 00:29:03.759 --> 00:29:06.759 like, just give it a try, and so they did, and 362 00:29:06.799 --> 00:29:10.960 their minds were blown by how accurately it came back. And so the point 363 00:29:11.000 --> 00:29:15.759 being, think about the whole side of discovery. When you're doing data analysis, 364 00:29:15.000 --> 00:29:18.799 the discovery side is so important because you're trying to find the unknown, 365 00:29:18.920 --> 00:29:22.279 right. I mean, it's one thing if you're trying to find a known 366 00:29:22.799 --> 00:29:25.480 like with a query to the database, give me what have already persisted, 367 00:29:25.480 --> 00:29:29.319 But if you're trying to look for patterns that have not yet been identified, 368 00:29:29.759 --> 00:29:34.240 this kind of technology is incredibly powerful because it will on a moment's notice, 369 00:29:36.200 --> 00:29:42.759 absorb tremendous amounts of data and then throw back seemingly random reflections that are very 370 00:29:42.799 --> 00:29:47.079 accurate and very compelling. So on the discovery side, I mean, I 371 00:29:47.200 --> 00:29:51.200 kind of view these engines as being a new front end for almost everything in 372 00:29:51.200 --> 00:29:55.279 the enterprise. Eventually, what do you think about haha? I agree? 373 00:29:55.359 --> 00:30:00.599 I agree the discovery. That's exactly what Jem and I were trying to figure 374 00:30:00.640 --> 00:30:07.359 out last week is how we have an obscure platform that not that many people 375 00:30:07.200 --> 00:30:15.079 know about anymore and very hard to find resource for even if you had resource. 376 00:30:15.200 --> 00:30:19.880 Some of these platforms that had been in an organization for fifteen twenty years. 377 00:30:19.920 --> 00:30:27.160 The amount of organic growth and customizations and that governance on this platform not 378 00:30:27.400 --> 00:30:33.079 just mind blown. But you know, how do you really accelerate that discovery 379 00:30:33.119 --> 00:30:41.319 process so that you really get the engineers to where they produce results versus doing 380 00:30:41.359 --> 00:30:48.640 these tedious, unvalue added activities. Honestly, so AI, that ability is 381 00:30:48.839 --> 00:30:53.680 just it amazed me still and get me very excited. Well, and you 382 00:30:53.720 --> 00:30:57.319 do. So. The guys from Matilian were mentioning they did then need to 383 00:30:57.359 --> 00:31:00.319 go, so they figured something out about how to do it, and then 384 00:31:00.359 --> 00:31:04.559 they actually had to go do the hard work with their existing information systems to 385 00:31:04.599 --> 00:31:08.559 get the design work and the engineering done. But at least they knew which 386 00:31:08.559 --> 00:31:12.279 way to go. At least they knew what's possible. Jim, I'll throw 387 00:31:12.279 --> 00:31:17.759 it to you. Being able to suss out what is genuinely possible in any 388 00:31:17.759 --> 00:31:22.680 given situation is half the battle. Then you have options that you can choose 389 00:31:22.720 --> 00:31:26.640 to go in this direction or that direction. But if you haven't effectively discerned 390 00:31:26.960 --> 00:31:32.920 the nature of the challenge or the range of possibilities, you're still in the 391 00:31:33.000 --> 00:31:36.920 dark and you're shooting in the dark. So this discovery component of these large 392 00:31:36.960 --> 00:31:41.640 language models, I think it's very compelling for being able to understand what's happening 393 00:31:41.720 --> 00:31:45.119 and then have a really good idea of where to go next. What do 394 00:31:45.160 --> 00:31:51.480 you think, Jim, It's interesting because the discovery process can be enhanced by 395 00:31:51.519 --> 00:31:57.079 asking the generative engine give me ten questions. Ask me ten questions as I'm 396 00:31:57.079 --> 00:32:02.960 going to discover something, and it will prompt you, with best practices of 397 00:32:04.039 --> 00:32:10.680 discovery to feed itself information so it can start offering you better analysis. The 398 00:32:10.759 --> 00:32:19.519 example how I was talking about was so much fun because it's an obscure platform 399 00:32:19.599 --> 00:32:24.640 that really hasn't been leveraged, let's say in tens of years, and we 400 00:32:24.640 --> 00:32:30.720 are able to find an old syntax manual with two thousand pages. So doing 401 00:32:30.759 --> 00:32:37.279 the pag approach or grounding or language chaining, you feed that two thousand page 402 00:32:37.319 --> 00:32:44.839 manual in and now it's making what I'm going to say, very precise outputs 403 00:32:45.440 --> 00:32:51.160 that are specific to the rules of the language and the syntax of the language 404 00:32:51.160 --> 00:32:55.799 that it was built in. And so you can take things that are ancient 405 00:32:58.160 --> 00:33:01.319 in the computer world and make very modern to the answers that can give with 406 00:33:01.400 --> 00:33:07.960 the very accuracies that you're seeing and you know it could be one hundred percent 407 00:33:07.000 --> 00:33:10.720 accurate eighty percent of the time, and the times that it's not accurate, 408 00:33:13.160 --> 00:33:15.920 you're going to have to have enough knowledge to call that out. The hallucinations 409 00:33:15.960 --> 00:33:21.319 of the biases, the blind spots. However, what we found is that 410 00:33:21.400 --> 00:33:28.039 as you're going through a discovery process, it's those those gotcha's were basically things 411 00:33:28.079 --> 00:33:30.799 I wasn't thinking about. It will think you know, have you thought about 412 00:33:30.920 --> 00:33:34.559 and it will give you some things. Have you thought about this? Have 413 00:33:34.640 --> 00:33:37.359 you thought about that? And it's like, no, actually I haven't. 414 00:33:37.640 --> 00:33:42.480 So it's a companion, right, It's kind of that person that that can 415 00:33:42.720 --> 00:33:45.799 can have a conversation with you and throw things out at you, but you're 416 00:33:45.799 --> 00:33:50.720 not going to reject them because you're asking them to do that for you. 417 00:33:51.319 --> 00:33:53.640 If that's a really good way to put it. And we'll talk about this 418 00:33:53.680 --> 00:33:57.799 in the next segment coming up here in just a moment, but using it 419 00:33:57.839 --> 00:34:02.160 as a companion, like an incredible, knowledgeable intern who will tell you whatever 420 00:34:02.240 --> 00:34:06.319 you ask it may not be right all the time. You kind of reminded 421 00:34:06.319 --> 00:34:07.800 me of the old Yogi bearra quote. Right, ninety percent of the game 422 00:34:07.840 --> 00:34:12.920 is half mental or something like that that can be completely correct eighty percent of 423 00:34:12.960 --> 00:34:15.960 design the clock the broken clock is correct two times a day, so well, 424 00:34:15.960 --> 00:34:17.559 don't touch that. He folks will be right back. You're listening to 425 00:34:17.599 --> 00:34:30.039 a very cool episode of Inside and Mills. Welcome back to Inside Analysis. 426 00:34:30.239 --> 00:34:37.440 Here's your host, Eric Kavanaugh. All right, folks, back here on 427 00:34:37.559 --> 00:34:44.000 Inside Analysis, Rocket and Rolling today with Baalja Whet from FPT and distinguished engineer 428 00:34:44.000 --> 00:34:47.639 and fractional CTO Jim Wilt. This guy knows a lot and a lot of 429 00:34:47.679 --> 00:34:50.440 people too. You know, there's a lot of cool people. He does 430 00:34:50.480 --> 00:34:52.840 a lot of stuff which is always fun. And we want to talk about 431 00:34:53.159 --> 00:34:58.639 pitfalls. There are always pitfalls. I think I'll just throw it out there. 432 00:34:59.039 --> 00:35:02.440 There are all these anecdotes that get thrown out. It's amazing how fast 433 00:35:02.480 --> 00:35:07.599 this stuff happens. Everyone's probably heard about the lawyer who went out and threw 434 00:35:07.679 --> 00:35:10.679 citations in It turned out to be nonsense citations. Well, shame on that 435 00:35:10.800 --> 00:35:15.559 fool for not just taking the extra step to make sure. And this is 436 00:35:15.599 --> 00:35:17.480 where you get into the rag model concept right. In other words, you 437 00:35:17.519 --> 00:35:22.920 want to use a trusted data source like whatever sources they have for cases, 438 00:35:22.920 --> 00:35:28.840 for historical cases. And I'm telling you, folks, lawyering as an industry 439 00:35:29.000 --> 00:35:32.440 is in the crosshairs. Accounting as an industry is in the crosshairs of AI. 440 00:35:32.639 --> 00:35:37.760 All these things that we've done traditionally are absolutely positively in the crosshairs of 441 00:35:37.880 --> 00:35:40.239 AI. And as Jim was kind of suggesting, eighty percent of the time 442 00:35:40.280 --> 00:35:44.679 it'll be one hundred percent accurate, you do have to know when it's the 443 00:35:44.719 --> 00:35:47.559 twenty percent to take a hard look at stuff. So that's the biggest pitfall 444 00:35:47.599 --> 00:35:52.079 I think is don't just rush in and start throwing this stuff it generates out 445 00:35:52.119 --> 00:35:57.400 into the world. Be careful about what you make this customer facing. There's 446 00:35:57.480 --> 00:36:01.480 several issues already, but those are probably the biggest ones. Jim Wilt, 447 00:36:01.519 --> 00:36:06.079 I'll throw it over to you, what are any other major pitfalls you've seen 448 00:36:06.079 --> 00:36:08.760 that people should watch out for with jen AI. I think one of the 449 00:36:08.800 --> 00:36:14.360 things I run into and you almost have to take a pitfall as comical and 450 00:36:14.960 --> 00:36:20.159 enjoy the entertainment aspect of it. When I was writing an article, I 451 00:36:20.320 --> 00:36:23.679 was tweaking hyper parameters just to see what happens when you break it right, 452 00:36:24.320 --> 00:36:29.880 and I had a test explain quantum entanglement. And I did it with different 453 00:36:30.199 --> 00:36:34.840 temperatures, and I chose one that was out of the parameter range and it 454 00:36:34.880 --> 00:36:38.960 created gibberish. I mean, my, you know, one year old granddaughter 455 00:36:39.719 --> 00:36:43.960 could have done better. But it was just random characters group together. So 456 00:36:44.000 --> 00:36:46.119 it wasn't even English language. It wasn't any language at all. It was 457 00:36:47.039 --> 00:36:52.400 kind of thing. So it will break and you and part of the learning 458 00:36:52.440 --> 00:36:57.480 process, you need to know when it breaks, how to break it by 459 00:36:57.519 --> 00:37:00.119 it breaks, you know, and you need to be on guard for these 460 00:37:00.199 --> 00:37:06.199 kinds of things. But as I was finding things, question the answers. 461 00:37:06.599 --> 00:37:08.719 That's probably one of the things I would say in every situation. What it's 462 00:37:08.800 --> 00:37:14.199 going to be critical. You talked about adoption in another earlier segment. You 463 00:37:14.239 --> 00:37:17.480 want to start with non mission critical and work your way towards mission critical. 464 00:37:17.639 --> 00:37:22.960 You don't start with mission critical. That's the lawyer example you gave and others 465 00:37:22.960 --> 00:37:25.320 that are out there in the last week. You don't want that. Start 466 00:37:25.320 --> 00:37:29.920 with non mission critical and work your way with confidence and learning and maturity. 467 00:37:30.719 --> 00:37:34.519 I've asked times, when it gives me a quote, can you give me 468 00:37:34.559 --> 00:37:37.880 the source of the quote, And it will say, oh, I'm sorry 469 00:37:38.079 --> 00:37:40.639 that I made that up. I mean literally, that's the words that we'll 470 00:37:40.760 --> 00:37:45.480 use. I'm sorry I made that quote up. That didn't come from that 471 00:37:45.719 --> 00:37:51.199 Forrester Gardner article, or if it does refer to an article, I'll say, 472 00:37:51.199 --> 00:37:53.719 can you give me the number enumeration of the articles so I can look 473 00:37:53.719 --> 00:37:57.800 it up and it will give me a false number. So you have to 474 00:37:58.320 --> 00:38:04.280 drill down into, you know, the material before you publicly go somewhere with 475 00:38:04.360 --> 00:38:09.320 it. If it's just helping you make a decision, it can be somewhat 476 00:38:09.599 --> 00:38:13.599 wrong and you're not going to hurt anybody with it. But if it's anything 477 00:38:13.599 --> 00:38:17.280 you're going to be then promoting forward. You have to be accurate about the 478 00:38:17.360 --> 00:38:21.679 data and the results, and you can ask the questions, can you give 479 00:38:21.719 --> 00:38:24.360 me the source for that answer? Can you give me references to where I 480 00:38:24.400 --> 00:38:29.119 can refer others to learn more about the answer. If it can't do that, 481 00:38:29.239 --> 00:38:31.440 you're probably in the hallucination zone and you want to get out of there 482 00:38:32.199 --> 00:38:37.760 right. Well, you know so, Jim, you talked about making sure 483 00:38:37.840 --> 00:38:44.039 that you check the words right. And part of the pitfall of adoptions also 484 00:38:44.320 --> 00:38:49.199 is moving too fast for the organizations. I think that we really need to 485 00:38:49.280 --> 00:38:54.800 watch for is the organization ready and if not doesn't mean that you shouldn't adopt, 486 00:38:54.800 --> 00:39:00.400 but you really have to build almost like a safety net, an on 487 00:39:00.599 --> 00:39:07.159 ramp for the organizations to adopt. So meeting your organization, your user where 488 00:39:07.199 --> 00:39:12.559 they are, making sure we understand where people are with the ability to use 489 00:39:12.599 --> 00:39:15.760 what we're going to put out there, and whether or not we have provided 490 00:39:16.280 --> 00:39:22.280 the appropriate level of handholding for this kind of work is very important too. 491 00:39:23.039 --> 00:39:25.519 You're reminding me of something. I'm so glad you brought this up because you 492 00:39:25.760 --> 00:39:29.280 put this concept of an on ramp, So you want to be careful at 493 00:39:29.280 --> 00:39:31.079 this pace at which you go. You want an on ramp and you also 494 00:39:31.159 --> 00:39:35.679 want an off ramp, right, I think you want an off ramp, 495 00:39:35.880 --> 00:39:37.679 And what I'm referring to is that, And this is going to be an 496 00:39:37.719 --> 00:39:43.920 ongoing question. Where do you actually insert the generative capability in your workflow? 497 00:39:44.199 --> 00:39:45.760 There's going to be some point at which it comes in and you're going to 498 00:39:45.760 --> 00:39:49.440 want to know, of course where that is, and you're going to want 499 00:39:49.440 --> 00:39:52.800 to have a toggle to turn it off or on. So if you use 500 00:39:52.880 --> 00:39:55.440 it, for example, for customer service, for chatbots. This is going 501 00:39:55.480 --> 00:40:01.239 to be probably one of the most common use cases is fueling a chatbot and 502 00:40:01.280 --> 00:40:07.679 feeding it enough information from past conversations to know where to go. That's the 503 00:40:07.719 --> 00:40:10.480 beauty of having an existing system is that you can just take the database of 504 00:40:10.559 --> 00:40:15.880 everything your chatbot has already said, do some analysis on when the chat bot 505 00:40:16.039 --> 00:40:20.519 did a good job and when the customer was satisfied. Okay, that's the 506 00:40:20.679 --> 00:40:23.599 behavior we want to model and mimic. But then at some point, if 507 00:40:23.639 --> 00:40:27.719 it starts going walky, you want to have the off ramp. Oh, 508 00:40:27.960 --> 00:40:30.960 turn it off like this, shut it down. It's just like an employee. 509 00:40:30.960 --> 00:40:34.480 You maybe drank too much before going into work that night, and like, 510 00:40:34.559 --> 00:40:37.360 okay, you better go home, buddy, it looks like you're both 511 00:40:37.480 --> 00:40:40.360 Well. That come up and comes down in any chatbot is so important. 512 00:40:40.480 --> 00:40:46.119 You really need to be able to collect the information when your chatbot start making 513 00:40:46.159 --> 00:40:51.199 no sense that you know, be able to really train that because it's a 514 00:40:51.239 --> 00:40:53.920 continuous training as well. Right, Well, that's the thing too. So 515 00:40:54.079 --> 00:40:59.719 my buddy Usama Fayad, who runs the Institute for Experiential AI I heard this 516 00:40:59.760 --> 00:41:02.400 a bit, Jim. He said, the whole thing about experiential means human 517 00:41:02.480 --> 00:41:05.960 in the loop. And he makes a really good point. And there is 518 00:41:06.000 --> 00:41:08.679 this narrative in the media that, oh, AI, it's going to just 519 00:41:08.679 --> 00:41:10.800 take over the business and run everything. No, no, no, no, 520 00:41:12.159 --> 00:41:14.599 you sure don't ever want to do that. You want to have someone 521 00:41:14.679 --> 00:41:17.360 watching it all the time to make sure that it does what you want it 522 00:41:17.400 --> 00:41:21.760 to do, because it will move it blinding speeds, and so if you 523 00:41:21.800 --> 00:41:24.400 don't course correct in time, it's going to be with blinding speeds when careen 524 00:41:24.480 --> 00:41:29.639 off the tracks, right, Jim, Yeah, I think the chatbot example 525 00:41:29.679 --> 00:41:34.440 is a good segue into what I'm going to say augmenting. I'm going to 526 00:41:34.480 --> 00:41:38.719 say services and everything that's augmented means as a human it's in that loop. 527 00:41:39.079 --> 00:41:45.400 They have the accountability and responsibility to have the final stay. So maybe what 528 00:41:45.519 --> 00:41:49.960 we'll see I would love to see in the future is opposed to just chatbots 529 00:41:50.039 --> 00:41:54.159 doing it randomly and anonymously, that it connects you directly to a person with 530 00:41:54.360 --> 00:42:00.440 information that's augmented by the conversation, so the person on the other end can 531 00:42:00.519 --> 00:42:06.480 understand your situation much faster, and then through the generative AI get you to 532 00:42:06.920 --> 00:42:12.199 your resolution much faster. But that human is going to if you will facilitate 533 00:42:12.440 --> 00:42:19.960 that process or you know, in the sense that it's not completely blund But 534 00:42:20.039 --> 00:42:24.159 you brought up a good point too. There's an article I wrote where I 535 00:42:24.199 --> 00:42:30.320 started it with the tractor didn't replace the farmer. You know, a generative 536 00:42:30.360 --> 00:42:36.400 AI won't replace your job, right, However, I am the article with 537 00:42:36.559 --> 00:42:42.000 the tractor didn't replace the farmer, but farmers with tractors did replace those without. 538 00:42:42.920 --> 00:42:45.639 And I think that's a big distinction to what you said, Eric, 539 00:42:45.719 --> 00:42:51.400 because it's very specific. The human in the loop is a necessity. The 540 00:42:51.440 --> 00:42:52.960 power of the human and the loop, if you will, in terms of 541 00:42:53.039 --> 00:43:00.199 accuracy and impact, is going to increase astronomically, right, And that's going 542 00:43:00.280 --> 00:43:02.679 to now create what I'm going to say what Baja was saying earlier. Am 543 00:43:02.719 --> 00:43:07.280 I going to ban papers from being written by jenator of AI? Or am 544 00:43:07.360 --> 00:43:13.320 I going to allow it to augment people who are writing papers? Different question, 545 00:43:13.440 --> 00:43:16.719 But that's kind of the thing you have to get through. Well, 546 00:43:17.000 --> 00:43:20.360 Jim was a T shirt. I have never been a T shirt. So 547 00:43:20.440 --> 00:43:24.960 I'm advocating for allow Jenny I to write paper. I think for augmenting, 548 00:43:25.079 --> 00:43:29.719 and I think one of the cool. Okay, that is augmenting. Yeah, 549 00:43:29.840 --> 00:43:35.920 you've got I wrote something. Read this and tell me what I wrote. 550 00:43:36.239 --> 00:43:37.800 Okay, Okay, that is not what I was trying to say. 551 00:43:38.400 --> 00:43:42.039 All right, correct it for me a little bit. Let it correct it, 552 00:43:42.079 --> 00:43:44.320 and I can say, Okay, that's good. Now I understand where 553 00:43:44.360 --> 00:43:47.719 I made a mistake. Now I'm going to continue writing from that, not 554 00:43:47.800 --> 00:43:52.239 take it. I'm going to take what it corrected and make it my voice 555 00:43:52.679 --> 00:43:55.039 and my intention and then have it read it back to me again. So 556 00:43:55.079 --> 00:44:00.280 it's a give and take when you think about it, Well, it's yeah, 557 00:44:00.320 --> 00:44:02.039 go ahead, Eric, Yeah you made you brought up such a such 558 00:44:02.039 --> 00:44:05.960 a great use case, and I'll give an example of why. And then 559 00:44:06.000 --> 00:44:08.320 we've got a podcast ponus segment coming up here next. But think about in 560 00:44:08.360 --> 00:44:13.079 customer service, and this is what really fascinates me about these engines is how 561 00:44:13.119 --> 00:44:16.039 it decides to choose what it chooses, right, And that's why you have 562 00:44:16.079 --> 00:44:19.199 to try it, to learn and to kind of figure it out, because 563 00:44:19.199 --> 00:44:22.360 there is a method to the madness. Obviously, it's a computer system, 564 00:44:22.400 --> 00:44:24.599 right, I mean, it's got rules and instructions and things that's supposed to 565 00:44:24.639 --> 00:44:30.880 do. But you think about customer service and historically you have a customer service 566 00:44:30.920 --> 00:44:34.400 center, it's going to bring on bringing up your system, bringing up your 567 00:44:34.440 --> 00:44:37.159 record, et cetera. It's a very slow process and it's typically from one 568 00:44:37.239 --> 00:44:42.519 system where there are lots of other systems where customers have touched the organization touch 569 00:44:42.519 --> 00:44:45.679 points as they call them, on the call center, on the website, 570 00:44:45.719 --> 00:44:49.440 in a store, maybe I wrote a letter or something. You never know. 571 00:44:49.880 --> 00:44:53.679 But these Jenai engines can in a moment, like as you're as you're 572 00:44:53.719 --> 00:44:59.239 getting to the customer service rep, whip up a whole bunch of stuff that's 573 00:44:59.280 --> 00:45:02.079 accurate in religt it and just give suggestions to the call center person. So 574 00:45:02.199 --> 00:45:06.719 now it's not this slow, painful thing. It's going real fast and the 575 00:45:06.800 --> 00:45:08.960 human can decide what to use and what not to use. That's why it's 576 00:45:09.000 --> 00:45:13.159 so powerful. Folks, Podcast bone a second coming up next. You're listening 577 00:45:13.199 --> 00:45:19.280 to Inside Analysis. All right, folks, time for the podcast bonus segment 578 00:45:19.280 --> 00:45:22.920 here and a fantastic inside Analysis. We've been talking to Boo Ho We of 579 00:45:23.159 --> 00:45:30.719 FPT America's and Jim Wilt fractional CTO and distinguished engineer, and we're talking all 580 00:45:30.760 --> 00:45:32.760 about jen AI use cases, what to do, how to get started, 581 00:45:34.000 --> 00:45:37.800 And as we were getting excited about that last use case, maybe about how 582 00:45:37.840 --> 00:45:40.519 I'll throw this over to you first. You know, one thing I figured 583 00:45:40.519 --> 00:45:43.800 out early is well, wait a minute, if this thing can write in 584 00:45:43.880 --> 00:45:47.639 German and French and Spanish, I bet it can write in HTML and JavaScript 585 00:45:47.679 --> 00:45:52.000 and C plus plus. And short answer is yes, it's your can and 586 00:45:52.039 --> 00:45:57.400 it can also absorb that kind of information too. So think about systems and 587 00:45:57.960 --> 00:46:00.559 information systems talking to each other in bits and bytes, and you know, 588 00:46:00.840 --> 00:46:05.480 you get log files basically, which to a human if you know what the 589 00:46:05.480 --> 00:46:08.440 log file means, okay, now it's meaningful. But the scale of log 590 00:46:08.480 --> 00:46:12.760 files you get, like thousands and thousands of log files you're trying to scan 591 00:46:12.840 --> 00:46:15.239 through, well ninety eight percent of them are the same, and then the 592 00:46:15.280 --> 00:46:17.079 ones that are different, Ah, that's when something probably went wrong. You 593 00:46:17.119 --> 00:46:21.039 can even throw that stuff at these engines and say hey, I got all 594 00:46:21.119 --> 00:46:22.639 these log files, what the heck does that mean? And get back some 595 00:46:22.719 --> 00:46:27.360 interesting answers. What do you think, boja? Oh absolutely, actually that 596 00:46:27.599 --> 00:46:32.360 was cybersecurity. I think leverage AI at least a few years ago. Yeah, 597 00:46:34.079 --> 00:46:39.159 there's been systems that knew exactly what you said. If you in a 598 00:46:39.320 --> 00:46:45.199 network management at all, one of the dreaded activity is read the lock file. 599 00:46:45.639 --> 00:46:50.119 You know, you can monitor your network all day long, but if 600 00:46:50.119 --> 00:46:52.400 you don't analyze that log file and read the log file and figuring out what 601 00:46:52.760 --> 00:46:58.960 it's telling you is quite less and useless. Right, So that's one of 602 00:46:59.000 --> 00:47:07.000 the applications that had been produced, and it basically general use jen Ai or 603 00:47:07.039 --> 00:47:13.519 a large language model to read the log file and then just detect that differences 604 00:47:13.840 --> 00:47:21.760 the abnormality and call out the abnormality and then do adjuscency analysis and to understand 605 00:47:21.880 --> 00:47:27.280 what kind of abnormality it is and where the threat is coming from. So 606 00:47:27.559 --> 00:47:34.719 absolutely, well that was specific for cybersecurity, but you know applications application debugging, 607 00:47:35.119 --> 00:47:40.840 right, Oh my gosh, can you those things? Because it's brutal, 608 00:47:40.920 --> 00:47:45.840 I mean very brutal. I'll throw it over the gym. Debugging has 609 00:47:46.000 --> 00:47:52.199 historically been one of the hardest things to do, and even with sophisticated technology. 610 00:47:52.239 --> 00:47:55.039 And I remember fifteen sixteen years ago interviewing a guy I always mentioned this. 611 00:47:55.079 --> 00:47:59.039 He it's such a cool name, Zohar Gilt. That was his name. 612 00:47:59.440 --> 00:48:01.960 He was with the company called Precise, and they got bought by Idea. 613 00:48:02.400 --> 00:48:05.760 But what they would do is help you this is a long time ago. 614 00:48:05.800 --> 00:48:09.960 They would help you do monitoring of systems and application dynamics and why did 615 00:48:10.000 --> 00:48:13.559 this break? Why did this go down? Even back then, you would 616 00:48:13.599 --> 00:48:16.719 have interesting heuristics. You'd see CPU usage went down on a timeframe, and 617 00:48:16.719 --> 00:48:21.320 the application speed went up, and all this stuff happened. But the human 618 00:48:21.360 --> 00:48:23.960 beings still had to parse all that together in his or her head to make 619 00:48:24.039 --> 00:48:27.519 sense of it, and then you would learn patterns of things. Oh okay, 620 00:48:27.559 --> 00:48:29.920 if this goes up and that goes down, it's usually because there's a 621 00:48:29.920 --> 00:48:34.519 disk failure or something. Well. Now, in the world of development, 622 00:48:34.880 --> 00:48:37.239 in part because of Kubernetes, but in part because of just the complexity of 623 00:48:37.280 --> 00:48:43.199 the cloud, we have containers and containerization, and we have all kinds of 624 00:48:43.280 --> 00:48:45.480 observability. I mean, there are so many tools out there. I joke 625 00:48:45.519 --> 00:48:50.400 about this. There's a company Mesmo, which just sits on top of your 626 00:48:50.440 --> 00:48:52.920 observability data and helps you marshal it and go in the right direction. So 627 00:48:52.960 --> 00:48:57.559 there's so much activity that there's a meta tool now that sits on top of 628 00:48:57.599 --> 00:49:00.599 it. That's how much observability we have, but it's still up to the 629 00:49:00.639 --> 00:49:05.320 developer to piece it all together. So this GENAI stock can be tremendously powerful 630 00:49:05.360 --> 00:49:09.960 at absorbing massive amounts of these log files, finding some correlation between them, 631 00:49:09.960 --> 00:49:13.199 and at least getting you on the right track. What do you think, 632 00:49:13.280 --> 00:49:17.960 Jim, Yeah, there's definitely the ability to scan and look for anomalies or 633 00:49:19.440 --> 00:49:23.960 anything along the non what I'm going to say traditional paths, because logs can 634 00:49:24.000 --> 00:49:29.519 be mundane and you're looking for that needle in the haystack can be very frustrating 635 00:49:30.199 --> 00:49:34.599 and to broker it across multiple logs. What's really going to be able to 636 00:49:34.639 --> 00:49:38.760 start doing with AI is pitting multiple logs against each other. It could be 637 00:49:38.760 --> 00:49:42.760 see the time stamps, there could be through events. Whatever you can do. 638 00:49:42.840 --> 00:49:45.559 You need to train it enough to understand that this log is connected to 639 00:49:45.599 --> 00:49:52.079 this log this way. Then you're going to be looking at multiple facets of 640 00:49:52.159 --> 00:49:57.559 the same problem from multiple viewpoints, so that you can have better ways of 641 00:49:57.639 --> 00:50:00.760 understanding what's going on. That. The other thing is, you know, 642 00:50:00.800 --> 00:50:04.280 with generative AI, you can write your test cases. It can look at 643 00:50:04.280 --> 00:50:07.199 your code, look for anomalies in your code. I think one of the 644 00:50:07.239 --> 00:50:10.079 things. That's really interesting is it can read back to you what your code 645 00:50:10.119 --> 00:50:15.400 is doing. If you sometimes are trying to understand there's code that you don't 646 00:50:15.480 --> 00:50:20.119 understand what it's doing. Comments don't make sense. Who comes to their code 647 00:50:20.119 --> 00:50:22.920 anyway? Then you can make a correction to the code, or it can 648 00:50:23.000 --> 00:50:27.719 augment you to making a correction to the code. Then you get another model 649 00:50:27.920 --> 00:50:31.199 to look at that change that you made and explain what it's doing. Is 650 00:50:31.239 --> 00:50:36.360 it still doing what it was said before or is it doing something different? 651 00:50:36.480 --> 00:50:38.719 Is that what you wanted for an outcome? So we're going to get to 652 00:50:38.760 --> 00:50:44.679 where I'm going to say, we're going to leverage these tools increasingly more complex, 653 00:50:45.079 --> 00:50:47.480 you know, around the way that we can solve problems that we never 654 00:50:47.519 --> 00:50:51.440 would have attacked the problems before. Before I would look at one log at 655 00:50:51.480 --> 00:50:55.559 a time, or I'd get a room. I've done this and telcom where 656 00:50:55.599 --> 00:50:59.440 you put people, you know, a dozen people in a room for six 657 00:50:59.480 --> 00:51:02.880 weeks at all the logs to find out where did the error occur? You 658 00:51:02.880 --> 00:51:07.920 know, because the CEO was contacted by a text and you can't find it. 659 00:51:08.599 --> 00:51:14.000 With Generator AI, we can start training the models to look at things 660 00:51:14.119 --> 00:51:17.239 multifaceted so that we can actually start finding errors that we never would be able 661 00:51:17.280 --> 00:51:21.679 to find on our own before because it just has more eyes. If you 662 00:51:21.760 --> 00:51:25.079 will on the screen, then we can put go ahead baha. Well, 663 00:51:25.119 --> 00:51:30.280 and you know, just on top of that, you need experience engineer to 664 00:51:30.360 --> 00:51:36.199 be able to read the lock and understand the lock and really recognize the issues 665 00:51:36.239 --> 00:51:39.400 when you come across it. Now with jen Ai, you really can take 666 00:51:39.440 --> 00:51:45.480 all of that and abstract it and make it so that the people you need 667 00:51:45.599 --> 00:51:52.119 one experienced engineer versus you need a room full of engineer to do it. 668 00:51:51.760 --> 00:51:55.599 That's such as Yeah, that's such a great way to put it, and 669 00:51:55.599 --> 00:52:00.599 it's exciting that I think we have to close on the line that was brilliant. 670 00:52:00.639 --> 00:52:02.239 The future is here and it's exciting. Well, folks will be talking 671 00:52:02.280 --> 00:52:07.800 about ha, we of FPT America's look up this company FPT. They're huge 672 00:52:07.960 --> 00:52:12.719 there. I mean they're a massive company. I have a wonderful origin story 673 00:52:12.760 --> 00:52:15.440 about where they came from. Very interesting stuff. And of course Jim Wilt 674 00:52:15.480 --> 00:52:21.800 distinguished engineer and fractional CTO. We do archive all these events for later listening 675 00:52:21.880 --> 00:52:23.079 and viewing. Send me an Nemei if you want to be on the show, 676 00:52:23.079 --> 00:52:25.360 info at Inside analysis dot com. We'll talk to you later. You've 677 00:52:25.400 --> 00:52:35.039 been listening to Inside Analysis and now the voices of ACAA was an exciting announcement. 678 00:52:35.239 --> 00:52:38.320 Want to hear NBC News or KCAA anywhere you go, Well, now 679 00:52:38.320 --> 00:52:43.400 there's an app for that. 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The 697 00:54:01.119 --> 00:54:06.559 complete website is to Hebota club dot com or call us at eight one eight 698 00:54:06.760 --> 00:54:10.599 six one zero eight zero eight eight Monday through Saturday nine am to five pm 699 00:54:10.679 --> 00:54:15.239 California time. That's eight one eight six one zero eight zero eight eight to 700 00:54:15.320 --> 00:54:22.880 hebotclub dot com. With sixty years of fascinating facts. This is the Man 701 00:54:22.960 --> 00:54:30.159 from Yesterday and Back in Time, due this time in nineteen eighty debuting ABC 702 00:54:30.400 --> 00:54:35.000 News Nightline. The show is an outgrowth of the hostage updates called America Held 703 00:54:35.000 --> 00:54:45.440 Hostage hosted by Ted Copple. This is ABC News Nightline reporting from Washington, 704 00:54:45.960 --> 00:54:50.239 Ted Copple, Good Evening and from March of nineteen sixty nine. Looks like 705 00:54:50.320 --> 00:54:53.440 the Fifth Dimension is having your biggest hit so far, Aquarius, Let the 706 00:54:53.440 --> 00:54:58.519 Sunshine in is still moving off the charts. It looks like a solid number 707 00:54:58.559 --> 00:55:01.639 one hit for the Fifth Dimension, who again making hits in nineteen sixty seventy 708 00:55:12.760 --> 00:55:16.440 and from this time in nineteen sixty seven. One of the funniest DJs around 709 00:55:16.519 --> 00:55:22.199 here in nineteen sixty seven is doctor Don Rose of station WQXI in Atlanta, 710 00:55:22.239 --> 00:55:25.280 where he does the morning show. And of course, to simplify things, 711 00:55:25.440 --> 00:55:29.239 the fastest way to get a man on the mood is to put a woman 712 00:55:29.280 --> 00:55:32.960 there first. Right, Excite done? Of course. 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Schiff was recently interrupted by pro Palestinian protesters while celebrating his 752 00:58:45.599 --> 00:58:50.840 victory in the primary for California's open Senate seat. He said Democrats will have 753 00:58:50.880 --> 00:58:54.000 to work hard to motivate young people to turn out and vote. South Carolina 754 00:58:54.039 --> 00:58:59.320 Senator Lindsey Graham is accusing President Biden of screwing up the world every way you 755 00:58:59.440 --> 00:59:02.679 can. In another interview on NBC News's Meet the Press, Graham A slammed 756 00:59:02.679 --> 00:59:07.920 Biden's foreign policy and approach to the US Mexico border. Graham, who supports 757 00:59:07.920 --> 00:59:12.239 Donald Trump for president, pushed back against Biden's comment that Israeli Prime Minister of 758 00:59:12.280 --> 00:59:16.079 Benjamin Netya who is hurting Israel more than helping with its military operations. He 759 00:59:16.239 --> 00:59:21.960 argued that Biden should be more forceful against Iran and Hamas. Senator rafae Ol 760 00:59:22.000 --> 00:59:27.920 Warnock says the situation in Gaza is a catastrophic humanitarian nightmare. We've seen some 761 00:59:28.239 --> 00:59:34.440 thirty thousand Gazans already killed, many of them innocent men, women, and 762 00:59:34.519 --> 00:59:37.519 children. Speaking on CNN State of the Union, Warnock talked about caring for 763 00:59:37.599 --> 00:59:40.920 his son, who's sick with a severe coffin, thinking about parents in Gaza 764 00:59:42.000 --> 00:59:45.840 caring for their own children in the midst of war. The Georgia Democrats says 765 00:59:45.840 --> 00:59:49.599 President Biden is pushing hard for a pause in the fighting between Israel and Amas 766 00:59:49.719 --> 00:59:53.719 to surge AID into the region. The National Transportation Safety Board is investigating Friday's 767 00:59:53.719 --> 00:59:59.400 deadly crash of a National Guard helicopter near the Texas border. Military officials say 768 01:00:00.000 --> 01:00:04.239 two soldiers who were killed were Army National Guard members from New York. A 769 01:00:04.239 --> 01:00:07.159 Border patrol agent also died in the crash. A fourth New York Army National 770 01:00:07.159 --> 01:00:08.519 Guard soldier was seriously