WEBVTT 1 00:00:00.000 --> 00:00:05.240 Speaker Jeffries said on NBC's Meet the Press that they have had informal conversations about 2 00:00:05.280 --> 00:00:08.880 a bipartisan governing coalition. He said the House needs to vote on bills that 3 00:00:08.919 --> 00:00:13.000 have substantial support from both sides of the aisle. Jeffries blamed a small group 4 00:00:13.000 --> 00:00:17.280 of Republican extremists for trying to dictate the agenda. I'm Chris Caragio, NBC 5 00:00:17.359 --> 00:00:22.320 News Radio, kc AA Radio, Lo Melinda, Where no listener is ever 6 00:00:22.519 --> 00:00:28.039 left behind. The information economy has a rid. The world is teeming with 7 00:00:28.120 --> 00:00:34.399 innovation as new business models reinvent every industry industry. Inside Analysis is your source 8 00:00:34.439 --> 00:00:39.119 of information and insight about how to make the most of this exciting new era. 9 00:00:39.399 --> 00:00:44.719 Learn more at Inside analysis dot Comsideanalysis dot com. And now here's your 10 00:00:44.759 --> 00:00:53.600 host, Eric Kavanaugh. Ladies and gentlemen, Hello, and welcome back once 11 00:00:53.640 --> 00:00:56.920 again to the only coast to coast radio show in the US of A that's 12 00:00:56.960 --> 00:01:00.240 all about the information economy. Inside Analysis, your host Eric Kavanaugh here, 13 00:01:00.240 --> 00:01:03.879 and folks. I am tickled pink to be talking to a legend in our 14 00:01:03.879 --> 00:01:08.959 industry, mister Andreas weigand he has been in this business for thirty odd years. 15 00:01:08.959 --> 00:01:15.680 He has an incredible history in our industry. He advised Jack Ma and 16 00:01:15.799 --> 00:01:19.599 Angela Merkel, their data coach. He worked with Jeff Bezos as chief scientist 17 00:01:19.719 --> 00:01:25.879 over at Amazon, and he got his PhD some well, he started it 18 00:01:25.959 --> 00:01:30.200 some thirty years ago. He knows a couple of things about artificial intelligence AI, 19 00:01:30.400 --> 00:01:34.480 its applications, how to get it right, how maybe we won't get 20 00:01:34.519 --> 00:01:38.599 it right. So we're so pleased to learn from him about what is going 21 00:01:38.640 --> 00:01:42.760 on in this crazy world of ours. And you also want to book data 22 00:01:42.840 --> 00:01:47.319 for the people. But let's talk about AI for the people and what this 23 00:01:47.359 --> 00:01:51.680 stuff really is. And I'll just give you some of my thoughts. I 24 00:01:51.680 --> 00:01:57.280 mean, what I find fascinating about these new foundational models and the transformers is 25 00:01:57.280 --> 00:02:01.719 that what we're really witnessing here is a reflection of our culture fed through a 26 00:02:01.840 --> 00:02:10.120 very high compute power engine. So these CHATGPT barred technologies, they're basically predictive 27 00:02:10.199 --> 00:02:15.240 engines for language. And what the folks who built these things did, to 28 00:02:15.280 --> 00:02:22.280 my understanding, is they really vectorized information language. So letters, words, 29 00:02:22.439 --> 00:02:27.199 sentences, paragraphs, headings, et cetera, all vectorized, in other words, 30 00:02:27.199 --> 00:02:32.120 turned into mathematical quotients, if you will, and that allows for traditional 31 00:02:32.159 --> 00:02:39.280 statistical analysis, and that is the engine behind being able to predict language as 32 00:02:39.319 --> 00:02:45.520 you would predict in other use cases whether or not someone's going to buy something. 33 00:02:45.800 --> 00:02:50.400 So these are predictive engines. They do reflect our culture and there are 34 00:02:50.639 --> 00:02:54.439 some shortcomings, and the shortcomings I think could be solved with embeddings and with 35 00:02:54.560 --> 00:02:59.879 some guard rails, but we'll find out from the expert. So Andreas, 36 00:03:00.000 --> 00:03:02.960 thank you so much for your time today. Tell us about your take on 37 00:03:04.080 --> 00:03:09.039 AI and how can we ensure that AI becomes AI for the people already thank 38 00:03:09.080 --> 00:03:15.960 you for having me. So let's reflect over the last year, in the 39 00:03:16.120 --> 00:03:23.919 minds of the people, AI has greatly taken more importance than it was the 40 00:03:24.000 --> 00:03:29.120 case prior to that. Why is that the case? I think the are 41 00:03:29.120 --> 00:03:37.719 two reasons. One, the amount of computation, the amount of data has 42 00:03:37.759 --> 00:03:44.120 been grown exponentially for the last thirty years or something like that, so there 43 00:03:44.159 --> 00:03:49.400 is no clear moment where there was like a face transition or something like that. 44 00:03:50.120 --> 00:03:58.759 But it is the perception of the people that something amazing has happened with 45 00:03:59.360 --> 00:04:03.639 GPTs three for instance. And what is that? Why is that the case. 46 00:04:04.520 --> 00:04:11.000 I think it's the case because suddenly we have a way where the computer 47 00:04:11.319 --> 00:04:16.759 interacts with us through language, through our normal language, and everybody on the 48 00:04:16.800 --> 00:04:23.959 planet can get a few thing about how far things some path come along. 49 00:04:24.800 --> 00:04:32.040 So for me, the sudden popularity of GPT three or GPT four now is 50 00:04:32.839 --> 00:04:41.399 because journalists realized, Wow, this is amazing, and then quickly the world 51 00:04:41.480 --> 00:04:46.120 realized just how amazing it is. And I have to confess I've spent many 52 00:04:46.160 --> 00:04:53.040 hours of my life in an earlier version Sam Altman had given me access to 53 00:04:54.079 --> 00:05:00.800 maybe two years ago, and I had promised not to talk to anybody about 54 00:05:00.800 --> 00:05:03.959 the results, so I still want do that now. But then, of 55 00:05:04.000 --> 00:05:14.000 course with tatt and I have to also say, I'm amazed that it knows 56 00:05:14.040 --> 00:05:26.560 more than any friend I have now lendth amount go ahead one. Google also 57 00:05:26.639 --> 00:05:31.560 knows more than any friend I have. So we shouldn't be that surprised because 58 00:05:32.240 --> 00:05:38.519 we have seen this in the late nineties that while you suddenly have a box 59 00:05:39.399 --> 00:05:44.399 that you enter something in, and no single friend of yours can give as 60 00:05:44.439 --> 00:05:49.319 many answers as cool who were good at the time. So we have had 61 00:05:49.360 --> 00:05:53.399 this and we forgot it. We got so used to it. That's number 62 00:05:53.439 --> 00:06:00.360 one. Number two is, however, that it has much more capability then 63 00:06:00.480 --> 00:06:06.879 just returning snippets of web pages what googles and studus. And for example, 64 00:06:08.160 --> 00:06:13.319 I'm doing a workshop in a week, and I wrote an abstract what I 65 00:06:13.360 --> 00:06:15.680 wanted to do in those three hours, and then I thought, I run 66 00:06:15.720 --> 00:06:23.959 it through GPT whatever the current version was, and writing the abstract probably was 67 00:06:23.959 --> 00:06:32.040 an hour. Carefully comparing sentence by sentence what I wrote with what GPT suggested 68 00:06:32.560 --> 00:06:38.879 took probably two hours because it really this engagement. This you know, I 69 00:06:38.920 --> 00:06:43.759 really engaged with those alternatives as if a friend has suggested, oh, andreez, 70 00:06:44.279 --> 00:06:48.879 you should make me write it this way. So I maybe reverted to 71 00:06:48.959 --> 00:06:55.120 my own in eighty or ninety percent of the changes, but that is just 72 00:06:55.120 --> 00:07:00.399 because I have my style of writing, and GPTs it's most that is the 73 00:07:00.439 --> 00:07:04.000 right way of writing. So I think at the end of the day it 74 00:07:04.040 --> 00:07:08.959 was definitely not worth the time for me, but it was another interesting experiment 75 00:07:09.759 --> 00:07:16.120 and maybe I should take data for the people my book and put it into 76 00:07:16.920 --> 00:07:27.000 GPT and see what's coming out. Particularly so it's coming out in a few 77 00:07:27.079 --> 00:07:30.920 languages, in German, Korean, Japanese, Russia quite good. The Chinese 78 00:07:30.920 --> 00:07:39.480 translation is very bad because the publisher took one page for a person or one 79 00:07:39.519 --> 00:07:46.240 person page for translating it, so it's extremely inconsistent. It's basically non comprehensive 80 00:07:46.519 --> 00:07:55.680 Chinese. So maybe maybe I should just use GVT and produce an English translation 81 00:07:55.800 --> 00:08:03.480 of it. It's at least it would be consistent, right anyways, So 82 00:08:03.600 --> 00:08:09.079 that is my impression. It is absolutely shocking. It remains shocking even for 83 00:08:09.279 --> 00:08:13.360 me. And the question that is coming now is what will be the roll 84 00:08:13.480 --> 00:08:20.519 of people in a world that we have GPT as much around as we have 85 00:08:20.639 --> 00:08:26.759 running water coming out of faces? Right right? This is the big question. 86 00:08:26.199 --> 00:08:31.160 And it seems to me that this is another technology. I mean, 87 00:08:31.199 --> 00:08:33.840 you look around the world. You don't see too many farmers using yaks to 88 00:08:33.960 --> 00:08:37.919 till the soil because we now have big machines to do that. And of 89 00:08:37.919 --> 00:08:45.200 course the world of agriculture around this planet has become heavily industrialized, and that's 90 00:08:45.240 --> 00:08:50.159 because big machines get the job done faster and more efficiently. And I've always 91 00:08:50.159 --> 00:08:52.399 believed the machine can do the job better and faster and more efficiently than the 92 00:08:52.440 --> 00:08:56.720 human. Let the machine do the job as long as it's economically feasible. 93 00:08:58.480 --> 00:09:03.080 But there is something to be said for boundaries. So a good friend of 94 00:09:03.080 --> 00:09:05.639 mine, Eugene Burke, came up with this great concept. He says that 95 00:09:05.759 --> 00:09:09.559 right now, which at GIPTA doesn't have an epistemological boundary, in other words, 96 00:09:09.559 --> 00:09:13.519 it doesn't know what it doesn't know. And I think the magic here 97 00:09:13.639 --> 00:09:18.120 is going to come in the embeddings and and you're let's call it first class 98 00:09:18.159 --> 00:09:24.039 citizen embeddings. What do you want to do from a grounding perspective? And 99 00:09:24.320 --> 00:09:28.600 all the folks I talk to are saying the same thing, that they are 100 00:09:28.840 --> 00:09:33.799 focusing on particular industry verticals because that way they can get to a working model 101 00:09:33.960 --> 00:09:39.559 pretty quickly, as opposed to using a large language model as a general purpose 102 00:09:39.919 --> 00:09:43.240 tool and then trying to train it on your corporate data. That's going to 103 00:09:43.320 --> 00:09:46.200 take probably a long time and not get you where you need to go. 104 00:09:46.679 --> 00:09:48.919 But think about, just think about this idea. For example, think about 105 00:09:48.960 --> 00:09:52.000 the history of media. And I'm a media guy, okay, so I 106 00:09:52.080 --> 00:09:56.200 understand how media works, and it's largely been a push model for decades, 107 00:09:56.240 --> 00:10:00.759 for centuries. Quite frankly, it turned into a pull model for kind of 108 00:10:00.759 --> 00:10:03.240 a short period of time with Google, and we had all these bloggers who 109 00:10:03.360 --> 00:10:07.559 kind of flourished, and then that there was sort of a race to the 110 00:10:07.559 --> 00:10:11.240 bottom with content around things like big data and AI, and so it gets 111 00:10:11.360 --> 00:10:15.960 harder and harder to get to really good content unless you know where to look, 112 00:10:16.000 --> 00:10:18.120 and that's hard to do. But what I think is interesting is that 113 00:10:18.240 --> 00:10:26.279 most media companies take a very let's call it cherry picking approach to coverage. 114 00:10:26.399 --> 00:10:30.720 Someone decides, I would say, sometimes arbitrarily, Oh, let's focus on 115 00:10:30.720 --> 00:10:33.399 this story. Let's focus on that story. That's the old way of doing 116 00:10:33.799 --> 00:10:39.320 media. I think the new way is going to leverage AI both for generation 117 00:10:39.559 --> 00:10:43.120 and consumption and what I want to do. And you might like this because 118 00:10:43.120 --> 00:10:46.559 you talked about data for the people and AI for the people. Right now, 119 00:10:46.679 --> 00:10:52.600 AI largely guides or governs what you see in places like Facebook and LinkedIn 120 00:10:52.720 --> 00:10:58.000 and Instagram and all these different tools, but the model is behind the scenes, 121 00:10:58.120 --> 00:11:01.200 so you don't get to see the model. And I and some colleagues 122 00:11:01.200 --> 00:11:05.000 of Mind are working on something where I want to make that model transparent. 123 00:11:05.120 --> 00:11:09.879 I want you, Andreaswigan, to own your feed and to understand your own 124 00:11:09.000 --> 00:11:13.679 model. I want to make that model transparent to you so you can adjust 125 00:11:13.759 --> 00:11:20.559 it and fine tune it, and then create this new conduit to information around 126 00:11:20.600 --> 00:11:24.679 the world that will be dynamically generated by systems of record. So think like 127 00:11:24.720 --> 00:11:28.399 the Federal Register here in the United States, that is a system of record. 128 00:11:28.799 --> 00:11:33.039 When something goes out there, that means something changed and people want to 129 00:11:33.080 --> 00:11:35.759 know about that. And I think people are going to be able to subscribe 130 00:11:37.120 --> 00:11:43.799 to dynamic newsletters where these AI engines will capture and process and deliver highly targeted, 131 00:11:43.879 --> 00:11:48.519 highly personalized information for me based upon the things I'm trying to research on, 132 00:11:48.600 --> 00:11:52.840 and I think that is going to be tremendously disruptive to the traditional media 133 00:11:52.960 --> 00:11:56.159 models. And you talked about fake news. There's that, and there's also 134 00:11:56.399 --> 00:12:03.440 just biased news. So I think run the beginning of an absolutely massive transformation 135 00:12:03.639 --> 00:12:09.480 in how people research information, how information is generated dynamically from like I say, 136 00:12:09.519 --> 00:12:13.279 systems of record, and then how it's channeled to us. And I 137 00:12:13.320 --> 00:12:16.720 am all in favor of putting individual people all around the world in control of 138 00:12:16.759 --> 00:12:22.240 their feed to be able to see what they want to see, especially in 139 00:12:22.279 --> 00:12:24.639 the government like government spending, where does all the money actually go, who 140 00:12:24.720 --> 00:12:26.919 gets it? What do we get for that? All that kind of fun 141 00:12:26.919 --> 00:12:31.440 stuff I think is coming. What are your thoughts about all that? Do 142 00:12:31.519 --> 00:12:37.519 you want to narrow it down and go back to the interview? Yes, 143 00:12:39.080 --> 00:12:43.080 okay, sorry I threw a lot at you. I know. Yeah, 144 00:12:43.200 --> 00:12:50.559 so let's look at I think the role of people. I think what's very 145 00:12:50.600 --> 00:13:01.399 important is that you learn to ask good questions. So curiosity is more important 146 00:13:01.440 --> 00:13:05.480 than it has ever been. I used to say that, you know, 147 00:13:05.840 --> 00:13:11.960 years ago with Schogle, but I'm saving it even with more emphasis now with 148 00:13:13.840 --> 00:13:24.000 Tragical Team. Second one is the idea of modeling. So you talked about 149 00:13:24.000 --> 00:13:28.799 modeling, and what's the essence of modeling. It's two things. One that 150 00:13:28.840 --> 00:13:35.799 you have an objective function which you're trying to optimize, and two that you 151 00:13:35.840 --> 00:13:43.879 are very clear what's within them model and what's outside the model, which that 152 00:13:43.960 --> 00:13:52.240 decision we call the error right. And I can, for example, quote 153 00:13:52.360 --> 00:14:01.279 a Ted talk I heard many years ago in Long Beach where a person in 154 00:14:01.279 --> 00:14:07.000 the first half of your talk make the point that you really really should use 155 00:14:07.120 --> 00:14:11.960 paper bags as opposed to plastic bags. And then you know, you see 156 00:14:11.000 --> 00:14:18.440 the images of the fish in the ocean and we've all seen sure. And 157 00:14:18.480 --> 00:14:24.639 then exactly halfway your first talk, they switched over and they told you why 158 00:14:24.759 --> 00:14:31.320 you should show use plastic bags rather than paperbags, and the images were that 159 00:14:31.399 --> 00:14:39.080 of deforestation. And okay, So it is the question about what are the 160 00:14:39.159 --> 00:14:45.080 externalities, what do we consider outside the model, and what do we consider 161 00:14:45.120 --> 00:14:48.879 inside the model. So those two remarks I think I would like to make 162 00:14:50.200 --> 00:14:58.159 what you said, one curiosity is king, and two understanding the objective function 163 00:14:58.360 --> 00:15:05.679 of Spitus said, understanding the boundaries of the models in the model and what's 164 00:15:05.720 --> 00:15:11.080 not in the model is key. Yeah, those are very very good points. 165 00:15:11.679 --> 00:15:15.000 And so let's dive into transparency. We've got about two minutes left here. 166 00:15:15.399 --> 00:15:20.000 I'm a huge fan of transparency, and certainly for anything government related, 167 00:15:20.039 --> 00:15:24.240 because it's our government, and so why shouldn't we know what our government is 168 00:15:24.320 --> 00:15:28.759 up to, because I think they wind up going down wormholes for various reasons. 169 00:15:28.799 --> 00:15:33.440 But transparency in data. Of course, in GDPR, someone can request 170 00:15:33.519 --> 00:15:35.759 an EU, citizens can say, hey, show me all the data you 171 00:15:35.799 --> 00:15:39.679 have about me. Years ago that would have been basically impossible. It's not 172 00:15:39.720 --> 00:15:43.279 impossible today. It can actually be effected today, which I think is a 173 00:15:43.279 --> 00:15:48.399 fantastic balance. And I do believe that you look at how all these big 174 00:15:48.440 --> 00:15:52.039 companies are training their models on our data through Facebook and Google and Twitter x 175 00:15:52.080 --> 00:15:56.480 whatever you want to call it, and I think that we should also have 176 00:15:56.600 --> 00:16:03.720 public facing, public accessible reposit stories of information about transactions, the kinds of 177 00:16:03.720 --> 00:16:08.159 things that SAP and Amazon or others can learn by aggregating this data. I 178 00:16:08.200 --> 00:16:14.360 want a public service where citizens and businesses can subscribe to that and use that 179 00:16:14.480 --> 00:16:17.600 data to better understand which products they should buy, et cetera. But what 180 00:16:17.639 --> 00:16:19.320 do you think about all that and what do you think about transparency in particular 181 00:16:19.360 --> 00:16:29.000 on dreams? Yeah, transparency. In my book, I talk about transparency 182 00:16:29.120 --> 00:16:33.320 rules right to see the data, to see what's happening to your data, 183 00:16:33.480 --> 00:16:41.840 and to have the right to see the reward ratio between what you and out 184 00:16:41.840 --> 00:16:45.159 of the data you give. So transparent is a good thing, but very 185 00:16:45.159 --> 00:17:00.919 difficult to implement. I think a more important question is to just talk about 186 00:17:00.919 --> 00:17:03.960 transparency, but really to talk about what can we do with it? The 187 00:17:04.119 --> 00:17:08.119 action I mean, what is the value of data? The value of data 188 00:17:08.240 --> 00:17:17.279 is only the action difference different actions make based on the data. So I 189 00:17:17.480 --> 00:17:25.440 like to focus more on what do pee to do differently and how can we 190 00:17:25.599 --> 00:17:30.920 educate people so data literacy, is there any chance left? It's a very 191 00:17:32.000 --> 00:17:41.240 honest question for people to know whether it is fake image of somebody that you 192 00:17:41.400 --> 00:17:48.559 see that was created by image generator, or whether it's a trimp. So 193 00:17:48.599 --> 00:17:52.880 those things we took for granted all over our lives, we cannot take for 194 00:17:52.960 --> 00:17:57.880 granted anymore, and it would be a very interesting world. What is next 195 00:17:59.079 --> 00:18:08.640 now? I'm a big fan of the Center for Humane Technology and their fears 196 00:18:08.680 --> 00:18:17.519 of what can go wrong and what can we do to try to stir things 197 00:18:17.519 --> 00:18:23.480 in the right direction. I don't subscribe as many of my colleagues do to 198 00:18:23.640 --> 00:18:27.920 the Moratorium, and I because having lived in China, I had a house 199 00:18:27.960 --> 00:18:37.200 to Shai, my husband is from China. I am of no illusion that 200 00:18:41.160 --> 00:18:47.440 even if in the US we you know, do everything to stop you know, 201 00:18:47.839 --> 00:18:52.319 any research China, and if not China then Russia. Will we just 202 00:18:52.799 --> 00:19:03.440 laugh all the way to the bank to have the ad ah. But the 203 00:19:03.480 --> 00:19:08.160 thoughts the Center for Human Technology, what they have, it's basically education. 204 00:19:08.599 --> 00:19:14.839 It is having people learn what can go wrong, and successfully we managed to 205 00:19:14.880 --> 00:19:18.720 deal with this with nuclear wars. We haven't had nuclear war since World War 206 00:19:18.759 --> 00:19:22.319 Two. Thank god. We lived in a very good, very lucky period 207 00:19:22.359 --> 00:19:26.119 in the last fifty six years. No period in the history of universe has 208 00:19:26.160 --> 00:19:33.599 been as good as the last fifty six years. And we can only whatever 209 00:19:33.640 --> 00:19:42.519 we say is probably not strong enough that we should take more time to think 210 00:19:42.559 --> 00:19:47.119 about it, to think about the actions we take, and to play with 211 00:19:47.200 --> 00:19:53.839 it, and to get used to that exponentially growing world and to the question 212 00:19:53.880 --> 00:19:59.759 about how can we ensure And I don't have an answer, but how can 213 00:19:59.799 --> 00:20:04.680 we shure that this is not an AI against the people, but that it 214 00:20:04.720 --> 00:20:10.519 would be an AI for the people? Mm hm wow. What a great 215 00:20:10.799 --> 00:20:14.079 bit of wisdom there from Andreazwevegan. Thank you so much for your time today, 216 00:20:14.160 --> 00:20:15.359 sir, it's an honor to be talking to you. We'll be right 217 00:20:15.400 --> 00:20:27.039 back. You're listening to Inside Analysis to welcome back to Inside Analysis. Here's 218 00:20:27.079 --> 00:20:33.119 your host, Eric Tabanac. All right, ladies and gentlemen, welcome back 219 00:20:33.160 --> 00:20:37.759 to Inside Analysis. I'm pleased to be sitting at Boomy World in Silicon Valley 220 00:20:37.759 --> 00:20:42.960 now talking to Ken Maglio of Worldwide Technology, and they're a big Boomy customer. 221 00:20:44.000 --> 00:20:47.319 In fact, you use this from what I can understand as a core 222 00:20:47.400 --> 00:20:49.640 component of your solution architectures. Right, tell us a bit about it. 223 00:20:49.640 --> 00:20:52.279 How do you use it? How has it changed your day to day life? 224 00:20:52.480 --> 00:20:57.880 Yeah, so worldwide technology. We utilize BOOMY to integrate our data with 225 00:20:57.920 --> 00:21:03.200 our customers. So when we are integrating with them for all of our ticketing 226 00:21:03.279 --> 00:21:07.559 needs and providing managed services to our customers, we need to be able to 227 00:21:08.000 --> 00:21:14.680 bond our systems with theirs. Okay, so effectively we're working with one set 228 00:21:14.720 --> 00:21:19.119 of data, one Golden record as a BOOMY term as between both our systems. 229 00:21:19.240 --> 00:21:26.799 So that's where we utilize BOOMY today to integrate those systems together so that 230 00:21:26.960 --> 00:21:30.160 we're all operating on the same ticket, the same data, the same comments, 231 00:21:30.200 --> 00:21:33.200 the same work being done, regardless of we're a separate company from our 232 00:21:33.200 --> 00:21:37.200 customers, right, we're doing managed services for them. We also utilize it 233 00:21:37.240 --> 00:21:44.039 in automation self service, you know, for onboarding purposes, reaching into OEM 234 00:21:44.079 --> 00:21:51.000 and other vendors where maybe in integration or alerting or another system can't produce something, 235 00:21:51.039 --> 00:21:52.720 but we can if we get the wrong data. So we utilize BOOMY 236 00:21:52.759 --> 00:21:56.559 to do a lot of extractions and then also like I said, this e 237 00:21:56.680 --> 00:22:00.279 bonding process that we've built, that's interesting. So it's really a core composent 238 00:22:00.440 --> 00:22:03.119 of your solution delivery strategy, right, yes, yes, it is. 239 00:22:03.160 --> 00:22:07.720 It is core components included in every offering that we give out with our managed 240 00:22:07.720 --> 00:22:12.079 services to our customers because it's so core to how we work with them as 241 00:22:12.119 --> 00:22:17.799 a trusted advisor. Yeah, that's very interesting, and so you can cover 242 00:22:17.880 --> 00:22:22.240 any number of solutions with this. You talked about networking, you talked about 243 00:22:22.640 --> 00:22:26.559 operations, efficiency gains, things of this nature. So any number of the 244 00:22:26.599 --> 00:22:29.480 things that an IT consulting firm would come in and do for a company, 245 00:22:29.799 --> 00:22:33.079 that's what you're doing. And a component sort of backbone of that is the 246 00:22:33.079 --> 00:22:37.839 Boomy platform, right yep. So we build a system within Boomy to be 247 00:22:37.920 --> 00:22:42.480 able to synchronize all of that data between ours systems and instead of trying to 248 00:22:42.519 --> 00:22:48.480 build individual integrations just between one customer and us and then the next customer repeat 249 00:22:48.559 --> 00:22:52.640 down the road, we built an entire engine or factory if you want we 250 00:22:52.759 --> 00:22:56.119 call it, to be able to allow us to repeat this and almost have 251 00:22:56.240 --> 00:23:00.839 a product that we can offer to customers. Say okay, as a handshake, 252 00:23:00.880 --> 00:23:03.720 we'll agree that we're sending this same data between us. But now this 253 00:23:03.880 --> 00:23:11.519 same process is repeatable. And so now we used to initially start our integrations 254 00:23:11.720 --> 00:23:15.839 when we first did our first one between just one technology and another without boomy, 255 00:23:15.519 --> 00:23:21.759 and it was six months and quite expensive of an effort. I'm now 256 00:23:21.799 --> 00:23:26.000 measuring what the team can do in about forty man hours of effort, not 257 00:23:26.119 --> 00:23:32.599 duration, but about I've taken that from what used to be six months down 258 00:23:32.640 --> 00:23:37.640 to about forty hours. Wow. So what we're trying to do is provide 259 00:23:37.039 --> 00:23:42.680 that efficiency. Now, obviously there's some nuances, but that's the biggest gain 260 00:23:42.839 --> 00:23:47.759 for what we're looking at now is we can actually move at the speed of 261 00:23:47.759 --> 00:23:51.359 our customers. And this isn't a six month long project. After we're go 262 00:23:51.400 --> 00:23:53.839 alive. This is a great we're live here a month or two after we're 263 00:23:53.920 --> 00:23:57.039 up and running with them. Yeah. No, what you've done is you've 264 00:23:57.079 --> 00:24:02.839 optimized the process by building an engine that is versatile enough to handle lots of 265 00:24:02.839 --> 00:24:07.240 different kinds of projects such that you don't have to go and build out bespoke 266 00:24:07.319 --> 00:24:11.480 solutions for every different customer because to your point, that takes a lot of 267 00:24:11.519 --> 00:24:14.960 time. And there's all I mean in our industry, we've talked about the 268 00:24:15.000 --> 00:24:19.200 sort of reuse, reduced recycle, like remember the old de mantra of environmentalism, 269 00:24:19.279 --> 00:24:22.480 and you're practicing what you preach, right, Yeah, And that's our 270 00:24:22.519 --> 00:24:26.079 big thing is I didn't want to build a system, you know, and 271 00:24:26.119 --> 00:24:32.240 then the team to have that overhead and management of Well, now once we 272 00:24:32.279 --> 00:24:36.519 get past five or ten, we've got a rat's nest of integrations and we've 273 00:24:36.559 --> 00:24:40.720 got problems because this integration fired. One, it didn't realize this integration but 274 00:24:40.799 --> 00:24:44.400 you know, circular logic, all these types of things. So instead we 275 00:24:44.480 --> 00:24:48.119 needed a system that was repeatable. Two, we needed to to scale because 276 00:24:48.160 --> 00:24:51.720 I don't know when the end of our customers are going to you know, 277 00:24:51.799 --> 00:24:56.519 be But there's another aspect that we didn't plan for that when we built the 278 00:24:56.559 --> 00:24:59.799 engine, we all of a sudden just said, well, if we tweak 279 00:24:59.799 --> 00:25:02.839 a things, the engine could handle it. And that was we started to 280 00:25:02.960 --> 00:25:07.039 engage partners, and our partners wanted us to bond with their systems. So 281 00:25:07.119 --> 00:25:14.160 now I have the scenario where it's us, our system and a customer system. 282 00:25:14.200 --> 00:25:18.839 But now that customer can have one or five partners, and I have 283 00:25:18.880 --> 00:25:22.559 to know traffic cop where that ticket goes to, which partner, who has 284 00:25:22.599 --> 00:25:26.039 the ball in through all of the system, and we were able to build 285 00:25:26.079 --> 00:25:30.799 that simply using a lot of the out of the box features of Boomy's Master 286 00:25:30.880 --> 00:25:37.279 Data Hub, and we didn't have to write that code. Now we had 287 00:25:37.279 --> 00:25:40.319 to build this integration, this factory to make it all work and tie it 288 00:25:40.359 --> 00:25:45.519 all together. But a lot of what was created was some of the core 289 00:25:45.599 --> 00:25:49.279 way that Boomy works. And I don't know if what we've done is completely 290 00:25:49.400 --> 00:25:53.960 unique, but when we've talked to others like HM, we didn't think of 291 00:25:55.279 --> 00:25:59.640 having Mastered Data Hub as that type of a use case interesting. So it's 292 00:25:59.640 --> 00:26:04.039 been a very very interesting story to be able to tell to other companies and 293 00:26:04.079 --> 00:26:08.680 even even Boomy themselves to go that's kind of a unique use case that isn't 294 00:26:10.599 --> 00:26:12.599 always the way that it's used. So well, it's funny. I've found 295 00:26:12.599 --> 00:26:17.960 that one of the most interesting parts of the software world when you develop an 296 00:26:18.039 --> 00:26:22.559 enterprise application, when you some years later talk to your clients, you will 297 00:26:22.599 --> 00:26:25.880 find them doing the most creative things that you never thought of. Yes, 298 00:26:26.039 --> 00:26:27.759 and that's why you have user conferences, that's why you talk to people, 299 00:26:27.799 --> 00:26:32.920 that's why you have strategic engagements in partners, right, is to learn Wow, 300 00:26:33.079 --> 00:26:36.880 so you did that as a sort of reset a stone of integration essentially, 301 00:26:36.960 --> 00:26:41.119 right, right, And that's some of the nice things about these conferences 302 00:26:41.160 --> 00:26:44.400 is sharing that feedback as well, right, and being able to sit down 303 00:26:44.440 --> 00:26:47.519 and say, hey, by the way, there's this other the other scenario 304 00:26:47.640 --> 00:26:49.680 that you might not think of, and they're like, oh, that is 305 00:26:49.680 --> 00:26:52.720 a good idea. Let's we need to get on that, you know, 306 00:26:52.920 --> 00:26:57.000 type type of scenario. Or talking to other individuals where what you've solved they 307 00:26:57.039 --> 00:27:00.039 need and then vice versa, like oh, you did it that way, 308 00:27:00.119 --> 00:27:03.599 that's an interesting way to do that, right, and or even the validation 309 00:27:03.759 --> 00:27:07.839 of we all did it the same way because we all got to the same 310 00:27:07.920 --> 00:27:11.880 you know, we all came to the same conclusion. So that's that's been 311 00:27:11.079 --> 00:27:15.680 a great thing about these conferences and being able to connect with booming as open 312 00:27:15.680 --> 00:27:18.640 as they are. Yeah. Well, and you know, every company is 313 00:27:18.799 --> 00:27:23.039 unique, every situation is unique, but still there are broader patterns that are 314 00:27:23.480 --> 00:27:29.079 similar across different environments. And what you figured out is that you can carve 315 00:27:29.119 --> 00:27:33.359 out x number of patterns that will get you eighty percent of the way there 316 00:27:33.440 --> 00:27:37.200 with new solutions such that, yes, you do have to finish the twenty 317 00:27:37.200 --> 00:27:40.880 percent, but you're eighty percent there from the get go, and that's kind 318 00:27:40.880 --> 00:27:44.799 of chat gipt string right gause eighty percent of the box then you find too 319 00:27:45.079 --> 00:27:48.799 and that's exactly the right when we when we sat down the team and I 320 00:27:48.880 --> 00:27:52.920 built this, it was we knew it's not going to cover one hundred percent 321 00:27:52.960 --> 00:27:56.720 of the scenario. We know we have to continue to modify and make a 322 00:27:56.839 --> 00:28:02.400 little bit unique each customer. What we didn't want was every time we sat 323 00:28:02.480 --> 00:28:06.559 down it to be a snowflake every time. So we needed to start with 324 00:28:06.599 --> 00:28:10.920 more of a product that we can offer that has the levers and the knobs 325 00:28:10.960 --> 00:28:15.720 that we can turn, or the availability that well, because we're in boomy, 326 00:28:15.799 --> 00:28:18.480 we have even more power than that. You know, we can swap 327 00:28:18.519 --> 00:28:22.480 something out, but at least we're starting from a template. Plus, now 328 00:28:22.519 --> 00:28:30.000 that we've built this system, certain things like air handling or data validation is 329 00:28:30.200 --> 00:28:33.599 universal throughout all of this, and we don't have to go re implement its 330 00:28:33.640 --> 00:28:37.519 boiler plate. It's included from the get go, so we're able to track 331 00:28:37.599 --> 00:28:41.240 and do things that if we had to build this that would be something you'd 332 00:28:41.279 --> 00:28:47.559 have to build over and over again potentially. So we've been able to gain 333 00:28:47.599 --> 00:28:52.039 efficiencies and be able to really build I believe in a very good robust system 334 00:28:52.720 --> 00:28:56.359 within Boomy, a system of systems within Boomy to be able to allow us 335 00:28:56.400 --> 00:29:03.160 to really integrate and hand a lot of different use cases of our customers. 336 00:29:03.200 --> 00:29:08.839 Because one system service now very prevalent in the industry, everybody implements service now 337 00:29:08.839 --> 00:29:12.279 differently. There's not one company that just uses it straight out of the box, 338 00:29:14.160 --> 00:29:17.720 and so when you come into that, it's not necessarily the technical piece 339 00:29:17.799 --> 00:29:22.000 that's the hard part. It's the business processes and what they do and what 340 00:29:22.039 --> 00:29:26.880 they've configured above and beyond the out of box that, well, now we 341 00:29:26.960 --> 00:29:30.559 have to bond with that. How do we make something that we don't have 342 00:29:30.920 --> 00:29:34.799 or our processes different work with their process So through mapping and other things we've 343 00:29:34.839 --> 00:29:38.359 created, we've been able to mitigate some of that, to be able to 344 00:29:38.480 --> 00:29:45.119 change the data as it's coming in into our global Golden record universal model, 345 00:29:45.680 --> 00:29:51.079 so that all of their uniqueness gets transformed into one standard model, which happens 346 00:29:51.079 --> 00:29:55.039 to be ours. But and vice versa. If we had something very unique 347 00:29:55.039 --> 00:30:00.000 that we did as an MSP as a manager provider to make it work, 348 00:30:00.079 --> 00:30:02.960 or we would handle that in our integration back to the hub, back to 349 00:30:03.000 --> 00:30:07.440 that record, so that Golden Hub, that record becomes the universal standard of 350 00:30:07.680 --> 00:30:14.799 what represents our data in our different models across the board. It's one record 351 00:30:15.119 --> 00:30:22.880 for everyone. So it's an interesting challenge and working with customers to understand it's 352 00:30:22.920 --> 00:30:26.039 not separate copies and you have your different cop we have our. It's one 353 00:30:26.119 --> 00:30:30.759 thing. That's we're now operating on one playing field. That's very interesting. 354 00:30:30.799 --> 00:30:33.759 Well, and as a consultancy, I'm sure you know this. I don't 355 00:30:33.799 --> 00:30:37.240 have to tell you that you need to deliver value to your clients on a 356 00:30:37.279 --> 00:30:42.920 regular basis and there cannot be big long pauses and value and months of time 357 00:30:44.039 --> 00:30:47.000 going by where people are scratching their heads and getting her it's like, all 358 00:30:47.079 --> 00:30:48.960 right, what are these guys doing for us again? So you've kind of 359 00:30:49.000 --> 00:30:53.880 solved that by creating this engine, this multifaceted engine, and every time you 360 00:30:53.960 --> 00:30:57.000 learn something, you bake that into the stack. Right, Yes, And 361 00:30:57.000 --> 00:31:00.680 that's something we're actually on Version two just rewrote it this year, Version two 362 00:31:00.799 --> 00:31:07.440 of our e bonding engine that we've written because we did some organizational and system 363 00:31:07.519 --> 00:31:12.559 changes that we wanted to use Boomy as the center point for everything. Now. 364 00:31:14.039 --> 00:31:18.359 We started as doing a traditional integration system to system and so that has 365 00:31:18.400 --> 00:31:22.359 existed for a long time that just got replaced with this new version two oh 366 00:31:22.440 --> 00:31:25.359 and in doing that, we also learned how to mature our own model. 367 00:31:25.400 --> 00:31:29.000 Like you said, we learned from one point zero, and now that we 368 00:31:29.079 --> 00:31:30.720 have that, we have all this from two point zero, and we've even 369 00:31:30.759 --> 00:31:36.519 gone beyond that to really start utilizing a lot of Boomy features that we were 370 00:31:36.599 --> 00:31:38.640 touching on in the first one, because the first time around you're a little 371 00:31:38.640 --> 00:31:44.920 probably gun shy, so but this time around we've just jumped in with two 372 00:31:44.960 --> 00:31:49.680 feet and we're already seeing the benefits from that. Because, for example, 373 00:31:51.000 --> 00:31:56.359 we always have to with a ticket tell what device, right, if we 374 00:31:56.400 --> 00:32:00.799 have an issue with an alert happened and we created a or an incident, 375 00:32:00.720 --> 00:32:05.039 we have to pass that along to you in the ticket itself. In the 376 00:32:05.079 --> 00:32:07.799 case, well, originally we were just passing the name of the device back 377 00:32:07.799 --> 00:32:12.160 in the day in the version one point zero. Well, with two point 378 00:32:12.240 --> 00:32:15.440 zero, we now have an entire relationship. We have that actual device as 379 00:32:15.480 --> 00:32:21.039 an entity so eventually, if we want to make two point zero have a 380 00:32:21.079 --> 00:32:24.480 device synchronization with our customer, it's native. It's there. We just have 381 00:32:24.519 --> 00:32:28.759 to build the endpoint to push the data to the customer. But it's in 382 00:32:28.799 --> 00:32:32.480 our system already, so we have features like that, or being able to 383 00:32:34.119 --> 00:32:37.599 respond to data that's being changed. Hey, it went from a priority two 384 00:32:37.640 --> 00:32:42.400 to a priority one. Well, Boomy can see that now it couldn't before. 385 00:32:43.000 --> 00:32:45.400 Well, now that I can see that, I can actually reach out 386 00:32:45.400 --> 00:32:47.680 to someplace, like if I want to hook it to Twilio and text people 387 00:32:47.680 --> 00:32:51.519 that, oh there's a critical alert text to get everybody on the horn. 388 00:32:52.200 --> 00:32:54.759 We can now do that because all of our data is now centralized and boomy. 389 00:32:54.880 --> 00:32:59.359 So we're getting a ton of benefit. Interesting. So one of the 390 00:33:00.200 --> 00:33:02.599 on our show years ago joke to actually the pre show that in the IT 391 00:33:02.960 --> 00:33:07.400 world, we're always saying that one more layer of abstraction can solve all our 392 00:33:07.440 --> 00:33:09.160 problems. Right, But that's kind of what you've done, right. You've 393 00:33:09.200 --> 00:33:14.039 created a layer of abstraction. You're using Boomy as the technology that runs this 394 00:33:14.240 --> 00:33:16.359 engine, but it is an engine that you have built on top of that 395 00:33:16.440 --> 00:33:21.160 solution. So it is this it's a deep layer of abstraction, right, 396 00:33:21.920 --> 00:33:23.960 it's our as I always call it, the system of systems, right, 397 00:33:24.920 --> 00:33:29.279 But that's that's one of the things that I try to do and then work 398 00:33:29.279 --> 00:33:31.400 with a team to make sure they understand it. And like I said, 399 00:33:31.720 --> 00:33:36.000 this huge team effort to get this done. Even though we're a small team, 400 00:33:36.240 --> 00:33:39.759 we're very mighty in getting this work done. So's we're doing a lot 401 00:33:39.759 --> 00:33:44.880 of things, even pulling some stuff out of boomy. And because we're trying 402 00:33:44.920 --> 00:33:47.759 to build this repeatability of scale, we actually pulled some of it out into 403 00:33:47.839 --> 00:33:57.039 sequel and tabilized and data like data drive driven drove the actual integration itself. 404 00:33:57.240 --> 00:34:00.119 So now when we templatize them, we could start from our stand well that 405 00:34:00.160 --> 00:34:05.119 already has those knobs and levers. We can do but it's not a redeployment. 406 00:34:05.200 --> 00:34:07.639 It's just changed some data values and we're off to the races. So 407 00:34:08.079 --> 00:34:14.199 we're doing things like that at a basic level to really kind of drive what 408 00:34:14.239 --> 00:34:17.719 we do so that there's less overhead of It's just like a deploying code I 409 00:34:17.719 --> 00:34:22.440 wouldn't want to change. If I had a business rule engine, I wouldn't 410 00:34:22.440 --> 00:34:24.599 want to redeploy that whole engine and the code that runs it, just because 411 00:34:24.599 --> 00:34:29.480 my business rule change now I want to have that it driven by data and 412 00:34:29.519 --> 00:34:32.440 a table or something. We've taken that same approach with high integration. Yeah, 413 00:34:32.559 --> 00:34:37.480 we got about a minute and a half left here. The pace of 414 00:34:37.679 --> 00:34:42.800 change has accelerated so much in the past really ten months, I would say, 415 00:34:42.960 --> 00:34:45.920 when chetchipt came out of these other things. And I think some people 416 00:34:45.960 --> 00:34:50.920 are getting a bit over their skis. But what's your advice to clients about 417 00:34:50.960 --> 00:34:58.440 how to mitigate the potential risk of change and how to just stay on track 418 00:34:58.480 --> 00:35:01.360 and make sure you just maintain com and keep going. Yeah. So it's 419 00:35:01.800 --> 00:35:06.920 a great challenge because one of the things is you have to keep going with 420 00:35:07.000 --> 00:35:09.840 change. And that's the one constant is there will be changed. So building 421 00:35:10.199 --> 00:35:15.079 whatever you take and utilize with any of your system boomy, et cetera. 422 00:35:15.840 --> 00:35:21.159 Is being able to handle that change in a way that you're not painting yourself 423 00:35:21.159 --> 00:35:24.400 into a corner. Because it will change. It will come down the pipe, 424 00:35:24.440 --> 00:35:28.559 right, you will have to make that process or you will have to 425 00:35:28.559 --> 00:35:31.920 make that integration do something different. So if you can take the time to 426 00:35:32.440 --> 00:35:37.199 I'm not saying make it perfect because that's analysis paralysis. But if you can 427 00:35:37.679 --> 00:35:40.960 take the time to build yourself just a little bit of room. You came 428 00:35:42.039 --> 00:35:45.199 up with the perfect term. Don't paint yourself into a corner. It's very 429 00:35:45.280 --> 00:35:49.000 dangerous these days. You have to watch out for that because it'll be very 430 00:35:49.000 --> 00:35:51.719 painful if that happens. Oh, you're gonna be a very unhappy camper. 431 00:35:51.960 --> 00:35:53.920 Yes, probably looking for a new job or a new client is something. 432 00:35:54.039 --> 00:35:58.840 Yes, well, look this gentleman up online, folks. Ken Maglio from 433 00:35:58.840 --> 00:36:01.039 Worldwide Technology can congratulations. Thank you, sir. We'll be right back. 434 00:36:01.039 --> 00:36:13.039 You're listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host, 435 00:36:13.559 --> 00:36:19.760 Eric Tabanat all right, folks, back here on Inside Analysis, here 436 00:36:19.760 --> 00:36:22.559 in Boomy World. And I'm very excited to be with a Jay Natarajen. 437 00:36:22.840 --> 00:36:27.800 He is with United Techno and you are an enterprise architect. That's good. 438 00:36:28.000 --> 00:36:30.360 And we were just talking a moment ago about how cloud is really a new 439 00:36:30.440 --> 00:36:35.159 center of gravity, which is quite fascinating. But personally, I believe that 440 00:36:35.199 --> 00:36:37.119 on prem data centers are not going to go away anytime soon. We're going 441 00:36:37.199 --> 00:36:42.039 to live in a hybrid world probably forever. That's just my personal theory. 442 00:36:42.480 --> 00:36:47.079 But how does Boomy play into the modern enterprise architects world? Yeah, thanks, 443 00:36:47.320 --> 00:36:52.280 thanks for the opportunity. The CIO of one of our major retail customers 444 00:36:52.679 --> 00:36:58.119 just said today to me that Boomy is the center of his universe. It 445 00:36:58.199 --> 00:37:04.760 is the most impart and application in his enterprise application portfolio. If you do 446 00:37:04.760 --> 00:37:09.679 all ten years behind, the most important application in enterprise portfolio was the ERP. 447 00:37:10.199 --> 00:37:15.599 Now the ARP is getting smaller, the monolith is getting broken. It's 448 00:37:15.639 --> 00:37:22.960 become the best of breed SaaS application composable. And they're all desperate, they're 449 00:37:22.960 --> 00:37:29.000 all spread spread and they can be changed very quickly. What and you need 450 00:37:29.320 --> 00:37:35.400 a smart, intelligent platform to make sure all of this is connected and everything 451 00:37:35.679 --> 00:37:42.079 is smooth, is growing smoothly and operationally ready for our customers. The operational 452 00:37:42.079 --> 00:37:47.880 importance is extremely important for our customers. The ability to connect these desperate systems 453 00:37:47.920 --> 00:37:51.559 and to change them right. If they don't like one, they can change 454 00:37:51.599 --> 00:37:54.800 the other to a strategy to be able to change them is extremely important to 455 00:37:54.840 --> 00:38:00.360 our customer and Boomy provides that platform. Yeah, what's a beating heart now 456 00:38:00.440 --> 00:38:06.440 of the modern de facto enterprise architecture which is hybrid but cloud first, cloud 457 00:38:06.519 --> 00:38:10.840 native ideally, but reaching into all these endpoints, whether that's the traditional data 458 00:38:10.840 --> 00:38:15.039 center, whether that's the edge, whatever the case may be. You have 459 00:38:15.159 --> 00:38:19.920 this beating heart of intelligent automation and integration. And as you're discussing, that's 460 00:38:19.920 --> 00:38:24.079 booming. Yep, that's true. Yeah, And what we do and boomy 461 00:38:24.320 --> 00:38:30.639 is a great platform for this, and it's not just an integration platform now 462 00:38:30.599 --> 00:38:35.480 and now it's become an intelligent integration platform with a new AI capability. Is 463 00:38:35.880 --> 00:38:39.880 all that is going to do is to make this more accessible, make this 464 00:38:40.039 --> 00:38:46.960 more easily deployable. It's so much more easily consumable. They have a good 465 00:38:47.000 --> 00:38:54.519 events stream event streams product that was just released makes the connectivity and scalability of 466 00:38:54.519 --> 00:39:00.679 this platform a lot more usable for our customers. And that's why Big Enterprise 467 00:39:00.880 --> 00:39:04.440 wants. They want something that is obviously easy to use and quick to use, 468 00:39:04.440 --> 00:39:07.679 but they want something that can scale, that can moderate well, that 469 00:39:07.800 --> 00:39:10.639 can take not just like today's traffic, but three years down what the traffic 470 00:39:10.719 --> 00:39:14.960 is going to be that then they should be able to do that well. 471 00:39:15.039 --> 00:39:19.480 So you kind of alluded to something here, which is the rate of change 472 00:39:19.639 --> 00:39:22.800 in the world at large, right now, which is off the charts. 473 00:39:22.920 --> 00:39:24.880 I mean, it's just crazy how fast things are changing. And if you're 474 00:39:24.920 --> 00:39:29.599 a CIO, you want to be as agile as possible, and so you 475 00:39:29.639 --> 00:39:31.199 want to be able to leverage new technologies. You want to be able to 476 00:39:31.320 --> 00:39:36.760 leverage new processes. You want to be able to bake insights into your existing 477 00:39:36.800 --> 00:39:38.880 processes, and you don't want a lot of lead time. When you come 478 00:39:38.960 --> 00:39:42.280 up with the idea to get something done, you want to go, Aha, 479 00:39:42.360 --> 00:39:45.119 let's do this, and you get it done. And that's what this 480 00:39:45.280 --> 00:39:50.760 marshaling area for integration and automation and complex have been processing boils down to. 481 00:39:51.159 --> 00:39:55.719 Is your engine your de facto business integration engine? Is that about right? 482 00:39:55.880 --> 00:39:59.760 Yeah? That's good. I mean what I keep saying, I have a 483 00:39:59.800 --> 00:40:04.400 bl B that I'd like to plug in linked and I blog every two weeks. 484 00:40:05.079 --> 00:40:07.480 And it's not about what we do, it's about why we do, 485 00:40:07.199 --> 00:40:09.920 why we do things. Why I don't wake up at two in the morning 486 00:40:09.960 --> 00:40:15.320 to fix something because we catch something ahead of time using develops and c s 487 00:40:15.440 --> 00:40:20.199 in automation. One of the blogs I say, integrate, don't complicate, 488 00:40:20.960 --> 00:40:24.639 and it's all about how do you simplify the process in paper and make sure 489 00:40:24.639 --> 00:40:30.000 that there's skill visibility between what you're trying to do and the end goal and 490 00:40:30.039 --> 00:40:34.360 continue to iterate and build on that. Right now, we see Boomy's platform 491 00:40:34.519 --> 00:40:37.440 is a great platform for us to do that, right, whether it's simply 492 00:40:38.440 --> 00:40:44.079 simplified UI driven approach and the ability to connect different systems all in a single 493 00:40:44.119 --> 00:40:49.320 screen gives us the ability to do that. You need, well, no, 494 00:40:49.360 --> 00:40:53.519 that's okay, So you need you need a command and control center basically, 495 00:40:53.519 --> 00:40:57.559 and that's kind of what Boomy is becoming to a certain extent, because 496 00:40:57.880 --> 00:41:00.360 again, you have the cloud, you have on POM data centers, you 497 00:41:00.360 --> 00:41:02.159 have all these edge places, and you have all these different ways of doing 498 00:41:02.199 --> 00:41:06.639 things. New startups are coming every single day for just about every kind of 499 00:41:06.679 --> 00:41:12.280 functionality you could want. And so if you have a stable foundation for intelligent 500 00:41:12.360 --> 00:41:16.079 integration and automation, that becomes this this de facto center of your environment. 501 00:41:16.199 --> 00:41:22.239 Right, that's good. I mean the platform is you know, gives it 502 00:41:22.760 --> 00:41:27.599 has the ability for us to see everything together in a single place. Right. 503 00:41:28.519 --> 00:41:32.920 Hybrid integration is the norm. It's not it's not like an exception. 504 00:41:34.079 --> 00:41:37.079 It's the rule. Right, It's the rule. There's always going to be 505 00:41:37.079 --> 00:41:38.679 on premise systems. There's always going to be cloud systems. There's going to 506 00:41:38.679 --> 00:41:43.000 be hybrid with the clouds. I have data sitting in a zoo, I 507 00:41:43.000 --> 00:41:45.639 have data sitting in a w S and GCP that I need to connect and 508 00:41:45.679 --> 00:41:50.239 those that's where the systems are and the ability for me to connect everything, 509 00:41:50.320 --> 00:41:52.480 see everything in a single platform. The command and control, as you said, 510 00:41:52.519 --> 00:41:57.960 it's pretty good, right, and that's exactly what the platform provides. 511 00:41:58.960 --> 00:42:02.000 But it also provides the with control, which is important. And there is 512 00:42:02.599 --> 00:42:07.840 fine grain access control so that even though everybody can you can see something, 513 00:42:07.920 --> 00:42:13.559 only people who are allowed to see can see it. There is tight security 514 00:42:13.599 --> 00:42:17.239 and authentication. There's type security and what you can do and what you and 515 00:42:17.400 --> 00:42:22.559 everything else and you know you cannot. I think that's that's also very critical. 516 00:42:22.880 --> 00:42:25.360 And for most of the platform you're talking about, especially integrate, that 517 00:42:25.519 --> 00:42:29.440 data is on PREND or not on PREND. The data is with the customer. 518 00:42:29.480 --> 00:42:31.920 It doesn't kind of leave wooly in most sense, it's sitting on your 519 00:42:32.239 --> 00:42:37.079 file shaft systems and it's it's displayed and rendered that so that's also pretty important 520 00:42:37.119 --> 00:42:44.559 for some of our customers. So Booby think about it like it's a platform 521 00:42:44.719 --> 00:42:49.800 wh's ubiqulous that anybody can see, whether you're a business executive, an I 522 00:42:50.079 --> 00:42:53.199 executive, a developer, an operation person, you can see and integrate and 523 00:42:53.239 --> 00:42:57.840 play the same platform across the board. But with type security and controls and 524 00:42:57.920 --> 00:43:00.920 access, you can make sure the only the right people will have access to 525 00:43:00.960 --> 00:43:06.760 that data. And that's critical and that's why many of our customers choose Boomy. 526 00:43:07.639 --> 00:43:09.880 No, that makes a lot of sense. What do you see with 527 00:43:10.079 --> 00:43:14.840 AI and AI being infused in the system. Where do you see the low 528 00:43:14.920 --> 00:43:17.280 hanging fruit of how that can be applied and how it can change day to 529 00:43:17.360 --> 00:43:22.199 day operations. That's a great question, right. So Tom Davenport was a 530 00:43:22.239 --> 00:43:29.800 keynote speaker today and he said, the only people who lose with AI are 531 00:43:29.840 --> 00:43:34.239 people who don't work with AI, and that's true for platforms and companies. 532 00:43:35.760 --> 00:43:42.000 What Boomy is doing with AI is required in this day. It is required, 533 00:43:42.559 --> 00:43:45.440 and it's also a great stepping stone because they've been doing this for the 534 00:43:45.480 --> 00:43:51.320 last ten years. They have two and twenty terra by building gazillion data that's 535 00:43:51.320 --> 00:43:54.920 what Ed mentioned, and they've been doing this secretively, honestly they've been doing 536 00:43:54.960 --> 00:44:00.519 this any so, they've been doing this for the last ten fifteen years. 537 00:44:00.559 --> 00:44:07.199 So when this chat GPT was released, it was a youtubeling. Uh they 538 00:44:07.800 --> 00:44:10.639 are they leverage what they already had and just made it available for us. 539 00:44:10.679 --> 00:44:15.400 I think some of the features are gad today, which is pretty good. 540 00:44:15.440 --> 00:44:21.679 So we are seeing the evolution of it. There will be iterations where it's 541 00:44:21.760 --> 00:44:28.519 it is going to get better today with a single With a single prompt, 542 00:44:28.599 --> 00:44:35.880 I can generate an integration from CRM to it's crazy crazy Yeah, but that's 543 00:44:35.880 --> 00:44:38.400 still step zero, step one right, Yeah, that still requires some work 544 00:44:38.480 --> 00:44:43.760 to make sure it's productionalized and deployable. What I would love to see is 545 00:44:44.119 --> 00:44:49.400 you know that being production ready code and that code being generated based on what 546 00:44:49.480 --> 00:44:54.039 I've previously deployed. AI should help And I see in the roadmap that at 547 00:44:54.039 --> 00:44:59.119 some point of time AI will help with operational issues. When can I schedule 548 00:44:59.159 --> 00:45:04.400 these deployments so I do not impact an existing system? Right? With a 549 00:45:04.480 --> 00:45:09.719 loader in today in our customers, we do about twenty two million documents a 550 00:45:09.840 --> 00:45:15.719 week. Wow, how do I know I should be doing twenty one or 551 00:45:15.760 --> 00:45:20.199 twenty four? Am I missing a million? If am I getting one million 552 00:45:20.199 --> 00:45:23.320 too much. I don't know, to be honest, And we are reactive 553 00:45:23.440 --> 00:45:30.320 in this place, and the whole industry is more reactive in this This is 554 00:45:30.360 --> 00:45:35.920 an area of an AI can shine and help HI, which is human intelligence, 555 00:45:36.199 --> 00:45:38.960 to do a much better job. I've always believed that if a machine 556 00:45:38.960 --> 00:45:43.480 can do a job faster than a human and more efficiently, let the machine 557 00:45:43.519 --> 00:45:46.400 do the job. You don't see farmers using yaks to till their land anymore. 558 00:45:46.440 --> 00:45:50.960 They have combines and big machinery to get that done. And that's the 559 00:45:51.000 --> 00:45:52.719 kind of the world we're in the right now. Yeah, yeah, yeah, 560 00:45:52.760 --> 00:45:57.000 I mean I drive a Tesla. I don't knowssemble wheels, but you're 561 00:45:57.079 --> 00:46:01.840 right. I mean, AI is a friend that we can employ with care 562 00:46:02.599 --> 00:46:06.480 and love. It's a mutual respect and love. I mean, we kind 563 00:46:06.480 --> 00:46:09.079 of use it, and we've got to make sure that there's governance and control 564 00:46:09.360 --> 00:46:14.280 on the AI itself, but we can. We can do a lot more. 565 00:46:14.440 --> 00:46:17.800 Today. I'm able to write in languages and never learned. Right, 566 00:46:19.119 --> 00:46:22.840 that's right, that's right. The generative AI. You know, I was 567 00:46:22.000 --> 00:46:27.159 afraid of using new languages or even using some complex things in Python. But 568 00:46:27.239 --> 00:46:32.239 I can ask AI to make it better with with privacy and everything considered, 569 00:46:32.559 --> 00:46:37.920 and I think I'm getting slightly better in my job and and that's that's that's 570 00:46:37.920 --> 00:46:40.519 always helpful. Well, that's wonderful. We've been talking to a Jing not 571 00:46:40.599 --> 00:46:44.559 orogen from United a techno. Thank you so much for your time. We'll 572 00:46:44.559 --> 00:46:53.920 be right back. You're listening to Inside Analysis. Welcome back to Inside Analysis. 573 00:46:54.360 --> 00:47:01.199 Here's your host, Eric Tavanaugh. Okay, folks back here on Inside 574 00:47:01.199 --> 00:47:06.599 Analysis times for the podcast bonus segment. And I'm pleased they have Damien Black 575 00:47:06.760 --> 00:47:09.119 with me. He was the founder and CTO and I guess for a while 576 00:47:09.159 --> 00:47:13.400 CEO of Sequel Stream, and he's going to tell us a bit about what 577 00:47:13.440 --> 00:47:15.599 they're really trying to do with these large language models and kind of how they 578 00:47:15.639 --> 00:47:21.199 work. So Damien, take it away. Yeah, just I can just 579 00:47:21.239 --> 00:47:24.559 sort of demystify this. And so first of all, the first sort of 580 00:47:24.559 --> 00:47:29.800 big breakthrough is the transformer architecture. And what really the important part about that 581 00:47:30.000 --> 00:47:36.239 is before they would what they're doing is they're looking at in neural nets. 582 00:47:36.239 --> 00:47:39.559 You're basically trying to predict the next say word they say token part of a 583 00:47:39.599 --> 00:47:43.400 word. But really you can think of it as predicting the next word, 584 00:47:44.840 --> 00:47:47.400 and you do it from what's been before, and so you can then train 585 00:47:47.960 --> 00:47:58.000 neural nets. You can change train neural nets based on for example, Wikipedia 586 00:47:58.599 --> 00:48:01.559 go through and I you'll be trying to do each time is train it to 587 00:48:01.880 --> 00:48:07.800 what is the next word. The problem with that is you know, you 588 00:48:07.840 --> 00:48:12.880 have a whole context of what's gone before and then the next word that's coming 589 00:48:12.960 --> 00:48:15.480 up, and it's a sort of very inefficient way of having to retrain. 590 00:48:15.519 --> 00:48:22.760 When you're retraining, what effectively doing is you're kind of solving thousands and thousands 591 00:48:22.800 --> 00:48:24.480 of equations, and that's what's really going on here. I can tell you 592 00:48:24.519 --> 00:48:28.920 a little about that what the neural nets are, but that effectively is what 593 00:48:29.079 --> 00:48:35.199 is really going on or and it's not actually solving, but trying to get 594 00:48:35.239 --> 00:48:39.920 better solutions or better approximations to the solution. So what the transformer architecture instead 595 00:48:39.960 --> 00:48:47.239 does It allows you to have a number of words predicting the next token, 596 00:48:47.320 --> 00:48:54.719 so you have a buffer of them all at the same time. And so 597 00:48:54.880 --> 00:48:59.639 there's a benefit in that because you can actually parallel You're not just sort of 598 00:48:59.639 --> 00:49:02.280 moving on one with one word at a time. You're actually doing one word 599 00:49:02.320 --> 00:49:08.639 in the context of a sentence of words. Now, part of the challenge 600 00:49:08.840 --> 00:49:13.800 in trying to actually get the sort of the full semantic meaning from that is 601 00:49:14.239 --> 00:49:16.079 there are a couple of things that you have a problem. One immediate problem 602 00:49:16.119 --> 00:49:21.719 is then is that you actually have a kind of look ahead capability, because 603 00:49:21.760 --> 00:49:24.079 when you've got up you know, fifty words say that are being used when 604 00:49:24.119 --> 00:49:29.199 you're training against the next word that's coming, it's got all of the past 605 00:49:29.280 --> 00:49:35.159 history these fifty words, and then predicting the next word. The system when 606 00:49:35.199 --> 00:49:38.320 it's solving the equations can look ahead and you can actually be looking ahead at 607 00:49:38.360 --> 00:49:42.920 words that are yet to come. Right, that's a problem. So they 608 00:49:43.000 --> 00:49:46.599 have a mechanism to try and stop that. Add in some masks so that 609 00:49:46.679 --> 00:49:53.920 the words we even when you're solving the problem any in trying to predict the 610 00:49:53.920 --> 00:49:58.079 next word, you're not allowed to look forward. Then the model can't actually 611 00:49:58.159 --> 00:50:01.239 see can't take any waitings from the words that are yet to come. So 612 00:50:01.320 --> 00:50:05.639 that's one thing that it does to try and stop. Otherwise it just you 613 00:50:05.679 --> 00:50:07.559 know, it's a bit like you know, playing cards and you know what 614 00:50:07.599 --> 00:50:10.360 the next card is. It's going to be delta on the top of the 615 00:50:10.400 --> 00:50:15.079 deck. It kind of ruins all the predictive models. So that's cool. 616 00:50:15.079 --> 00:50:22.079 That's a call of masking. The attention behavior is basically provides a number of 617 00:50:22.079 --> 00:50:29.519 different extra waitings where words can refer to other words or take waitings from other 618 00:50:29.559 --> 00:50:32.559 words that have happened in the same area in that same buffer, So you 619 00:50:32.679 --> 00:50:37.719 allow and that's really important because of the context of a word. When you 620 00:50:37.920 --> 00:50:42.119 for example, particularly using things like pronouns and so on, you don't necessarily 621 00:50:42.159 --> 00:50:49.440 sure what the thing is that you're actually referring to. So the attention mechanism 622 00:50:49.679 --> 00:50:54.679 is to allow in the training algorithm waitings to come in for words based on 623 00:50:55.519 --> 00:51:02.719 the other words that are present in that buffer. And so the you could 624 00:51:02.719 --> 00:51:07.519 it can when it's solving the equations and changing the waitings. It has a 625 00:51:07.599 --> 00:51:12.199 number of different ways of changing parameters and for each of the sort of the 626 00:51:12.239 --> 00:51:20.159 attention references or like the attention head, it can actually tweak waitings to try 627 00:51:20.159 --> 00:51:22.840 and come up with the best possible solution. And just adding in that extra 628 00:51:23.360 --> 00:51:30.400 capability where there are waitings based on references from basically one from the position and 629 00:51:30.760 --> 00:51:37.920 one word to another word, it allows the it allows the extra expressiveness that's 630 00:51:37.960 --> 00:51:44.960 required to be able to capture spoken language or natural language. I don't know 631 00:51:45.000 --> 00:51:47.639 if I put that very well. But the way to think about this, 632 00:51:47.719 --> 00:51:54.239 if you understand what what a you're on net is, it's basically, you 633 00:51:54.280 --> 00:52:00.079 know, it's it's modeling information as equations, so you can write, you 634 00:52:00.079 --> 00:52:02.440 know, it can have ax one plus b x two plus b x three 635 00:52:02.519 --> 00:52:07.639 plus you know, c x three plus d x four and so on, 636 00:52:08.079 --> 00:52:13.760 and then it's all about how you actually what are the waitings you're going to 637 00:52:13.840 --> 00:52:17.800 put onto these different waiting factors on how many variables you have. Then they 638 00:52:17.840 --> 00:52:23.920 then those equations then roll into they feed into another variable at the next level 639 00:52:23.960 --> 00:52:28.719 of equation. These equations all basically slot in and you what they're trying to 640 00:52:28.760 --> 00:52:32.880 do is is have a series of equations that is trying to model what's going 641 00:52:32.920 --> 00:52:37.960 on in the real world. If you had enough of these equations, you 642 00:52:37.000 --> 00:52:39.920 know, rather than if you're just you should be able to measure, you 643 00:52:39.960 --> 00:52:44.280 know, model any kind of shape, and as you have enough dimensions, 644 00:52:44.280 --> 00:52:49.239 you can model anything. That's the idea and the way it's so what they're 645 00:52:49.239 --> 00:52:53.360 trying to do is change change through these complex equations that roll into one another, 646 00:52:53.360 --> 00:52:55.960 which is what the neural nets are, and the waitings are finding the 647 00:52:55.960 --> 00:53:02.400 waitings on those variables. What they do then they then say, they write 648 00:53:02.440 --> 00:53:07.920 a function which is describing how close you know the function which describes those equations 649 00:53:08.119 --> 00:53:13.039 compared to the ultimate answer that you want. The difference between them is the 650 00:53:13.199 --> 00:53:17.280 error function. So by taking the separation of those you have in another etceter 651 00:53:17.360 --> 00:53:25.840 equations that describes which describes basically how accurate your model is for the natural language. 652 00:53:25.840 --> 00:53:30.719 So if you have your equations describing how you represent the understanding of English, 653 00:53:30.719 --> 00:53:32.960 and you've got your predictions, you can see then okay, what is 654 00:53:34.000 --> 00:53:38.400 the difference Because you've modeled it all with mathematic equations. Now you think, 655 00:53:38.440 --> 00:53:42.519 well, how, how on earth what can I do about that? Well, 656 00:53:42.559 --> 00:53:46.599 what they do is they use simple calculus. It's basically how the actual 657 00:53:46.639 --> 00:53:51.760 algorithms are working to train the network. They're trying to basically reduce their weightings 658 00:53:51.840 --> 00:53:54.159 to reduce the error function. So it's all it's all, you know, 659 00:53:54.159 --> 00:54:00.840 It's all about solving very large numbers, literally thousands and thousands of equations, 660 00:54:00.519 --> 00:54:07.559 thousands and thousands of parameters, billions of possibilities, and that's what's going on 661 00:54:07.639 --> 00:54:09.639 with these systems. Well listen, thank you so much for your time today 662 00:54:09.679 --> 00:54:12.920 and great stuff. Will talk to you next time. Folks, you've been 663 00:54:12.920 --> 00:54:20.320 listening to Inside Analysis, Southern California's mind spread progressive talk in Southern California, 664 00:54:20.559 --> 00:54:28.559 the legends you love and the best talk open for takeout and delivery. Hell 665 00:54:28.679 --> 00:54:32.440 Tapiac, Mexican food restaurant in the Tri City Center of Redlands, is back. 666 00:54:32.559 --> 00:54:37.360 Their entire family is on hand to serve up their delicious burritos, machaka 667 00:54:37.480 --> 00:54:42.480 charizo, wavos, Ranchero's steak and eggs just part of their mouth watering great 668 00:54:42.480 --> 00:54:45.920 food. 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This is the man from yesterday and from this time. 693 00:56:32.800 --> 00:56:37.599 In October of nineteen fifty five, the new Mickey Mouse Club over ABC 694 00:56:37.760 --> 00:56:43.079 TV is going to add a filmed serial series titled Spin and Marty and their 695 00:56:43.159 --> 00:56:50.159 Adventures on the Triple R Ranch. Spinning Marty was an unbelievably popular thing in 696 00:56:50.199 --> 00:56:55.360 our time, but we were the only thing on that was for kids. 697 00:56:55.960 --> 00:57:00.519 You know. There were pampa shows, there were a cartoons, but there 698 00:57:00.519 --> 00:57:04.760 weren't any live action shows. And from this time. In nineteen eighty eight, 699 00:57:05.159 --> 00:57:07.920 Leondard nied Way of Star Trek Fame directs a new Disney movie called Three 700 00:57:07.920 --> 00:57:10.880 Men and a Baby. Two of its stars are known to TV, Tom 701 00:57:10.920 --> 00:57:15.360 Selleck and Ted Danson. It also stars Steve Gutenberg. It's a baby. 702 00:57:15.679 --> 00:57:19.079 It's a baby. Of course, it's a baby. It's you a baby. 703 00:57:19.360 --> 00:57:22.320 No, it's not my baby. It's Jack's baby. If your turn 704 00:57:22.360 --> 00:57:23.960 to changer. I'll give you a thousand dollars if you'll do it, And 705 00:57:24.000 --> 00:57:28.480 from this time. In October of nineteen sixty seven, at Sullivan signs the 706 00:57:28.599 --> 00:57:32.280 Calcils to an exclusive TV contract for a minimum of ten appearances on the Ed 707 00:57:32.360 --> 00:57:37.840 Sullivan Sunday Night CBS TV show. Their first date is October twenty ninth, 708 00:57:37.880 --> 00:57:39.920 and they're going to perform their big hit, The Rain, the Park and 709 00:57:40.079 --> 00:57:52.320 other things. Many know it as I Love the Flower Girl. She sent 710 00:57:52.440 --> 00:58:02.559 me done with more at Man from yesterday dot com. It's that time of 711 00:58:02.679 --> 00:58:07.000 year again, No, not the holidays. 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I'm Chris Gragio, Secretary of State Anthony Blincoln will 733 00:59:52.239 --> 00:59:57.320 be heading back to Tel Aviv tomorrow to continue a world win diplomatic mission. 734 00:59:57.360 --> 01:00:00.679 He made stops in Egypt and Jordan today to urge Arab nations to work together 735 01:00:00.719 --> 01:00:05.719 and keep the conflict from expanding. Blincoln says the US stands with Israel, 736 01:00:06.039 --> 01:00:08.440 and that's one of the four key objectives of his trip to the Middle East.