EP 02: Talking AI in Asset management and Data

00:00:01.683 --> 00:00:02.124 All right. 

00:00:02.283 --> 00:00:03.443 We are live, everybody.

00:00:03.684 --> 00:00:04.985 Welcome to the show here today.

00:00:05.365 --> 00:00:07.804 That still does not have a name.

00:00:07.844 --> 00:00:09.685 So please drop down your

00:00:09.824 --> 00:00:11.346 name suggestions in the comment below.

00:00:11.726 --> 00:00:12.766 Let us know if you think of

00:00:12.805 --> 00:00:13.885 a cool podcast name.

00:00:14.545 --> 00:00:14.885 Otherwise,

00:00:14.906 --> 00:00:16.766 we're going to continue without a name.

00:00:17.407 --> 00:00:18.646 "My name is Yassen Horanszky.

00:00:18.786 --> 00:00:19.907 I'm the founder and CEO of

00:00:19.986 --> 00:00:21.446 Luniko and your host for today.

00:00:21.928 --> 00:00:22.928 And I've helped digitally

00:00:22.987 --> 00:00:24.468 transform businesses of all

00:00:24.507 --> 00:00:25.827 shapes and sizes from

00:00:25.908 --> 00:00:26.827 publicly traded

00:00:27.047 --> 00:00:27.949 multibillion dollar

00:00:27.989 --> 00:00:29.228 international organizations

00:00:29.289 --> 00:00:30.388 to local nonprofits

00:00:30.905 --> 00:00:32.085 and everything in between.

00:00:32.506 --> 00:00:33.305 And just like you,

00:00:33.807 --> 00:00:34.646 I'm wondering where

00:00:34.726 --> 00:00:36.107 artificial intelligence and

00:00:36.148 --> 00:00:37.529 this disruptive technology

00:00:37.548 --> 00:00:38.670 that we've seen dominate

00:00:38.710 --> 00:00:40.890 headlines last year fits in and how.

00:00:41.771 --> 00:00:43.231 And that's why we've created the show,

00:00:43.593 --> 00:00:45.014 to bring on guests of all

00:00:45.054 --> 00:00:46.134 levels in all types of

00:00:46.154 --> 00:00:47.554 businesses and talk about

00:00:47.594 --> 00:00:49.595 AI and its business adoption,

00:00:49.676 --> 00:00:52.478 but strictly from a non-technical lens,

00:00:52.978 --> 00:00:53.658 meaning that we'll be

00:00:53.679 --> 00:00:56.200 touching on topics of strategy, process,

00:00:56.479 --> 00:00:57.820 people, emotions,

00:00:58.121 --> 00:00:58.942 all of that sort of thing.

00:00:59.676 --> 00:01:00.796 So if you're curious about

00:01:00.996 --> 00:01:02.478 AI and its business impacts

00:01:02.518 --> 00:01:03.198 and challenges,

00:01:03.218 --> 00:01:04.197 then this show is for you.

00:01:04.998 --> 00:01:05.679 And we're going to try to

00:01:05.698 --> 00:01:07.278 drop the corporate jargon

00:01:07.319 --> 00:01:08.179 as much as possible,

00:01:08.239 --> 00:01:10.739 keep things as simple as we can.

00:01:11.219 --> 00:01:15.340 Now, today, I am joined by Norm Pointer,

00:01:16.662 --> 00:01:18.162 who is the Managing

00:01:18.201 --> 00:01:20.283 Director at Asset Management Advocates,

00:01:20.882 --> 00:01:21.822 a New York and

00:01:21.882 --> 00:01:23.603 Calgary-based organization.

00:01:25.024 --> 00:01:27.564 Now, Norm is a very special guest,

00:01:28.204 --> 00:01:28.724 at least for me.

00:01:29.543 --> 00:01:31.465 Uh, so we worked together at Nexen Energy,

00:01:31.525 --> 00:01:32.945 which is now CNET Petroleum

00:01:32.986 --> 00:01:33.665 North America.

00:01:34.346 --> 00:01:35.066 I was working with the

00:01:35.126 --> 00:01:36.587 operational excellence team.

00:01:37.046 --> 00:01:38.347 Norm was leading some of the

00:01:38.388 --> 00:01:39.628 internal improvement groups

00:01:39.668 --> 00:01:41.308 for maintenance and reliability,

00:01:41.328 --> 00:01:42.310 and we worked well together.

00:01:43.230 --> 00:01:43.290 Uh,

00:01:43.390 --> 00:01:45.411 but he kind of took on a mentorship

00:01:45.450 --> 00:01:46.031 role for me.

00:01:47.212 --> 00:01:47.871 Um,

00:01:48.412 --> 00:01:49.653 I always say that there were three

00:01:49.753 --> 00:01:51.052 people at Nexen who were

00:01:51.112 --> 00:01:52.713 instrumental in me quitting

00:01:52.754 --> 00:01:54.555 my job and pushing me to

00:01:54.594 --> 00:01:56.635 pursue entrepreneurship and

00:01:56.695 --> 00:01:57.635 Norm was one of them.

00:01:58.373 --> 00:01:59.334 So I wouldn't be here.

00:01:59.373 --> 00:02:00.275 You guys wouldn't be here at

00:02:00.355 --> 00:02:01.875 all if it wasn't for somebody like Norm.

00:02:01.894 --> 00:02:02.715 So thank you, Norm,

00:02:03.096 --> 00:02:05.016 for being here with us today.

00:02:05.296 --> 00:02:06.016 How are you doing, man?

00:02:06.417 --> 00:02:06.856 Hey, good.

00:02:06.876 --> 00:02:07.177 Yes.

00:02:07.236 --> 00:02:07.798 And yourself.

00:02:07.858 --> 00:02:09.198 Thanks for those kind words.

00:02:09.258 --> 00:02:10.198 I mean, it was a pleasure.

00:02:10.217 --> 00:02:12.118 It was a pleasure those

00:02:12.158 --> 00:02:13.900 years working with you and to see you.

00:02:15.310 --> 00:02:15.889 see you grow.

00:02:15.909 --> 00:02:17.350 And I, I, I mean, we're,

00:02:17.550 --> 00:02:19.752 we're roughly 20 years apart in age, so,

00:02:20.012 --> 00:02:20.231 you know,

00:02:20.252 --> 00:02:22.492 I'll be sitting in my wheelchair

00:02:22.532 --> 00:02:23.453 and you'll still be working,

00:02:23.492 --> 00:02:25.354 which is a nice thought actually.

00:02:25.693 --> 00:02:26.653 I'll be pushing you, man.

00:02:26.693 --> 00:02:27.634 Don't worry.

00:02:27.754 --> 00:02:29.115 I'll be pushing you around the park.

00:02:29.776 --> 00:02:30.036 Right.

00:02:30.056 --> 00:02:32.796 Don't worry about that.

00:02:32.877 --> 00:02:33.097 Right.

00:02:33.217 --> 00:02:35.418 So, um, but look, I, you know, let's,

00:02:35.437 --> 00:02:36.437 let's kind of get to some

00:02:36.457 --> 00:02:37.938 of the questions that we have here today.

00:02:37.998 --> 00:02:38.139 Right.

00:02:38.158 --> 00:02:39.019 So Norm's got a very

00:02:39.058 --> 00:02:39.878 interesting background.

00:02:39.919 --> 00:02:41.139 He started out as a mechanic.

00:02:41.747 --> 00:02:44.008 and a programmer, and, you know,

00:02:44.248 --> 00:02:46.871 he runs a successful consulting firm,

00:02:46.931 --> 00:02:48.772 right, specializing in asset management.

00:02:49.713 --> 00:02:52.415 He speaks all over the world as well,

00:02:52.596 --> 00:02:54.056 right, when it comes to SAP,

00:02:54.417 --> 00:02:56.118 enterprise asset management, you know,

00:02:56.179 --> 00:02:57.560 data, hierarchy,

00:02:57.620 --> 00:02:59.782 and all of that sort of thing, right?

00:03:00.401 --> 00:03:01.682 So let's start, I think,

00:03:01.703 --> 00:03:02.743 just with the basic thing I

00:03:02.764 --> 00:03:04.064 want to ask most people here, Norm,

00:03:04.085 --> 00:03:07.728 which is what does AI mean to you, right?

00:03:07.768 --> 00:03:08.588 How would you define that?

00:03:10.414 --> 00:03:12.037 It's a tough one to define

00:03:12.556 --> 00:03:15.579 because there's a lot of

00:03:15.658 --> 00:03:17.700 influence in the market or

00:03:17.860 --> 00:03:19.382 in our social media feeds

00:03:19.703 --> 00:03:21.283 trying to define it for us.

00:03:21.704 --> 00:03:22.784 And so for me,

00:03:22.805 --> 00:03:27.968 I don't really believe that

00:03:28.128 --> 00:03:29.569 AI in the true sense of the

00:03:29.629 --> 00:03:32.752 term actually truly exists.

00:03:32.853 --> 00:03:34.954 I think we have artificial

00:03:35.093 --> 00:03:36.536 narrow intelligence or we

00:03:36.556 --> 00:03:39.277 have large language models that have

00:03:40.020 --> 00:03:41.941 a degree of intelligence, but again, it's,

00:03:42.042 --> 00:03:43.322 it's a narrow intelligence.

00:03:43.382 --> 00:03:43.522 And I,

00:03:44.203 --> 00:03:46.044 I constantly try to go out and

00:03:46.444 --> 00:03:47.786 search and I'll even ask

00:03:48.126 --> 00:03:50.728 chat GPT questions like that and,

00:03:50.888 --> 00:03:51.147 you know,

00:03:51.168 --> 00:03:52.468 see what response it gives me

00:03:52.528 --> 00:03:55.510 back because that's probably the most, um,

00:03:56.292 --> 00:03:58.332 collated set of data to

00:03:58.413 --> 00:03:59.674 give a response like that,

00:03:59.734 --> 00:04:01.034 that I'm aware of.

00:04:01.254 --> 00:04:01.475 I mean,

00:04:01.656 --> 00:04:02.977 short of Googling and getting a

00:04:03.597 --> 00:04:04.978 thousand or 10,000 or a

00:04:05.018 --> 00:04:07.199 million or a billion responses, but, um,

00:04:08.393 --> 00:04:13.475 To me, AI is, you know,

00:04:14.076 --> 00:04:16.499 we have to continually

00:04:16.999 --> 00:04:20.781 improve our practices with

00:04:20.841 --> 00:04:23.302 respect to our domains, meaning for me,

00:04:23.362 --> 00:04:24.524 it's plant maintenance or

00:04:24.564 --> 00:04:25.665 maintenance and reliability.

00:04:26.245 --> 00:04:27.745 And we've been stuck on some

00:04:27.826 --> 00:04:29.307 very simple problems in

00:04:29.346 --> 00:04:30.608 that space for a long time,

00:04:30.668 --> 00:04:31.949 mainly master data,

00:04:32.629 --> 00:04:33.750 planning and scheduling

00:04:33.810 --> 00:04:37.612 concepts and the ability to eradicate

00:04:38.714 --> 00:04:40.456 engineered in defects in

00:04:40.516 --> 00:04:42.559 equipment has been

00:04:43.019 --> 00:04:45.081 problematic since the

00:04:45.122 --> 00:04:46.403 concept of reliability

00:04:46.423 --> 00:04:47.343 centered maintenance came

00:04:47.463 --> 00:04:49.966 out back in the 50s and 60s

00:04:50.026 --> 00:04:53.569 right so for for us not to

00:04:53.730 --> 00:04:55.130 take a look at something

00:04:55.552 --> 00:04:58.495 like as a society that ai

00:04:58.595 --> 00:05:01.218 offers which would be to expedite

00:05:02.646 --> 00:05:04.166 solutions to some of these

00:05:04.247 --> 00:05:05.947 common problems we have I

00:05:06.007 --> 00:05:07.048 think would be

00:05:08.488 --> 00:05:09.949 short-sightedness now what

00:05:10.129 --> 00:05:11.850 what does it mean you know

00:05:12.050 --> 00:05:12.812 you and I've talked about

00:05:12.831 --> 00:05:13.572 this a little bit in the

00:05:13.612 --> 00:05:15.192 past I'm I'm sort of an if

00:05:15.273 --> 00:05:16.434 then else kind of guy

00:05:16.454 --> 00:05:17.494 because of my programming

00:05:17.533 --> 00:05:18.894 background you know I

00:05:18.915 --> 00:05:20.576 started when I was 12

00:05:20.576 --> 00:05:21.716 programming on a commodore

00:05:21.776 --> 00:05:23.456 vic-20 I remember begging

00:05:23.516 --> 00:05:25.918 my parents for a computer um

00:05:26.706 --> 00:05:27.327 back in those days,

00:05:27.367 --> 00:05:28.007 you were going through the

00:05:28.067 --> 00:05:29.369 Sears catalog when it came

00:05:29.408 --> 00:05:30.550 out at Christmas time and

00:05:31.029 --> 00:05:32.370 looking for something.

00:05:32.411 --> 00:05:33.271 And there was a Texas

00:05:33.331 --> 00:05:34.533 Instruments little computer

00:05:34.572 --> 00:05:36.555 that was much cheaper than a Commodore.

00:05:36.574 --> 00:05:37.956 I begged for that.

00:05:38.055 --> 00:05:40.036 They finally got me a Commodore VIC-20,

00:05:40.076 --> 00:05:42.098 but my neighbor had a Commodore 64.

00:05:42.098 --> 00:05:42.519 And of course,

00:05:42.559 --> 00:05:43.620 I was very jealous of that

00:05:43.639 --> 00:05:44.980 because he had much more

00:05:45.060 --> 00:05:45.901 memory than I had.

00:05:46.661 --> 00:05:49.283 But the element of

00:05:50.406 --> 00:05:52.086 Programming then was, well,

00:05:52.127 --> 00:05:53.367 it was all if then else,

00:05:53.447 --> 00:05:54.747 unless you were trying to

00:05:54.848 --> 00:05:56.028 figure out machine code and

00:05:56.048 --> 00:05:57.249 then it was zeros and ones.

00:05:57.369 --> 00:05:58.709 So to me,

00:05:58.829 --> 00:06:03.509 AI is a large if then else string

00:06:03.910 --> 00:06:05.391 of decisions that are

00:06:05.430 --> 00:06:06.571 coming to a conclusion,

00:06:07.290 --> 00:06:08.911 storing that conclusion and

00:06:08.932 --> 00:06:09.732 then comparing it.

00:06:10.413 --> 00:06:10.973 each time.

00:06:11.074 --> 00:06:12.675 So if you run into that condition again,

00:06:12.735 --> 00:06:14.115 you get another conclusion

00:06:14.136 --> 00:06:16.358 that matches and you get a

00:06:16.759 --> 00:06:18.079 positive statistical

00:06:18.139 --> 00:06:19.360 response that then

00:06:20.002 --> 00:06:21.423 influences some sort of

00:06:21.463 --> 00:06:22.483 decision in the future.

00:06:22.644 --> 00:06:26.367 So maybe that makes sense to some folks,

00:06:26.408 --> 00:06:27.509 maybe it doesn't.

00:06:27.709 --> 00:06:30.130 But I don't think AI,

00:06:31.031 --> 00:06:32.552 as I started this conversation,

00:06:33.581 --> 00:06:35.742 can truly think for itself

00:06:35.862 --> 00:06:38.843 yet and can truly exist

00:06:39.363 --> 00:06:41.646 without that if-then-else

00:06:41.725 --> 00:06:43.927 construct somewhere in the

00:06:43.947 --> 00:06:46.869 life cycle of its origins.

00:06:49.810 --> 00:06:50.290 Right.

00:06:51.211 --> 00:06:53.812 Yeah, that makes sense to me, Norm.

00:06:53.992 --> 00:06:55.213 So you've obviously seen

00:06:56.132 --> 00:06:57.153 Things evolve,

00:06:57.872 --> 00:06:58.593 and you've been part of

00:06:58.634 --> 00:07:00.394 various digital phases, right?

00:07:00.415 --> 00:07:02.615 Obviously, since you were 12,

00:07:02.615 --> 00:07:03.497 getting yourself involved

00:07:03.536 --> 00:07:05.057 in computers and zeros and ones,

00:07:05.117 --> 00:07:05.637 everything that you're

00:07:05.658 --> 00:07:06.598 talking about there, right?

00:07:06.619 --> 00:07:08.939 So how is this wave, right?

00:07:08.959 --> 00:07:11.641 I'm going to call it the AI wave, right?

00:07:11.661 --> 00:07:12.843 I know it's been around for a while.

00:07:12.983 --> 00:07:14.423 Machine learning is not anything new.

00:07:15.485 --> 00:07:16.725 But at least since last year,

00:07:16.745 --> 00:07:17.906 it's just dominated

00:07:17.966 --> 00:07:21.048 headlines in every single space, right?

00:07:21.067 --> 00:07:22.288 Every single conference you go to.

00:07:22.776 --> 00:07:24.377 There's some sort of talk about AI.

00:07:24.418 --> 00:07:27.600 If not, it's typically predominantly AI,

00:07:27.661 --> 00:07:27.860 right?

00:07:27.901 --> 00:07:29.242 So how is it different?

00:07:29.343 --> 00:07:30.723 How is this wave different

00:07:31.285 --> 00:07:32.365 than some of the previous

00:07:32.425 --> 00:07:33.687 waves that have come before it?

00:07:37.949 --> 00:07:40.791 Well, I guess it's a little more real now,

00:07:40.992 --> 00:07:43.293 although we're still seeing it,

00:07:43.434 --> 00:07:45.214 especially in my space,

00:07:45.274 --> 00:07:46.855 we're still seeing it as a

00:07:46.915 --> 00:07:49.536 marketing term and companies are saying,

00:07:49.596 --> 00:07:52.538 well, we have an AI, we've embedded AI.

00:07:53.600 --> 00:07:55.940 And if you lift the covers, of course,

00:07:56.062 --> 00:07:58.642 it's nothing more than I just described.

00:07:58.682 --> 00:07:59.564 It's an if-then-else

00:07:59.663 --> 00:08:02.305 construct that is arriving at a solution,

00:08:02.365 --> 00:08:02.586 right?

00:08:04.247 --> 00:08:04.487 I think...

00:08:05.221 --> 00:08:08.764 ChatGPT clearly has changed

00:08:08.824 --> 00:08:11.427 that game because it's

00:08:11.708 --> 00:08:13.290 readily available to everyone.

00:08:14.331 --> 00:08:17.954 And I started out on the free version.

00:08:17.973 --> 00:08:20.817 I quickly upgraded to the

00:08:20.817 --> 00:08:22.017 $20 a month version.

00:08:22.557 --> 00:08:26.701 And I would say that I use ChatGPT

00:08:27.728 --> 00:08:31.571 daily for some purpose right

00:08:31.711 --> 00:08:32.791 now I was never great at

00:08:32.850 --> 00:08:35.613 math I failed math 30 with

00:08:35.673 --> 00:08:39.096 a 30 I dropped out of it I

00:08:39.115 --> 00:08:40.316 because I i just wanted to

00:08:40.355 --> 00:08:41.636 paint cars so I'm like I'm

00:08:41.677 --> 00:08:42.778 never going to need math so

00:08:42.798 --> 00:08:45.019 I got out of that so I was

00:08:45.059 --> 00:08:45.960 never very good at math

00:08:46.441 --> 00:08:47.380 ultimately I had to take it

00:08:47.400 --> 00:08:48.522 three more times to get

00:08:48.562 --> 00:08:49.962 into the program I chose at

00:08:50.023 --> 00:08:51.344 college but nonetheless

00:08:51.384 --> 00:08:52.344 that's a bit of another

00:08:52.384 --> 00:08:56.667 story I i used chat gpt yesterday to

00:08:58.018 --> 00:08:59.677 confirm some calculations

00:08:59.717 --> 00:09:00.938 that were in a spreadsheet

00:09:00.999 --> 00:09:03.019 that I didn't quite think were right,

00:09:03.058 --> 00:09:05.000 or I didn't understand the formula.

00:09:05.700 --> 00:09:07.820 So I put it into chat GPT

00:09:07.919 --> 00:09:08.921 for confirmation.

00:09:09.660 --> 00:09:11.260 And so I think that is the

00:09:11.900 --> 00:09:16.581 big difference today from, you know,

00:09:17.001 --> 00:09:19.043 years past is there was

00:09:19.062 --> 00:09:20.903 always promise of something

00:09:21.082 --> 00:09:22.203 machine learning or, um,

00:09:25.177 --> 00:09:27.118 some language around that concept.

00:09:27.498 --> 00:09:29.717 But today it's, it's, it's real.

00:09:29.778 --> 00:09:30.918 There's something there that

00:09:30.938 --> 00:09:32.219 you can actually physically

00:09:32.438 --> 00:09:33.778 use and touch and get a

00:09:33.818 --> 00:09:34.980 result from that can

00:09:35.039 --> 00:09:37.620 support your work and accelerate your,

00:09:37.780 --> 00:09:38.620 your solutions.

00:09:38.801 --> 00:09:41.442 So just as a segue or an

00:09:41.501 --> 00:09:44.642 example of that is in 2018,

00:09:44.642 --> 00:09:47.322 I was at the Australia

00:09:47.383 --> 00:09:49.384 conference for mastering

00:09:49.524 --> 00:09:52.245 SAP plant maintenance and supply chain,

00:09:52.924 --> 00:09:53.684 Paul Karchina,

00:09:54.565 --> 00:09:56.546 And I gave a presentation on

00:09:57.285 --> 00:09:59.966 the future and it included AIs.

00:10:00.307 --> 00:10:03.267 And in 2018, that was pretty theoretical.

00:10:03.287 --> 00:10:04.927 Now, the sad thing was,

00:10:05.067 --> 00:10:06.488 is that I didn't pay

00:10:06.528 --> 00:10:08.428 attention to anything after that.

00:10:08.548 --> 00:10:09.889 Like I did that presentation

00:10:09.908 --> 00:10:10.708 and I moved on because I

00:10:10.749 --> 00:10:11.509 didn't believe in it.

00:10:11.769 --> 00:10:13.090 I didn't believe that really

00:10:13.129 --> 00:10:14.910 in my lifetime or in the

00:10:14.951 --> 00:10:15.971 near future that I would

00:10:16.051 --> 00:10:17.250 see anything related to it.

00:10:18.071 --> 00:10:18.751 The funny thing was,

00:10:18.792 --> 00:10:19.751 is that this past year,

00:10:19.851 --> 00:10:22.993 I did another keynote related to chat GPT,

00:10:23.033 --> 00:10:23.993 which we can talk about.

00:10:24.860 --> 00:10:26.522 And I had to go back to that

00:10:26.522 --> 00:10:27.582 2018 presentation.

00:10:27.643 --> 00:10:29.403 I was like, oh, boy, whoops.

00:10:29.803 --> 00:10:31.004 I didn't take my own advice.

00:10:33.346 --> 00:10:34.086 So, you know,

00:10:34.225 --> 00:10:37.248 I ate some crow there because NVIDIA,

00:10:37.307 --> 00:10:38.107 for example,

00:10:38.187 --> 00:10:39.928 was part of that presentation.

00:10:39.989 --> 00:10:40.249 And, well,

00:10:40.288 --> 00:10:41.970 we all know where NVIDIA stock

00:10:42.049 --> 00:10:42.951 is at these days.

00:10:43.922 --> 00:10:46.283 I even went to Palo Alto with Paul,

00:10:46.624 --> 00:10:48.664 I think it was maybe early

00:10:48.664 --> 00:10:51.606 2019 and we visited Nvidia

00:10:52.067 --> 00:10:53.288 and I didn't buy their stock.

00:10:53.668 --> 00:10:53.889 Right.

00:10:54.269 --> 00:10:55.029 So I,

00:10:55.269 --> 00:10:57.551 I guess that to me is the life lesson

00:10:57.591 --> 00:10:59.692 that says you,

00:11:00.552 --> 00:11:01.573 sometimes you gotta believe,

00:11:01.614 --> 00:11:02.394 and sometimes you gotta,

00:11:02.414 --> 00:11:04.056 you gotta pay attention to

00:11:04.696 --> 00:11:05.917 what the hype is and make

00:11:05.956 --> 00:11:07.798 your own decisions versus

00:11:07.857 --> 00:11:09.158 just ignoring it.

00:11:09.198 --> 00:11:09.860 Cause you think it's,

00:11:10.100 --> 00:11:11.441 it's simply that hype,

00:11:12.500 --> 00:11:13.182 if that makes sense.

00:11:13.692 --> 00:11:13.971 Yeah,

00:11:14.052 --> 00:11:15.092 and I like the point that you're

00:11:15.113 --> 00:11:16.313 bringing up right about it

00:11:16.413 --> 00:11:18.034 feeling tangible because

00:11:18.054 --> 00:11:20.216 that's exactly what ChatGPT did.

00:11:20.275 --> 00:11:21.576 It made it very accessible

00:11:22.258 --> 00:11:24.578 to people and put it in

00:11:24.698 --> 00:11:26.900 front of a human to use it

00:11:26.980 --> 00:11:29.162 however they choose to use it.

00:11:29.403 --> 00:11:29.623 Right.

00:11:31.163 --> 00:11:32.104 So it's interesting to see

00:11:32.144 --> 00:11:33.065 how things have evolved.

00:11:33.144 --> 00:11:34.245 I wish I bought some video

00:11:34.265 --> 00:11:36.827 stock early on as well.

00:11:36.967 --> 00:11:37.868 And I think all of us

00:11:38.568 --> 00:11:39.369 looking at the way that

00:11:39.408 --> 00:11:40.250 things are in the market

00:11:40.309 --> 00:11:41.770 now wish that we would have done that.

00:11:42.414 --> 00:11:42.695 Right.

00:11:43.054 --> 00:11:43.596 Um, I,

00:11:43.916 --> 00:11:45.015 I think one of the things with this

00:11:45.135 --> 00:11:45.716 wave that's,

00:11:46.236 --> 00:11:47.417 that's crazy to me is just

00:11:47.456 --> 00:11:48.998 how quickly things are accelerating.

00:11:49.758 --> 00:11:50.018 Right.

00:11:50.158 --> 00:11:51.958 And how many new uses and

00:11:52.019 --> 00:11:52.918 new applications.

00:11:52.958 --> 00:11:53.720 And I don't mean like the

00:11:53.779 --> 00:11:55.059 actual software applications,

00:11:55.100 --> 00:11:55.880 but I mean more the

00:11:55.921 --> 00:11:57.861 applications to our daily

00:11:57.922 --> 00:12:00.643 lives or to whether it's professional or,

00:12:00.802 --> 00:12:02.864 or personally speaking, um,

00:12:03.024 --> 00:12:05.024 I find things new every single day, man.

00:12:05.225 --> 00:12:05.544 And it's,

00:12:05.784 --> 00:12:07.284 and I find new ways to use it

00:12:07.365 --> 00:12:08.025 every single day.

00:12:08.086 --> 00:12:09.405 And it's sometimes it's the

00:12:09.446 --> 00:12:10.787 technology that gets better.

00:12:11.642 --> 00:12:11.741 Um,

00:12:11.841 --> 00:12:13.604 sometimes it's our understanding of how

00:12:13.644 --> 00:12:15.304 to use the technology that gets better.

00:12:15.325 --> 00:12:16.044 Uh,

00:12:16.085 --> 00:12:17.466 sometimes it's a combination of both

00:12:17.547 --> 00:12:18.267 and sometimes it's just

00:12:18.506 --> 00:12:20.408 asking and trying to figure out like,

00:12:20.589 --> 00:12:21.950 and kind of exploring

00:12:22.549 --> 00:12:23.650 messing around with it.

00:12:24.231 --> 00:12:24.491 Right.

00:12:24.591 --> 00:12:27.514 So what is you, you obviously, um,

00:12:27.573 --> 00:12:31.777 you did this new chat in Australia, uh,

00:12:31.817 --> 00:12:32.857 which had a funny name to

00:12:32.878 --> 00:12:33.698 it when I first saw it.

00:12:33.759 --> 00:12:33.938 Right.

00:12:33.958 --> 00:12:34.940 So it was norm versus AI.

00:12:36.265 --> 00:12:38.567 Yeah, Norm versus chat GPT.

00:12:38.587 --> 00:12:39.889 It was kind of like a stump

00:12:39.948 --> 00:12:41.509 the chump session.

00:12:41.529 --> 00:12:42.812 And I was the chump.

00:12:44.472 --> 00:12:46.094 So tell me more about that.

00:12:47.174 --> 00:12:47.414 Well,

00:12:47.535 --> 00:12:49.937 I think what we wanted to do at the

00:12:49.998 --> 00:12:52.219 conference was the conference itself,

00:12:52.320 --> 00:12:53.159 the structure of it.

00:12:53.240 --> 00:12:54.140 And I've been going to that

00:12:54.181 --> 00:12:55.542 conference every year since 2006,

00:12:55.542 --> 00:12:56.383 aside from, you know, the conference.

00:13:00.341 --> 00:13:02.202 COVID year or two,

00:13:02.283 --> 00:13:03.644 and Australia was hard to get into.

00:13:03.703 --> 00:13:05.085 And then there was another

00:13:05.105 --> 00:13:05.784 year I didn't go,

00:13:05.845 --> 00:13:07.365 but I've been there for years.

00:13:07.385 --> 00:13:08.586 I don't know why I didn't

00:13:08.606 --> 00:13:10.107 just say that rather than that long,

00:13:10.869 --> 00:13:12.629 elongated discussion.

00:13:13.830 --> 00:13:15.451 But it was the same old, same old.

00:13:15.490 --> 00:13:16.552 There was really nothing on

00:13:16.572 --> 00:13:19.293 the agenda that spoke to this wave of

00:13:19.969 --> 00:13:21.831 change that's occurring with

00:13:22.351 --> 00:13:25.075 technologies like chat gpt

00:13:25.154 --> 00:13:28.058 so um we decided that we

00:13:28.078 --> 00:13:28.980 should do something kind of

00:13:29.000 --> 00:13:31.023 fun and you know crazy and

00:13:31.043 --> 00:13:32.985 of course I'm typically up

00:13:33.024 --> 00:13:35.508 for some public humiliation

00:13:35.567 --> 00:13:37.770 on stage so um I said okay

00:13:37.791 --> 00:13:38.392 sure let's do this

00:13:39.433 --> 00:13:40.073 So what that did,

00:13:40.114 --> 00:13:41.895 and I always encourage people,

00:13:41.956 --> 00:13:42.855 and I think I've encouraged

00:13:42.895 --> 00:13:43.756 you in the past, Yas,

00:13:43.797 --> 00:13:45.477 and to go out and speak at conferences,

00:13:45.557 --> 00:13:47.720 because what it does is it, one,

00:13:47.759 --> 00:13:49.520 it forces you to really

00:13:49.561 --> 00:13:50.682 know your subject well,

00:13:50.741 --> 00:13:52.123 and maybe it pushes you out

00:13:52.163 --> 00:13:53.182 of your comfort zone and

00:13:53.222 --> 00:13:55.044 you end up in a space where

00:13:55.065 --> 00:13:55.804 you have to learn.

00:13:55.904 --> 00:13:57.066 So at the end of the day,

00:13:57.125 --> 00:13:59.027 it's a win-win for you and

00:13:59.086 --> 00:13:59.788 for the audience,

00:13:59.888 --> 00:14:03.049 because the audience

00:14:03.090 --> 00:14:03.910 wouldn't have heard what

00:14:04.692 --> 00:14:05.874 your knowledge is if you

00:14:05.894 --> 00:14:06.754 didn't go speak and you

00:14:06.774 --> 00:14:07.514 wouldn't have gotten that

00:14:07.553 --> 00:14:08.414 knowledge unless you had

00:14:08.475 --> 00:14:10.075 some experience behind it.

00:14:10.836 --> 00:14:12.716 So that's what it did for me

00:14:12.777 --> 00:14:14.876 when we laid out the title, it was like,

00:14:14.998 --> 00:14:15.557 okay, oh boy,

00:14:15.577 --> 00:14:16.518 what is this going to look like?

00:14:16.778 --> 00:14:18.099 So, um,

00:14:19.158 --> 00:14:21.639 I started to actually use it with

00:14:21.700 --> 00:14:23.620 some data that I pulled

00:14:23.801 --> 00:14:26.863 from one of our joint previous customers,

00:14:27.003 --> 00:14:27.383 actually.

00:14:28.115 --> 00:14:30.875 and analyze that data, analyze text,

00:14:30.995 --> 00:14:31.655 basically,

00:14:32.275 --> 00:14:33.956 to see what's embedded in the text.

00:14:34.015 --> 00:14:37.057 Because in MySpace, in an SAP,

00:14:37.096 --> 00:14:39.418 there's a data object called long text.

00:14:39.477 --> 00:14:42.558 And it's basically what it describes.

00:14:42.639 --> 00:14:44.578 It's infinite text that a

00:14:44.798 --> 00:14:46.158 user can put into the system.

00:14:46.538 --> 00:14:47.759 But nobody looks at it.

00:14:48.879 --> 00:14:50.301 pulls it out and analyze it.

00:14:50.421 --> 00:14:51.400 It's difficult.

00:14:51.461 --> 00:14:54.022 It's either you come up with

00:14:54.702 --> 00:14:56.423 a library of words to apply

00:14:56.482 --> 00:14:57.864 against it in a loop and

00:14:57.884 --> 00:14:59.043 you try to pull the words

00:14:59.163 --> 00:15:00.445 out and then figure out if

00:15:00.485 --> 00:15:01.684 there's any meaning behind it,

00:15:02.206 --> 00:15:03.265 or you have to do it manually.

00:15:03.426 --> 00:15:05.326 Well, with ChatGPT,

00:15:05.527 --> 00:15:07.567 you can feed that text in and say, hey,

00:15:08.427 --> 00:15:09.428 analyze this for me.

00:15:09.548 --> 00:15:11.109 Find all the words that are

00:15:11.149 --> 00:15:12.009 maintenance-related,

00:15:12.429 --> 00:15:13.830 and it goes through and does that,

00:15:13.870 --> 00:15:14.910 and then you can prompt

00:15:15.631 --> 00:15:19.232 for more detail or send it

00:15:19.253 --> 00:15:20.033 in a different direction.

00:15:20.634 --> 00:15:22.374 So that's what we did with that.

00:15:22.854 --> 00:15:23.533 I stood in front of the

00:15:23.615 --> 00:15:25.174 audience and first off said,

00:15:25.215 --> 00:15:26.394 how many people have heard of it?

00:15:26.455 --> 00:15:27.816 Well, everybody raised their hand.

00:15:28.275 --> 00:15:29.296 How many people are using it?

00:15:29.355 --> 00:15:30.856 We felt that that question

00:15:30.956 --> 00:15:33.957 actually would have a very low response,

00:15:34.038 --> 00:15:35.717 but quite interestingly,

00:15:36.317 --> 00:15:37.519 there was a large number of

00:15:37.558 --> 00:15:39.298 the 600 people in the audience that said,

00:15:39.318 --> 00:15:41.019 yes, I'm using ChatGPT.

00:15:42.017 --> 00:15:43.457 And then the next question was, well,

00:15:43.618 --> 00:15:44.638 how many people are

00:15:44.697 --> 00:15:45.918 actually using it at work?

00:15:45.977 --> 00:15:49.038 And then the number went down, right?

00:15:49.158 --> 00:15:50.200 And then, okay, well,

00:15:50.220 --> 00:15:51.059 how many people are using

00:15:51.100 --> 00:15:52.139 it to analyze data?

00:15:52.159 --> 00:15:52.980 And then we got down to the

00:15:53.020 --> 00:15:53.721 point where there was like

00:15:53.841 --> 00:15:55.402 one person raised their hand.

00:15:55.881 --> 00:15:56.802 So then I just did a little

00:15:56.881 --> 00:15:58.582 demo with it and

00:15:58.682 --> 00:16:00.243 demonstrated what was

00:16:00.363 --> 00:16:02.183 possible and then talked

00:16:02.224 --> 00:16:03.364 about how that could

00:16:03.403 --> 00:16:07.166 transform our current thinking, which is,

00:16:07.206 --> 00:16:07.385 well,

00:16:07.405 --> 00:16:10.206 let's ask someone to pull a dropdown

00:16:10.267 --> 00:16:11.386 and populate a data field.

00:16:13.370 --> 00:16:14.250 And that's for the purpose

00:16:14.289 --> 00:16:15.791 of equipment history and

00:16:16.130 --> 00:16:17.350 tracking what happened to

00:16:17.390 --> 00:16:19.011 that asset over its lifecycle.

00:16:19.711 --> 00:16:21.192 But the ironic part of that is,

00:16:21.312 --> 00:16:22.552 is that the history that

00:16:22.572 --> 00:16:23.432 we've been collecting for

00:16:23.472 --> 00:16:24.513 years could actually

00:16:24.633 --> 00:16:27.693 propose statistically what

00:16:27.813 --> 00:16:29.634 is the likelihood of what

00:16:29.654 --> 00:16:31.575 you're trying to identify today.

00:16:32.416 --> 00:16:34.316 So instead of asking someone

00:16:34.395 --> 00:16:37.297 to be a data capturer,

00:16:38.136 --> 00:16:40.918 we could ask them to be a data validator,

00:16:40.999 --> 00:16:41.759 which is a much more

00:16:41.879 --> 00:16:43.100 valuable role in an

00:16:43.179 --> 00:16:44.780 organization than just

00:16:44.900 --> 00:16:46.942 someone doing data entry, right?

00:16:47.722 --> 00:16:50.323 And that was the real

00:16:50.403 --> 00:16:51.524 message from that session

00:16:51.585 --> 00:16:53.986 was if we shift and if we

00:16:54.147 --> 00:16:56.248 use this technology and the

00:16:56.868 --> 00:16:58.509 mounds of data that we have

00:16:58.609 --> 00:16:59.809 to predict or to propose,

00:17:03.504 --> 00:17:04.565 Then we start to use the

00:17:04.944 --> 00:17:06.507 tacit knowledge that is in

00:17:06.567 --> 00:17:07.887 an individual and we start

00:17:07.907 --> 00:17:09.108 to make it more explicit in

00:17:09.128 --> 00:17:09.910 the organization.

00:17:10.851 --> 00:17:12.192 So that was that was sort of

00:17:12.211 --> 00:17:13.353 the message in that session.

00:17:13.373 --> 00:17:14.814 And I think that's one of

00:17:14.854 --> 00:17:16.955 the largest benefits that

00:17:16.996 --> 00:17:18.698 we currently have both

00:17:20.239 --> 00:17:23.061 industry and as well as personally is.

00:17:24.082 --> 00:17:26.083 Now, before I go to write a document,

00:17:26.282 --> 00:17:28.624 I go to chat GPT and I ask

00:17:28.663 --> 00:17:30.243 for a template or I ask for

00:17:30.284 --> 00:17:31.023 some guidance.

00:17:31.144 --> 00:17:33.484 I ask for that collated

00:17:33.565 --> 00:17:34.905 information and knowledge

00:17:34.965 --> 00:17:37.906 to be presented to me versus, you know,

00:17:37.946 --> 00:17:40.688 I've got a dream up at a meeting agenda.

00:17:41.048 --> 00:17:41.288 Well,

00:17:41.307 --> 00:17:45.769 I feed that in and I ask for what's

00:17:45.949 --> 00:17:47.509 out there in that knowledge base.

00:17:48.029 --> 00:17:49.191 And then I can validate it.

00:17:49.490 --> 00:17:51.073 So I think that's, you know,

00:17:51.113 --> 00:17:52.773 that's the benefit.

00:17:52.814 --> 00:17:53.634 And that's that's what we

00:17:53.674 --> 00:17:54.434 did at that session.

00:17:55.355 --> 00:17:55.836 And frankly,

00:17:55.875 --> 00:17:57.597 there was a great number of

00:17:57.637 --> 00:17:58.597 people in that session that

00:17:59.239 --> 00:18:00.159 came back and said, wow,

00:18:00.180 --> 00:18:00.819 that was amazing.

00:18:00.859 --> 00:18:02.240 I hadn't thought about using

00:18:02.300 --> 00:18:03.561 it this way or I hadn't

00:18:04.202 --> 00:18:06.243 considered how we could do that.

00:18:06.304 --> 00:18:08.125 And of course, you also got that.

00:18:08.145 --> 00:18:08.445 Well,

00:18:08.526 --> 00:18:09.666 I don't want to put my data in the

00:18:09.707 --> 00:18:11.488 cloud and I don't want to send my data.

00:18:11.548 --> 00:18:11.867 And, you know,

00:18:11.887 --> 00:18:14.069 you got all the sort of risk

00:18:14.130 --> 00:18:16.152 adverse people talking as well.

00:18:18.817 --> 00:18:20.078 this is the future now.

00:18:20.719 --> 00:18:22.461 Like SAP is embedding,

00:18:22.480 --> 00:18:24.541 they've got a partnership

00:18:24.582 --> 00:18:27.463 with IBM called Jewel that

00:18:27.523 --> 00:18:29.265 they're embedding in SAP.

00:18:30.326 --> 00:18:32.247 Adobe just yesterday offered

00:18:32.326 --> 00:18:33.847 me or wanted me to restart

00:18:33.968 --> 00:18:34.929 because they've embedded

00:18:35.009 --> 00:18:37.529 some chat GPT connection or

00:18:37.611 --> 00:18:38.371 something similar.

00:18:38.671 --> 00:18:39.332 So like you say,

00:18:39.571 --> 00:18:40.652 every day there's something new.

00:18:42.493 --> 00:18:42.733 Right.

00:18:42.913 --> 00:18:43.173 Yeah.

00:18:43.534 --> 00:18:44.035 It's interesting.

00:18:44.055 --> 00:18:45.355 Two things kind of come to mind there.

00:18:45.736 --> 00:18:47.196 First of all, you know,

00:18:48.223 --> 00:18:50.005 Nor versus ChatGPT, who won there?

00:18:51.306 --> 00:18:51.546 Well,

00:18:54.628 --> 00:18:55.909 the interesting thing was that we

00:18:55.989 --> 00:18:57.390 asked some very specific

00:18:57.590 --> 00:19:00.011 SAP questions around configuration,

00:19:00.732 --> 00:19:03.213 and it answered them way

00:19:03.555 --> 00:19:05.955 more detailed than I could, right?

00:19:06.076 --> 00:19:06.916 Way more detailed.

00:19:09.278 --> 00:19:10.199 It doesn't have the human

00:19:10.239 --> 00:19:11.539 element of experience, though.

00:19:12.661 --> 00:19:13.480 And it doesn't have the

00:19:13.500 --> 00:19:14.342 bright shirts either.

00:19:14.761 --> 00:19:15.482 That's correct.

00:19:15.643 --> 00:19:16.564 Yeah, that's correct.

00:19:17.044 --> 00:19:17.183 Yeah.

00:19:18.051 --> 00:19:18.332 Right.

00:19:18.432 --> 00:19:18.692 Okay.

00:19:18.813 --> 00:19:19.133 And then,

00:19:19.292 --> 00:19:20.394 so one of the things that you

00:19:20.433 --> 00:19:22.976 said was about people using

00:19:22.996 --> 00:19:23.817 ChatGPT at work.

00:19:23.876 --> 00:19:26.398 I just saw a stat maybe a week ago.

00:19:26.439 --> 00:19:28.180 I wish I had the actual numbers in there.

00:19:28.661 --> 00:19:29.902 But it was surprising how

00:19:29.942 --> 00:19:32.163 many people are now using ChatGPT at work,

00:19:32.503 --> 00:19:36.467 but not in a public way, right?

00:19:36.487 --> 00:19:37.688 They're using ChatGPT at

00:19:37.708 --> 00:19:38.388 work without their

00:19:38.429 --> 00:19:39.609 employers knowing about it.

00:19:40.017 --> 00:19:40.176 Right.

00:19:40.557 --> 00:19:40.978 Um, and,

00:19:41.018 --> 00:19:42.057 and I think that's probably one of

00:19:42.077 --> 00:19:42.738 the things that we're gonna

00:19:42.758 --> 00:19:44.318 start to see a lot more, right.

00:19:44.419 --> 00:19:46.420 People using these tools,

00:19:46.500 --> 00:19:49.281 but not using them in a secure way.

00:19:50.041 --> 00:19:53.663 Um, because their employer doesn't want to,

00:19:53.884 --> 00:19:55.164 or they're afraid of it, or,

00:19:55.285 --> 00:19:56.464 or there's some policies

00:19:56.505 --> 00:19:58.006 that go against it to

00:19:58.046 --> 00:19:59.987 protect data or to protect privacy or,

00:20:00.146 --> 00:20:01.047 or whatever that might be,

00:20:01.086 --> 00:20:02.887 but it's gonna happen pretty quick.

00:20:03.749 --> 00:20:03.888 Yeah.

00:20:03.909 --> 00:20:05.028 What are your thoughts on that?

00:20:05.829 --> 00:20:07.951 Well, of, of course, I mean, the, um,

00:20:09.942 --> 00:20:12.064 The likelihood of somebody

00:20:12.584 --> 00:20:14.304 having a strategy in place

00:20:14.663 --> 00:20:18.224 as a corporation today to

00:20:18.744 --> 00:20:20.945 manage the use of chat GPT

00:20:21.006 --> 00:20:22.987 and ensure the integrity of

00:20:23.866 --> 00:20:25.047 the data that's being

00:20:25.106 --> 00:20:26.208 shared or the information

00:20:26.228 --> 00:20:28.288 that's being shared is

00:20:28.508 --> 00:20:30.249 arguably pretty low unless

00:20:30.528 --> 00:20:31.429 you started work on

00:20:31.489 --> 00:20:32.828 something like that three

00:20:32.868 --> 00:20:34.490 or four or maybe five years ago.

00:20:35.325 --> 00:20:36.924 So maybe the large companies

00:20:37.025 --> 00:20:40.205 that saw that coming, that had a CIO,

00:20:40.326 --> 00:20:43.666 CTO role or a board that

00:20:43.727 --> 00:20:44.767 put emphasis on that,

00:20:45.027 --> 00:20:46.207 has that in place and was

00:20:46.267 --> 00:20:47.268 able to respond.

00:20:48.528 --> 00:20:49.688 I think other companies have

00:20:49.728 --> 00:20:51.208 simply added a sentence

00:20:51.268 --> 00:20:53.328 maybe to their policy or to

00:20:53.388 --> 00:20:56.730 their IT architecture to say,

00:20:57.769 --> 00:20:59.630 we don't connect to chat

00:20:59.650 --> 00:21:01.990 GPT from inside the firewall.

00:21:05.040 --> 00:21:07.742 Or if you're a small company,

00:21:08.163 --> 00:21:10.505 mid-sized or even smaller,

00:21:11.605 --> 00:21:13.707 maybe even you're run by a family,

00:21:13.826 --> 00:21:14.867 it's been a family business

00:21:14.907 --> 00:21:18.869 for years that is maybe

00:21:18.970 --> 00:21:20.510 stale wartish in nature

00:21:20.530 --> 00:21:21.491 when it comes to technology.

00:21:22.502 --> 00:21:22.682 Well,

00:21:22.742 --> 00:21:24.605 I'm not sure that they even know how

00:21:24.625 --> 00:21:25.445 to respond to it.

00:21:25.506 --> 00:21:27.267 And so some of those folks I

00:21:27.346 --> 00:21:31.050 fear are at risk of really

00:21:31.111 --> 00:21:33.732 lagging behind because they

00:21:34.212 --> 00:21:35.914 cannot react quickly enough

00:21:35.994 --> 00:21:40.759 and aren't well educated in

00:21:40.778 --> 00:21:43.461 the space to know how to react.

00:21:43.521 --> 00:21:46.944 I think that's a big challenge, honestly.

00:21:49.194 --> 00:21:50.816 Yeah, no, I mean,

00:21:50.855 --> 00:21:51.916 we've seen historically

00:21:51.977 --> 00:21:53.178 companies don't react fast

00:21:53.218 --> 00:21:54.618 enough when it comes to technology.

00:21:55.140 --> 00:21:56.320 This is one where I think

00:21:56.340 --> 00:21:57.221 they need to react and

00:21:57.261 --> 00:21:58.323 react really fast because

00:21:58.343 --> 00:21:59.323 the people using it.

00:21:59.343 --> 00:22:01.545 I mean, it's so powerful.

00:22:01.565 --> 00:22:03.487 There's so many potential

00:22:03.586 --> 00:22:05.528 good use cases for it that

00:22:05.568 --> 00:22:06.548 people are just going to do

00:22:06.588 --> 00:22:07.809 it whether they're allowed

00:22:07.829 --> 00:22:08.510 to do it or not.

00:22:08.871 --> 00:22:09.852 And I was very surprised.

00:22:10.112 --> 00:22:12.493 I'll try to find the research.

00:22:12.554 --> 00:22:13.295 I'll send it your way.

00:22:13.315 --> 00:22:14.516 But I was very surprised at

00:22:14.556 --> 00:22:16.096 how many were using it

00:22:16.176 --> 00:22:17.357 without their employer kind

00:22:17.397 --> 00:22:18.219 of knowing about it.

00:22:18.519 --> 00:22:20.279 it doesn't surprise me yeah

00:22:20.480 --> 00:22:22.619 and even the schools um how

00:22:22.660 --> 00:22:23.901 have the schools responded

00:22:24.721 --> 00:22:25.701 they had to respond in a

00:22:25.721 --> 00:22:27.902 heartbeat to students right

00:22:28.741 --> 00:22:32.223 and and so that's not even

00:22:32.344 --> 00:22:33.344 that's not possible we know

00:22:33.364 --> 00:22:34.963 that you know governmental

00:22:35.204 --> 00:22:37.365 public uh organizations

00:22:37.404 --> 00:22:38.806 like that can't respond

00:22:38.925 --> 00:22:39.806 quickly they're just not

00:22:39.865 --> 00:22:41.026 nimble enough they don't

00:22:41.046 --> 00:22:42.106 have the resources they

00:22:42.126 --> 00:22:43.987 don't they're not strategic

00:22:44.027 --> 00:22:45.448 in nature oftentimes so

00:22:47.056 --> 00:22:48.317 I think that's a real, real challenge,

00:22:48.336 --> 00:22:49.557 but you'd be curious to see those,

00:22:49.957 --> 00:22:50.698 those stats.

00:22:50.738 --> 00:22:52.278 Cause I'm sure it's, it's massive.

00:22:52.298 --> 00:22:54.558 Like I, I think when you go to open AI,

00:22:54.578 --> 00:22:56.000 it tells you how many users

00:22:56.160 --> 00:22:57.300 are in one screen.

00:22:57.340 --> 00:22:58.040 It happens to,

00:22:58.080 --> 00:22:58.760 I can't remember where it

00:22:58.800 --> 00:22:59.721 shows that or it used to.

00:23:00.721 --> 00:23:01.522 And it it's,

00:23:02.163 --> 00:23:03.163 it's astounding how many

00:23:03.222 --> 00:23:04.663 people have signed up for that service.

00:23:06.404 --> 00:23:06.684 Yeah.

00:23:07.184 --> 00:23:08.885 And how many more will continue, man?

00:23:09.385 --> 00:23:11.467 So Lauren versus chat APT,

00:23:11.666 --> 00:23:12.667 there was a competition,

00:23:12.708 --> 00:23:13.867 but it sounds like now it's

00:23:13.928 --> 00:23:15.348 not about compete, but it's spoken.

00:23:16.396 --> 00:23:17.518 And it sounds like Chachi

00:23:17.557 --> 00:23:18.779 Petit has completed you.

00:23:19.280 --> 00:23:23.065 Is that a fair statement to say there?

00:23:23.144 --> 00:23:24.547 Well, yeah,

00:23:24.827 --> 00:23:26.750 I can't really remember how we

00:23:26.829 --> 00:23:27.411 summed it up.

00:23:27.490 --> 00:23:29.153 I mean, it was more to the effect of...

00:23:33.126 --> 00:23:35.309 This is a tool and this is

00:23:35.430 --> 00:23:36.551 something that's valuable

00:23:37.011 --> 00:23:38.013 to each and every one of us

00:23:38.914 --> 00:23:41.397 in some way and go out and explore,

00:23:41.637 --> 00:23:43.980 go out and learn and try

00:23:44.181 --> 00:23:46.183 and test it and look for,

00:23:46.203 --> 00:23:49.146 I've always hated this term in IT,

00:23:49.548 --> 00:23:51.410 use cases for chat GPT.

00:23:53.031 --> 00:23:54.553 But if you do nothing,

00:23:55.133 --> 00:23:57.013 you're definitely going to fall behind.

00:23:57.173 --> 00:23:59.375 And it's astounding to see

00:23:59.515 --> 00:24:02.616 how quickly Microsoft, Google, SAP,

00:24:03.616 --> 00:24:06.239 everybody pivoted to have something,

00:24:06.759 --> 00:24:08.220 some type of interface

00:24:08.259 --> 00:24:09.901 connection or association

00:24:10.540 --> 00:24:15.242 to these generative transformers, right?

00:24:17.252 --> 00:24:19.015 tools that will take large

00:24:19.075 --> 00:24:20.617 language models and provide

00:24:20.738 --> 00:24:21.999 or large data models and

00:24:22.038 --> 00:24:23.161 provide insights to them.

00:24:24.122 --> 00:24:27.246 So, yeah, that's interesting, man.

00:24:27.266 --> 00:24:28.428 So obviously things have

00:24:28.488 --> 00:24:29.229 evolved quite a bit.

00:24:30.150 --> 00:24:31.911 And you're in the asset management space.

00:24:31.931 --> 00:24:33.090 And just for our listeners,

00:24:33.111 --> 00:24:34.270 when we talk asset management,

00:24:34.290 --> 00:24:34.951 we're not talking about

00:24:34.991 --> 00:24:35.932 financial instruments.

00:24:36.172 --> 00:24:39.252 We're talking the big assets, facilities,

00:24:40.292 --> 00:24:41.874 pipelines, infrastructure,

00:24:41.913 --> 00:24:44.273 those sorts of assets, managing those,

00:24:44.535 --> 00:24:45.795 the maintenance, the operations,

00:24:45.835 --> 00:24:47.935 the reliability, the planning,

00:24:47.976 --> 00:24:49.496 the scheduling, all of that stuff.

00:24:50.655 --> 00:24:52.416 Now, let me ask you this, Norm.

00:24:52.457 --> 00:24:55.438 What trends do you foresee

00:24:55.577 --> 00:24:57.298 in asset management with

00:24:57.337 --> 00:24:58.838 the continuous evolution of AI?

00:24:59.818 --> 00:25:00.945 And how should businesses

00:25:01.387 --> 00:25:02.133 prepare to adapt?

00:25:04.875 --> 00:25:05.115 Well, yeah,

00:25:06.036 --> 00:25:07.237 I don't think we have to just

00:25:07.317 --> 00:25:08.176 limit it to asset

00:25:08.436 --> 00:25:09.798 management per se because

00:25:09.837 --> 00:25:10.878 we're experiencing it in

00:25:10.959 --> 00:25:12.839 our consumer lives as well, right?

00:25:12.940 --> 00:25:15.281 So if you want to draw more

00:25:15.422 --> 00:25:16.603 comparison to make sure

00:25:16.623 --> 00:25:18.044 your listeners understand

00:25:18.723 --> 00:25:20.285 what asset management is,

00:25:20.325 --> 00:25:22.626 let's talk about our personal stuff,

00:25:22.707 --> 00:25:22.967 right?

00:25:22.987 --> 00:25:27.410 So our fridges, our stoves, our houses,

00:25:27.549 --> 00:25:30.971 our cars, we're seeing, of course,

00:25:31.353 --> 00:25:33.134 more and more sensors going

00:25:33.193 --> 00:25:34.335 into those devices.

00:25:35.174 --> 00:25:37.375 in our homes and

00:25:37.777 --> 00:25:38.977 subsequently then we're

00:25:39.017 --> 00:25:41.679 getting insights back from

00:25:42.740 --> 00:25:45.260 power usage or do you need

00:25:45.421 --> 00:25:49.703 milk or what is going on in your house?

00:25:49.784 --> 00:25:52.744 So that's exactly the same

00:25:52.825 --> 00:25:54.306 thing that's happening in

00:25:54.486 --> 00:25:56.928 industry in that there are

00:25:57.428 --> 00:25:58.848 more and more sensors.

00:25:58.888 --> 00:26:01.329 That's another good stat to go look for.

00:26:01.390 --> 00:26:02.010 I can't remember

00:26:03.023 --> 00:26:05.366 the number of IoT devices or

00:26:05.527 --> 00:26:07.028 Internet of Things devices

00:26:07.470 --> 00:26:08.471 connected to the Internet

00:26:08.652 --> 00:26:11.516 off of equipment, I'll put that in quotes,

00:26:12.016 --> 00:26:15.039 has exploded over the last few years.

00:26:15.401 --> 00:26:15.661 Right.

00:26:16.082 --> 00:26:18.744 And there's two segregations

00:26:19.775 --> 00:26:22.436 between that there's the IOT device,

00:26:22.477 --> 00:26:23.717 which is your nest

00:26:23.817 --> 00:26:25.238 thermostat and your house

00:26:25.258 --> 00:26:26.679 that you can connect your app to,

00:26:26.778 --> 00:26:28.579 or your digital lock on the

00:26:28.619 --> 00:26:30.101 front door that you can connect to.

00:26:30.560 --> 00:26:31.261 And then there's the

00:26:31.382 --> 00:26:32.762 industrial internet of things,

00:26:32.823 --> 00:26:35.044 which are devices that have

00:26:35.084 --> 00:26:38.185 sensors on them behind the firewall in,

00:26:38.445 --> 00:26:38.526 in,

00:26:38.786 --> 00:26:40.467 in industry that may or may not

00:26:40.586 --> 00:26:43.229 connect out to the public cloud,

00:26:43.729 --> 00:26:46.309 so to speak for security or, um,

00:26:46.711 --> 00:26:47.770 proprietary reasons.

00:26:48.832 --> 00:26:48.991 But,

00:26:50.336 --> 00:26:52.477 That continues to be the push.

00:26:52.958 --> 00:26:54.238 And unfortunately,

00:26:54.258 --> 00:26:57.240 a lot of it's being driven

00:26:57.319 --> 00:26:59.421 by the lack of resources.

00:26:59.480 --> 00:27:00.721 I had a call with a water

00:27:00.801 --> 00:27:03.523 company just yesterday

00:27:03.784 --> 00:27:07.025 where they put out a job

00:27:07.905 --> 00:27:10.027 posting for a water operator.

00:27:10.627 --> 00:27:11.807 And that would mean you're

00:27:11.827 --> 00:27:13.068 going to go into a water

00:27:13.128 --> 00:27:14.068 plant and you're going to

00:27:14.650 --> 00:27:15.849 operate the equipment and

00:27:16.030 --> 00:27:18.112 monitor the production of water.

00:27:19.038 --> 00:27:20.940 which is highly regulated

00:27:21.000 --> 00:27:23.000 and obviously we have to

00:27:23.080 --> 00:27:24.461 protect the water source, right?

00:27:25.922 --> 00:27:28.042 They don't get any responses

00:27:28.522 --> 00:27:30.423 to those job postings.

00:27:30.963 --> 00:27:32.525 And so then if you don't

00:27:32.565 --> 00:27:34.625 have the human to do the work,

00:27:35.465 --> 00:27:38.207 how do you alleviate that gap?

00:27:38.346 --> 00:27:39.708 Well, you automate.

00:27:39.768 --> 00:27:41.249 So automate means you have

00:27:41.288 --> 00:27:44.210 to take advantage of sensor data.

00:27:44.230 --> 00:27:45.871 You're gonna have to take advantage of

00:27:46.875 --> 00:27:49.776 AI automation techniques.

00:27:49.855 --> 00:27:50.435 If you wish,

00:27:50.496 --> 00:27:51.455 you're going to have to

00:27:51.556 --> 00:27:55.057 somehow use mechanization

00:27:55.297 --> 00:27:58.317 or technology to eradicate

00:27:58.458 --> 00:27:59.817 the human position.

00:28:00.417 --> 00:28:00.877 So the,

00:28:01.358 --> 00:28:05.358 the push in my industry is sensor data.

00:28:05.720 --> 00:28:08.579 How do we predict what, oh,

00:28:08.619 --> 00:28:09.701 I just did the thumbs up.

00:28:09.740 --> 00:28:10.441 That's funny.

00:28:10.980 --> 00:28:13.201 Um, how do you predict the, um,

00:28:14.761 --> 00:28:15.602 the machinery?

00:28:17.022 --> 00:28:18.904 failing and how do you prevent it?

00:28:18.984 --> 00:28:20.086 How do you be proactive?

00:28:20.768 --> 00:28:22.670 And arguably that's what's happening.

00:28:22.690 --> 00:28:22.890 You know,

00:28:22.930 --> 00:28:25.333 Tesla does a download of updates

00:28:25.432 --> 00:28:28.376 to the software system in a Tesla,

00:28:29.337 --> 00:28:30.398 same concept.

00:28:30.419 --> 00:28:32.221 They're constantly monitoring that car.

00:28:32.300 --> 00:28:33.542 They're capturing

00:28:34.153 --> 00:28:35.894 road data they're capturing

00:28:35.953 --> 00:28:37.194 driving data they're

00:28:37.214 --> 00:28:39.277 feeding that back google is

00:28:39.376 --> 00:28:40.958 using our phones to update

00:28:41.057 --> 00:28:42.798 maps every time we drive

00:28:42.878 --> 00:28:43.940 it's updating the map

00:28:43.980 --> 00:28:45.101 that's how it keeps it up

00:28:45.141 --> 00:28:46.961 to date and consumers

00:28:47.021 --> 00:28:49.384 expect that right we if the

00:28:49.423 --> 00:28:50.684 map isn't correct from

00:28:51.065 --> 00:28:53.047 google or our gps takes us

00:28:53.067 --> 00:28:54.667 to the wrong location we

00:28:54.688 --> 00:28:55.429 don't accept that

00:28:56.209 --> 00:28:57.590 industry is lagging behind

00:28:57.611 --> 00:28:58.751 that but they're having

00:28:58.771 --> 00:29:00.252 they're being forced just

00:29:00.313 --> 00:29:03.576 based on labor and costs to

00:29:04.478 --> 00:29:06.098 more automation and then

00:29:06.559 --> 00:29:08.141 more analytics using

00:29:09.843 --> 00:29:11.505 artificial in quotes

00:29:12.465 --> 00:29:14.887 technologies right to make

00:29:14.928 --> 00:29:16.128 those to come up with that

00:29:16.148 --> 00:29:17.130 information and come up

00:29:17.150 --> 00:29:19.432 with those intelligences so um

00:29:22.050 --> 00:29:23.551 It's a weird time.

00:29:23.652 --> 00:29:24.873 It's a very weird time

00:29:24.893 --> 00:29:25.692 because everybody's trying

00:29:25.732 --> 00:29:28.015 to do it and it's very difficult.

00:29:28.095 --> 00:29:29.776 It still relies on

00:29:30.655 --> 00:29:33.718 foundational data being accurate,

00:29:34.038 --> 00:29:35.858 a quantity and a quality of

00:29:36.219 --> 00:29:38.039 data that's acceptable to

00:29:38.079 --> 00:29:41.041 an if-then-else statement, right?

00:29:42.061 --> 00:29:42.301 Right.

00:29:42.722 --> 00:29:44.523 So let me ask you this, Norm, right?

00:29:44.564 --> 00:29:45.463 Because one of the things

00:29:45.483 --> 00:29:46.064 that you've done

00:29:46.084 --> 00:29:47.105 tremendously well

00:29:47.125 --> 00:29:48.665 throughout your career is

00:29:48.746 --> 00:29:50.727 help organizations essentially set up

00:29:51.464 --> 00:29:54.987 the data structures for their systems,

00:29:55.126 --> 00:29:55.448 right?

00:29:55.548 --> 00:29:57.209 And it's one thing that you

00:29:57.229 --> 00:29:58.970 bored me with the first

00:29:59.009 --> 00:30:00.471 time you were kind of talking about it.

00:30:00.490 --> 00:30:01.590 I remember you showing me on

00:30:01.631 --> 00:30:02.832 the whiteboard how everything works.

00:30:02.912 --> 00:30:04.732 And I'm like, I don't care about this.

00:30:04.792 --> 00:30:05.773 Never going to use it in my life.

00:30:06.233 --> 00:30:08.154 One of the most valuable

00:30:08.736 --> 00:30:09.996 things that I learned very

00:30:10.116 --> 00:30:11.837 early on in my career, right?

00:30:11.897 --> 00:30:12.597 And I didn't kind of

00:30:12.637 --> 00:30:13.778 appreciate it back then.

00:30:14.818 --> 00:30:15.980 But that's one thing that

00:30:16.019 --> 00:30:17.560 your career has been, you know,

00:30:18.221 --> 00:30:19.122 you've been very successful

00:30:19.142 --> 00:30:19.721 in your career for.

00:30:21.423 --> 00:30:22.404 companies don't understand

00:30:22.525 --> 00:30:24.205 how important it is to set

00:30:24.266 --> 00:30:26.748 up that initial system structure, right?

00:30:26.768 --> 00:30:27.508 The master data,

00:30:27.548 --> 00:30:28.409 like how everything's kind

00:30:28.449 --> 00:30:30.190 of going to flow, the various taxonomies.

00:30:31.269 --> 00:30:32.111 Now it's going to be even

00:30:32.191 --> 00:30:34.472 more important than it ever has been,

00:30:34.512 --> 00:30:34.673 right?

00:30:34.712 --> 00:30:36.173 At least for those companies

00:30:36.193 --> 00:30:37.294 that want to leverage that,

00:30:37.795 --> 00:30:38.815 they need to have those

00:30:38.855 --> 00:30:40.455 structures in place.

00:30:40.496 --> 00:30:41.757 They need to be defined well.

00:30:41.777 --> 00:30:42.998 Do you want to talk a little

00:30:43.038 --> 00:30:44.078 bit about that and maybe

00:30:44.159 --> 00:30:45.960 what companies should be thinking about

00:30:47.093 --> 00:30:49.055 when they're redesigning or

00:30:49.115 --> 00:30:50.194 resetting up certain

00:30:50.255 --> 00:30:51.855 systems so that they can be

00:30:51.895 --> 00:30:53.837 compatible with artificial

00:30:53.877 --> 00:30:54.678 intelligence tools?

00:30:56.219 --> 00:30:56.439 Sure.

00:30:56.499 --> 00:30:57.940 That's a big topic.

00:30:58.000 --> 00:30:59.040 We could probably talk for

00:30:59.060 --> 00:31:00.142 an hour and I could get a

00:31:00.162 --> 00:31:01.221 whiteboard out and I could

00:31:01.301 --> 00:31:02.403 bore everybody to death.

00:31:02.462 --> 00:31:05.464 But, you know, unfortunately,

00:31:05.484 --> 00:31:08.227 I think there's a human

00:31:08.267 --> 00:31:09.468 condition that takes things

00:31:09.508 --> 00:31:10.887 for granted and maybe it's

00:31:10.948 --> 00:31:12.869 our inherent laziness that

00:31:13.939 --> 00:31:15.740 which just assumes that

00:31:15.779 --> 00:31:20.342 because things work, they, there was no,

00:31:20.923 --> 00:31:21.982 they should always work or

00:31:22.042 --> 00:31:23.023 there was not really a

00:31:23.064 --> 00:31:24.044 great deal of effort went

00:31:24.084 --> 00:31:26.306 into them or something to that effect.

00:31:26.486 --> 00:31:30.208 But I'll go back to the map example again,

00:31:30.228 --> 00:31:30.968 because I think that's the

00:31:31.488 --> 00:31:36.131 most connected to each and

00:31:36.151 --> 00:31:37.290 every one of us of any

00:31:37.351 --> 00:31:38.832 example I could probably give.

00:31:40.133 --> 00:31:40.173 If,

00:31:40.996 --> 00:31:43.877 if I move into a house in Calgary,

00:31:43.917 --> 00:31:45.318 let's use Calgary as an example,

00:31:45.358 --> 00:31:46.700 because it's a quadrant city.

00:31:47.721 --> 00:31:48.821 If I move into a house in

00:31:48.922 --> 00:31:52.525 Calgary and I don't like my

00:31:52.585 --> 00:31:53.665 house number and I don't

00:31:53.705 --> 00:31:55.047 like my street name,

00:31:56.488 --> 00:31:58.909 I just don't go out and change that.

00:31:59.470 --> 00:32:01.651 Like if I don't like it's nine, six, eight,

00:32:01.691 --> 00:32:03.952 I don't change it to nine, nine, nine.

00:32:04.032 --> 00:32:06.575 And if I don't like the fact it ends in,

00:32:07.076 --> 00:32:10.097 you know, uh, Avenue or place or,

00:32:11.242 --> 00:32:12.824 clothes or whatever I don't

00:32:12.844 --> 00:32:14.105 go change the street sign

00:32:14.144 --> 00:32:16.446 because if I do well for

00:32:16.586 --> 00:32:17.627 one nothing is going to

00:32:17.728 --> 00:32:19.369 happen because the inherent

00:32:19.450 --> 00:32:21.991 foundation of mapping is so

00:32:22.771 --> 00:32:25.513 deeply entrenched in in the

00:32:25.554 --> 00:32:27.276 found the fundamentals of

00:32:27.675 --> 00:32:29.817 of uh the earth if you wish

00:32:29.877 --> 00:32:31.199 that it wouldn't work and

00:32:31.259 --> 00:32:32.400 two if I did that well

00:32:32.881 --> 00:32:34.260 My mail would never get to

00:32:34.320 --> 00:32:37.342 me if I had delivery to my

00:32:37.402 --> 00:32:40.403 house because Amazon wouldn't get to me,

00:32:40.442 --> 00:32:41.462 et cetera, et cetera, right?

00:32:42.323 --> 00:32:45.903 But in industry and in our

00:32:46.064 --> 00:32:49.203 professional lives, in many cases,

00:32:49.364 --> 00:32:53.244 we don't have control over

00:32:53.444 --> 00:32:55.205 that addressing system.

00:32:56.145 --> 00:32:59.326 So a company will implement

00:32:59.526 --> 00:33:02.166 SAP or JD Edwards or Oracle or

00:33:02.840 --> 00:33:04.623 Microsoft Dynamics or whatever,

00:33:05.763 --> 00:33:07.605 and they allow it to be an

00:33:07.805 --> 00:33:09.006 island unto itself.

00:33:09.046 --> 00:33:09.826 And then within it,

00:33:10.007 --> 00:33:11.969 they allow islands of data.

00:33:12.690 --> 00:33:13.549 And then you've got

00:33:13.671 --> 00:33:14.951 engineering data and you've

00:33:14.991 --> 00:33:16.153 got other departments that

00:33:16.173 --> 00:33:17.253 aren't using that system.

00:33:17.314 --> 00:33:18.615 And they come up with all

00:33:18.654 --> 00:33:20.817 their own addressing and

00:33:20.876 --> 00:33:21.778 naming conventions.

00:33:23.219 --> 00:33:25.560 If we think about maps, though,

00:33:26.582 --> 00:33:28.943 maps don't allow that.

00:33:29.384 --> 00:33:30.025 Maps don't

00:33:30.520 --> 00:33:32.982 allow for that address to be

00:33:33.522 --> 00:33:35.065 changed or for that naming

00:33:35.105 --> 00:33:36.786 convention to be changed, right?

00:33:36.826 --> 00:33:38.748 If I tell you to go to 7th

00:33:38.887 --> 00:33:43.111 Avenue and 7th Street in Calgary, Alberta,

00:33:43.151 --> 00:33:47.414 Canada, well, I sent you to four places,

00:33:47.974 --> 00:33:48.796 but you'd likely

00:33:48.855 --> 00:33:50.096 immediately recognize that

00:33:50.116 --> 00:33:51.357 I didn't say Southwest,

00:33:52.018 --> 00:33:54.039 being that it's a quadrant city, right,

00:33:54.079 --> 00:33:55.441 which is the old Nexen address.

00:33:57.442 --> 00:33:57.843 But in

00:33:58.519 --> 00:33:59.859 Every organization I've ever

00:33:59.900 --> 00:34:01.461 worked in and it's been

00:34:01.681 --> 00:34:03.502 really frustrating is that

00:34:03.563 --> 00:34:07.986 they allow that breakage of the address,

00:34:08.445 --> 00:34:08.905 and so,

00:34:09.847 --> 00:34:12.369 when we when we look at information.

00:34:13.023 --> 00:34:15.326 it will magnetically attract

00:34:15.505 --> 00:34:16.266 to an address.

00:34:16.646 --> 00:34:20.550 And again, using that 7th Ave, 7th Street,

00:34:20.650 --> 00:34:22.311 Southwest location.

00:34:22.771 --> 00:34:24.672 We can go on Google Maps,

00:34:24.753 --> 00:34:26.614 we can see the businesses around it,

00:34:27.074 --> 00:34:28.275 we can see the streets,

00:34:28.356 --> 00:34:29.637 we can go to Street View,

00:34:29.717 --> 00:34:32.139 we can see recent images of it,

00:34:32.880 --> 00:34:35.101 we can likely see reviews

00:34:35.161 --> 00:34:35.983 of the restaurants,

00:34:36.043 --> 00:34:37.304 reviews of the buildings,

00:34:38.467 --> 00:34:39.929 news associated to it.

00:34:39.969 --> 00:34:41.210 If we want to drill far enough,

00:34:41.250 --> 00:34:42.849 we can see tax information,

00:34:42.931 --> 00:34:44.510 street repairs, et cetera.

00:34:44.630 --> 00:34:46.112 Like everything magnetically

00:34:46.152 --> 00:34:46.693 attracts to that,

00:34:46.733 --> 00:34:49.333 just like it does to your home address,

00:34:49.414 --> 00:34:50.074 because that's where

00:34:50.153 --> 00:34:53.315 everything flows to or organically.

00:34:53.456 --> 00:34:56.737 That's how it works, right?

00:34:56.818 --> 00:34:57.759 Even the universe is

00:34:57.818 --> 00:34:59.420 structured that way from a

00:34:59.500 --> 00:35:01.059 planetary system and a

00:35:01.159 --> 00:35:03.061 solar system and a universe perspective.

00:35:03.121 --> 00:35:03.902 We want to go far enough.

00:35:05.523 --> 00:35:06.784 but get to an organization

00:35:07.164 --> 00:35:08.923 and turn somebody loose

00:35:08.985 --> 00:35:10.985 that doesn't understand addressing.

00:35:11.005 --> 00:35:13.806 That's going to create data.

00:35:14.246 --> 00:35:15.407 Think about all the years

00:35:15.447 --> 00:35:18.469 you spent filing documents

00:35:18.809 --> 00:35:21.389 into a folder structure on a shared drive,

00:35:23.170 --> 00:35:23.371 right?

00:35:23.990 --> 00:35:26.231 It's chaos because everybody

00:35:26.271 --> 00:35:27.293 thinks differently about that.

00:35:27.773 --> 00:35:28.652 Go to the library.

00:35:28.693 --> 00:35:30.693 What keeps a library organized?

00:35:30.753 --> 00:35:33.335 It's a Dewey decimal system, right?

00:35:34.635 --> 00:35:35.096 We don't,

00:35:35.916 --> 00:35:36.476 follow,

00:35:36.835 --> 00:35:38.356 we don't have that architecture

00:35:38.496 --> 00:35:39.998 thinking in organizations.

00:35:40.757 --> 00:35:43.039 So that's the first problem, right?

00:35:43.498 --> 00:35:44.920 And let me build on that, Norm, right?

00:35:44.960 --> 00:35:46.320 So do you think the problem

00:35:46.380 --> 00:35:48.121 there is it's obviously a people problem,

00:35:48.521 --> 00:35:48.721 right?

00:35:49.141 --> 00:35:50.561 Now, my question to you is,

00:35:51.103 --> 00:35:52.922 is it a lack of education

00:35:52.943 --> 00:35:54.224 where people are not aware

00:35:54.284 --> 00:35:55.043 and understand the

00:35:55.083 --> 00:35:56.525 importance and how everything connects?

00:35:57.164 --> 00:35:57.965 Or is it a lack of

00:35:58.045 --> 00:35:59.786 discipline where they may understand,

00:35:59.826 --> 00:36:01.407 but they want it different and they say,

00:36:01.427 --> 00:36:02.666 fuck it, I'm going to do it this way?

00:36:02.726 --> 00:36:03.688 What do you think is more?

00:36:04.351 --> 00:36:05.313 I didn't know we could swear

00:36:05.353 --> 00:36:06.112 in this podcast.

00:36:06.152 --> 00:36:06.673 That's awesome.

00:36:07.653 --> 00:36:10.996 You can say whatever you want.

00:36:11.777 --> 00:36:13.077 It's all of the above.

00:36:13.097 --> 00:36:15.498 Plus, it's a degree of laziness.

00:36:15.818 --> 00:36:16.059 Right.

00:36:16.199 --> 00:36:18.981 Again, not an inherent human condition.

00:36:19.342 --> 00:36:23.945 So if I feel that my background,

00:36:24.425 --> 00:36:25.505 because I did some

00:36:25.545 --> 00:36:26.786 programming early on and

00:36:26.806 --> 00:36:27.867 because I learned about

00:36:27.927 --> 00:36:28.588 databases earlier,

00:36:29.797 --> 00:36:32.161 it's plain as day to me that

00:36:32.280 --> 00:36:33.742 I can't really accomplish

00:36:33.882 --> 00:36:35.965 anything unless I structure

00:36:36.025 --> 00:36:38.206 that data in a way that I

00:36:38.246 --> 00:36:39.509 can connect to it.

00:36:39.528 --> 00:36:40.869 And in database terms,

00:36:40.929 --> 00:36:42.351 it's called a concatenated

00:36:42.472 --> 00:36:44.653 key or a foreign key where

00:36:44.693 --> 00:36:46.536 you join things together by

00:36:47.036 --> 00:36:48.097 related information.

00:36:48.237 --> 00:36:50.320 So, you know, 7th Street,

00:36:51.842 --> 00:36:53.942 7th Ave, Southwest, Calgary, Alberta,

00:36:54.083 --> 00:36:54.702 Canada,

00:36:54.742 --> 00:36:56.523 with a postal code is a

00:36:56.563 --> 00:36:58.222 concatenated address that's

00:36:58.422 --> 00:37:01.083 unique and doesn't exist anywhere else,

00:37:01.384 --> 00:37:01.603 right?

00:37:02.543 --> 00:37:03.384 When we get to an

00:37:03.443 --> 00:37:06.244 organization and we name a

00:37:06.405 --> 00:37:08.965 file folder something, well,

00:37:09.105 --> 00:37:10.425 it's easily repeatable.

00:37:10.585 --> 00:37:11.726 Somebody else can duplicate

00:37:11.766 --> 00:37:12.666 that someplace else.

00:37:12.706 --> 00:37:13.545 There's no check.

00:37:14.065 --> 00:37:16.186 There's no referential integrity check.

00:37:16.266 --> 00:37:17.467 And, you know, for the

00:37:18.153 --> 00:37:19.275 Those of you that don't know

00:37:19.295 --> 00:37:20.516 what referential integrity is,

00:37:20.576 --> 00:37:22.097 it's a good go look it up.

00:37:22.277 --> 00:37:25.818 But the integrity between

00:37:25.838 --> 00:37:26.599 the data isn't there.

00:37:26.659 --> 00:37:29.222 So when we get to a large data model,

00:37:29.422 --> 00:37:32.623 now this starts to degrade

00:37:32.684 --> 00:37:33.684 this theory a little bit

00:37:33.704 --> 00:37:37.067 because large models can be

00:37:37.206 --> 00:37:38.288 very abstract.

00:37:38.827 --> 00:37:42.369 They don't have to have perfect indexes.

00:37:43.831 --> 00:37:46.373 But it takes a lot longer to

00:37:46.492 --> 00:37:47.554 train that model

00:37:48.034 --> 00:37:49.976 when you have to validate everything.

00:37:50.036 --> 00:37:50.675 So, you know,

00:37:50.695 --> 00:37:52.056 there's the supervised

00:37:52.117 --> 00:37:52.657 training and the

00:37:52.697 --> 00:37:55.599 unsupervised training and where,

00:37:55.719 --> 00:37:57.960 where my organization is at

00:37:58.019 --> 00:37:58.980 and where we are with

00:37:59.021 --> 00:37:59.900 organizations that we're

00:37:59.920 --> 00:38:00.860 working with is we're

00:38:00.900 --> 00:38:02.461 having to train their

00:38:02.541 --> 00:38:05.384 models for the algorithm to

00:38:05.623 --> 00:38:06.364 come out with a

00:38:06.403 --> 00:38:08.744 statistically accurate

00:38:08.885 --> 00:38:10.085 result because the

00:38:10.326 --> 00:38:12.007 underlying foundational

00:38:12.487 --> 00:38:13.907 indexes and addresses

00:38:14.668 --> 00:38:16.028 aren't congruent with each other.

00:38:17.981 --> 00:38:18.380 So that,

00:38:19.001 --> 00:38:21.101 although it's probably one day not

00:38:21.121 --> 00:38:22.101 going to be necessary

00:38:22.161 --> 00:38:23.021 because we'll probably have

00:38:23.061 --> 00:38:24.322 enough data that we can

00:38:24.442 --> 00:38:25.561 sort all that out.

00:38:25.621 --> 00:38:27.362 There's only so many ways to

00:38:27.422 --> 00:38:29.322 do things and there's only

00:38:29.503 --> 00:38:30.882 so many combinations.

00:38:30.963 --> 00:38:32.463 It's like solving the Rubik's cube,

00:38:32.903 --> 00:38:34.304 just a large Rubik's cube.

00:38:35.284 --> 00:38:36.664 But today where we're at

00:38:36.704 --> 00:38:37.563 with technology and

00:38:37.603 --> 00:38:39.625 understanding and use is

00:38:39.684 --> 00:38:41.585 that those indexes that are

00:38:41.644 --> 00:38:43.045 broken or those addresses

00:38:43.085 --> 00:38:44.806 that are broken become

00:38:44.865 --> 00:38:46.646 outliers of data that

00:38:47.644 --> 00:38:50.266 create a lot of human work

00:38:50.505 --> 00:38:52.865 to intervene to train to

00:38:52.925 --> 00:38:54.266 say yeah that's a valid

00:38:54.347 --> 00:38:56.427 record or potentially to go

00:38:56.527 --> 00:38:57.726 out and have to physically

00:38:57.907 --> 00:38:59.867 look and see right like

00:38:59.987 --> 00:39:01.887 back to my example of

00:39:01.947 --> 00:39:03.809 changing the address so had

00:39:03.849 --> 00:39:05.068 I changed the address of my

00:39:05.108 --> 00:39:06.849 house and then I phoned the

00:39:06.929 --> 00:39:09.150 city and said hey I never

00:39:09.170 --> 00:39:12.811 get my mail or the gps doesn't work well

00:39:14.061 --> 00:39:15.483 somebody's going to have to come look.

00:39:16.103 --> 00:39:18.025 So that's a lot of work, right?

00:39:18.684 --> 00:39:21.067 Whereas if we are constantly

00:39:21.126 --> 00:39:22.527 thinking about addressing

00:39:22.728 --> 00:39:23.688 and maintaining that

00:39:23.768 --> 00:39:26.030 continuity through our data

00:39:26.110 --> 00:39:28.311 and letting it magnetically attract,

00:39:28.972 --> 00:39:29.753 concatenate,

00:39:30.574 --> 00:39:37.197 then those algorithms and AI tools can,

00:39:37.338 --> 00:39:39.320 I think, be much, much more effective.

00:39:41.461 --> 00:39:42.382 That's interesting.

00:39:42.402 --> 00:39:43.384 I always like hearing the

00:39:43.423 --> 00:39:46.766 data perspective from you on that front.

00:39:47.007 --> 00:39:47.266 Right.

00:39:48.007 --> 00:39:49.889 Um, now here's a question I have for you,

00:39:49.989 --> 00:39:50.168 right?

00:39:50.208 --> 00:39:52.972 So you've there's, we talk,

00:39:53.012 --> 00:39:55.474 you work with a lot of big organizations,

00:39:55.554 --> 00:39:55.793 right?

00:39:55.853 --> 00:39:56.114 Really,

00:39:56.173 --> 00:39:57.554 really big organizations when it

00:39:57.574 --> 00:39:58.615 comes to asset management.

00:39:59.396 --> 00:39:59.516 Um,

00:39:59.717 --> 00:40:01.878 now businesses that are either big or

00:40:01.938 --> 00:40:03.059 small doesn't really matter,

00:40:03.099 --> 00:40:06.583 but that are the early stages of, okay.

00:40:06.623 --> 00:40:07.463 I see promise in AI.

00:40:07.543 --> 00:40:08.844 It sounds like you're pretty bullish.

00:40:09.391 --> 00:40:09.891 on AI,

00:40:10.032 --> 00:40:11.592 I'm obviously super bullish on AI

00:40:11.652 --> 00:40:13.072 kind of going all in with

00:40:13.112 --> 00:40:14.574 it from an entrepreneurial standpoint.

00:40:15.074 --> 00:40:16.315 But those companies that are

00:40:16.474 --> 00:40:18.414 just starting to say, okay,

00:40:18.434 --> 00:40:20.135 I'm going to make, it's not,

00:40:20.155 --> 00:40:21.255 I'm not going all in on it.

00:40:21.536 --> 00:40:22.496 It's a small mental

00:40:22.516 --> 00:40:23.996 commitment and organization

00:40:24.056 --> 00:40:24.757 commitment that I'm going

00:40:24.777 --> 00:40:25.777 to make towards AI.

00:40:26.757 --> 00:40:28.539 And they're asset management

00:40:28.579 --> 00:40:29.838 driven organizations, right?

00:40:30.360 --> 00:40:31.920 What are some of the initial

00:40:31.980 --> 00:40:33.960 things that you think the initial steps

00:40:34.625 --> 00:40:35.585 that they should consider

00:40:35.606 --> 00:40:36.907 and that they should kind

00:40:36.947 --> 00:40:39.568 of go through before they really dive in?

00:40:41.550 --> 00:40:43.990 Well, when I started programming,

00:40:44.010 --> 00:40:46.193 the first thing I ever did

00:40:46.293 --> 00:40:48.393 was the Hello World program,

00:40:48.434 --> 00:40:50.255 which you basically learn how to,

00:40:50.275 --> 00:40:51.496 you know,

00:40:51.615 --> 00:40:54.097 print out Hello World on the screen.

00:40:55.179 --> 00:40:56.820 And I think if you're going

00:40:56.900 --> 00:40:59.362 to embark on the AI journey,

00:40:59.461 --> 00:41:01.023 then you're going to start

00:41:01.063 --> 00:41:03.643 with something that that's very small.

00:41:04.661 --> 00:41:05.422 but provable,

00:41:07.125 --> 00:41:09.327 something that you could do manually,

00:41:09.347 --> 00:41:12.351 that you can have AI do,

00:41:12.731 --> 00:41:14.152 but that you can also prove.

00:41:14.713 --> 00:41:18.998 And so pick a data set that is

00:41:21.443 --> 00:41:23.103 Reasonable in size because

00:41:23.123 --> 00:41:24.284 you need a certain amount

00:41:24.304 --> 00:41:26.985 of data for the isolation

00:41:27.085 --> 00:41:29.306 forest algorithm to work

00:41:29.425 --> 00:41:30.706 and you need a certain data

00:41:30.786 --> 00:41:31.786 series and there's all

00:41:31.827 --> 00:41:34.327 these parameters around that.

00:41:34.407 --> 00:41:36.688 So pick something that will work.

00:41:38.168 --> 00:41:40.150 And and start testing it and

00:41:40.210 --> 00:41:41.210 see what you get back,

00:41:41.371 --> 00:41:42.891 because you might be

00:41:42.931 --> 00:41:44.791 surprised you might get nothing back.

00:41:45.719 --> 00:41:47.300 Or you might've had a hunch

00:41:47.380 --> 00:41:48.702 that that data contained

00:41:49.262 --> 00:41:51.043 information that if you

00:41:51.083 --> 00:41:52.144 could get would be really

00:41:52.184 --> 00:41:52.945 valuable to you.

00:41:53.025 --> 00:41:55.827 And if you can get a nugget back, then,

00:41:56.748 --> 00:41:58.550 well, then you can expand from that.

00:41:59.110 --> 00:42:00.811 But you're gonna have to validate it,

00:42:00.911 --> 00:42:01.132 right?

00:42:01.612 --> 00:42:02.492 That's the difference.

00:42:02.552 --> 00:42:03.514 As I mentioned earlier,

00:42:03.574 --> 00:42:05.835 no longer should we really

00:42:05.896 --> 00:42:08.697 have to have people be data entry people.

00:42:08.938 --> 00:42:09.759 We should have people that

00:42:09.798 --> 00:42:10.960 are data validators.

00:42:11.039 --> 00:42:12.260 And that's a big difference.

00:42:13.708 --> 00:42:15.829 From a value proposition perspective.

00:42:15.909 --> 00:42:17.851 So what I'll give you an

00:42:18.130 --> 00:42:19.731 example where we're focused

00:42:19.851 --> 00:42:24.295 right now is equipment history.

00:42:24.375 --> 00:42:26.115 So service and repair history,

00:42:26.835 --> 00:42:28.317 preventive maintenance history,

00:42:29.978 --> 00:42:31.559 operational context.

00:42:31.820 --> 00:42:33.159 So how did that piece of

00:42:33.239 --> 00:42:35.001 equipment operate over time?

00:42:35.802 --> 00:42:36.882 What were its temperatures

00:42:36.943 --> 00:42:39.043 and pressures and what happened?

00:42:39.835 --> 00:42:41.615 And then trying to glue all

00:42:41.635 --> 00:42:44.117 of that together to look for patterns,

00:42:44.318 --> 00:42:45.679 because we believe there's

00:42:46.360 --> 00:42:47.721 patterns in that data.

00:42:48.461 --> 00:42:51.143 Then comparing oil analysis.

00:42:51.244 --> 00:42:52.985 So every so often on

00:42:53.505 --> 00:42:55.226 equipment that has oil in it,

00:42:55.327 --> 00:42:57.929 like a combustion engine or

00:42:57.969 --> 00:42:59.769 a gearbox or something like that,

00:42:59.809 --> 00:43:00.731 we're sampling oil.

00:43:01.251 --> 00:43:02.211 We're comparing those

00:43:02.271 --> 00:43:04.213 results against the other stuff.

00:43:05.077 --> 00:43:05.916 We're looking at what

00:43:05.976 --> 00:43:07.637 materials were consumed and

00:43:07.677 --> 00:43:09.599 what manufacturing process

00:43:09.739 --> 00:43:10.619 manufactured those

00:43:10.679 --> 00:43:11.960 materials because maybe

00:43:11.980 --> 00:43:13.521 there's defects in the materials.

00:43:14.362 --> 00:43:16.021 And we're just trying to glue data.

00:43:16.041 --> 00:43:16.262 Again,

00:43:16.282 --> 00:43:18.384 we're trying to concatenate data on

00:43:19.364 --> 00:43:21.965 to that address until we see a pattern.

00:43:22.585 --> 00:43:23.726 We're going to include the weather.

00:43:24.407 --> 00:43:25.266 I want to include

00:43:26.505 --> 00:43:27.927 the people's behavior.

00:43:28.367 --> 00:43:29.248 I want to include the

00:43:29.309 --> 00:43:30.849 process in the organization.

00:43:30.909 --> 00:43:31.130 Well,

00:43:31.630 --> 00:43:33.972 who created the PO that bought the parts?

00:43:34.112 --> 00:43:36.954 Who did the service on that equipment?

00:43:37.235 --> 00:43:38.175 Who had the last,

00:43:38.396 --> 00:43:39.737 when was their last sick day?

00:43:39.777 --> 00:43:41.478 When was their last vacation day?

00:43:42.059 --> 00:43:42.199 Right.

00:43:42.239 --> 00:43:43.840 There's so many factors and

00:43:43.860 --> 00:43:46.342 that that's the value of,

00:43:47.163 --> 00:43:49.664 of a tool such as AI in

00:43:49.704 --> 00:43:54.449 that we can feed it large unrelated data.

00:43:56.577 --> 00:43:58.637 unrelated meaning different contexts,

00:43:58.737 --> 00:44:02.679 but related via an address and say,

00:44:03.498 --> 00:44:04.358 what does this look like?

00:44:05.119 --> 00:44:07.019 And it's going to run on it

00:44:07.179 --> 00:44:09.159 until it comes back with a

00:44:09.219 --> 00:44:11.079 pattern that makes sense or

00:44:11.099 --> 00:44:12.141 until it comes back with

00:44:12.721 --> 00:44:14.021 outliers that then you can

00:44:14.041 --> 00:44:14.920 go investigate.

00:44:15.722 --> 00:44:17.742 Or if you're asking it the right question,

00:44:18.001 --> 00:44:20.083 it might come back with a

00:44:20.123 --> 00:44:21.023 solution that says,

00:44:22.164 --> 00:44:23.146 Hey, when this happens,

00:44:23.626 --> 00:44:24.967 you need to change oil or, Hey,

00:44:24.987 --> 00:44:25.728 when this happens,

00:44:25.768 --> 00:44:28.230 you need to slow down or, you know, uh,

00:44:29.210 --> 00:44:30.351 and that's arguably what's

00:44:30.891 --> 00:44:33.333 happening with self-driving technology,

00:44:33.393 --> 00:44:33.534 right?

00:44:33.554 --> 00:44:34.614 They have mounds of data.

00:44:35.135 --> 00:44:37.396 Here's what happens when it's this speed,

00:44:37.476 --> 00:44:39.398 this temperature, this road condition,

00:44:39.858 --> 00:44:40.599 this road.

00:44:41.480 --> 00:44:43.340 So the car can adjust

00:44:43.822 --> 00:44:46.503 quickly based on patterns

00:44:46.563 --> 00:44:47.565 it's already seen.

00:44:47.605 --> 00:44:48.644 And that's really the

00:44:48.704 --> 00:44:50.867 simplest way I think I can describe it.

00:44:51.708 --> 00:44:51.788 Um,

00:44:53.206 --> 00:44:55.246 but you gotta figure out

00:44:55.467 --> 00:44:57.469 where is that spot in your

00:44:57.509 --> 00:45:00.630 organization to look for those patterns?

00:45:01.050 --> 00:45:02.612 So you're basically saying like, okay,

00:45:02.632 --> 00:45:04.293 start with something that's low risk.

00:45:04.313 --> 00:45:05.414 Start with something that's small.

00:45:05.434 --> 00:45:06.373 Start with something you can

00:45:06.414 --> 00:45:09.096 get to the end and validate that it works,

00:45:10.996 --> 00:45:12.338 which I agree with you, right?

00:45:12.398 --> 00:45:12.617 I mean,

00:45:12.677 --> 00:45:14.119 I think we've seen any digital

00:45:14.159 --> 00:45:15.519 transformations that we've

00:45:15.559 --> 00:45:16.260 done in the past,

00:45:16.681 --> 00:45:17.521 the ones where they're the

00:45:17.561 --> 00:45:18.782 most successful is you have

00:45:18.802 --> 00:45:20.163 this balance of

00:45:20.925 --> 00:45:22.146 incremental wins, right?

00:45:22.186 --> 00:45:23.166 The quick things that you

00:45:23.286 --> 00:45:25.007 use to generate that momentum, right?

00:45:25.027 --> 00:45:26.969 You validate kind of near the end,

00:45:27.009 --> 00:45:27.909 you show value,

00:45:28.250 --> 00:45:29.731 but then you've got the step changes,

00:45:30.110 --> 00:45:30.632 the things that

00:45:30.692 --> 00:45:32.952 dramatically change performance.

00:45:33.833 --> 00:45:36.034 And you need a combination of both, right?

00:45:36.054 --> 00:45:37.175 So you're saying don't start

00:45:37.195 --> 00:45:37.876 with the big stuff,

00:45:38.257 --> 00:45:39.297 start with the small stuff.

00:45:39.818 --> 00:45:42.900 I don't know where that came from, man.

00:45:42.920 --> 00:45:43.039 That's

00:45:49.519 --> 00:45:50.980 I don't know about your webcam,

00:45:51.099 --> 00:45:52.400 or let's see if it does it for me.

00:45:52.420 --> 00:45:53.981 It doesn't do it for me.

00:45:54.141 --> 00:45:55.260 But that's basically kind of

00:45:55.501 --> 00:45:56.762 what you're saying there, right?

00:45:56.782 --> 00:45:57.702 Like start small,

00:45:57.802 --> 00:45:58.961 validate it until the end,

00:45:59.422 --> 00:46:02.623 and then use that to find other use cases,

00:46:02.782 --> 00:46:03.083 right?

00:46:04.202 --> 00:46:04.563 Yeah.

00:46:04.623 --> 00:46:04.842 I mean,

00:46:04.862 --> 00:46:06.903 I guess I've always grown up with

00:46:06.963 --> 00:46:11.125 that methodology because back in the day,

00:46:11.144 --> 00:46:12.844 you wrote your program.

00:46:12.864 --> 00:46:14.525 You did your program manually.

00:46:14.585 --> 00:46:15.846 If you could do it manually,

00:46:15.905 --> 00:46:16.766 then you could automate it.

00:46:18.324 --> 00:46:19.465 We're a little beyond that,

00:46:19.784 --> 00:46:22.746 but I think because there's distrust,

00:46:22.885 --> 00:46:23.126 right?

00:46:23.367 --> 00:46:27.447 Not many people believe that, I mean,

00:46:27.927 --> 00:46:28.967 the algorithms that we're

00:46:29.088 --> 00:46:30.608 faced with on social media

00:46:30.648 --> 00:46:32.309 certainly predict what we want to see,

00:46:32.329 --> 00:46:33.650 but that's because Siri is

00:46:33.690 --> 00:46:36.411 listening to us and things are happening,

00:46:36.471 --> 00:46:36.690 right?

00:46:36.771 --> 00:46:39.411 So I don't think we trust

00:46:39.452 --> 00:46:41.152 that in our industrial or

00:46:41.192 --> 00:46:43.012 our professional lives at work.

00:46:44.065 --> 00:46:47.467 that some computer can tell

00:46:47.626 --> 00:46:49.628 us when something's going

00:46:49.648 --> 00:46:51.409 to happen or how to do something.

00:46:51.929 --> 00:46:55.172 And I mean, it can support us today.

00:46:55.231 --> 00:46:57.634 I think people are open to, Hey,

00:46:57.673 --> 00:46:58.594 here's a suggestion.

00:46:58.715 --> 00:46:59.454 Kind of like you get when

00:46:59.474 --> 00:47:01.356 you go on Amazon and you, you know,

00:47:01.396 --> 00:47:03.498 you buy a smoke detector and it says, Hey,

00:47:03.538 --> 00:47:04.559 you should buy a carbon

00:47:04.579 --> 00:47:05.759 monoxide detector too.

00:47:06.639 --> 00:47:06.980 Okay.

00:47:07.039 --> 00:47:07.481 Well, that's,

00:47:08.061 --> 00:47:09.422 I can get my head wrapped around that.

00:47:09.461 --> 00:47:10.963 I can get the logic behind that.

00:47:11.043 --> 00:47:11.123 And,

00:47:11.704 --> 00:47:13.264 And by the way, there's been, you know,

00:47:13.264 --> 00:47:14.806 5000 people in the last

00:47:14.907 --> 00:47:16.467 month did the exact same thing.

00:47:17.489 --> 00:47:17.568 OK,

00:47:17.608 --> 00:47:19.070 it must be a good idea and it must make

00:47:19.090 --> 00:47:19.351 sense.

00:47:20.231 --> 00:47:23.273 But to have that happen in your job where,

00:47:23.293 --> 00:47:23.934 you know,

00:47:23.954 --> 00:47:26.215 a month ago you were making

00:47:26.255 --> 00:47:28.197 those decisions to now your

00:47:28.237 --> 00:47:29.840 managers come along and said, well,

00:47:29.860 --> 00:47:31.041 we're going to implement AI

00:47:31.061 --> 00:47:32.282 and it's going to tell us what to do.

00:47:33.376 --> 00:47:34.737 I think that's a bit of a stretch.

00:47:34.978 --> 00:47:37.659 So pick something that is, is, um,

00:47:38.259 --> 00:47:40.340 absorbable or palatable for

00:47:40.420 --> 00:47:41.920 the organization and the

00:47:41.981 --> 00:47:42.701 people within it.

00:47:43.181 --> 00:47:45.202 I think that's the only way.

00:47:45.242 --> 00:47:48.744 And I mean, I don't know,

00:47:48.764 --> 00:47:51.385 I don't have any really

00:47:51.445 --> 00:47:54.065 good AI stories other than

00:47:54.106 --> 00:47:56.547 the ones that I've told.

00:47:56.646 --> 00:47:59.847 Like I'm not seeing in my industry anyway,

00:47:59.867 --> 00:48:00.829 there's not a,

00:48:01.650 --> 00:48:01.909 you know,

00:48:01.929 --> 00:48:03.650 a truckload of people coming to

00:48:03.690 --> 00:48:05.110 conferences or coming out

00:48:05.150 --> 00:48:06.150 of the woodwork saying, well,

00:48:06.190 --> 00:48:08.431 we did this with AI, right?

00:48:08.492 --> 00:48:10.692 Even, even SAP has,

00:48:11.112 --> 00:48:13.333 has groups that are working on AI,

00:48:14.612 --> 00:48:16.673 but where are the stories?

00:48:16.773 --> 00:48:17.813 Where, where are the,

00:48:18.213 --> 00:48:19.094 where are the actual

00:48:19.155 --> 00:48:20.954 customers that have done something and,

00:48:21.974 --> 00:48:23.076 and churned that out?

00:48:23.235 --> 00:48:26.617 Certainly in our, in, in high margin,

00:48:27.277 --> 00:48:28.436 very profitable business

00:48:28.456 --> 00:48:31.157 businesses like social media or,

00:48:32.393 --> 00:48:33.592 companies that are dealing

00:48:33.652 --> 00:48:35.313 with human behaviors,

00:48:35.373 --> 00:48:37.173 we're seeing what's

00:48:37.213 --> 00:48:39.855 happening with the algorithms, right?

00:48:41.434 --> 00:48:44.956 But in professional industrial cases,

00:48:45.235 --> 00:48:46.396 it's not that prevalent yet.

00:48:46.456 --> 00:48:48.996 So I think we have to start

00:48:49.077 --> 00:48:51.038 small because I think we're doubters.

00:48:51.157 --> 00:48:53.057 We've always done it this way.

00:48:53.777 --> 00:48:55.219 We've always gotten a certain result.

00:48:55.298 --> 00:48:57.298 Now you want me to trust something else.

00:48:58.800 --> 00:48:59.019 Right.

00:48:59.579 --> 00:49:00.559 That's good, Narek.

00:49:01.628 --> 00:49:03.289 So let me ask you one final question,

00:49:03.329 --> 00:49:03.489 right?

00:49:03.510 --> 00:49:05.391 So you've been very helpful

00:49:05.451 --> 00:49:06.492 in my career instead of

00:49:06.572 --> 00:49:08.572 pushing and helping me push

00:49:08.612 --> 00:49:09.673 the boundaries, right?

00:49:09.833 --> 00:49:11.315 And think differently,

00:49:12.594 --> 00:49:13.476 which I'm obviously very

00:49:13.536 --> 00:49:14.516 grateful to you for.

00:49:15.697 --> 00:49:15.876 Now,

00:49:16.237 --> 00:49:17.878 you've mentored and worked alongside

00:49:17.918 --> 00:49:19.438 many professionals, right?

00:49:19.659 --> 00:49:20.820 So what's one piece of

00:49:20.880 --> 00:49:23.382 advice that you would find

00:49:23.402 --> 00:49:24.362 yourself constantly

00:49:24.461 --> 00:49:27.083 offering about embracing technology?

00:49:30.543 --> 00:49:32.264 Maybe I'll try to wrap it up into one,

00:49:32.324 --> 00:49:34.065 but I believe if you've

00:49:34.204 --> 00:49:35.565 never taken a programming

00:49:35.626 --> 00:49:36.565 course or you've never

00:49:36.626 --> 00:49:39.086 taken a database course and

00:49:40.827 --> 00:49:42.849 you are trying to apply

00:49:42.889 --> 00:49:45.391 some of these technologies, you're likely,

00:49:45.411 --> 00:49:48.731 you need a little more education.

00:49:48.891 --> 00:49:50.333 So I think you have to learn.

00:49:50.353 --> 00:49:51.833 I think you have to do some learning,

00:49:53.255 --> 00:49:54.574 be curious about it and

00:49:56.815 --> 00:49:57.677 I think you have to have

00:49:57.717 --> 00:50:02.581 some personal reason for doing so, right?

00:50:02.661 --> 00:50:06.025 Like if you're faced with a, you know,

00:50:06.045 --> 00:50:06.686 you're going to log into

00:50:06.726 --> 00:50:08.387 chat GPT as an example,

00:50:08.929 --> 00:50:09.989 it's a blank screen,

00:50:11.331 --> 00:50:13.253 what are you going to ask it, right?

00:50:13.914 --> 00:50:14.655 And I think,

00:50:15.315 --> 00:50:16.356 I suspect there's people out

00:50:16.376 --> 00:50:17.538 there that just all day

00:50:17.597 --> 00:50:18.858 long have a conversation with

00:50:19.266 --> 00:50:20.987 chat gpt because you can

00:50:21.086 --> 00:50:22.108 right you can treat it like

00:50:22.208 --> 00:50:24.690 a entity and be nice to it

00:50:24.730 --> 00:50:26.530 and compliment it and you

00:50:26.570 --> 00:50:28.291 can set it up so that you

00:50:28.311 --> 00:50:29.672 know it takes on your

00:50:29.753 --> 00:50:31.054 persona as well and it

00:50:31.094 --> 00:50:32.255 responds in a way you want

00:50:32.295 --> 00:50:34.135 to be responded to so I

00:50:34.396 --> 00:50:35.376 think it's a combination of

00:50:35.838 --> 00:50:37.978 learning and having a basic

00:50:38.039 --> 00:50:38.900 understanding of the

00:50:39.460 --> 00:50:40.400 principles of what are

00:50:40.501 --> 00:50:42.601 what's going on and then

00:50:43.242 --> 00:50:44.403 exploring and learning

00:50:45.824 --> 00:50:45.905 And,

00:50:46.025 --> 00:50:48.085 and constantly being curious about it.

00:50:48.206 --> 00:50:51.367 Otherwise, otherwise there's, there's no,

00:50:51.847 --> 00:50:53.268 there's no point to it.

00:50:53.329 --> 00:50:53.509 Right.

00:50:54.748 --> 00:50:55.010 Right.

00:50:55.329 --> 00:50:58.110 That theme of curiosity is

00:50:58.130 --> 00:50:58.911 something that we keep

00:50:58.952 --> 00:51:00.452 hearing over and over and over again.

00:51:00.771 --> 00:51:01.012 Yeah.

00:51:01.152 --> 00:51:02.373 And maybe just to add to that.

00:51:02.413 --> 00:51:02.672 Yes.

00:51:02.713 --> 00:51:03.333 And like, I think,

00:51:03.353 --> 00:51:05.114 I think we're in this type

00:51:05.155 --> 00:51:06.735 of business and certain

00:51:06.755 --> 00:51:07.655 people are in this type of

00:51:07.695 --> 00:51:09.577 business because they're

00:51:09.617 --> 00:51:11.998 problem solvers and they

00:51:12.018 --> 00:51:13.778 like the problem they like to.

00:51:14.637 --> 00:51:16.157 unpack the problem and come

00:51:16.197 --> 00:51:17.097 up with a solution.

00:51:17.157 --> 00:51:17.398 Right.

00:51:18.097 --> 00:51:20.318 I would, I always say, if you said to me,

00:51:20.358 --> 00:51:21.559 Hey, you know,

00:51:21.579 --> 00:51:23.179 here's a brand new Ferrari.

00:51:23.219 --> 00:51:23.820 What wouldn't be well,

00:51:23.840 --> 00:51:24.480 it might be brand new,

00:51:24.519 --> 00:51:25.800 brand new Ferrari Enzo.

00:51:26.340 --> 00:51:27.021 Do you want that?

00:51:27.661 --> 00:51:29.001 Or do you want this smashed

00:51:29.181 --> 00:51:30.682 up Corvette over here?

00:51:30.983 --> 00:51:31.503 And I go, Oh,

00:51:31.523 --> 00:51:32.762 I want the smashed up Corvette.

00:51:34.023 --> 00:51:34.224 Right.

00:51:34.304 --> 00:51:36.103 Because I want to fix it.

00:51:36.625 --> 00:51:37.925 I want to take it apart.

00:51:38.065 --> 00:51:39.126 How did they do this?

00:51:39.746 --> 00:51:40.905 How can I straighten this out?

00:51:40.945 --> 00:51:42.086 How can I put it back

00:51:42.126 --> 00:51:43.327 together and make it something?

00:51:44.938 --> 00:51:46.179 And I think if you have that

00:51:46.360 --> 00:51:49.242 sort of aptitude or problem

00:51:49.302 --> 00:51:50.945 solving thinking,

00:51:51.045 --> 00:51:52.646 then I think there's a lot

00:51:52.666 --> 00:51:53.427 of opportunity.

00:51:53.447 --> 00:51:55.568 There's a ton of opportunity

00:51:55.608 --> 00:51:56.170 in this space.

00:51:56.230 --> 00:51:57.110 I shouldn't say a lot.

00:51:57.130 --> 00:51:57.871 There's a ton.

00:51:57.951 --> 00:51:58.492 It's endless.

00:52:00.233 --> 00:52:02.434 Yeah, that's a good point, Norm.

00:52:02.514 --> 00:52:04.516 I would take the ENSO, right?

00:52:04.737 --> 00:52:07.400 But I see where you're going with that.

00:52:08.271 --> 00:52:09.634 in terms of problem solving, man.

00:52:09.653 --> 00:52:10.594 So that's awesome.

00:52:10.614 --> 00:52:11.014 Look, Norm,

00:52:11.034 --> 00:52:13.197 thank you so much for coming on the show,

00:52:13.237 --> 00:52:13.396 right?

00:52:13.456 --> 00:52:14.617 It's been great chatting with you.

00:52:14.657 --> 00:52:15.318 I feel like, you know,

00:52:15.338 --> 00:52:16.300 we do these chats all the

00:52:16.340 --> 00:52:18.902 time over beer typically, right?

00:52:18.922 --> 00:52:19.682 So I think we'll have you

00:52:19.722 --> 00:52:20.844 back on the show at some point.

00:52:21.224 --> 00:52:22.545 But everybody that's been watching,

00:52:22.565 --> 00:52:24.467 thank you guys for listening, right?

00:52:24.487 --> 00:52:25.568 We're obviously very excited

00:52:25.989 --> 00:52:26.630 about the show,

00:52:26.690 --> 00:52:27.351 being able to have

00:52:27.391 --> 00:52:28.452 different conversations and

00:52:28.472 --> 00:52:29.472 just get the perspective.

00:52:30.376 --> 00:52:31.777 of different people, right?

00:52:31.818 --> 00:52:33.577 And in terms of what they think about AI,

00:52:33.617 --> 00:52:34.378 how they think it's going

00:52:34.398 --> 00:52:36.378 to shape our future and how

00:52:36.438 --> 00:52:37.099 it's going to shape

00:52:37.239 --> 00:52:39.599 organizations and our personal lives.

00:52:39.639 --> 00:52:41.039 I think next time we bring you on the show,

00:52:41.059 --> 00:52:42.521 let's talk more about the

00:52:42.561 --> 00:52:44.101 personal side of things.

00:52:44.161 --> 00:52:45.021 I think that there's a lot

00:52:45.061 --> 00:52:46.621 of interesting applications.

00:52:47.021 --> 00:52:48.163 I'm one of those people that

00:52:48.202 --> 00:52:50.043 talks with Chai GPT and

00:52:50.103 --> 00:52:51.983 Copilot basically the entire day, man,

00:52:52.664 --> 00:52:52.844 right?

00:52:53.556 --> 00:52:55.197 And that's probably one of

00:52:55.237 --> 00:52:56.858 the biggest benefits that I get.

00:52:56.918 --> 00:52:58.838 It's like for me, I like to problem solve,

00:52:58.878 --> 00:52:59.898 but I like to do it with

00:52:59.978 --> 00:53:01.278 other people and talk to

00:53:01.298 --> 00:53:02.940 people and get on a whiteboard,

00:53:02.980 --> 00:53:04.481 facilitate things and all

00:53:04.521 --> 00:53:05.320 of this kind of stuff.

00:53:05.820 --> 00:53:07.282 Now, I mean, you know,

00:53:07.842 --> 00:53:09.202 I can do it at any time, right?

00:53:09.222 --> 00:53:10.543 If I wake up at four in the morning,

00:53:10.603 --> 00:53:12.244 I can start doing it with Chachi PT.

00:53:12.563 --> 00:53:13.264 If I'm, you know,

00:53:13.304 --> 00:53:14.385 going to bed late and I've

00:53:14.405 --> 00:53:16.425 got the spur of creativity

00:53:16.465 --> 00:53:17.226 and inspiration,

00:53:17.306 --> 00:53:18.427 I can go ahead and do that.

00:53:18.907 --> 00:53:19.788 And it's always there.

00:53:20.268 --> 00:53:20.608 You know,

00:53:20.748 --> 00:53:22.469 it gasses me up when I need some

00:53:22.509 --> 00:53:22.969 gassing up.

00:53:23.617 --> 00:53:23.918 Right.

00:53:24.539 --> 00:53:24.938 Um, and,

00:53:24.998 --> 00:53:26.079 and I think a lot of people can

00:53:26.119 --> 00:53:26.780 benefit from it.

00:53:26.800 --> 00:53:28.380 So I like the theme of exploration.

00:53:28.860 --> 00:53:29.742 I like the theme that you're

00:53:29.762 --> 00:53:31.242 talking about curiosity and

00:53:31.302 --> 00:53:33.023 just playing with it and starting small.

00:53:33.423 --> 00:53:34.465 I think that's some good

00:53:34.505 --> 00:53:35.585 advice that a lot of people

00:53:35.666 --> 00:53:36.967 and organizations can take.

00:53:37.706 --> 00:53:40.748 So guys, thank you so much for joining us.

00:53:41.268 --> 00:53:41.329 Um,

00:53:41.369 --> 00:53:42.949 we're always looking for feedback in

00:53:42.989 --> 00:53:44.271 terms of how to improve the show,

00:53:44.291 --> 00:53:45.391 what you would want to hear,

00:53:45.411 --> 00:53:46.413 who you would want to have

00:53:46.452 --> 00:53:47.092 us on the show.

00:53:47.512 --> 00:53:49.474 And we need a name, right?

00:53:49.675 --> 00:53:50.815 We will continue to be

00:53:50.856 --> 00:53:52.076 nameless because that will

00:53:52.115 --> 00:53:52.976 not slow us down.

00:53:53.831 --> 00:53:55.293 But yes, thank you, Garland.

00:53:55.373 --> 00:53:57.275 Help us title our show.

00:53:58.719 --> 00:53:59.460 That's it, everybody.

00:53:59.480 --> 00:54:01.163 Thanks, guys.

00:54:02.865 --> 00:54:03.327 Cheers, Norm.