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Tim Huckaby on the Future of AI and Computer Vision - Episode #10 · The Adults In The AI Room

• 0:28
  SPEAKER_00: And now you're your host, Seth Early and Chris Featherstone.
• 0:36
  SPEAKER_03: Welcome to today's podcast.
• 0:38
  SPEAKER_03: I'm Seth Early.
• 0:39
  SPEAKER_03: I'm Chris Featherstone.
• 0:40
  SPEAKER_03: And before we get started, I want to give a shout out to our sponsors, Simpler Media, Marketing AI Institute, and Early Information Science.
• 0:48
  SPEAKER_03: And our guest today is an industry luminary focused on AI, computer vision, machine learning, augmented reality, mixed reality, and emerging user experiences.
• 1:00
  SPEAKER_03: His list of accomplishments awards are very long.
• 1:03
  SPEAKER_03: We need the whole hour just to list those out.
• 1:06
  SPEAKER_03: He's really been around for a long time and he's done some amazing work.
• 1:10
  SPEAKER_03: He's sought after as a highly rated speaker.
• 1:13
  SPEAKER_03: He's an author.
• 1:14
  SPEAKER_03: He's had thousands of publication, uh, publications and uh publishing credits to his name.
• 1:20
  SPEAKER_03: And we're really lucky to have him today.
• 1:22
  SPEAKER_03: Uh, please welcome Tim Huckabee.
• 1:24
  SPEAKER_01: Wow.
• 1:26
  SPEAKER_01: Luminary.
• 1:27
  SPEAKER_01: You know, when they wrote that about Luminate.
• 1:30
  SPEAKER_01: Yeah, when they wrote that comfortable with it.
• 1:33
  SPEAKER_01: So, yeah, okay.
• 1:35
  SPEAKER_01: That's awfully nice of you.
• 1:36
  SPEAKER_01: I I think to sum that up, it just means I've been around a long time and I have gray hair.
• 1:42
  SPEAKER_03: You've showed up.
• 1:43
  SPEAKER_03: What is Woody Allen says?
• 1:45
  SPEAKER_03: It's uh it's uh 90% of life is showing up.
• 1:47
  SPEAKER_03: You've been showing up a lot of a lot of the uh places in life.
• 1:50
  SPEAKER_03: So that's great.
• 1:51
  SPEAKER_03: Um and and I think you were luminous, I think you were luminous the last time I saw you.
• 1:56
  SPEAKER_03: Actually, I saw little sparks or something coming off.
• 1:59
  SPEAKER_03: Uh Chris, tell you've known Tim for a long time.
• 2:02
  SPEAKER_03: Tell tell us how you met.
• 2:04
  SPEAKER_04: Yeah, but before he was uh luminous.
• 2:07
  SPEAKER_01: Um now I'm gonna just gonna be mocked, aren't I?
• 2:12
  SPEAKER_04: Uh yeah, for me, uh, for sure.
• 2:14
  SPEAKER_04: No, I've known Tim for probably 20 something years now.
• 2:18
  SPEAKER_04: It's been it's been a long time.
• 2:20
  SPEAKER_04: And um yeah, he and I met, and I think you know, I was grateful.
• 2:25
  SPEAKER_04: I think he took me under his wing a little bit, but you know, I was at Microsoft, he was one of our key partners, and we just did some fun, fun work together and laughed a lot, played volleyball, and because we're both down in Southern California, so it just made for um just a really good time.
• 2:40
  SPEAKER_04: And then over the years, it's been fun because you know, we've been able to again work in different deals, you know, him our families know each other, so we hang out.
• 2:49
  SPEAKER_04: So it's just been it's been really, really fun.
• 2:51
  SPEAKER_04: Plus to see, you know, from the outside in terms of what he's been able to do with um a lot of the companies.
• 2:57
  SPEAKER_04: I'll I'll say some nice things about him, actually, um, which actually are spot on.
• 3:02
  SPEAKER_04: One of the things that I think has separated Tim from just the industry at large is really Tim, your ability to help um facilitate showing the actual capabilities of, let's say, some core technology, whether it be super brand new and bleeding edge or even contemporary, and actually really helping people one, understand it from a business value perspective, but more importantly, getting people super excited to use it and to know how to use it, and then showing them how to use it, even the likes of the organizations that create the technology, showing them actually how to use it in the market as well.
• 3:44
  SPEAKER_04: And I think that's one of the things that I've always loved about is the ability to help those technologies that come out separate it from, let's say, the noise that's out there and say, okay, you created this really unbelievable technology.
• 3:56
  SPEAKER_04: Now, now that you've created it and the customer, let me show you really the value of it.
• 4:01
  SPEAKER_03: I I want to make a comment there because so much of the time organizations don't know what they have, and it's separating the noise internally as well.
• 4:10
  SPEAKER_03: So you worked in the 90s uh at Microsoft.
• 4:12
  SPEAKER_03: Tell us about you know what you were surfacing at that time.
• 4:16
  SPEAKER_01: Well, that's a great segue, and and thanks for the kind words, Feather.
• 4:20
  SPEAKER_01: Wow, yeah, it's funny because I have this 25-year speaking career, you know, in front of audiences.
• 4:26
  SPEAKER_01: And I mostly do keynotes these days, which are are fun and different.
• 4:31
  SPEAKER_01: Um, but I kind of walked bass acords into it.
• 4:34
  SPEAKER_01: I I was working on uh within the Windows NT server group on a not so famous, not so good server product uh that lived within NT Option Pack, if you if you remember this, if you're old enough to remember this, um, you know, Microsoft's first internet server, IAS4.
• 4:54
  SPEAKER_01: And and I worked on the the site server part of this, the e-commerce part of it.
• 4:59
  SPEAKER_01: But anyways, the the story is, you know, when it after we shipped the product, that Microsoft had this huge conference back then called TechEd.
• 5:07
  SPEAKER_01: And uh the product team, I can remember this vividly.
• 5:11
  SPEAKER_01: They're all, you know, the the the powers that be said, all right, we need, you know, we needed this speaker and this speaker to do this presentation, this presentation.
• 5:19
  SPEAKER_01: And and all my bosses and all my bosses' bosses were like, screw this, I'm not doing that.
• 5:24
  SPEAKER_01: I'm not standing in front of an audience, no way, you know, stand in front of 3,000 people and wherever it was, Dallas or Orlando or whatever.
• 5:32
  SPEAKER_01: Let's make Huckabee do it.
• 5:35
  SPEAKER_01: Because I yeah, I seem to be the only one out of 2,500 people on that team.
• 5:41
  SPEAKER_01: If you could if you can imagine that's how software was built back to with 2,500 people, I seem to be the only one who liked smile and laugh and could entertain.
• 5:50
  SPEAKER_01: So so yeah, without any experience, I stood in front of 2,500 people and um I guess I just nailed it.
• 5:58
  SPEAKER_01: I had the highest rated, I found out later I had the the highest rated um session in the entire conference, and and that that agitated the famous speakers and such because no one had ever heard about this, you know, 25-year-old kid from San Diego.
• 6:14
  SPEAKER_03: So are there any archive uh videos of that?
• 6:18
  SPEAKER_03: There's gotta be some.
• 6:22
  SPEAKER_04: That was before that was before flip phones, man.
• 6:25
  SPEAKER_04: That was when we had like um you know, no Kia phones with just you know monochrome screens, yeah, blackberries.
• 6:32
  SPEAKER_04: Uh yeah, yeah.
• 6:35
  SPEAKER_03: Motorola Razor.
• 6:36
  SPEAKER_03: I should add this.
• 6:38
  SPEAKER_04: If you know what what the um install path was for NT Option Pack, I'm sorry that we just gave you facial ticks.
• 6:48
  SPEAKER_01: That the install team for that particular product oh went, I think 700 people.
• 6:56
  SPEAKER_01: The install was so complex.
• 6:58
  SPEAKER_01: You know, that that's honestly at Microsoft, that's where I learned how enterprise software was built.
• 7:03
  SPEAKER_01: But you know, back then testing under the 36 languages was a nightmare.
• 7:09
  SPEAKER_01: Like, you know, we didn't have automated testing tools, so a lot of it was manual.
• 7:13
  SPEAKER_01: And and if you've ever done you know internationally international language software, you know that German is what you test on because all the words in German are like a mile long.
• 7:24
  SPEAKER_01: You click start and then a German word, and it you know bleeds over the control, and and you're like, oh holy shit, what what do we do now?
• 7:34
  SPEAKER_01: Am I allowed to say that?
• 7:35
  SPEAKER_01: Yeah, of course.
• 7:37
  SPEAKER_01: That's so interesting.
• 7:38
  SPEAKER_02: This is interesting.
• 7:39
  SPEAKER_01: Oh God, Seth, you would not believe how difficult it was to build software back then.
• 7:44
  SPEAKER_01: And and I know it bothers the engineers, the brilliant people that I get to work with, it bothers them.
• 7:50
  SPEAKER_01: But I'm just I'm telling you, it's just not that hard to build good software anymore because the tools and the plumbing and the platforms are so good as opposed to plenty years ago.
• 8:02
  SPEAKER_01: Right, right.
• 8:03
  SPEAKER_01: There's so much more that has been built.
• 8:06
  SPEAKER_04: Yeah.
• 8:07
  SPEAKER_04: Go ahead, Tim.
• 8:07
  SPEAKER_04: I mean, the the the fun part too was, you know, if I, you know, and this is this is um idealistic of what uh Tim and is all about is if I wanted to, you know, I'm at Microsoft, I'm an actual, you know, senior technologist or whatever architect or doing stuff at Microsoft.
• 8:26
  SPEAKER_04: If I wanted to prove a technology would work, I would get find the money and go engage Tim and his resources to actually show it, show it and get it done.
• 8:36
  SPEAKER_04: It made no sense, which is awesome.
• 8:38
  SPEAKER_04: It made no sense for me to actually go back to the product group to say, hey, can we build something like this, this, or this?
• 8:43
  SPEAKER_04: Because they didn't know.
• 8:45
  SPEAKER_04: And so putting it into you know, into Tim's hands and saying, Hey, let's create something that's you know really never been done before, or use this for this.
• 8:53
  SPEAKER_04: I have an idea.
• 8:54
  SPEAKER_04: What do you think?
• 8:55
  SPEAKER_04: You know, he'd take it, run with it, and then bring something back that was you know 10 times better than I ever thought.
• 9:01
  SPEAKER_04: In fact, um, Tim, I don't know if you remember, but uh we had the original sur uh Microsoft Surface was that game table thing, and it was super crude.
• 9:12
  SPEAKER_01: Bill Gates Dream, that was Bill Gates Dream, the Surface Table.
• 9:16
  SPEAKER_04: Yeah, surface table.
• 9:17
  SPEAKER_04: And uh, and so my thought was because I was doing federal healthcare stuff at the time.
• 9:23
  SPEAKER_04: I thought was, well, let's do a 3D heart and just show the power of what you could do with 3D hearts.
• 9:29
  SPEAKER_04: So we went to get some 3D, you know, rendered hearts and things like that.
• 9:33
  SPEAKER_04: And I basically just said, Hey, here's the idea, Tim.
• 9:35
  SPEAKER_04: What do you want to do?
• 9:36
  SPEAKER_04: You know, take it.
• 9:37
  SPEAKER_04: Actually, it was it was actually something off of what you had built for intermittent healthcare, actually.
• 9:43
  SPEAKER_04: And I wanted to take it and port it to the surface.
• 9:45
  SPEAKER_01: Yeah, you're you're saying all these names, and now it's 20 years later, so it doesn't matter, the NDA doesn't matter, but yeah, we we built we built the a 3D rendering of the heart so they could annotate the heart surgeries.
• 10:00
  SPEAKER_01: God, we were so before our time.
• 10:03
  SPEAKER_01: Talk about heart if you remember, Feather, uh an engineer named Kevin Kennedy was like the brilliance behind that, just a brilliant young man.
• 10:12
  SPEAKER_01: Um, yeah, and yeah, we repurposed that that heart numerous ways, shapes, and forms.
• 10:18
  SPEAKER_01: And and I know it saved lives, and it was super cool on the surface table because the surface table is a touch table, right?
• 10:26
  SPEAKER_01: So you could spin it and render it and and look at all, you know, annotate on the heart surgery and see the effects and blah blah blah.
• 10:32
  SPEAKER_01: But it also made it to the TV show Gray's Anatomy.
• 10:37
  SPEAKER_01: I don't know if you remember that feather.
• 10:39
  SPEAKER_01: Yeah, we and and you know how in the I don't know how much you know about you know television.
• 10:46
  SPEAKER_01: We're we're I'm in Southern California and we've built a lot of stuff for Hollywood, but you know, it's all smoke and mirrors, right?
• 10:52
  SPEAKER_01: That the these these actors, you know, spew out this medical mumbo jumbo.
• 10:57
  SPEAKER_01: They don't know what the heck they're saying.
• 10:58
  SPEAKER_01: And more times than not, it doesn't mean anything medically.
• 11:02
  SPEAKER_01: But yeah, they used the surface table in a couple episodes of Gray's Anatomy, and then we built them a wall version of it.
• 11:09
  SPEAKER_01: So that Dr.
• 11:10
  SPEAKER_01: McDreamy and what's the Dr.
• 11:12
  SPEAKER_01: McSteam.
• 11:14
  SPEAKER_01: And then uh, everyone, the people listening to your podcast are gonna want to know this trivia.
• 11:20
  SPEAKER_01: The answer is yes.
• 11:21
  SPEAKER_01: Sandra O, the actress, is like the nicest person in the world.
• 11:26
  SPEAKER_03: That's wonderful to hear.
• 11:27
  SPEAKER_03: You know, a lot of times you have these images of folks and you think, oh, they seem so nice, and then they're complete, not nice.
• 11:34
  SPEAKER_01: Honestly, in Hollywood, nine out of ten are not the type of people.
• 11:37
  SPEAKER_04: The the the other key thing too, Tim, is um it is amazing to me at how many people also, you know, utilized the the things that that you guys built and that we did and all that kind of stuff together as their promotions to other you know lofty heights, you know, stepping on the backs of others there, you know.
• 11:56
  SPEAKER_01: So yeah, I if there's a a question in here about what's your proudest accomplishment in your career, Tim.
• 12:04
  SPEAKER_01: It is without a doubt, all the careers that that my companies launched, you know, these these brilliant engineers just flourished uh in the you know, the internalogy company and then and then the the other company, the visibility company.
• 12:21
  SPEAKER_01: And and I'm I'm yeah I'm more proud of that.
• 12:24
  SPEAKER_01: And they come back now, you know.
• 12:26
  SPEAKER_01: They live a lot of our employees for Microsoft or Facebook or Amazon or or whatever.
• 12:33
  SPEAKER_01: Um they come back when Microsoft buys their venture.
• 12:37
  SPEAKER_01: Yeah, well, well, they they come back and say, honestly, the the happiest parts of my career were at internology, and they launched my career, and you know, but then they say these nice things, and I'm I'm proud of that.
• 12:48
  SPEAKER_01: I'm really proud of that.
• 12:49
  SPEAKER_01: That's awesome.
• 12:50
  SPEAKER_01: Yeah.
• 12:51
  SPEAKER_03: So you um you built uh an app dev company.
• 12:54
  SPEAKER_03: Tell tell us what uh, you know, when you when you left, when did you move on from Microsoft?
• 12:59
  SPEAKER_03: It was kind of a chronology, what what yeah?
• 13:01
  SPEAKER_01: I'll I'll try and make it quick, Seth.
• 13:04
  SPEAKER_01: But uh, so I worked on that that product team at Microsoft and I commuted actually from San Diego to Redmond, Washington.
• 13:12
  SPEAKER_01: It was brutal.
• 13:13
  SPEAKER_01: I was home for 32 hours a week over the weekend for almost two years.
• 13:21
  SPEAKER_01: You know, I'd fly in on um Sunday night and then fly home on Friday night.
• 13:27
  SPEAKER_01: And um, you know, that that's back in the day, but it was a different Microsoft back then.
• 13:31
  SPEAKER_01: You know, you you worked 18-hour days and you had all three meals in Building 25 because it that many people and so much to do.
• 13:40
  SPEAKER_01: It was just crazy and the pressure was nuts.
• 13:42
  SPEAKER_01: But it but you know, with me, I I love people.
• 13:45
  SPEAKER_01: So I met so many people.
• 13:47
  SPEAKER_01: I mean, if you know the name Scott Guthrie, he would be like the number two guy at Microsoft.
• 13:52
  SPEAKER_01: He owns he owns everything all up, right?
• 13:55
  SPEAKER_01: He's he's being groomed to be the next CEO.
• 13:58
  SPEAKER_01: Like he started about two months after me.
• 14:03
  SPEAKER_01: We we went to the same meetings.
• 14:05
  SPEAKER_01: He's now a friend of mine.
• 14:06
  SPEAKER_01: You know, we were so young back then, and now he's like one of the most powerful people in the world, if not, if or technology, if not the world.
• 14:15
  SPEAKER_01: So, anyways, um, the Queen, as I affectionately call her, my lovely bride of almost 32 years, said, uh, there is no way in hell we are moving to Redmond, Washington.
• 14:28
  SPEAKER_01: And you know, we're we're spoiled Southern Californians, and you know, I could have stayed at Microsoft and my career would have taken a much different path.
• 14:36
  SPEAKER_01: Um, and I'm like, yeah, yeah, cool.
• 14:39
  SPEAKER_01: You know, I get it.
• 14:40
  SPEAKER_01: The kids were small, small enough that I could do that travel thing, but they were getting big enough that I was missing stuff.
• 14:46
  SPEAKER_01: So I came home and I just founded another part of my life.
• 14:50
  SPEAKER_01: I walked bass backwards into an angel investment, not a lot of money, but enough of uh a college buddy of mine, still a dear friend, who um said, Tim, you want to start your own company?
• 15:01
  SPEAKER_01: Here's a bunch of money.
• 15:03
  SPEAKER_01: And sometimes in life you you regret what you've uh you know, you asked for.
• 15:07
  SPEAKER_01: And now I'm like, holy shit.
• 15:09
  SPEAKER_01: And my partner at the time, Dave Lynn from Microsoft, said, uh, well, we got to do this now.
• 15:15
  SPEAKER_01: So we started this custom app dev company because we didn't know better.
• 15:18
  SPEAKER_01: And and we were all technical people, you know, there's no business people in the firm back then.
• 15:23
  SPEAKER_01: We we we survived those tough times um economically on sheer grit and engineering talent.
• 15:30
  SPEAKER_01: And um, you know, it's a service-based company.
• 15:33
  SPEAKER_01: We didn't build a product, we were building products for other companies.
• 15:37
  SPEAKER_01: And I mean, Feather knows this, but that's I mean, we were building for the Fortune 1000 some huge companies, completely NDA type stuff, including Microsoft.
• 15:49
  SPEAKER_01: We were building product bits for Microsoft, and um, you know, um my joke at the time, I I left internology just just a few months ago, by the way.
• 16:00
  SPEAKER_01: Um, and and some other people run it now.
• 16:03
  SPEAKER_01: I uh sold my interest in in internology.
• 16:06
  SPEAKER_01: And but my joke about internology always was an awesome company to work at, you know, great projects, high profile, like the CNN Magic Wall and things like that.
• 16:16
  SPEAKER_01: Just a really tough company to own, you know, because it you're it you're always on the elephant hunt.
• 16:22
  SPEAKER_01: It you know, you're always looking for different projects.
• 16:25
  SPEAKER_01: It's not like you're doing maintenance on, you know, you have this legion of sales force and things like that.
• 16:32
  SPEAKER_01: Right.
• 16:32
  SPEAKER_03: So, you know, right during the living on licensed revenue, you're not living on licensed revenue, featable revenue.
• 16:39
  SPEAKER_03: You're you're you get to eat what you kill.
• 16:42
  SPEAKER_01: So you get that, you definitely get that because that's a great way to describe it.
• 16:48
  SPEAKER_01: Um, but it's you know, it's a hard business, you know, that you can only build so many CNN magic walls, and you can only build so many components for Microsoft's latest server product or latest.
• 17:00
  SPEAKER_04: Hey, hold on, hold on just a sec.
• 17:01
  SPEAKER_04: So Seth, one thing I want to highlight.
• 17:03
  SPEAKER_04: So you know, he's he brought up the CNN magic wall.
• 17:06
  SPEAKER_04: That was the key tool that they utilized to um go through which, you know, for the presidential um race, which you know, projections that they were gonna go.
• 17:18
  SPEAKER_04: So everybody in in the world has seen this thing, and the and the news guys were going through and saying, Well, look at if this one goes here and and this and you know, this county gets this many votes and this one, so they had to they built that entire thing.
• 17:31
  SPEAKER_01: And that actually has a great story behind it because the first version we built was in Win32 or dot dot WPF specifically, and um, because of the SLA on that application, which is 100%, you cannot crash software when you're live not 99.9 no no no when you're live to 250 million people worldwide cannot crash software.
• 18:02
  SPEAKER_01: So we were exposing bugs in XAML.
• 18:06
  SPEAKER_01: I think it's cool to talk about this now.
• 18:08
  SPEAKER_02: Yeah, yeah, yeah.
• 18:09
  SPEAKER_01: We were exposing bugs in in the XAML stack uh of WPF, and Microsoft was fixing them hot because it was so high profile.
• 18:20
  SPEAKER_01: Just imagine that world, you know?
• 18:23
  SPEAKER_01: Um amazing app, DirectX, oh god, XAML.
• 18:28
  SPEAKER_01: Yeah, and it and they're still using it, right?
• 18:30
  SPEAKER_01: You still see it almost every on CNN.
• 18:33
  SPEAKER_01: And the other the other companies now copied it.
• 18:38
  SPEAKER_03: How long were those projects?
• 18:39
  SPEAKER_03: When you did something like that, I mean, you know, how many people, how long were those projects?
• 18:44
  SPEAKER_01: That was a big one.
• 18:45
  SPEAKER_01: That was abnormally large because of the 100% SLA, because it was WPF, and because of the speeds of the data that you took.
• 18:55
  SPEAKER_01: And then also it had a it had a producer console because I don't know if you know how this works.
• 19:02
  SPEAKER_01: Um, like John King from CNN or Wolf Blitzer or Anderson Cooper, whoever, you know, they've they've got an earphone in their in their mic, and the producer or director or whoever's driving the show is talking to them constantly while they're talking, you know.
• 19:17
  SPEAKER_01: So and and uh so so we had a separate application where you could introduce content into the magic wall depending on what was going on live.
• 19:29
  SPEAKER_02: Yeah, that's awesome.
• 19:30
  SPEAKER_01: Elaborate application.
• 19:31
  SPEAKER_01: So that one took, I don't know, six months.
• 19:34
  SPEAKER_01: Um, probably a team of four to six brilliant people.
• 19:38
  SPEAKER_01: But the typical internality project at the time uh would you know was you know three, four months with with a small team of three, four people.
• 19:47
  SPEAKER_01: I I can tell you this this there's a if we do another interesting Hollywood thing.
• 19:51
  SPEAKER_01: John King of CNN really gets all the credit for the design on the magic wall and the functionality.
• 20:00
  SPEAKER_01: That was his brainchild, and he was literally our product manager driving features.
• 20:06
  SPEAKER_01: By the way, he is a totally nice guy, um, John King.
• 20:11
  SPEAKER_01: But realize this John King is like any other actor from CAS uh from what Creative Argents Agency, Creative Artists Agency in Newton Hills, right?
• 20:22
  SPEAKER_01: He's like any other actor, he makes a ton of money, it's negotiated by his agent, and he could move to Fox in a heartbeat if they doubled his salary.
• 20:35
  SPEAKER_01: You know, he he's not you know pinned to to liberals or or or uh a political party or anything like that.
• 20:43
  SPEAKER_01: He is just good, you know, he could survive on any network, Fox, whatever.
• 20:48
  SPEAKER_04: Yeah.
• 20:49
  SPEAKER_04: Let me ask let me ask you this, because I think the one key thing, you know, throwing out some of those those terms, you know, centered around what you know XAML is and the Windows Presentation Foundation and some of those key areas, which basically, from from my lens and perspective, has led you into a ton of because those are unbelievably simple, and that's the complexity, unbelievably simple visual interactive applications.
• 21:17
  SPEAKER_04: And you know, I'd love to get your take from where you went, you know, with building all that cool stuff to now, you know, what this looks like and what it means for you know, the you know, the artificial reality piece and what this looks like for you know for the future and what you guys are focused on and what you have been focused on and how those core areas have got you now thinking about contemporary pieces, metaverse type stuff, all of those those areas.
• 21:43
  SPEAKER_04: I'd love to get your take on on you know how you know those visual elements are now basically preceding these new visual elements and the interactivity of it, right?
• 21:54
  SPEAKER_04: And what we're gonna be facing now and in the future.
• 21:57
  SPEAKER_01: Yeah, that that sounds like a seated question, feather.
• 22:00
  SPEAKER_01: But I know we didn't seed that one.
• 22:01
  SPEAKER_01: You know, I'm uh I guess infamous for some bold predictions.
• 22:06
  SPEAKER_01: Yeah, uh, which is great.
• 22:09
  SPEAKER_01: If you if you have self-deprecating humor like I do, meaning you can make fun of yourself, uh it's great to to make predictions.
• 22:18
  SPEAKER_01: And one of my really wrong predictions was the death of the internet when I was when I was young, you know, because I came up from a I came up from a Win32 background.
• 22:32
  SPEAKER_01: Well, while I was working at Microsoft, of course.
• 22:35
  SPEAKER_01: And why was it going to die?
• 22:36
  SPEAKER_01: What was the what was the context?
• 22:43
  SPEAKER_01: I vividly remembered my boss saying, well, you know, or Bill Gates writing that famous email which I got, which is we're late to the internet, you know, we got a switch on everything.
• 22:53
  SPEAKER_01: And so so part of my team's job was that the first internet server, IAS4, I believe.
• 22:58
  SPEAKER_01: It literally was the first version, but they called it four for some bizarre reason.
• 23:02
  SPEAKER_01: Oh, because of MT.
• 23:03
  SPEAKER_01: That's why.
• 23:04
  SPEAKER_01: Because MT was in its its fourth version.
• 23:06
  SPEAKER_01: So, in any event, I looked at this HTML thing and went, God, this is bullshit.
• 23:11
  SPEAKER_01: You know, this isn't programming, this is stupid.
• 23:13
  SPEAKER_01: This form submit is a joke.
• 23:16
  SPEAKER_01: And then so I started studying.
• 23:17
  SPEAKER_01: I I I I love to learn still at my age, I just love to learn.
• 23:21
  SPEAKER_01: So I looked into this T TCPIP thing.
• 23:24
  SPEAKER_01: And because we at the time, you remember, Seth, we had beautiful binary protocols, you know, and they were stateful.
• 23:33
  SPEAKER_01: And and uh so I looked at this TCPIP thing, I'm like, this is bullshit.
• 23:38
  SPEAKER_01: Who wouldn't who would do this?
• 23:40
  SPEAKER_01: This is gonna die because it just the way it routed traffic, you know, with all the router hops was so inefficient.
• 23:49
  SPEAKER_01: I'm like, this is gonna die under its own scale because you know, you if you make a cell phone call, you know, it's like two hops.
• 23:58
  SPEAKER_01: But if you pick, you know, TimHuckabee.com, which I broke last night and his the homepage is all screwed up, um, it, you know, that's like 35 router hops.
• 24:09
  SPEAKER_01: What I didn't know, what I didn't anticipate was that Cisco would make a bad thing worse by creating that first 2550 router.
• 24:21
  SPEAKER_01: And because of routing, they overcame the hop problem.
• 24:25
  SPEAKER_01: They they made it efficient.
• 24:27
  SPEAKER_01: And then the next, so that leads into Feather's question.
• 24:31
  SPEAKER_01: So we're we, you know, we're building Win32 apps in WPF, which is basically a wrapper of Win32, right?
• 24:37
  SPEAKER_01: Uh anything.NET is.
• 24:40
  SPEAKER_01: And uh you have this incredible surge of the internet.
• 24:43
  SPEAKER_01: And I probably said something, well, they can never do the CNN magic ball in a web app.
• 24:48
  SPEAKER_01: Well, guess what?
• 24:51
  SPEAKER_01: Yeah, we had to rebuild the whole thing in web, and it was actually a pleasure uh with uh some JavaScript libraries like um uh uh what's called something 3D, uh Web3D.
• 25:06
  SPEAKER_01: There's a there's a just a fantastic 3D JavaScript script library we could pull off all those animations with.
• 25:13
  SPEAKER_01: And uh now, you know, cut cut to now, even in our mobile apps.
• 25:18
  SPEAKER_01: Well, so one of the famous predictions I made was hey, we're gonna the the web got really popular, popular web apps, and then I said, Well, now the smartphone is here, you know, we're gonna start building Win32s again, or essentially compiled apps.
• 25:33
  SPEAKER_01: That was my bold prediction.
• 25:34
  SPEAKER_01: And that one I was spot on with.
• 25:37
  SPEAKER_01: But cut to today, and back to Feather's point.
• 25:40
  SPEAKER_01: You know, we're we're building most of our mobile apps with web technology because the web tech has gotten so good.
• 25:48
  SPEAKER_01: And I just underestimated how good it got.
• 25:52
  SPEAKER_01: And at the same time, the problem to overcome, of course, is wrapping a wrap wrapper, meaning latency and and performance and stuff like that.
• 26:02
  SPEAKER_01: Well, Moore's Law has continued on its path, and you can equate Moore's Law essentially to processing speed.
• 26:10
  SPEAKER_01: I mean, you can get there.
• 26:12
  SPEAKER_01: And our procs are so good these days.
• 26:15
  SPEAKER_01: I mean, Intel and its competitors are really kicking ass in these high-performance procs, CPUs, which can overcome, you know, these these really high-level platforms and language, programming languages.
• 26:31
  SPEAKER_01: I find that super interesting.
• 26:34
  SPEAKER_01: Yeah, and then the other the other problem, of course, that we all pointed at was well, battery um tech has to improve, or else we're screwed.
• 26:44
  SPEAKER_01: Feather, you were around when we were we were on the Windows team when we were uh testing the compact iPack.
• 26:51
  SPEAKER_01: Oh yeah, yeah, it was there.
• 26:52
  SPEAKER_02: Yeah.
• 26:53
  SPEAKER_01: Here's another here's another great story.
• 26:56
  SPEAKER_01: We were running Windows on the compact iPack, and the current battery tech um gave us 90 seconds at at full charge.
• 27:07
  SPEAKER_01: At full charge gave us 90 seconds.
• 27:10
  SPEAKER_01: This is you know, 20 years ago.
• 27:12
  SPEAKER_01: Uh I don't know if that was acceptable today.
• 27:15
  SPEAKER_01: Yeah, but guess what?
• 27:16
  SPEAKER_01: Battery tech has has kind of kept pace.
• 27:19
  SPEAKER_01: And we always said whoever invents the battery wins.
• 27:23
  SPEAKER_01: So, you know, those companies that are doing these, you know, high-tech batteries like Tesla, I guess, and and companies like that, are are doing well, really well.
• 27:35
  SPEAKER_03: It is so astounding to think about, you know, I'm having I'm holding my my iPhone in my hand here, and that was a physical impossibility 20 years ago.
• 27:45
  SPEAKER_03: You know, when you think about all the problems that had to be in in most people's minds, right?
• 27:49
  SPEAKER_03: In the experts' mind, these things could not be solved and put to the scale, and and it's like problem after problem after problem that had to be solved at so many different levels to make this stuff possible.
• 28:02
  SPEAKER_03: And now, you know, no one could really imagine it uh 20 years ago that we would have this kind of power and capability, and you know, no one knew that they needed, you know, all of their music available all the time, maybe connected all the time.
• 28:14
  SPEAKER_03: It's just a fantastic, right?
• 28:16
  SPEAKER_03: So now we're we're here at this really critical inflection point, I think, in really in human history, where so many things are coming together.
• 28:24
  SPEAKER_03: And when you start looking at, you know, the work that you've done with with with um visual and video and and uh and all of those things that that came before that you've been working with all these years.
• 28:36
  SPEAKER_03: Now you're really looking at uh how computer vision, facial recognition, uh AI, and you know, all of these other elements around meshing the physical with the digital are coming together.
• 28:49
  SPEAKER_03: Talk about what tell me about your perspective now and and what are we on the cusp of?
• 28:55
  SPEAKER_03: Because that's what's really exciting to me.
• 28:57
  SPEAKER_03: I think we're gonna see many, many changes just in the next few years.
• 29:00
  SPEAKER_03: So you're gonna continue to accelerate this pace of change and innovation.
• 29:05
  SPEAKER_03: And what are we what are we going to be looking at?
• 29:07
  SPEAKER_03: What's what's is uh how is the metaverse world and and I know there's this question down here about quantum computing.
• 29:14
  SPEAKER_03: I know that's kind of jumping ahead, but to me, that's like so difficult for me to comprehend and there seems to be so many uh issues to overcome.
• 29:23
  SPEAKER_01: I know I just jumped around a little bit, but I just wanted to kind of look at Seth, you are spot on, just like Feather's comment, uh, you're just spot on with what I believe.
• 29:35
  SPEAKER_01: And and um let me just start with, you know, we just a few years ago when we had this wave of machine learning, just maybe four or five years ago, you know, I like you guys, I had the pleasure to meet with some really sharp scientists who were trying to solve some really hard problems, whether it be genetic or even physics at an atomic level.
• 30:02
  SPEAKER_01: Uh, and and at the time, four or five years ago, this machine learning craze hit a big dead end because we didn't have enough processing power.
• 30:14
  SPEAKER_01: And I just couldn't get my arms around that because we had this cloud thing and we had more processing power in the two major cloud players than I could ever dream of needing.
• 30:27
  SPEAKER_01: I'm like, what do you mean you don't have enough processing power?
• 30:30
  SPEAKER_01: It's like, well, I would need Azure and I would need AWS and I'd need to replicate them by you know 100,000 for the processing power I need to track photons and electrons to truly battle, you know, some type of cancer or something like that.
• 30:53
  SPEAKER_01: And you know, it's hard to get your your head around needing that much processing power.
• 30:59
  SPEAKER_01: And then we're we're back to Moore's law, you know, we would it's still continuing on that, you know, what is it, doubling the amount of trist transistors on a circuit board every two years.
• 31:10
  SPEAKER_01: Yeah.
• 31:10
  SPEAKER_03: And we're coming up on we're coming up on a when it when it runs into it seems to run into physical limits, there's another dimension, right?
• 31:21
  SPEAKER_01: Because you can only make things so thin, right, in terms of the number of of uh atoms uh, you know, that a that an element can be, but but then there's three-dimensional, and then there's there's just so many other when when you get and this was just explained to me by Pat Hines, who feather you may remember.
• 31:40
  SPEAKER_01: Yeah, for sure.
• 31:42
  SPEAKER_01: He's uh got famous in security, and he's the one who's teaching me about quantum physics and quantum computing.
• 31:49
  SPEAKER_01: But in any event, um the these problems get multidimensional, and that's why we scale out um in our current tech, um, which to me is just mind-boggling.
• 32:04
  SPEAKER_01: But so that leads us to well, if we don't have enough CPU power, and by the way, if if you follow Moore's law by 2025, and and we're in 2022, so just in three years, if Moore's law continue, and there's nothing indicating it won't, our CPUs will calculate at the speed of the human brain.
• 32:25
  SPEAKER_01: Just keep your arms around that.
• 32:27
  SPEAKER_01: That doesn't, you know, that doesn't mean the machines are taking over the singularity, but it does mean we can solve some really interesting problems in artificial intelligence and medical and research and healthcare.
• 32:42
  SPEAKER_03: I think that before we go on, I just want to take a moment um and uh uh be sure to remind our listeners that we're uh talking to AI machine learning expert Tim Huckabee.
• 32:53
  SPEAKER_03: And I want to thank our sponsors, Marketing AI Institute, Simpler Media, and uh CMS Wire, and of course early information science.
• 33:00
  SPEAKER_03: So with that, let's get back to it.
• 33:02
  SPEAKER_03: And uh Tim, I want to just make a comment about biological processes in the human brain.
• 33:07
  SPEAKER_03: And this is where my biology and chemistry background kind of come in.
• 33:12
  SPEAKER_03: Uh, I'm still a big geek, and you know, I I I subscribe to multiple science scientific publications uh and try to get through them.
• 33:19
  SPEAKER_03: But you know, the one thing when people start talking about equating computing technology and computing power to the human brain, one of the things that I want to say is that there's a hundred different neurotransmitters in the brain, a hundred different ones, and they're all analog, right?
• 33:34
  SPEAKER_03: They're all analog, they all work at varying levels of concentration and and uh along gradients.
• 33:41
  SPEAKER_03: And then you add to that, you know, a single neuron can be connected to 10,000 other neurons, and you have three billion of them.
• 33:49
  SPEAKER_03: And I think that level of complexity is you know, the the the scale of of combinatorial uh mathematics and combinatorial physics and and all of that is just still beyond what what we can conceptualize right now.
• 34:03
  SPEAKER_03: But then you know, you add in these other dimensions like quantum computing, which again is still beyond my my ken.
• 34:10
  SPEAKER_03: I I uh I have a hard time with uh uh uh positrons traveling backward in time and being annihilated before they're created and other positive right to see dimensions change.
• 34:27
  SPEAKER_03: It's like what so tell us what your thoughts are about quantum computing and how you know when you look at those levels of complexity and the possibility and the fact that you know a quantum computer will allegedly be able to break any encryption.
• 34:42
  SPEAKER_03: Well, where does that leave us?
• 34:43
  SPEAKER_03: So maybe you could just kind of posit a little bit on those themes and those ideas.
• 34:49
  SPEAKER_01: Fair enough.
• 34:50
  SPEAKER_01: Just understand, like you, Seth, I fully admit I am no expert and literally have zero expertise in quantum computing.
• 34:59
  SPEAKER_01: I'm just trying to get my arms around it from like a high level because it had it like artificial intelligence, it has this potential to end the world and at the same time to make the world so much better.
• 35:17
  SPEAKER_01: Right?
• 35:18
  SPEAKER_01: It's it's it's it's it's like the the metaphor between nuclear technology, you know.
• 35:23
  SPEAKER_01: In nuclear technology, we have the worst bombs in the world and can literally exterminate the planet, but at the same time, we got the microwave oven, which is pretty darn cool, right?
• 35:33
  SPEAKER_01: So nuclear magnetic resonance spectroscopy, and you know, yeah, that's all this greatness came out of nuclear technology, right?
• 35:44
  SPEAKER_01: Yeah, so and quantum, as you mentioned, quantum computing does have, in fact, the experts are predicting the end of security as we know it within just a few short, like three, four years.
• 36:00
  SPEAKER_01: Um, because it can crack any supposedly quantum computer can crack anything we have right now, which means the whole the whole Bitcoin thing, the whole the whole blockchain thing, you know, every corporation, it all gets exposed to bad guys.
• 36:20
  SPEAKER_01: Um at the same time, you know, if you were able to build the algorithms, the inputs, the problem with quantum computing is you know that the outputs, it it it doesn't have it doesn't really have inputs or outputs.
• 36:36
  SPEAKER_01: What it has is this incredible analytical engine in between.
• 36:40
  SPEAKER_01: So you literally have to remember when when computers you had to flip switches and stuff like that to get it to run anything, right?
• 36:48
  SPEAKER_01: Those are inputs, right?
• 36:50
  SPEAKER_01: Right.
• 36:50
  SPEAKER_01: So the problem with quantum computing is there's a ton of really high-end, really hard work just to get an input into the thing so it can analyze it.
• 37:04
  SPEAKER_01: And um, I'm sure smart people are working, you know, on that.
• 37:08
  SPEAKER_01: Oh, yeah.
• 37:08
  SPEAKER_01: Um, so that we can get inputs and outputs, and its potential is great.
• 37:12
  SPEAKER_01: And yeah, they're talking about commodity quantum computers and things like that.
• 37:17
  SPEAKER_01: But getting your arms around how it works, like you mentioned, Seth, is really tough.
• 37:22
  SPEAKER_01: Like we came, me, feather, and I just met you, but we came from ones and zeros, and it's really easy to conceptualize binary, it really is, or hex for that, yep, right?
• 37:36
  SPEAKER_01: But in quantum computing, as I'm told, a one can turn into a zero, or it could be half of a one, or it could morph back to one and a half.
• 37:45
  SPEAKER_01: You did that.
• 37:46
  SPEAKER_01: That's what I'm trying to get my arms around.
• 37:48
  SPEAKER_01: And that's that basic concept of quantum is not only its bold potential, but also why it's so difficult to comprehend.
• 37:59
  SPEAKER_01: Yeah, yeah.
• 38:00
  SPEAKER_04: Let's let's let's do this because I I'd love to get your take.
• 38:04
  SPEAKER_04: I mean, part of the path of of uh of where you've gone is now getting into augmented reality.
• 38:10
  SPEAKER_04: And you know, the the interesting part, you know, the the the key about like all the quantum stuff is that you know there's definitely nefarious people that will prove it out before we can figure out how to use it for business, right?
• 38:23
  SPEAKER_04: Is usually how it goes.
• 38:24
  SPEAKER_04: Unfortunately, yeah.
• 38:25
  SPEAKER_04: Unfortunately, yeah.
• 38:26
  SPEAKER_04: The but I I love the fact though that people are trying to get ahead and figure out what this looks like for augmented reality type of scenarios and how that's gonna, you know, play a role.
• 38:36
  SPEAKER_04: I'd love to get your take, you know, on what you've been doing there, because I know you've you've had a lot of work that you've done there and what that looks like.
• 38:43
  SPEAKER_01: Well, I I have this love affair with computer vision, like this 10-year love affair with the you know, the AI stack, if we could call it that, is huge.
• 38:53
  SPEAKER_01: There's a lot, a lot of stuff in AI.
• 38:56
  SPEAKER_01: But I I just love computer vision.
• 38:59
  SPEAKER_01: And that's that's a computer, some form of lens or endpoint device, uh interpreting what it sees.
• 39:06
  SPEAKER_01: Picture that in quotes.
• 39:07
  SPEAKER_02: Yep.
• 39:08
  SPEAKER_01: Um, and that was the the genesis to the augmented mixed reality thing.
• 39:13
  SPEAKER_01: You know, we we'd been or I'd been working in in computer vision for for a long time.
• 39:19
  SPEAKER_01: And granted, some of the projects we did were a little bit creepy, you know, looking at the human face.
• 39:25
  SPEAKER_01: You know, we didn't do it for ourselves, we did it for obviously for ISVs and companies.
• 39:30
  SPEAKER_01: But um, they used it to, you know, look at the human face and get an emotional profile, get a demographic profile, age, race, gender, um, you know, age profile.
• 39:41
  SPEAKER_01: And of course, person identification, which is called mostly called facial recognition.
• 39:47
  SPEAKER_01: But, you know, the world of object recognition was was so much more enticing to me.
• 39:53
  SPEAKER_01: And that would mean, you know, say I want to, I don't know, identify animals or trees or traffic patterns or, you know, yeah, uh cellular structures for that matter.
• 40:04
  SPEAKER_01: Because it, you know, this doesn't this doesn't have to be, you know, a 2D type world.
• 40:08
  SPEAKER_01: This that you go into uh atomic level and and have computers look at stuff.
• 40:14
  SPEAKER_01: And that, you know, that led into augmented reality and then mixed reality, which I think is a term made up by Microsoft.
• 40:22
  SPEAKER_01: But you know, as opposed to virtual reality, if you're not familiar with this world, virtual reality is like you see in the TV commercials at the Super Bowl.
• 40:30
  SPEAKER_01: You put on a pair of lenses and you don't see anything else in the real world.
• 40:33
  SPEAKER_01: You know, you're only looking into this view that the headset is giving you.
• 40:39
  SPEAKER_01: Augmented reality is where you're looking at the physical world, you're looking to the real world, but but these artificial things appear, these virtual things.
• 40:48
  SPEAKER_01: So, you know, picture me gazing across the street, which I'm doing now, and looking at a tree, but I'm seeing a bunch of animals in a virtual way in the tree.
• 41:00
  SPEAKER_01: Um and then mixed reality, which I get, did Microsoft make that up?
• 41:07
  SPEAKER_01: This holo lens thing, um, you're basically wearing glasses and now you're interacting, you know, and the the best holo lens use cases to get your arms around are where you're you're learning about some type of procedure, meaning I'm looking at a broken, I don't know, automobile engine, and it's showing me how to, it's identified what's wrong with it and showing me how to fix it in an you know augmented type way.
• 41:35
  SPEAKER_01: Well, it's the same problem, or at least it has been uh processing power and battery.
• 41:42
  SPEAKER_01: Same problem.
• 41:44
  SPEAKER_01: And then of course, so so and no one wants to wear a holo lens, at least my wife wouldn't let me wear a holo lens in a public place like an airport.
• 41:53
  SPEAKER_01: So we we we remember used to see people in Google glasses walking around, and you think like, oh my god, you're total geek.
• 42:02
  SPEAKER_01: You're never gonna that way.
• 42:05
  SPEAKER_04: The best were the parodies of people walking around with those glasses running into stuff, you know.
• 42:10
  SPEAKER_04: Right.
• 42:12
  SPEAKER_01: So funny.
• 42:13
  SPEAKER_01: So reality went to the the smartphone because the smartphones started to get real CPUs, like you talk about your iPhone, and I have a fancy iPhone, and and you you could do some really interesting stuff by pointing your phone at something.
• 42:29
  SPEAKER_01: And especially what what really got me is this concept of spatial anchors, and and that would be you pin something virtual to a very physical place in the world to like a two-inch resolution, meaning I stick this this spatial anchor.
• 42:53
  SPEAKER_01: I'm looking out the window across the street, so I stick it over there next to that tree, and that means anytime someone comes by with their smartphone and runs an application, they point it at this tree, and it gives them this augmented reality experience.
• 43:09
  SPEAKER_01: And the experience might be I don't know, who's a famous scientist?
• 43:12
  SPEAKER_01: Who is it?
• 43:13
  SPEAKER_01: Who's the guy that ate trees back in the 70s?
• 43:16
  SPEAKER_01: Remember the pine, the guy that ate pine trees?
• 43:19
  SPEAKER_01: I I ate a lot of trees in the 70s.
• 43:21
  SPEAKER_01: So I, anyways, your famous scientist point at the tree.
• 43:26
  SPEAKER_01: Yule Gibbons, there we go.
• 43:28
  SPEAKER_01: Yule Gibbons, you point at the tree, and Yule Gibbons appears on your phone and starts talking about the tree and the animals there, and you ask him questions like, hey, Yule, what what are there any mammals that hang around this tree, or does this tree produce fruits?
• 43:44
  SPEAKER_01: And and Yule, virtual Yule, who's been long gone, probably for 50 years, is interacting with you back and forth.
• 43:52
  SPEAKER_01: I mean, that that that's and you're learning by him talking.
• 43:56
  SPEAKER_01: And if you're a sports guy, like I am, and Feather is.
• 44:00
  SPEAKER_01: You know, maybe maybe you pointed at the the stadium down here in in Petco Park, the baseball stadium down here in San Diego, and Tony Gwynn appears, who who is no longer with us, died of cancer, and you start talking with Tony Gwynn, right?
• 44:14
  SPEAKER_01: Hey Tony, what was your favorite year?
• 44:17
  SPEAKER_01: Um, and Tony replies back, well, most people would think was the year we went to the World Series in 1997 against the Yankees, but really it was when I was at San Diego State and we were playing, you know, I don't know, BYU in the national championship, you know, stuff like that.
• 44:35
  SPEAKER_01: Um, that we're talking about intense processing power and battery.
• 44:39
  SPEAKER_01: We're all we're all back to that.
• 44:41
  SPEAKER_01: That we've got the algorithms, we got the tech.
• 44:44
  SPEAKER_01: We're we're just waiting for the hardware to get its shit together, basically.
• 44:48
  SPEAKER_01: And we have been for 25 years.
• 44:50
  SPEAKER_01: You know, software is all waiting for the hardware to get better.
• 44:54
  SPEAKER_01: And the data.
• 44:56
  SPEAKER_01: The data.
• 44:56
  SPEAKER_01: Uh, that's its own unique problem.
• 44:59
  SPEAKER_01: Yeah, that's another that's another project I'm on right now.
• 45:04
  SPEAKER_01: You know, the whole yeah, go ahead.
• 45:08
  SPEAKER_01: Go ahead.
• 45:08
  SPEAKER_01: Sorry, Seth, I stepped on you.
• 45:10
  SPEAKER_01: Well, the the whole machine learning thing dies a quick, awful death because of data.
• 45:18
  SPEAKER_01: It is so hard to get data in computer vision.
• 45:22
  SPEAKER_01: Imagine training my what was my my uh use case earlier, recognizing trees.
• 45:29
  SPEAKER_01: Imagine the amount of data you'd need to recognize every tree in the universe.
• 45:35
  SPEAKER_01: You'd need literally millions of images, and they need to be categorized, right?
• 45:41
  SPEAKER_01: You draw a bounding box around them, and then you say this is a pine tree, and you draw you know another picture up, this is a cottonwood, but millions, if not tens of millions of images.
• 45:52
  SPEAKER_01: Data is our huge, huge problem in this world.
• 45:56
  SPEAKER_01: There's an awesome company.
• 45:57
  SPEAKER_01: I don't know if you guys know this, but they're they're literally the only player in the space that I know of called Inno Data.
• 46:05
  SPEAKER_01: It's a huge company, by the way, publicly traded company, huge company.
• 46:10
  SPEAKER_01: Um, and that's what they do.
• 46:11
  SPEAKER_01: That's the old that's all that they do.
• 46:14
  SPEAKER_01: Catalog data?
• 46:15
  SPEAKER_01: Data, yeah.
• 46:16
  SPEAKER_01: And and they they create it virtually, they get it synthetically, they get it naturally, they license it.
• 46:24
  SPEAKER_01: But if you want if you want to recognize trees, you go to inadata and you say, How much is it going to cost to get the data for a tree project?
• 46:34
  SPEAKER_01: And they figure it all out for you.
• 46:35
  SPEAKER_01: Great business to be in.
• 46:38
  SPEAKER_03: And you know what?
• 46:38
  SPEAKER_03: Here's the other though.
• 46:40
  SPEAKER_03: But then there's really the last mile of I what I call cognitive AI and the interpretation.
• 46:46
  SPEAKER_03: Like when you talk about that uh machine uh that that augmented reality or mixed reality to show you how to repair the engine, well, that has to come from you know the heads of engineers and design drawings and uh corporate knowledge based.
• 47:04
  SPEAKER_03: And so for organizations to say, yes, we want to use this, they have to get that data house in order.
• 47:10
  SPEAKER_03: And that's not going to be genericized or or a generalized model or even a generalized language model.
• 47:17
  SPEAKER_03: That's going to be based on the unique knowledge, expertise, terminology, uh, processes, procedures, expertise of the enterprise.
• 47:26
  SPEAKER_03: And so I think a lot of these things that we are going to see are just going to take a lot of work in getting those uh corporate data houses in order.
• 47:35
  SPEAKER_03: Which is different from what you're talking about.
• 47:36
  SPEAKER_03: Inadata.
• 47:37
  SPEAKER_03: Inadata is doing things around you know large data sets where they can provide training uh data and label data for more public things.
• 47:45
  SPEAKER_03: But when you look at the the inner workings and the competitive advantage, that's going to be an internal piece.
• 47:50
  SPEAKER_03: But I but I do want to I do want to before we finish, we only have a couple of minutes left.
• 47:54
  SPEAKER_03: I do want to get your thoughts on the ethics of AI because when you start looking at all these data issues, whether it's corporate data or public data or personal data or private data or you know, the algorithms.
• 48:06
  SPEAKER_03: And I know we can do a whole session on this, but why don't you give a couple of minutes of your thinking around what's going on in in and the ethics space around AI?
• 48:15
  SPEAKER_01: Yeah, sure.
• 48:16
  SPEAKER_01: Um, but I just have to comment.
• 48:18
  SPEAKER_01: What?
• 48:18
  SPEAKER_01: We're we're we're done.
• 48:19
  SPEAKER_01: You know, we could talk about this for hours.
• 48:23
  SPEAKER_04: We can keep going.
• 48:24
  SPEAKER_01: I mean, no, no, no.
• 48:27
  SPEAKER_01: You gotta you gotta continue.
• 48:29
  SPEAKER_01: Yeah, you guys are welcome.
• 48:30
  SPEAKER_01: I'd love to come back.
• 48:32
  SPEAKER_01: Um, yeah, most of the keynotes I do these days, and then I have a technical session that augments the keynote is about the ethics of AI.
• 48:41
  SPEAKER_01: And it's it's super interesting for me and the audience.
• 48:45
  SPEAKER_01: I I'm a I'm a demo type guy on stage.
• 48:48
  SPEAKER_01: I just can't rely on PowerPoint, but you know, and hold the audience, at least I think I can.
• 48:54
  SPEAKER_01: So I I demo some compelling stuff, and then I demo some stuff that's been used for bad.
• 49:00
  SPEAKER_01: And um, well, well, well, let's face it, a a 12-year-old can get the tools for free on the internet to do some really bad stuff.
• 49:10
  SPEAKER_01: But a lot of the ethics of AI do is around computer vision, because that's my thing, right?
• 49:15
  SPEAKER_01: And and uh we have this privacy law issue.
• 49:19
  SPEAKER_01: Um, and it's not just a US thing, this is a worldwide, super interesting debate.
• 49:25
  SPEAKER_01: And it's a balance between security and what you call what one calls privacy.
• 49:33
  SPEAKER_01: So there's a to make it complicated, we privacy law is at a federal level in the United States, and um, there's also privacy law that supersedes it at a state level, and now we have uh city civic-based uh privacy law that supersedes that.
• 49:53
  SPEAKER_01: And the leading one is is San Francisco.
• 49:56
  SPEAKER_01: San Francisco has its own privacy law, which is more strict than the state of California, which is more strict than the United States.
• 50:03
  SPEAKER_01: In San Francisco, you can't use computer vision.
• 50:06
  SPEAKER_01: The police have the their tools been taken away from them.
• 50:10
  SPEAKER_01: So they can't look for the bad guy.
• 50:12
  SPEAKER_01: You know, you see those, you see those um, you know, on the news all the time or on some of those those shows where the police are wearing cameras and you know, they the the they they have tools in their police cars that are looking at license plates and you know, figuring out who it is and if they're bad guys and all that.
• 50:31
  SPEAKER_01: All that has been taken away in the city of San Francisco uh because they the policymakers there, not the people, I would say, but the policymakers have decided that's an invasion of privacy.
• 50:47
  SPEAKER_01: So there's where you get into, you know, do you really want terrorists running freely through San Francisco?
• 50:55
  SPEAKER_01: You know, what what's this balance between privacy just being safe?
• 51:03
  SPEAKER_01: Right?
• 51:03
  SPEAKER_01: So that there's an but at the same time, you've got this ethical issue, and and let's face it, you know, the Googles and the Facebooks and the Instagrams of the world have built multi-billion dollar industries on breaking privacy law, they are tracking everything you do, and some of it is super creepy, super creepy, right?
• 51:26
  SPEAKER_01: Yep, yeah.
• 51:28
  SPEAKER_01: So it's an interesting debate.
• 51:30
  SPEAKER_01: Um, and that's why I love talking about it.
• 51:32
  SPEAKER_01: Yeah.
• 51:33
  SPEAKER_01: Yeah.
• 51:33
  SPEAKER_01: Right.
• 51:35
  SPEAKER_03: Um, anything else coming up that uh you want to let people know about?
• 51:39
  SPEAKER_03: Um, do you have a keynote coming?
• 51:41
  SPEAKER_03: Do you have a project that we should uh let people know about?
• 51:45
  SPEAKER_04: What's next for Tim?
• 51:47
  SPEAKER_01: Well, uh feather and I, feather and I, and I think Seth, you were in that conversation the other day.
• 51:52
  SPEAKER_01: We're I'm joking about how you know I'm miserably failing at being semi-retired.
• 51:59
  SPEAKER_01: You know, there's just so much opportunity out there.
• 52:04
  SPEAKER_01: I'm I'm currently doing some consulting projects for one, you know, for for the large chip maker I mentioned, who I've been working with forever, uh, or at least for the last six, seven years, and some other interesting consulting projects.
• 52:19
  SPEAKER_01: And I'm I'm trying to make this the year of fly fishing, but oh my goodness, there is so much opportunity from starting another company.
• 52:29
  SPEAKER_01: You know, there's there's plenty of funding out there to start another company.
• 52:33
  SPEAKER_01: And I would do it in in some of the things we've talked about.
• 52:36
  SPEAKER_01: I at this stage in my career, I'd like to be, you know, a life-saving, a world-saving type project, as opposed to building software for the company that kills people with its cheeseburgers.
• 52:48
  SPEAKER_01: Um, you know, that you know, who knows?
• 52:51
  SPEAKER_01: Maybe I'll I'll take a big job if they'll give it to me at one of the other big companies I mentioned, including my former employer.
• 53:00
  SPEAKER_01: Um, there's just I I love software and I love learning.
• 53:04
  SPEAKER_01: And I just can't imagine not just stopping doing it.
• 53:08
  SPEAKER_01: So I'll I'll I'll figure out something, but I'm really trying to stave it off for at least two or three months.
• 53:15
  SPEAKER_01: Plus, I've got so many fly fishing trips booked this year that there's no way anybody's gonna hire me.
• 53:22
  SPEAKER_01: I love it.
• 53:23
  SPEAKER_01: Yeah, that's the kind of speaker.
• 53:24
  SPEAKER_03: Well, listen, we've really come to the end of our time today.
• 53:28
  SPEAKER_03: It's been a tremendous pleasure talking with you today, Tim.
• 53:32
  SPEAKER_01: Exactly.
• 53:33
  SPEAKER_01: Right back at you guys.
• 53:34
  SPEAKER_01: Super fun.
• 53:35
  SPEAKER_04: Yeah, love you, brother.
• 53:36
  SPEAKER_04: Thanks for doing this.
• 53:37
  SPEAKER_04: It's yeah, it's it's fun to hear um, you know, just where you know, just some of the pioneering stuff that you've done.
• 53:43
  SPEAKER_04: Oh, it's exciting too.
• 53:45
  SPEAKER_04: It's exciting.
• 53:46
  SPEAKER_04: Yeah.
• 53:47
  SPEAKER_01: Say hi to the family, feather.
• 53:49
  SPEAKER_01: I will for sure.
• 53:50
  SPEAKER_03: All right.
• 53:50
  SPEAKER_03: Just before we close, I just also want to thank sponsors Simpler Media, uh Early Information Science, and the Marketing AI Institute.
• 53:58
  SPEAKER_03: And again, uh, thank you all.
• 53:59
  SPEAKER_03: Thank you, Sharon, for uh your work behind the scenes and producing the episode.
• 54:04
  SPEAKER_03: Thank you, Chris, and uh and thank you, Tim.
• 54:06
  SPEAKER_03: Appreciate it.
• 54:07
  SPEAKER_01: Thank you, Seth.
• 54:08
  SPEAKER_01: Super fun.
• 54:09
  SPEAKER_04: Thanks, guys.
• 54:10
  SPEAKER_03: Bye.
• 54:14
  SPEAKER_00: Welcome.
• 54:15
  SPEAKER_00: You're listening to the early AI Podcast.
• 54:18
  SPEAKER_00: Here we talk with those leaders and practice on what's possible and practical with AI and enterprise as we move from the current day into the future of the future.