AI is one of the most talked about technologies.
However most people increasingly see AI as either a kind of super technology that can perform a limitless range of tasks. Or as technology gone rogue with sci-fi images of AI powered machines taking over the world.
Turns out neither really defines AI. In fact, it could be said that AI is one of today’s most misunderstood technologies.
There’s a lot that AI can do and even more that it will be able to do … but it has its limitations too.
In today’s episode Robb Wilson, founder, lead designer, and chief technologist behind OneReach.ai, joins us on A Seat at The Table.
Amongst its many achievements, OneReach is the highest-scoring company in Gartner’s first Critical Capabilities for Enterprise Conversational AI Platforms report.
Robb has spent more than two decades applying his deep understanding of user-centric design to unlocking hyper
His forthcoming book, "Age of Invisible Machines: A Practical Guide to Creating a Hyperautomated Ecosystem of Intelligent Digital Workers" is a must read. It provides an easy-to-understand look at what’s really happening with AI.
Unlike many books about technical topics, Age of Invisible Machines is - as its title implies - a practical guide to bring AI into the work place.
Am I recommending you read this book? Yes I am! It’s a must read for anyone looking to determine how AI and specifically hyper automation, is the next big thing to stay competitive.
In this episode Robb discusses ...
5 ways that AI is programming us and how to stop it.
3 questions to ask when charting a course toward hyperautomation in your organization
Common myths about conversational AI
... and a lot more.
OneReach’s website: https:// onereach.ai
Connect with Robb Wilson: https://www.linkedin.com/in/invisiblemachines/
Learn about The Current Situation in Sourcing: https://thecurrentsituation.net
A Seat at The Table website: https://seat.fm
Visit A Seat at The Table's website at https://seat.fm
[00:00:00] Rob. I'm really so happy that you're able to come onto the show because AI is an increasingly controversial topic. It seems the general view has gone from fascination, to, in some respects alienation, we all like the idea that AI could do so many things for us. And then at the same time, there are all these rumors that it's controlling our lives and that it's going to ... world domination. So to speak, take over everything. Yeah. Can you tell us more about that?
[00:00:28] this is a tough one, right? Okay. Because yeah, cause no one can tell the future. Right, right .
[00:00:33] So, and, and I think a lot of people try to and they, you know, whether it's dim or positive. And when it comes to AI and, you know, we're all predicting what we're gonna invent next. And as we know, not too many of us are very good at that. So, but I, I will say that like anything if we're afraid of smart machines, which AI is just a buzzword, right?
[00:00:57] We're just talking about smarter machines [00:01:00] than we've built in the past. If you're gonna, try to manage it, you gotta understand it. Right. I mean, that is always the beginning of any problem. Is what are we talking about the boogeyman or are we talking about something real here? And, so what we're just talking about is should we be more afraid of smarter machine?
[00:01:21] And should we be more scared of smarter machines? And I think the answer is, yeah, we should . But we should be really scared of dumb machines. The atomic bomb is a machine the missile that it rides in is a machine. And the launchpad is a machine and the button you push is a machine.
[00:01:40] But I'm more scared of Putin and the brain and the human right than I am of the machine. And it might just be true that it's the machine that saves us. Right. Right. Like, thank God there's a machine between him. and that device, and now you ask, do you wish [00:02:00] it was smarter or dumber? Right. Do you just want a button that just works or, or should it do check some balances
[00:02:05] And so I think from the moment that man sharpened a stick machines became a threat and we've always had to understand them and try to manage within them. And I think that's, I just have hope for smarter machines being easier to manage than.
[00:02:19] I think that's an interesting point of view. It, it's not one that I have heard put forward to often.
[00:02:24] I think people look at AI as getting so advanced that it has a mind of its own that it's not a person controlling a smarter machine, that, like you said, instead of just pushing one button, it actually has checks and balances, but that the machine itself becomes a. I can't find the right word for it at the moment, but it acts on its own.
[00:02:45] Is that myth or is that reality?
[00:02:48]You know, the idea that we would program self preservation into a machine certainly is plausible. I think it, it really comes down to the will machines be more dangerous [00:03:00] than they are now. right. Not, will they be dangerous? I think that's the tough part of the problem.
[00:03:05] Nobody can deny that machines are dangerous and will continue to be dangerous. It's just, will they be more dangerous or less dangerous? And that's the part where I really don't think anybody knows. I don't. I think there's a lot of people who are making assumptions that they could get worse, but I'm just looking at where we are today.
[00:03:23] And, and how scared we should be of the current machines we have, given the world situation. And I'm like, how much worse could it get? right. well, I mean, I hate to be the naysayer, but it does seem that there may be a ceiling on better, but worse tends to be yes. A bottomless pit that extends clear to infinity.
[00:03:42] They seem always to come up with worse. Agree with that. Yes.. Yes. So kind of the moment you say, right. That's what I just said. yeah. Something comes up that you never you're like, oh my God, I didn't think it would go there. yes, yes. Yeah. Yeah. So I think that's the, some of the, the [00:04:00] concerns around AI, but you know, like you're pointing out some of that is more sci-fi than, than what might realistically happen.
[00:04:07] Right, right. And yes, it really does come down to responsible deployment of machines and, are we capable of doing that? And the answer is, well, history says, no, right. And are we gonna get better or worse at it? You know, I don't know. That's, that's a tough one. I just don't.
[00:04:26] I guess the point I'm trying to make is you can't put machines back, you know, you can't, we can't roll the clock back to right before we, we invented the atomic bomb. We can't get rid of the machines. We have. It's possible that the way out of the dangers of the current machines we have is through smarter machines.
[00:04:42] It's possible. Right. Yeah. I think it's an interesting hypothesis. Now, bringing, you know, AI back into what you might call day to day life. As we, as most of us know it, how can companies leverage hyper automation to stay ahead of the competition and maybe start [00:05:00] out by just explaining to us what exactly is hyper automation?
[00:05:03] You bring that up in your book. Sure. Yeah, I, the easiest way to explain it is like when your, when your kids come home from grandma grandpa's house and they're wired on candy and they're running around the house, tearing everything down and then you say, okay, now imagine. That you could replicate them instantly to infinity and, and actually get them to do productive things like clean the room and do the laundry instead of tear down the house dare to dream.
[00:05:32] Right. Okay. Yeah, exactly. Now we're in a place where we're just talking about hyper automation, it's automation done really fast. Um, and, and it might even, you might even go as far as to say it might be automation creating itself. So it's where oh, interest. Automation is automating itself and finding ways to automate by watching what we're doing, , and, and making suggestions.
[00:05:57] And of course, back to the, in a controlled [00:06:00] way, in a moderated way, but responsible way. But hyper automation is just automating fast. And I think when you think about it, you kind of look at every company has this ratio of human, tasks to automated tasks. and we all think that that ratio's gonna be static and that the more we automate the fewer human tasks will be required in the company.
[00:06:29] But history's showing us that the more we automate, the more human tasks we invent and that ratio doesn't seem to be closing the gap. It seems like we keep having finding jobs and creating new jobs for humans to do as we automate the. Interesting. So that ratio is the thing to watch. Not don't assume it's gonna be St.
[00:06:52] It hasn't been the cappuccino machine in your house was the beginning of barista's and Starbucks. Not the end of it. [00:07:00] Interesting. Yeah. so I think hyper automation is the act of closing that gap quicker than everybody else in a, in a kind of rapid way that gives you a competitive advantage. It's interesting that you said that the more we automate, the more other jobs we create, do you think that's sort of like, you know, nature of horrors of vacuum or is it something.
[00:07:23] I think it's just, you know, our purpose is to have purpose, right?
[00:07:28] well said.
[00:07:29] So, so we're gonna make and create purpose. We're very, very good at inventing jobs. Most of the jobs we do aren't necessary, it's this idea that productivity is a measure of our human worth. And I just, I think the future is a rethinking.
[00:07:46] Of how we measure ourselves and is productivity really our number one metric for how we should measure is that on, on our deathbed? Is that what people say? I wish I was more productive in my life. yeah, it's a good [00:08:00] point. And you're right. I mean, you and me and, and so many other people were raised with that ideology about productivity, that it's just an essential to, like you said, measuring, I don't wanna say your, your human worth, but pretty close to it.
[00:08:16] Right. Yeah. And I think that's that it's a healthy idea to start saying, it's maybe it's connection, right. Maybe it's not, you know, it's and, and maybe, and what is connection, maybe connection is creativity and art. And like this conversation not being prewritten right. You know, is what makes it fun and interesting.
[00:08:37] And, and if we were just saying the same words every day, you know, that gets boring. And, and so maybe connection is art and then it kind of, that's kind of going out there, but what if it is creativity in art? And then we sort of measure ourselves by our creative lives, not our productive lives. I think that's a really good point and something that more people are [00:09:00] gonna start to consider.
[00:09:01] I think that they're. In general in a very broad based way, you can sort of sense that people are starting to question, right? Whether more is really more, satisfying, valuable, et cetera, um, a, across many different areas of our lives, whether it be the, acquisition of, of different things, right? Whether it be acquisitions of money in general, of, of more goods, right.
[00:09:24] Of more experiences or whether like you're saying it's something deeper and more thought. . Yeah, I think about that every time I complain about younger generations, I stop myself and say, you know, maybe they get it right. Right. Exactly. Yeah. Sometimes you do have to turn it on its head and, and, uh, say, what if the other guy is actually right.
[00:09:44] yeah. Yeah, absolutely. Now, well, carrying on, from what you're saying here, a lot of companies, are trying to replace human beings with, um, with bots or with some version of automation. Right. And a lot of [00:10:00] work or a lot of conversation has come out around actually having artificial intelligence that can carry on a conversation with you, from your experience.
[00:10:10], you talk about there's situations where conversational AI actually can provide better experiences than a human being. Can you, can you share what your thoughts are on that? . Yeah. And it, it really like to sort of begin that you kind of realize that automation has no purpose unless it improves the experience of our lives.
[00:10:31] Right. That matches it. And I think that's one of the biggest problems with how automation's deployed today is that they deploy it to save money. Right. But not to improve the customer's experience or the employee's experience. And if you deploy it to save money, but it doesn't. Improve the experience you've really stepped backwards.
[00:10:51] Right? Yeah. And I think that's one of the, the big challenges out there is we understand that for cars to drive themselves, they're gonna have to [00:11:00] drive better than humans. Not as well, not worse. Right? We're gonna expect that our automation does a better job. And then the other side of that, the, you know, the less sort of, you know, the more sort of positive side, the opportunity side is to realize like automating what you do.
[00:11:20] What about automating what you should be doing, ? Right. It's it. A lot of people look at what they're doing and think that could be automat. and instead of saying, okay, that's what you're doing. What should you be doing? Right, right. Should we automate that? And does that elevate your organization from automating what you do, taking that next leap to, to doing better and thinking about automating how you should be doing these, how you should be interacting with customers.
[00:11:49] So I think that's, that's a big opportu. yeah, I think you're absolutely right. And, and that same point has been brought up around digitization, which is, don't [00:12:00] just take what you're doing and digitize it, take the opportunity to rethink what you're doing to begin with. Right. Hundred percent. Yeah. And, and, but it's hard though.
[00:12:08] It is, you know, it is. Yeah. Yeah, that's the hard thing. That's the, I think the harder part is not the, the adding AI to it or digitizing it. It's rethinking the actual process itself and challenging yourself as you're pointing out it. No, is this the best way we could do it? right. And I, every design like this, this is kind of common, this process of, of S schism.
[00:12:34] And it's this, you know, it happened in graphic UI design where all of a sudden, if we had a calculator that was, on an iPhone, We would add these prop shadows and we'd make it look like a real calculator. You know, the buttons, we'd actually try to make them look like they were physical buttons because, because we wanted to, you know, connect people to, to [00:13:00] what it was.
[00:13:00] But. None of that was necessary. It was just to create a familiarity with the device. And then over time people were like, why are we doing this? This is a lot of extra work. It's not efficient. So they go to flat design and we see like moving away from S schism to just more practical ways to interact with machines.
[00:13:21] And I think. Conversation is the same thing. We're beginning by saying, oh, machines will talk like other humans will talk to us instead of wait. No, they're not other humans. They're machines. We're gonna create a new language to talk to machines. We don't need that. So I think that we're still in that sche morphic phase of, of conversations with machines that we're gonna evolve out of and say, okay, there's no reason to bring a machine.
[00:13:50] Yes. Yeah. We don't need all of that. Hello. How are you today? Yeah, that's such a good point. And until you brought it up, I actually hadn't even thought about it.
[00:13:57]So I think, I think these are really good points [00:14:00] because it, you know, there are things that they may seem obvious to someone who works in the field, but to somebody who doesn't, you don't really think about it until someone like yourself puts this concept forward.
[00:14:10] And then it does seem, very obvious. yeah, we call those the obvious, but not so obvious ideas. It's the ideas that once you hear them, you're like, oh yeah, that makes sense. But it isn't until you hear it, that you can act. And it's funny. That was like, we actually measured that in the book.
[00:14:27] We highlighted every idea. And we user tested it with saying, this is what people love is these obvious. So we've made sure we had enough of them to write a book with them. It's it's kinda, yeah, I think that's great. I think it makes it a lot more meaningful to people as well, because you're, you're taking your, the theory, the hypothesis and so forth than you're bringing it forward in things that people can actually say.
[00:14:50] Oh yeah, actually, yeah. I can see how that works, makes it, makes it much more real, right. Much more applicable, right. right. Unless [00:15:00] trust me, you're sort of like, oh, this makes sense. I'm trusting my own judgment. Yeah. I think that's fundamental. I think. One of the hardest things is, is to get people to accept something new is to get to, like, you're saying that point of where they actually feel that they've come to that conclusion.
[00:15:16] Right. And that, that their good judgment is telling them right. That this is true. As opposed to just having to have faith in what someone else is saying. Yeah, absolutely. Yep. Yeah, I think I was there a while ago and I realized, yeah, people don't yeah, I understand that. It's one of the hardest things about anything that's new to get people to embrace it on a widespread scale.
[00:15:41]Absolutely. Absolutely. Now, when we talk about AI, um, what do you see as some of the, the common myths? I mean, I think. It's taken on a life of its own, a persona of its own, so to speak. And a lot of that might be media hype around it. And because it is new and because it can [00:16:00] do a lot of extraordinary things, but as someone who really has your hands in the clay, what do you think that people are looking at AI that it, it could do either positively or negatively and that you're thinking?
[00:16:12] Well, actually, no. . Yeah. So the, I'd say the top, the first myth that I I see is that is kind of what we talked about. Touching on that earlier is that we should interact with machines in the way we interact with each other. Um, I think that's the first myth, uh, machines are capable of, of creating buttons and dropdowns and maps and all kinds of things instantly, um, to improve the communication, to speed up the communication.
[00:16:41] It can finish our sentences for us, you know, it can anticipate what we do. So a lot of people misunderstand that, that the pinnacle of AI is that it can understand what you say. And understand what you mean by what you're saying, but actually it's higher than that. The [00:17:00] pinnacle of AI is anticipating what you're gonna say and what you want before you want it.
[00:17:05] And that's the ideal state. That's where you get that delightful moment that it, and. And not, not the auto, correct. That's your, you know, best friend that finishes your sentence is wrong. But the one that actually like anticipates correctly, that's, that's the best use of AI, right. Is where it's, where it's actually anticipating why you're calling or before you call that you're gonna need something.
[00:17:32] So, so I think that's, that's the first thing is it's not a person. You know, let's create a new language. It's not gonna be just words. It's not gonna be language based. It can hold a lot of context. It's very good at keeping past conversations, top of mind and factoring that in a really fast way. So I think that's the, probably the first one on the second one is that a lot of people think that designing [00:18:00] conversations is easy because they do it every day.
[00:18:03] Because they have conversations, but if you. Talk to Jerry Seinfeld who like designs his set. And you realize that it's easier to design a comedy set because it's not two way. You don't have to anticipate what the audience might say and then how you might react to it. Right. So, so a lot goes into a set, a lot, goes into designing conversations.
[00:18:28] Ted talks are very well rehearsed. So I think a lot of people run into this and they don't take that time up front to design and, and value that the design of that conversation's really, really important. And then the other thing I see, that's the mistake that people make.
[00:18:47] Companies make is they start automating with their customers. They start with customer interactions first, which makes no sense because they're your most important resource. So we try to [00:19:00] convince companies like start with a more friendly audience, start automating and using AI in conversations with your employees who are gonna be forgiving and on board with you.
[00:19:10] And once you get your toe in the water and figure that out. and, and you feel comfortable that you've got that then moved to your customers? Such a good point because you're right. People always think about this stuff. In trying to get rid of that onerous help desk, situation, which caused companies.
[00:19:30] In some cases, hundreds of millions of dollars, right. And it's such a frustration that typically that's where they put that AI and, and it, it doesn't work out maybe as well as they had planned initially. So it's interesting. I would've never thought to actually use it internally. Yeah. Yeah. I, I it's, it's amazing how well it can work and, and you figure that it's also usually the place that companies do the worst.
[00:19:55] true by communicating, you know, to their employees. So, so the [00:20:00] bar's really low, you know, it's a great place to kind of start. Yeah. That's an excellent idea. That's really sort of a, a great test opportunity, to do that. And, I think that people do look at AI. maybe because if you don't work with it every day, you're sort of being sold.
[00:20:17] What people who are maybe selling you that service want you to, what, what they think you wanna hear as opposed to in there's a lot, as opposed to what may actually be best practices. . Yeah, there's a lot of, um, we call 'em party tricks. You know, a lot of, a lot of demos are just party tricks, you know, on, on AI that are very well staged and seem amazing.
[00:20:43] And then when a company tries to implement them, find out that it's either too complicated or, or the texts not there yet. Right. So talking about the tech is not there yet. Where is the tech right now? I mean, in reality, and I know that maybe this is a very, very broad [00:21:00] question, but what should we be looking to utilize this for based on where the technology is at this point?
[00:21:07]Yeah. So the, text's pretty advanced. You can do a lot more than I think most people think, you know, I think when we, uh, show people, what you can do, they're usually blown away and they're like, I thought we were a lot further than we are. But with that said, Where I think the focus is on feasibility, just because you can, .
[00:21:29] Doesn't mean you can, and companies, it's so complicated and hard to implement that right. You can't most companies can't and so feasibility and the, and the ease in which you can. Implement complex interactions and AI solutions is really where people should focus instead of pulling out the feature list and checking all the boxes.
[00:21:57] Can you do this? Can you do this? It's more like, [00:22:00] can I do this? Can I do this? show me how I can do this. And is it feasible? And I, I even think that's really tough. I find that most companies are pretending that they can evaluate this stuff, but they can't, they can't tell the difference between one solution and another.
[00:22:17] So I have this like simple rule that says, especially in the world of conversational AI is if, if that company can show you that they use it all over their company. Right then it's feasible if they don't use it. If they give you the, you know, cobbler's kids have no shoes story, , don't buy it. The bottom line is if it's not feasible enough for them to use it, it's not gonna be feasible enough for you to use such a good point.
[00:22:45] And I think you're absolutely right. I mean, even with something that's. Less complex, from, um, AI, anytime people are trying to evaluate a tech solution, if that isn't an industry that you work in, if you're not the actual, an actual developer, [00:23:00] I think people find it very difficult to actually analyze what they need, whether any particular technology's the right one for them.
[00:23:09] We all sort of fall victim, right? Shiny objects syndrome. The fact that tech companies tend to have to square, being a feature creature because features are a good marketing ploy with taking what you have and actually making sure they consistently work and doing that sort of unglamorous backend constant improvement, debugging.
[00:23:29]As, as a user, it's hard to, it's hard to sort all that. . Yeah. Yeah. And I have friends that, that work at Amazon and Google and IBM. And, and you ask 'em so how often as an employee, does the average employee interact with a bot and they're like almost never.
[00:23:50] Yeah. So they're not using it. Want you to use it, right. Right, exactly. Right. Wow. That's so well said because you see that all the time, the, the myth. [00:24:00] Takes on a life of its own. Um, it becomes legend yes. So if, if you're looking at AI where we stand right now, What do, I mean, it's very hard as you said earlier and very correctly, so that it's very difficult to predict the future, but what do, where, what direction do you see it going in?
[00:24:19] What do you see as are the next things coming that are actually going to be applicable to a wider spread group of companies or users? I think that the thing that that's easy to underestimate is. So this kind of this journey, and I promise to keep this shorter than it's gonna sound. No, go ahead. but this started for, for me when, I was, I had a prior company that focused on, design and.
[00:24:50] Development, one of the first UX firms. And we were hired by Boeing to help with the, design of the cockpit of the 7 [00:25:00] 87, the dream liner. And you know, I'm gonna oversimplify, but the problem was, Hey guys, we got this new airplane and it's far more complicated than the 7 47, the old. and it's got bigger windows because we have this, you know, carbon fiber body.
[00:25:22] So basically we don't have enough wall space for all the buttons that we need to put in this thing in the cockpit. So what are we gonna do here? Right. And if you just Google the 7 47 dashboard and the 7 87, you would see that the 7 87 is just a series of six, really important. and, and our piece was working with Jefferson on the flight planning.
[00:25:47] So think of the flight planning as the context, right. It was a big piece of what to put on the screen. When are we taking off? Are we landing? What we're able to do now is narrow the number of choices. [00:26:00] That a pilot has, in front of them in terms of what they need to do next. Right. Really contextualizing the choices and things like that.
[00:26:09] The bottom line is that I'd never flown an airplane in my life, but I sat in the simulator and I was able to take off and land 7 87 with 10 minutes of training. . Yeah. And you like, press this button. Okay. We're taking it off time to land, press this button. Oh, incredible. Right. Um, now granted, could I handle all of the, the exceptions,
[00:26:35] no. Yes. But that's amazing. Right? So I'm sitting here going, wow. Like. What just happened, right? What if all software and all machines became that easy to operate? If, if you can do that to a 7 87, all of our software could be a lot easier to use than it is. And you go, well, we've spent all this time consolidating all our back end.
[00:26:58] what about the front end? [00:27:00] What would happen if one, we had one consolidated front end for all of our software and you realize traditional UIs will never scale. Like there's, that's a thousand tabs designed by a thousand people. There's no way that would be usable, but with conversation. Applications, the more things it can do the better and the fewer things, the worse it is.
[00:27:22] So when we ask our conversational application to do something, it says, I can't, that's actually worse. We want it to do more and more and more, the more it can do the better, the experience when our apps get overburdened with buttons, it becomes a worst experience. So this idea of one interface for all software.
[00:27:45] Just makes it accessible to the world. It makes it accessible to people that aren't computer literate. It's just this world changing moment, you know, and it's super, I'm super passionate obviously about that idea of connecting [00:28:00] and catching people up with technology. This whole, like technology is leaving people behind.
[00:28:05] Isn't about teaching people, how to code it's about teaching machines, right. To go to the people and talk their language. That's such an interesting point of view. And, and I can definitely see what you mean by consolidating things on the user interface side of things. Because if you look at it in the early days, you needed all those C prompts and so forth. And then in a sense, um, and I'm, I'm probably glossing over a lot of different things. Apple came out with that graphic interface that made things a little bit easier. And now you're saying to consolidate a lot of these steps.
[00:28:37] So I suppose a lot of those steps would be happening behind the scenes. Thanks to. . Yep. Yeah. It's not that 7 47 dashboard that pops up. It's all Photoshop. it's just, Hey, I want to crop this image and a little thing comes up with a slider. Right. And we do it, you know? Yeah. I think it's so interesting that, you know, you frame it that way because we don't [00:29:00] look at it typically if we're not inside the industry, like you are from that perspective, but you're right.
[00:29:05] You know, as you're describing this to me, I can definitely see. The validity in, in what you're putting forward and why that really is, is the solution and will help more people be able to utilize this technology because otherwise the learning curve is, is just, it's too extensive for people who are using this as an auxiliary to doing another kind of a job.
[00:29:26]Yeah. And that when, you know, when we set out to write the book, we, it wasn't good. I wanted to write a book. Like I, I was very busy and I'd done it once before I was, you know, there was no allure. I was not, yeah. I was not excited about it, but, but I, I just realized we had a. Important things to share.
[00:29:47] And what, what really drove me was that I'd read so many books, that were so high level. And they talked about AI in the future and how it's gonna change everything, but it didn't get down to the nuts and bolts of how you would [00:30:00] implement it today. How would an average company or a person sit down, where do they start?
[00:30:05] What do they do next? The paint by numbers version of, of how to implement this in a responsible way and in a practical way it's to make so in a sense, we wanted to write a book to make AI feasible, you know, so it really does get into the details. It's not just this high level like's gonna change. Yeah. I, I think that's so important because you're right.
[00:30:27] A lot of things are written and they're just too theoretical for anybody outside of the industry to really be able to understand it and, and see the value. A practical value in what's being done. So I think it's wonderful that you've written the book. Where can people find the book? Rob? I know a lot of people will wanna get their hands on.
[00:30:47]yeah, it's published by Wiley. Okay. So you can get it through them. It's on Amazon and barns and noble and all the usual places, you just have to, you know, search age of invisible machines and, um, the [00:31:00] invisible machines part, you know, is the idea that when machines talk right, they disappear.
[00:31:05] Right. So yeah, age of, I'm gonna put that in the show notes. And how can people connect with you? I think a lot of people would probably wanna reach out to you as well as reading the book. Yeah. Yep. So there's a website age of invisible machines.com and, uh, and then, you know, on LinkedIn, under, you know, Rob Wilson and you can always connect with me.
[00:31:29] I'm always sharing stuff and, and, and as well as the, the blog on the, on the well that's one. We also have a Q code in the book. Every chapter kinda connects to a bot of course. Right. so you can ask questions and talk and you, you, we created a, a kind of digital twin of, of myself. So you can ask questions to me.
[00:31:52] That's. You know, that's kind of baked into AI, about look, I mean, that alone is gonna be, [00:32:00] a must experience. Well, Rob, thank you so much for joining us on a seat at the table. You've shared so many interesting things. I mean, this has just been an education in a box for me, so, uh, thank you again. Yeah.
[00:32:15] Thanks for having me. It's been fantastic. I really appreciate the opportunity to share, share ideas.