#044: Artificial Intelligence with Dennis Mortensen

The following is a straight-up machine translation. It has not been human-reviewed or human-corrected. However, we did replace the original transcription, produced in 2017, with an updated one produced using OpenAI’s WhisperX in 2025, which, trust us, is much, much better than the original. Still, we apologize on behalf of the machines for any text that winds up being incorrect, nonsensical, or offensive. We have asked the machine to do better, but it simply responds with, “I’m sorry, Dave. I’m afraid I can’t do that.”

00:00:04.00 [Announcer]: Welcome to the Digital Analytics Power Hour. Tim, Michael, and the occasional guest discussing digital analytics issues of the day. Find them on Facebook at facebook.com forward slash analytics hour. And no, the Digital Analytics Power Hour.

00:00:24.77 [Michael Helbling]: Hi everyone, welcome to the Digital Analytics Power Hour. This is Episode 44. Imagine a future where your website will optimize itself based on what’s best for users. Artificial intelligence. While it’s not necessarily a core part of what an analyst does today, the concept of machine learning is already moving into every space. A couple years ago, Stephen Hawking famously said that artificial intelligence could spend the end of the human race. But until then, it’s fun to experience it as it becomes more and more a part of the real world. Hi everyone, I’m Michael Helbling, Analytics Practice Lead at Search Discovery, and of course I’m joined by Tim Wilson, my co-host, senior partner at Analytics Demystified. Hello Tim. Hey Michael. And who better to be our guest on this show than someone who has spanned both the digital analytics world as well as AI, our good friend Dennis Moertensen. Dennis is a serial entrepreneur. He’s had many successful exits. He was the CEO of Index Tools, which was acquired by Yahoo. He then founded Visual Revenue, which he successfully exited, sold OutBrain. And now he’s the CEO of X.ai, which is an amazing company that takes over scheduling meetings for you with the power of machine learning. Welcome to the show, Dennis.

00:01:46.71 [Dennis Mortensen]: Thanks much for having me.

00:01:48.85 [Michael Helbling]: Yes. So Tim is curious that are we really talking to a real person or is this just another expression of the amazing AI that runs x.ai? So Dennis, you somehow have to prove your humanity to Tim, apparently.

00:02:09.09 [Tim Wilson]: I’m always suspicious. I always think there’s a turning test going on. I never know.

00:02:13.14 [Dennis Mortensen]: Something fishy is always going on. You’re absolutely right.

00:02:17.26 [Michael Helbling]: There you go. Well, to kick things off, I think it might be great for our audience to first understand maybe the real definition of artificial intelligence or at least how you define it and then talk a little bit about how that has applied in terms of the work that you’re doing now.

00:02:34.06 [Dennis Mortensen]: I am sure that any definition I come up with or really any definition that the three of us come up with today will at some point in the not too distant future be rendered invalid and that’s been the case really for the last what almost six decades. I do like this idea of taking what used to be a human process and see if you can somewhat emulate that as and the machine process. And I think in the given kind of vertical that we’re attacking here for where we’re trying to emulate, if not completely replicate that of the personal assistant for where if you ask her to set up a meeting, our agent can do just that. And that’s certainly a definition that continues to be true today. It was also true yesterday. And I feel good about it being true tomorrow.

00:03:28.89 [Michael Helbling]: So I want to ask a follow on question, which is, so as I read up on the topic, there’s sort of some people who would demarcate sort of a narrow versus broad AI kind of concept. Would X dot AI be considered a narrow AI in that context because it’s focused on a singular task?

00:03:48.10 [Dennis Mortensen]: Correct. We’ve certainly had this fantasy of coming up with some human level AI and that’s not a single Hollywood movie that wouldn’t immediately suggest that it’s just around the corner and as it arrived it will most certainly be evil. However, I think those fantasies and those doomsday scenarios are a little bit further out so we can just kind of park them over here. But then there’s this idea that there’s plenty of narrow AI for where real applications and real use can be seen through applications today, whether that is Facebook figuring out that you’re in the picture. So you don’t have to tag it yourself or Amy being able to set up a meeting for you. So narrow AI indeed.

00:04:36.98 [Tim Wilson]: So how, how narrow will it stay for how long? How do you have to, do you perfect, do you perfect the narrow and then start to go broader? It’s a good question, right?

00:04:47.88 [Dennis Mortensen]: I certainly don’t believe that there’ll be some mourning in 2025 for where you receive a press release from Google or Facebook or some other company that will say, ta da, we’ve done it. And that’s the end. It’s the last piece of software that will ever develop because we now have human level AI. create that scenario or create some sort of belief for where I can see us get there. However, I do see the near-term and mid-term have some distinction between what I define as horizontal AI or some sort of enabler and think of that as the Siri Cortana Alexa and so on and so forth and a set of vertical AIs for where highly specialized jobs that you want done with the horizontal AI cannot do will have to connect with. So the future is not one for where I compete with Apple and certainly not really one for where Apple will consume all of the jobs that we need done. It’s more likely Ryan with you saying, hey Siri, could you have Amy? at x.ai, reach out to my friend Tommy instead of a meeting for when he’s in Manhattan, first week of September, please. And it then becomes the horizontal AI’s job to figure out who is Amy and figure out what did I just collect, hand that over to that agent, see that agent do that job, sometimes either immediately, sometimes over days. And upon conclusion, come back and say, hey Siri, that job you asked me to do, I’ve done it. You can now go back to have asked you that question. And I think we’ve almost kind of seen this before, if you ask me, just like if they had the capacity to create this oracle that can do all the jobs we want done or have all the answers to all the questions that we might have, that seems almost to rhyme with the idea that there shouldn’t be any app store, that why didn’t they just do all the apps themselves? Which seems just ludicrous when you say it out loud. I said, not only would they never have the resources to do so, they wouldn’t even have the imagination to know what apps do a 43-year-old Danish American with two teenage daughters need. They have no idea of what I’m looking for in that app store. I think it will be the same with these AIs that there needs to be some agent marketplace or intelligence marketplace for where you go hunt for intelligence that you need to do what you want done.

00:07:37.99 [Tim Wilson]: So that that seems like an incredibly like pragmatic and believable and plausible way to approach it. So I didn’t know if we were going to because it seems like you’re part of the like the Hollywood’s one example, but you also look at you know, Silicon Valley and maybe that’s because you’re grounded, you’re a grounded east coaster. You know, talk about that we are kind of on the verge of the singularity and what you’re describing is much more of a, if you have these smart or components, so they’re not, I say do X and it does X, I say, I need to get to why and you figure out how to get me there and like you know Google Maps or whatever being the example so if you have those kind of targeted I guess kind of more on the narrow AI front and then if you have a large enough catalog of those then Then you have, I guess, the horizontal AI is just kind of connectors into that. So stuff that I could never give two shits about, that my horizontal AI is never going to go and use those specialized components.

00:08:43.73 [Dennis Mortensen]: I think you’re right. And I think where it becomes really sexy or where things start to rhyme with whatever fantasy we have for AI is when these horizontal agents start to talk to each other. So right now, if you think about any piece of software, I think there’s two changes about to happen. So the first one is that most software today, whether you’re an accountant, an analyst, A programmer, a designer, software will assist you in doing a job a little bit faster, a little bit more accurate. But you are certainly the one doing the job. It’s not like you can go back and say, I had a piece of software do the job for me. No, you did it. But hopefully the application makes sure you can do it much, much faster. I think that is about to change for where you will start to describe objectives of what you want done and then the software will do that for you. Such as in the most simple of ways, Amy, would you be so kind and set up a meeting between Dennis and I when I’m in downtown Manhattan, second week of August? That is not you doing the job. That is you describing the job which you want done. And I think there will be plenty of software that will move from this one paradigm to the next and it will start with little chores like setting up meetings and so on and so forth. But then it would come almost obvious in any piece of software for where. No, I have the question. That’s the hard part. I really just wanted to figure out whether the margin on our customers on these given campaigns is better on the East Coast versus what we do in Europe. That’s the question. Somehow, I think there’s a disconnect. So you go do that for me, whatever analyst agent I might have. And once you’re done, you come back and tell me. So that’s the first kind of major shift I see happening in software. The second major sift I see happening is that almost all interaction with software is in a very syntax-driven environment. Even applications, if they show for where you use buttons and drop-downs and this and that, however good UI UX it might be, it is still a syntax-driven environment. Or even when two machines speak to each other, it’s kind of an API-driven environment. And that puts the burden on whoever’s got the question. As in, if you want something done, you need to teach yourself the UI, the UX or the syntax. I think there needs to flip. for where the burden should be on the receiver. As in, if I ask a human something, one of my employees, could you go do this for me? The burden is on him to understand what I just asked him to do. And if he doesn’t understand it, he needs to tell me, but otherwise, I’ll just walk away and assume he’ll do that job. Why is it not the same with software? Such as, if I ask Expedia, Could you be so kind and find a trip to Miami this weekend for me, my wife and my daughters for less than a thousand dollars and I need to be back Moenday by 10 a.m. for meetings? That’s my request. You go figure out how you want to interpret that. I don’t care whether you use Muppets in outer space, people in Manila, machine learning, just do what I told you to do. And I think those two combined that you describe objectives and the burden is on the receiver. That will allow us to see these agents kind of come full circle here, start to talk to each other. So if I ask Amy to set up a meeting with one of my potential partners in Miami come next week, then she’ll have the whole meeting set up, but somehow Things change. Things get delayed. Then I would much rather that Amy reach out to my travel agent, make sure that I have the whole thing rebooked. My travel agent reach out to my receipt agent that keeps all my receipts because there was $150 rebooking charts. And I would much rather that my receipt agent reach out to my CPA and have the whole thing stored. And I want them to be able to talk to each other, but none of them should think about, ooh, We need like 17 different APIs. No, just tell Julie the travel agent and Julie could be human. She could be machine. Doesn’t matter. The burden is on her. So those are certainly the near term changes I see. And then you can see it becomes super fucking sexy when all these agents start to talk to each other. You don’t even know why you’re staying in extra day in Miami, but somehow these agents agreed that it was better that you stayed the night. And you just assume that I guess I’m staying in Miami for a night. And here we are.

00:13:52.81 [Tim Wilson]: Well, that seems like one of the the travel travels an interesting one because I’ve I’ve grappled with you know when you go to travel you don’t realize how many factors you’re actually dealing with when you think that you’re you just need to get to Miami and you need to be back by this amount of time and you have a price point but then it turns out that Well, you really don’t want to fly through O’Hare. And you’ve got all these other criteria. And for the agent to, I mean, where does kind of the learning preferences over time? Because part of it seems like it’s natural language processing. Part of it seems like it’s having a very robust rule set that can kind of grow kind of organically. And then is there a learning part of over time I’m going to get better at booking you, these sorts of things? Is that falling one of those two? So travel is a funny one, right?

00:14:42.28 [Dennis Mortensen]: For where we’ve gone backwards. So we used to have travel agents for where the query I just described would be the exact query. I would walk into my travel agent and give him and he would somehow have some relationship with me for where that wouldn’t be crazy talk and he would work out a few options and I would pick one of those options. But then we were fooled into believing that no, you don’t need that. What you need is to sit alone in the dark at 1 a.m. on orbits or Expedia until 4 30 a.m. and solve this yourself. Oh, yeah, I guess so. No. That is just fucking retarded. But somehow we got lured into it and now for 15 years straight, the three of us have been travel agents and none of us want to be travel agents. I just want to be in Miami. So somehow we need to kind of figure out how to bring this back. And I think what you described and certainly what we figured out is that for any one of these agents to survive, We need to have them exist within a universe that is so well-defined that when I say scheduling agent, you and I think the same. Otherwise, my universe is different to yours and Amy can’t exist in these two different universes. What makes travel agents, at least in my opinion, quite hard is that when I say travel agent, my view of that is different to yours. There’s not an immediate overlap. We have a clear understanding that I asked Amy to set up a meeting. The input is clear. Her negotiation is clear. The output, which is an invite, is clear. And that have certainly this end up being possible. A travel agent might actually be seven different distinct agents that we need to solve first before we have this more casual agent that I just described before. I think there’s other agents before the travel agents, but look no further than to your inbox. I think anything arriving in that inbox have already been digitized by the pure fact that it’s already a natural language in your inbox and completely right for potential automation and thus for an agent to take over.

00:17:02.26 [Michael Helbling]: I like this vision of the future. This sounds exciting. Well, and it sounds realistic.

00:17:07.58 [Tim Wilson]: I mean, that’s well, I mean, it’s already here for scheduling meetings, right? So well, but it’s not it’s not Watson, right? I mean, that’s the thing is it what you’re describing is I would say almost almost exactly the opposite of how IBM presents Watson in the media as, look at any of these brilliant minds, they can sit down and have this conversation, just throw data at it and it will solve it. So that’s kind of like the machine learning of, just throw data at the machine and it’s gonna figure out, it’s gonna figure out what your inputs are, what your outputs are, it’s gonna figure out what your dependent variables are, what your independent variables are, what the relationship is between them. And that is kind of this amorphous, broad thing, whereas what you’re describing is saying, no, break it down into the components that can be repeated. And it turns out that’s challenging. But if you bake those really well, then you can start hooking them together. Right?

00:18:02.02 [Dennis Mortensen]: I also have the same fantasy. I also want the same as they want. Some Oracle for where whatever pain I might have, whatever chore I arrive in my inbox, I can just give to some machine and you do it. So I can just run around the office with a Diet Coke, work on the whiteboard, think grand thoughts, but I just don’t see any immediate pointers to us being able to deliver on that promise. So the only way we’re going to get from here to there, I think, is if we break it down. And to give you a good example here, so we are 70 people who spent almost three years in the basement trying to solve this Single agent which can only schedule meetings. She does that really well though, but that’s the only thing she can do Imagine then of all the things you want done just multiply that with 70 and 35 million dollars by the way Which we raised and then that receipt from the end of that dinner that will be pretty darn expensive and I’m not even sure it becomes possible because there’s just some steps for where I You can’t go from A to D. You need to go through these steps to kind of get there. And I think certainly without this turning into some sort of therapy for me and it’s not even an interview anymore, there’s just Dennis crying on Skype and we do that once a week. But I think one thing that people underestimate here is the data set required. So many of those stories or neural nets that you will have seen people play around with will mimic something. So you’ll see any half decent paper mimic a conversation on some topic. The interesting thing about that is that The key word here is mimic. As in, the conversation is actually not between the human and the machine. The conversation is in your head. As in, you can see what the machine said, what the human said, what the machine said, and somehow you look at that and conclude that that’s a conversation. No, it’s a conversation if the machine understands what we talk about, not as if it comes up with a string that somehow relates to the string before it and you believe it’s a conversation. And I think that’s certainly where we cannot cheat, as in, if you ask Amy to set up a meeting between the three of us in Atlanta at a given conference for an hour at that hotel in that lobby, she cannot not understand that. She cannot write something back that looks like we’re having a conversation. She needs to write something back for where everything you said, I understood, as in, I understand the people, the locations, the dates, the times, the constraints, your preferences, our past, and all of that, I can look at and say, given that my response will be this, not a set of strings. No, given that understanding, my response will be this. And that is certainly where in all honesty, we’ve underestimated the amount of effort we needed to apply to do two things to bring Amy to life. One, define this universe in which she exists. And it sounds almost too grand like Dennis. Don’t use the word universe when you talk about meetings. Use some other word, just not that. But that universe, however simplistic it might sound as in, Dennis, let me describe it for you here. It’s called location, participant, date and time. What else do you need? I think I just defined it for you. But we probably spent a year and a half, two years alone just describing it. Forget about one machine learning algorithm over another, one neural net over another. Just defining this universe, because if you don’t have that defined, how can you predict any outcomes within this universe if you don’t know what the boundaries are? That was hard. Then the second major challenge, again completely underestimated by us included here, is the data collection challenge. It’s almost like The three of us figured out that we want to build a tunnel from Manhattan to New Jersey. And all we talk about is the tunnel and how great is going to be. Trump style. And that sounds like a good idea. But the tunnel is not the job. The drill is the job. all we should be talking about is what drill do we need to get from here to there and all the work that we need to do to actually do the tunnel is in assembling the drill and once the drill will reach the middle those two drills will get disassembled and probably never used again but the drills were the job and that analogy kind of comes back to all of the work that we’ve done have been on the data annotation console, the annotation guidelines, not on picking out features in some machine learning model to increase precision or recall, but annotation. Like, just grunt work. It’s so unsexy that there’s no word for it, but that’s what’s needed.

00:23:37.36 [Tim Wilson]: Well, it’s funny as you’re talking about defining the universe and having a common definitions and kind of being a digital analytics podcast and thinking of how often we run into on a website. They crunch the data and give us the insights and make the recommendations and we stumble across that we haven’t really gotten crystal clear on what success is. I mean, I think that the easiest way for web optimization is, you know, optimized to a conversion on the website and, you know, a B testing multivariate testing bandit problem, whatever, you know, crank it through. And as you’re talking, I’m kind of hearing the same thing. You still have to define. what that problem is, and you’re still splitting that, yeah, there’s a bunch of different stuff you can throw at it, the tools to kind of crank it through. But that may be me falsely trying to draw a connection between not scheduling meetings and optimizing a website. Not really.

00:24:39.52 [Dennis Mortensen]: So we come from the same place, and there’s very few that will collect even good clickstream data, even though we spent the last decade and a half trying to persuade people to do just good, not even great, just good, but just getting to good is hard, as in really hard. I don’t think I ever looked at just the most rudimentary clickstream data sets thinking, damn, that is stellar. You’re almost always just a little bit disappointed, but I guess we’ll make the best of it. That’s your starting points. And I think that is certainly that data set. And then the next question is, then you tell me what you want to achieve here. And that could be multiple objectives, sometimes even competing objectives. But if you don’t tell me what you want to achieve, how do I help you get there with our poor data sets? So it’s not even like I’m going to be sailing there. So you don’t know where we headed. And the boat is kind of leaking. I guess we’re heading to sea now. Yeah. Okay. So I can certainly relate and have nightmares from the past that are very similar to the current nightmares.

00:26:00.03 [Michael Helbling]: Well, and I think we’re, you know, as you’re talking and we’re thinking about it, it does express why it’s been so difficult to do things that are machine learning in analytics and things like that, because the universe is not defined well enough. and one universe for one company may be completely different than another.

00:26:21.09 [Dennis Mortensen]: And we make all sorts of assumptions and obviously I have in my little venture here the challenge for where I have to exist in a place for where I can’t allow time for me to educate the user. He will look at Amy, make a set of assumptions for what she’s supposed to do, and if it’s not crystal clear, she’ll just fail. we just happen and I’m heavily biased here to stumble upon something for where we believe certainly there is a good agreement amongst most people on what an agent that can set up meetings is supposed to do and we can see that in the intents that we’re trying to predict that that’s a finite list. It’s not a never ending list of things people want Amy to do. And that was really, to a large degree, a major bet on the company, because had that list not been finite, we would have died trying. As in, we cannot have some set of infinite amount of intense, as in, what is it you want her to do? Intense, really her skill set, right? And that needed to end and perhaps, year, year and a half into it, we saw us kind of getting to the end of that. And that was obviously a ride towards, you know, you keep paddling, you know, further to see hoping that, you know, there will be something beautiful out here. And we certainly got to the end of it. But that was a little bit of a bet. And I think many other people who start on other agents will also be sailing into the sunset, but there will be a There’ll be no islands when they get there. They’ll just keep sailing and there’ll be an infinite amount of intents.

00:28:09.66 [Tim Wilson]: So when you say intents, is that, I mean, is that a list of, you have a list of 10 things or 20 things or 30 things or two things? Is that, is that how you knew you were there when, when a week went by and there was nothing else that wasn’t fit within that list? Is that how it worked? Yes.

00:28:27.28 [Dennis Mortensen]: Just a longer list and a longer time period. So it seems like one of those things where you and I can quickly start to imagine those intents. New meeting, canceling meeting, rescheduled meeting, running late, adding participant, Michael is optional, change conference number, and so on and so forth, right? Those are all logical. But it’s when you get to the end of that list where things might not be logical. And when you start to see there might be some overlap here. because there can’t be any overlap in those intents. There needs to be unique and mutually exclusive so that I can predict one from the other. And that have been a long journey. Even just, so forget about the intent prediction. There’s three primary entities which we need to deal with. So temporal data, location, and people. And if you think about temporal data, just time, I said, Dennis, don’t tell me you spent more than 20 minutes defining time. I’ll tell it to you. August 1st, 2016, 200 hours, 39 minutes, EST, daylight saving. But you know what? That would be cool if everybody talked like that, but they don’t. And they’ll say things like, let’s do one. One what? First of August, 1 p.m., 1 a.m., you’re on the West Coast. Are you talking to me? Are you talking about your own time zone? Or even worse, later. Let’s meet up later. This week, today. Next year. Or even worse, let’s meet up upon my return. Where are you at, Michael? When are you coming back? And somehow you need a time model for where you are trying to communicate time and I still need to understand what you’re trying to communicate and somehow be able to store that. So that have taken a lot of time. No pun intended.

00:30:48.23 [Michael Helbling]: Well, this is awesome. I’m really enjoying this conversation. One thing that we forgot to do at the beginning that we should probably go back and do now is maybe Dennis, just talk a little bit about your journey from analytics into this field and sort of kind of what steps or what key points kind of can you point to that kind of got you to, you know, to where you’re doing this today.

00:31:11.30 [Dennis Mortensen]: So here’s the story that I’ve come up with. The funny thing is that I believe it, but you are allowed to call bullshit from the end of it. And it goes like this, which is that back in the mid 90s, we did enterprise web analytics consulting off of mostly raw log files or rudimentary web trends version 1.0, by the way. and did really well. And the way we looked at that was really a venture for where we would go find the data, take it back, analyze it, come up with some sort of insight, go out on a monthly, bi-monthly, quarterly basis and provide that insight to management teams. That was my first venture. The next one was one for where that seems heavily delayed. How about we create a platform for where instead of me providing the insights, I create some software for where we will still collect, store the data, but allow you to retrieve and slice and dice it in a traditional enterprise web analytics SAS piece of software. And that was certainly a next step in that journey. At VR, we took it one step further. So instead of me analyzing and coming up with the insight, instead of me giving it to you so you come up with the insight, how about I create a recommendation system so that not only do I collect, store, retrieve, I will model on the data and tell you what I suggest you should do. And that was the next natural step. If you think about x.ai, that’s kind of a fourth step on that journey for where I will collect, store, retrieve, model, suggest, and take the action. So instead of me suggesting you meet up next Thursday at four, how about I just have my agent figure that out for you and do it? So that’s my 20-year justification for how I got from one place to the next, and it might just be all based on looking back at it. I can see that.

00:33:22.44 [Tim Wilson]: You could sell that, that you had that vision from the get-go, you knew you were ahead. The world couldn’t move quite as quickly as you had a plan. I’d buy it. Only a computer could be that long.

00:33:34.58 [Dennis Mortensen]: Yeah, except now.

00:33:37.24 [Tim Wilson]: We’re back to the Turing test.

00:33:40.78 [Dennis Mortensen]: But we need that little You need that set of questions that you should have prepared for me. Yes.

00:33:48.18 [Michael Helbling]: That’s right. Well, but it is interesting because in the world of digital analytics, there are lots of people who are trying to do things around you know, discovery of insights or hey, let’s do some things to try to push some of the things you should be looking at to you programmatically. And there’s there’s sort of this balancing act in these more, I would say this is a more complex system than the one that you’ve chosen to tackle. Hence, you know, let’s multiply it time 70 times 70 with the other millions and millions of dollars, right, that it would take to evolve it, is it something where we actually should be thinking more like human with intuition sits and sends out agents on specific tasks and those agents should just know how to go pursue a very specific set of tasks within the data that the user then takes in as my intuitive understanding of what we need to accomplish? And is that a better pursuit in the short term or should we just keep on trucking?

00:34:49.00 [Dennis Mortensen]: I think so, because what you describe in the most extreme sense is really just a company for where there’s one KPI, let’s call it profit. and everything else is optimized before that and no human need apply. As in, I just want max profit, you can do campaigns, you can not do campaigns, you can allocate more for R&D, you can not, you can hire people, you can not, you can do whatever you want as long as I’m maximizing this one KPI. I think we are far away from that. What I think is much more likely is this second scenario which you describe, which is, what I tend to see as the bring your own agent generation for it used to be certainly the three of us young kids had a bring your own device generation for where we could bring our own laptop bring our own smartphone start to access company data through those devices and I think we’ve just reached a point for where That’s just normal. Nobody will bitch about you bringing your own mobile phone or even reading the company email on your mobile device. I think the next step is bring your own agent. As in when I hire Tim to do a job or come work together with me for the next two years, we are hiring, by the way, a customer acquisition analyst. You should go apply. on to the next point. And as I hired Tim, who am I hiring here? Am I hiring Tim or I’m hiring Tim who have employed 11 distinct agents that he worked with for years. They’re machine agents and he trained those agents. So he think they are super optimal, not just in the universe at last, but in their relationship with Tim. And that I find interesting. I find that super interesting that I don’t want just Tim alone. Tim coming alone would seem handicapped as in that’s all. I’m not sure we’re ready for that. I would have some campaign optimization agent for content that you’ve been working with for years that you go deplore if there’s any questions in that domain. You obviously wouldn’t do that yourself. Or are you telling me that you do keyword optimization or keyword analysis in spreadsheet yourself? Ah Tim, that’s a little bit too eccentric. We probably need some agent to help us with that. So I think that is certainly what’s going to happen near term. And then it’s interesting, who’s paying those agents? So if I pay Tim his six figure salary, is he paying his own agents because I kind of hired him under the guise of those are his agents because he keep those agents as he walks out the door. So he needs to pay them. So I probably need to somehow see some setting for where I need to pay for this little army of agents that he bring along. And that’s I think is It’s interesting and realistic, by the way.

00:38:16.13 [Tim Wilson]: If we look at an individual at our careers and for those of us who are in consulting, absolutely, we’re supposed to show up when we’re working with clients and saying, I’m not starting off saying, well, let me learn how to use a spreadsheet. Let me learn how to use your web analytics platform. There’s kind of an expectation that, OK, you’ve got experience. So that’s kind of the that’s maybe the more amorphous, you’re more efficient because you’ve done this, but it does seem like there’s the potential to say whether you’re a consultant or inside a company as you’re growing your career, what are those conceptual agents? The techniques that you’ve codified, be it process, be it a tool you’ve developed, there’s certainly a wealth of technology vendors out there that are saying they’ve built the magic agents and you can just plug in their software which doesn’t happen you still need to have a human being who can operate those but some kind of maybe extrapolating a little too far but thinking that that’s certainly what I feel like a lot of what I’m doing right now is trying to figure out what can I do what can I what what machine little mini machines can I build that I can apply in multiple places and then grow and develop those those agents, and that’s kind of a technical agent, but I guess there’s a process piece too. I think I’m on the same page as you are.

00:39:35.93 [Michael Helbling]: Well, the first thing that has to happen is a pattern or system has to be developed as the right or best way to approach that problem. Like in software development, there’s design patterns.

00:39:48.08 [Dennis Mortensen]: And I think if we look at the job of a junior analyst, even that is way too sophisticated to dream up some agent that can do the job even off a junior analyst. I do think though if you look back at the first six months of this year there’s many tasks which you’ve done over and over again and some of those tasks as you repeatedly do them might actually be up for automation and some agent could do that for you and I think the interface to that agent will not be a drop down menu or some other interface that will be natural language where you will describe to that agent what you want done on whatever given data set and that agent will do that for you and the more you work with that agent The more that agent will learn both your language, your objectives, and what you want done, and the more you train it, the smarter it becomes. So even one agent from the same company versus the same agent from the same company with another person, yours might just be better trained because you’ve honed a skill set over the last two decades. So it’s not about necessarily just the vendors and their agents, I think it’s about the people who deploy the agents and how they train them.

00:41:11.78 [Tim Wilson]: So is there, but if you’re bringing your own agents, is there the potential, the challenge you were talking earlier about needing to know the syntax that if I’m using my own agent and I think I’ve certainly run into all sorts of people who’ve kind of built their own, their own tools and they talk about how this thing they’ve built is super smart, super, uh, super flexible. And then, but they’re kind of the only ones who can interface with it, which is still fine. If I’m asking them to do a task and they become the agent to get to the agent to, to do that. But is that, does that short circuit? Does that have the ability to short circuit half of the equation that you were describing?

00:41:50.73 [Dennis Mortensen]: I don’t think so. So whatever agent you go employ will be an extension of Tim. and is for Tim to be able to communicate with that agent. I do think though that the agent will communicate in plain English. I think it will learn your preferences, but it will still be able to communicate with other similar agents or whatever agent universe exists within your world for where it needs to also communicate with these other five that you have implored and you should be aware of the fact that they exist and things sometimes will happen because they had a conversation not behind your back but in front of you because that would just be natural why wouldn’t they and that I think certainly is That exciting step, but I just want to lower our ambition and not have us believe that there’s some junior analysts coming along. I think there’ll be some highly specialized agent for one type of campaigns in one specific setting. for a given data set that somebody might come up with. And instead of you spending two hours every Thursday on that task, you have an agent, and that agent can do what you ask it to do on that data set in that given scenario. That will be that very first beginning.

00:43:15.53 [Tim Wilson]: So why don’t they just productize it instead and go and sell it to the masses?

00:43:20.06 [Michael Helbling]: They will. Because whoever has the best agent, you should be able to sell that to everyone so we can get the best. They’ll put it in the app store.

00:43:27.53 [Tim Wilson]: We are back to where we started.

00:43:30.52 [Michael Helbling]: Horizontal vertical. It’s just many, many vertical AIs. So that raises another question of which horizontal AI is the best right now?

00:43:41.25 [Dennis Mortensen]: See, that’s a good question. And there’s obviously multiple answers to that. Which one of these horizontal agents have the most knowledge? As in if I just shout out a random question, will it have the answer to it? But I think let’s just go a little bit further up and I can tell you what certainly have turned me on, which is the form factor of the Amazon Echo for where you have a bigger speaker, better microphones, true always on, is a game changer. I actually don’t think they have the best knowledge, the best NLP, and the best kind of concept of the universe at large, but that always on better microphones have changed everything. If I look at how much I’ve talked to Alexa over the last couple of months, it has surpassed how much I’ve talked to Siri for eight years, right?

00:44:45.19 [Michael Helbling]: Right, or how much my kids have talked to Alexa. No, I think that’s right. It’s not the amount of knowledge in the horizontal. It’s more how vertical can it be and how fast.

00:44:56.41 [Dennis Mortensen]: And you can see that they have a very similar attitude towards tomorrow as I have. And they’ve gone, created that skill store, I believe they call it, which is their vertical AIs. So they just assume that, no, we won’t have all the answers. No, we won’t be able to do all the jobs. So how about people just go engineer skills, which can tap into our ecosystem.

00:45:23.54 [Michael Helbling]: So when can I expect Amy to be a skill in the Amazon Echo?

00:45:29.69 [Dennis Mortensen]: In the not too distant future. Outstanding. Yes. And I think what you’ll see us do to begin with is actually just you being able to get status. Hey Alexa, what is Amy working on? So she can speak back and say, hey, I’m still trying to reach Tim. I’m on the second reminder. He’s a little bit tardy, but hey, you know Tim. This was just an example, by the way.

00:45:55.40 [Tim Wilson]: So apparently you looked up our records before you got into this.

00:46:00.99 [Dennis Mortensen]: It’s usually the other way around, but I like your example.

00:46:07.44 [Michael Helbling]: No, that’s, and that’s amazing. And that is sort of, you know, just in the past year, I think Amazon has done something that Google and Apple who were ahead have failed to do a little bit more. But, but then again. Well, it’s about to happen too. It’s going to go. Yeah. Google gets it. Google just announced earlier this year their device.

00:46:28.73 [Dennis Mortensen]: But in hindsight, it’s just so obvious. And given that it’s so obvious, why didn’t Michael, Tim, or Dennis come up with the Moenica home device? We will be killing it right now. Have we done so? But somehow it was only obvious the day it arrived that you really just need a semi big ass speaker with a set of really good microphones and plug it into the wall. So it’s always on.

00:46:59.65 [Tim Wilson]: I was too busy cleaning up my crappy clickstream data. So that’s my excuse.

00:47:05.92 [Michael Helbling]: So there. My excuse is I only hang out with the people with good ideas. I don’t personally have them. But yeah, well, and it helps probably that, you know, that’s where you buy everything from.

00:47:18.05 [Dennis Mortensen]: It was just a good idea. Such a good idea that it’s almost disappointing to see that my new Apple TV isn’t always on with Siri. Because that is the one thing for where, for crying out loud, I’m staring into it as we speak. And it doesn’t talk back to me. It’s right there. You could just add it to microphones and everything would have been hunky dory. But they didn’t. All good to Amazon. Well spotted.

00:47:46.76 [Michael Helbling]: Johnny down notes of things I want to go invent, because my head is exploding right now. And I can’t do them, but my agents will be able to. And that’s pretty exciting. And for someone who has never had like, I’m not a data scientist, like some of the people on my team were like, Tim is learning R. Like those are the skills I don’t possess, but what if I had agents who I could deploy with the intuition I have, and that then makes someone like me much more able to go and do great analysis, but without necessarily having to learn all the ins and outs of how to utilize those systems.

00:48:27.49 [Dennis Mortensen]: And that’s the keyword. I think you just describe what I believe will be one of the most valuable skills in the future. So it’s not the one who have the best answer. Answers will be cheap. It’s the one who have the best question. So answers, I can just get an agent to go work on that. So there, Moenica, no questions. That is gold. And that will see I think the dynamic in the workplace changed dramatically as in right now we reward people with answers and we should because that’s the environment we live in but I think we don’t fully realize how valuable the questions are.

00:49:15.33 [Tim Wilson]: I feel like I’ve been beating the drum of, because right now, I think a lot of times analysts are expected to, they’re asked to provide answers in the absence of a question. Give me insights and recommendations, or to your other early example, when I say, well, let’s talk about what you’re really trying to achieve. They’re like, I just want to maximize profit. I’m like, well, that’s great, but you’re selling toilet paper. And you’re asking me about social media. So we’re going to have to get to questions that are a little more focused that rely on your intuition. So, but I like, I like this is really helping me bridge the, live just in web analytics, try to get people to ask questions, generate hypotheses. You know, because the other way to look at a question is I have a hypothesis. I have a question. I can state that is I, I believe the world operates this way, whether I really do or not, it doesn’t matter. And I want to go validate it. And to me, that’s gold for an analyst, because the analyst just then has a very specific thing they can operate on. I like, this actually has helped me bridge the machine learning to the soft world of discovering hypotheses. So, nice. Group Hawker. It’s outstanding.

00:50:25.05 [Dennis Mortensen]: Group Hawker.

00:50:25.33 [Tim Wilson]: Once again, no idea where the conversation would go.

00:50:27.89 [Michael Helbling]: And this has been awesome. This has been a great discussion, and I’d love for us to continue, but we do got to start wrapping up. One thing we do on the show is we love to go around and do a last call, which is anything you think is interesting right now, you’ve run across in the last few weeks or days. So anybody, I’ll throw it out there. Anybody can start.

00:50:48.75 [Tim Wilson]: I’ll throw one. I’m calling an audible during the episode based on the discussion. I had a couple of podcasts I was going to recommend, and instead I’m going to recommend one specific episode of one specific podcast, which is the Planet Moeney episode number 626, which is This is the End. It was from May of 2015, but they went around to economists. There’s this whole, there’s this idea that as the machines get smarter and all these agents are out doing stuff, we’ll have more leisure time. And what does that mean? What’s the impact on the world if we actually achieve this point where we don’t need to work? you know, 60 hours a week. And I remember it being very fascinating is that one, arguing how close are we to that, which we touched on that a little bit of how close are we to this nirvana of the machines just doing everything. But then also the economic impact of it. So it just seemed so yin to this episode. So it was episode 626 of Planet Moeney.

00:51:49.05 [Dennis Mortensen]: I will in the same vein of not clarity, but perhaps further Disconnect with where we believe we were because there is this blog post that I keep rereading Every two months. Do you know there’s one of those where you fall in love then you have to read it again and I keep coming back and it’s the one by Tim over at wait, but why the one on the Fermi paradox and Whenever you think you are in control and you have some understanding of the real universe and the whole thing almost makes sense you read that blog post and nothing makes sense and whatever guideline for tomorrow I might have had I have nothing I guess I’ll just go to sleep and when I wake up I’ll be much better but I love that blog post because I can’t fathom why we’re alone why they’re not others And why haven’t they talked to us yet? And that sounds like I’m ludicrous. Then you read the blog post thinking, they should have called us. Why didn’t they call us? And then when you finally get to kind of believe that, ah, so there’s this great filter concept. That’s why they haven’t called us. But then we’re doomed. Ah, so we doomed if they haven’t called us. But if they do call us, they’re going to be so far ahead that we probably also doomed. And then you go to sleep again. So it’s one of those every two months. You should go read it. It’s a wonderful blog post. 15 minutes.

00:53:33.13 [Tim Wilson]: We’re fucked. The great filter is ahead of us. Oh, this is going to be fun.

00:53:37.43 [Michael Helbling]: Awesome. All right. So actually, my last call is not that dissimilar in some senses, although it’s kind of out there. Recently, a lot of people have been recommending to me a certain show on television called Rick and Moerty. So I’ve begun to watch it and it’s very interesting. So that would be my last call is Rick and Moerty which also deals with time travel and multi-dimensional universes. So not that dissimilar Apparently the science is pretty solid. So I hear I’m only a few episodes in so I’ll keep learning All right, well, this has been phenomenal. What a great discussion. You know, as we’ve been talking, you heard Dennis say the question is the thing. So if you have questions, hit us up on our Facebook page. We will relay them to Dennis and maybe if he’s not too busy working on what’s going on over at x.ai, he can jump in and help us answer them or hit us up on the measure slack. We always love to hear from everybody. Also, if you haven’t yet and you have a mind to, feel free to rate us on iTunes. Apparently that helps us out in some way. Or develop an agent to go rate a son. We should work. We’re going to work on that as right now. I think we’re the number one explicit digital analytics podcast on itunes. We’re hoping to stay that way. But anyway, it’s been such a profound delight Dennis to have you on the show. And this conversation has been one that’s been sort of mind blowing a little bit and delightful in that it gives you so many great avenues to go down. So I’m excited to see some of the conversations that come after that. So thanks again, Dennis. It’s been so delightful to have you on the podcast. Really enjoyed your insights. Thank you so much for joining us.

00:55:36.42 [Dennis Mortensen]: Thank you very much for having me. This was absolutely fun. Michael, Tim, cheers. And we’d love to hear from you as well.

00:55:46.19 [Michael Helbling]: But as you know, until there’s agents to do it for you, keep analyzing.

00:55:55.89 [Announcer]: Thanks for listening and don’t forget to join the conversation on Facebook, Twitter or Measure Slack Group. We welcome your comments and questions.

00:56:04.16 [Announcer]: Facebook.com forward slash analytics hour or at analytics hour on Twitter.

00:56:19.67 [Tim Wilson]: Yep, yep, we’re recording. We are recording.

00:56:23.49 [Michael Helbling]: No, no. Oh, Tim gets me with that every time. Already recording and I see something really stupid. Tim will ask a lot of questions and I will try to get a word into us. Oh, I’m a user, absolutely. Very good. If you’re going to peel your face off here at the end of this, then that’s right. Technically, now that you’re in the world of AI, you have to accept your role as sort of more of the Bond villain now.

00:56:57.87 [Dennis Mortensen]: Because I want that money. So just go wild.

00:57:05.40 [Michael Helbling]: Like on television television? Well, I mean, it’s prerecorded. I don’t watch live television.

00:57:11.09 [Tim Wilson]: Come on. Rock flag and artificial intelligence.

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