( bright upbeat music) >> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube conversation. >> Hey, welcome back. You're ready, Jeff Frick here with theCUBE, we are still getting through COVID. It's a hot August day here in San Francisco Bay Area. It is 99, somebody said in the city that's hot, but we're still getting through it. We're still reaching out to the community, we're still talking to leaders in all the areas that we cover. And one of the really interesting areas is natural language processing. And it's a small kind of subset. We'll get into it a little bit more detail, we are very specific place within the applied AI world. And one of my very good friends and Cube alumni, who's really an expert in the space, he's coming back for his second startup in the space. And we're joined by Ben, he's Ben Chung, the Co-founder of Ogmagod. Did I get that right Ben, Ogmagod. >> That's correct. That's right. >> Great to see you again. >> Thank you for inviting to the show. >> Well, I love it. One of the topics that we've been covering a lot Ben is applied AI. 'Cause there's just so much kind of conversation about artificial intelligence, the machine learning is kind of this global big thing. And it kind of reminds me of kind of big data or cloud, in the generic it's interesting but it's really not that interesting, 'cause that's really not where it gets applied. Where I think what's much more interesting and why I wanted to have you back on is, where is it actually being applied in applications? And where are we seeing it in solutions? And where is it actually changing people's lives, changing people's days, changing people's behavior, and you seem to have a propensity for this stuff. It was five years ago, I looked July five years ago, we had you on and you had found Genie, which was a natural anti processing company focused on scheduling. Successful exit, sold that to Microsoft and they baked it into who knows, there probably baked in all over the place. Left there now you've done it again. So before we get into it. What so intriguing to you about natural language processing for all the different kind of opportunities that you might go after from an AI perspective? What's special about this realm that keeps drawing you back? >> Yeah, sure, I mean it, to be honest it was not anything premeditated, I kind of stumbled on it. I before this, I was more like an infrastructure guy spent a number of years at VMware and had a blast there and learned a lot. Then I kind of just stumble on it. Because when we started doing the startup, we didn't intend it to be a AI startup or anything like that. We just had a problem that my co-founder Charles Lee and I really wanted to solve, which is to help people, solve people's scheduling problem. But very shortly after getting into and start looking at some use cases, we thought that the easiest way is to communicate with people like humans do to help them do the scheduling. And that's kind of how I stumbled on it. And it wasn't until that I stumbled on it that I realized that it has a lot of attraction to me, because I throughout my whole life, I'm always very interested in the human emotions of it, how humans relate to each other. And that's always been the hidden side project thing, I do traveling to figure out stuff and get a little bit of that. But once I start getting into this field, I realized that there's a lot about it, about humanity and how humans communicate that it was kind of like a hidden interest for me. That now suddenly coming out and it kind of just got me hooked. >> Right, that's awesome. So one of the things and we'll just get into it is people are a little bit familiar with natural language processing, probably from Siri and from Google and from Alexa and increasingly some of these tools but I think, you kind of rapidly find out beyond what's the weather and play a song and tell me a joke that the functionality is relatively limited. So when people think about natural language and they have that as a reference point, how do you help them see that it's a lot more than, asking Siri for the weather. >> Yeah, there are a lot of capability but also hopefully not offensive to some of the tech visionaries. Just as a guy who is dealing with it every day, there are also lots of limitation is not nearly to the degree of refinements. Like what might being preach out there saying that the machines are going to take over everything in one day, we have a lot of struggles that are very basic stuff with machines. However, there has been definitely a lot of breakthroughs in the last few years and that's why I'm dedicating my life and my time into this area because I think that it just, there's going to be huge amount of innovation continuously going in this area. So that's at the high level, but if you talk about, in terms of artificial intelligence and in general, I think, I have my own understanding, I'm more like an apply guy, lot of academics so what I'm going to say might make some academics cringe because I'm more like a everyday practical guy and try to re conciliate these concepts myself. The way that I view is that artificial intelligence has really tried to help mimic some human capabilities that originally thought that is the domain of human, only humans are able to do it, but machines now try to demonstrate that machine can do it, like as though the humans could. So and then usually people get that mixed up with machine learning, to me is actually quite different thing. Artificial intelligence just like what I mentioned, machine learning is just a technique or a science or way of applying like to leverage this capability, machine learning capability in solving these artificial intelligent problems, to make it more achievable to raise the bar on it. So I don't think we should use them interchangeably, artificial intelligence and machine learning. Because today machine learning is the big deal that are making the progress wise, tomorrow might be something else to help improve artificial intelligence. And in the past, it was something else before machine learning. So it's a progression, the machine learning is the very powerful and popular technique right now to being used. Now within artificial intelligence, I think you mentioned that there are various different domains and topics, there is like object recognition deals with image processing, there's speech detection, there's a video and what I would call action or situation detection. And then there's natural language processing, which is the domain that I'm in that is really in that stage of where we seeing quite a bit of break through, but it's not quite there yet. Whereas versus speech detection and image processing actually has done a tremendous progress in the past. So and in you can say that like the innovation there is not as obvious or as leap frogging as the natural language processing. >> Right, so some of the other examples that we know about that are shared often for machine learning or say, the visual thing, can you identify a chihuahua from the blueberry muffin, which sounds kind of funny until you see the pictures, they actually look very, very similar. And the noise stated that Google and their Google Photos, right, has so many pictures such a huge and diverse data set in which to train the machines to identify a chihuahua versus a blueberry muffin. Or you take the case in Tesla, if you've watched any of their autonomous vehicle stuff and their computer vision process and they have the fleet, hundreds of thousands of cars that are recording across many, many cameras reporting back every night. With natural language processing you don't have that kind of a data set. So when you think about training the machine to the way that I speak, which is different than the way you speak and the little nuances, even if we're trying to say the same thing, I would imagine that the variety in the data set is so much higher and the quantity of the data set is so much lower that's got to be a kind of special machine learning challenge. >> Yes, it is. I think the people say that there is, we are at the cusp of, being able to understand language in general, I don't believe that we are very far away from that. And even if when you narrow scope to say, like focus on one single language like English, even within that, we still very far from it. So I think the reality, at least for me, speaking from the ground level, kind of person tried to make use of these capabilities is that you really have to narrow it to a very narrow domain to focus on and bound it. And my previous startup is really that our assistant to help you schedule meetings, that assistant doesn't understand anything else other than scheduling, we were only able to train it to really focus on doing scheduling, if you try to ask it about joke or ask anything else, it wouldn't be able to understand that. So, I think the reality on the ground at least from what I see of a practical application and being successful at it, you really need to like have a very narrow domain in which you apply these capabilities. And then in terms of technology being used broadly in natural language processing in my view there are two parts of it, one is the input, which is sometimes call natural language understanding. And then that part is actually very good progress. And then the other part is the natural language generation, meaning that the machine knows how to compose sentences and generate back to you, that is still very, very early days. So there is that break up and then if you go further, I don't want to bore you Jeff here with all these different nuances, but when you look at natural language understanding, there are a lot of areas like what we call topic extraction or entity extraction, event extraction. So that's to extract the right things and understand those things from the sentences, there is sentimental analysis knowing that where some a sentence expresses somebody angry or some different kinds of emotions, there is summarization, meaning that I can take sets of texts or paragraphs of text and summarize with fewer words for you. So and then there is like dialogue management, which manages the dialogue with the person. So they're like these various different fields within it. So the deeper you look, there's like the more stuff within it and there's more challenges. So it's not like a blanket statement, say like, "Hey, we could conquer on this." And if you digging deep there's some good progress in certain this area. But some areas like it's really just getting started. >> Right, well we talked about in getting ready for this call and kind of reviewing some of the high level concepts of and you brought up, what is the vocab? So first you have to just learn what is the vocabulary, which a lot of people probably think it stops there. But really then what is the meaning of the vocabulary, but even more important is the intent, right, which is all driven by context. And so the complexity, beyond vocabulary is super high and extremely nuanced. So how do you start to approach algorithmically, to start to call out these things like intent or I mean, people talk about sentiment all the time, that's kind of an old marketing thing, but when you're talking about specific details, to drive a conversation, and you're also oh, by the way, converting back and forth between voice and text to run the algorithms in a text based system, I assume inside the computer, not a voice system. How do you start to identify and programmatically define intent and context? >> Yeah, just to share a little anecdote, like one of the most interesting part of, since I started this journey six years ago and also interesting was a very frustrating part is that, especially when I was doing the scheduling system, is that how sloppy people are with their communication and how little that they say they communicate to you and expect you to understand. And when we were doing the scheduling assistant, we're constantly challenged by somebody telling us certain things and we look at it's like, well, what do they mean exactly? For example, like one of the simple thing that we used to talk a lot with new people coming on the team about is that when people say they want to schedule next week, they don't necessarily mean next week, what they mean is not this week. So it doesn't, if you like take it literally and you say, "Oh, sorry, Jeff, there is no time available next week." And actually Jeff probably not even remember that he told you to schedule next week, to what he remember, what he told you not to schedule it this week. So when you come back to them and say, "Jeff, you have nothing available this week or next week." And Jeff might say like, while your assistant is kind of dumb, like, why are you asking me this question? If there was nothing available next week, just scheduled the week after next week. But the problem is that you literally said next week, so if we took you literally, we would cause unhappiness for you. But we kind of have to guess like what exactly you mean. So don't like this a good example where they're like lot of sloppiness and lot of contextual things that we have to take into account when we communicate what humans, or when we try to understand what they say. So yeah, is exactly your point is not like mathematics is not simple logic. There are a lot of things to it. So the way that I look at it, there are really two parts of it. There's the science part and then there's art part to it. The science part is like what people normally talk about and I mentioned earlier, you have to narrow your domain to a very narrow domain. Because you cannot, you don't have the luxury of collecting infinite data set like Google does. You as a startup, or any team within a corporation, you cannot expect to have that kind of data set that Google or Microsoft or Facebook has. So without the data set, huge data set, so you want to deliver something with a smaller data set. So you have to narrow your domain. So that's one of the science part. The other part is I think people talk about all the time to be very disciplined about data collection and creating training data sets so that you have a very clean and good training data set. So these two are very important on the science part and that's expected. But I think a lot of people don't realize this, what I would call the art part of it, is really there are two parts to that. One is exactly like what you said Jeff is to narrow your domain or make some assumption within the domain, so that you can make some guesses about the context because the user is not giving it to you verbally or giving you to you into text. A lot of us we find out visually by looking at the person as we communicate with them. Or even harder we have some kind of empathetic understanding or situational understanding, meaning that there is some knowledge that we know that Jeff is in this situation, therefore, I understand what he's saying right now means this or that Jeff is a tech guy like me, therefore, he's saying certain thing, I have the empathetic understanding that he meant this as a tech guy. So that's a really hard kind of part of it to capture or make some good guesses about the context. So that's one part. The other part is that you can only guess so much. So you have to really design the user experience, you have to be very careful how you design the user experience to try what you don't know. So that it's not frustrating to the user or to put guardrails in place such that the user doesn't go out of balm and start going to the place where you are not trained for that you don't have to understand it. >> Right, because it's so interesting, 'cause we talked about that before that so much of communication, it's not hard to know that communication is really hard, emails are horrible. We have a hard time as humans, unless we're looking at each other and pick up all these nonverbal cues that add additional context and am I being heard, am I being understood? Does this person seem to understand what I'm trying to say? Is it not getting in? I mean, there's so many these kind of nonverbal cues as you've expressed, that really support the communication of ideas beyond simply the words in which we speak. So and then the other thing you got to worry about too, as you said, ultimately, it's user experience if the user experience sucks, for instance, if you're just super slow, 'cause you're not ready to make some guesses on context and it just takes for a long time, people are not going to to use the thing. So I'm curious on the presentation of the results, right? Lots of different ways that that can happen. Lots of different ways to screw it up. But how do you do it in such a way that it's actually adding value to some specific task or job and maybe this is a good segue to talk about what you're doing now at Ogmagod, I'm sorry I have to look again. I haven't memorized that yet. 'Cause what you're also doing if I recall is you're taking out an additional group of data and additional datasets in beyond simply this conversational flow. But ultimately, you've got to suck it in, as you said, you've got to do the analysis on it. But at the end of the day, it's really about effective presentation of that data in a way that people can do something with it. So tell us a little bit about what you're doing now beyond scheduling in the old days. >> Sure, yeah, I left Microsoft late last year and started a new startup. It's called Ogmagod. And what we do is to help salespeople to be more effective, understand the customer better so that they have higher probability of winning the deal or to be able to shorten the sales cycle. And oftentimes, a lot of the sales cycle got LinkedIn is because of the lack of understanding and there's also, I say, we focus on B2B sales. So for B2B salespeople, the world's really changed a lot since the internet came about. In the old days is really about, tell it to explain what your product is and so that your customer understand your product, but the new days is really about not explaining your product because the customer can find out everything about your product by looking at your website or maybe your marketing people did do such a good job, they already communicated to the customer exactly what your product does. But really to win out against other people you really like almost like a consultant to go to your customer and say, like, I have done your job, almost like I've done your job before I know about your company. And let me try to help you to fix this problem. And our product fit in as part of that, but our focus is let's fix this problem. So how would you be able to talk like that, like you've done this job before? Like you worked at this company before? How do you get at the level of information that you can present yourself that way to the customer and differentiate yourself against all the other people who try to get their attention, all the people sending them email every day automatically, how do you differentiate that? So we felt that the way that you do it is really have the depth of understanding where your customer that is unrivaled by anybody else. Now sure, you can do that, you can Google your customer all day, reorder news report, know all the leadership, could follow them on social media-- >> Right, they're supposed to be doing all this stuff, right Ben, they're supposed to be doing all this stuff and with Google and the internet there's no excuse anymore. It's like, how did you not do your homework? You just have to get the Yellow Pages. >> Yeah, why didn't you do your work? Yes, people get beat up by their management saying like, "Oh, how come you miss this? "It's right there go on Google." But the truth is that you have to be empathetic to a salesperson. A lot of people don't realize that for a salesperson, every salesperson, you might own 300 accounts in your territory. And a lot of times in terms of companies, there might be thousands of companies in your territory. Are you going to spend seven hours, follow all these 300 companies and read all tweet. Check out the thousands of employees in each of these company, their LinkedIn profiles, look at their job listings, look at all the news articles. It's impossible to do as a human, as a person. If you do that you'll be sitting in your computer all day and you never even get to the door to have a conversation with the customer. So that is the challenge so I felt like salespeople really put up impossible tasks, because all this information out there, you're expected it to know. And if you screwed up because you didn't check, then it's your fault. But then on the same time, how can they check all 300 accounts and be on top of everything? So, what we thought is that like, "Hey, we made a lot of progress "on natural language processing "and natural language understanding." And salespeople what they look for is a quite narrow domain. They are looking for some very specific thing related to what they selling, and very specific projects, pinpoints budget related to what they're selling. So it's a very narrow domain, we felt like it's not super narrow. It's a little bit broader than I would say scheduling. But it's still very narrow the kind of things that they're looking for. They're looking for those buying triggers. They're looking for problem statements within the customers that relate to what they selling. So we think that we can use, develop a bunch of machine learning models and use what's available in terms of the web. What's out there on the web, the type of information out there. And to be able to say, like, salesperson, you don't need to go and keep up and scan, all the tweets and all the news and everything else for these 300 companies that you cover, we'll scan all of them, we will put them into our machine learning pipeline and filter out all the junk, because there are lots of junk out there, like Nike, that's like, I don't know, hundreds of news release probably per week. And most of them are not relevant to you. It doesn't make sense for you to read all of those. So but how about we read all of them and we extract out, we it's difficult topic extraction, we extract out the topic that you're looking for and then we organize it and present to you. Not just we extracting out the topic. Once we get the topic how about we look up all the people that are related to that topic in the company for you so that you can call on them. So you know what you want to talk to them about, which is this topic or this pin point. And you know who to talk to, these are the people. So that's what we do. That's that's really interesting. It's been a tagline around here for a long time, right separating the signal from the noise. And I think what you have identified, right is, as you said, now we live in the age where all the information is out there. In fact, there's too much information. So you should be able to find what you're looking for. But to your point, there's too much. So how do you find the filter? How do you find the trusted kind of conduit for information so that you're not just simply overwhelmed that what you're talking about, if I hear you right, is you're actually querying publicly available data for particular types of I imagine phrases, keywords, sentences, digital transformation initiative, blah, blah, blah. And then basically then coalescing the ecosystem around that particular data point. And then how do you then present that back to the salesperson who's trying to figure out what he's going to work on today. >> B2B salespeople, they start with an opportunity. So opportunity is actually a very concrete word at least in the tech B2B sales-- >> We know, we see the 60 stories in downtown San Francisco will validate statement. (laughs) >> Yes, so yeah, so it starts with the word opportunity. So the output is a set of potential opportunity. So it speaks to the salespersons language and say, when you use us, we don't just say "Hey, Jeff, there's this news article about Twilio and you cover Twilio, that's interesting to you." "Oh, there's a guy at Twilio that matches the kind of persona that you sell into." We don't start with that, we start with, "Jeff, there are six Opportunities for you at Twilio. "Let's explain what those things are." And then explain the people behind these opportunities so that you can start qualify them. So get you started, right way in your vocabulary in a package that you understand. So that I think that's what differentiates us. >> Right, and at some point in time, would you potentially just thinking logically down the road, you have some type of Salesforce API. So it just pumps into whatever their existing system is. That they're working every day. And then it describes based on the algorithm, why the system identified this opportunity, what the attributes are that flagged it, who are the right people, et cetera. Awesome, so what kind of data are you requiring-- >> Yes, you are designing our product wise. >> (laughs) Since Dave and John, watch this. They're going to want to talk to you, I'm sure. But what type of data sets are you querying? >> There are lots of them. We learned most of it by through the process working for salespeople, meaning that we work for salespeople, we may be quote, unquote, stand behind their back and see what they're searching. They're searching LinkedIn. They're searching jobs. They're searching endless court transcripts, they're looking at 10K 10Q's, they dig up various, some people are very, very creative, digging out various parts of the web and find really good information. The challenge is that they can't do this to scale. They can't do it for 300 accounts, 'cause we're doing for one accounts very is laborious. So there are various different places that we can find information. And in terms of the pattern that we're looking for. It's not just keyword, it's really concepts. We call it a topic. We really looking for very specific topics that the salesperson looks for. And that's not just a word, because sometimes words is very misleading. For example, I tell you one of the common words in tech is called Jenkins. Jenkins is a very popular technologies, continuous delivery technologies step but Jenkins is also happens to be a very common last name for people. >> (laughs) Well, I'm always reminded of our Intel days with all the acronyms, but my favorite is ASP 'Cause you could use ASP twice in the same sentence and mean two different things, right? Average selling price or application service provider back in the days before we call them clouds, but yeah, so the nuances is so tricky. So within kind of what you're doing then and as you described working within defined data sets and keeping the UX and user experience pretty dialed in and within the rails, are there particular types of opportunities in terms of B2B types of opportunities that fit better that have kind of a richer data set, a higher efficacy in the returns what do you kind of seeing in terms of great opportunities for you guys. >> We're still early, so I can't tell you that like from a global view because we are like less than one year old experience, quite honestly. But so far we are being led by the customer. So meaning that there is an interesting customer, they ask us to look for certain topics or certain things. And we always find it to my surprise, because and that really is, like, I'm constantly surprised by how much is there out in the web, like what you were saying, like customer ask us to look for something. And I thought for sure, this thing we couldn't do it, we can find it. And we gave it a try and low and behold, there it is. It's out there. So, to be honest, I can't tell you at this point, because I have not run into any limits. But that is because we are still a very young startup. And we are not like Google. We're not trying to be all encompassing looking for everything and looking over everything. We're just looking over everything that a salesperson wants, that's it. >> So I'm going to make you jump up a couple levels. Since you've been thinking about this and working on this for a long time, there's a lot of conversation about machines taking everybody's jobs, then there's the whole kind of sidetrack launch to that, which is no, it's all about helping people do better jobs and helping people do more higher value work and less drudgery. I mean, that sounds so consistent with what you're talking about, I wonder as somebody down in the weeds of artificial intelligence, if you can kind of tell us your vision of how this is going to unfold over the next several years, is it just going to be many, many, many little applications that slowly before we know it are going to have moved, along many fronts very far, or do you still see it's such a fundamental human thing in terms of the communication that the these machines will get better at learning, but ultimately, they can kind of fulfill this promise of taking care of the drudgery and freeing up the people to make what are actually much harder decisions from a computer's point of view than maybe the things that we think about that a three year old could ascertain with very little extra effort. >> Yeah, if you take a look at what we do and hopefully it didn't sound like we're underselling our startup but a lot of it really is we taking away to time consumer and also grunt work process of the data collection and cleaning up the data. The humans, the real human intelligence should be focused on data analysis to be able to derive lots of insights of the data. So and to be able to formulate a strategy, how to win the account, how to win the deal. That's what's the human intelligence should be focused on. The other part by struggling with doing the Google search and in return 300 entries, in 30 different pages and you have to click through each one and then give up the first week, that kind of data collection data hunting work, we are really, it should not, I don't think it's worthy, quite honestly, for a very educated person to deal with. And we can invest it back in helping the human to do what the humans are really good at is that, how do I talk to Jeff? And I'm going to get a deal out to Jeff, how can I help and through helping him solving his problem, how can I take the burden of solving the problem from Jeff's head and solve the problem for him? That's what human intelligence for me as a salesperson, I would prefer to do that instead of sitting in front of my desk and doing googling, so net net what I'm trying to say using ourselves as an example is that we're not taking over the job of a salesperson, there was no way that we can close a deal for you. But what we're doing is that we're empowering you so that you look like you're on top of 300 accounts and you talk to any of those accounts, you'll be able to talk to the people, your customer, their particular customer, like you know them inside out. And without you being the superhuman to be able to do all this stuff, but as far as that customer is concerned, sounds like you were on top of all this stuff all day and that's all you do, you have no other customers, they're the only customer. In fact, you on top of 300 customers. So that's kind of the value that we see, to provide to the human is to allow you to scale by removing these grunt work that are preventing you from scaling or living up to your potential how you wanted to present yourself, how you want to deliver yourself. There's no way that we can be smarter than human, no way. I just don't see it not in my lifetime. >> I just love, we've had a lot of conversations over the years and you talking about the difficulty in training the computers on some really nuanced kind of human things versus the things that they're very very good at and keeping the AI in the right guard wheel is probably just as important as keeping the user interface in the right lane as well to make sure that it's a mutually beneficial exchange and one doesn't go off and completely miss the benefit to the other. Well, Ben, it's a great story. Really exciting place to dedicate yourself and we are just digging watching the story and we're going to enjoy watching this one unfold. So thanks for taking a few minutes in sharing your insight on natural language processing and this applied machine learning techniques. >> Thank you, Jeff. It's always a pleasure. >> Yep, all right. He's Ben, am Jeff, you're watching theCUBE. Thanks for watching. We'll see you next time. (bright upbeat music)

>> From Phoenix, Arizona, The Cube at Catalyst Contracts. Here's your host, Jeff Frick. >> Hey welcome back everybody, Jeff Frick here with The Cube. We are in Phoenix, Arizona at the Girls in Tech Catalyst Conference. It's funny, seems something about Phoenix that this is where all the great women in tech conferences are. We were here two years ago for our first Grace Hopper and it's really fun to return now to this one, the Girls in Tech Catalyst Conference, which, a little bit smaller, about 400 people, their fourth year, but again it's all about empowering girls, empowering women to think differently, to take charge and to be more successful so really excited for our next guest, Jim McCarthy, brought in to motivate the troops. >> That's right. >> So first off, welcome. >> Thank you. >> So-- >> Thanks Jeff. >> Your keynote was all about a career without regret, have a great impact on what you care about. That is so topical right now, and especially these people that talk about, you know, the millennials and you know, kind of the younger generation coming up, they want to do things that they care about >> Yeah, I think all the research indicates millennials, more than maybe prior generations, really are looking for work that has impact and has meaning. >> Is it because they can? You know, that things are a little bit easier, they know they're not necessarily y'know, suffering to get by? Why do you think there's the change and then once you've made that decision, how do you implement that in kind of your day to day life? >> Well I'm not sure I could explain how the millennials are perhaps different, maybe they just see some of the challenges in our world like climate change for example and realize wow, there's some very serious challenges we face. That might be why they're looking for more of an impact, but in terms of what to do to find more meaning in life I always encourage people to do work that they really love, that they're passionate about, and in this conference a lot of the women have talked about passion and what you're good at and really doing that 'cause that's what you're going to be most successful at. >> Right, but that's a really common theme >> Yeah. >> We've heard that forever, to close your parachute, y'know, if you could find something that you get paid well and you're passionate about, but often times there's a conflict, right? Sometimes it's just harder, people get stuck in something that they're not happy with, but they're not really willing to make the change, not really willing to make the investment or take the chance so what are some of the things you tell people that are specific actionable, that will help them y'know, make those changes to get some place where they're y'know, feeling better about what they're working on? >> Well, so for me part of my talk was I talk about how I had a career in Silicone Valley, early employee at Yahoo! and different internet companies and then about three and a half years ago I was diagnosed with cancer and that was a big wake up call for me. And even though my health seems to be okay right now, it really sort of helped me realize that wow, I'm not going to live forever and by embracing my mortality I've started living much more fully and I decided okay, if I wanted to be a motivational speaker, I always wanted to, never had the courage to do it, I thought okay, I'm not going to live forever, I might as well dive into it, have the courage to try even if I fail. But at least I'll be happy and I'm not living a life with regrets. >> Right. >> So that was part of my workshop yesterday. >> So that's really interesting and a powerful story I mean, we often hear when there's these, y'know, kind of life changing events, these big moments, y'know that is the catalyst. Does it take that to make the change? Can people do it without the change? I mean, we can't hardly get anyone to lift up their face out of email. (laughing) I mean, how do we do it without that or does it really take that? I mean, is that really what happens, whether it's yourself or a loved one or someone you care about, it's interesting 'cause that's powerful catalyst >> Yeah so, I think for some people it does take getting, y'know, hit with a ton of bricks like that in order to really realize what they need to do and have the courage to do it and just realize y'know, this may not work out but I'm just going to go for it. In part of my workshops I try to help people think about their mortality, think about if you were to die today, how would you feel about your relationships. If you were to die today, how would you feel about the work that you've done. And then I always have them write out action plans for okay, based on what I wrote, based on what we discussed, what do you want to change in your life and what's the deadline to do it? So that's kind of the process that I use in my workshops so it's not just nice story and inspiration but it's really okay, how can we bring this back to what am I going to do with my career, what am I going to do with my relationships and there's also very practical things that people can do that I think will help them a lot, one is mindfulness to reduce their stress, one is affirmation in which you can actually train your brain to be much more positive thinking and there's a lot of neuroscience behind that today which shows that you can actually sculpt your brain to have a much more positive attitude. So those are some and then the goal setting is important too. So -- and then gratitude, I'm sorry, there's another practice. So these are very, this is not just nice ideas but actually daily practices you can do, mindfulness and meditation, gratitude and affirmations, these are all things that can really have a daily impact in a very positive way. >> Right, and I'm sure people say, "Jim, that sounds great, I printed it out, it's on my fridge, but jeez, I wake up, I have 472 unread emails, the boss is calling me," how do I really actually do it? I want to do it but I'm drowning in email, whoever invented email is problematic, I'm glad that young kids don't use it 'cause it's going to die soon. (laughing) But y'know, practically, what do you tell folks? >> What I tell people is if you meditated 10 minutes a day, that's about 1% of your waking hours and that 1% would improve the other 99% of your waking hours and meditation used to be very weird and funky and new-agey and now you see more and more people saying, "No actually, 10 minutes of mindfulness or meditation or breathing or whatever can make a huge positive impact on your health both physically and mentally". There's all sorts of very serious scientific research, neuroscience, which underscores that. So if you invest 10 minutes of your day in being at peace, reducing stress, focusing on your breathing, then the other 99% of your day is going to be calmer, you're not going to be freaking out so much, you get an email in your inbox that you may not like but you can say, "okay, let me breath, okay let me think about this, okay", don't have to do an immediate flame mail response and then you're doing a lot less damage control in your life and you're being much more focused on how do I want to spend my day. And so that is one way to reduce your stress and yet still get stuff done, the most important stuff done. >> It's interesting, I have an unwritten book that I always wanted to write, kind of on some of the things you said before about y'know, don't forget your death bed, 'cause at some point you're going to be laying on your death bed-- >> That would be the title of your book? >> And you're going to have those questions. >> Yeah >> Yeah Y'know, did I do what I want to do? Did I spend too much time at the office, or too much time at the beach or too much time with the kids or not? >> Well if I can say, there's a woman who wrote a book named "The Top Five Regrets of the Dying" and regret number two was "I wish I had worked less". And every single man in her survey that she talked to said "I wish I had worked less". And these are men on their deathbeds. But it applies to a lot of women as well. >> So I want to shift gears a little bit, back to your tech days, >> Yeah (laughing) >> Just looking at your background, obviously some of our homework and you y'know, you did a summer at McKenzie, you're kind of at the leading edge of business and smart people and you -- >> You're too kind Jeff, okay? (laughing) >> No, and then you decide I haven't finished the story, and then you go to San Jose Mercury News to work in classifieds. >> Actually to do marketing. >> To do marketing >> Yeah >> But you were involved in classifieds and I only bring up the classifieds 'cause it's interesting because then you left and went to Yahoo!, right at the main, I mean really at a pivotal time in the transformation of classifieds moving from the newspaper to online. >> Yes >> So you lived kind of this digital transformation long before Uber and some of the other examples that are so often cited. >> Yeah. >> So I'd just love to get kind of your perspective on, y'know, kind of digital transformation, it happened, this was 97 so what 20 years ago, I can't believe it's 20 years ago, to now and then in the context of what you're doing now. >> So I graduated from business school in 1996, and went to the San Jose Mercury News and was doing marketing things. But right when I was graduating I was like, "Oh jeez, y'know this internet thing is going to be huge!", and after a few months at the Mercury News, I said, "Look, I really want to do something with internet", and they said, "Sorry, can't do that, keep helping us sell papers." And I said, "Well screw this!", and so I went to Yahoo! In July 1997, I was employee number 258 and I was hired to be a product manager for Yahoo! classifieds, so realizing, 'cause I remember sitting in the Mercury News at my computer and looking at, wow, Yahoo! has some like, online classifieds for autos? And careers? And this is way better than the newspaper! I can have long descriptions here and you can even see pictures of things, so I went to Yahoo! classifieds and out of that we created Yahoo! Autos, Yahoo! Careers Yahoo! Personals, Yahoo! Real Estate. And yes, this absolutely-- And then later there was the category killers where there was Match.com, where there was Monster or Monster Board, and on down the line-- >> Monster Park, remember Monster Park, one of the first sponsored stadiums back in the day. >> Yeah, yeah. >> After 3Com. Excuse me, I'm sorry to interrupt. >> No it's okay. So it was an amazing transformation and it was one of these things where the internet just does things so much better and you could say it also sort of helped destroy an industry, right? I mean, I'm certainly a big believer in the power of local newspapers and investigative journalism, and that's really been damaged a lot from the last 20 years, but sometimes it's like this technological imperative where the web is so much better, people have to figure out different business models, different ways to fund their journalism, different revenue models that work. But I mean it's just amazing to see what's gone on with how classifieds has developed, e-commerce has developed. I worked later on Yahoo! Auctions and Shopping, you can talk about that more if you want. >> Yeah, a friend of mine works at the Yellow Pages, I was like dude, you probably need to get a new job. >> Really? Still? >> It's YP.com now. Well turns out they have a huge online business which is good for them. No still, I was like c'mon, (laughing) You need to get out of that. >> Gosh (laughing). >> So, anyway. It's just interesting, the digital transformation that we're under now y'know, has happened over and over again, we just happen to be kind of in the current iteration, sometimes people forget-- >> Yes, yeah. >> That there was a time before Google, it was called AltaVista (laughing) or WebCrawler if you want to go back even further. Anyway, we regress. So Jim, what're you working on now, what're you looking forward to in the next six months, any special projects? You just traveling the country and spreading good word? >> I travel the country and I travel internationally doing my workshop. So basically the workshop's where I teach companies how to build happy, high performance teams. >> Awesome. >> And in the workshop, some of them are a little bit more, much more sort of inspirational and about mortality and about what you want to do for life purpose, I have a workshop called, "Happiness Workshop: Keep Calm and Get Stuff Done" and then so there's ones which are much more goal setting, there's more which are inspirational and yeah, I travel and teach companies how to -- whether it's an hour workshop or a six hour workshop, that's what I do. >> Jim, thanks for stopping by, it's a great story and I think it's just so important, y'know there's a lot of great inspirational stories out there but really y'know, how you do you help people, give them actionable things that they can put on the fridge, put on their calender and-- >> And have in their daily routine. >> Right and do it right, and do change behavior 'cause it's hard to change attitude, really hard, and the way you do it is you change behavior, that you can actually change. Thanks for-- >> Yeah, yeah. >> Thanks for sharing a few minutes with us. >> Thank you Jeff, very kind of you. >> Absolutely >> Thank you >> Jim McCarthy, I'm Jeff Frick, we're in Phoenix, Arizona at the Girls in Tech Catalyst Conference, you're watching The Cube. Thanks for watching.