One is the ability to look at immense amounts of content and understand it in a fairly humanlike way. I mean, truly, the computers don’t understand it as a human does, but different than traditional computing, cognitive allows that understanding in the others. The learning nature of these platforms, these cognitive solutions, learn by usage. The more you use them, the more you train them, the smarter they become and I look at that as these kind of two.
The Artificial Intelligence Dilemna
Big differences versus a traditional computer. Yeah, that’s really well aligned with how we look at Watson. We we take it a little bit of a step further, sort of that basis of understanding large amounts of data excuse me, reasoning. Understanding what that is in the context of where that information is coming from, being able to learn by interacting with humans as there’s feedback from that and that interactive piece. So being able to interact and partner with humans who are providing that training data or making decisions from that unstructured data. I love that because because I’m not from that space, I tend to say it’s about enabling you to be a better master. You make better decisions, help you be faster and smarter, leveraging data that you probably couldn’t analyze yourself because there’s just tons and tons of data being generated. But now I see that using that term enablement can be really, really confusing because it it mixes up with all of these existing technologies, technologies that are available for marketers today. So what are some of the confusion that you’ve seen with marketers around the topic of why? We think we think robots, we think cars that drive themselves in. A lot of people can’t really grasp how marketing can be affected by A.I.
So when you’re speaking to prospects and customers, what is the message that you give? I think the first sort of myth that we like to dispel at Watson is that this is not magic. There’s no magic ball anywhere. And that I, like anything else, is a technology. And you can break it down into small pieces to be used for a particular application such as marketing. So I think the first real hurdle to get sort of folks who are unfamiliar with the world over is that there’s not a magic button. There’s really exciting companies like Apple three who have built what appears to be magic and magic buttons on top of a lot of different technologies. But at its core, these are sort of transactional technologies that can be applied in specific applications, such as personalized marketing. And maybe you can expand a little bit on what you’re doing there. But that’s the myth we like to dispel here. Yeah, that’s actually that’s a great setup because we do run into that problem where people see a demo, they see it all work and it’s like, oh, it just magically works. And so one of the things we’ve had to educate people on is we have found there’s a significant difference between a cognitive project and a traditional one in a traditional or at least for us, you know, it kind of gives all the development, all the back and work and the business user waits for the result. But here, because it isn’t magic, we actually can implement Lusi very quickly. But then it’s incumbent on the business user to train their new associate, their new companion, and they actually have to spend a fair bit of time in training and mentorship so their companion can be effective.
How To Build Artificial Intelligence Systems
And so rather than a finished system and then waiting for it to do more, especially on the business user, to do the training and really do a lot of the work. Interesting, so out of the box, you’re not going to get this magical assistant that can automatically sync with all of the data sources that you have and give you the recommendations you need. You need a little bit of time to work with the system itself, because while it’s smart, it’s not that smart yet. I want to assume it’s got add to that. Well, you know, that’s actually will end up being a great segment. Segway into the demo because I can actually show how this all works. Excellent. Well, going back to Alissa, tell us a little bit about IBM Watson in the product portfolio that you manage. You’ve talked about things like emotional elements that you teach Watson. Can you give us a little bit more color on that? Because I think that’s something that we as marketers, at least today, without touching the other the product know very little about. Yeah, absolutely. So Watson is a fabulous part of the broader IBM company where we’re like a very well funded startup within IBM. And so we’re sort of out of the bleeding edge here. Creating technologies that are at the platform level are a series of APIs that each do discrete functions, and each one of those functions serve something a little different. But they are basically like Lego blocks and companies like Equals Three are stacking them together to solve a particular challenge in a particular industry. So in this case, marketing what a lot of people don’t necessarily realize about Watson, since there’s a lot of attention and hype around A.I., is that how much is actually already being used in reality? And you might not know it because it’s sort of like this sort of.
I had a friend at one point call sort of as sort of this digital toilet paper. Right. It’s something that you don’t necessarily know exists, but it’s there. Right. So major banks, major insurance companies that you already interact with, lots of companies, large and small, are using this today. And they may or may not be super clear that they’re using it behind the scenes to help automate some of the customer interactions that they’re serving with. We’re really big into transparency at Watson. And so we like to be really clear and open around what data is being used to train. Why so in the emotional intelligence space, as you were saying, that’s a really fun and exciting area for us. We have sort of three different technologies in that space today. We have a tone analyzer which takes text and analyzes the tone of that conversation. So today, actually, we released a major update in that space focusing on the customer care market. So, for example, if you’re interacting with a customer service agent and you are perhaps calling a company because you’re frustrated, likely we can understand that you’re frustrated and help escalate that conversation faster or direct you to the right place and really be attuned to what those emotions are in the customer care space. Right. That might be a little different from emotions that you’re expressing more generally on social media or other venues. The emotion stuff is really interesting. And because it’s not language that we as humans often talk about and they’re reasonable, humans can disagree around what emotion is contained. In the particular phrase, emotion is a very sort of multifaceted way of expressing yourself. You have facial expression and you have your tone of voice. You have the actual words that you’re saying themselves.
You have the context in which you’re coming into the situation. Right. Are you on the phone? Are you on social media? What is that background that’s got on to that interaction that you’re having? What do I know about you? But you may look happy today as pleasant or she’s not angry, but maybe I am angry and I’m not telling you. So it’s a really tricky space. It’s really exciting. We’re really proud of what we’re doing there. And one I know that equals three and quite a bit of personality insights, which is taking personalities and understanding sort of intrinsic natures about people. And when we say someone has a really strong IQ, right, we mean they’re good at reading people and understanding. And so how can we build suites of technologies that help understand people better and interact better and apply those towards delightful client experiences? Now, that’s fascinating. The minute you talked about scanning for tone, I, I was thinking of a certain airline that issued an apology that nobody appreciated. Wonder what Watson would have had to say if we had run the text through the system. I wonder if that would have gotten published, that’s actually a pretty classic use case around understanding what what people are saying or even if you’ve written email. Right. And you might not be aware that it comes across as really assertive. So you can have there’s companies that have built little widgets that integrate with lots of say, hey, look, this is an aggressive eval. Do you really want to be aggressive? You know, here’s some suggestions of how you can alter things. So there’s a lot of exciting work going on. Yeah. And I know some email marketers would probably love to get their hands on that type of technology and make sure that their emails are effective in conveying the tone that they’re completely right, that that matches with your brand.
Right and that matches with what you’re trying to communicate because not everyone has a good sort of third party independent editor to review what they’re saying and how it comes across in the same way that me as a human, I’m always interested in how people are responding to what I’m saying and how I’m communicating it. And I might think that I’m being pleasant and open and conscientious, but I may come across as abrasive and assertive and different from how I’m interested in communicating my emotions. So fascinating Stot So is kind of set you up to tell us a little bit more about Lucy or your product. What what is it? What is what can it do with a little bit about what you’re working on today? So, yeah, the so Lucy’s the cognitive companion to the marketing professional. She’s built for the Fortune one thousand and the agencies that serve them. The problem that we set out to solve working with IBM and Watson was really the idea that marketers have so much content in so many different systems. They have the content that they own on their own databases, marketing, analytics, website, analytics, media data. They have third party data and they have all the own documents, all of the PowerPoint PDF and the like.
So I’ll just turn on screen sharing. Now, I love seeing real life applications of what I can do, especially in the marketing space, even to me, I’ve done a lot of research into it. It’s kind of just cloudy topic where I don’t really know what it is. So to see an actual example is always super valuable, I think, for the audience that we have here.
Absolutely. She’s a software as a service. She lives in IBM’s bulimics environment. And the and she has three major components research, audience, persona, modeling, building a really tight persona, models along the lines, what Alice was just talking about and then helping with media planning. And so what I must show here is research. And you can see I’ve asked a question of Lucy, what is the latest information on self-driving cars in this instance? I’ve got a demo that’s around automotive marketing and Tesla specifically. Lucy gets trained around the data of the company that hires her. So we have to be pretty specific in that regard. So here’s what she’s done. When I ask the question, what is the latest information on self-driving cars? She comes up with a list of responses. These can come from databases. They can come from power points and PDF documents in our file systems or can come from third party relationships like the marketers and foresters and the like. So she’s showing the list of responses. And you can see on the left her confidence score. Her confidence is based on her natural understanding language, which comes from Watson, as well as the training that is given to her by the company hired her. So here I can see her responses to this question on self-driving cars with a ninety four percent confidence. Now, keep in mind, this is a trained Lucy. She has found some information from eMarketer on level of interest, attitudes and opinions about self-driving cars. So great stuff. And the bottom right was this was this answer relevant? I can say yes. Give Lucy four stars as the training that notifies and gives her confidence. As I go through this, I can see other examples.
I see more information from eMarketer. I can see additional reports from eMarketer. And as I go through this, she’s giving us the components of eMarketer, reports that she thinks best answer the question just below her confidence in those eMarketer reports. She has some great data from City Star. So throughout I can be creating these responses saying how she did a great job that impacts her confidence. If I see something I like, I can save it to a project. So by clicking on the star here, I can pick a project and save this to it. And so that’s how we end up interacting with Lucy. We ask a natural language question. She goes through all the data that’s available to her, shows her confidence and gives you her best responses. Now, another example is I want to find a SWOT analysis for test. Someone ask, do you have a SWOT analysis for Destler? Now, in a world without Lucy, what would happen is I would think I’m in a marketing department, we have dozens or hundreds of people here, and I might say I know somewhere we created this, but where and I might post to an internal social network, like a Facebook for business or to a chatter I might email around. I might not find some balls, but the chances of my finding such a specific component as this within the thousands of documents that are in enterprise is very, very difficult. I’m more likely to recreate it than anything else. But here I ask, do you have a SWOT analysis for test? So I just as easily could have said, what are the strengths for tests or what are the weaknesses? What are the threats? And Lucy found it. So where did she find this? And then the bottom left.
I see the source in the eMarketer and statistic reports that source would have taken me to my subscription. In this case. The source is going to take me to the specific file. And so when I saw this downloading a file, it’s a PDF, it’s a forty six page PDF that is like so many documents are within an enterprise where a singular document could answer dozens of different questions. So as I scroll through this, this is a document that Lucy read. But she answered with the precision of the specific answer that’s within this document. So as I eventually get to Page. 19, I see that SWOT analysis, and so it’s not just me saying, here’s a series of documents or here’s the documents, here’s the component in that document, and then I can save that component for later use. Now, a lot of what wants and has been known for so wants in jeopardy was the amazing ability to look through huge amounts of content text, and then we think of that as unstructured data. So these initial examples, either my own documents or the license documents from eMarketer and other sources are examples of unstructured content. The thing is, marketers need to work with data that’s structured and unstructured, they need to ask questions of their marketing automation platforms like HubSpot. They need to be able to ask questions of databases like Google Analytics or Omniture or other website analytics. They need to ask questions of media data from sources like comScore or Kantar or Nielsen. So Hiraman ask a question, which is how much did BMW spend five months last year? This is an example of a natural language query that’s going to go against a database. Without Lucy, I would have to go into a platform like Cantare or Nielsen or comScore to ask this question.
I’d have to be trained on how to write scripts or how to do reporting. But here we bring a natural language interface to this source of data. So here we’re working with Cantar data. You can see the data that we connected via API to Cantare and extract it to answer the question. And you can see the visualizations of this data that we’re able to provide. So if I ask something like, who are the competitors? For BMW, that’s another question that can be answered from data that exists at Cantar. And here we see the competitors, if I want to ask a question like how much did BMW spend versus a Jaguar versus Audi and versus what Ford and Cheah and some others buy month last year. You can see that she will go out to Cantare and formulate this question, come up with the response. And it’s really pretty amazing how we can work with various structured sources of data all through this natural language interface. And Lucy works with this very quickly to give us what can be some fairly complex reporting and. Brilliant. So there’s one other thing I wanted to show you, and you brought up United Airlines. And you brought up what could we learn from checking things like tone, from messaging, and so one of the things we’re doing with Lucy is we are combining multiple sources, news sources and social all together in one component. So the initial example, this is brand insights and Lucy does is she is reading through roughly a million pages of content a day. And here we’ve looked at United Airlines over the last 10 days. We’re looking about 10 million pages of news content coming from common news sources like Washington Post, New York Times, CNN, Reuters and a thousand others.
And what Lucy is doing is she’s saying the sentiment in articles about United Airlines is really, really negative. Seventy four percent to the negative, only 12 percent to the positive. There is a ton of content here that Lucy has gone through. You can see under the sources and articles, you can see the volumes of mentions. So New York Times has written about United Airlines thirty three times in the last 10 days. And if I click on this, I can see which articles were considered negative or positive. We’re looking at that tonality per article and so we can easily go through the list. If I want to see when did it get bad for United? I can click on United Airlines, the brand under the topics and I can see where five hundred negative mentions on the 10th, a thousand bad ones on the 11th. Nine hundred more on the 12th. This is just a crisis. It’s just been, you know, you can see that bubble. And when the news which is so bad, we can see hashtag analysis, we can see image associations and you see the what was a united customer being dragged out of the plane. So all this is being combined by bringing social and news sources together into one place. And by the way, we also compare how sentiment runs in news, which is seventy four percent negative and social, which isn’t quite as harsh, which is a little surprising in any case. What you’re seeing are the research components of Lusi, the ability as natural language questions against unstructured content that’s licensed like eMarketer. Ask questions against your own data like the PDF, ask questions against databases like the Cantar database, as well as how we’re able to use Watson’s ability for measuring tone and sentiment to look at huge amounts of content and to do things like a brand insights.
So Rucci has all kinds of other features we’d love to show. But this gives you a good idea of how we’re working with those core Watson components into a package solution for marketers and Lucy. That is especially the middle piece where you talked about being able to scan assets, I know that that’s a pain that we feel at home. So we’re not perfect. We have lots and lots of CDs and files and we have it in our internal wiki system. And it gets lost to totally being able to search our own archives like that would be just I wanted to sign me up. Even so, I wanted to go back to the first example where you were training Lucy by simply giving it a star rating. I think that’s just a wonderful visual testament to how simple it can be, because I think a lot of people, when they think about A.I., they think about having to jump in a lot of data to train it. Right. And then complicated algorithms get that out and you have to have a PhD to really navigate your way through the system to make it do the thing that you want to do. But these overlays of the technology that you’re building, what IBM is enabling, it can be as simple as the Netflix. Thumbs up, thumbs down. You’re going in the right direction. It could be as simple as that. You kind of teach or train your eyes to do what you needed to do, which is a wonderful example. I have no question for that. I just wanted to point that out. That that’s fabulous. All right, so thanks, Scott, for the demo, super, super interesting. So from the site’s perspective, we’ve been trying to dabble in a I don’t think we’re as far along as as U2, obviously, but we wanted to definitely share with our own customer base and what marketing automation can become with the help of A.I..
We’ve got our own kind of natural language processing bot where we allow people to dig into their CRM prospect, look for new leads and also create new blog posts, just bypassing kind of our menu navigation system completely. Right. We just have to say create a new blog post for me and I’ll pop out and give you a link. I think for a lot of folks, though, the concept of A.I. is a little bit frightening. And so do you think that with all of these new technologies that are being built, I think will definitely change our jobs for sure. But do you think that jobs will be gone, that we are going to be obsolete, that the machines are going to take over? Obviously, you can tell from my tone that I have a bias in how they answer, but I think it is something that is part of every single conversation nowadays that’s around. So I’d love to get your thoughts on that. So, you know, for us, the whole idea of the name equals three is about the idea that one plus one equals three, that better than the individual or better than the machine and the two together. So I think that we will see scenarios, look at how they can make changes to staff based on automation, we’re certainly seeing that in many industries. I think the business that complements the talented individual with the companion will outperform those who don’t adopt or embrace at all or those that rely too heavily on the A.I. to do the job itself. And so we’re pretty bullish on the idea that this is all about supplementing and enhancing the individual. I think what’s going to happen is that we’re going to see more expected or demanded by the marketing department, more expected and demanded of the agency, and that the way they keep up with that is it’s going to enhance their service delivery in their performance.
But we look at it that more will be expected and more will be achievable. People be able to drive better results in better outcomes because of their embracing of the A.I. versus the displacement of people. Yeah, I think that that’s one hundred percent with how IBM really comes to market and talks about this. We see this as man plus machine and Jenny gone on about that many times. It’s about the partnership here between humans and cognitive technologies. We actually at IBM, when we say I, we meant we meet, we talk about augmented intelligence. Right. Which is all about augmenting what a human is already doing and extending that to be able to do things that they could not have done before. So one example in the marketing space from another client working actually with IKEA, a company called AI, and they are interested in social media listening. Right. Similar to what, Scott? Just demo. And they actually wanted to understand, if you could ever put together an IKEA product, it can be a challenge sometimes. And sometimes people get frustrated or they do really creative things with a bookshelf like turning into a bed that IKEA didn’t necessarily anticipate or think of. And so they did a project that again extended the reach of the marketing team by looking at Google video, YouTube videos and understanding, visually speaking, where were the IKEA products that they were particularly interested in appearing in those videos? And then what was going on in the context of the videos? Was it positive? Was it negative? What did it associate with in the product Skewes that actually sold? And so that was an example where there were hundreds of thousands, millions of videos. They couldn’t possibly have done that with their marketing team.
Right. It’s something that from a human perspective, it’s way too hard, way too overwhelming. But if you can do that using A.I. by training a visual classifier to understand visually where are those products and when, which ones look like ones I sell, you can start to have an intelligence that that was not possible before. But that was only possible because the humans trained the visual classifier to say, hey, here’s what it looks like. Here’s what I want to see. Can you go find that? Tell me where this exists. So that’s just a good example where the nature of the work may be shifting a little bit or if some one may have different responsibilities than they did before. But Scott’s point, the winning companies are going to be the ones that embrace this idea of both. Excellent. So before I ask the next question, I’m just going to pause and let folks on the webinar know that you can ask questions of our panelists by tweeting at HubSpot Academy, use the hashtag webinar and someone will come in with the questions and we’ll be sure to answer them if you have any time left over. So if you have any burning questions for the panelists, please do tweet at us and we will try to get them answered in the remainder of the webinar. So I just have a kind of a not a personal question, but more about why you two decided to you started your own company, Scott, on around like what was the potential that you saw? What kind of motivated you to get started with this? What what caused you to think this is this is it. And I’m going to dabble in it. I’m going to build it up because I see X amount of potential in return and what I’m going to build.
Yeah. So I’ve always been fascinated with the A.I. space, and when IBM showed up on Jeopardy, it was like, wow. And that’s something I just explored. It was super interesting. And then it was about two years ago, it became clear to us that the Watson platform was being made available to developers. And so I sat down with their business partners and said, what could we do if we had this? What is the problem domain where if we could apply everything IBM has invested, the billions and years they put into developing this platform. If we had at our disposal, what could we do with it? And we thought about all the marketing technology platforms. We have stood up for customers over the years and thought about just how much data is in so many different platforms. If we could find a way to bring the power of Watson to all that data, whether it was structured, unstructured Olander license. What we do with that, and that became the impetus, and then we started to we worked with IBM, they were great to work with. We love the tech. And we started to build out the MVP around Lucy and bring it to some of our trusted contacts. They were blown away and that gave us the energy and excitement that let’s go for it, make it happen. I think for me, I was really attracted to Watson in the space generally because I see the potential to change the world for the better. It’s a cheesy answer, but it’s really it’s what gets me out of bed in the morning is exactly what Scott and many of our other customers are doing with the technology and how they’re applying it to a whole host of different industries and business problems.
And it delights me to hear our customer stories and to work with those customers around how they’re actually using this to make something easier or better or delightful for that end customer. That really is exciting and something they could not do before. So that’s that’s what gets me out of bed in the morning. And I think it’s it’s certainly a privilege to be in my position. Do you think that the reason why we held this panel, even though we’re not really in the game, is that we undertook a lot of research into it because we saw a lot of potential, obviously, but a lot of confusion, least among our marketing audience. People could tell that it was something that was important. It may disrupt their jobs, but they just didn’t know what it even was. And so we kind of wanted to unpack that a little bit. And we just had the pleasure of working with you to even develop this webinar to kind of educate our audience. I kind of asked you this question before, but why are so many professionals just not aware of what the potential impacts of is? Is it because it’s nascent and so the tools are still developing their messaging? Is it because we’ve been told in popular culture that this is what we should expect it to be? And so we have this preset notion. There’s so much potential, there’s so much interest and yet so little clarity. Why? I think one of the challenges that this faces has is it’s been a promise for the last 50, 60 years. Hollywood has promised this this magic future world. And so there’s a lot of preexisting ideas around what it will be or what it should be. And I think in some ways, this technology and this space in general is very nascent.
Right. We’re just starting to see in the last five years, I would say real businesses use this in reality at scale in production to really and truly solve heart problems. But it’s not new technology, but there’s sort of a confluence of data, of hardware, of accessibility, of this technology that is new. Right. And we’re able to do things that we were not able to do 10 years ago or 15 years ago. And so I think that’s one of the the challenges that we face is sort of educating people around what’s magic and then what’s now and then. I think the other side of it is that one of the biggest pieces that I think we really work with our customers on, which is what Scott touched on a minute ago, which is that he sat down and starting equals three and thought about what problem do we want to apply this to? And that really is the hardest problem with any technology that can sell you a knife or a MacBook or anything. But it’s just technology. The magic happens when you apply it right, as a chef and you create a masterful dinner, or if you put a book in the hands of a iOS developer and they create wonderful mobile apps like anything, this technology is a tool. It’s a new tool. Right. New ish. But it’s really up to our customers and the users of this to make that magic happen and apply it to particular industries, particular business problems. And so I think there’s a lot of people who one, you have to understand how the tool works and learn it. Right. And so that can be a challenge to understand. Hey, here’s what it does. Here’s what it doesn’t do.
Right. You cannot make breakfast with your MacBook. You might be able to make dinner with a knife. So you need to understand sort of the limitations of the tools and what they do. And then you have to think about, hey, like, how am I going to make Vietnamese food tonight or am I going to make Italian? You have to get specific around what you’re going to do with that and how you’re going to create a delightful experience. So I think that that hurdle with A.I. is around. Do I want to do customer listening and social media? Do I want to optimize my call center or do I want to apply this to medicine? What applies to health care? Do I want to cure cancer? Right. How do you want to take this tool and apply it to the problem that you care about? And breaking down that problem that you care about into its parts and pieces can be a big challenge. Right? I want to do social media listening. I want to understand everything anyone has ever said about my company ever and magically have an insightful dashboard. That’s a lot of work. Right. And consumers have have proven that and really made that easy. But there’s a lot of smaller tools and smaller parts and pieces that go into making that magic happen. And so I think when. People get overwhelmed sometimes it’s because they’re trying to break down that problem into smaller and smaller components of which I can be applied to. Now, just add to that a little bit, which is one of the practical challenges for marketers, is they’ve never done this before. So I totally agree with everything Melissa said that you apply that to. Yes, identify the problem.
But if you’ve never bought it, I mean, for us, there are no our for cognitive agents, there’s no marketing departments that have preexisting budgets for I’m going to put X dollars into EHI. And so because of that, you don’t have people with experience of having run a project before they even bought it before. They’re not quite sure what it’s going to look like. So there’s a huge level of market education. Events like this are immensely helpful because people can walk away and say, oh, I get it. I get at least that’s one facet of of my job that could be impacted by and as much as we all have a permeating our daily lives. Google is an amazing tool for A.I. It’s no longer a search engine. Your Facebook news feed is completely driven by A.I. To some degree, people use services like Siri and things like that. So we have a in our day to day lives, but we haven’t put them necessarily into our business lives in this way. And so it’s new for people. So market education is just a huge, huge element to get to that point where you can say what are the problems I could solve? Excellent. So I do have a question that actually ties into what we’re just discussing. So I’m going to ask it. It’s for Scott. And it has a little to do with this confusion around, like what it enables versus something that exists today. So the question is, how is Lucy different from other listening platforms? So the third example you gave in your demo, eMarketer, is asking, well, that kind of looks like Omniture kind of looks like something. But what’s what are the chances of your product that makes it more advanced? So here’s the thing.
If your whole life is in social media, you’re going to live in products like sprinkler systems and you’re going to go a mile deep. If your life is as a data scientist, you’re going to use Adelmo or a Tableau or Watson analytics type product. If you’re in marketing automation, you’ll use one of the marketing clouds. But to the VPE, the product manager, the the strategist, the planner, somebody who’s omni channel, somebody who either isn’t using all of their data or they’re sitting there with 20 windows open at once, Lucy becomes amazingly helpful to them because through one login, through one natural language interface, they’re able to get at that data that would otherwise be an Omniture. And perhaps it’s only in the hands of very few people in the organization. They’re able to get to that cantare data that would otherwise only be in the hands of a few people. They’re be able to fully utilize eMarketer because if marketers got great content, but too often an enterprise is not used as universally as it ought to be or that be true of Forrester and others as well. And so we’re saying through one login, one natural language interface, I can query dozens of different sources and have it all come together. Now, if my life is only in one source, then the deeper tools are going to be use. Use that, use that to. If I’m a cantare operator and I know how to write a script and I know how to do the reporting, I should just live in Cantare. But if I’m the agency account director of the VP, I just want to know how much did we spend versus our three top competitors. And I don’t want to ask the decision.
Science or the media team was overworked. I can just ask Lucy. Got it. All right, last question and then we’ll dig into some more of the submitted questions. So where do you see heading the not so distant future and how can we get started in using today? We dabbled a little bit in that case, but tell us about what you’re excited about, new developments that are coming. So from our end, and this probably got a broader perspective on this being an IBM, but from our standpoint, we see that it’s going to permeate everything. I get so excited when we connect to a new source and Lucy learns it and she becomes smarter and smarter. And it’s amazing to see how that data gets stronger. The longer somebody has a companion, the better it performs for them. And so and then the other part is for us, just product roadmap. We’re constantly inventing that. What’s next? It’s exciting to sit around with a team, listen to customers and get their feedback on what they would want to see and then make a real. Yeah, we’re we’re similar at Watson with a potentially little bit of a broader scope. We’re really excited about the future of everything that we’re bringing out, like teams have releases. I think we have three releases this week. So we’re constantly iterating and releasing new stuff. And that’s just my team. There’s many others that are that I work closely with. So we’re developing sort of at a lightning fast pace to keep up with market demand for different features and functionality. As I mentioned today, the customer care tone models are just available. Last week, we released a visual recognition tool around making training easier and we’re coming out with some more exciting stuff in the next couple of weeks.
Why Should We Be Worried About Artificial Intelligence
I think more broadly, though, there’s a perception that this is hard and it’s difficult to use and take advantage of. Something people don’t know about me is I don’t have a background in computer science. I have a liberal arts degree and I don’t code on a regular basis, but I use I write and I can use these developer tools. There’s a 13 year old in Germany who’s gotten a lot of press with IBM and he’s always the first to adopt whatever we put out there even before some pre-release beat out and he’s 13 years old. But this stuff is is there’s free versions of all of it. It’s easy to use and if it’s not easy, call me. I’m not doing my job well. But the idea is that this stuff is easy for developers of any skill level to get started with. And there’s certainly an expertise and a training as you get more advanced and more sophisticated with the tools that we want to do. But at its most basic level, these are API. So if you can integrate an API or even better, some of our services have tooling on top of them. If you’re your business user like me, you can log in and build a chat bot using our conversation service. So as an example, I got sick of people asking me the same question over and over and over again about visual recognition and pricing, where if I did everything else and I was like, Watson could handle this and I built a little chat bot, right? I’m a business user. I don’t code right. I was in my hotel room and an hour later I was done and I launched it. So I think that’s dispelling that myth that this is hard is something that I’ve tried to reiterate and dispelling the myth that it’s expensive because these cost fractions of a cent and it’s something that can it’s easy to get started with and scale up as you grow.
I feel like this is going to be a natural question that some folks are tweeting at us. Where can we find these resources that you’re talking about? Lessa. Oh, just go, go, go, go to IBM Watson dot com, get started with a blue mix account just like equals three did a couple of years ago and start making API calls. But what IBM wants a platform is hosted within Bluebox Accounts are free checkout. Great. All right, so let’s bring it back to market a little bit. I think you you two are definitely really well aware of what’s available on the market. So there’s some questions around it. All right. How can you help me deliver the right content to the right person at the right time? What exists out there that I can leverage to do that? And if at all? Once a ticket. Scott, go ahead. Well, I’ll just say that as of the moment, Lucy’s fairly unique in what she does, the ability to do both some of the research things you saw, as well as the audience persona modeling and the media planning capabilities within a package solution are currently unique for the most part. So my answer to the question is, well, talk to us about Lucy. We have yet to have any significant client interactions or they’re evaluating us head to head with another cognitive solution. It’s really the evaluations. Are they ready for cognitive? What are their use cases? What are their sources of data? What are the expectations for how that data looks within a platform? So our point of view in the marketplace is we we certainly know there will be competitors. There has to be. But at the moment, we haven’t seen a lot of that as of yet.
I think certainly what Bill does is unique and unusual in the space. I think there are many agencies that we’re working with that are doing components of this right and and using different APIs for similar types of use cases around ad targeting, personalization as an example or something that are a little bit more on the fringes of what equals three does. So Ogilvy is one that has had a lot of attention and case study. And what they’ve done for the US Open or Wimbledon is well documented. Examples of how they’re delivering with brands, right. The right messaging for the right person at the right time. Right. With all of their sponsors and many different brands involved. To that end. Agencies like she did some interesting work that was put into the Cannes Film Festival that was driven, you’ve got agencies like Covas will develop cognitive practices, but in general, the cognitive practices are also creating bespoke solutions versus providing packaged offerings. Got it. I think we were partnered with Salesforce and they have quite a engine that’s being built out for their marketing in their CRM platforms and on the outside, we are seeing a lot of interest and we’re certainly working on allowing our own customers to examine previous emails. They’ve sent content that they’ve written, channels that they’ve explored. And then eventually our goal is to package it in the way using a natural language processing to allow people to understand, OK, this was a successful channel that we or we pursued a prospect. This is a great conversion lane that we can kind of enrich and enable in the future using some of these capabilities. I think there’s definitely a lot of morphing automation. Companies like HubSpot definitely feverishly working on it.
It’s such I think you mentioned Salesforce specifically, and I’d be remiss if I didn’t sort of bring up our we announced recently a really large partnership with Salesforce, and many of those cases are in the marketing automation space. I think Salesforce is really interested around how do they take the richness of the data that they have around customers and really use it to deliver personalized messaging and enable sales teams, marketing teams to really delight that end customer. So we’re really excited about the work that Salesforce is doing on integrating the Watson Technologies into their platform. And to that end, a lot of the effort and in marketing, which is not the area of our focus, isn’t that area of cognitive engagement. How can I use cognitive to optimize how I’m fitting in media? How can we optimize performance at the e-commerce level or conversion of some kind? And there are a fair number of solutions out there that are working in that space. So very different than what we have been talking about as far as research and things like that. But there’s a fair amount of energy there. Certainly that’s a big area that Einstein is focusing on and trying to drive optimization of the marketing cloud and creating a better one to one experiences for their customers. Doing some very cool work there. Yeah, the ad buying piece is really, really, I think, compelling for a lot of marketers. I saw one of your colleagues actually demoed pieces of Watson that was kind of just focused on ad purchasing, optimizing, getting the right type of channels, hitting the right people at the right time, all powered through the engine. I think for a lot of folks, advertising is the biggest crapshoot for marketers because we just don’t know for hitting the audience that we’re getting.
The conversions are actually going to generate the revenue. And I think tying into that, that the the purchases that you make are the smartest possible. And hopefully with all the data sources coming in and tie it back to an actual sale. Right. That’s super, super powerful. And I think a lot of marketers today just don’t have that ability to track it and that type of detail. Yeah, right. The Holy Grail is the market is the attribution and the automation. Right. Of all that. And I think even looking ahead around the attribution problem, which is often a disparate data problem, it’s also looking at the impact. So let’s say you did reach that customer, right. And they did make a purchase. But how do they feel about that purchase? Was it a good was a successful one, how they feel about that product? Are they that encouraging others to buy it? So there is more than just did it happen or not? Did I get that view? Did I get that click or not? But was that click meaningful? Was it positive? Was it can you can you get further right than just a more sort of wrote it happened or it didn’t. So I think one more question, this is from someone from Southeast Asia, so international is a top of mind for this particular person. How is it being adopted around the world and specifically when it comes to localization, as people are going across boundaries, across geographies, across languages, is something that can help us bridge that gap? Yeah, we’re really focused on that problem at IBM and we have a huge amount of resources and attention on solving that internationalization problem. IBM operates at one hundred and seventy countries, I believe. And so we need to have Watson understand not only the languages, but the cultures and the context of our global client footprint, because I’ll go back to tone and emotion.
Right. Those cultural norms impact. How do you understand and apply in different places? So one example that I use there is something like color. For example, in Japan, the notion of green is a concept that is different than green in the United States. Right. So a simple tag like that around, hey, this this tree is green, that that concept is different because a green is not a fact. Right. It’s this abstract creation that we have of color. Right. And so how when we do global expansion, how do we not just simply translate something into a different language, but how do we make sure that we are being aware of the cultures and the learnings that we can create context, specific, relevant solutions for that market? It’s a lot more than just language got any thoughts? I think it’s a great question. It’s something that we’re we’re mostly focused on really us. All of that said, Lucy takes in questions from dozens of different languages, although providing English language responses and we have plenty of global customers, again, working against English data. It’s interesting to think through. How do you start to compare different cultural norms? How do you have content from different geographies and how do they compare equally within that environment? And then IBM just as much better, better and bigger perspective on how to solve that because they’re immersed in it. I think some of the other challenges for expanding globally are around security and data sovereignty laws. For example, we just opened a data center in Frankfurt that we’re really excited about to serve our European customers. And then we’re also working with partners. So you mentioned Southeast Asia. We have a big partner in Korea and we have other clients who are partners of ours serving those and customers.
Now, you kind of mentioned it tangentially. The last question is, are there any concerns of being hacked or trade secrets for data? How how does IBM approach this? How does how do you Scott, when you’re building up this product, you’re compiling quite a lot of inside data is what’s the approach there? Yeah, we take security really, really seriously. At IBM, it’s not trivial at all. We try to differentiate from our competitors in the space, actually on security and on the approach that we take to data. So when you’re Wevers, you reserve the right as our customers, not for IBM to store or learn from your data. Right. You can use Watson without us storing or using anything that you’re sending to us. So that’s the first sort of big way that we differentiate and then we offer different sort of levels of separation. We offer our public cloud. We also offer premium and dedicated options for different security environments and what’s appropriate, given what type of data that you’re looking to do analysis on. Right. So one example would be our health care. IBM, Watson Health. Right, is a hip, compliant, totally separate type of environment. Then if you’re just looking to understand social media, someone posted this image. What is this image of? It’s a very different types of data security requirements. Yeah, on our end, the security side has to be supportive of the enterprise, and so we have a couple of really important tenants for the agency customers. Lucy needs to help them stay in compliance with their MFA’s to their end customers. If they’ve got multiple brands, they’ve got internal firewalls. The right users can only see the results of the content that they should have access to. And then for the data partners, those that are the providers of third party data, we need to ensure that Lucy’s helping our customers stay in compliance with their third party data.
Right. So that if you’ve got 10 names, seats to source X, those 10 people and Lucy will see those results, whereas the rest won’t. And that ends up being a very important part of how we’ve architected Lucy. The other thing is partnering with IBM, leveraging the security they have for the data that is within their environments has been really critical because they’ve got a world class infrastructure to support us in that. Excellent. All right, so now this is the real last question. Thank you, everyone, for joining this session. My question is, when the first self-driving car rolls out of the factory, are you guys buying one? I’ll say I already have a Tesla and I use it to autonomous driving features a ton and I love it. So it’s not true self-driving, but I got a lot of mileage steering wheel. I’m not like when there’s no steering wheel at all in the car.
Artificial Intelligence Technology
I’m really excited about self-driving car technology with no friends with Tesla and I think it’s really exciting. I think we’re just getting started. So I was in an accident, a car accident last week. I got a concussion and I was thinking to myself, I can’t wait till it’s self-driving and this doesn’t happen. Right, because it’s human error today. So I can’t wait for it to drop me off and then drive home and pick me up so I don’t have to find parking. That’s like I’m excited for that. So I’m an early adopter of everything that self-driving cars have a way to go. We’re not there yet, but I’m looking forward to it. All right, folks, we’ll thank you so much for your time and your insights. It’s amazing that you are so coherent after a concussion.