Start time, so I'm going to get started. Thank you all so much for joining this webinar. My name is Mimi An and I write on market research. Today's webinar is presented by the thought academy and the IBM learning lab. These are both great resources for continuing education. You should definitely check it out after this webinar. So as a reminder for folks online, we are recording this webinar, and you will all be sent this link later today.
So, to start, we have two amazing panelists, Alyssa Simpson and Scott Lipman, and I'm going to pass it to Alyssa to introduce herself briefly. Alyssa.
Hi, Alyssa Simpson, Director of Product Management at IBM Watson. My portfolio covers what we call the sensory services, so teaching Watson to see and hear, speak, and the emotional portfolio of feeling and understanding emotional intelligence.
Thank you Alyssa. Scott?
This is Scott Lipman, I'm the co-founder of Equals Three, we are a very close business partner to IBM, and in my past I've had the great opportunity to build marketing services and ad tech companies that have had great field success, working with some of the world's biggest marketers on how digital technology is transforming their business.
Excellent, thanks so much for joining, we're so happy to have you two. So let's start with an easy question, and by saying easy, it's not easy. How would you describe AI, and let's keep it brief, because we're going to dig obviously deeper into this in the next hour, but, you know, when you're talking to someone and you say, "I work for Watson, I work for Equals Three, and we dabble in AI," how do you explain the concept to someone who may not be familiar with it?
So, from my end, what I look at is different than traditional computing. There are two attributes we really focus on. One is the ability to look at immense amounts of content, and understand it in a fairly human like way. I mean, truly the computers don't understand it at the human does, but different that traditional computing, cognitive allows that understanding and the others the learning nature of these platforms, these cognitive solutions learned 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 big differences versus a traditional computer.
Yeah, that's really well aligned with how we look at it at Watson. We take it a little bit of a step further, so that basis of understanding large amounts of data. Reasoning and understanding what that is, and the context of where the 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 I'm not from that space, I tend to say, it's not enabling you to be a better marketer. It should make better decisions, make 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 do see that using that term enablement can be really, really confusing, because it mixes up with all of these existing technologies that are available for marketers today. So, what is some of the confusion that you've seen with marketers around the topic of AI? We think robots, we think cars that drive themselves, and a lot of people can't really grasp how marketing can be affected by AI, so when you're speaking to prospecting customers, what is the message that you give?
I think, you know, the first sort of myth that we like to dispel at Watson is, this is not magic, there's no magic ball anywhere, and that AI, 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 of ours is that there's not a magic button.
It's really exciting companies like Equals Three who have built what appears to be magic, you know, 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 Equals Three, you can expand a little bit on what you're doing there, but that's the myth we like to dispel here.
Yeah, 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 world, at least for us, you know, IT kind of does all the development, all the back end work, and the business user waits for the result. But here, because it isn't magic, we actually can implement Lucy 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.
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 to 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 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 Scott.
Can you add to that?
Well you know that's actually, that will end up being a great segway into the demo, because I can actually show how this all works.
Well, going back to Alyssa, tell us a little bit about IBM Watson and 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, I think today, without touching the product, know very little about.
Yeah, absolutely. So, Watson is a fabulous part of the broader IBM company. We're like a very well funded startup within IBM, and so we're out on the bleeding edge here, creating technologies that are, at the platform level, sort of a series of ATI's that each do discrete functions. And each one of those functions does 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, right. 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 AI is that how much is actually, already being used in reality, and you might not know it, because it's sort of like this ... I had a friend at one point call, sort of, AI this digital toilet paper, right. It's something that you don't necessarily know it exists, but it's there.
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. And so in the emotional intelligence space as you were saying, you know, that's a really fun and exciting area for us. We have sort of three different technologies in that space today.
We have Tone Analyzer, which takes text and analyzes the tone of that conversation. So today 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 are 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.
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 reasonable humans can disagree around what emotion is contained in the particular phrase.
Emotion is a very sort of multi-faceted way of expressing yourself, right. You have your facial expression, 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? You know, what is that background that's gone on to that interaction that you're having? You know, what do I know about you?
But, you know, you may look happy today, Alyssa's pleasant or she's you know, not angry, but maybe I am angry and I'm not telling you, so it's a really tricky space that's really exciting. We're really proud of what we're doing there and one, I know that Equal Three's using quite a bit of personality insights, which is you know, taking personalities and understanding sort of intrinsic natures about people. When we say someone has a really strong EQ, right, we mean they're good at reading people and understanding.
And so how can we build suites of technology that help understand people better and interact better, and apply those towards delightful, quiet experiences?
Yeah that's fascinating, and the minute you talked about scanning for tone, I was thinking of a certain airline that issued an apology that nobody appreciated. I wonder what Watson would have had to say if we had run that text through the system. I wonder if that would've gotten published.
That's actually a pretty classic use case around understanding what people are saying or even if you've written an email, and you might not be aware that it comes across as really assertive. So you could have those companies that have built little widgets that integrate with Watson, and say hey, like, this is an aggressive email, 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 who'd probably love to get their hands on that type of technology and make sure that their emails are effective and conveying the tone that their company-
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. Scott, so Alyssa kind of set you up too. Tell us a little bit more about Lucy, your product. What is it? What can it do? Show us a little bit about what, you know, you're working on today.
So, yeah, so Lucy's the cognitive companion to the marketing professional. She's built for the Fortune 1,000 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 in their own databases, marketing analytics, website analytics, media data, their third party data, like Forrester, eMarketer, Kantar, and others, and they have all their own documents, all of the power points, pdf's, and the like.
And if you're okay, should I bring up Lucy?
So, I'll just turn on screen sharing.
Yeah I love seeing real life applications of what AI can do, especially again in the marketing space, because even to me, I've done a lot of research into it right, it's just kind of this cloudy topic, right, don't really know what it is. So to see an actual example is always super valuable. So I hope that's the same case for the audience that we have here.
Absolutely, so this is Lucy, and she's a software service. She lives in IBM's Bluemix Environment. And she has three major components, research, audience persona modeling, building really tight persona models along the lines of what Alyssa was just talking about, and then helping with media planning.
And so, what I'm going to 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 an 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 asked 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's and documents in our file systems, or it can come from third party relationships, like the Emarketers and Forresters, and the like.
So, she's showing a list of responses, and you can see on the left, her confidence score. Her confidence is based on her natural understanding of language, which comes from Watson, as well as the training that is given to her by the company that's hired her. So here, I can see her responses to this question on self driving cars. With a 94% 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, where it says was this answer relevant? I can say yes, give Lucy four stars, that's the training that edifies and gives her confidence. As I go through this I can see other examples, I see more information from eMarketer, I can see, yeah, 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 a little of her confidence in those eMarketer reports, she has some great data from Statista, 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 swat analysis for Tesla. So I'm going to ask, do you have a swat analysis for Tesla? 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 knock on some cue balls, but the chances of my finding such a specific component as this within the thousands of documents that I have in enterprise, is very, very difficult.
I'm more likely to recreate it than anything else. But here, I asked do you have a swat analysis for Tesla? I just as easily could have said, what are the strengths for Tesla? What are the weaknesses, what are the threats? And Lucy found it. So where did she find this? And in the bottom left, I see the source. In the eMarketer and Stratista reports, that source would've taken me to my subscription. In this case, the source is going to take me to this specific file, and so when I click on source, you can see Lucy's downloading a file. It's a pdf, it's a 46 page pdf that is like so many documents that are within an enterprise, where a single document could answer dozens of different questions.
As I scroll through this, this is the 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 swat analysis, and so it's not just, you know, Lucy 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 reuse.
Now, a lot of what Watson has been known for, you know, when you saw Watson on Jeopardy, was the amazing ability to look through huge amounts of content, text. And we think of that as unstructured data, so these initial examples, either my own documents, or the licensed 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 be able to ask questions of their marketing automation platforms like Hub Spot. They need to be able to ask questions of databases like Google Analytics or Armature, or other website analytics. They need to ask questions of media data, from sources like ComScore or Kamtar, or Nielsen.
So here I'm going to ask a question, which is, how much has B&W spent by month last year? This is an example of a natural language query that is going to go against a database. Without Lucy, I would have to go into a platform like Kantar, 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 in a natural language interface to this source of data.
So here we're working with Kantar data. You can see the data that we connected by API to Kantar, and extracted 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 B&W, that's another question that can be answered from data that exists at Kantar.
And here we see the competitors. If I want to ask a question like how much did B&W spend versus say Jaguar, versus Audi, and verus Ford and Kia and some others, by month last year, you can see that she will go out to Kantar, and formulate this question, come up with a 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 this one other thing I wanted to show you, and you brought up United Airlines. And you brought up, what can 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, this for example, this is Brand Insights. And what 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, so about 10 million pages of new 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, 74% to the negative, only 12% 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 the New York Times has written about United Airlines 33 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. 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 500 negative mentions on the 10th, 1,000 bad ones on the 11th, 900 more on the 12th. This is just a crisis, you can see that bubble when the news was just so bad, we can see hashtag analysis, we can see image associations, and you can see the ... What was a United customer being dragged out of the plane.
So all of 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 74% 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 Lucy. The ability to ask natural language against unstructured content that's licensed, like eMarketer, ask questions against your own data, like the pdf's, ask questions against databases, like the Kantar 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 to do things like brand insights.
So, Lucy has all kinds of other features, I'd love to show, but this gives you a good idea of how we're working with those core Watson components into a packaged solution for marketers and Lucy.
That is so fascinating, especially in the middle piece where you talked about being able to scan assets. I know that's a pain that we even feel at Hub Spot, you know, we're not perfect, we have lots and lots of pdf's and files, and we have it in our internal kind of wiki system, and it gets lost totally. Being able to search our own archive like that would be just, I want it, 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 AI, they think about having to dump in a lot of data to train it, right. And the complicated algorithms get spit 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 that you're building, what IBM in enabling, it can be as simple as the Netflix, you know, thumbs up, thumbs down, you're going in the right direction, it could be as simple as that, the kind of teacher trainer AI system to do what you needed to do, which is a wonderful example. I have no question for that, I just wanted to point that it, because that's fabulous.
Alright, so thanks Scott for the demo, super, super interesting. So from the Hub Spot perspective, we've been trying to dabble in AI, I don't think we're as far along as you two obviously, but we wanted to definitely kind of share with our own customer base on what automation can become with the help of AI. We've got our own kind of natural language processing bot where we allow people to dig into their CIM prospect, look for new leads, and also create new blog posts just bypassing kind of our menu navigation system completely, right. You can just have the bot say create a new blog post for me and it'll pop out and give you a link.
I think for a lot of folks though, the concept of AI is a little bit frightening. And so, you know, do you think that, with all these new technologies that are being built ... I think AR will definitely change our jobs for sure. But do you think that jobs will be gone, that we're going to be obsolete? That the machines are going to take over? Obviously you can tell from my tone that I have a bias now, the answer, but I think it is something that is part of every single conversation nowadays, that's around AI. So I wanted to get your thoughts on that.
So, you know, for us, the whole idea of the name Equals Three is about the idea that 1+1=3. Better than an individual, or better than the machine, to have the two together. So, I think that we'll 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 compliments the talented individual with the AI companion will outperform those who don't adopt or embrace AI at all, or those who rely too heavily on the AI to do the job itself.
And so, we're pretty bullish in the idea that this is all about supplementing and enhancing the individual. Now I think what's going to happen is that we're going to see more expected or demanded by the marketing partner, more expected and demanded of the agency, and that the way they keep up with that is AI. It's going to enhance their service delivery and their performance.
But we look at it, that more will be expected and more will be achievable, people will be able to drive better results and better outcomes because of their embracing of the AI, versus the displacement of people.
Yeah, I think that's 100% with how IBM comes to market and talks about this. We see this as man plus machine, right, and Jenny's gone on about that many times. It's about the partnership here, between humans and cognitive technologies. We actually, at IBM, when we say AI, we sort of talk about augmented intelligence, right, which is all about augmenting you know, 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 with Ikea, a company called iTrends, they ... Ikea's, interested in social media listing, right, similar to what Scott just demoed.
And they actually want to understand if you were to put together an Ikea product, it can be a challenge sometimes. And sometimes people get frustrated or they do really creative things with a book shelf, like turn it 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 YouTube videos, and understanding, visually speaking, where were the Ikea products they were particularly interested in appearing in those videos.
And then, what was going on in the context of those videos, was it positive, was it negative, what did it associate with in the products that Ikea 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 AI by training a visual classifier to understand visually where are those products, and which ones look like ones I sell, you can start to have an intelligence 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 of where the nature of the work may be shifting a little bit, or someone may have different responsibilities than they did before, but, to 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 Hub Spot Academy, use the hashtag hub spot webinar, and someone will come in with the questions and we'll be sure to answer them if we have any time left over. So if you have any burning questions for the panelists, please tweet at us, and we will try to get them answered in the remainder of the webinar.
So I just have kind of a, not a personal question, but more about why you two decided to, you know, you started your own company Scott, around AI. Like what was the potential that you saw? What kind of motivated you to get started with this? What caused you to think, this is it, and I'm going to dabble in it, I'm going to build in it, because I see x amount of potential in return in what I'm going to build.
Yeah, so, you know, I've always been fascinated with the AI space, and when IBM showed up on Jeopardy, it was like wow, and that's something I 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 my business partners and I said, what could we do if we had this? What is the problem domain where we could apply everything IBM has invested, you now, the billions and years they've put into developing this platform, if we had it 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 of that data, whether it was structured, unstructured, owned or licensed, what would 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 loved the tech, and we started to build out the MVP around Lucy and bring in 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, and this 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 sort of 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 what gets me out of bed in the morning, and I think it's certainly a privilege to be in my position.
Do you think that, so the reason why we at Hub Spot held this panel even though we're not really in the AI game is that we undertook a lot of research into AI, because we saw a lot of potential, obviously. But a lot of confusion, at 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 just have the pleasure of working with you two, 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 AI is? Is it because it's recent and so the tools are still developing and there's not a lot of messaging? Is it because we've been told in popular culture that this is what we should expect AI to be and so we have this pre-set 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 space has had is it's been a promise for the last 50, 60 years, right. Hollywood has promised this magic, you know, future world. And so there's a lot of preexisting ideas around what it will be or what it should be. And I think, you know, in some ways, this technology and this space in general is very nascent, right? We're just starting to see, in the last, you know, five years, I would say, real businesses use this in reality at scale in production to really and truly solve hard problems.
But it's not new technology, but there's sort of a confluence of data, of hardware, of you know, 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 challenges that we face, is reeducating people around what's magic and then what's now. And 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, when 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.
I can sell you a knife or a math book or anything, but it's just technology. The magic happens when you apply it as a chef and you create a masterful dinner, or if you put a math book in the hands of an 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 math book, but you might be able to make dinner with a knife. So you need to understand sort of the limitation of the tools and what they do. And then you have to think about, hey, like, you know, 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 hurdle with AI is around, you know, do I want to do customer listening and social media? Do I want to optimize my call center? Do I want to apply this to medicine? Do I want to apply this to health care? Do I want to cure cancer? 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.
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, and Scott and his team 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 AI can be applied to.
Yeah I'll just add to that a little bit, which is, you know, one of the practical challenges for marketers is they've never done this before. So, I totally agree with everything that Alyssa said, that you apply that to, yes, identify the problem, but if you've never bought it, I mean, for us, there are no ROP's for cognitive, there's no marketing departments that have pre existing budgets for, I'm going to put x dollars into AI. And so, you know, because of that, you don't have people with the experience of having run AI projects before, they haven't 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 my job that could be impacted by AI. And as much as we all have AI permeating our daily lives, you know, Google is an amazing tool for AI, it's no longer a search engine. Your Facebook news feed is completely driven by AI to some degree, people use services like Siri and things like that.
So, we have AI 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 were just discussing. So I'm going to ask it. It's for Scott, and it has a little to do with this maybe confusion around what AI 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, a marketer is asking, well that kind of looks like Armature, it kind of looks like something like that. What are the nuances 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, Cisnos, and you're going to go a mile deep. If your life is as a data scientist, you're going to use Edomo or a Tableau or a Watson Analytics type product. If you're in marketing automation you'll use one of the marketing clouds. But, to the VP, the product manager, 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 in Armature, and perhaps is only in the hands of very few people in the organization, they're able to get to that Kantar data, that would otherwise only be in the hands of a few people.
They would be able to fully utilize eMarketer, because eMarketer has got great content, but too often, enterprise is not used as universally as it ought to be, or that'd 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 that tool. If I'm a Kantar operator, and I know how to write a script and I know how to do the reporting, I should just live in Kantar.
But if I'm an agency account director the VP and 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, who's over worked, I can just ask Lucy.
Got it. Alright, last question and then we'll dig into some more of the submitted questions. So where do you see AI heading in the not so distant future, and how can we get started in using AI today? I think we dabbled a little bit in that piece, but tell us a little bit about what you're excited about, new developments that are coming.
So, from our end, and Alyssa's probably got a broader perspective on this, being at IBM, but, from our standpoint, we see that it's going to permeate everything. I get so excited when we connect to, you know, a new source, and Lucy learns it, and she becomes smarter and smarter, and it's amazing to see how that data gets stronger. You know, the longer somebody has an AI companion, the better it performs for them.
And so, and then the other part is for us just product road map. 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 it real.
Yeah we're similar at Watson with potentially a little bit of a broader scope. We're really excited about sort of the future of everything that we're bringing out. My 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, you know, many other 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, you know, 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. 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 any regular basis, but I use AI. And I can use these developer tools.
There's a 13 year old, Tom May, who's gotten a lot of press with IBM, and he's always the first to adopt whatever we put out there, even before, in some pre-release beta, right. And he's 13 years old. But this stuff is, there's free versions of all of it, it's easy to use, and if it's not easy, you know, call me, I'm not doing my job well. But the idea is that this stuff is easy for developers at any skill level to get started with.
And, you know, there's certainly an expertise and a training as you get more advanced, and more sophisticated with the tool, with what you want to do, but at its most basic level, these are API's, so you can integrate an API, or even better, some of our services have tooling on top of them for a business user like me. You can login 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 digital recognition, and pricing, and where to find docs, and everything else, and I was like, Watson can handle this. And I built a little chat bot.
Again, I'm a business user, I don't code. And I was in my hotel room and an hour later, I was done and I launched it. And 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, the basic API costs fractions of a cent. And it's something that is easy to get started with and scale up as you grow.
So this is going to be a natural question, some folks are tweeting at us. Where can the find these resources that you're talking about with us?
Just go to Google, just go to ibmwatson.com, get started with a Bluemix account, just like Equals Three did, you know a couple of years ago, and start making API calls. But, IBM Watson platform is hosted within Bluemix, accounts are free, check it out.
Great. Alright, so let's bring it back to marketing a little bit. I think you two are definitely really well aware of what's available on the market. So there's some questions around, alright, how can AI 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. Who wants to take it?
Sure, Scott, do you want to 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 our packaged solution are currently unique, for the most part. So, my answer to the question is, you know, talk to us about Lucy.
We have yet to have any significant client interactions that are evaluating us head-to-head with another cognitive solution, really the evaluation is, are they ready for cognitive? What are their use cases? What are their sources of data? What are the applications for how that data looks within a platform? So, you know, our point of view in the marketplace is, we certainly know there will be competitors, there has to be, but at the moment, we haven't seen a lot of it, as of yet.
I think, certainly what Equals Three has built is unique and unusual in the space. I think there are many agencies that we're working with that are doing components of this, and using different API's for similar types of use cases around ad targeting personalization, as an example. Or something better, a little bit more on the fringes of what Equals Three does, so, Ogleby is one that has had a lot of attention and case study in what they've done for the US Open or Wimbledon, it's well documented examples of how they're delivering with brands the right messaging for the right person at the right time, with all of their sponsors and many different brands involved.
Yep, to that end, agencies like Sachi did some interesting work that was put into the Cannes Film Festival, it was, you know, AI driven. You've got agencies like Havas-
Yep, Rocket Fill is another one.
That develop cognitive practices. But in general the cognitive practices are also creating bespoke solutions versus having provided a package offering.
Got it. You know I think we were partnered with Sales Force, and they have quite a AI engine that's being built out for their marketing and their CRM platform. You know, on the Hub Spot side, we're seeing a lot of interest and we're certainly working on allowing our own customers to examine the pre-existing emails they sent, content that they've written, channels that they've explored. And then, eventually our goal is to package that in a way using AI and actually with processing to allow people to understand, okay, this is a successful channel, or we pursued a prospect, and this is a great conversion lane that we can kind of enrich and enable in the future, using some of these AI capabilities.
I think there's definitely a lot of marketing automation companies like Hub Spot for example that definitely are feverishly working on it.
Yeah, I think you mentioned Sales Force specifically, and I'd be remiss if I didn't sort of bring up, we announced recently a really large partnership with Sales Force, and many of those use cases are in the marketing automation space. You know, I think Sales Force 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 on the marketing teams to really delight that end customer?
So we're really excited about the work that Sales Force is doing on integrating the Watson technologies into their platform.
And to that end, a lot of effort in AI and marketing, which is not the area of our focus, is in that area of cognitive engagement, how can I use cognitive to optimize how I'm bidding on media? How can I optimize, you know, performance at ecommerce 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've 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's focusing on and trying to drive optimization of their marketing cloud, and creating better one-to-one experiences for their customers, and they're 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 a piece of Watson that was kind of just focused on ad purchasing, optimizing, getting the right type of, you know, channels, hitting the right people at the right time, all powered through the iengine. I think, for a lot of folks, advertising is like the biggest crapshoot, you know, for marketers, because we just don't know if we're hitting the audience that we're getting and the conversions are actually going to generate the revenue. And I think tying AI into that, the purchases you make are the smartest possible and hopefully with all the data sources coming in, you can 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 in that type of detail.
Yeah, right, the holy grail of the market is the attribution and the automation 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, and they did make a purchase, but how do they feel about that purchase? Was it successful? How are they feeling about that product? Are they then encouraging others to buy? So there's 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, you know, can you get further, right? Than just a more serose, it happened or not, if it didn't.
Totally, so I think, one more question, this is from someone from Southeast Asia, so international in the top of mind for this particular person. How's AI being adopted around the world? And specifically when it comes to localization as people are going across boundaries, across geographies, across languages, is AI 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 in 170 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, you know, tone and emotion, those cultural norms impact how do you understand and apply AI 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. So a simple tag like that around hey this tree is green, that concept is different. Because, green is not a fact, it's this abstract creation that we have of color, 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 we are being aware of the cultures and the learnings that we can create, you know context specific, relevant AI solutions for that market. So it's a lot more than just language.
Scott, any thoughts?
I think it's a great question, it's something that we're mostly focused on, on really US, although 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 AI environment? And then IBM just has a much 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 SK, a big partner in Korea, and we have other clients whoa are partners of ours serving those end customers.
Got it, you kind of mentioned it, again gently, the last question is, are there any concerns of AI being hacked, for trade secrets, for data? How does IBM approach this? How do you Scott, when you're building up this product, you're compiling quite a lot of inside data. 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 ... You reserve the right as our customers not for IBM to store or learn from your data. 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 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 healthcare, IBM Watson Health is a HIPAA compliant, totally separate type of environment than if you're just looking to understand social media, someone posted this image, what is this image of? It's a very different type of data security requirement.
Yeah, on our end, the security side has to be supportive of the enterprise, and so we have a couple of really important tenets. For the agency customers, Lucy needs to help them stay in compliance with their MSA's to their end customers. If they've got multiple brands, if you'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 rights, so that if you've got 10 name seats to source x, those 10 people in Lucy will see those results, whereas the rest won't. And that ends up being a very important part of how we architected Lucy. The other thing is partnering with IBM, leveraging the security they have for the data that is within their environment is really critical, because they've got world class infrastructure to support us in that.
Excellent. Alright, 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 would say I already have a Tesla and I use its autonomous driving features a ton and I love it. So it's not true self driving, but I got a lot of miles-
I have to have the steering wheel, I'm not going to like when there's no steering wheel at all in the car. How about you Alyssa?
I'm real excited about self driving car technology, I have a number of friends with Tesla's and I think it's really exciting. I think we're just getting started, so I was in a car accident last week, I got a concussion, and I was thinking to myself, I can't wait until it's self driving and this doesn't happen because of human error today.
Yeah, I can't wait for it to drop me off and then drive home and then pick me up so I don't have to find parking, that's like, I'm excited for that.
I'm an early adopter of everything, but I think self driving cars have a way to go, we're not there yet.
Yeah, but sometime.
I'm looking forward to it.
Alright folks, well thank you so much for your time and your insights. Alyssa, it's amazing that you are so coherent after a concussion, oh my goodness, but thank you again, I hope for the audience that this is an insightful and interesting topic of conversation, continue it on Twitter. And we'll see you next time. Thanks folks.