Click HERE to return to the contents page

Kore, Msg.AI, Fandango and more demo their bots at MobileBeat 2016

Youtube link

Published on 20 Jul 2016

Amit Aghara, Global Head of Solution Management at Kore;
Mike Brevoort, CTO of Robots & Pencils/Beepboop; Puneet Mehta, CEO of Msg.AI; Eugenia Kuyda, Founder and CEO of Luka; Mark Wasiljew, Director of Business Development at Fandango; Sam Vasisht, CMO of MindMeld; and Dror Oren, cofounder of Kasisto unveil their bots at VentureBeat's MobileBeat 2016 conference.

English Subtitles

0:04 thank you everybody
0:12 so thanks
0:16 thanks for joining us today and thanks for going to be for giving us the
0:18 opportunity present
0:19 so I'm glad this light is actually working better now so I know we had some
0:23 technical difficulties yesterday as well so i'm actually going to simply show you
0:26 a bunch of slides and some examples and obviously i can actually do more bots
0:31 if you get a chance to meet me in person so first of all core is a intelligent
0:35 bots platform we are announcing our boss back from officially to the public
0:40 we've been actually in beta for several months now working with roughly about 40
0:44 different companies
0:45 he basically offered three separate things one is there is a platform for
0:49 you to bird bots any custom bots second is of course there is a actual box store
0:56 where you can simply go use the box that you want to simply use and third is
1:00 and those are the individual box and third is we have our own Universal
1:03 bought that comes out of the box so that you don't have to build your own
1:06 Universal body can start from there
1:08 so I think everybody here knows what about is so no surprise there
1:13 chat bots are becoming very very popular no doubt we allow you to do a human
1:19 initiated conversation of course where the human start the discussion by
1:22 wanting to take an action like go create a lead in the case of a b to e scenario
1:27 crm use case or maybe help me find some products i'm looking for from a commerce
1:33 perspective or bring information to the user like an alert about a potential
1:39 particular item going on sale and maybe you want to buy it but we also love you
1:43 take actions on those alerts right so we made these alerts more actionable so
1:47 that i can actually first talked to the bar like it
1:50 in this case the one I'm showing you right now I can ask for a forecast
1:55 report from my crm system and the bots will actually have this out of the box
1:59 and not be enabled so it will actually have a dialogue conversation with you if
2:03 it needs more information will come back and you ask for more information and get
2:06 me my forecast report then of course if i choose to act on that forecast report
2:11 such as
2:12 so I want to go update an opportunity or something on those lines
2:15 it actually has the context to know that you actually a bit an option that was in
2:19 your forecast report
2:20 so actually asked you choose to ask you on its own saying would you like now be
2:24 the forecast so that context handling and the relevance is also baked into
2:28 sort of the way the body interaction response
2:30 now you can have box for individuals and for teams so I just to quickly go back
2:36 so the context can be bought from an individual perspective as well as from a
2:40 team perspective doesn't have to be just for individuals and one of the
2:45 interesting things that we're seeing a lot with our customers is they want to
2:49 be able to decide how they want to aggravate the bonds because for a large
2:54 enterprise like citibank in this example they may choose to decide multiple nurse
3:00 about four different groups of individuals in their company
3:03 so for example they may choose within the employee Rome they create a specific
3:06 sales bought that talks to various sales related systems only like crm etc or on
3:12 the consumer side they may create a bot that is specifically for certain kinds
3:16 of product owners so they are actually wanting to choose how the aggregate the
3:21 different parts into a unique offering to for a particular class of user
3:26 the other thing is what's interesting is more complex use cases one of the things
3:31 that we're seeing a lot is like this is an example of travel everybody
3:34 recognizes this I had to travel since she was just asking i came from orlando
3:38 so I had my own travel process I book the ticket I charge it to my corporate
3:43 card card let's say expensed it blocked my calendar get an uber whatever the
3:47 case may be the universal bought Cora that we provide to you out of the box
3:51 actually gives you a way for you to interact with a single bar and talk to
3:57 all the underlying bots and need the covers
4:00 so quarter will actually talk to let's say your control board which is what we
4:03 use for travel and expense management for booking travel
4:05 and then expensing the travel order to actually talk to my google calendar but
4:10 because we use Google's there to block my calendar for example or the you body
4:14 so it underneath the covers will actually talk to various different parts
4:18 in order to drive the conversation forward now one of the things that
4:21 you're doing here is making sure that BOTS don't just live in one channel or
4:26 the other about is about as a box
4:28 we wanted to live in n number of channels because every Enterprise let's
4:33 see a bank is not going to be able to dictate where their users are they're
4:37 going to want to reach the users where they are
4:39 so in our case once you build a park or you can actually give it an email
4:43 address you can get a phone number so you can SMS the box you can basically
4:47 say i want the board to live in slack or facebook messenger or even within your
4:51 website so obviously a bank has an online banking or a mobile application
4:54 have the bottle of there but it's one definition of about one conversation and
4:59 it's omnipresent omni-channel so you can actually start a conversation from one
5:02 channel to continue on to another channel with the same but in this case
5:06 so here's an example of Disney that we are working with where they want the
5:10 board to live within the website obviously they want to brand and
5:13 personalize the board it's a Mickey but in this case so I completely agree with
5:17 the previous comment that fish made we do believe that there is a room for BOTS
5:21 to live within existing properties
5:24 so here in this case it's more of a companion type of conversation where I
5:28 can interact with the bart and i can tell the bar hey I want to book a room
5:32 which one of the disney resorts and then the UI is actually driven by the board
5:37 and vice versa
5:38 where if I do something in the UI the ball comes back and says I want to do
5:41 this
5:42 so we have a full platform that allows you to actually build your own bar and
5:45 of course as I said you know we have a box store with a bunch of different
5:48 boxes you can start using from day one
5:50 thank you and see you later and we'll be able to show you more box
5:56 thankss terrific
5:58 next up we have Mike Brevoort CTO robots and pencils
6:07 beep boop hi everybody wow I I thought that you had me a nickel for sure but
6:12 you actually a really good job on my name which one here
6:17 see all right
6:26 all right
6:29 all right the real alright so Mike report i'm the founder of beep boop and
6:34 the CTO of robots and pencils
6:36 beep boop is about hosting platform has been launched for about six months now
6:39 with some pencils who make mobile app spots games things for people and
6:44 customers and clients and so today we're announcing that beep boop as a generic
6:49 bottles and platform is now going to focus exclusively on slack and as a
6:54 slack integration platform and in addition to that we're announcing a
6:59 better support for teams to collaborate on boot and and one of the reasons that
7:04 we're doing this is that we found that the consumer versus the enterprise use
7:09 cases for bots are actually really different
7:11 we're in the consumer side there's lots of attention to marketing advertising
7:16 brands games entertainment on the enterprise side it's more about
7:20 collaboration and collaboration in context
7:23 we think that we could have a much bigger impact on how people work
7:26 together and so let's just talk a little bit slack and so I think slack is so
7:32 powerful it's been transformative to our organization but what you have is a we
7:38 have this convergence of of people systems and data into a very specific
7:42 context that it from a slack perspective which i think is different unique is
7:46 it's a very humane way and so the way I look at slac is more than just a
7:52 messaging platform
7:53 I look at slack as a human first operating system and so if we look at
7:58 how we create applications in the past or have communicated with each other in
8:01 the past
8:02 it's very much been through computers & and now with platforms like slack
8:08 we have this opportunity to not just talk through computers but actually talk
8:12 two systems to computers on the same level which that we talk to each other
8:15 so as humans understand each other our systems and start to understand each
8:20 other as well and so it's not walking through a really quick demo here how it
8:25 jumps in and started already but it starts with a mobile push notification
8:28 from slack and this is for leads coming in from Salesforce and so using a slack
8:32 interactive message here and the first thing we'll see is down here josh is one
8:36 of our people on this channel
8:38 he passes on the opportunity so lead comes in
8:40 their claim or past the opportunity i pass on the opportunity but I don't want
8:45 the opportunity to be missed
8:46 so I'm going to reach out the tracy in context in here in the conversation and
8:49 notice how structured and sort of ad hoc workflows all work together seamlessly
8:53 with this this box so i asked Traci if she can take this lead because i don't
8:58 want to be dropped she said sure I have time and she go ahead and claims the
9:02 lead we update the message that's the record and we're also keeping track of
9:05 how long did it take to claim the lead
9:08 we're storing them as an analytic is the business analytics which is really
9:10 important to us and then notice we create a new channel here automatically
9:14 for that lead to get associated to a sales force record and then we can
9:18 continue the conversation here and have notes get applied to that Salesforce
9:22 record and see notes that are applied to the Salesforce record as they come in
9:26 and the thing I think here is that is that this isn't a very complex use case
9:31 however if this is your process you very well might not find this in the in the
9:38 slack app directory not this specific one and actually you don't actually see
9:41 a generic sales for spot because sales force is so very specific to how you use
9:46 it it's hard to generalize it so this is one particular process for one client
9:50 that we have that we created and and mimic their workflow in in slack and is
9:54 very powerful because you have slack as a mobile app and a platform that you can
9:58 build upon and you get more push notifications and you get all this
10:02 integration and that's particularly with what they wanted and what they needed
10:05 and so that's the other point is that you know your company is very unique and
10:10 I think I know I've been part of rfp processes and companies where you're
10:16 basically looking for products that don't exist that features that don't
10:20 necessarily exist and in the past you know custom software is also as always
10:24 sort of been this black sheep with a really high tea co on and very difficult
10:29 right and very costly and so we think there's a significant opportunity to use
10:34 slack as an operating system to build custom software and that's either for
10:38 your company or that's where people offering products in slack that are
10:41 customizable and we want to do is get used to that last mile right so that
10:45 basically ninety percent of the cost is built on top of slacking on top of beep
10:50 boop
10:50 and we can enable you to really you know open up slack
10:53 and take most advantage of this platform where you have all of your company
10:57 connected all the time are reachable on any platform so you get you know
11:02 authentication authorization group management you get native client's web
11:05 clients if you ever create an application for the enterprise
11:08 you have to make these decisions of like is it a web app is a mobile app
11:12 i'm going to put another trash app in my company's hands so you know we think
11:16 this is tremendous opportunity to do this
11:18 and so if you're interested talking about how your company better leverage
11:21 slack or how your product to better reach users I'd love to talk to you so
11:25 thanks everyone
11:30 okay next up we have in heat
11:38 Ptah CEO message AI
11:47 well hello hello
11:51 if you had a seat belt i would ask you to put it on now because what you're
11:56 going to see is the biggest take off since the origin of bots you go
12:02 so we are message I we are a deep learning platform for conversational
12:09 commerce and bots that brands build mostly fortune 500 companies on our
12:15 platform are deployed across messenger app chat and SMS
12:19 now when we started working with brands some of the most innovative ones around
12:24 the word we figure out they were looking for three things from bots and this was
12:30 not surprising
12:31 the first one they wanted to conversion left the second is they wanted to figure
12:35 out a way to reduce service costs and the third they wanted to delight
12:40 everyone that the boat's talk to now if i was a part right
12:46 and I spoke to you slowly in a slower pace than a normally do
12:52 maybe you like that but maybe you want me to speak really fast
12:56 maybe you do understand my Indian accent and I can go on and on and on in indy
13:01 right now are those three different parts or is that the same Bart
13:07 so what we figured out and what we are launching today is the biggest
13:12 advancement in Bart's since the original parts is multivariate testing for chat
13:17 BOTS
13:18 why do we need this now this is a new interaction UI which can have so many
13:24 different dimensions in a tap and type interface
13:28 you're tapping on certain boxes but you're also talking in natural language
13:31 but there's so many different ways of talking in natural language with
13:35 websites and apps
13:36 we have normalized an interaction model with clicks and created a lot of
13:41 predictability
13:42 now we are entering allow
13:44 and of unpredictability the second is this is also the most personal customer
13:50 connection brand would ever have
13:52 you're entering a space which is normally owned by their friends
13:55 I mean and lastly this has never been done before so i would love to show you
14:02 a quick demo of how this actually works
14:05 so since we are deep learning platform we have been able to in our PhD data
14:10 scientists have been able to put together a bunch of algos so that brands
14:14 don't need to hire phd's to do this
14:17 and in this case you're seeing someone configure an actual you know variant
14:22 list
14:23 in this case the two variants and their weight is distributed across the two
14:27 variants and just by configuring different types of cards different type
14:31 of you know responses that they can attach in this case they are mapping it
14:35 to a weakened versus a weekday and what ends up happening just in this user
14:41 interface like if you can actually use facebook you can use this interface you
14:44 don't need a PhD
14:45 that's the kind of that's our purpose in life but what ends up happening is when
14:50 you attach it to different conditions and different situations
14:53 consumers from different age groups consumers from different parts of the
14:57 customer journey get a different experience in this case of women fashion
15:01 retailer has an onboarding flow and they recognize the age group of the customer
15:08 and they're talking about specific products instead of a general on
15:13 boarding experience and they know that this person is looking for offers so
15:17 they bring up that kind of menu right in the beginning and when you do a product
15:22 drill down again
15:23 they have specific offers that are mapped to customer segments from a part
15:27 of the customer journey
15:29 so when you start talking about conversational commerce this becomes
15:31 supercritical because this drives personalization this is what consumers
15:35 are not looking for not a one-size-fits-all experience now in case
15:40 of another consumer you know we would switch screens in a quick second
15:45 so in this case you see that the customer was given a coupon in a in
15:49 another experience a different customer from a different part of the word
15:55 okay i think some issues with the video but what I wanted to say is a different
16:00 color from a different part of the word would get a completely separate
16:03 onboarding flow maybe even a different regional language now
16:07 the result of this is massive personalization at scale but really
16:12 focused towards optimization for those three KPIs that you noticed and what we
16:16 are doing behind the scenes using deep learning is we're doing user attribute
16:21 analysis
16:22 so we were able to create customer clusters and married at a certain
16:25 conversational you I we are doing contextual driver mapping everything
16:29 from what the weather is like to know what people in your social networks are
16:32 saying and we are doing a quick switch optimization matrix what that means is
16:37 low-hanging fruit
16:38 there's so much low hanging fruit that you can leverage so when you start
16:41 optimizing your final you find where the leak ages you find where you're losing
16:46 customers and this is the only way to fix that
16:49 so we are launching today the industry's first enterprise-grade analytics and
16:55 multivariate testing for chat parts of your message and I i'll be around
16:59 come see me thank you all right who needs a good job
17:04 next up we have Genia cuda founder and CEO of Luca
17:15 hey really hard to talk about this group after this great presentation but i
17:24 wanted to tell you a little bit about Luke oh here it is
17:28 I first met Luca when he was nine I he's a kid
17:34 he's the son of my friends and you know how you make connections with those kids
17:37 that are kind of like you so I told him skateboarding and snap chatting and a
17:41 bunch of other things and so when he moved to boarding school and he was
17:44 lonely and sad
17:46 he would text me almost every day and we became really really close friends
17:52 now he's thirteen and although i moved here to yourself i only see him maybe
17:56 once a year he still texts me every day
18:00 and you know I guess I'm he's measuring friends or something like that
18:04 it's very important friendship to me for the last two years I've been working at
18:08 a company that builds conversation box built a I and when I was thinking about
18:14 the name for the company
18:16 I kept thinking what were what are the conversations that are most valuable in
18:20 my life and I call the company Luca for 10 year old friend of mine that makes me
18:25 smile every time he texts me chris messina white marlatt nailed this term
18:32 conversational commerce but we had Luca kept thinking to ourselves
18:35 what if there are other one of their conversations that are not necessarily
18:38 selling you something on solving some the practical use case for you
18:42 they can still be available here ago we started building a Dalek model that was
18:48 based on your network
18:50 we were the first company to publicly launched an english-speaking chat bot
18:54 that was based on a neural network
18:57 we called it Marv on it's only goal was to become your best bad friend
19:06 it quickly got to the top charts charts on telegram and the engagement was kind
19:13 of crazy people send 45 tax on average procession and some of the active users
19:19 send even over 2000 messages
19:21 um what was interesting about Martha that it was the body was genuinely
19:25 interested in the user
19:27 so if everyone wants to build a bod that speaks we want to kind of wanted to try
19:33 to build a bond that will listen to you
19:34 this is one of the conversations just to give you an example
19:39 but people were would share so much in in the conversations with more from they
19:45 would this is one of the profile that marker got from from from the user and
19:50 what we realized that people would turn their frustrations or personal societies
19:55 very very personal things and it was incredibly interesting to get all the to
20:01 get all this data
20:02 what was more interesting that good that they're good at being a good listener
20:08 listener Marfa also managed to make connections with people
20:13 so there are some of the reviews reviews that people left from our phone
20:17 there was a lot of set stuff but i guess that was also inevitable there were a
20:24 lot of kids that were chatting
20:36 but we also realize that one personality does not necessarily fit all some fun
20:42 while market works for most of you for some of the users some found her
20:47 manipulative and meaty
20:48 so we started thinking what other personalities we can build in Silicon
20:53 Valley show was coming out with its season three we decided to build the
20:58 chat box for Silicon Valley show characters but there was a problem
21:04 I only had tweets and subtitles to train your network for for example for our
21:12 like that was all that was only like five thousand lines just to give an
21:17 example from our fur we had a baseline data set of over 35 million lines so
21:23 clearly that was not enough data but then I thought what if we don't need to
21:27 train the system from scratch
21:29 what if we take the general baseline neural network and then add Ehrlich's
21:35 tweets and subtitles on top of it just to tweak its behavior
21:39 you know to have a personal to do at the personal touch the vocabulary the tone
21:44 of voice
21:45 the way Orlick makes jokes or use emoji all those tiny things that kind of make
21:51 us who we are and it all work
21:54 we had the Silicon Valley's show characters Russ Hanneman Richard
21:59 Hendricks an early Backman in the app for the users to chat with and then we
22:03 also added a bought for prints
22:06 hey I does not need to pass the Turing test to become a part of our everyday
22:15 life we as humans don't tend to interpret the files everything they
22:20 would come into contact with cars cats dogs even thunder and lightning
22:26 we seek emotional connection with we tend to bond with the stuff that we
22:32 interact for a long time with so the only trick is to stick around long
22:36 enough to make this mechanism is kicking
22:39 and this is what we're building a look on a I with personality that you will
22:45 feel connected to technology even the most sophisticated one tends to
22:51 commoditize over the years the relationships don't
22:56 and this is what matters far as most Thank you Thank You Genia next up we
23:05 have Sam beseeched CMOS vp global marketing
23:11 pretty good I thought you surely we're going to watch that last name but the hi
23:17 everyone
23:18 so mind-meld is in advance the company based here in San Francisco and we are
23:24 building conversation interfaces for any device or application and what that
23:28 really means is that the conversation interface can take the shape of a bottle
23:32 or can take the shape of a voice assistant or you can take the shape of a
23:35 combination of these things and then i'm going to show you will show you the sort
23:38 of multi-modal approach that we take and the reason I mention that is because as
23:46 of yesterday and also generally what I've been hearing that seems to be camps
23:50 forming around different ideas around voice assistance vs bots apps vs
23:54 messaging text versus touch and so on and we believe that at least our
23:59 philosophy is that conversation interface embrace all these different
24:03 modalities and ultimately the goal is to provide a terrific
24:06 user experience no matter what the application is no no matter what the
24:09 platform is
24:10 and in order to do that we want to embrace everything that's available to
24:14 us
24:14 obviously we applied in different measure for different applications not
24:20 to focus on the eye chart here but i want to point out that we are b2b
24:23 platform so we don't have our own but we don't have an application we work our
24:28 our platform is designed to enable our customers to achieve these end goals and
24:34 what that means is unlike many other companies we don't rebuild anything we
24:37 don't rebuild content domains we don't pre built in natural language models
24:41 instead we use our customers data which is often proprietary data to then build
24:46 these solutions for them build a natural language models built there are content
24:50 domains and so on
24:51 and one last point on the last night is that that the platform is enables to or
24:58 enables us to do this in a very fast scalable way
25:01 so the customers and prospects that you're talking to fall into many
25:03 different categories as you can see these are large categories all very
25:06 distinct categories and even within a category there are large enterprises
25:10 that have very unique needs that we need to cater to
25:14 so the demo that i'm going to show you today out of many demos that I could
25:17 have shown i'm going to show you one that's an in-store shopping my dad and I
25:20 chose us for a very specific reason we will see in the evolution of online
25:25 commerce e-commerce over the last 10 or 15 years and continues to evolve you
25:30 know based on some of the demos we saw yesterday and at the same time in store
25:35 shopping has stagnated it hasn't really evolved over the course of time except
25:40 for now with the availability of conversational interfaces it's possible
25:44 to transform this experience that people have when they go into a store and so
25:48 i'm going to show you a video that highlights a few vignettes that
25:53 illustrate this point that i just made so i'm going to show you is a
25:57 multi-model conversational interface where users can interchangeably use
26:00 voice type touch and swipe to search navigate and transact in a physical
26:04 store
26:05 I think the search in the navigate part of the ones that are interesting the
26:08 transaction part is just basically checking out of a cart and that's
26:12 something that you just have to sit through unfortunately but it's it's it's
26:15 all wrapped into the same video
26:18 one caveat demo does not represent any specific customer that we work with any
26:23 specific project i had don't have the
26:25 liberty to disclose any of those details and so any resemblance to any business
26:30 living or dead that you might see is purely coincidental
26:34 ok so the video is going to scroll through three vignettes want to call
26:39 them out so that you know what you're looking at
26:41 I was the first one is and these are three scenarios that everybody
26:45 experiences and walk into a store in fact one of them was pointed out by the
26:48 gentleman from flipkart which is you walk into a store and the first thing
26:51 you want to know is where something is located
26:53 so you want to see how not only can you identify where something is located
26:56 using a conversation interface but it also greatly enhances the experience
26:59 that a person has the second thing is you see something in a store that you
27:04 like maybe something on a mannequin that you want to find out if it's in
27:08 available in your size different color may be a difference a slightly different
27:11 styles and you can basically just scan the tag and get that information
27:14 the third one is you find something on the rack that you like but it's not
27:17 available you can find one in your size you want to check if it's available if
27:21 it's not available
27:22 what options do you have so these are the three vignettes that are going to be
27:25 in the video and since we're running out of time just going to ask them to roll
27:28 the video thank you
27:31 i'll give you a pic narrative and if you want to see the video put it up on
27:35 youtube if you don't mind
27:36 you can take my email Sam at my know . com send me your send me an email with
27:41 your contact and i'll send you back the link for the video but what you would
27:46 have seen as basically a voice interface used to to find products and it would
27:51 give you something like this record give you not only the location i also give
27:55 you a the items that you looking for now notice that is very specific query men's
28:00 competition graphic designs
28:01 you see the same example in text when somebody says men's t-shirts because
28:05 nobody's going to type the kind of type in the way that they speak so you and
28:09 you get a different set of results because it's a it's a different query
28:12 and so that's one example then you can drill down in terms of continuing to
28:17 interact you can you can find a t-shirt you can scan the tag you can tap on an
28:21 item get information things like that so now you've got this multimodal
28:24 interaction taking place and then subsequently the the second one is where
28:28 you find an item you want you to you just can't the tag and then you can
28:31 start saying is available in different colors available in sleep less available
28:36 in different design doesn't those kind of things you can start doing so that's
28:40 an example where you're actually not speaking but you're using touch and type
28:44 and scanning to to interact well with the conversation interface and the third
28:51 one would be similar where you find an item you scan it and then it asks you or
28:55 you can ask it do you have in my size
28:57 whether that be through a touch button or you could even type in the text bar
29:01 below you can type anything regardless of what the menu items are and it will
29:05 basically understand that you are asking something very specific
29:09 that is not necessarily part of the built-in menu that is intelligently
29:13 generated but it's designed to facilitate a much faster workflow but
29:18 you can always override that and essentially go to the text borrow the
29:22 others voice bar to the microphone to basically initiate a new query or
29:28 modification to the options that are available to you
29:31 that's what I have thank you and we're mind melt the conversational interface
29:35 company
29:36 thank you
29:37 thank you Sam
29:41 next up we have draw RN vp of product and co-founder of cassis toe
29:48 hello
29:53 so I felt that it was the wrong decision to make a to do an actual live demo but
30:01 now i'm not sure maybe it will work better than the video when i'm going to
30:05 need my hands for that so
30:08 hello everybody and my name is Lauren i'm one of the co-founders and the 50 of
30:12 product with your sister
30:14 and so in this kind of conference I guess I don't need to tell you guys that
30:20 not all bots are created equal
30:23 we had consistent believe that high IQ bots required two main things one is
30:28 deep a I and the other one is domain expertise as a spin-off of sra
30:33 international the company that also created Cirie and a bunch of other
30:36 companies were deeply rooted within the AI and we train our BOTS to speak fluent
30:42 banking now what I want to show you today is Mike I are smart banking bought
30:48 and we created Mike I they're basically - whoo
30:53 oops i wanna steal my thunder so we created my Chi and to show a little bit
30:59 of the glimpse of into the future of what smart banking what can look like it
31:05 help you track expenses manager money and in two payments and it does all of
31:10 that on leading messaging platforms including SMS facebook Messenger and
31:15 slack
31:16 mike is available today so I'm actually going to be showing again a live demo so
31:21 if we can move to the to my phone so i'm going to be opening up
31:26 and my real phone and my real facebook Messenger so no no judgment
31:34 oops yeah that's real and i'm just going to show you a little bit of what Mike I
31:44 can do so
31:45 Mike I can answer a lot of questions about your accounts things like how much
31:50 money do i have in my account
31:52 you know what's my balance in my wells fargo checking account or you know how
31:56 much I on my credit card so I can say things like how much money do i have
32:02 this is connected to real account
32:08 hopefully the network is strong enough then another thing that my can do is so
32:17 it's conversational so you know it comes back and actually ask you a question you
32:20 know they want to break it up
32:21 obviously not a good balance right mortgages in the bay area are pretty
32:25 intense
32:26 so i can ask to see the actual transactions
32:29 the wife is a little slow here so i apologize for the delay but you can see
32:33 these are real
32:34 you know account will kind of like banks I can ask questions like
32:39 about transactions so we support transaction question including
32:42 categories merchants questions around you know date ranges that fees deposit
32:49 so i can say things like for example how much did I spend again no judgment on
32:58 junk in 2015
33:08 that's not too bad i can actually ask to see the transactions or can you know
33:11 continue just to show that these are real and i can say something like what
33:17 was my largest hotel transaction in 2016
33:30 or ask something like well we do live in the Bay Area
33:35 so how much as I spend 1 <operand> twenty </operand> in May and again these
33:43 are all obviously nature language conversational interactions or just say
33:48 you know I'm looking for 200-250 check
33:56 so you know I don't know exactly if it's 250 or not but I trust guy to actually
34:01 find within a range and actually find the checks that I was looking for guys
34:06 are such a train to actually know a lot of general banking kind of like
34:10 questions we can ask stuff like what is that CD or what is a routing number
34:14 i'm going to skip that could I don't have a lot of time and I want to respect
34:17 my five minutes
34:18 it also does payment so i can with venmo i can say and let's say pay sasha was my
34:23 co-founder let's say twenty dollars for dinner
34:31 and you know kind of stands that but i'm actually doing this demo is way too
34:35 often so I'm not going to put in twenty dollars going to say make that you know
34:40 50 cent and I'm going to say it I'm going to pay tues or was our CEO because
34:44 you know it's obvious how much the kind of stands that and you know i can just
34:48 go ahead with the with the payment so this is mike I i welcome all of you guys
34:52 to guys and ladies to go
34:54 it took a sister . com and get my Chi and if we can go back to the slides we
35:02 can continue and tell everybody about our exciting news
35:07 so what we're basically introducing today is that we're extending KY to also
35:12 support investing so for the first time users will be able to invest ask about
35:18 market about your portfolio create investing alerts and actually make that
35:23 make actual trading using natural language interface on facebook Messenger
35:27 and in order to support that we partnered with traded trade with it
35:32 which is a leading technical ed provider of investing API they allow quick and
35:39 secure investments and this is going to be available in the fall and until then
35:45 like I said I invite you guys to go on everybody to go to assist the dot-com
35:49 get my kind start playing with a smart banking but in your phone
35:53 thank you all right for our
35:56 that was brave up next
36:01 mark walsall do director of business development for fandango
36:13 hi everyone I'm back again
36:18 so as I think I mentioned yesterday we have a pre-recorded demo here but
36:22 everything you're going to see is live today you can access the bot via
36:25 facebook Messenger and follow along if you'd like to play the video
36:29 so this is our movie discovery and ticketing bot built for facebook
36:34 Messenger its effect you effectively a virtual concierge for moviegoers
36:37 movie-going is obviously very social experience and so Facebook's a natural
36:42 platform for us to to be starting here leading into existing behaviour
36:46 ultimately moviegoers have a course that it needs a movie theater and a showtime
36:58 and what we've done is basically built as many routes as possible for a user to
37:02 go and and get to that sent a 10 points
37:05 so in this case we're going to start with movies
37:08 one thing you'll notice is it it knows my location and so it can quickly get to
37:14 a point where you can find local local movies that are showing within any list
37:18 of movies you can quickly jump into a trailer if you don't know what the
37:21 movies about or you want to get familiar with with what that movie might be
37:24 quickly jump back into the list and easily pull up another trailer until you
37:34 can find what it is that you want to see that night
37:43 now we're all in the early stages of biotechnology the bottom fortunate
37:47 doesn't do everything that we wanted to do always perfectly
37:49 so one of the things that we've done is that in any of these carousels
37:54 if you go all the way to the right we've added an outlet for the user
37:57 so if they want to get to fandango to find a more robust experience if we're
38:01 not able to deliver exactly what it is they want they can get out get back over
38:04 to fandango get a full experience serving out of course and and then
38:10 quickly jump back to get what they need
38:13 now i'm a big Anna Kendrick and aubrey plaza fan
38:16 so I'm going to see Mike and David wedding dates select the movie click on
38:20 theaters you'll see we we provide maps of the theaters if you don't know the
38:23 area
38:24 I'm a lot of familiar with the area so find a theater that I want click on
38:29 Showtime's and we'll we'll set you up with showtimes for for this evening
38:32 I'd like to go to the late showing tonight so click on buy tickets and will
38:37 drop your right into the purchase funnel where you can complete your purchase
38:39 select your seats and be on your way
38:46 now we also offer some other options as well so you can go buy theater
38:51 if you have a favorite movie theater that you always go to you can start
38:53 there and see what's playing at that theater and quickly and get to a point
38:58 where you can find your tickets as well
38:59 the third option is training this week we editorially curate on a daily basis
39:03 the five most popular movies so it may not be something that's playing today
39:07 but something that's opening this weekend that you that you might have in
39:09 mind
39:10 so that's a bit of the core functionality like to get into something
39:15 a little more fun now we're starting to play with some of the natural language
39:18 processing so there's a newborn will be coming out later this month
39:22 when is that you'll see not only can we give you the opening data the movies you
39:26 know what's coming but we'll also pull up a bunch of other bourne movies
39:29 we're pretty excited about some of the functionality that will have coming up
39:32 with that where we can we can sort you out with the solutions for bourne
39:37 identity bourne legacy and some of the previous films in the franchise but the
39:43 good news is you can have already buy your tickets for opening night you go
39:46 through the flows that we just talk through and you're all set for july
39:49 twenty nine for the the first night of the board movie
39:53 now one other thing i want to point out is the way we set this up is that you
39:58 can effectively enter these queries these natural language queries at any
40:01 point and so you're not stuck within the flow you can you can always get out
40:07 we also have the start / buttons prominently and so we want to ensure
40:10 that the user never feels like they went down a path
40:13 they're stuck they can't get out and they don't know what to do now for the
40:17 movie that I think we're almost excited about this year
40:19 unfortunately we're all gonna have to wait til december to see that one
40:27 that's the end of the demo one thing i do want to mention we are doing more
40:30 with natural language processing and it's coming pretty fast
40:33 we want to be able to allow users to start to skip some of these steps so you
40:36 can type in something like I want to see the purge on Saturday night and the
40:40 bottle recognize the date and the movie and quickly get you into a flow where
40:44 you you're simply picking a theatre - Showtime
40:46 and we're doing more and more that and that's all coming very quickly
40:49 I do want to say thank you to our friends facebook our partners that
40:53 assists who helped us put the spot together and the folks of entropy for
40:57 having me today
40:57 thank you thank you Mark