>> From Palo Alto, California, it's theCUBE. Covering The Conference Board's 6th Annual Innovation Master Class. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at the Innovation Master Class that's put on by The Conference Board. We're here at Xerox PARC, one of the original innovation centers here in Silicon Valley. Tremendous history, if you don't know the history of Xerox PARC go get a book and do some reading. And we're excited to have our next guest because there's a lot of talk about tech but really not enough talk about people and where the people play in this whole thing. And as we're seeing more and more, especially in downtown San Francisco, an assumption of responsibility by tech companies to use some of the monies that they're making to invest back in the community. And one of the big problems in San Francisco if you've been there lately is homelessness. There's people all over the streets, there's tent cities and it's a problem. And it's great to have our next guest, who's actually doing something about it, small discrete steps, that are really changing people's lives, and I'm excited to have him. He's Kevin Adler, the founder and CEO of Miracle Messages. Kevin, great to meet you. >> Great to meet you too Jeff. >> So, before we did this, doing a little background, you knew I obviously stumbled across your TED Talk and it was a really compelling story so I wonder A, for the people, what is Miracle Messages all about, and then how did it start, how did you start this journey? >> Miracle Messages, we help people experiencing homelessness reconnect to their loved ones and in the process, help us as their neighbors reconnect with them. And we're really tackling what we've come to call the relational poverty on the streets. A lot of people that we walk by every day, Sure, they don't have housing, but their level of disconnection and isolation is mind boggling when you actually find out about it. So, I started it four years ago. I had an uncle who was homeless for about 30 years. Uncle Mark, and I never saw him as a homeless man. He was just a beloved uncle, remembered every birthday, guest of honor at Thanksgiving, Christmas. >> And he was in the neighborhood, he just didn't have a home? >> He was in Santa Cruz, he suffered from schizophrenia. And, when he was on his meds he was good and then he'd do something disruptive and get kicked out of a halfway house. And we wouldn't hear from him for six months or a year. >> Right. So, after he passed away, I was with my dad, and not far from here, visiting his grave site in Santa Cruz. And I was having a conversation with my dad of the significance of having a commemorative plot for Uncle Mark. I said, he meant something to us, this is his legacy. So that's nice, but I'm going to go back in the car, pull out my smartphone, and see status updates from every friend, acquaintance I've ever met, and I'm going to learn more about their stories on Facebook, with a quick scroll, than I will at the grave site of my Uncle Mark. So, I'm actually a Christian. I have a faith background, and I asked this question: "How would Jesus use a smartphone?" "How would Jesus use a GoPro camera?" Cause I didn't think it was going to be surfing pigs on surf boards. And I started a side project where homeless volunteers, like my Uncle Mark, wore GoPro cameras around their chests. And I invited them to narrate those experiences and I was shocked by what I saw. And I won't regale you with stories right now but I heard over and over again, people say "I never realized I was homeless when I lost my housing, "only when I lost my family and friends." >> Right. >> And that led me to say, if that's true, I can just walk down the street and go up to every person I see and say "Do you have any family or friends "you'd like to reconnect with?" And I did that in Market Street, San Francisco four years ago, met a man named Jeffrey, he hadn't seen his family in 22 years. Recorded a video on the spot to his niece and nephew, go home that night, posted the video in a Facebook group connected to his hometown, and within one hour the video was shared hundreds of times, makes the local news that night. Classmates start commenting, "Hey, "I went to high school with this guy, "I work in construction, does he need a job? "I work at the mayor's office does he need healthcare?" His sister gets tagged, we talk the next day. It turns out that Jeffrey had been a missing person for 12 years. And that's when I quit my job and started doing this work full time. >> Right, phenomenal. There's so many great aspects to this story. One of the ones that you talked about in your TED Talk that I found interesting was really just the psychology of people's reaction to homeless people in the streets. And the fact that once they become homeless in our minds that we really see through them. >> Totally. >> Which I guess is a defense mechanism to some point because, when there's just so many. And you brought up that it's not the condition that they don't have a place to sleep at night, but it's really that they become disassociated with everything. >> Yeah, so I mean, you're introduction to me, if you had said hey there's this guy, there's no TED talk, there's nothing else, he's a housed person, let's hear what he has to say. Like, what would I talk... That's what we do every single day with people experiencing homelessness. We define them by their lack of one physical need. And, sure, they need it, but it presumes that's all there is to being human. Not the higher order needs of belonging, love, self-actualization. And some of the research has found that the part of the brain that activates when we see a person, compared to an inanimate object, does not respond when we see a person who's experiencing homelessness. And in one experiment in New York, they had members of a person's very own family, mom and dad, dress up to look homeless on the streets. Not a single person recognized their own member of their own family as they walked by 'em. >> Yeah, it's crazy. It's such a big problem, and there's so many kind of little steps that people are trying to do. There's people that walk around with peanut butter and jelly sandwiches that we see on social media, and there's a couple guys that walk around with scissors and a comb and just give haircuts. These little tiny bits of humanization is probably the best way to describe it makes such a difference to these people. And I was amazed, your website... 80 percent of the people that get reconnected with their family, it's a positive reconnection. That is phenomenal because I would have imagined it's much less than that. >> Every time we reconnect someone, we're blown away at the lived examples of forgiveness, reconciliation. And every reunion, every message we record from a person experiencing homelessness, we have four, five messages from families reaching out to us saying, "Hey I haven't seen "my relative in 15 years, 20 years." The average time disconnect of our clients is 20 years. >> Right, wow. >> So what I've been doing now is, once you see it like this, you walk down the street, you see someone on the streets, you're like that's someone's son or daughter. That's someone's brother or sister. It's not to say that families sometimes aren't the problem. Half of the youth in San Francisco that are homeless, LGBTQ. But it's to say that everyone's someone's somebody that we shouldn't be this disconnected as people in this age of hyper-connectivity and let's have these courageous conversations to try to bring people back in to the fold. >> Right, so I'm just curious this great talk by Jeff Bezos at Amazon talking about some of the homeless situations in Seattle and he talks, there's a lot-- >> He's a wealthy guy, right? >> He's got a few bucks, yeah, just a few bucks. But he talks about there's different kind of classes of homelessness. We tend to think of them all as the same but he talks about young families that aren't necessarily the same as people that have some serious psychological problems and you talked about the youth. So, there's these sub-segments inside the homeless situations. Where do you find in what you offer you have the most success? What is the homeless sub population that you find reconnecting them with their history, their family, their loved ones, their friends has the most benefit, the most impact? >> That's a great question. Our sweet spot right now, we've done 175 reunions. >> And how many films have you put out? >> Films in terms of recording the messages? >> Yeah, to get the 175. >> 175 reunions, we have recorded just north of about 600 messages. And not all of 'em are video messages. So, we have a hotline, 1-800-MISS-YOU. Calls that number, we gather the information over the phone, we have paper for 'em. So 600 messages recorded, about 300, 350 delivered and then half of them lead to a reunion. The sweet spot, I'd say the average time disconnected of our clients is 20 years. And the average age is 50, and they tend to be individuals isolated by their homelessness. So, these are folks for decades who have had the shame, the embarrassment, might not have the highest level of digital literacy. Maybe outside of any other service provider. Not going to the shelter every night, not working with a case worker or social worker, and we say hey, we're not tryna' push anything on ya' but do you have any family or friends you'd like to reconnect with. That opens up a sense of possibility that was kind of dormant otherwise. But then we also go at the other end of the spectrum where we have folks who are maybe in an SRO, a single room occupancy, getting on their feet through a drug rehab program and now's the point that they're sayin' "Hey, I'm stably housed, I feel good, "I don't need anything from anyone. "Now's the time to rebuild that community "and that trust from loved ones." >> Kevin, it's such a great story. You're speaking here later today. >> I think so, I believe so. >> On site for good, which is good 'cause there's so much... There's a lot of negative tech press these days. So, great for you. How do people get involved if they want to contribute time, they want to contribute money, resources? Definitely get a plug in there. >> Now, or later? Right now, yeah, let 'em know. >> No time like the present. We have 1200 volunteer digital detectives. These are people who use social media for social good. Search for the loved ones online, find them, deliver the messages. So, people can join that, they can join us for a street walk or a dinner, where they go around offering miracle messages and if they're interested they can go to our website miraclemessages.org and then sign up to get involved. And we just released these T-shirts, pretty cool. Says, "Everyone is someone's somebody." I'm not a stylish man, but I wear that shirt and people are like "That's a great shirt." I'm like, wow, and this is a volunteer shirt? Okay cool, I'm in business. >> I hope you're putting one on before your thing later tonight. >> I have maybe an image of it, I should of. >> All right Kevin, again, congratulations to you and doing good work. >> Thanks brother, I appreciate it. >> I'm sure it's super fulfilling every single time you match somebody. >> It's great, yeah, check out our videos. >> All right he's Kevin, I'm Jeff. We're going to get teary if we don't get off the air soon so I'm going to let it go from here. We're at the Palo Alto Xerox PARC. Really the head, the beginning of the innovation in a lot of ways in the computer industry. The Conference Board, thanks for hosting us here at the Innovation Master Class. Thanks for watching, we'll see you next time. (bright ambient music)

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, its theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer event in San Francisco, Spring, 2018. About 100 people, predominantly practitioners, which is a pretty unique event. Not a lot of vendors, a couple of them around, but really a lot of people that are out in the wild doing this work. We're really excited to have a return guest. We last saw him at Spark Summit East 2017. Can you believe I keep all these shows straight? I do not. Alfred Essa, he is the VP, Analytics and R&D at McGraw-Hill Education. Alfred, great to see you again. >> Great being here, thank you. >> Absolutely, so last time we were talking it was Spark Summit, it was all about data in motion and data on the fly, and real-time analytics. You talked a lot about trying to apply these types of new-edge technologies and cutting-edge things to actually education. What a concept, to use artificial intelligence, a machine learning for people learning. Give us a quick update on that journey, how's it been progressing? >> Yeah, the journey progresses. We recently have a new CEO come on board, started two weeks ago. Nana Banerjee, very interesting background. PhD in mathematics and his area of expertise is Data Analytics. It just confirms the direction of McGraw-Hill Education that our future is deeply embedded in data and analytics. >> Right. It's funny, there's a often quoted kind of fact that if somebody came from a time machine from, let's just pick 1849, here in San Francisco, everything would look different except for Market Street and the schools. The way we get around is different. >> Right. >> The things we do to earn a living are different. The way we get around is different, but the schools are just slow to change. Education, ironically, has been slow to adopt new technology. You guys are trying to really change that paradigm and bring the best and latest in cutting edge to help people learn better. Why do you think it's taken education so long and must just see nothing but opportunity ahead for you. >> Yeah, I think the... It was sort of a paradox in the 70s and 80s when it came to IT. I think we have something similar going on. Economists noticed that we were investing lots and lots of money, billions of dollars, in information technology, but there were no productivity gains. So this was somewhat of a paradox. When, and why are we not seeing productivity gains based on those investments? It turned out that the productivity gains did appear and trail, and it was because just investment in technology in itself is not sufficient. You have to also have business process transformation. >> Jeff Frick: Right. >> So I think what we're seeing is, we are at that cusp where people recognize that technology can make a difference, but it's not technology alone. Faculty have to teach differently, students have to understand what they need to do. It's a similar business transformation in education that I think we're starting to see now occur. >> Yeah it's great, 'cause I think the old way is clearly not the way for the way forward. That's, I think, pretty clear. Let's dig into some of these topics, 'cause you're a super smart guy. One thing's talk about is this algorithmic transparency. A lot of stuff in the news going on, of course we have all the stuff with self-driving cars where there's these black box machine learning algorithms, and artificial intelligence, or augmented intelligence, bunch of stuff goes in and out pops either a chihuahua or a blueberry muffin. Sometimes it's hard to tell the difference. Really, it's important to open up the black box. To open up so you can at least explain to some level of, what was the method that took these inputs and derived this outpout. People don't necessarily want to open up the black box, so kind of what is the state that you're seeing? >> Yeah, so I think this is an area where not only is it necessary that we have algorithmic transparency, but I think those companies and organizations that are transparent, I think that will become a competitive advantage. That's how we view algorithms. Specifically, I think in the world of machine learning and artificial intelligence, there's skepticism, and that skepticism is justified. What are these machines? They're making decisions, making judgments. Just because it's a machine, doesn't mean it can't be biased. We know it can be. >> Right, right. >> I think there are techniques. For example, in the case of machine learning, what the machines learns, it learns the algorithm, and those rules are embedded in parameters. I sort of think of it as gears in the black box, or in the box. >> Jeff Frick: Right. >> What we should be able to do is allow our customers, academic researchers, users, to understand at whatever level they need to understand and want to understand >> Right. >> What the gears do and how they work. >> Jeff Frick: Right. >> Fundamental, I think for us, is we believe that the smarter our customers are and the smarter our users are, and one of the ways in which they can become smarter is understanding how these algorithms work. >> Jeff Frick: Right. >> We think that that will allow us to gain a greater market share. So what we see is that our customers are becoming smarter. They're asking more questions and I think this is just the beginning. >> Jeff Frick: Right. >> We definitely see this as an area that we want to distinguish ourselves. >> So how do you draw lines, right? Because there's a lot of big science underneath those algorithms. To different degrees, some of it might be relatively easy to explain as a simple formula, other stuff maybe is going into some crazy, statistical process that most layman, or business, or stakeholders may or may not understand. Is there a way you slice it? Is there kind of wars of magnitude in how much you expose, and the way you expose within that box? >> Yeah, I think there is a tension. The tension traditionally, I think organizations think of algorithms like they think of everything else, as intellectual property. We want to lock down our intellectual property, we don't want to expose that to our competitors. I think... I think that's... We do need to have intellectual property, however, I think many organizations get locked into a mental model, which I don't think is just the right one. I think we can, and we want our customers to understand how our algorithm works. We also collaborate quite a bit with academic researchers. We want validation from the academic research community that yeah, the stuff that you're building is in fact based on learning science. That it has warrant. That when you make claims that it works, yes, we can validate that. Now, where I think... Based on the research that we do, things that we publish, our collaboration with researchers, we are exposing and letting the world know how we do things. At the same time, it's very, very difficult to build an engineer, an architect, scalable solutions that implement those algorithms for millions of users. That's not trivial. >> Right, right, right. >> Even if we give away quite a bit of our secret sauce, it's not easy to implement that. >> Jeff Frick: Right. >> At the same time, I believe and we believe, that it's good to be chased by our competition. We're just going to go faster. Being more open also creates excitement and an ecosystem around our products and solutions, and it just makes us go faster. >> Right, which gives to another transition point, which would you talk about kind of the old mental model of closed IP systems, and we're seeing that just get crushed with open source. Not only open source movements around specific applications, and like, we saw you at Spark Summit, which is an open source project. Even within what you would think for sure has got to be core IP, like Facebook opening up their hardware spec for their data centers, again. I think what's interesting, 'cause you said the mental model. I love that because the ethos of open source, by rule, is that all the smartest people are not inside your four walls. >> Exactly. >> There's more of them outside the four walls regardless of how big your four walls are, so it's more of a significant mental shift to embrace, adopt, and engage that community from a much bigger accumulative brain power than trying to just trying to hire the smartest, and keep it all inside. How is that impacting your world, how's that impacting education, how can you bring that power to bear within your products? >> Yeah, I think... You were in effect quoting, I think it was Bill Joy saying, one of the founders of Sun Microsystems, they're always, you have smart people in your organization, there are always more smarter people outside your organization, right? How can we entice, lure, and collaborate with the best and the brightest? One of the ways we're doing that is around analytics, and data, and learning science. We've put together a advisory board of learning science researchers. These are the best and brightest learning science researcher, data scientists, learning scientists, they're on our advisory board and they help and set, give us guidance on our research portfolio. That research portfolio is, it's not blue sky research, we're on Google and Facebook, but it's very much applied research. We try to take the no-knowns in learning science and we go through a very quick iterative, innovative pipeline where we do research, move a subset of those to product validation, and then another subset of that to product development. This is under the guidance, and advice, and collaboration with the academic research community. >> Right, right. You guys are at an interesting spot, because people learn one way, and you've mentioned a couple times this interview, using good learning science is the way that people learn. Machines learn a completely different way because of the way they're built and what they do well, and what they don't do so well. Again, I joked before about the chihuahua and the blueberry muffin, which is still one of my favorite pictures, if you haven't seen it, go find it on the internet. You'll laugh and smile I promise. You guys are really trying to bring together the latter to really help the former. Where do those things intersect, where do they clash, how do you meld those two methodologies together? >> Yeah, it's a very interesting question. I think where they do overlap quite a bit is... in many ways machines learn the way we learn. What do I mean by that? Machine learning and deep learning, the way machines learn is... By making errors. There's something, a technical concept in machine learning called a loss function, or a cost function. It's basically the difference between your predicted output and ground truth, and then there's some sort of optimizer that says "Okay, you didn't quite get it right. "Try again." Make this adjustment. >> Get a little closer. >> That's how machines learn, they're making lots and lots of errors, and there's something behind the scenes called the optimizer, which is giving the machine feedback. That's how humans learn. It's by making errors and getting lots and lots of feedback. That's one of the things that's been absent in traditional schooling. You have a lecture mode, and then a test. >> Jeff Frick: Right. >> So what we're trying to do is incorporate what's called formative assessment, this is just feedback. Make errors, practice. You're not going to learn something, especially something that's complicated, the first time. You need to practice, practice, practice. Need lots and lots of feedback. That's very much how we learn and how machines learn. Now, the differences are, technologically and state of knowledge, machines can now do many things really well but there's still some things and many things, that humans are really good at. What we're trying to do is not have machines replace humans, but have augmented intelligence. Unify things that machines can do really well, bring that to bear in the case of learning, also insights that we provide. Instructors, advisors. I think this is the great promise now of combining the best of machine intelligence and human intelligence. >> Right, which is great. We had Gary Kasparov on and it comes up time and time again. The machine is not better than a person, but a machine and a person together are better than a person or a machine to really add that context. >> Yeah, and that dynamics of, how do you set up the context so that both are working in tandem in the combination. >> Right, right. Alright Alfred, I think we'll leave it there 'cause I think there's not a better lesson that we could extract from our time together. I thank you for taking a few minutes out of your day, and great to catch up again. >> Thank you very much. >> Alright, he's Alfred, I'm Jeff. You're watching theCUBE from the Corinium Chief Analytics Officer event in downtown San Francisco. Thanks for watching. (energetic music)