Justin Sargent What is father's day? Is it coming up or did it just pass? Sunday. Thank you. This story is about my father Well, it's actually about me, but it features my father kind of the way that grandgay men Ross features Alec Baldwin My father I admire him a lot. He's a real man's man Grumpy Vermont Fisher hunter hikers. I can build anything out of anything And he's a doctor too, so he's got the smarts and He's always been very supportive. He had my mother make a great team. I think they raise us pretty well very supportive all the time Even supported us with the somewhat questionable decisions we made going into college. I kind of tricked them in terms of Major choice. I always told him I wanted to do classics, but I made it more powerful by saying I wanted to also study Pre-law and become a lawyer. That's great. So after the first year I was like, actually I'm going to change that to computer science It's still okay. I was like after that I changed my linguistics and then English and then you know But they just kept supporting me through all that and they The college I went to was Duke University, which was A lot like former years of high school. It's very isolated from the strata community. They encouraged me to live on campus So I didn't have a job. I didn't pay rent or anything like that. I was just, you know, kind of continued the whole dependency thing and After college I decided to move my brother to New York City He was pursuing a career in acting and had a program that he was participating in and I had nowhere better to be and it had some Fancy ideas about being a writer or something. So I thought New York would be a good place to be But when you've never paid rent, never had a job You're kind of going from zero to 60 by moving to New York City And I'm also somewhat of a country mouse. So again, a lot of culture shock. I didn't It didn't hit me right away at first. It's just very exciting to be there. There's a real energy from New York City Um, but after that initial excitement were off. It just became really impressive I remember in the first couple days Of the excitement. I just walked around downtown everywhere. I thought I walked from like Harlem all the way to Battery Park and then back again. It was exhausting, but it was it was exciting to see everything Um, and at the time we were still looking for a place. We eventually found one in Washington Heights And the rent was I think like $1,300 a month. So That didn't mean anything to me. I mean, I've never had to pay rent. I've never really had a job So Everything was fine for a little while, but then I started to feel that responsibility way down And my brother was okay because he was in a program So he was kind of he had somewhere to be and something to do and I had I had nothing I was looking for jobs. I had been to bartending school in college, but Nobody in New York cares at all that you've been to bartending school And so all the restaurants that I tried to get jobs that nobody wanted me I tried briefly to substitute at some schools and that didn't really work out So I just ended up I remember walking through midtown and looking up at the skyscrapers and just Knowing that each building had hundreds and thousands of offices in it and each of them had people of them And I was just wondering what could they possibly be doing that many people I couldn't figure it out and I just started hiding in my room In our apartment and the weeks went by and I had I had nothing to do and nowhere to go and I would Try to you know do some searches online try to find something but nothing was happening and and I I would call my mother because Both of my parents was supportive, but when you call for like People make me feel better kind of talk. I was usually talking to my mom um, and so These calls became somewhat regular a couple times a week. It's calling like help help. What am I doing here? I I can't I can't do this and you know try to butter me up and try to support and like, oh, no You're great. Are you writing? You know try to try to you know, call me down and and that would It worked pretty well but It just couldn't go on like that And eventually I reached a breaking point My brother and I both did and We had already borrowed Almost $3,000 from them. Well, I say borrowed That's just an information for taking $3,000 From our parents for the first month's rent and security deposit and um I after after several weeks being unable to find a job not knowing what to do and just doing this crushing massive humanity We decided we had to get out of new york and called the management company and asked them how we could do that and he was sick of us because You know, we never rented before We've sent our first rent Money in as cash And they had to call us specifically tell us not to do that because it's on both broken by machines and the money So he already didn't like us And he told us the only way to get out of the lease was to buy it out And so I was like, okay, and called up parents and said You know, we can't do it. You know, we tried but we have to leave new york So we need like $5,000 about the lease And um, that's when my mom gave the phone to my dad And you might expect that he would Use the the money as a reason to say no, but that's not what he did He told me about his time in the Navy as a doctor About how many times Young man would come into his office who had signed a contract joining the military Accepted all the benefits and then when it came time for them to Take their duty station They would come to him to try and find a medical excuse to get out of it And he said I don't want you to be the kind of man who puts their name on something and then backs out and these Kind of coming of age moments sometimes they happen Slowly over time and you look back and you realize and you came and sometimes they happen like that And this is one of those times where I I heard the words And I thought yeah, I don't want to be that kind of man either and so we we stuck it out and um Things got better and I ended up spending two years there and probably three quarters of the stories I told that example. I've come from my time in the york city So I I really appreciate all the times in my life that my dad said yes It supported me and I really really appreciate the time that he said no Bill tory Picked up my grandson on his last day of school and they got me to thinking about what it was like when I was in school Back when I was in the third grade I was incarcerated with my classmates at the little white school house on the corner of group 15 and 128 yes at the center All the other kids were at the main school house about 200 yards down 128 and for us kids At the little white school house. It was like beyond out the trash Cut off on the mainland In the warden of the prison was my third grade teacher Mrs That we called her old lady asperd Yeah It was 1968 and corporal punishment was alive and kicked in at the little white school health She had many ways to straighten this album Like for instance, if you gave her any lip or spoke out of turn she made to stick your tongue out And then she clipped a colis pin on to it Her favorite was uh the old stand with your nose in the corner routine Trude but effective, but you know it only made some of us want to rebel against her even more John patri spent a good deal of third grade with his nose stuck in one corner or another John was the unofficial ringleader of the Loosely foreign band of brothers that came to get into an annoyed pester and pissed off old lady asperd She once made John roll a crayon apart across the floor with his nose In retaliation somebody I won't squeal Oh Put a tack on old lady asperd's desk. Yeah, I'm gonna rise out of it Well, the the battle seesaw back and forth Headquarters for the resistance was down in the bathroom of the old school house before the bathroom Yeah You went down about nine flight at nine steps to a little land and then you turned nine degrees to the right Went down another three or four steps on the right hand side.  They're still going strong probably could have gone farther Of course, we was all laughing and squealing like eight-year-old boys tend to do And it was by turn Well, I started blasting away at the urinal I backed right up to that first step step up onto the first one still going strong Like quickly with expert.  She says yeah, I just to remember her great coffee cigarette candy breath in my face And she says You so much as put your head one inch one side or the other and I'm going to paste you one you'll never forget Foo-ree I don't know how long I stood there with my nose in the corner Lord knows I I couldn't see a car I do know it was all the way through recessive most of English class Boy, did I learn my lesson that day One that I put up a fight for the whole school year held for my whole life And that lesson being When it comes to a pissing contest it really ain't no winners Kelly two summers ago I signed up to be a host for the fresh air fund Now if you don't know the organization the fresh air fund's been around for 140 years It's based in New York City And it helps underserved Intercity kids escape the city For a week or two in the summer and spend that time with a host family And it operates from Maine down to Virginia And I'd always wanted to have an exchange student or something where my kids were in school But I was a single working mom and I traveled for work and I just never felt like I could manage it But now it was just me in Vermont by myself. I had The perfect house pets great community knew all the fun things to do. So I thought I was a perfect host But then I started thinking about How this 10 or 12 year old girl Would see me through her eyes coming out to spend a week with a stranger in the country in Vermont First of all, I was 55 years old. I was old enough to be a grandma And it was just me there were no other kids So I talked to the fresh air fund folks and we agreed maybe I should have two girls So I signed up to have two 10 year old girls come out and stay with me And a couple weeks later I get an email with the information about them and I'd be hosting And ISIS both from Brooklyn So I sat down and wrote their parents an email and told them about myself and my house and all the fun things I had planned for them and I told them I was 55, but I said I'm really fun and active And I've got kids of my own so I'll make sure they have a great time So the day before the girls were arrived I go to the grocery store and I buy a bunch of bad kids food You know that stuff that comes in blue and red boxes I had every intention I've introduced them to all the good vegetables in my garden and the wild blackberries that grew down in my Meadow, but I knew that would take a little transition time So the morning they're due to arrive I go to the bus stop and I painted a bright sign that said welcome Kamori and ISIS And they pile off the bus and I load them into the car And they're just strapping in in the back seat when Kamori takes a look at ISIS and says She not a old lady? Now I want to be clear about this. I have to talk in their voices with their inflection or you won't get How clever and how funny these girls were Disrespective So the first morning they're at my house I take them out after breakfast to meet my horse and show them how to take care of a horse Of course, you're scared to death ever But as soon as I get her in the stall and she's mentioned around hey, they're fine So I grab them a break and I go out and start cleaning up the horse feet With the birth of one of our expressions for the week That nasty usually followed by So a couple of days later we go into Montpelier and we go to the farmers market We're wandering around town. We've been there for a couple hours when Kamori came over to me and she said There are no black people in this town.  And I said people in Vermont, maybe not as many here as in other places, but there are And so we're walking around town and after lunch I see this Darling african-american couple who's two kids. I'll be eight and six So I go to the girls and I said see Families in Vermont So that's saturday on sunday the next day We're in chelsea at the spare And the same family is there And the same family Completely figured out comes over to me. She says let me you pay in that family to follow with myself, you know I should probably be dry The week of her here. I don't know other family situations. I don't know how alcohol fits in or doesn't This is not normal for me, but I decided okay. I'm not so grateful to her So the fortnight there with me we're helping out at a wine and cheese tasting at my In my town a little community event Of course, I'm dipping into the samples of wine So we get home that night. We flopped down the sofa. We're watching these silly shows on tv And then we start telling knock knock jokes and then we're making fun of each other's toes And I'm drinking a couple more glasses of wine. We had a glass set up to like two in the morning And the next morning at breakfast Isis looks over at me with her wise little smile and she says Oh And that same night spawned our one of our next catch phases, which was let me you owe what you fund Where the girls were to leave I decided to take him to the contra dance at the Grange in Montpellier And I've been to the dances before and I knew they were up last And it wasn't real sure how they feel about dancing with you know, sweaty old Vermont man with long straggly old white But they were great sports. They had so much fun. We stayed for four or five dances. They really seemed to enjoy themselves But true to form We get the car to leave and as soon as the door shut Kamori starts in the last session you dance was this tall thin young guy with bare feet Oh, no, no, this Matthew bare feet. He has some old scraggly toe. He's scratching me. That old nasty scraggly toe He just couldn't get anything past him So the next morning I put him on the bus and they head back to New York City and I am exhausted And I tell anyone who will listen Do not let me ever do that again Then I realized for nine months straight. I told hilarious stories About my time with Camorian Isis and I knew I had to have a back So sure enough they came back last summer and we had so much fun I mean, this is only half the stories. They're coming back this year for another week and I'm really looking forward to it Which just goes to show you When you think you're doing something to help somebody else You end up benefiting yourself the most And remember I'm old but I'm fun Dina Frankel years ago I Did a freelance job where I got paid With an all expenses paid free trip to Jamaica to a Resort to a clothing optional resort It seemed like a good idea at the time But when I got there I looked around and I realized this was really probably a big mistake All of the guys that I could see were like bodybuilders from New Jersey Six pack abs hair gel everywhere And this was definitely not my taste in men But so I'm trying to figure out what to do and I see this one guy who doesn't fit the stereotype He's got a wild mane of blonde hair Pulled back with this piratical red bandana And a luxurious mustache that just frames The most sensual mouth He's wearing like cut off jeans and boat shoes and it has all effect to sort of a hippie sailor This is my taste So we just had this magnetic attraction the two of us came together as like something out of the movies You can like practically hear the violins It turns out we've got some things in common Um, well, I find out that this is captain jim. He's not actually a guest at the resort He owns a sailboat that is used as a charter takes guests from the resort out on day sales for $25 a head And we um, we find out we're both from Connecticut. We're both scuba divers. We both French we both Have memorized every Monty python routine Well, there's so much chemistry. He says to me, do you want to see the real Jamaica? The next thing you know, he's carrying my suitcase to my room And whisking me away on the back of his motorcycle So he takes me to this magical place On the cliffs above the Caribbean where the waves Thunder in under the cliffs and shoot up through these holes in the rocks and make these geysers into the sky And then we get back on the bike and he takes me up into the mountains and let me tell you Spending a few hours on the back of this straddle in this motorcycle with my arms wrapped around this sexy stranger It was Stimulated So we stopped a week for lunch at this little roadside shack that served Jerk chicken and salt fish and red striped beer And while we're eating Jim Panthers with the provider in this Jamaican patois that's so thick it sounds more like reggae than english to me And then when we're done we get a we have a nice red striped buzz going here And we get on the bike and head further up into the hills So as the afternoon goes on the sky opens up and it rains like a monsoon The road has gotten slick and so Jim pulls the bike off into this dirt track And as soon as we made the turn we were just mired So we had no choice we ditched the bike and we started hiking up this track and there in the jungle Is a treehouse? It's really like this cottage built up into the canopy that belongs to an expatriate friend of Jim's And so we we're able to take shelter from the rain Now You know where this is going right you can probably imagine that a young American tourist and a sexy sailor Who are trapped in a treehouse in the jungle for several hours while their clothes dry are not playing doxy Let me just put it this way it was The most romantic afternoon of my life Um, uh at the beginning of one of the most romantic weeks of my life I never really went back to the resort except to get my suitcase We swam and sailed and so forth The air was just full of the smell of jasmine and Jim made me feel like I was the center The night before I was supposed to leave came to me and he asked for a favor Would I bring a package back to the united states? Yeah, the up and up he said But you know, I had been with this guy for a week now and I kind of trusted him So the next day he brings me this package and it contains 480 $20 bills And they smell like dirt Like pirates. So here was his mo He offered a five dollar discount to the american tourists who paid for their boat rides in us currency, which was strictly illegal in jamaica And so he had amassed this huge pot piles of 20 dollar bills, which he kept buried in the front yard of his house And from time to time you would befriend an american tourist I suspect usually a female american tourist And he would dig up just enough cash to stay under the 10,000 dollar legal limit for bringing cash back into the united states and he would ask them to Mail the cash back to him At his home in the in the states So you know what I did I took the cash I flew back to Miami And I fed X to him just like I had promised Tim told me years later that he had never lost a penny in this tax shelter scheme I don't know why no one just kept Just keep the cash, but I'll tell you it never crossed my mind Now I've probably made you think that he was kind of a scoundrel any user, but you know He and I 30 some years later are still good friends We traveled all over the world together He's one of the most stand-up guys I've ever met And that was the end of my smuggling career. Thank you I'm squire story begins in 1920s When a real estate developer in los angeles decides to build a community That he calls Beverly Hills But apparently the first several houses don't sell very well So he needs to up the marketing And so he reaches out to the great movie star will rogers and asks will rogers he would be the mayor of the town And uh, will rogers says no, I wouldn't be a member We are the mayor of any town that doesn't have a church So very quickly the developer Sets three very prominent lots right in the middle of Beverly Hills and donates the land to the Presbyterian Episcopal and catholic churches and the churches are built and they're there today And if you go there, you can see the presbyterian episcopal and catholic church is right on centimonic bolder So fast forward to the mid 1990s iowide in los angeles and I started having a really great time I'm going out at night going out tonight clubs Monday to the wednesday thursday friday and saturday and So I figure out the square you got to get you got to get right here and uh Maybe what I'll do is I'll go to church on sunday and try to just clean this act up a little bit So not being from any particular religious tradition. I just walked into the first church that I saw And I go in I And all the people are way down front They're up in the first couple of views and I kind of quietly and she kind of sit towards the back Behind my own business. I'm just sitting there But uh, there's a moment in the service when we're supposed to stand up and Did the people decide you piece? Well, there's nobody near me But I kind of look around and it's quietest little mouse This man had crept in during the service and sat down right behind me, but it's not just a man It's jimmy stewart The jimmy stewart and I look at mr stewart A bit of peace and these people up front are all taking a lot of time So kind of whispering talk and I said i'm romp squire and he says i'm jimmy stewart Yes, yes, you are your jimmy stewart this far Completely geeking out because I've been a big fan of jimmy stewart my whole life And it's just just a great moment and and I've just been piece of jimmy stewart and then A couple of minutes later in the service. They want you to hold hands with the people next to you and pray But there's you know, no one around me. So I turn around and i'm holding jimmy stewart's hand And we're praying together You know hit us this day our daily bread and i'm sitting here with this man. I'm like This is incredible. I didn't know church was like this I feel like I died and gone to heaven and here I am. I'm like, this is great. I'm gonna come every week I'm gonna get to know jimmy and we're gonna start hanging out and we'll start to pray with jimmy stewart So this is great And so I'll catch him that coffee and donuts after service and we'll make this thing a regular thing jimmy sweeps out before the end of the service He leaves just as quietly as he came in But that doesn't deter me. I decided I'm coming back next week. So I came back next week I go to church jimmy doesn't show up And I figure well, he must accept I come back the next week jimmy doesn't show up Oh, he must be on vacation I saw him again in the fourth week jimmy doesn't show up again. This is a long vacation But I keep going back jimmy never shows up again But I keep going back and I start making friends and I get active in the community and I actually get confirmed in the church Suddenly we uh You know the rector of the church and the bishop of la come to me and they say hey, well, have you ever thought about doing this professionally? What like, you know join the clergy like no And he said well, you know, we've got a lot of bullet gale and we've kind of spoken to them There's a spot for you to live in the school bunch. Go check it out So the rector and I fly to new haven we go I take the tour I do the interviews and all that and they tell me Yeah, we've got this spot for you.  You'd have to finish up the application I go back to LA with this application That's right at this time that I discovered Why jimmy steward is not in church And why jimmy steward is never gonna be in church And that's because the first week I went to church. I walked into the presbyterian church The second week I walked into the episcopal Waiting for stewardy's next door man. It's worse now. I've got this application And I swear to god that the first question the first question on the application says What is it about your religious journey and your search for god that has brought you to the episcopal church? I had to do it. I wrote I didn't come to the episcopal church looking for god. I came looking for jimmy steward And you don't have him I wrote that I signed it. I mailed it in They did not accept my application They did not join the clergy and instead I moved to Montpelio


This is Think Tech Hawaii, Community Matters here. Good morning, and aloha. My name is Mark Shklav. I am the host of Think Tech Hawaii's Law Across the Sea program. And today, we have a topic that is interesting to me and the future of law. It's called Artificial Intelligence and the Practice of Law, and my guest is Priya Rasheed, Ms. Rasheed is a Juris Doctoral Candidate, Class of 2018 at the University of Hawaii's William S. Richardson School of Law. As part of her studies, Ms. Rasheed has developed an insight into the influence of artificial intelligence on the practice of law. And today, we're going to talk about where that is now, where it's going, and what it means, what it means for the practice of law in the future, what it means right now, what do we have to learn. First, however, I want to ask Ms. Rasheed, or Priya, may I call you Priya? Yes, of course. Priya, why do you want to become a lawyer? How did you get into that? Isn't there enough stress in life without taking an occupation or career that gives you more? Well, first of all, thank you so much for having me, Mark. You know, I'm a 30-year law student, and I'm still answering that question. I came to law school because I loved to read, and I loved to write, and they said, you know, there's a profession where that's literally all you do is read, write, and help problem solve, and so I'm still figuring that out. I have loved school so far, and I've loved studying in Hawaii, and I have been exceptionally lucky to study international law and technological ethics and problem solving, so I've been extremely lucky. Okay, all right, so that's the real Priya. Now, artificial intelligence, what is artificial intelligence? Give us a definition of that, please, and then I want to go into what it does to the law or what's happening in the law today. First, tell me, put a face, if you will, on artificial intelligence, a real face. Well, that's a mouthful, but artificial intelligence is the systems and the designs that essentially want to replicate and enhance human intelligence, and they generally require a high amount of human intelligence, such as visual perception, speech recognition, decision making. Anything that you've thought of as a smart computer probably has an aspect of artificial intelligence, and as we enter into an increasingly technological world, more things will have this smart intelligence, this human intelligence without the human. Okay, now, you said it includes human intelligence, that's part of it. Now, right, you said that artificial intelligence includes human intelligence, is that right, but does that mean you have to be intelligent to watch it or get it, or does it mean that's how you create it? A little bit, maybe, as somebody that is not a tech savvy person, I don't think you need a high level of intelligence or tech savvy to appreciate artificial intelligence, I want that to be known. Okay, that's good, thank you. I think my computer is very old, I think three years old, which is very old in the tech world, and my cell phone is five years old, so that gives you any indication of my tech savvy, it's minimal, but it's important because if we talk about artificial intelligence, from the very beginning, in the 1950s when artificial intelligence became a buzzword and something important, it always was an imitation game to make it as human like as possible, and we want our computers to be, that's what we aspire, is that humans are the special thing and computers enhance them. So the computer takes these traits and that's part of artificial intelligence and enhances them? Well, I would preface this with, excuse me, computers don't really do anything. Okay.  They don't have a soul, they don't have an intent. They don't have power unless we give it to them. I see. Give it to them, and even I do it when I'm speaking about computers. Okay, so computers, we know about computers, artificial intelligence, I'm getting a feel for it. It enhances the human qualities and it's something we can go online maybe and find out about. What is artificial intelligence and the practice of law? So to begin with, artificial intelligence and the practice of law is just like any other profession in any other area, and I joke around with people and I say the first people that are going to get hit are the doctors, the truck drivers and the lawyers because it just shows how unspecial we are and for us the unique and special thing about our profession is that we're paid to think, we're paid to problem solve and so doing that accurately, doing that in a socially proper manner, those are things computers can't do. But having the right information, having the perfect processing for e-discovery, knowing that there are 30,000 cases that are applicable to this problem that your client is facing, it can be overwhelming, but it can also be extremely empowering to an attorney. So alright, how did it start? How did it begin? For artificial intelligence and the law, what were the beginnings of it? And how did attorneys begin to use artificial intelligence to their advantage and I guess to the disadvantage of opponents at times, you know? I guess that would be the logical non-computer conclusion that I would make. Well, I would say that as a profession we have been extremely resilient and stubborn when it comes to integrating lawyers, stubborn, never know. But we actually should be surprised at how resilient we have been to automation and artificial intelligence and to tell you the truth, we borrow so much technology and so many ideas from other areas of our lives. So for example, the computer processing and the computer power that has emerged in the last 20 years, we are being given extreme power and instead of utilizing it early on in the 70s and 80s, we pretty much decided that we were going to use junior associates as our robots for these massive due diligence projects and massive discovery projects when we could have been making initial investment into the technology. We chose as a profession because of how special our client relationships are, how important the consequences were that these tasks belong to humans and we made that decision early on but now we're competing against the medical field and for example gene sequencing, these areas that require a lot of computer processing are now saying, how come the lawyers never use these computers? How come they aren't using these for these millions of cases around the world to problem solve? And we're looking at them without a very good answer because we could have saved clients a lot of money, we could have saved junior associates a lot of suffering maybe if we had started to automate and integrate artificial intelligence earlier. At the current state that we're at right now, all of the magic circle firms, all of the larger firms over 400 people have either started massively investing into artificial intelligence technology or have already integrated into their due diligence, their discovery processes and their major billable hours, their major income generating areas. And so it's pretty scary to think that maybe the small and mid-sized firms are threatened by a massive disruption in the legal field and so that's where I'm interested in how do we protect these small and mid-sized firms from this automation disruption. Okay, so who are the magic firms, magic circle firms, what does that mean? So that's a colloquial phrase to reference these traditionally large, large mega firms. They use Swiss-Ferrine structures to have I should use this colloquially, franchises around the world and essentially they run the corporate structures and network whether it's corporate contracting, arbitration, dispute and litigation, mediation all of these large private international law and they're the ones who are harnessing artificial intelligence. When did this start? I mean I think that when it came to the control function on our keyboard and us realizing that in discovery actually having it on a computer was faster than having the printout was when lawyers started realizing these big cardboard boxes were not going to be the future and most of it was cheap, most of it was what you could get on a Microsoft word processor to what was built in every single computer and then they realized it wasn't specialized enough and so in the late 90s lawyers started realizing there are specialized tools for example dragon dictation which was commonly used by doctors lawyers said why should I hire a paralegal to do my dictation when I could but now with most attorneys being high level typists that also became a little bit of a fad and so we've been trying to integrate these fads as quickly as possible and see what stays. But I sense from what you said earlier there was initially kind of a reluctance that we our profession deals with other people and really you really need a human attorney we can't send this out to a machine but that's changed because of the other professions so I sort of spurred that on it's what I hear you saying Yes and I think there were two factors to consider in the reason that has happened one we have a very insular profession we believe in the client relationship we are one of the few professions that we regulate ourselves we license ourselves and that's a privilege but it's also a great responsibility and when it came to technology almost we're a bit arrogant in saying that it wasn't going to be us it was going to be the doctors and the truck drivers but not us and I think that that also happened to coincide with the increasing litigation costs and clients saying that you're not bringing the value that I need and so for example several major contracts in the last ten years in the United Kingdom, in Spain, in Italy the United States actually started saying we refuse to pay in the contract we refuse to pay for anything that could be done by a paralegal we refuse to give billable hours to anything that could be done by a junior associate and it started to make firms, large partners think to themselves well why should I have a junior associate if I'm not going to be getting billable hours for them and then it became another matter of how do you get the work done in the most efficient way and a lot of the times the answer became computers for large firms who could overcome the cost barriers so what I hear you saying actually is that it's all about money in a way, in a way that sort of started especially with the international firms and now we hear that the international firms are developing at full speed ahead is artificial intelligence and I'm going to, we're going to take a break but I want to ask you after the break what are they doing with it and what can the small firms do to compete and then where, how far does artificial intelligence go and you told us it doesn't have a soul you intimated that, I said that but can we blame it for anything but let's take a break and then we'll ask those questions This is Think Tech Hawaii, raising public awareness You can be the greatest You can be the best You can be the king Come play in all your chess You can be the one who can talk to God Don't bangin' on his door I'm Pete McGinnis-Mark and every Monday at one o'clock I present Think Tech Hawaii's research in Manoa where we bring together researchers from across the campus to describe a whole series of scientifically interesting topics of interest both to Hawaii and around the world So hopefully you can join me one o'clock Monday afternoon for Think Tech Hawaii's research in Manoa Welcome back I'm Mark Schlaufe, host of Think Tech Hawaii's Law Cross the Sea and my guest, Priya Rashid and I are talking about artificial intelligence and the practice of law and Priya, when we broke you were talking about how large law firms are now getting into it this makes more sense from the bottom line it appears and at one time the profession felt well, you only have to deal with attorneys that kind of sacrosanct feeling is kind of dissolved What are the law firms doing? What are the basic uses of artificial intelligence with these large law firms, first of all how are they being used? Well, so for example most large law firms have started to integrate natural language processing and machine learning and so that means intake instead of having a paralegal or a junior associate integrate clients a machine might do it and then this secondary or tertiary step is a junior associate or a paralegal So what does the machine do in that respect? So the machine actually tries to I'm sorry, do we call the machine or artificial intelligence? I mean, there are probably I think roughly about 20 or 30 mainstream systems and systems is a great way to catch all and these systems try to read things such as word patterns I'm worried about my divorce we have been fighting for 8 months there's been a crime committed things like that and they try to almost problem solve pre-problem solve and give the lawyer a time-saving tool Like a summary or something? A summary is just a minimal top of thing that can happen but for example it might actually begin to take the next step to say this is the problem this is the choice of forum this is the forum of where the case should be disputed this is the probability of success and this is the probability of cost Do you think that this client and also the probability of payment sometimes which is really important for mid-sized and small firms of client payment What else besides that? Well, I think that on the other side so for small and mid-sized firms many of them have already started integrating case law analysis so for example Bloomberg is a legal service provider and they have started to do case mapping so making information much easier to take in and absorb so that a lawyer can become faster and take on more clients so things like that are common tools common machine learning tools and another example I would use is for example legal aid and legal aid whole they already have a chat box that was made this past year and they never Richardson candidate in my cohort and what they did was they had a grant from Microsoft and AmeriCorps and they actually gave them the funds to create an intake box a chat box and ironically the students name is Chad so we call it the chat box but it's if you think about legal aid as a smaller mid-sized firm and they're extremely under resourced they're helping indigent populations and people that really need this service they deserve that type of artificial intelligence and that automation so they can help as many people as possible so somebody would call up describe their problem and the system would the system would give them a range of possibilities so for example when you go on to legal aid site and you type in your problem you might say I have landlord-tenant dispute and I've been living there for four years but I don't have a lease the computer can pick up that it's going to be a matter of a non-written contract these big problems that happen when you do or don't have a contract and the computer can guide the information in a certain way and kind of guide a conversation almost the way a human being would ok and so the system the computer, the machine will take, do an analysis of some sort internally by I guess going through and finding out what words are in common and then send something to an associate or attorney then what does the attorney do with that well so for example the attorney can triage the cases what cases need to be responded to now versus this afternoon versus tomorrow morning which is extremely important those types of decisions are much greater efficiency but also like I told you about adding predictability and information to the client that client can get information immediately which lawyers have mixed feelings about but in terms of the legal profession and also society there is really nothing wrong with people having information faster and in fact it's an indication of a good society when people can have the things they need and whether that's comfort or information as quickly as possible and artificial intelligence through the legal field can provide that ok so let me do a couple of follow ups here now what's going to happen to these associates and these paralegals and these secretaries law secretaries and assistants with if something comes in isn't that going to take jobs away or are we what is technology doing here what's going to happen well fundamentally if you think about what we do as humans what do we enjoy doing we enjoy problem solving we enjoy client relationships generally if you come to work and you can do something robotic and you click 30 times a day there is a justification that a robot should maybe do your job for efficiency but I don't believe that the members of the legal profession are likely to be impacted by immediate or unexpected suffering through unemployment in fact what I believe is I think that when I graduate I'm going to get a job that I love much more than if I had to do 80 hour weeks of due diligence checks because I think that the partner will expect from me to be a better problem solver because that is the best investment into this associate and so I do wonder if members of my class are going to be maladapted or ill adapted to the new legal profession but I also think that if you're a critical thinker if you're a hard worker and you bring something new to the table every day you wake up with a creative mind as a problem solver I don't believe that your job will be as at risk as you might believe it to be so I hear you saying use the technology to advance your job you'll have a job but you'll use the technology the technology won't take your job away you will use the technology to enhance your job well I think that in all professions the theme of artificial intelligence is really that technology can enhance the things that make us human and special and so for example problem solving with a client is often limited because of the research the sheer amount of time it takes to research draft memos, file briefs and the high stakes require you know strict scrutiny but if you had a computer checking you double checking you how much time could you spend conversing with the client how much time could you spend thinking of novel ideas so those are the things that most lawyers enjoy and love and those are the things that make us human ok so I got a couple follow up things I want to ask you first of all do law firms charge for the time of the computer? is that a billable hour? do we have a system that now goes on the bill saying I am charging so much per hour for that is that how they do it I don't think that the model rules of ethics would let computers receive billable hours because it's just you know not the way of the world but I also believe that there is a serious cost barrier to entering into artificial intelligence and I do believe clients will feel it in their charges and in their cost layouts but there may not be a world with billable hours in the next 20 years it may be service based fees but not quite contingency fees I think that it actually will make the legal profession have to reconsider why is it paying so much why is cost so high in litigation and things like that and I think it will bring the total price of legal services down and I don't know if our salaries will actually go down but it will so that actually is good for the market and good for efficiency okay so will the small firms be able to compete still I think I'm hearing you say that they can use this technology they're not going to be bought out it's within the price range of smaller firms well there is a cost but as we know we have some of the highest numbers of solo practitioners in the country per capita in Hawaii I believe that it's up to each individual attorney whether you are flexible and able to adapt to this new cost sharing scheme and using artificial intelligence is a very difficult question to answer especially for individual attorneys but I would say this it is an opportunity for small firms to leverage technology and go head to head with larger firms and I do believe that in a client perspective you get the attention of a small firm and if the technology is there that means that the best quality is the best of our profession high quality, low cost okay now my last question for you okay what do we do to prepare for the future what's your advice and suggestions for lawyers to prepare for the future with artificial intelligence well my conclusion and advice, personal advice is that you should consider this a positive thing, a good thing because it is inevitable and I truly believe that the greater flexibility any person has but especially a lawyer it will only make you a better problem solver and a better service provider for your client if you can be flexible, if you look and know about the tools that are available to you so you can be a better tool for your client and so like I said accept it embrace it, maybe internalize it and think to yourself in a new place that I'm going to be successful in and also continue to be a human, a real person in light of the artificial intelligence still can maintain your strength as a person yes exactly, I think that when we practice law we should harness the things that make us special our ability to interpret political, moral, social constructs whether something is good whether something can be done versus whether it should be done where we can be true stewards of this artificial intelligence movement as lawyers for corporations, for governments that's what lawyers are meant to do and then hopefully we'll have more time because of artificial intelligence to go home and hug our children and be with our families that's very nice very nice thought, thank you Priya Rashid thank you very much for being my guest today you're welcome Mark


This is George Gilbert. We're at Databricks. We are on the ground. We're interviewing Michael Armbrust, who is the creator of the Catalyst Query API, which as we'll see is a very fundamental piece of Spark. He also has responsibility for Spark SQL and Spark Streaming, which when you put the three together pretty much underpin much of what's new in Spark 2.0. So Michael, welcome. Yeah, thanks for having me. All right. Let's start with how the work that you've done with Catalyst and what comes on top of it does so much for unifying and simplifying both the API in Spark and the engine. So what was the motivation behind Catalyst? Tell us where it fits and then and then why you built it the way you did. Yeah, that's kind of a fun story actually. So Catalyst started initially as this kind of out there research project. I was working on another relational database and I realized that the query optimizer, well, a very important part of the system, was very rigid and very difficult to change and I was trying to implement some of my thesis research on top of it and I kind of took a step back and said wait a second. Why is this so difficult? To add new concepts into the query optimizer. Maybe there's an easier way to do it. So we looked at a bunch of research on composable query optimizers and decided to try and build it. Once we had this really cool tool, kind of applying it to Spark was kind of the next logical step. There was already this system called Shark, but it was borrowing the query optimizer from Hive and that was really starting to become the limitation. So we took this next step of kind of giving Spark its own query optimizer and that was really how Catalyst began. Okay, and so just for our, for some of our viewers who haven't gotten under the covers, the query optimizer is the core sort of it's the core value add that allows you to take a query and turn it into something high performance that gives you an answer back really quickly. Yeah, exactly. And I think kind of a key part of this is it also comes along with this logical language for describing what kind of data flow you're trying to accomplish. Okay, elaborate on the data flow. So you can actually say things like I want to do a join, I want to do an aggregation and this is a higher level than the RDD API, which is really kind of more like individual code operators that you are implementing yourself. And RDD was the original API for Spark.  Exactly. I think the key difference here is it's what we like to call declarative programming where you're telling the system what you want to do but not necessarily how to do it. Once you have that language for saying what you want to do, now the system has all of this new flexibility for kind of trying different ways to accomplish that can actually explore the space and find the best way to do it. Okay, so still on the database and the SQL track, taking the composable capabilities, which I assume means that you could add rules without breaking the engine apart and rewriting it over a period of 10 years as Oracle would probably tell you. Combine that with this declarative capability. What did that allow you to start expressing that was not possible before? Yeah, so when we first started we were focused primarily on SQL and we realized that that was a great use case. But there were a lot of people who actually wanted to express things that are difficult to do in SQL. They had complicated UDFs, they wanted to do machine learning, user defined functions, like their code that they wanted to inject into the query engine. And what we found was the RDD API was actually a very natural way to do this. So we wanted to come up with some way where you could marry the declarative programming of SQL with the kind of low level language integration that RDDs gave you and that's where data frames came from. Okay, just to recap, so declarative is, here I'm just going to say what I want to do, not how, so that's a lot simpler. That's like punching in my navigation system. I want to go here, you tell me how. Exactly, you say like compute an average, not one plus and count. Okay, and then the RDD gave you the ability to put your own functions and data types. But in RDDs you couldn't have that declarative capability other than if you were working with this Hive engine that was kind of broken from Hadoop. Exactly, exactly. So okay, the two came together and that created data frames. Tell us then, starting in SQL, what did that allow you to do, including the richness of the UDFs and the UDTs? I think what it allowed developers to do is when they wanted to do kind of relatively simple but very important tasks like filtering, aggregating, joining kind of the bread and butter of SQL, they can do that without having to re-express that logic on their own. But then when they want to do something kind of doesn't fit in that box, they can seamlessly transition into their own code without switching languages, without switching frameworks. Before you'd have to actually take multiple tools and compose them together. So this would be the old complaint with SQL was even though it was great at saying what I want but not telling you, but not requiring you to say how to get it, if you did something that wasn't in the SQL language, which was, you know, there was a lot that you couldn't fit in there, now you were able to surface those capabilities or allow it to be extended with user-defined types and functions. Exactly. So it was for people who are familiar with Microsoft Link, you know, it was basically one language that gave you access to the data and anything else that you wanted to do to process the data. Is that fair to say? Yeah, absolutely. I think we took a lot of ideas from Link and we actually then implemented them on Spark's distributed runtime, which is where a lot of the power comes from. Okay, so how did that capability migrate then into all the other Spark APIs? Yeah, that's a great question. Once we had this kind of more general framework for piping data through integrating with different data sources and expressing all of these transformations, things like MLlib naturally wanted to take advantage of it. They had spent a bunch of time building up their own ways to kind of allow users to flow data through a machine learning pipeline and, you know, say like I want to use this column as features and I want to transform this column in this way. Kind of having the data frame API and having this kind of logical plan structure that Catalyst provides was kind of a natural way for them to take advantage of a lot of that without having to reimplement it inside of machine learning. So data frames became a structure for people to represent machine learning pipelines. And so you, underneath data frames, you could also use Catalyst to optimize how that data flow worked. Exactly. So the same performance optimizations you applied to essentially SQL queries you could now apply to machine learning pipelines. Yep, exactly. I would kind of think of data frames as like the skinny waist of expressing data transformations inside of Spark. Okay, and then how did that work with structured streams? How did that help make structured streams so powerful? Yeah, so maybe before I dive into that, just a little bit of history. So there was another project before structured streaming just called Spark Streaming, which used this thing called the DStream API. It was a very low level API, very similar to RDDs, where you actually express exactly how you want the streaming computation to proceed. After kind of hearing a lot of feedback from users, they liked the power of this API, but they didn't like having to kind of express these complicated concepts on their own. If you have a stream and you want to do a count or you want to compute an average, you just want to say that. And so adding data frames to streaming was kind of a natural next step. Once you've answered the question once, you want to answer the question over and over in real time incrementally. Okay, so in other words, data frames sort of elevated the, for lack of a better term, abstraction of the entire Spark API. Exactly. I think like once you had this high level way of expressing things that's very general, kind of lets you go back and forth between a declarative and functional programming, you want to use it in many different places, not just for batch queries. And of many of the popular Spark applications, whether, you know, data warehousing, business intelligence, recommendation engines, how did you see their functionality changing or how much simpler and more accessible did they become for developers? Yes, I mean, we're just scratching the surface with structured streaming. It was just released in 2.0, I think, two weeks ago. But I think what we're seeing already with customers is cases where they would have to write a significant amount of Dstream code, both for doing integrations with different data sources and then expressing the transformations once the data is inside of Spark became significantly simpler. Instead of having to rewrite it and rethink it in terms of streaming, you take exactly the same code that you wrote for your batch job and you can turn it into a continuous application that incrementally computes the answer as new data arrives. Oh, so the idea that you go from a table which is finite and when you're dealing with batch to an infinite or never-ending table, would it be fair to say that when you're dealing with streaming, you're also pretty much giving up the notion that you're going to get a definitive answer because you might have stragglers in terms of items coming in and you can't wait infinitely. Sure. And one of the things that's sort of a feature of big data and fast data is that you don't get a definitive answer, but you get the best effort answer or a probabilistic answer. Is that a trade-off you made to make data frames and structured streaming accessible to people who just understood batch? Yeah, so we spent a lot of time thinking about the model. Traditional streaming systems often make you actually think about streaming, think about late data, think how you're going to adjust things as data arrives out of sequence. With the data frame API, we actually wanted, in structured streaming, we wanted to take a slightly different tact. We came up with this notion of triggers and I think the right way to think about it is you have an ever-growing table as data arrives. You execute a query at certain trigger intervals and we give you the best answer as of the time that the trigger fires. But the nice thing about this is then the trigger fires again in five seconds and now you have a new up-to-date answer that is logically consistent. It doesn't backtrack from things that you saw before. It may continually update if more data has arrived since the last trigger interval, but you can still think about it as a batch query that ran at that time on some set of data. Okay, and now that we have the data frame as a higher level API, the whole issue of micro batches versus per event processing, that is now under the covers. Absolutely. And sometime in the future you might have a lightweight per event processing stream, structured stream processor that's out at a gateway near IOT devices. Yep, so this was another pain point that we heard from users of D-streams. In particular, the correctness of your answer actually could depend on the micro batching interval that you chose. This was like a key part of the user-facing API. If you look at data frames, since it is the same API for batch and streaming, there is no notion of exactly how the computation is happening under the covers. You just expressed the computation we want and we figure out how to execute it incrementally. As a result, it's very easy for us in the future to change out exactly how the execution is done under the covers without you having to rewrite your application. Okay, so that sounds important and it sounds like the trigger corresponds to the window? Yeah, see this is actually kind of an important thing. The triggers in the windows have nothing to do with each other. Windows are based on event time. It is the time that is stated inside of the stream of data based on the semantic of your application. Triggers are just the intervals at which we're going to produce answers. You can change the trigger interval without changing the results of the query. It's just going to change when you see the results of the query. Okay, okay, that sounds... All right, I think I understand. So how will structured streams roll out within the product over time to affect all the different APIs or integrate with the different APIs? Yeah, that's a good question. So I think in 2.0, our primary focus was putting a lot of thought into this model and the API that we were presenting to users. We wanted to make sure that it was going to be a solid foundation to build on so that when people started building applications, they wouldn't have to rewrite them with each new release of Spark. Moving forward, I think you're going to see a lot of progress in a couple of different areas. One is integrating with different sources. So right now we can read files from HDFS, but we want tight integration with S3 and Kinesis and Kafka and many of these other kinds of things in the streaming ecosystem. Another thing that we're working on is integration with other parts of Spark, in particular, machine learning. So Spark MLlib is great if you have a batch of data and you want to train a model on that batch of data, but usually you actually want to incrementally improve the model as new data arrives. I'm actually working with an intern here at Databricks this summer on building model updating into the structured streaming framework. Okay, and so I assume that's the sort of online learning. Would there be a corresponding sort of persistence and then instantiation for online predicting in a like a parallel stream? Absolutely, yeah. I mean, once you've trained the model, now you actually want to be able to use it to do things. And we're looking at both kinds of types of model serving as well as being able to do this learning or this scoring directly on streams of data. Which would be when you take the model that you've updated and you put it into production on potentially the same stream. But there's got to be some work in terms of, I assume if you're updating the model you might even add new features but unsupervised potentially.  Yeah, I mean that's, we're just scratching the surface here. Okay. I think there's a lot of interesting questions to answer. Right now what we're looking at is just kind of how do you even do this and how do you express it in terms of the APIs that we have just to get a kind of continually updated model. Using multiple models doing ensembles, that's all kind of open questions. Okay. What about, I imagine that streams, potentially high volume streams with online learning can be very computationally expensive. Can that work in a clustered environment if the underlying algorithms are scale out? Absolutely. And this is why we're working closely with the ML Lib team who's experts at building these scale out models so that any of the training we're doing can be done on a distributed cluster and scale up as the volume of traffic coming in goes up. Okay. With that, Michael, let's break and return to database scenarios. This is George Gilbert. We're at Databricks with Michael Armbrust and we're on the ground at Spark Central. We'll be back.


Not anymore. So let's do this one, PCL5. So we're trying to do this hybridization thing here. So how do we justify? Because we would expect, of course, hopefully everybody thinks phosphorus is in the middle of this molecule. All the chlorines are on the periphery. And phosphorus is rehybridizing, becoming hypervalent. Is everybody OK with that thought process? So how does that happen? So well, we got to think of the valence electrons and phosphorus and where they're located. So let's go ahead and write those valence electrons. So 3s. And remember, we skip that 4s. I can go to the 3d's. We're filling. Boss, is everybody OK with what we've done so far? Why is that 3d there? Like what we said, right? We're skipping the 4s. Why? Because 3d is actually lower in energy when we think about it. Remember, we were talking about that. Those d orbitals, when they get filled, they can come condensed and things like that. So go back and watch those lecture videos if you have any trouble. OK, so how do we fill this up with the electrons or when we hybridize? We're hybridizing with those d orbitals. So of course, if we looked here, right, phosphorus should only be able to make three bonds. We know it makes five. We look here also. We know that those bonds, if we saw them, should be 90 degrees apart, right? Because the d orbitals, I guess this is something we didn't talk about in the last one, are on these three axes, x, y, and z axes. So those axes are 90 degrees apart from each other. And if you look, it's not the case with all of those bonds. In fact, PCL 5, we should be able to predict that it would be trigonal by a parameter of five bonds. So it should look like this. I guess you've got a 90 degree angle between those. But here, you've got a 120 degree angle. And here, you've got a 180 degree angle. Is everybody OK with that? So if you want to, you can pass this thing. Here, we can do this for you guys. So of course, those bond angles are not 90 degrees, or at least not all of them. So we need to be able to make five bonds. OK? How do we do that? Well, we need five orbitals to do that, and five electrons, which we've got five electrons. And we've got more than five orbitals. So the ones that we're going to use are the ones that are lowest in energy first, of course. So all of the s's, all one of the s's, all three of the p's. And that would give us four orbitals. So we need still one more. So we've got to go snag one of those d's. So if we're using one s, three p's, and one d, what is the hybridized orbital stuff? s, p, three d. And we put how many into the blender? Five. So let's write a hybridized theorem. So we're going to get how many out? Five, four, five. And they're all going to be equivalent in energy. And we're still going to have our four 3-d orbitals up there, OK? Now we're going to hybridized orbitals, our s, p, 3-d orbitals. And hopefully, now we realize that we can make five bonds. And again, I guess we use chlorine again. So remember, chlorine, it's got its half-filled orbital. So it's just going to use that d to stick on, OK? So we can, we'll just look at one of these bonds. So when we do that, we're going to have that s, p, 3-d bond, our orbital. And it's going to interact with the 3-p orbital of the orbital of the bond. Is everybody OK with that? Does that answer most of your questions then? OK, so when we see this, how many bonds are we expecting to have? Five, right? So phosphorus can make five bonds. So I'm going to erase this part, and we're going to kind of do Vesper theory on it, too. Is that OK? We've got it up here, like building the thing. We can justify that structure that I showed you. So in fact, the electronic structure is the same as I3 minus, if you recall. That was like an example we did, I think, two examples of them. So phosphorus, that's going to give us the Lewis structure. Phosphorus can do this, because it's period 3 or below. If we want to draw this in, you know, I'll get out of the way. If we want to draw this in, can I erase this, OK? I guess I expect you to be a speedrider, so. OK, so when we're going to draw something like this, in perspective, right? Equatorial coreings and axial coreings, remember we talked about this. What's the electronic structure? The cellular. Whatever the cellular structure is. Trigonal bioparamable, right? And I know I already said this a million times. But what's the molecular challenge? Trigonal bioparamable. How did you know it was trigonal bioparamable? The electronic geometry that is? There's no lone hair. I have electronic. Because there's five electron groups. Five electron groups. Everybody OK with that? If there's six, that's when it's octahedral. Then it's octahedral, yeah. We can do an octahedral one eventually. I'd like to get back and do a smaller one. Any other questions on this one?


proudly we hail from Hollywood this is CP McGregor speaking and welcoming you to another broadcast of proudly we hail a program of your ward apartment through the courtesy of the Hollywood Coordinating Committee to present Harold Gildersley Perry as the star of our show titled Lily packs a wallop written by Rich Hall with music by Eddie Scrivenic. Chester Lelywhite who live very quietly with his elderly housekeeper Mrs. Edwards wasn't the kind of a fellow you'd want to play tackle on your football team. Chester had two obsessions his flowers and his next door neighbor Mr. Dunkel both he'd like to see planted underground Mr. Dunkel six feet under Mr. Dunkel shared billing with the ham and eggs in the conversation that morning at breakfast. Mrs. Edwards these muffins are delicious this morning. Well now I'm glad you like them. You feel good don't you Mr. Lelywhite. Good perfect. Why I'm as chipper as a J bird. You always feel good when Mr. Dunkel goes away in his vacation. Oh please Mrs. Edwards don't mention Dunkel. I only think of him when I buy my insect spray. Oh it's true though. That man is so edgy and and so muscular and I had to plant my renunculus right by his fence and renunculus requires so much water. You'd better warn the new gardener not to get water on Mr. Dunkel's fence. Yes the new gardener. Don't you remember the employment agency is sending a gardener out today. Oh yes so they are so they are. Well we won't have to worry about Dunkel's fence not for quite a while. Since Dunkel's been gone I've watered those renunculus to my heart's content. Why his old fence is dripping with water at this very moment. I don't see why Mr. Dunkel has to make so much trouble but I do think you should stand up to him more. Mrs. Edwards you're absolutely right and when he comes back I'm going to be very firm with him. Well you always say that when he's on his vacation. This time I've made up my mind. But hasn't it been wonderful having Dunkel away. Hasn't it been wonderful. That's me. Oh dear he's back. Oh dear the water all over his fence. Excuse me Mrs. Edwards. Definitely. Nice to have you back Mr. Dunkel. Well Mr. Dunkel did you enjoy your vacation. I've got a good notion to push your nose right back between your ears now now Mr. Dunkel that's not resort to violence I pay a hundred and eighteen dollars and sixty cents to get this fence painted I go away on my vacation I come back and what do I find yes what does he find spots spots all over spots now Mr. Dunkel remember I have to water my renunculus you do now listen Lily White is crumpling my tie Mr. Dunkel I'll crumple something else next time if you have to water your renunculus water them use anything use an eyedropper but don't spot her my fence yes Mr. Dunkel and what's more you better get a nice new scrub brush scrub brush yes Mr. Dunkel and clean every one of those spots off my fence spot yes Mr. Dunkel and if this ever happens again it won't Mr. Dunkel no and now me I have my neck tie back again and my neck thank you it's certainly nice to have you back home again well Mr. Lily White were you firm with Mr. Dunkel did you stand up for your rights oh yes Mrs. Edwards I most certainly did by the way do we have a nice new scrub brush which you mind getting it out he's making you clean his fence again making me not at all I simply offered to do it as a gesture of good neighbor policy all right but I wish I were ten years younger I'd take care of Mr. Dunkel I've hit me husband rest his soul for less oh the new gardener is here in the living room in the living room oh dear oh thank you that's excellent I'll go talk to him oh dear such a day well my good man greetings mr. Lily White moran's the name butch Moran oh I'm glad to know you Mr. Moran likewise I'm sure the employment agency sent you out that's right and I heard every word at the employment agency of the slight altercation you had with the guy next door oh that was nothing I'm disappointed mr. Lily White they're sub-pointed you are sure you should have popped him right on his quister oh please mr. Moran please one must never resort to such mundane things as physical combat one mustn't one must not but it's so gratifying to bounce one's fist off the chin of a cat or a bully definitely mr. Moran you're so right if I only could but he's so large he played fullback on his college team the bigger they come mr. Lily White the harder they fall that's what I keep thinking every time I look at those hams that dunkel has fists well pardon me for praying into your personal affairs quite all right mr. Moran quite all right now you're here to apply for the job of Gardner have you had any experience well mr. Lily White I am taken up horticulture in night school I have already learned the life cycles of the A.S. that the hydrangea the hollyhock and the marigold my next lesson is on the petunia oh that's excellent I love petunia but now what about your practical experience with flowers well I personally placed a reed the 12 dozen roses under pyramid good friend lippy McGrew when he was bumped off oh no mr. Moran what I mean is what practical experience have you had with gardening you got me mr. Lily White I was born and raised in Brooklyn but we didn't even have a tree oh I'm sorry mr. Moran I have quite a large garden here I need a more experienced man of course I could take care of that neighbor yours permanently if you was to hire me the most pleasant thought of the day of any day but no mr. Moran the dispute is between dunkel and myself well why not let me coach you then coach me sure get you in trim give you a little footwork a little of that one-two stuff why you will be spitting right in his eye how dreadful I will sure I fought some of the best boys in me class didn't you ever read the police cassette oh no I can't stand pink paper but you've given me a thought how long would it take you mr. Moran to prepare me shall we say for combat well now you're putting me on the spot look at what I got to work with but I know I can do it mr. Lily White I know it very good mr. Moran you're hired and she thinks say I'm just like Lutoboy bank developed in a grapefruit and I'm starting with a lemon too we pause briefly from our story the Lily packs a wallop starring Harold Gildersley Perry to bring you an important message from your war department 30 years ago very few men could afford college training today all young men can obtain college education at government expense by joining the regular army the educational provisions of the GI Bill of Rights still apply any man who serves 90 days in the new regular army at least one day of which is previous to the official termination of the war is eligible for one year free schooling in addition he is entitled to 30 days education for every month served before the termination up to a maximum of 48 months of schooling here is an opportunity for the young man of today that his father never had while accruing his education credits the regular army soldier is working at a responsible high-paid job learning a trade or scientific skill he can also be preparing for a higher education by taking off-duty or correspondence courses of his choice ask today at your nearest army recruiting station about the educational opportunities in the new regular army you'll be glad to explain it no obligation to you act two of the Lily packs a wallop starring Harold Gildersley Perry is Chester Lily white mr. Dunkels fence has been scrubbed clean but his comeuppance is in the making Chester is proving a willing student butch Moran is teaching him all he knows a basement room has been converted into a gymnasium and Chester decked out in long underwear is at the moment getting his daily instruction well butch am I progressing oh immensely mr. Lily white you know when I first seen you in them long John's I thought to myself here is the before of the before but now you see your muscles are hardening up you're getting to look almost like the after don't go better order a large bottle of liniment well I won't damage him too much a couple of gallons ought to bring him around now let's get back to where we was at the one to hit now come on try it on me okay one to that's right one to that's it now you're getting some sock into it now watch out I'm coming back at you oh darn it why did I do that oh dear butch Moran what have you done to mr.   Lily white there now blow I don't have a handkerchief it not your nose I mean blow and scramb isn't that right butch all right mr. Lily white you don't have to resort to violence and don't you give me any more trouble or I'll push your nose right back between your ears kind of polished him or for a right mr. Lily white didn't I though he was a cinch sucker for a left but you don't seem very happy about this butch happy I'm miserable but my good man why look at what you went and done you trampled all my beautiful run non-killers this is CP McGregor speaking I hope you've enjoyed our proudly we hailed story starring Harold Gilda sleep Perry before leaving you don Forbes has an important message for all of us is the cold getting you down these days if it is be thankful you're not with Task Force will a war now maneuvering in the frigid climate of Alaska Task Force will a war the United States Army ground forces experimental unit is based at a DAC Alaska an average daily temperature of 40 degrees below zero is not unusual here studies are being made to learn which types of food are most suitable for top of the world conditions army scientists with this group are measuring the daily number of calories required for heavy exertion maintenance of body heat clothing footwear gloves tentage and cooking utensils are being tested to provide maximum utility against the wintry blasts as a result of these army experiments living for everyone during the cold winter months is expected to be made immeasurably more comfortable testing under severe Arctic conditions is just one phase of the new regular army program of research and development in the fields of atomics electronics aviation engineering and many more army technicians are daily making new experiments and discoveries in medicine to army developed devices were used last summer during an infantile paralysis epidemic one was a breathing device which kept the patient alive despite the closing of his larynx the other was the oxymeter by means of which the doctor can determine the length of periods which the patient can be taken from the respirator and when he can be removed permanently yes your new regular army is a scientific organization in the months to come many men will have earned the right to discharge high caliber replacement the rate of 40,000 a month must be found for them men who are capable of absorbing the technical training necessary to carry on the scientific work of the army here is an opportunity for a worthwhile career in an organization which serves mankind in war and in peace any ambitious young man between 18 and 34 17 with parents consent who can qualify is eligible ask at your nearest army recruiting station today about a career and our new technically trained regular army thank you Harold Gildesley Perry for a wonderful performance proudly we hail will come to you again next week listen in