Hey guys, I'm Chad from WIRE and we just released High Definition Group Calling. I want to show you how it works. So you go and you create a conversation with any of your friends up to five at once and once you enter the conversation all you have to do is call. Give it a shot, it's really great, plug in some headphones and it'll sound like everyone is sitting around you with our stereo feature.


A central Minnesota man is in custody after a two hour standoff Thursday evening in Crowing County. The incident happened on the 5400 block of County Road 145 where a male had barricaded himself in his car with a rifle when an officer from the Pequot Lakes Police Department was attempting to conduct a criminal investigation on behalf of the Baxter Police Department. The officer attempted to negotiate with the individual before seeing the rifle in the vehicle. The officer immediately backed away and a perimeter was set up. Law enforcement also evacuated a neighboring home. Crisis negotiators established phone contact with the male and he eventually agreed to exit the vehicle and surrender shortly before 8 o'clock last night. Members of the tactical response team took the man into custody without further incident. The individual was transported to Ascension Health in Brainerd. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


Hello. I'm June from the integration team. Yeah. So in this past three weeks, we're basically very busy making sure that reviewing all the issues that needed to be fixed for the solar point to release. So in this past three weeks, we've landed 123 issues and 11 rejected 11 from these integrated issues. And of this issues came from CRR, our component with review. And then this Monday, we had minor releases for model 3.9 up to 3.4.2. And of course, we have our major release for model 4.3. And from a quick number of quick stats, we landed 847 issues in total. And 220 of these came from the community. And 264 of these are new features and improvements. And of course, we have like this course like from 106 bugs. Yes. And next slide, I'll be presenting on behalf of CME. Of course, during this past four weeks, we also had a very busy QA cycle, and where we had 491 tests completed. And yeah, of course, 34 of these testers, 34 testers performed the UA help participate on the QA's test. And 400, I think 414 of these issues were tested by the community, which is like amounts to about 84% of our of the numbers. So it's a really great effort from the community. So yeah, so here's takes to thanks to everyone who participated. So in terms of preparation for fall three of grades of the community sites, thanks Mary for the updates. So we have a full three new features page, this tiny portion here in the screenshot because it's a very long page, as we know, a lot of brilliant stuff in four three. So all except four new features have now been documented and the labels sorry, doc requires labels have been removed from the trackers. And the next four need developer input for clarification on the comments have been left on those track ratios. And as I've just mentioned, in earlier, the school demo and some boxes have been upgraded to fall three. If you want to have a look at all the features page on the documentation, there is a link here.


Maybe have some gloves on, think about it, let me lift your waist a little bit. They got a weight belt, what is he doing? Waist train has been out, man. I don't know who said, man, maybe we can make something for some bitches. Talk about their waist shapers, they squeezing themselves. They just waking up like, push this side up, push this side up. Roll it. Oh my goodness. Constantly tall. Sit down in their seats, cause it's the 85 South Show and we don't repeat. Nothing on the stage when we have to kick in the floor. You know it's going down so don't you sit on the floor. The 500 people out there standing in line, don't worry about it. We gonna take our time, cause we got five hundred people. Five hundred people who be standing outside so they can come and see the guys who be on 85. We got five hundred people standing outside just so they can see the guys who be on 85. My name is Lose Yellow. How you doing this evening? Know you standing outside, well let me tell you the reason. The show sold out and everybody wanna see the dudes from 83 hold up two plus me. That's 85, brilliant.   But we gonna wait for the people to get in the... And since we got a little time, I tell you, look the show until you get in bro. And I'm gonna say it again, if you brought your girlfriend in there, so we ain't got no time. These niggas know I'm off with it. Cause you know we bout to go all the way off.   Know that that is Jokes Hill's laugh. You watch the show, you can get him bro. Be like, ah! I mean the jokes won't... They just in there and they heard nothing. You sell it in the rock. Is that work with right man? No.    Let's get back to the show we started. But he don't know the people outside and they ain't parted. It's somebody.


If you want a one-stop shop for all things TESOL related, you should head over to the ITTT blog where you will find a vast amount of interesting information. The blog contains all manner of fascinating posts from professional TESOL experts and our very own course graduates. You will find helpful tips and visas and work permits, salaries and budgets, and general TESOL course information. There is also a section on teaching locations that looks at many of the most popular destinations from a wide variety of angles, as well as some great teaching ideas that you can use to transform your own classroom into a modern, fun and productive environment. A large part of the blog is given over to our TESOL course graduates who take the time to write about their experiences of teaching English abroad, both in and out of the classroom. If you are serious about heading overseas to teach, the blog can offer plenty of advice, tips and insight information that could make your journey a much smoother process and point you towards information you didn't even know that you needed. As the blog is regularly updated with fresh and insightful content, make sure you head there often to keep abreast of the latest insights into the current state of English language teaching worldwide. You might even want to add your own story to help inspire other potential teachers to take the leap.


Hello, everyone. Happy Monday. Welcome to Dragnet Live from otrgold.com. I'm your host, Austin Vaughn. I'm glad to be with you today.  Let me highlight this one on screen. It's funny. In true dragnet fashion, he starts with the weather report 11 of one Monday. It was warm in Alabama. Anyway, that one made me laugh. Thomas, you're correct. I was having technical difficulties. I was actually a little late to start the stream because I was finishing my taxes. It is the last day to file your taxes in the United States. It's my least favorite day of the year, but listening to some dragnet softens that a little bit. I think today is going to be good. Taxes are done and now I'm listening to dragnet and everything's going to be better from here on out. Great. As you can see, we have comments going down the side. If you want to participate in the conversation, you can head over to youtube.com forward slash OTR gold. This is broadcasting to two different channels that I manage. You need to be on OTR gold, subscribe to OTR gold to see and then I'll be highlighting relevant comments throughout the stream up here on screen. Everything else. Hang on a second. Look at this. This is unplanned, but I was going to pitch this Bluetooth speaker, but look, it actually receives calls. Let me ignore this one. I don't know. Probably spent a scammer, but that's a good segue. These are vintage style Bluetooth speakers. They can play music. They obviously connect to your phone, which I didn't know about, but if you want one of these two speakers, go over and subscribe to OTR gold. Excuse me, the YouTube channel OTR gold. Once we hit 500 subscribers, I'll pick one at random and you can choose which of these two that you want. And then when we hit a thousand subscribers, I'll give away the other one.  So let me turn those off real quick.  Anyway, they're cool. I didn't tell you this, this little dial right here is actually the volume. So it's kind of cool. Um, anyway, yeah, so go, go do that. Today we're going to be listening to episode 11 of dragnet. It's called master jewel thieves. The original title that I had up on the live stream said 16 jewel thieves, but then doing a little bit of prep work for this stream. Um, it was the, according to Wikipedia, which is the most reliable source I have, the title was either 16 days of jewel thefts or master jewel thief. Sometimes, um, as episodes of rebroadcasts are given different names or as they, as they appear in different marketing material, I guess from the period they had different titles. So the one I'm going with today after listening to the story is master jewel thief. It originally aired on August 18th, 1949. And the two little bits of history that I found that I can share with you now are, um, in that week in history, um, August 15th, it's a couple of days before this episode aired WOTV in Grand Rapids, Michigan began broadcasting. That was an NBC station. And on August 18th, the same day this episode aired originally, Hungary adopted their constitution. So I'll do a bit more research as we're listening and I'll share any other relevant tidbits, um, on screen. Um, I think that covers it all. We talked about the giveaway. We talked, oh, if this is your first stream, we're here every Monday, Wednesday, Friday listening to dragnet in every episode currently available. I have access to 310 of them. And we're just on the 11th episode today, but we're going to go all the way through. So if you like dragnet and you like old time radio, subscribe, join us, uh, Monday, Wednesday, Friday, at 9am Pacific. We have listeners from all over. Um, I asked that if this is your first time, tell us we're listening from, I'll go through and highlight. It looks like those that have been here before know the drill. They're already telling us where, and I'll highlight those as we get started. But, um, that's enough talking for now. Let's get to the story. Thanks all for being here and enjoy Master Jewel Thief. NBC brings you dragnet.  You're assigned to burglary detail. A sudden wave of jewel thefts is sweeping the city. In 16 days, 16 burglaries have been committed, one each night. They bear the same trademark. Thousands of dollars of jewels are missing. The thief is a master at his trade. Your job. Get him. Dragnet, the documented drama of an actual crime, investigated and solved by the men who unrelentingly stand watch on the security of your home, your family, and your life. For the next 30 minutes in cooperation with the Los Angeles police department, you will travel step by step on the side of the law through an actual case transcribed from official police files. From beginning to end, from crime to punishment, dragnet is the story of your police force in action. It was Tuesday, June 17th. It was warm in Los Angeles. We were working the day watch out of burglary. My partner's Ben Romero. The boss is Ed Backstrand, chief of detectives. My name's Friday. I was on the way into work that morning and it was 7.53 a.m. when I got to room 45, burglary detail. Hi, Joe. How are you, Walker? Gonna be a scorcher out today. Yeah, just like yesterday. Been in yet? I think he's over in communications picking up the mail. Thanks. You guys been busy? Kind of. Jewel thefts. Anything big? I don't know big halls, but he's consistent. Sixteen nights in a row. Same guy? Think so. Same MO. Everybody's got troubles. Gotta check some records. See you later, Joe. Okay, Willie. Burglary, Friday. Yeah. Okay, Mike.  Right. Bye. All right, Joe. Hi, Ben. Hannon just called. Chief wants to see us. Take a look at these first. What do you got overnight reports? Yeah, I have these two. Two of them. Three diamond rings, one sapphire, one necklace, jade, big hall. Look at the other one. Ladies, watch. Diamond band, emerald bracelet, tourmaline brooch. What's tourmaline, Ben? I don't know. Must be valuable. It's gone. Let's see. Owner left house about 9 p.m. Returned about 1.30 a.m. Found property gone. Scratches on the door. Probably using a cellophane method. Hasn't missed yet. Two in one night. Now he's picking up his pace.  You get the description sheet from bone job detail. Yeah, got it right here. You take half of them. Let's see what luck we got this morning. Nothing so far. We need it. I'll get it, Joe. Berger, emerald. Hi, Ben. Chief still wants to talk to you, boys. He's got an appointment at 8.30. He wants to see you before he leaves. Okay, Mike. Just checking some by sheets. We'll be right in. Better make it fast. He's in a bad mood this morning. Okay, Mike. Thank you. Backstrand again? Yeah, he's in a bad mood. Come on. Wonder what's bothering him. Something's bad. He doesn't blow very often. Chief of Detectives Office, Hanon. Go ahead in, boys. He's waiting. Thank you, Mike. All right, ma'am. I'll connect you. Friday, Romero, sit down. Wait till I get the phone. Backstrand. Oh, yes, Mrs. Winthrop. Yes, ma'am. We're doing all we can. Yes, ma'am. Yes, ma'am. Yes, I'll do that. Yes, ma'am. Goodbye. Got trouble, Ed? Taxpayer. Mrs. Winthrop. You two ought to remember the name. We do, Skipper. 10 days ago, somebody lifted $2,000 worth of diamonds from her bedroom while she was at the symphony. Last night, she was hit again. A diamond watch, an emerald bracelet, and some kind of a brooch. Turmaline. I don't care what kind of was, it's gone. What's the pitch? We just read and reported a couple of minutes ago, Skipper. Could be a time when those other 16 jobs. 16 jobs in 16 days. You haven't got a lead on the thief yet? Nothing shows up. No prints, nothing. What about the pawnshops? Nobody's tried to soak any hot stuff as far as we know. We double-check the detail. We've got every hot shop in town on the alert. Whoever it is, they've got to try to pawn the stuff sometime, unless they're going to give diamond rings for Christmas presents. They haven't tried to pawn shops yet, Skipper. We sure they. Look, 16 burglaries in 16 days. Jewels and watches. Good ones. It's got to stop.  We'll stay right on top of it, Ed. We're doing all we can. For two weeks, I've had half a dozen women calling me every day. Society women. Some of them figure they should get extra treatment. They're only DR numbers to us, Skipper. They all get the same attention. I'm trying to explain that to some of them. They think you're in on the racket. Maybe you boys would like to take these calls some morning. No thanks, Ed. All right, then let's get some action. Keep the pawn shop operators on their toes and get after every known fence in town. That's all. I've got an appointment. All right, Ed. Check you later. Holding a call for you, Ben. Oh, thank you. Now, well, hi, Max. What? Well, hold it. Be right down. First break, Joe. What do you got? Necklace and a watch. Both have them on the stolen property list. Where? Henry's pawn shop, 5th and main. Six minutes later at 8.25 a.m., Ben and I drove up in front of Henry's square deal pawn shop. Quick cash, no red tape, watches bought and sold. The proprietor was Max Murphy, an old friend of Ben's. Well, pal, of all days, it had to happen yesterday. Took the day off and went fishing up at Big Bear. I left my nephew in charge. Harry, a real nuthead, that kid. How do you mean, Max? Joe, if I told him once, I told him a hundred and once, whatever you do, whatever they come in with to hock, check it with the list. Check it with the stolen property list. What does he do? He forgot. He forgot. Oh, a real nuthead, that boy. How old is he, Max? Thirty-two. A real nuthead. I checked the slips from yesterday. Then I checked the stolen property list. There it is. Hot stuff. When did the stuff come in, Max, do you know? About four o'clock yesterday afternoon. Can we look at it? Oh, sure. Back here behind the car. There it is. Did you check out the serial numbers on the watch yet, man? When I found out, yes. They matched to a tee. All right, let's see. Yeah, description on this necklace matches too. Let's have a look at your buy book, huh, Max? Yes, sir, Joe. Here you are. There's a deal right there. Here? Yeah, that's him. That's how he gave his name. Walter Tracy, one thirty-two and a half Blackstone Court, Los Angeles. Let me check the book for the description, man. Oh, sure. Yeah, here. Okay, Max, thanks. We'll be checking with you later. Sure, Joe. Anytime. Sorry. All right, Max. See you later. Yeah, you fellas, take it easy. Right. I want to check and see if we're clear, Joe. Yeah, I will. 80K to control one. 80K to control one. Are we clear? Control one to 80K, stand by. Good lead, Joe. Got a description in here. Well, it's too bad Max's nephew to slip up. Control one to 80K. Call your office immediately. Call your office immediately. Wonder what that's about. No, no. Call in. You got some change? I'll use Max's phone. Use your phone a minute, Max. Oh, you bet. Sure, help yourself. Thank you. City Hall. Two five, two four. Two five, two four. This is Ben, George. You got something? Oddwin, universal loan shop, sixth and bought in place. Guy just took in a couple of rings. He checked too late. What you mean? He checked the form after the guy left. It was signed, Walter Tracy. There they are, Sergeant. Both rings. Fine quality diamonds. Don't you usually check your stolen goods list before you take in stuff like this? Usually, yes. Last night? No. I don't know what I was thinking about. We have a look at your buy book. Right here. There it is. Walter Tracy. $699 street. 145 pounds, five foot nine, dark hair, billed, thin. We'll have to slap a hold on these rings. I know. I should have thought. Can you think of anything else that might help us to identify the man? No. Had a light suit on. Nice cut. Very well dressed. Thank you. That's all for now. Here's a card. If the guy happens to drop back, give us a call, will you? Sure will, Sergeant. Say, I've got some nice watch pens. Yours look gold. Can I interest you? No, thanks. Some other time. Come on, Ben. That afternoon in the following morning, despite our alert and our warnings, two more pawn shops called in with reports of stolen watches taken in. We checked them out. The serial numbers on the watches matched those on the stolen property list. On the pawn shop account books, the loan was listed under the name Walter Tracy. The addresses were given as number 12, St. Vincent Place, and 700 East Flower. The descriptions of the man were the same. Slight build, well dressed, about 145 pounds, five feet nine inches tall, dark wavy hair. We had the name and description distributed to every pawn shop in Los Angeles and surrounding communities. Through our informants, we checked up on every known fence in the city. For the next two nights, we received no reports of stolen jewels. That made up for the double burglary the night before. On June 19th, the box score read 18 successive nights, 18 successive jewel burglaries. At 3.25 in the afternoon, Ben and I sat down to check over the late incoming reports. Got anything, Joe? Oh, not yet. No. Nothing here. Maybe the guy's left town. Nope. No such luck. Take a look. That's it, number 19. He may set a record.  Look, man's watch, lady's watch, Chinese amber necklace, diamond shirt, studs, and a bracelet with two large rubies. He's getting ambitious. How's the value listing? That's $1,800. One haul. I'll get it. Burglary Friday, yeah. What? Yeah. Be right down. Stalem. Let's go, Ben. Where? Kaplan's down in East Second. Walter Tracy's in there now, trying to hawk a gold watch. Ben, cover the door. I'll give it a look like I'm shopping around. Right, but watch your step. We don't know this guy. Yeah, stay close to the door, huh? Yeah, that's the best we can do on the watch. Look, Mac, this is gold, 21 jewels. Well, that's the best I can do. Ah, drop dead. What's the best I can do? Don't get sore. Yeah, sure. See you later. That's him, Sergeant. Walter Tracy. I stole them as long as I could. All right, I'll check back with you later. Did you spot the guy that just came out? Yeah, I went up the street. Let's follow him. Hustle him. Spot him, Ben. Straight ahead, about 15 yards. He's crossing the street. Yeah, let's get up a little closer. We'll lose him, sure, if the light changes. Come on, run for it. What's the traffic like? That was close. You might have spotted us as going faster. Come on, Joe, run. Yeah. Don't lose him. This crowd's not helping. Hey, hey, wait a minute. I thought you were a cop. You're chasing somebody. All right, let go of my arm, mister. Let go. Well, you don't have to get tough. Lazy cop, stick to your own retreat. I'm going to write the mail soft. Come on, Joe. He's running for it. Yeah, I see him. Watch the signal up ahead. Hurry, Joe. Almost up. Come on. Into the parking lot. Hey, you, stop. Look out, Joe. A gun. Get away. Get away. All right, spot got. Nice job. Yeah, he's too fast for an honest man. Let's take him in. When we got back to headquarters, Walter Tracey was under technical arrest. We took him directly to the interrogation room. We searched him thoroughly. We had him take everything out of his pockets and put it on the table. Then we had him take all the money he had in his wallet, count it out, and hold it in his hand. What is all this routine? That's all the money you have on you? $47.17, right? Yeah. Okay, keep it in your hand. Ben, shake him down. All right, Tracey.  What kind of a picture is this? I'm no hood. Take them all. Two big cops. You're not pinning anything on me. I don't care what you do. Sleeves, pockets, lining. Nothing in the coat, Joe. Get his shirt. Take a light with the threads, huh? Cost money. How about the trousers, Ben? I'm seeing. Curves, pockets. No. Let me get the belt. Zipper on the inside of the belt. No, it's clean. Shoes are okay. All right, Tracey. Let's see the soles of your feet. I hope you don't mind, uh, they're dirty. Why don't you take a shower? Let's see. All right, Joe, nothing. Put your toes back on. Yeah, thanks. All right, Joe. What's your name? Huh? I said, what's your name? You telling jokes? Walter Tracy, you know that. Your real name. How old are you, Tracy? 27. Where do you live? No place. Just got in town a couple of days ago. Where are you from? Salina, Kansas. Where have you been sleeping the last two nights? The Park, Pershing Square. Clothes don't show it. Pretty nanny. I had impressed. Where? Down by the square. I don't remember. You ever been arrested before? No. Where'd you get this gun, Tracy, the one you pulled on us? I didn't know who you were. Could have been a couple of hoods. You kind of look like it. Where'd you get the gun?  Where'd you get the watch? Graduation present. You want to run a make on them, Joe? The gun and the watch? Yeah, I'll call them. Go on, check. You can't prove a thing. Pawn shop records, Gilmore. Gil is Friday. Can you give me a make on a watch? Sure, Joe, go ahead. Time master, yellow gold, man's wrist watch. Okay. Case number, 716-F23. Right. Movement number, B351-708. Okay. Now give me a make on this gun, huh? 32 S&W automatic, serial number, 579-461. Okay. Call me back. Right. What's your station number? 2572. I'll ring you, Joe. Thanks. Having fun?  When do I get out of here? I don't think you're going to get out. You got nothing on me. How tall are you, Tracy? Get your tape measure, 59. How much you weigh? 140. I'm 27. My name's Walter Tracy. I come from Salina. I've been in town two days, and I don't know what you guys are talking about. You sound smart. You don't act it.  I'm Brian Copper. What'd you do with those jewels you stole? I don't know what you're talking about. What color are your eyes? I don't know. I'm colorblind. What color would you say your hair is? You colorblind, too? You ever been arrested before? Straighten out. He asked me that. I'm asking you. No. You ever done any big time? No. All right. I don't care if you're level with us or not. We're going to make you on those proud jobs, all 19 of them. Sure, sure. You guys are smart. You got in Los Angeles two days ago. Is that right? Yeah. You don't know anything about any jewel thefts. That's what I said. And how come your name and your hand writing's on the account books and four pawn shops in Los Angeles? Not mine. You can't prove it. We can, Tracy. Come clean. What'd you do with the stuff you stole from 1250 Maraga Drive, June 5th? I didn't steal any stuff. What'd you do with the rings and watches you took from 1400 Placerville Road, June 9th? I wasn't in town. What'd you do with the diamond dress pins you stole June 13th, 123 South Van S? Did I do that?  You prove it. Order, get you a saw, buck. Your prints bounced, Tracy. Our handwriting, man, is going to work on those signatures of yours. You haven't got a chance. Now, come on. Where'd you hide this stuff? You can't prove a thing. Where'd you say you been sleeping the last two nights? In the park, Pershing Square. You want a map? Close, sure, doesn't that? I said I had them pressed. But you can't remember where. No, I can't remember where. Is that a crime? Friday time. Joe, this is Gilmore. Here's the stuff you asked for. Let's have it, Gil. No make on the watch. No make on the gun. Okay, Gil, thanks a lot. Yeah.  You said that, didn't you? You're going to talk, Tracy. I'm tired. All right, we'll let you sleep on it. Come on, Ben, let's book him. All right. I'll get your jobs, coppers. Sure, come on. We took Walter Tracy to the county jail and had him booked on suspicion of burglary. He was still sullen. We knew we had the guilty man, now we had to prove it. As it often happens, the victims never see the burglar. They only know he's been there. They can't identify him, but they can identify their property. Our job was to find the property. When we did, we'd have Walter Tracy. And the 19 victims would have their property returned. But Tracy wasn't talking. We knew he'd never talk unless he thought it might help him. We took the problem to Ed Backstrand. Smart punk skipper, but he's done time before. How do you know? Tried him out last night when we brought him in. He talks like it and he acts like it. But he won't cop out. Are you sure? He won't talk in a hundred years. He knows he's got us in a spot. And one thing's sure, we're not going to send him up without finding the loot first. He's planted the stuff somewhere in this city. We've got to find it. Ben and I have got an idea, Ed. It's not going to be easy, but it might work. What is it? Tracy tried to soak some of the stolen property at four separate pawn shops in the downtown area. Yeah? At each one of those four pawn shops, he gave a local address. We're sure he must have a room or an apartment someplace in town. All right. Where? That's where a guesswork comes in, Skipper. Every one of those addresses he gave falls within a certain area. Oh, big an area. You've got that street diagram, Joe? Yeah. Here it is, Ed. From Figaroa here to San Pedro. And from Pico down to First Street. The area's about 12 blocks wide, 14 blocks long. That's a lot of territory. How are you going to cover it? On foot, we'll take Tracy with us. Plenty of legwork. You sure it's the answer? We've got to find the stuff. And it's the only way we can figure it. Hotels, apartments, rooming houses. There must be hundreds of places he could stay in that territory. It'll take a couple of weeks. Yeah, on foot it will. All right. It's tough, but it's your idea. Go to it. An hour after we left Chief Backstrand, we got Tracy out of his cell in the county jail and started our canvas of the appointed area. We took the usual precautions and handcuffed Tracy's wrists to our own. We started the search for his hideout at First Street in Figaroa. It was a warm day in Los Angeles. The temperature was 91. After the first three hours, I could tell Ben's feet were ready to give out. And so were mine. We couldn't even have the comfort of complaining that it encouraged Tracy and he was cocky enough already to be cursed and threatened every step of the world. My legs off. All right. Quit pulling, will ya? Come on, Tracy. Up the stairs. Another one to check. Warm day, Jill. Yeah, a little. What do you mean a little? Must be 110. Yes. What is it? You the manager? Yes. Could you tell me what apartment this man has in your house, ma'am? Oh, him? Yes, ma'am, this one. Never saw him before. He don't live here. All right, ma'am. Thank you. Yeah. Hot, ain't it? When are you gonna get wise? Come on, Tracy. Well, that finish is this side of the street. You want to cross over, Jill? Yeah. Let's go. I'm hungry. I want to eat. After we cover the other side of the street. You can't do this to me. I'm gonna get a lawyer. I'll have your jobs, both of you. Yeah, mm-hmm. Come on. We only got a couple of hundred places to go. Hi, Jants. What can I do for you? You the manager? I run the place, yeah. Which room does this man have in your place? Him? You made a mistake. He doesn't live here. All right, thanks. My feet are killing me. Wait till I get a lawyer.  What do you think you're doing anyway, my kind of guy? All that day and the day after that and the day after that, Ben and I, with Tracy handcuffed to our wrist, canvassed the designated areas from hotel to hotel, from rooming house to rooming house, and the apartments, too. Every day, our feet ached a little more. Our pace slowed down. Tracy got more irritable and the weather got hotter. The second day, it reached a high of 92. The third day, 94. The fourth day, 94. Police regulations say plain clothes officers must wear a coat and necktie on the street at all times. We wore our coats and neckties. The search continued into the fifth day. Our pace got even slower. Ben and I started to lose heart. After a while, we forgot our object was to recover the stolen jewels. All we wanted was to find Tracy's hideout. We knew we were right. We knew Tracy was our man. It was a point of pride. Whether your feet hurt or not, you don't give in to a thief. Yes. What do you want? You're the landlady here. I am. Which apartment does this man have in the building? Well, none of them. He's not one of my tenants. Thank you, ma'am. Come on, Tracy. By the sixth day, all three of us had special pads in our shoes. Our feet ached worse than ever. Tracy let us know about his every three minutes. By late afternoon of the sixth day, we'd covered more than half of the designated area. The temperature was 95.  I'm sweating like a horse. I'm getting tired of your moaning. That looks like the manager behind the desk. Yes, sir? You're the manager? Yes, sir. What can I do for you? Can you tell us which room this man has in the hotel? Him? He doesn't live here. Hey, you fellas look awfully warm. Like to cool off in the lobby? We're air conditioned. No thanks. I'm hungry. When do we eat? You're always hungry.  All right. Yeah. I'm hungry. I want to eat now. Wait till I give this story to the papers. Mistreating innocent guys. They'll break. All right. Come on. Up the stairs. I'm gonna get a lawyer tonight. I'll show you. Yes? Why, Mr. Baker, where have you been? We questioned the landlady of Miss Elizabeth Hunter. She told us that Baker alias Tracy had rented an apartment from her about two months before. That's all the information she could give us. Tracy clammed up. He would admit nothing. We asked Miss Hunter to accompany us as a witness. We took the elevator up to Tracy's apartment on the sixth floor. Miss Hunter, Tracy, Ben and I. Down this way. Here. Do you want me to open it? Please, Miss Hunter. There's a gut. Water? What is water? I told you to get out of town if I didn't come back. I didn't want to leave. I didn't want to leave without you. I thought you slept in the park. Ah, take a jump. Where is this stuff hidden? All right, Ben. Handcuff him to the chair. The girl behind him will find the stuff ourselves. All right, Tracy. All right, you're next. I haven't done anything either. See, you can't prove it.  That's better. No talking between you two. First, we'll separate you. All right, Ben. You take the living room here. I'll try the kitchen and get a window open. It's hot in here. All right. Sergeant, you will be careful of the furniture. Yes, ma'am. I had no idea. You, Mr. Baker, of all people. Don't talk to him, please, ma'am. Oh, yeah. Tin cup. Why don't you fail? Ben, look. It's only the beginning. He's got the stuff scattered seven ways for Sunday. We're going to need help. In the milk bottle? Yeah, two rings, three loose diamonds, and this bottle of mayonnaise. We found some kind of a broach in it. A couple of watches taped to the underside of the kitchen sink. All right, you. Convinced? OK, Ben. Call Backstran. There was a definite possibility that Walter Baker, alias Tracy, had stored some of his stolen loot outside his apartment. We stood little chance of ever recovering it unless we got him to break. Ben called Chief Backstran, and in 10 minutes he arrived at the apartment with another man from burglary detail, George Levine. Together we went over the four room apartment foot by foot.  Who's your girlfriend? All right, I'll ask her. What's your name? I said what is your name? Billy. Billy Crawford. He didn't do anything. He didn't. All right, Billy. Maybe you can tell us. Where's the rest of the stuff he stole? He didn't steal. He didn't steal anything.  Keep quiet, Hugh. Ed, wait a minute. What? Just a minute. I want to look over here. Papers. Tape to the underside of that top drawer. What is it, Walter? No, no, you can't. You can't. Look at these. All right, you found them. I'll talk. No, Walter, don't. They're all in papers. Use the next con. Yeah, I'll cop out. Don't do it, Walter.  Dumb dame. The rest of the stuff. Where is it? On the roof. Inside the ventilator, the one near the front, you'll find a couple of paper bags. That's it. I mean, got it, chief. I'll check it. Your papers say you did time in Oregon. What for? Fell for robbery. Did five. I owe him seven. What about the girl? Walter, I'm going with you. Her? I don't know. You figure it. All right, Frandy. Romero, take the girl to Lincoln Heights and Booker. We'll take him. Right, Ed. Come on, Ben. No. No, Walter, I want to go with you. I'm sorry, ma'am. This way out. All right, easy, lady. No, no. Wait a minute. Just a minute. Walter.  So long. Walter. All right, come on. Let's go. What's the matter? You feel all right? You lied. You said you loved me. You lied to me. Don't feel hurt, lady. You lied to everybody. The story you have just heard is true. Only the names were changed to protect the innocent. Walter Baker alias Walter Tracy was tried and convicted on three counts of first degree burglary and received the maximum sentence prescribed by law. He is now serving out his term in the state penitentiary. A hold has been placed on him by the state of Oregon where he will serve out seven years for violation of parole. Billy Crawford Baker's accomplice was tried and convicted of receiving stolen property and is now serving time in the state penitentiary for women. You have just heard the 11th in a new series of authentic cases transcribed from official police files. Technical advice for dragnet is furnished by the Los Angeles Police Department. Tonight's program is dedicated to warden Clarence A. Larkin of Folsom Prison, Sacramento, who on the evening of September 24th, 1937 gave his life so that yours might be more secure. Dragnet came to you from Los Angeles. If you enjoyed tonight's production of dragnet, you'll want to listen this Saturday evening to a pair of adventure shows featuring two well-known Hollywood personalities. You'll enjoy Brian Donlevy, star of dangerous assignments. Also on Saturday schedule is Richard Diamond, private detective, as played by the screen's romantic tough guy Dick Powell. Listen to both of these exciting programs this Saturday over most of the same NBC stations. This is NBC, the national broadcasting company. Hope you enjoyed that episode. I want to highlight something. I want to highlight a comment from Life with Roscoe. Also, if you are watching this from the OTR archive channel, it started with comments. I want all the comments, since we're broadcasting to multiple places, I'm using a system that pulls them all into one location and then I can respond, but then it only sends it back to the original source. If you're seeing responses that don't make sense, it's because you're missing half the conversation. You'll see everything up here on the screen, but I went in and I changed it so that you can watch from OTR, from the old time radio archive channel, but you can't comment. If you're watching over there, thank you for watching. Please also click the link in the video description. It'll take you to the OTR Gold channel. Subscribe to that one, watch the live streams from there, and then you'll be able to comment and see the stuff on the side. It was confusing to me because I could always see everything because I'm using a software that pulls it all in, but then I would look at replays and see broken conversations. Anyway, that's that. Then I'm highlighting this thing on screen because Life with Roscoe asked this last time, but I had just ended the stream, so I saw the question come in that I couldn't respond to it. At the end of these episodes, we hear the three NBC chimes. NBC, so you asked the question, the question is, when did a fourth, when they did a fourth, what did it mean? I'm guessing this is like a trivia question for me. I don't know that they ever did a fourth. So did NBC had a fourth chime that I'm unaware of? If they did, Life with Roscoe, please tell me why, because now I'm curious, and when can I go hear this fourth chime? So I'm going to wait just a second in case you're still on the stream, and you can comment. In the meantime, Joshua must be a musician or something, because half the time your comments are, this would be a great album title, Broken Conversations. We created, at Joshua's request, they created just a simple Google doc where we were chatting about things that they said in the year that made us laugh, and we were capturing some of those, or I put one down there. So if you scroll through the chat, you'll see it. I should probably find a more permanent place for that. Also looking and considering, well, I have this idea for crowdsourced transcripts of these shows, because it's really easy with AI to get audio converted to words, but what the AI can't do super well is say, who is saying this? They might say person one, person two, but is person one Joe Friday, is it Ben Romero? And it would be cool if there was a way, kind of like a Wikipedia page or something, where as we listened to these things, or went back through and listened to them, we could build out a library of basically scripts. If you see a film script, it has character, what they're saying, the transition sounds or anything like that. We can kind of crowdsource awesome old time radio transcripts. So if anybody has an idea on how we could successfully do that, please let me know, either leave a comment, or you can contact me at contactotrgold.com. Okay, let's see here. According to Wikipedia, it began as a seven chime jingle, going down to four, five, four, and then three, to it being difficult to play. You know, three notes, seven notes is too hard. That seems crazy. Okay, let's see what Roscoe says. When they did a fourth, it was all news people reported to your newsroom. Listen to the D-Day broadcast. When they did a fourth, it was all news people reported to your newsroom. So did that, was it like NBC news? Was that like the fourth one was news? I'll have to listen to the NBC D-Day broadcast to do that. Maybe if I find a good clip, we can, I can play that at the beginning of our next stream. Oh, Sam, thanks for this context. Bear in mind the early days that announcers played the chimes live. What were they playing to get that kind of electronic NBC chime? I mean, it doesn't, it's not like a xylophone, right? Or maybe is it just like a, like the organs that they have at baseball games that kind of, I can't remember what they call it, like a seaside organ or something? Is it a seaside organ or is that just a city organ that I named?  Like I'm by no means an expert on an old time radio. I'm a fan of it, but that's why I love having people that know this stuff.  I don't have any answers to them though.  Invite all your friends that know as much as you. And, uh, and please come answer all my questions. Seaside organ would be a great band name.  Bear with me one second. There's a type of organ. I remember how I had a keyboard organ, C organ. No, that's not it. Dang it. I'll find it. What type of organ are, what do they play like in when they played the seventh inning stretch or something? You know, all the transitions when people are walking up to the plate.  I'm going to look it up. There's some type of organ that's popular. I'll, I'll share it. Anyway, thank you all for being here. Thanks for the conversation. Again, if you're watching this on OTR archive, please go subscribe to OTR gold. You get entered to win one of these cool Bluetooth radios. And then also you can join in the conversation. And we keep calm and we keep all the comments in one place. So it's easy for everyone to see. I hope you all have a great, sorry, a great band name would be a great band name. Indeed. Indeed. Okay. Last comments. NBC chimes article on wiki has a lot of stuff on them. Good to know. I'll check it out. Thank you all for being here. Have a great week. I hope to see you on Wednesday.


Hey guys, sorry the lighting is real bad, I'm shooting this video at like 1.30 in the morning because I don't really have any other options, I don't have a ton of time since I'm gonna start the process of moving to my new place but that's not what we're here. Frozen 2. The much anticipated sequel to the 2013 movie Frozen and much like Frozen, I neither love nor hate this film, though I have two main problems with it.  Alright, quick side note, I don't like Olaf because I find him to be a very annoying, very annoyingly written character if that makes any sense. He is the dope of the group, he is supposed to be the comic relief, but for me that doesn't work. To me he very much comes across as like a character written very quickly, I don't know how to describe what he bothers me because it's hard for me to explain what he does, he just does. Maybe it's honestly like how popular he is for how much of nothing he contributes to the plot, actually no that's probably, he contributes nothing, nothing tangible to the plots of any of these films, he's just there and then annoys me, beside the point. But honestly this movie's fun, especially if you're a little kid, I don't know what adults are going to get out of this but if you're a kid the movie's fine and so I think I would recommend it for a kid, but honestly if you like the first one you're probably going to like this one, if you don't like the first Frozen you're not going to like this, that's what it comes down to, it's a sequel. So you probably already have your mind made up on whether or not you want to watch it. For me I did think it was better than the first one, at least a little bit. More comes down to this one has more of like an actual plot with them trying to find out what happened and why the two wins got separated by that mist, as opposed to the first one which is, oh save the Runaway person because she definitely can't take care of herself when she gets magical eyes powers. So by the point when Elsa does discover what truly happened, almost nothing else has actually occurred throughout the movie. Almost nothing happens for the majority of the film, yeah no everything pretty much happens like that's actually consequential to the plot happens in the last 45-30 minutes of this hour and a half long movie. So why do we care about the other part, I don't know but the time stuff actually happens when the movie starts wrapping up, again it's a Deus Ex Machina ending because they find out what happened, they do the thing to stop it, and then the bad thing they thought was going to occur doesn't occur because they managed to stop it through the magic. Deus Ex Machina ending. So it's not a ton for me to talk about with this movie, if you like Frozen you're probably going to watch it, if you didn't like Frozen you're probably not going to watch it. I'd say that this movie rolls a 13. Entertaining enough for if I was drunk or something but not something I'm going to actively seek out because it's really not made for me. While I personally like to analyze movies on whether or not they're a good movie overall, that's hard to do when it is a good movie but it's made for kids. I honestly hope that Disney gets back to writing the story first and then focusing on whether or not they want the movie to be for kids because I love Disney, I grew up on it but a lot of their movies that I watch for kids adults can enjoy because they don't make it to be a kids movie and that's what I think their power was. So I would recommend it if you liked Frozen if not, don't but that's all I really have to say. So sorry this one didn't get super in depth or anything, it's not a ton for me to say. You've probably already made up your mind on whether or not you want to watch it. But yeah, that's all I really have to say guys, peace out.


Everybody forced here up in a box of 2022 tops chrome black baseball for David s. Good luck David. Here we go, man 2022 tops chrome black. Let's see a big low number here Hey guys getting real close on transcendent.  There's a redemption coming up Come on big name. All right. We got base of Christian Pache and Mike trout The green refractor of Miguel Cabrera nice miggy number and 63 of 99 and the redemption Mike trout orange refractor parallel autograph Boom big congrats David that trout's gonna beat a 25 Heck yeah, man That'll be so clean. It looks so good this year Once again, David. Thank you. Congrats. We will get this out to you


The winners and losers of free agency, let's talk about that real quick. And I think we have to mention the New England Patriots when we talk about this. I mean, there's no other team that I can think of so far that has made a bigger splash in free agency than the Patriots. Let's just recap the Patriots free agency period thus far. It all started when they re-signed Cam Newton to a one-year $14 million contract. And people were thinking $14 million for one year. Number one, I didn't think he was going to come back. I thought that experiment failed. I thought they were tired of Cam Newton. They were like, okay, this was a bus. Let's just move on. Let's get a quarterback in the NFL draft, which could still happen.  But like we mentioned with Tyra Taylor earlier, $14 million. That's not backup money. It really isn't. You're going to expect Cam Newton to be the starter at least for one more year for the New England Patriots. So what do you do at that point? You need to give him weapons at this point because we all know that Tom Brady left that Patriots team because one of the reasons was the lack of weapons. And because the front office and the general manager, who is also Bill Belichick, by the way, was not doing a very good job in trying to bring in some weapons for Tom Brady. And then we saw in 2020 that all collapsed. We saw what could happen when you don't have weapons for a quarterback. That's not Tom Brady.  So Cam Newton, if you want to bring him back for the 2021 season, let's sign some free agents. Who do we got? You need a tight end. I mean, Ryan Izzo, who just got traded to the Houston Texans for a seventh round pick. He's not going to be the guy. Asiata is not going to be the guy as well. We haven't found a solid tight end since Rob Gunkowski left. Let's sign Jono Smith, an underrated tight end athletic who played for the Tennessee Titans. Could be a force in the NFL. Let's bring him in to make us a dominant team on offense as far as the tight end position goes. Okay, Jono Smith, your starter. A lot of money. Four years, $50 million contract. You need some wide receivers on top of that. Let's bring in Kendrick Bourne, an underrated wide receiver. A little bit underneath the radar for the San Francisco 49ers can still be very good for this offense. Let's bring in another wide receiver on top of that for Cam Newton.  So you got two solid wide receivers right there. Okay, cool. Are we done? No. Let's bring in another tight end. Hunter Henry, which I believe was the best tight end on the market, signing a three-year $37.5 million contract with the Patriots. Head scratcher, you've got Jono Smith and Hunter Henry, two tight ends. Why do you need two tight ends to large contracts for this Patriots offense? Couldn't they be reverting back to a two tight end set? What Aaron Hernandez and Robert Gunkowski were for the Patriots a decade ago when they had so much success with that duo. I mean, you line them up in two tight end sets bunched up in the I formation or you can line them up wide if you wanted to as well. One outside, one in the slot, one at the tight end position. It doesn't matter. The possibilities are endless. This offense is getting better and better and better and they're still not done because according to multiple reports, they've tried to make a push for a star running back. Now, they haven't released the names of the star running backs that they pursued. There are two of them, but we presume that it was either Chris Carson or Kenyon Drake, one of the top running backs on the free agent markets at this point. They also make some moves on the defensive side of the football as well, signing Jalen Mills from the Philadelphia Eagles, Devon Gacheau from the Miami Dolphins, and then also trading for Trent Brown. So they're offensive line, they're trying to bolster as well and protect Cam Newton. Guys, there's a trend going on here. They want this offense to be great so that they can compete in the AFC East. Are they better than the Buffalo Bills at this point? No. Absolutely not. The Bills are obviously the better team getting to the final four last year. But can they compete with the Buffalo Bills, make it a little bit harder for them and winning the AFC East? Potentially, it's possible. I mean, they face twice a year and I would not be surprised if one of those games the Patriots pulled off the upset with this offense. I mean, year two with Cam Newton in this offense, Trent Brown coming in from a trade, all these weapons, four weapons for Cam Newton to play with. I'm liking what Bill Belichick and the general manager of Bill Belichick is doing in that Patriots offense. So that's why I believe that the Patriots are the biggest winners so far in free agency. That's my thoughts, but hey, if you guys have any sort of comments or opinions or anything, leave a comment down below, interact with us and let us know who are your biggest free agency winners thus far, which team has had the best free agency period in 2021.


Hello everyone. My name is Dr. Surendra Suvaslingh Gawar currently studying as resident in the radio diagnosis department of Dr. Balasaheb Vikhya Parties Rural Medical College, Loni, Amad Nagar. Title of my paper presentation is Epiploic appendicitis, presenting as a case of acute abdomen, a case report. Epiploic appendicitis is a rare self-limiting ischemic inflammatory process that affects the appendicitis epiploic of the colon. It may either be primary, spontaneous or secondary to the adjacent pathology. It usually affects patients in their second to fifth decades with a redilection for women and obvious individuals, presumably due to larger appendages. Patients typically present with abdominal pain and guarding. It many a times is indistinguishable from diverticulitis and acute appendicitis depending on the location of the pain. Because there is focal peritoneal irritation, pain is more localized than with other causes of acute abdominal pain. Epiploic appendicitis denotes inflammation of one or more appendicitis epiploike, which number 50 to 100 and are distributed along the large bowel with variable frequency. That is, the maximum are in rectosignal junction of the colon and the least are noted in the descending colon. The pathogenesis of epiploic appendicitis is due to a torsion of large or pedunculated appendage epiploike or spontaneous thrombosis of the venous outflow which results in ischemia and necrosis of the epiploic appendage. Now, the case which presented to our institute was a 33-year-old male that came to OPD with pain, complaints of pain in abdomen, which were incidents in onset gradually progressive over duration of presentation since two days. The pain was non-radiating, not associated with any aggravating or a living factors. There were no history of fever, urinary, bowel, complaints, trauma, no history of chronic comorbidities, no history of allergies or any drug consumption, no similar family history and no similar complaints in the past. On admission, patients vitals were stable. On examination, abdomen was soft with localized focal tenderness over the left eyelid for serration. Laptase for complete blood counts, LFTs, RFTs, serum electrolyte levels, protein levels, coagulation profile were all within normal limits. Radiological investigations were done for the patient to identify the pathology. Radiograph of abdomen and pelvis in erect AP view was done, USG abdomen and pelvis was done, CT scan abdomen and pelvis, plane and contrast was done. The imaging findings were on radiograph of abdomen and pelvis which was done, which were performed using Allenges 800MAs machine revealed that the radiograph of abdomen and pelvis was within normal limits. Abdomen and pelvis on USG was performed on Zario 200 machine with 6 C1 curvilinear transducer at the point of maximum tenderness. There was a 17 x 9 mm size well-defined oval, non-compressible, heterogeneously hyper-equic area with surrounding subtle hyper-equic region. On color dot this study, it did not show any vascularity. There was minimal surrounding fat-standing, however no adjacent bobble or thickening was noted, nor any enlarged lymph nodes were noted. Then the CCT of abdomen and pelvis was done using Siemens somatom perspective 64 rows and 120 slices machine which revealed that there was an 8 x 10 x 5 mm size well-defined oval shaped heterogeneously enhancing hyper dense fat density area. With peripheral hyper dense ring, this sign is also called as hyper attenuating ring sign, adjacent fat-standing. And this area was noted abutting descending colon in left eye leg force origin. However, no adjacent bobble ball thickening was noted and it was noted just beneath the anterior abdominal wall muscle on the left side. There was a tiny central hyper dense area noted within the lesion which likely suggests thrombose vascular pedicle. The radiograph of abdomen and pelvis erect AP view is shown which appears to be within normal limits. USG abdomen and pelvis was done as explained it shows a well-defined non compressible heterogeneously hypoequic area with surrounding hypoequic area. Now this we are provided with limited axial cross section view of CET abdomen and pelvis in the plane images. We can see that there is a well-defined oval shaped hyper dense fat density area with peripheral hyper dense ring that is called as hyper attenuating ring sign and adjacent fat-standing noted. After giving contrast in arterial phase, no enhancement was noted. Then in venous phase, there was heterogeneous slight enhancement and there is evidence of hyper dense tiny area in the center of the lesion which shows thrombose vascular pedicle. We are provided with coronal images of the contrast venous phase which also shows the same lesion heterogeneously enhancing avoid lesion in the left iliac posa region. The sagittal images of venous phase after giving contrast of CET abdomen and pelvis also show the same lesion. The diagnosis was based on history and imaging findings. Then on the imaging differences that were considered were diverticulitis, omental infarction and omental neoplasms. Diverticulitis was ruled out as bobble wall out bulging was not noted. No communication with the bobble wall was noted and no bobble wall thickening was also noted. Omental infarction was ruled out on the basis of imaging findings the size of the lesion which was less than 2 cm and also based on abdominal pain. As omental infarction classically involves right lower cortine and our patient presented with left lower cortine pain. Secondary omental infarction was also ruled out as there was no previous history of any surgery or any intervention. Omental neoplasms were ruled out on the basis of imaging findings as well the omentum elsewhere in the abdomen was within normal limits and not involved. No other pathology was found in the CET abdomen and pelvis scan. After the diagnosis of epiploic appendicitis was made the patient was given supportive treatment with analgesics and fluid therapy and rest following which the patient improved and was discharged from the hospital. The term epiploic appendicitis was the first time employed by Lyme in 1956 and first written description for the same was done in 1986 by Danielson. Epiploic appendicitis is a self-limiting disease and responds well to analgesics, NSAIDs, sometimes mimics acute abdominal conditions for which surgery is required and therefore correct identification on CT prevents unnecessary surgery. Also treatment options for epiploic appendicitis many a times do not involve surgery. Chronically and infacted epiploic appendage may get calcified and detached to form an intraperitoneal loose body also called as peritoneal mice. It may rarely involve the vermiform appendix's epiploic appendage and it is called as epiploic appendicitis of the vermiform appendix. Thus we are making appendicitis both clinically as well as potentially on the CT. Therefore knowing the condition and making the right appropriate diagnosis serves best for the patient, clinician, surgeons as well as the diagnosing radiologist. These are my references. Thank you for your patient hearing.


Hi everyone. Thanks for coming to my talk. Uh, today I'll be talking about how we can uncover bias in different machine learning models and what the implications might be. And I'm Eileen as John introduced me and I just became an assistant professor and today is my second talk as an assistant professor. Yesterday was my first one. And yes, let's see, is this working enough? Um, yesterday I talked about supervised machine learning being used on language of individuals so that we can identify these individuals based on their linguistic style. And this is a serious privacy concern. But when we look at language at the aggregate level, language of society, we see that there are some fairness problems there because this linguistic data that is coming from society also brings the biases of society, humans with it. And now we are going to be looking at this. And yesterday what I talked about was, uh, about coming up with a method to identify a style of individuals or these can also be programmers so that we can, uh, do attribution. And this has security enhancing properties, but at the same time we see that it's very privacy infringing in some real world cases. And today I'll be talking about a method for quantifying and detecting bias in linguistic data or linguistic machine learning models. And, uh, for this, I basically adapt the implicit association test for humans to machines. And given that, uh, there's the language universal that in every language semantics happens in the same context window. The method that I came up with can be, uh, used in any language basically. Okay, so, uh, in AI, under the umbrella of AI, uh, today we'll be looking at natural language processing and machine learning. And in particular, deep learning and unsupervised learning. In the past there have, there has been, uh, some work on looking at supervised machine learning to see where the bias might be happening, how it can be removed, but it's slightly more difficult to understand these in unsupervised machine learning models since they don't have direct classification outputs and so on. And again, talking about, uh, for example, individual language versus society's language, when we look at individual's language, the syntax that they use in language is the most identifying thing, uh, even in source code. Uh, but today we'll be focusing on semantics, the meaning in language at the societal level. And just a brief summary of, uh, this work on security privacy and machine learning, where I'm using stylometry, uh, which is the study of linguistic style. And since this is linguistic style, we can be looking at natural language, or we can also look at artificial languages, such as programming languages. And some examples are identifying the authors of English text, English as a second language, so that we can identify their native language or translated text and identify the native language or translator, as well as underground forum texts where underground forum users engage in business transactions. And even though, uh, these are very noisy data sets, we can still identify these authors, and sometimes these are suspect sets for, uh, uh, intelligence agencies. And because of that, uh, this work, the tools that we developed and that are open source on GitHub, any one of you can download and use, they're currently being used by the FBI or expert witnesses to be used as scientific evidence in court or European high tech crime units so that they can identify suspects online based on their language. But again, I'm reminding you, this is about individuals' language and the privacy implications that it has. And in artificial languages, focusing on programming languages, in particular Python, C and C++, source code, as well as binaries, uh, this work is being used by DARPA and being part of the Department of Defense, you might imagine why they would be interested in attribution problems, uh, for example, to find the authors of, um, malicious software or, you know, press regimes, authors of, uh, for example, censorship circumvention software. And expert witnesses, again, can now use this information as scientific evidence in court. And I've been collaborating with the US Army Research Laboratory, they are still working on, um, programmer, de-anonymization, programmer attribution. Okay, but today I want to talk more about fairness and language at, uh, societal level. And I'll start with an example. So, uh, the, do any one of you know this author, Robert Galbraith? Okay. So there was this, uh, novel, uh, crime novel called The Cuckoo's Calling by Robert Galbraith. But some people suspected that it might not be written by Robert Galbraith, but instead JK Rowling. And after performing stylometric analysis, it was shown that it was actually written by the famous JK Rowling of the Harry Potter series. And, uh, after the JK Rowling said that, okay, uh, yes, she's the author of this book, but she wanted to use a man's name because it's a crime novel, and the publisher also thought that it would sell better if it was published under the name of a man. And at the same time, they wouldn't know that it was written by JK Rowling, so she would get a more realistic evaluation of her work. So we can see even at high-profile people how bias is affecting society, even for such an important product that they are publishing. And here, uh, this reminds me of the interplay between privacy and fairness. Uh, Big Data's Evil Twins. This is what, what I call them. For example, with privacy, we can have a serious problem when sensitive information is leaked, but with fairness, when sensitive information or protected attributes are abused, then we have a fairness problem there. But, uh, in the upcoming slides, we are also going to see that privacy does not imply fairness. Okay, let's, uh, start focusing on, uh, natural language processing models, linguistic models, semantic spaces, uh, in machine learning. For example, recently, Google had this, uh, Cloud Natural Language API. Amazon, Google, many companies, researchers are making these tools available or for commercial purposes as well. And such tools are being used by developers, researchers, uh, just, uh, random folks, citizens, and so on. And, uh, we can also see that whenever we are dealing with a smart or digital application, if there is any text included in it, then usually these linguistic machine learning models are also being used. For example, web search, when you're trying to type something, and you can see some suggestions to fill that sentence, which is sequence prediction or text generation. Uh, at the context level, they are also looking at linguistic semantic spaces or machine translation, sentiment analysis, especially for market predictions to see how a commercial product is, for example, perceived. Is it negative, positive? And, uh, of course, it's using linguistic data and tokens, uh, words as tokens that are in certain context windows or named entity recognition and text generation. When you're receiving an automated call on the phone, usually that text, uh, text is automatically generated. Okay, why would this be a problem? Uh, let's look at this example. So, one of my native languages is Turkish, and Turkish is a genderless language. There are no gender pronouns. There is one pronoun and it means he, she or it. Okay. Uh, so I'm translating from English to Turkish. She's a doctor translated as he, she or it is a doctor. Taking the Turkish sentence, translating it back to English, it's translating as he's a doctor. It's not even asking if it should be a he, she or it. Okay, let's say that it's smart enough to understand that this is a human, so it shouldn't be an it. It's still saying, not saying he or she, but just choosing he as the most accurate answer. And another example to see if this is just one rare exception or is this happening at the larger scale. He's a nurse translated to Turkish. He or she is a nurse. Translated back to English, she's a nurse. Okay. Uh, you can maybe also see the difference. Doctor versus nurse, the prestige or the, uh, salaries of these people. And we see a pattern here. Uh, he or she or it is a professor. He's a professor, teacher. She's a teacher. Okay. Is this only happening in English? Uh, German is also a gendered language. Not only pronouns, but, uh, other things are also gendered. It's more gendered than English. And of course from Turkish. But again, we see that a doctor is translated as a male and a nurse is translated as a female. And then we have Bulgarian and in Bulgarian, uh, almost everything is gendered. Verbs are gendered. Adjectives are gendered. And again, we see that doctor is a male, whereas a nurse is a female. Okay. So, uh, it has been 62 years since they came up with the term, I believe it's still 62 years. Uh, they came up with the term artificial intelligence and what they, what can be done with old doomsday stories about when there is, uh, super intelligence or when machines are going to take over and so on. But they haven't been really thinking about immediate problems we might have with artificial intelligence. Okay. We know that when garbage goes into machine learning models, what comes out is usually garbage as well. Because if that's the quality of your training data, then the output would reflect the same quality. And one example here for how text is collected for generating semantic spaces and linguistic models is, uh, for example, there was the Microsoft tweet bot, Tay, and it was taken down the same day it was introduced because it very quickly turned into a very offensive, racist, biased, uh, bot. And Microsoft wasn't ready to account for such cases in the linguistic model. And this was done by fortune. It was basically model poisoning. It was adversarial machine learning. And it worked very quickly. And these were some of the examples that the tweet bot started, uh, tweeting. And of course it was taken down, but we can see how easily the bias can be embedded, uh, with a strong effect size in this example within a few hours. And how is this getting into these models? Let's take a step by step look at this. Uh, we know that humans are biased. This is not necessarily a bad thing because there are, uh, neutral biases as well. And sometimes biases are helpful in some conditions and I'm going to give some examples of those as well. But when we're biased and when we're speaking, we reflect that bias in our semantics. We have values, for example, we say that, okay, the snake is ugly or this butterfly is beautiful, for example. And these are neutral biases, but they are there. And as we are speaking, forming languages, uh, we tend to have similar patterns, uh, in same context windows. Let's say that we have a negative context window and snake tends to appear in that context window as a neutral, uh, bias, but it's still a negative bias for snakes. And that's called, uh, then distributional meaning. We see in the statistics that certain words end up in negative context, for example, and context windows. And after that, machine learning models, especially semantic spaces, look at this distributional meeting, uh, meaning and co-occurring statistics. And after that, it learns the co-occurring statistics of certain terms or certain people's names and what they are associated with. And this is reflected in machine learning models. Basically, the bias is propagated through this entire process. And at, sometimes it is even, uh, increased and augmented. It, it's not just perpetuated. How can we measure it? Because especially when we look at unsupervised learning, we don't really have something to directly control for and measure these things unless we look at the model at the construct level, like the intelligence of this model, let's say the, uh, the understanding of the world that this model has. And for humans, the implicit association test has been used as a way to measure the implicit biases that we might have, uh, Greenwald from University of Washington came up with the implicit association test in 1998. And there's a lot of criticism about this method as well, but at the same time it's revealing some patterns about the world and humans. And it's revealing subconscious biases that we might have and we might not be even aware of. And, uh, this test is basically asking you to associate certain societal groups, members or terms with certain stereotypical words. And how fast do you associate, for example, a butterfly with being negative or positive versus how fast do you associate a snake being positive or negative? And then when you're doing these associations on a computer, you are asked to click right or left to classify a positive term with butterfly or snake. And after that, there is a difference in reaction time and this differential reaction time in associating congruent, uh, stereotypes and incongruent stereotypes gives you the effect size for implicit bias that we might have. And this example is, uh, from a girl taking the implicit association test for males versus females about science versus arts. And the bias in general is that males are associated with science, whereas women are associated with arts. And you can go and take this test, uh, online at the Harvard's website, the implicit and I'm showing this because we see a few implicit association tests here that are listed. And in the experiments that I did, I took these, uh, previous experiments that have been, uh, generated by experts in societal psychology because I'm not an expert in that area at all. And I wanted to see if we can replicate these biases versus what happens when I try random things or things that are not biases so that, uh, I'm not just cherry picking the biases that I want to show you. You see that these are the main categories that were there and I'm using the same ones to see if they are reflected. Because the other thing is this test is taken by millions of people over decades. And another example is, uh, you can also take it in German if you are from Germany, let's say, because the same tests since they are based on context and words, uh, they they can exist in any language basically. And we can generate linguistic models in any language as well. So we can apply these tests in other languages for other cultures or countries. Okay, let's start looking into the details of generating language models. Another example here of, uh, where text is taken to generate these models. Basically, we can crawl the web, take all kinds of, uh, text structured, unstructured tweets here. We see some tweets from Donald Trump before he became the president, but we see a certain pattern in the text about maybe certain biases and so on. And this is blindly fed into, uh, in this case, neural networks. And the neural networks looks at the co-occurring statistics and all the data, uh, point-wise material information it has. And after that it produces a semantic space. Uh, in this semantic space, we have, uh, the common example is with 300 dimensions. We basically have a dictionary of a language and then, uh, this dictionary each word contains, each word in a language, uh, with, in a vector form, numeric vector form with 300 dimensions where each dimension is a combination of certain contexts. But when we have each of these words, we see that similar words are projected to the same points in that space. For example, things about, uh, positiveness, things about feelings, things about females and so on. And based on their vicinity, we can understand or answer many questions. And the types of, uh, semantic spaces that I focused on in my study were, uh, Wurttowek, uh, the algorithm and Glove from Stanford researchers. Wurttowek, when it was introduced, it was, it was extremely popular and the models that was produced by Wurttowek, which is, uh, from Google research, uh, it's being used by many developers, researchers or app developers and so on. So a lot of people use these for their applications and Glove, uh, is similar to that as well. It's produced by Stanford researchers around the same time and these two semantic spaces have about the same accuracy after evaluation. Even though it's not very clear how to evaluate these methods, it's like an approximation of an evaluation for these, uh, semantic spaces. And Wurttowek is based on Google news data and we would expect Google news data to be, uh, more neutral and objective because it's news data. But we see that that is not the case based on concurrent statistics and other type of data that Glove uses is Common Crawl. It's about 800 billion tokens from the internet. It's basically the crawl of the worldwide web. Basically, uh, the language of internet users, I would say in this case. Not in particular society in general but, uh, the internet using population. Okay, what can we do with these word embeddings? First of all, we can understand syntax. We can understand the meaning of a word better. Uh, we can perform analogies and, uh, get the answers or we can look for semantic similarity. Um, let's see if I have this example. No, but we can ask questions such as, okay, Rome is to Italy as Paris is to what? And it will be able to answer that. It's France. So it has some understanding of language. It has understanding of semantics but there is also knowledge and maybe even statistics embedded here. And by statistics, I don't mean the concurrent statistics. I mean statistics about the world. And looking into the details of these, uh, vectors, we have each word listed by frequency and the first word is usually A or D or just a comma and so on. And there are 300 features that represent this word in the semantic space. And in Glove, we have about two million, uh, tokens, two million words in this dictionary. And in a dictionary, you would expect much less words, but given that this has taken all the words above a certain frequency on the internet, we have words such as Obama or Michael Jackson and so on. Okay, what can we do with vector arithmetic? So when we, uh, project these vectors to 2D space, we see that for example, I hope at least the laser is working now because I didn't turn it on. Okay, now it's working. Okay, here we see that on the lower side there is brother, which is a male and here we see sister. And now we see the direction of gender here. Once we see the direction of gender, maybe we can look at kink and find what corresponds to the female version of kink, which is queen here. So basically we can perform vector arithmetics with cosine similarity most of the time or taking the principal components of these vectors and try to answer the questions that we have in syntactic form, analogy form or semantic form and so on. Uh, how can I use this information to measure bias in machine learning models? So the first thing I came up with was the word embedding association test instead of the implicit association test. And for this what I do is I try to quantify the, uh, implicit or in this case it's actually explicit and deterministic associations between societal categories and evaluative attributes which are stereotypes in this case. And what I do is I take the distance between the, uh, stereotypes and societal groups for the two groups and look at the difference in standard deviation between the means of these associations. And that gives me the effect size of a certain bias and we can also measure the statistical significance by generating a null hypothesis and then see if the result that we are getting from the effect size is significant or not. And for this the first thing I wanted to start with was looking at neutral, uh, stereotypes that are universally accepted or they are called so. For example, flowers being considered pleasant and insects being considered unpleasant. For some reason apparently most of the population just naturally intuitively have this, uh, stereotype or musical instruments being considered pleasant whereas weapons are being considered unpleasant. And since this is not dangerous for society or harmful to society it's considered neutral and in this case we see that the effect size is 1.5, uh, around 1.5 for both of them and this is a large effect size if it's above 0.8. And the highest effect size can get is two because it's bounded by the standard deviation here. And we see that these are both statistically significant and high effect sizes. And for the upcoming examples it will be the same case. They are all statistically significant with high effect sizes. Okay, let's start looking at the other major, um, implicit association test categories such as, uh, white people's names versus black people's names and trying to understand if they're considered pleasant or unpleasant and, uh, we get the congruent stereotype that white people are, uh, considered pleasant in this case. And when we look at the differences between genders we see that males are associated with career and females are associated with family and again, uh, I'm using the same words that the implicit association test is using to ask you to perform the classification association test here, the exact same words. And when we look at science versus arts, again we see that males are associated with science and females are associated with arts. Uh, I'm not going to talk about stereo threat here but at least we can see that bias is certainly perpetuated with these linguistic models and, uh, the two examples that I have with models here are from 2014, we are in 2018 and these are still the state of the art models that are being used by many people and they are not really updated on a frequent basis because they are quite large files and they require a lot of data and so on. Okay, let's look at some other, uh, health related stereotypes for example, uh, young people being considered pleasant whereas all people are being considered unpleasant or physical diseases considered controllable whereas mental diseases being considered uncontrollable and we see the stigma being, uh, reflected in these models or when we look at for example, um, heterosexual versus homosexual, uh, individuals we see the attitude towards the more straight versus trans gender and so on. And we can also perform these in German when we look at the main categories after generating the linguistic model and you can also generate your linguistic models. For example, you can download corporate online or use google ngrams data from different years, decades, countries, languages and so on to analyze what might be going on in these years and when we look at the most, uh, recent, uh, version of google ngrams for, uh, German we see that we can also replicate the stereotype, uh, or the prejudice for Turkish people. Turkish people went to Germany decades ago, millions of them as immigrants and, uh, there isn't a very positive attitude towards them and we are able to replicate that from, uh, Google ngrams data as well by performing the VEAT test, sorry the VEAT test. Okay, we saw that semantics and bias is, uh, embedded in these models. What about empirical information that doesn't depend on a context or a feeling but it's more about the statistics in the world. Can we replicate those as well or can those be a reason for these biases? Uh, for example, let's say that I would like to know how a certain name is associated with being male or female. Taylor, for example, it's an androgynous name and it's almost 50-50, uh, male and female and based on this I can do, I can perform a similar, uh, computation to see, uh, how much a word is associated with a certain stereotypical group. And, uh, based on this I went to US, uh, I, I went, I collected data from the, uh, US Census Bureau, 1990 I believe, uh, data where they included the gender, uh, of, uh, people with certain names and how many of them were out there. So I took all of these names, the most frequent ones, and tried to calculate, uh, their association with genders being female or male. And we see that this androgynous names had, uh, 0.84, uh, correlation coefficient. So it's almost about, let's say, 84 percent, uh, similarity in the statistics of the world versus the association that I'm getting only from a semantic model, which is very interesting to me. And I want to, and this is a illustration of that. We see here that, for example, Taylor is in the middle, almost white, 50 percent male, 50 percent female, from the effect size that I'm getting, uh, which is almost zero in this case. And we see that Carmen is almost 100 percent female, whereas Chris is 100 percent male. Okay, what about, uh, if we look at, uh, employment statistics, occupation statistics based on gender, and, uh, the Bureau of Labor Statistics publishes, uh, these, this information every year. And based on this, I took the data from, I'm trying to see which year this was. It might be 2017. And I took the, uh, occupation names and then tried to see their association with certain genders. And the correlation coefficient was 0.9, which is amazing. Uh, and after that, when we look at the result, we see that a programmer is, oops, where is my, it's been reflected from the mirror, I don't want to blind anyone, so I'm not going to use it. But on the upper left side, we see that programmer is almost 100 percent male, whereas a nurse is almost 100 percent, uh, female. And when you look at, for example, Google, Google Engram, searching for she's a programmer, uh, usually the result you get is zero, because things that are below a certain frequency are just, uh, cut from the Engrams. And, uh, until recently, she's a programmer with zero, and we can see that here reflected here as well. Okay, now we can understand that there are different types of bias that are embedded in semantic spaces. And there might be different reasons why these are getting in these models, but we can come up with three main categories for types of bias that we are dealing with. The first one is the vertical, uh, information, gender and occupations, for example. And this is not exactly bias, it's basically the statistic that we have in the world, and these might be caused by, um, the injustices in the past, or biases in the past, but we don't know any information about that, we just have the statistics here. And the models are learning from these statistics. But we also see that the universal biases are embedded in these models, as well as things that can be really prejudice, such as black versus white names being considered pleasant versus unpleasant. But I would like to remind that in some cases you want certain types of biases to be in your machine learning models, because they can be very useful. It depends on what kind of task you are dealing with. And some people have suggested fairness through blindness. Basically, just remove redundant, just remove protected attributes, or devise the system by completely taking the bias component from the vector space from all words. But this cannot be the right solution to this, like just turning a blind eye to this, because first of all, once we remove this information, we see that we're also removing statistical information about the world. And the other thing would be, we would end up with redundant encodings, which wouldn't have the same quality as the previous ones, and we don't know what exactly we are losing here. And another very important example is having proxies. Even if you remove protected attributes, there are still proxies for bias to take place. For example, when automated systems are deciding to give loans to certain people, the zip code is a proxy to the address. So even if you remove all protected attributes, the zip code since it's a proxy to having a certain financial status, it would give you the redlining example where certain people are just denied loans because of their zip code. So, and by the way, in law, the main discrimination criteria is using protected attributes for discrimination, and if those protected attributes are removed, and if you are proxying from the zip code, it's completely okay to be using the system like that right now. And I'm suggesting instead, fairness through awareness. First of all, we need to understand cultural bias. And then, based on this bias, we need to understand the protected attributes that come with them. And we also need to understand the machine learning task. For example, in bioinformatics or health informatics, we need to make sure that there's a certain bias. The bias for genders or certain ethnicities or racial backgrounds are taken into account, because one example is for cardiac disease. Symptoms of men and women are different. Treatment should be different as well, and we have to make sure that we are taking this into account in whatever model we are building. And I'm saying that fairness is task-specific for this purpose. And this work was published last year at Science, and there were a lot of news about it in media, and one of them said, after this random screenshot that I took, which I really liked because it says, in 2017, society started taking AI bias seriously. So in 2017, people really understood that there was a serious problem caused by these automated systems, and the bias that they are perpetuating in society that's already a huge problem to deal with. And this is not happening at the large scale. Okay, what am I going to be working on next? So this kind of covers the project that I was working on, and I'm trying to wrap up quickly so that you can also ask questions, and also I was going to mention in the beginning that this can be interactive, but I think it's too late now. Okay, I'm not going to be focusing on the singularity or transhumans or when machines are going to gain cognition because we have much more immediate problems right now. For example, focusing on computer vision and joint semantic visual spaces, and these are being current news, for example, for automated surveillance, and computer vision systems are known to have bias as well, but it's much harder to quantify those if you're not dealing with supervised machine learning, and imagine a system being biased for certain skin colors or ethnicities and how much of a problem this might become because a lot of these automated systems are being used to identify targets or they are even used in war zones or they are used all over the streets to identify, for example, anomalies for anomalous detection and so on, and we don't exactly know how these systems are working yet, and they might very well be biased because all the analyzable ones are showing bias. What about algorithmic transparency and interpretable machine learning? So, for example, with driverless cars we know that they have vision systems as well, and let's say that this is the classical trolley problem in the current sense where we have the driverless cars is going to crash into someone, there'll be an accident, it cannot avoid that, and it has to decide is it going to crash into the white male executive right across the car, or is it going to choose to run into the old black lady on the other side, and we don't know the answer to this yet, and because of that we have to be very careful about what kind of products that we are building because we don't want to be building the digital analogs of Robert Mousses' racially motivated love overpasses. So Robert Mousses is considered one of the best urban planners, he's the planner of New York City, but the overpasses that he built all around the parkways were quite low, so buses where people with lower financial status had to use public transportation, buses couldn't pass through those love overpasses, so people had to have their own cars so that they can go to the beaches in Long Island, or the parks that he built, and with these love overpasses people were basically just separated and this led to decades of segregation and we have to make sure that we are not causing the same problems again with the digital products that we are putting out there blindly, and for this I would like to keep working on a fairness framework to uncover bias in artificial intelligence and come up with ways to mitigate it while preserving the utility of systems and come up with fairness algorithms, but there are a lot of privacy and security implications here as well, for example when we have these machine learning models going from our phones to the cloud or from our fitness trackers to the cloud, can we guarantee fairness in a secure and at the same time private way? How can we avoid adversarial poisoning for these systems and so on? And there are many unanswered questions in this area and it's a very exciting area and I would like to thank all of my collaborators as well, none of this work would have been possible without them and I'm really grateful to them and now I think we have a few minutes for questions as well, so I would be happy to take questions, comments, questions, anything.