Here at Gillette Stadium, the Titans prepare to fight the Patriots and history. The first runner to play off, one game, one player at a time, start the game. Tana Hill, under pressure, fires to the end zone, touchdown, Titan Furkser. Hey, Furko, crazy. Henry, touchdown. Pass batted up at the Intercepted and returned for a touchdown, Titans, Logan Ryan. Everybody count us out, it didn't matter no. Man, it feels good to get this done, man. Nobody beats the Patriots before tomorrow. But we did. Tana Buff, bro, it's hanging up on that thing. Hey, those are all we need is one shot. One to the second round, man, let's go, baby. How to do a Titans role, man. We're a hot team right now on to Baltimore. Welcome to Baltimore, indeed. Welcome to the Mike Vrable Show. My name is Mike Keith. John Robinson is sitting in again this week for the head coach. As the Titans play once again Saturday night, 7.15 kickoff time in Baltimore at M&T Bank Stadium. We're on Titans Radio at 6 o'clock with Titans Countdown. What a win in Foxborough. What else can we say about it? And John, when you took this job nearly four years ago to the day, you knew that if you're going to come out of the AFC, you've got to be able to go into New England and beat the Patriots. Goal accomplished and a very special night for the entire organization.  The coaches and players did a great job putting the game plan together. The coaches did. Worked hard at it all week. We built momentum through the week of practice and then went and executed on Saturday night and just so proud of the way the team played. Let's take a look at the six pack from the win Saturday night in New England. Titans trail in the ball game early three to nothing as the Patriots went and scored on their first possession. But again, important that Tennessee gets an answer third and 10 from the 12. Yeah, really great recognition by Ryan here. They send a seven man pressure and it's basically man coverage. Firkshire does a great job kind of spraying his release, giving himself some ability to break inside and a great throw and catch, great execution overall by the offense. Tannehill gets just enough time. Firkshire makes the grab and the Titans take a seven to three lead. That's the score at the end of one. As we move to late in the second quarter, the Titans trail 13 to seven, they get the football back after a Patriots field goal and go run with Derek Henry. Back to the ground game. You know, just a really well executed play here. Great individual effort by Derek to get through there. But you can see Jones, he gets enough on high tower to kind of make him jump, slows him down on the front side of the play. Michael Pruitt does a really good job getting movement on the outside linebacker. Great combination by Conklin and Davis. And then it's really Derek on a corner and you see him kind of jump through and pull through a tackle there. And then it's one on one with the safety. Deron finally gets him out, but it's a huge gain for us offensively. It's a gain of 29 on the play, takes us to the two minute warning. So staying with the same drive, 47 seconds left in the half. Titans have gone five straight Derek Henry runs and then there's a bit of a surprise in the pass game.  Jonu Smith does a great job on the backside of kind of running an over route, holds the safety and great action by Ryan. He gives the lineman time to get over there, kind of set up the wall and just a great individual effort. He knows Derek knows he's going to take a shot there at the pylon, does a great job securing the ball. He's going to come and do a great job getting a hat on a hat. And we finish it off getting it inside the one there. Officials discuss it for just a moment. Think for a second he might have gotten the ball over the pylon. Instead, Henry ruled out of bounds at the one yard line. 38 seconds remaining in the half first in goal. Who else are you going to give it to? Big 22. Yeah, just old school football here, just, you know, a hat on a hat.  Don't let any penetration come through the line of scrimmage. Get Derek behind his pads, get him downhill, and then surge into the line of scrimmage. Great execution on that drive right before half to get a score right before half time and go in with some momentum was really huge for us. I guess Henry was technically the fullback on that play because you motioned AJ Brown into the tailback position. Yeah, just another little bit of a wrinkle to kind of get the guys looking. Maybe they're expecting some type of, you know, I don't know, pitch play or another screenplay. But all the while we're going to give it to Derek. It's 14 to 13 at halftime. It's hard to believe that 29 minutes and 35 seconds later, it's still 14 to 13, 25 seconds to go in the game. The Titans bring in the punter Brett Kern and he and the special teams come through big time. Huge play for us, just great situational football. They actually put Edelman back in the back to return and then walked him over to cover one of our gunners. They rushed nine guys, you know, a couple of the guys on the interior there had to block two guys. They did a great job holding it off. Brett did a good job. He knew he was going to have to one step kick that thing. And then great awareness by Dane, you know, to get his back to the goal line there and stop the ball at the one yard line and just really pin those guys. A critical, critical situation for us that was executed extremely well. Something that the Patriots do very well is play situational football. The Titans do it to help beat the Patriots in that instant. Dose of their own medicine. Dose of their own medicine. And to end it, Tom Brady's final pass of the 2019 season ends up with a Titan. Yeah, there's not a whole lot of time here. This was going to be a hook and ladder play. They were trying to throw it to the curl route there and then leak James White around for the pitch. Brock does a good job of choking down on Sanu there, getting a hand on the ball. And then Logan redeems himself for the one that he knows he missed earlier in the game. And to come away and pick the ball off, walk in touchdown to really seal the win for a special moment for him, you know, being a former Patriot as well. Just a really well, another well executed play situation. You could see Woodard, he's on the back. We knew that that was coming. So really, you know, just really well executed defensive play.  Shut out Tom Brady at home in the playoffs in the second half. We knew that's what it was going to take. It was going to take, you know, really sound defense. We're going to have to stand up to them offensively, to their offense. And then offensively, we're going to have to run a football. And we were able to execute both of those keys and come away with the win. Speaking of sound defense, we're going to see some in our Bridgestone clutch performance plays of the game. That's next on the Mike Brable Show. The Bridgestone clutch performance plays of the game, late second quarter Patriots first in goal at the Titans one and they don't get in. Yeah, just really a huge set of plays here. Forrest down there on the goal line. They're really trying to punch it in. Our defensive line, they do a really good job of knocking back a line of scrimmage, playing down the line of scrimmage, getting in on plays. And by their play, it really helped for Sean to come up with some huge stops for us there. Created creases in their blocking scheme and he ran through and splattered the back. Just a great team performance from the secondary, from the line of scrimmage guys and certainly from Sean on that set of plays. The Patriots end up having to kick a 21 yard field goal. Instead of going up 17 to seven, they go up only 13 to seven. The Titans drive back down the field and take the lead at the half. It ends up being the key sequence in the ball game. It really was. It kept four points off the board. It allowed us to get the lead back before half time and then really keep the lead for the rest of the game. Are you ready for the Delta Dental, guess the Titan? We'll see. I hope I can keep my track record intact here. John Robinson, two of two on the year filling in for Mike Vrable. Can you guess this Titan? Delta Dental, guess the Titan. That is a tough one. That's a tough one. I'm going to go out on a limb and say Khalif Raymond. Khalif Raymond is the guest. Show us Khalif Raymond. Oh, Kenny Vaccaro. I guess they went with Kenny Vaccaro because he had such a good game. He had a great game. I mean, he was aggressive. He was downhill. He had a great pass break up on the sideline there where he really got over the top and made a great play. I'm a little disappointed that I didn't get that. Oh, you're two for three. I'm two for three. You're two for three. Kenny Vaccaro playing good ball. Another guy playing good ball in the secondary, Logan Ryan. He's going to join us for the Geico Gladiator of the game. Amy Wells with number 26 when we returned on the Mike Brable show. Brady in the end zone, takes the snap, looks, fires pass, batted up in the area, intercepted and returned for a touchdown. Titans, Logan Ryan. Welcome back to the Mike Brable show. I'm Amy Wells joined by Logan Ryan, who is our Geico Gladiator of the game. Now, Logan, I've got to ask you because we're in the playoffs now. This is serious. Does the 24 hour rule still apply or does the window of celebration get a little smaller as the intensity picks up? That's a good question. I think we definitely enjoyed that one. It was a big win for Nashville. I saw the reaction in videos. It's great. I can feel the buzz in the city. But, you know, the 24 hours has passed, so we're on the ball tomorrow, which would be a great test for us. You're the guy in the locker room with the most playoff experience. How do you take your experience and help out some of the guys around you on this team? Tell these guys to be themselves because the playoffs is just, it's still football. You still got to do what you do. You got to bring your strengths to the table and do everything you can for the team. And I feel like when you're a team that started off two and four, every game we won was needed to get there. So even though we didn't view them as playoff games at the time, every single win, we had a scratch and claw way to get in. So every win mattered. And we got to keep doing the same thing, scratch and claw, and get these wins. It's got to be a little bit harder, though, to manage your emotions and really stay focused on the task when the stage is so much bigger. I think that's where the experience comes in, is be able to kind of silence everyone around you. And your family is excited. And man, my phone blew up after that last game. Like it never has before, but you got to still focus it back in and do what got you there and stick to your routine. It's good to feel good. I'm happy about our team. I love how we're playing. I love this group of guys. I love the attention that we're getting, but it's because we're winning and we got to continue to do the things that help us win.  Guys like Jeffrey Simmons, Amani Hooker, David Long have gotten a lot of playing time, even though they're in their rookie seasons. What do you, as a leader on that side of the ball, do to help them be so productive? Encourage them and make the most of their opportunity. I mean, when you play on really good defenses, it's a pride thing when you get a chance to play. And we have a lot of depth on our defense, and you need to have a lot of depth to make a championship run, because injuries happen, the violence of the game, the physicality. People are running the ball a lot more, and you're fighting for every inch out there.  So, if a guy like Jayon has to come out of play, David Long going there, and the defense can't drop from one snap, not when you're playing against Tom Brady, not when you're playing against Lamar Jackson. How much are you guys on the defensive side of the ball going to be able to lean on Dean Pease, who spent eight years with the Baltimore Ravens? I mean, a ton. I mean, he spent time with the Patriots as well. So, we lean on Dean for everything. He's our guru. We're a team that runs different defenses every week based on who we're playing. So, we trust the old Dean to go scheme it up for us and put us in a position to make plays, and we're going to go out there and do whatever he asks. I heard Nathan George Jr. like to run, even when no one else did. On December 17, 2000, back in Ohio where he made his name, in the snow and ice of Cleveland, with the cold wind whipping off Lake Erie, run he did, 34 times in all, for 176 yards and three touchdowns. He had teammates, yes. But Eddie George was a one-man wrecking crew as the opponent wilted against his continuous and enthusiastic assault. He loved to run, even when no one else did. Edward Nathan George Jr., a name befitting a star, one of the greatest stars of his generation in the National Football League. When the Baltimore Ravens played in Nashville in October of 2018, Joe Flacco was the quarterback and their offense was as traditional as any in the NFL. That started to change late last season, when Lamar Jackson took over as the Ravens quarterback and led the team to six wins in their final seven games and the AFC North title. Head Coach John Harbaugh had something and he knew it, so he promoted Greg Roman to be Baltimore's offensive coordinator and set out to put together the league's most diverse attack, and they have. After a two-and-two start, the Ravens have won 12 in a row, putting up amazing offensive numbers, 33 points per game, 206 rushing yards per game, 408 total yards per game. Baltimore starts their attack with Lamar Jackson's amazing ability to run the football. We see it in doses, but not as consistently as they'll run the quarterback. And so when you run the quarterback, everybody has to defend and there's an extra gap in there. There's an extra guy carrying the football, so you don't have to block them all. You can read one of them. They'll read the four technique, they'll read the five technique, they'll read the end or the outside linebacker. It just is a challenge to make sure that when you're playing option football, that based on what your call is, that everybody has responsibilities, and when somebody leaves that responsibility, that's where you give up big plays. But Lamar Jackson doesn't just make big plays in the run game. Yes, he's rushed for over 1,200 yards, but Jackson has also thrown 36 touchdown passes. You know, he's athletic enough to buy time when he has to, to move around. He throws the ball from different arm angles. So it's been impressive just in the few games that I've watched today. You know, because you kind of hear about what he's doing, but you've never really, you know, watched it. You've watched it a little bit just in the crossover games. But then to be able to sit there and watch him just improvise and make plays when there's guys there. Not sure that that's how they drew up the play, but that's what this league's about.  Lamar Jackson is a special player who will be named the league's most valuable player in about a month. But he's not the only talented Baltimore Raven. As a matter of fact, the Ravens have 11 other players outside of Jackson who've been named to the Pro Bowl and that's a record. Running back Mark Ingram has two things in common with Lamar Jackson. Both are Heisman trophy winners and both rushed for over 1,000 yards this season. Cornerbacks Marcus Peters and Marlon Humphrey are two of the best at their position in the NFL. And Justin Tucker has missed just three of 88 total kicks in 2019. The 2019 Baltimore Ravens are talented, versatile and complete. That's why they didn't lose in the final three months of the regular season. Of course, it's not the regular season anymore. We'll get the keys to the Titans having a chance to knock off the Ravens in the divisional round when the Mike Frable Show continues right after this. AFC divisional playoff game Saturday night at Baltimore. It's time for the keys to success, John Robinson. And with time of possession, you think that is key number one to beat the Ravens? Yeah, it's about controlling the football. We've got to do a really good job offensively. A, of taking care of the football. B, converting third downs, getting down to the red zone and scoring points. Because Baltimore, they're number one in the league in time of possession and they're number one in scoring. So the longer we can keep the ball offensively, that means their offense is on the sidelines. They've got us for 206 yards a game this year. They've got their quarterback Lamar Jackson with over a thousand. Mark Ingram, the running back with over a thousand. Gus Edwards, the big back with over 700. I mean, they can do it on the ground. So you've got to be able to keep it away from them. That's exactly right. All right, so then let's look at the red zone. Here's an area where the Titans have done a great job this year scoring touchdowns. Don't leave four points out there, finish the drive. And those points are critical. We saw that conversely last week with New England where we forced the field goal down there and those fourth points ended up costing them there at the end of the game. We've got to continue. We've been successful down in the red zone. We've got to drive the link. We've got to get into the red zone and we've got to get touchdowns. All right, so then the third key to success in this ball game is force Baltimore's offense to go the long, hard way. Don't give them the big plays. And they're capable of making them with this quarterback, with his legs, whether it's handing it to Ingram, who's an explosive back, Hollywood Brown at receiver. I mean, he is super fast. Lamar is able to extend place with his feet. He's able to get out of trouble, move around. Those receivers, the Titans, that's a really good position group for that football team. We can't allow them to have any explosive plays. Touch on the Titans for just a second, if you would, because they have three guys who can make a lot of things happen. I don't know if any team in the league has three guys like the Ravens do at tight end. Yeah, they've got three outstanding players at that position group. Nick Boyle is kind of there on the line of scrimmage blocker, but he catches the ball. He's really strong. He's really powerful. He's got strong hands that he gets into you and can move you off the ball, if you don't play with good technique. Andrews, he was kind of a split out receiver at Oklahoma, really athletic guy, tall, big target, big catch radius. And then Hayden Hurst out of South Carolina is another really athletic player, a former baseball player who can move around. He's got soft hands. He's really instinctive and savvy with finding voids in the zone coverage. So it's a huge challenge for us defensively. But the Titans will take confidence, certainly, into Baltimore, especially after the win at New England.  And then we just got as good an opportunity as every other team. We just got to go up there and play our style of football. There's a lot of belief in the football team, our football team with the players and the coaches. We believe that we belong in the playoffs. We just got to go up there and execute and play sound football. Certainly did a great job last week. It was exciting to see. Looking forward to seeing it again, hopefully. Absolutely. That's the plan, Mike. All right. So Tennessee at Baltimore, remember the game is Saturday night. The kickoff time is 715. We're on the air with Titans countdown at six o'clock. So again, Saturday, 715 kickoff airtime on Titans Radio is six o'clock for John Robinson, sitting in for Mike Brable. I'm Mike Keith. Thanks for joining us and we'll see you next time.


Do you ever wonder what happens when the police leave? Crime scene cleaners are private companies that handle the clean up after the police are gone. Spalling Decon is one of the nation's largest clean up companies handling the aftermath of homicides, suicides, decompositions, hoarding and much more. These are our stories. So we're on our way to a mold and rodent dropping job. So that's rat feces? Hey guys, so we're on our way to a mold and rodent dropping job. Woman's house kind of flooded, got affected two bathrooms and the ceiling of a utility room. Well, there's also rodent droppings in the walls too, so we're doing both, but... So today you get to be in there for a full demo of two bathrooms and a ceiling of utility rooms. So we're tearing it all out so it should be a lot of action shots, a lot of good stuff. Stay tuned. Steve, can you tell us a little bit about what we're doing and why? Yeah, so right now we're putting up a containment wall because we're going to be working on mold remediation inside this bathroom, okay? So as we're doing our work, we're going to be agitating the mold spores and as we agitate the mold spores, we don't want them to get out into the rest of the house. So you put up a containment wall, work inside the containment. As you come out of the containment, you will don and dock your PPE to make sure that you're not spreading mold spores throughout the house. Okay, so what I'm doing right now is I'm doing this half because we have a little bit of excess because we always try to make sure that we go a little bit wider on both sides because you can always cut less. It's hard to add more, so. The hardest parts for every technician, including myself, I actually got my butt kicked on doing my own containment over there in the week, is putting up these contaminants. Because depending on the door jam and the height of it, if you're trying to do it by yourself, really as you see, there's not a lot of space for both of us to work in here. So if this is really a one man job. So what you want to do is you want to start from the top, hang your sheet. Make sure it's nice and straight on the top as you're hanging your sheet and then make your cut so that it fits the door as best as possible so that you have a nice tight containment. You do not want a loose containment because if the air is on or a fan is blowing like our dehumidifier is in there and it has an exhaust fan and stuff like that, that puts a lot of play in that containment area. And we'll eventually pull that tape off the wall if not taped properly or there's too much slack in the containment wall. Let's see how this looks after he's done. Containment is here, nice and secure. Checking my tape every once in a while. You will see that as people come in and out sometimes that this loosens up and I always check to make sure we're nice and tight on the side. Now we've got to check the zipper. Okay, see here, see where my zipper goes down here in the gap because I want to be able to close it beyond where that gap is to actually provide containment. Zip up. You'll see I did the same thing up here. The plastic is up here. I can go a little bit higher and then I have access into my containment area. Basically, we're in one of three access points to the attic and we're removing the insulation. So the original insulation was a layer of batting. So it had like paper on one side. When we started pulling it, I noticed a lot of tunnels. So there is, you know, some rodent damage that's been up here. So there's tons of tunnels that we saw. They actually communicate through tunnels, lots of feces. So far we've have one fatality on record. Definitely had a skeleton that was plastered to that drywall, which we sucked right up. We almost had a second fatality.  So, you know, when you do a job like this, your equipment is like super important.  So challenges we're facing, as you can see, there's lots of lines running all over. So, you know, we've got to be very careful. Obviously the pitch in this area is also not forgiving. But we're getting it done. I'm excited for the homeowner because like once the insulation's out, when you have rodent infestations, you notice an immediate improvement of like the indoor air quality. And so she's going to, the house is going to feel a lot fresher. All right. How are you holding up, Isaiah? Are you doing all right? Pretty good.  That's all we got for you right now. We got to get down into that first. So get the tile out, clear your working space, and then start working down as we go. Okay? Problem is once we, there's a small bathroom, so we're also going to have to do our vacuuming and stuff in here. Okay? Steve, so as you're tearing up the wall, what are you seeing? So I'm seeing where the rest of the mold is. So we already did a mold assessment here. It came back positive at Culver remediation. We see here as I expose more of the wall discoloration on some of these beams here. Some major discoloration in here. But down here, this is bloated dry wood from where it's been away. As you see, I can visibly, I can break it apart with my hand because it's very weak at this point. Okay? So what we're going to do is get an access point so that I can start getting all of this bloated and nasty wood out of here and then treat the support beams and then seal them so that they are no longer a risk to create more mold within the house. Okay Steve, can you tell us why we need to remove the toilet? The leak happened on this wet wall that is the shower right here. But when I did my moisture readings, the moisture showed that this wall was wet behind this toilet. So this dry wall needs to come. I'm going to have to pop the baseboards, cut this, but I can't do that if the toilet's in the way so I got to remove it. This wall was wet so we got to pull that dry wall because that dry wall acts as a sponge and soaks up that moisture. We already know that there's mold. The mold will be growing inside of that dry wall as well. Mediation protocol shows that if we find more visible mold as we're doing this, we've got to keep on going on our two feet and treat. You can see all of this right here as we take care of. Here all these have been treated. Right here is different than what we see here. This is for a tile to be sat there. It's built to be water resistant because it is for the shower. This is decorative for the dry wall and stuff and it's a little less water resistant than this is. Once we get the HEPAVAC in here and we get to see this, we'll know more. What are you going to do now? So now all of our mold spores and dry wall dust and just general stuff that came out of the wall and debris.  So it just keeps everything inside where it's like your normal shop vac. It just blows kind of all those particles and stuff out the back. So it's self-sealing and closed.  This is where the roof started sagging and actually caved in. If you look right here, this frowning on the ceiling right here. This was the indication signs for them that it was leaking water first. And then come over here and look at this support beam. This support beam right here has visible blue. When it's black it's bad, when it's black it's white, plus it's evil worse. But our rule is two foot. So now we're going to go two foot beyond that. So I'm going to go at least right here. So let me make my marks. That's two of three. And we did half of it so far. Immediately when I went in it was just like the temperature was way off. And a lot of times you have heat build up. There's not a lot of exhaust initially. So that can also contribute to mold. I know you guys with Spalding Decon are working on that. But as I was removing the insulation, the other thing that I noticed is that when they did the construction of the home, they actually, there's a couple kinds of insulation. So here we have blown fiberglass but underneath it is rolled bats that's faced. So it's got a paper. So when they installed the bats they actually covered the soffits. So the bats were basically covering that. So the fresh air goes in from the outside. But unfortunately it wasn't allowing the fresh cool air to go in. And that's another reason why the heat was building up. So we pulled that out. Immediately we started noticing a difference. We got the hottest side of the house done. We typically try to work on the south-southwest exposure because that's the hottest part. So basically we've got the hottest part of the second story attic done. So I'm sure we'll find more things so far. There's no dead bodies in that spot. But we'll keep looking baby. That's it for now. So the next thing I'm going to do, take all these. Interesting enough, there's screw. There's nail. There's nail, there's screw. Sometimes these laborers, they'll just use whatever they have available. All right, so this piece looks pretty gnarly on the backside. Look, you got it. And I got to go this way. But I'm also seeing that this is a live electrical wire. So I got to pull this down, tuck it away. Let's tuck it back this way. It's going to be pretty interesting. We're going to go, go, go. So that's rat feces. As you know, Jessica's here doing the insulation. See, we are driving back to the office in the day one today. Very exciting day. We started the demo in the master bathroom. We found that there was more molds. We had to go an additional two feet. We are going to have to pull up the hardwood flooring tomorrow. So that's going to be an interesting task. We also pulled the majority of the roof that needs to be pulled from the utility room. We treated all the, all the support beams. We still have a little bit more to do to pull. But because we were wrapping up, we treated those. It's going to be a multiple step process to treat that. So that we make sure that the mold is completely killed within the side, that area that is between the first floor and the second floor. That where the leak happened and created molds. Very interesting day. Made for a little change on our pace and our direction. But we are going to get a good portion of it done today. Thanks for watching guys. Don't forget to like, share, subscribe and ring that bell to be notified of the next episode. For more information visit any of our locations.


Hello and welcome to Alden Library's Tips and Tricks to Finals Week. For Alden Library's finals week hours, refer to the hours shown here and go to the link displayed for more information. Whether you need to get out of your dorm or just like the ambiance, Alden will always be a safe place for you to work. Study-wise, maybe just talking about the topics like pick a general topic and just list off as much as you know about it. Just to see if there's any blank spots. One thing I do is make sure I stay ahead. I feel like that's the biggest tip I would have for anyone. Don't procrastinate, especially during finals week. I love the little cafe.  A little drink, something to bring you a smile today. I feel like just like writing stuff down on flashcards and just constantly memorizing. That's always going to be key for me. Every 30 minutes, we try to be like, okay, we got to talk for two minutes. What happened today? Nothing, because we've been studying. But then we just talk less on the song in our mind. I've found that working in little pieces every single day rather than all at once, it makes a huge difference for me. I make these study guides, so I'll go through my notes and say if it's like, what are the signs and symptoms of this disease, whatever. So you ask the question, you're like, what are the signs and symptoms of this? And then put a little bullet point, list the things, put it in white, so all you have is questions, right, all in black. So when you go through the question right, then you highlight the answer in green for, you got it right, or red because you didn't know it.  Keep studying. The main thing I would stress is that staff are available as long as we're open, even if you're not here in the building. You can use live chat to communicate with us. You can call. You can send us emails. Especially popular during finals would be our study spaces. But if they're wanting to use our study rooms, they'll want to book those as early as they can. You can book them two weeks in advance. I would encourage people to explore the building if you do need a place to study. You know, look on other floors.   Stick to it, because I feel like me personally, if I'm like in a different environment, I can focus a lot better, so yeah. Finals week always makes Alden a busy place on campus. If you head there to work but are unable to find a spot close to an outlet, head to the second floor service desk and check out an omni charge battery pack. Good luck on your finals and thanks for watching.


What's up everybody? Jason here for jaspyscasebrakes.com. We just sold out this random number block for Gilded, picker team number seven that includes 15 teams That includes the big ones like the Oreos, Angels, Mariners, Braves, Mets, Rangers, and of course plus more So it's 15 total teams, half the league guys, and again if any hits pop out of this throughout the whole case We go by the serial number, first number on the left side of the serial number dash And again one of ones go to one spot one, any and all redemptions for these teams go to spot zero So here's the dice rule, here's the customer names, there's the numbers, let's click roll See what you guys match up with after four times three to one One two three four Four times four four Danny, Danny, Danny, and then three to one four four One two Three four Zero down to five All right Danny, in and out redemptions four zero for you, Chris with seven, Eric with two, Brian with eight, Danny with six, and then Chris Three one nine four and then D-Cons Or sorry Danny, what you got same thing Five so there you go guys now again, of course, this is an R&B so I'm gonna quickly alphabetize it And then I'll paste the list for the sorting team So that way they know the 15 teams All right So there you go guys, good luck, break it's coming up next Oh, it's nearly a toner because it just wouldn't Yeah, it wouldn't want to print unless you had like actual like still toner in there Oh no, so once it's at zero it won't print out All right guys, so I'm gonna go grab the case again obviously number eight is on the website It is down to 14 on its own So it's a little closer than this one here that we're doing for the R&B That we sold out so you know depending on what we hit or don't hit obviously it doesn't affect the next case But I'll probably random number block the next case a gilded as well if you guys want to run it back similar style I'd assume it'd be a little bit cheaper, but sometimes there's like other teams that did Sell on the first half that's sold here vice versa So but it seems like I see the diamondbacks available in this one. So that's a cool one I see the braze tigers angels Dodgers Mets Yankees Padres Mariners No rangers, so it's almost the same I think the only difference was in the first one the Orioles didn't sell and the Diamondbacks did this time the Orioles Hold on this half, but the Diamondbacks didn't so little little switcher up right there But after that we're gonna know our basketball I'd be right back While other houses rush, we take it slow, some rakes sweet Italian tomatoes with open kettles, slow is good Unless you're face-to-face with a light backer, or you're homemade, make everyday delicious On a curse, thanks man, cheer the boogers, and you gotta get it to your face That's cool, that's super cool, love it when it's hot Hey guys here we go then


So last week we had a case come in and I mean it didn't come in I've been representing a lady for about four or five months and I talked to her and talked to her we talked to her often so last week she wanted to settle a claim and literally she wanted to settle gave me authority to settle a claim for $5,000. So I looked at the claim and I went through the medicals extensively I had an outline in my head and actually on paper and I got the defense lawyer on the phone and I went through the case with him point by point and the demand I made was $75,000 they paid her 50 and she was just beside herself. Now she was fine with just resolving the claim for $5,000 but what she didn't understand is we needed to build in future medical care for her we needed to build in future wage benefits for her we did refer her to in in-house to our Social Security Department they're gonna handle her Social Security case we had to make sure that her benefits were secured so that Social Security wouldn't take all of her money there was a lot involved but a lot more than her just saying okay I'll settle for $5,000 and this is what happens with people that don't have lawyers they just call the insurance company and they're so tired of no not good medical care not getting paid on time that they just fold up and say I don't just the heck with it it's not worth it just give me 10,000 I'm leaving I'm done well we're their voice and we don't let that happen we try and instill patience which is difficult sometimes because of their financial situation we understand that but we want to move the case along appropriately to compensate them the right way


Hello. Am I audible at the back? Yeah. So I'll start. I actually did not even expect these many people because even I don't reach office at this time. But yeah, I have a crowd. So I wanted to start with a quick show of hands. How many of you people know about natural language processing? How many of you have actually worked on it or used it? And how many of you know what embeddings? Okay, very few hands. And about deep learning, how many of you know the basics of deep learning? So I'll demystify these terms, deep learning and natural language processing. And I'll try to give an intuitive understanding of why deep learning works and how do you use it in the context of NLP. So let's get started. So internet nowadays is a huge source of data.  And the reason for that is the number of, the sheer number of users there are who are continuously generating data. When users browse websites, clickstream data. When they buy something, it's transactional data. But undoubtedly the largest amount of data on the internet is in the form of natural language. And NLP is about that. It's about computers being able to process that data, computers being able to understand that. And if you can effectively do that, the number of applications are actually limitless. I've listed down here some of the applications. Now, search, when you think about it, is all about NLP. An ideal search engine, what it will do is it will try to understand the query of the user and then map it to the relevant document. Customer support. Today, thousands of people send emails to companies and a team of hundreds of people go there and understand the email and try to solve the problem. Even that has a lot of scope of efficiency. Question answers, like I said, answers to most of our questions are out there on internet. Sentiment analysis has also become a big deal nowadays because companies want to understand our users liking my feature. If a customer is dropping off, why is he dropping off? And people are tweeting about it. They're blogging about it. They're literally shouting on the internet and it is being captured across various databases, but we need good enough techniques to understand sentiment analysis. And social data has so much of knowledge, like if, suppose for some reason Flipkart has a technical issue, people on Twitter will tweet it and we should be able to identify it from that data. We should be able to know about cases like net neutrality that people are not happy with this by automatically analyzing this data. I can go on and on about such examples of NLP, but you can use it in personalization, information extraction and so on. So NLP is basically the science of deriving meaning from natural language. And it has initially when computer science started, people thought, well, NLP wouldn't be that big a deal. I'll pass this sentence into some sort of a semantic data structure and I'll be able to run queries on it to understand language. But soon they realized that it wasn't as simple as that. People moved on to rule-based system, so they ended up writing a rule for each sort of thing and they also realized there that you cannot pre-think every possible rule. And what happens is when you put too many rules, these rules start to conflict with each other. So some good systems are there, still ELISA, chatbot and all, which are based on that. But the biggest breakthrough in machine learning came in the late 1980s when people started to apply machine learning-based statistical methods for this. Now you go to your Google inbox and all your spam is automatically cleared out. That's the power of statistical machine learning at work. You go to Google News and all the news talking about the same thing is clustered together. Even Google Translate till a while back worked on the principles of statistical machine learning. What it basically does is you give the computer the algorithm a bunch of positive and negative examples and it will find statistical patterns in that. So let me take a few examples for people who are new to NLP to appreciate why it is hard. So sentiment analysis is a problem that I've been working for a long while now. So I'll give some examples over there. This is an easy statement. Flipkart is a good website. You can just count the number of positive words and say that this is a positive statement. This one is slightly harder because receive, I didn't receive the product at some time. Now receive is not a positive word in any centi-word net or anywhere else and didn't receive is actually negating that. But a good statistical classifier will still catch this because it will see the biogram didn't receive occurred many times in the negative tweets and classify this statement as a negative statement. There is a problem of rare words. Suppose in your training data you never had the word shoddy. So the classifier wouldn't know that shoddy is actually a negative word and it will say this is probably a neutral statement. There are all sorts of things in social media. There's SMS lingo, there's misspellings and so on. So if it is not in the training data, it would be a problem for the classifier. And then of course there's the hardest problem for all is sarcasm. Well played flipkart. So statements like this even humans who do not know what IRCTC is would have a problem classifying it as a negative statement. So the bottom line is that NLP is not that easy and the kind of systems that I talked about in my first slide are not really there. The reason is accuracy is hard. We understand that NLP is hard but the end user doesn't and if you make mistakes over there, end user feels there are bugs on your website and so on. None of the e-commerce website has a good review summarization system and all. So how do we go about increasing the accuracy of these methods? This problem let me to try this exciting new technique that has been coming up a lot nowadays deep learning. A bit of overview about deep learning, it has shown some amazing results and it has dominated pattern recognition in the past few years. If you see ImageNet it is a database with around 5,000 classes and deep learning has an error rate of around 6% which is next to only humans which have around 5.6%. Facial recognition also these have near human accuracy. Those of you who use Google phones would have noticed that speech recognition accuracy has tremendously increased in past few years. Even deep neural networks are in work over there. I have some examples of the hard ImageNet problem. Now images, humans find it very easy to classify but if you look at them at raw pixels it is actually a very hard problem and they are doing it perfectly for so many classes of images. Even when they make errors, it is sensible errors. Even humans might classify the first example as snake or the second example as dog. So I come to this question of deep learning for NLP. NLP has its own specific problems but when I talk about applying deep learning, people sometimes say it is a hype, some people say it is overly complicated and so on but I think numbers speak best for themselves. What we observed on our internal data set was an accuracy increase from 85% to 96% in two class sentiment classification. I did not believe these results when I first tried them. I thought there might be a bug in my code or I might not have split the data sets probably and so on but when I actually looked at it, it was this significant. There was a 73% error reduction which is actually a lot. Generally, we see incremental gains like 2% increase, 3% increase but this was actually significant. The most amazing thing about this was that there was no sentiment-specific feature. I did not handle negation specifically. I did not give it sentiment-specific lexicon or anything like that and still it learned all of that. Obviously I applied it to many other tasks. I applied it for topic identification and I applied it on various data sets like tweets, reviews and emails. So it worked very well in all these cases. Most of my talk will be about text classification but in the end, I will give some hints on how to solve other problems using neural networks as well. Many of the problems that we receive right now can actually be reduced to tweet classification. Let's say you have to prioritize email, you have to do sentiment analysis. A lot of NLP stuff can be solved by training a simple classifier. So the burning question is why does deep learning outperform statistical models? Let's say you have a data like this.  So lay down like this. What a statistical classifier essentially does is that it learns a decision boundary here. Now, this could be anything. Let's say you are classifying users as male and female. So the way statistical classifier will work is when a new data point comes, you can classify it as male or female based on the side of decision boundary that it lies in. The problem is that most of the raw data is like this. You have images, you have data and you have sound waves and so on. So how do you actually get from here to here? The answer to that is features. I'm sorry, the slide has some issues. So features actually transform input data into a space where a classifier can learn a decision boundary. Now, most of you who would have worked in machine learning would know the pain of feature engineering.  After that, it's very simple. You just learn a classifier. And let me give an example for features. So let's say you have some data, you want to classify gender as male and female. So you can plot the browse counts in male store and female store. So that will be a discriminative feature for this data, and you will be able to learn a decision boundary on that. So features have to be manually identified. But deep neural, the power of deep neural networks is that they identify these features automatically. And sometimes they'll, like in case of images, they'll obviously do a better job at it than humans because they're looking at the data. So what is a neural network? This is an example of a simple three-layered neural network. You give a pass on the data to the input layer, and whatever, if you're learning a classifier, output layer will actually be the labels that you've given to your data. And hidden layer has the representation, hidden representation of data. I don't have enough time to explain this fully, but what essentially happens is that there is these lines that you see between layers are weights, and each of the neuron is doing what a simple classifier does. And when you actually train your network, what you do is you pass it the input data, you set labels around the output, and there's an algorithm called back propagation, which learns all these weights inside it. The power of deep neural networks is that higher layers from higher levels of abstraction. Now image has always been a good example to explain this. So let's say you're training a classifier which can recognize objects and animals and so on. So what the first layer will learn is essentially very basic things about the images, basic things like edges, and what the second layer will learn is probably more complicated shapes like circle or simple geometric shapes and so on. The next layer after that will learn progressively higher features like maybe furs or feathers, and the one beyond that will learn maybe complicated structures like eyes and ears. So this is actually how even the human visual cortex works on images. We have layers called V1, V2, V4 and IT, each learning higher and higher level features. So this is actually why deep neural networks work so well. And images, you cannot do that good of feature engineering because you'll need new feature for every new class. You'll have different features for images, for cats, you'll have different features for something like an ambulance and so on. So that's why in image recognition now deep learning has replaced all the techniques. People directly start with these. Another reason why deep learning works so well is this concept of unsupervised pre-training. Now a lot of data that we have, labeled data is only a small part of that. Let's say I'm doing sentiment analysis, I'll have say what, 2,000, 5,000, 10,000 labeled examples. But there is such a huge chunk of data in the form of Wikipedia on web. And deep neural networks have a way that they can learn and generalize from this unsupervised data. They can catch patterns, grammatical patterns, meanings of words from that data. So here is a graph, the blue line on the y-axis you see classification error as the network trains on an x-axis. So when the blue network is the one in which, which wasn't unsupervisedly trained and black network, black line is from the network which was, had unsupervised training. You can see that it generalizes, that it generalizes very well. This thing is not there in normal statistical classifies because you just ignore the unlabeled data. You only use the labeled data in most cases. So we saw why deep learning works. I gave you basic two reasons that higher levels from higher forms of extractions and unsupervised pre-training. But there's specific problems when you apply deep learning to natural language. Specifically that first of all, natural language can be of any length. I mean you can have a word with five sentence with five words or 50 words. And how do you pass that data to a neural network because it will always have fixed number of neurons in the input layer. Another problem is data sparsity and so on. But let us first go back to statistical models and understand how they work in case of Lp. Let's say you have a tweet like Flipkart is better than Amazon. You first would convert it into a bag of words representation which will have unigrams and diagrams of these words. And then you'll apply something called a one-hot encoding to encode this data. What this one-hot encoding is doing is suppose Flipkart is present in the sentence so the index of Flipkart will become one, rest all will remain zeros. So I have an example vector over there. Now this is the data that you actually pass. This is your feature and you learn decision boundary on a data like this. So obviously there are a lot of, this is a very simple approach because there are so many problems. First of all word ordering information is lost. So Flipkart is better than Amazon and Amazon is better than Flipkart have completely different meanings but they'll have the same representation if you're using unigrams. Data sparsity is another problem. Now you have so many zeros and so few ones. You'll have around 20,000 zeros and few ones. How will you learn a decision boundary in a thing like that? Another thing is words are atomic symbols. So now cat and dog are same distance apart as cat and ambulance. Although cat and dog have so many features in common. Both cat and dog are animals, both cat and dog are pets. So they are semantically similar but this guy will treat them as just an index in a vector. So it becomes very hard to find high level features. To solve these problems people use other techniques.  So we come to this problem that how do we encode a meaning of a word? Word is more than just an index in the vector. How do we represent that? One idea can be represented using synonyms in wordnet but obviously if you treat these words adept, expert, good, practiced, skillful as the same, they are very different. You cannot say that they are exactly the same and there is no way to get a distance between them or something like that in senty wordnet. So the biggest breakthrough in applying neural networks for NLP was word embeddings. So here is an example vector for cat and dog. So because they are semantically a little bit similar, the cosine distance between the vectors of cat and dog here will be less and in one or encoding it's the same for every word. So it doesn't matter. So this word vector somehow represent the meaning of the word. I'll explain that. I'll make substantiate my statement. So how this word embeddings are learned is, Bengiu in 2001 was training a language model using neural networks. Language model is, you can think of it as something like auto-suggest on your keyboard. So you type a sentence, the cat is a, and it predicts the next word. That is a basic language model. So they were training that using neural networks and they couldn't pass words directly so they created some random embeddings. Those embeddings changed when back propagating and that was actually the first example of word embeddings. When they, when they opened those hidden layers and saw those word embeddings they discovered that they had beautiful, they had really beautiful properties. So this is an example of closest words found in the word embedding space. So if you see the word France, it's closest to European countries. If you see Jesus it's closest to God and other such words and so on. So it has learned that from the data that these words are actually semantically similar in their interchangeable. They capture relationships. So if you subtract the vector of the man minus man it is same as aunt minus uncle or queen minus king. So it has the information of gender somehow and they have captured so many different relationships. France to Paris that's a country to capital relationship. Microsoft Windows that's an operating system and the amazing thing about this is that we never trained the network to learn this. It automatically in the process of learning a better language model. What the network was doing was simply predicting the next word but to predict the next word effectively it had to learn all these things effectively about human language. It had to learn the meaning, the relationship, gender and such semantic attributes. This is another example superlatives. They have a similar sort of angle in that space. If you subtract king minus man plus woman will actually give you a vector close to queen. So you can even do some analogy questions. The bottom line here is that first of all word embeddings are trained in a completely unsupervised way. You don't need labeled data. The data sparsity problem is essentially solved because now you're not dealing with huge number of zeros and ones. You have a lot of floating values. They've done some sort of semantic hashing. They're representing the information about meaning of the words. Semantic hashing is basically in that space. Semantically similar words are closer. And they're freely available for out-of-the-box usage. You do not have to have a GPU cluster to begin to start use them because Google has actually released word embeddings trained on Google News Group and Wikipedia and so on. You can get the word for cat, for dog, the word vector for that. Now word embeddings are good but they're not directly useful are they? I mean we're not interested in meaning of a word. We're interested in the meaning of a sentence. So how do we represent that? Now this is not a solved problem till now. There have been a number of approaches and we're still researchers going on in this. A simple approach can be word vector averaging. So you just average out the word vectors of each words. There's a huge loss of information over there. It's not a good approach. But even that gives performance sometimes equal to statistical classifiers. There are better approaches like weighted word averaging but even they don't work that well. So here is an image by social who has done a lot of work in how to compose word embeddings. So usage of language has some sort of a recursive structure. So what you do is you take word embeddings for each thing and recursive neural network is by the way different from recurrent neural networks and compose them. I won't be talking much about this because most of the work that I have done is on convolutional neural networks. Convolutional neural networks are excellent feature extractor in images because features are detected regardless of position in an image. Colibar back in 2011 had the first idea of applying these for text. Let's see how CNN basically works. This is a GIF explaining that. So a neural network will basically scan through the image and what this gives is temporal insensitivity because now if a cat is present in the top right corner or the bottom right corner it will still be detected because you are basically convolving and this yellow weights that you see are like a simple neural network. And this is the model that I use CNN for text. This is the overall architecture of the network and so I have laid down the word embeddings over here. These are the word embeddings. This is the convolution layer which is doing what the GIF just showed. Convolving over you can think of them as 1D images so it will convolve over them and this is like a normal deep neural network. I'll explain this thing in detail. So just to say so you will pass the word embeddings over here, the label over here and all these are weights. All these are weights which will be learned through BRAG propagation. So let's say I have a sentence called cat sat on a mat. I lay down the word vectors for each of these. Now I have to do some sort of composition. So let's say I have a window size of 3. So I'll convolve over this word with this window. So convolution operation that we are using right now is simple concatenation. So I'll concatenate these vectors. So the cat vector sat on is all concatenated into a single vector and then we're doing it a multiplication with weight matrix. These weight matrix are the weights of the convolution layer. So even these are learned when the network is actually training. So this multiplication gives us again 3 by 9 into 9 x 1 will give us a 1 by 3 vector. So this is how you can, this will be the same for even if you increase the window size. So you will pass over this window cat sat on, sat on a mat and then you perform something called max pooling over it. So the first vector over here represents the trigram cat sat on. The second vector represents sat on a and the third represents on a mat.  And by the way this is a toy example because in real life you'll have word emitting that 200 dimension or 300 dimension. This is just for simplicity sake. Max pooling what it will do is it will figure out the, it will just take the maximum of each vector. So here it's 0.46, here it's 0.81 and 0.40 and you, this can be considered as the most relevant vector of the sentence. And after this you perform, you pass it to a multi layer perceptron to, which is a neural network to finally get the output which is neutral, which will be neutral in this case. This is how the basic convolution neural network works. Now actually I and many other people lose the intuition at this max pooling layer. Now what is happening? You understand that this is representing that and this but what happened over here? So we created a debug console to actually understand that. Let's say I was intense. I have to say website experience was okay but you guys did a really shoddy job at delivering the products on time. Now this is a large sentence but your sentiment will essentially depend on the words over here, really shoddy job. If you just quickly look over it you'll catch these words and you'll say well this is a negative statement. This is exactly what the neural network is doing. So the number, this feature count here is the number of features that came from this trigram. By the way we are using 3, 4 and 5 grams in this thing. We're not using 1 and 2 grams. So really shoddy job did really shoddy job at. These are the things that it learned most of the things from. It even learned experience was okay because even this gives sentiment but these things overwhelmed it. Actually each of these we are actually working on it and these will become green, red because it doesn't show right now what it is saying about it. So what I showed in the animation was a simple thing but this is how it works in real life. You have window sizes of 3, 4 and 5. I just showed the window size of 3. I'm sorry about the presentation. The static mode, there's something called static mode in which we do not change the word vectors. In non-static mode what happens is back propagation runs on the word vectors as well. So word vectors change their value as you after you train them. There's a multi-channel mode in which you use both static and non-static mode and we even adopted the model to do multi-class classification. These are some of the results that we obtained. So first three are on Flipkart's internal data sets showing how the accuracy increased compared to normal statistical models and the rest two are from external data sets that we just tried and you can see we have applied it to a lot of problems. Sentiment analysis and topic identification and we've applied it on variety of sources, Twitter, emails, movie reviews and it works better than statistical classifiers, sometimes significantly better than statistical classifiers. Here if you see this is a statistical classifier which has used so many features. It had used lexicons and this is a paper by NRC Canada and we outperformed it just by using a simple neural network which knew nothing about restaurant reviews. No feature engineering nothing like that and it still outperformed a heavily feature engineered algorithm. SS2 is standard sentiment data set around on the net. Statistical classifier gives 79.4. Although some deep neural networks give 88% there. 87.5, it's nearly that. And these are other data sets on Flipkart where we obviously saw performance increases. Here is some examples that you might like. So this was the earliest sentiment classifier based on naive base and this is the convolutional neural enterprise. So in the first example, a naive base classifier overfit on the words the best and it ignored the word sold out or out of stock and gave this as a positive example. Here, max pooling what survived was sold out and out of stock. In the second example is the rear word example. So now shoddy would be caught because the word embedding for shoddy would be closer to some other negative vectors. So it will learn that shoddy is a negative word even without explicitly being trained on the word shoddy. Overfitted last year, so we had a data set of big billion day and a lot of people tweeted negatively on big billion day. So the naive base classifier actually learned billion as a negative word and CNN is not prone to such problems. What survived was good sign. So drawbacks and learning. So the main problem of neural networks right now is that it's computationally expensive and there are no ways around it as such you have to use GPUs as most companies are using. We could train on our data sets on CPUs but for larger data sets for experimentation we use GPUs. That's how you scale training and there are other problems like multi CPU parallelization is not their ingredient descent in Tiano. So all these problems make it incredibly hard right now. It's in a very nascent stage. There are no good libraries for this right now but what are used are Tiano, Pylone 2 and Torch. Torch is increasingly gaining popularity but my current work was in Tiano. How to scale prediction? Prediction is slightly easier to scale because it's not that computation heavy and we're actually just using a simple Tiano server for that. This is a cartoon that I said that if you did not understand anything in the presentation but you're interested about it you can go and read about it. I did not have time to cover it fully. Another thing is I have completely open source my code. So this is a generic library you can go. You can put your own data sets. You can try out. You'll have a state of the art sentiment classifier if you have good data of data. The link is in my slides as well. You can reproduce all these results. It's heavily derived from all other open source works as well. In the GitHub page I say that what exactly are the improvements I've made on the other codes but it's basically easy to use utility. Just give your sets and it will work. So I said I talked a lot about text classification but since my talk is deep learning about NLP there are a lot of problems beyond text classification also. So unsupervised learning and so to solve the composition problem there are basically three approaches right now. convolutional neural network is one recursive neural network is other and the third one is recurrent neural network or LSTM type of cells. So how they work is something like this machine translation and chat and classification these networks are performing well. So you pass it one by one in a recurrent nature. So this is actually the same neuron and this edge is going over here but we folded it out on the time. So these models are also giving some very good performances in sequence to sequence sort of problem like machine translation where you don't have a single sentence but you have to translate it into another language and suppose you want to learn a chat bot or classification also these models work well. So this is also an exciting area to try on but again libraries and good are not there. So yeah that's it. Any questions? Mono. Hello. Yeah. Yeah. Did you ever come across this semantic training where we train based on grammars and find the semantic relation between words and you compare it with neural network training. Okay you're talking about the linguistic approach where you first so where you first passed the sentence grammatically according to POS tags. There are also libraries like Sempre which Stanford has come up. Stanford NLP. Yeah. So I mean these gave incremental performance like when I said features other than other than POW so you pass these things as features. So actually one of my colleague tried it and what 85 to it the accuracy increased around 3-4% in that but these gave much more performance increases and the biggest problem is in Indian data you won't find grammatical structure. People don't write it that well so these perform don't perform well specifically on Indian data. Those grammatical approaches. So it's a representation I mean it's it's hard to say I mean if you were to represent the word cat can you think of a vector now cat has many different properties right. So it has actually learned them while training right. So it it represents many of the things but you cannot actually give it a label that this vector is this this vector is this because it has automatically learned that. But you can run experiments like the cat and King example to see that it has now gender is not a property single property because you're subtracting the vectors. So it has represented gender in that whole vector space somewhere but we can run experiments to figure these out but it's not very obvious that which is there. You'll have to run experiments to get that. Yeah. Hello. Yeah. What to make. Yeah. We even try training on a flip card data set our own word embeddings but they didn't work that well. They directly the what what Google has trained I just reuse them. But you can train on your own data set also but this performs better because it's a huge data. Yeah. There is a recent paper by Coakley and others which have some data is based on RNNs. So they have some very exciting results. Hey, good morning. So so the interhuman disagreement rate on sentiment analysis is 16 percent. It's here. I'm here. OK. Yeah. Yeah. The interhuman disagreement rate on sentiment analysis is 16 percent. Right. So how did you achieve 96 percent accuracy? I think that's a little bit no very clear. So actually it's not that how do you have references for that 16 percent interhuman? I mean that would be specific to a data set. You cannot say in general on some data set there might be 16 percent. Even here we observe things like that but we basically removed all the ambiguous example from training. In testing also we had a redundancy. So to measure ambiguity what most people are thinking was taking up to properly label the data. So even the examples in the rest 4 percent are actually ambiguous examples where some people might think of them as positive some people might think of them as negative. And 96 percent is two class positive negative. In the three class we have around 87 percent. It's not 96. Positive negative neutral class. In general you need a large data set to for this to work right. So what is the kind of guideline on that? How much data is required for you to use deep learning? Actually you do not I initially thought that you require a lot but it's not the case. So because word embeddings have actually been trained on such huge data what we had in our sentiment data set was just 1500 examples of positive negative and neutral. It wasn't a huge data set. Word embedding the one Google provides are generic. I trained them on Flipkart data but maybe because it wasn't so huge those word embeddings did not perform that well as the one on Flipkart on Google Word 2 performed. Because it catches generic patterns across things. Word embedding require huge data but using word embedding you can work with small data. Yeah, there are other implementations of Word 2 also. Hey Shankar on second. This is Vignesh here. So my question is with respect to the corpus. So for example if you consider Sachin Tendulkar he is regarded as the master blaster as well as the god. Like when you consider that when we pass each token or rather the annotators of a sentence how do you what corpus did you use that would say Sachin Tendulkar the master blaster would be the same. Like if that is the question like how do you cater such words because they are proper nouns. So that is like again a very linguistic sort of approach but so word embeddings work on the basis of co-occurrence. So when you say Sachin Tendulkar is a cricketer as well as a god they will be used in similar contexts. Sometimes people will write Sachin Tendulkar is god sometimes people will write Sachin Tendulkar is master blaster. So while training word embeddings since it is based on co-occurrence of words it will catch that master blaster and god in context of Sachin are similar words. That's how word embeddings will catch that thing. So the cosine value between those two words would be this. It would be placed together. Okay. Thank you. Okay. So is this a target dependent sentiment analysis and if it is target I cannot catch it. Can you repeat it? Yeah. So the sentiment the results that you presented right first and second row. Yeah. So is it target dependent? Yeah. And that one is on Flipkart Twitter data set so it will catch words like delivery delay and so on. And we train SSD2 is movie reviews data set. So that is like a generic sentiment classifier. Okay. And how do you figure out that the target of the sentiment is indeed split by there could be multiple entities in the same tweet or same sentence. Right. So how do you know that this target is actually not something else? That's a good question. So we are thinking of solving that by. Yeah, actually that will require a special formulation of the problem right now. The network catches is a general a general sentiment. So even if it is positive for Flipkart or Amazon it will give it as positive even from Flipkart context it might be negative. But that's an interesting research area as well. How do you solve that? Yeah. How do you how do you get embeddings for the words which are not in the corpus? Which is not in so which is not in the training corpus or which is not in the I mean let's put everything together. So if it is not there in word embeddings we'll randomly initialize a vector. So even though the word vector is not there there will be a random vector but all these weights will be learned corresponding to that word. Now there might be many words whose word vectors are not there. But still it will learn but it's better if you you can even start without word vectors. You can give a random vector to anything and will still give 85% accuracy. But word embeddings just increase the performance. Yeah. I have two questions in in large sentences where there are multiple entities. For example I don't know Flipkart has a very poor returns process but Amazon has a very poor catalog depth. How good has your experience been? And the second question is what about sarcasm? So like I read a tweet and no political affiliations here which said PM Modi will make a great business development manager for the Adhanis. Yeah. Would your experiments capture the sentiment of something like that? Yeah. So the first question about that I said we haven't solved that problem yet. We can think of approaches like there are again linguistic approaches but negates the things you divided into two parts and do separate sentiment analysis of that. That is one way to solve those things. And sarcasm still these networks won't catch until you explicitly give them a lot of sarcasm examples. And sarcasm also kind of has patterns. It will be highly positive and there are sometimes weird things like IRCTC might come generally in a negative in that example in generally in a negative context but you coupled it up with a positive thing. So that's how sarcasm might be caught. OK. Yeah. I had two questions. Question number one is when you decide the sentiments do you also scale them in a rank between one to five one being the least and five being the best or it's only positive or negative? It's only positive or negative. Right now the three classes positive negative neutral. So the accuracy will decrease on fine-grained sentiment. Now humans humans also like they said have an ambiguity in positive and negative and when you increase the number of classes that ambiguity becomes even further. Someone might say this is very positive someone might say positive. So you'll see accuracy decreasing on that. OK. Question number two was when you extract features from a given document from a given comment. How do you take care of phrases to two words which actually mean one? Phrases right now should be handled by the shingling that the CNN is doing. Right now we do not come up. Actually so if there are things like phrases we pre-pre- during preprocessing stage we sometimes so an example of phrase would be well done if you say well done. So well underscore done but that is not being done in this network. OK. Thank you. That will be the last question. Hello. Yeah. So I have two questions. First how big was the training corpus and how was the accuracy calculated and second is how big was it how big was the cluster on which you train and how much time was it taking to train on that cluster. OK. So we have formed experiments on a lot of data sets I'm assuming you're on the you're asking about the Flipkart sentiment data set. So that was 1500 training 500 tested that is all we could label with all the redundancy.  So that will take even longer to get the optimum configuration. And there was a one more question. OK. Yeah. Yeah. We use so I used a personal GPU laptop for that and GPU we got 30 X performance increase.


Bonne matin, bonjour, bonjour est-ce que c'est plus facile pour moi de le dire. Je suis là aujourd'hui pour vous parler de précision, de miséconnectivité, de l'élever votre faible à l'hôme, un projet qui accélère votre specialité. Vous pouvez inquiéir ce qui est faible à l'hôme. Oh, je peux changer le slide par moi-même. Donc, pour vous introduire moi-même, j'ai commencé dans cette industrie. Longtemps ago, I was a young man, mid-order now. I did quite a few experiences, which is a history of scanning, but I'm not here to talk about me. It was just to make a quick introduction basically. So, fibre to the home. Why someone from Tremble is talking about fibre to the home, you might wonder. So, first of all, what is fibre to the home? It refers to the use of fibre optic cable to deliver broadband internet. Directly to your home, to your office. And you know like today we have more and more need to use a cloud to exchange data. Even to stream Netflix or Amazon or whatever things or exchange data between all of us. If you do mobile mapping, you collect a lot of data. We need like fibre. Like for example in Germany they do a lot of development because fibre optics is not so much developed here compared to other countries like in Europe for example. And it replace a copper infrastructure that's telephone or correctional cable, as you can see here. So, we spent quite a lot of time to make that graphics. It's not just like a pretty picture that looks nice like a cartoon. It's actually a story. If you can see, I cannot really see, but the tree from the first phase of planning a network to building a network to make the operation and maintenance. You can see the trees they become a bit bigger too. The poles start to tilt a little bit with a mystery after storms or flooding and all those things. And you can see a little cloud on top to exchange the data between the field to the office and back to the field. So, I will try to explain the different phases of such a network to you. Because there are like several needs and the title was precision, misconnectivity. So, first of all, it's kind of nice for all of us to see. The fiber to the home, they have like an association. They have like a handbook, it's like my new Bible to learn about how they do things. And you can see they have like a person, like a surveyor with an instrument on a tripod for the first phase of planning, so to collect some data. Then operating and maintenance. And then what do they need? They need to make a choice if they want to lay the fiber underground by making trenches on poles. And in some countries, it's even on façade. So, a lot of time people use like an open street map or Google Maps to try to lay the design. But the accuracy is not the best. And is it like up to date as well? It could be very old images for example. So, that's why they start to look more and more at geospatial sensors and data to collect more accurate data. Because it's important for them to know the trench will be in private property or public property, so accuracy is key. Same for the poles, they need to know the location of the poles, who is the owner of the pole, all those things. They need all that information. So, that's why I'm not here to talk about products but just to give you an idea like when they start to do like a new country they need to do like first like a large scale reality. So, mobile mapping, that's the title of that session to talk about mobile mapping. So, mobile mapping is used like widely and then in some smaller areas, they can use like scanners. And once we mobile mapping, after collecting all the data, they send the data, people will analyze all this data and then they can send some crew in the field to analyze to see if it's correct or not. So, they can use some GNSS solution, for example, in that case. So, just to give you like a little bit of all the things that can be used but first of all mobile mapping to collect large area. I know I could try to change the slide, it might come shortly. I need fiber, maybe, that's the internet that's missing here. I cannot go back. I know if you can help me in the back to change the slide. Thank you very much. We said we talk about hardware to collect the data but the big piece of that puzzle is to analyze, process the data and then exchange, share the data. So, that's why the cloud Trimble Connect is a key part of the solution to be able to share the data, analyze and send back to the people in the field. And also when we talk with people doing fiber to the home, they don't know our software very well. They have their own dedicated software. SDK, APIs, are also key for the success of all this. It's not just collecting data, it's just one piece of the puzzle. They need after to analyze, process, exchange, all this. So, just to go look at the planning what they have to do first, they have to do like the pre-survey of the route to see which side they can go. They need to do some analysis of the natural extent of the work. Grand characteristic is that send, is that asphalt. Location, accessibility, all those things as well. The tree next to pole, so, that's the planning phase. And for this, I hope that video will play. So, they can use like a type of system, so mobile mapping to go and drive, could be like in a city, could be the countryside as well. And so just like to collect data, so point clouds on images at the same time. Obviously, that's the first phase to do like the pre-survey of the route. In that case, as a server of the route, so more accurate than using like Google Maps for example. Obviously, an up to date, which is also key for them to have data up to date. So after this, they get that point cloud and images at the same time. And from this, they can extract, analyze, they can import the design also to check if it will fit or not. To see which side of the road will be the cheapest to dig for example if it's the send or the asphalt, that's a good example. For example, and then send the data to some GIS. So of course, if you want to see more about this, you can come and see us on the Trimble booth. The MX publisher is what we discussed about the cloud as well is to be able to share the data inside and outside the company. Because if you just keep your data for yourself, it's no use. It has to be shared as well. That's a perfect tool to share the data but also to make some integration via plugins. People could use like Asry or QGIS or GeoStrike for example or Bentley, someone from Bentley just before, Autodesk to do all that analysis. The design phase, it's what they call a walk out. So they do a walk out of the design. They need to make sure the design will fit in the existing. And for this, they use of course mobile mapping in the office but they have all the data collected, import the design and see if it will fit or not. But also they can do some field survey verification and you can see that little surveyor is holding an instrument, some augmented reality. It's also recommended by the people doing fiber to use augmented reality for this. And we have what we call site vision. It's a Trimble site vision to high accuracy order. So again, accuracy is key also for them to know the location or exact location. And then they can also use PDF straight in the field because in that industry, people use a lot of PDF also. So you can have a look of what's hidden, so what's below the ground, could be cables. So it's so important for them to know the location of existing utilities. Could be water, gas, electricity for example before they place a fiber. They don't want to break anything. See the future, so once they use some poles they can see if the poles will fit into the forest or they have to get rid unfortunately of trees or maybe move the design to save the trees for example. That's also key. And see the progress once they install start they can see and follow straight in the field so they hold the device. They can see the design with the existing. So once they do the building phase they need to do the recording of all the data or all the things that have been installed. Do some GIS field update as well and do some inspection and record modification as well. So we found that the solution, the Trimble catalyst solution was the best for them because they don't need to be experts in surveying. They don't need to know anything about like a coordinate system if they want to connect to a VRS or to a base station. With that solution they can just grab an antenna connect and collect. We have something called Trimble app correction that will find the nearest base station automatically send you all the corrections straight in the field. It's what they do and then for the mobile app they can use our own app or they can use like Esri or QG or their own solution which is also key because that's an interface they know. For operate and maintain they have to know the precise as built they need to do some inspection as well. Like that pole is a bit tilted, quite a lot tilted the vegetation grows also and can affect the lines, the power lines and all type of elements. We see more and more unfortunately like flooding or storms that can damage and destroy things so they need to know location to be able to rebuild all this. So that's some example that's it's not a very nice example on the left, that's from my street where I live it was a little storm, there were some branches on top of cables some of them got damaged and collapsed in my street and what we could do is to collect the data using mobile mapping or a scanner or a tripod and then we can upload that data to Trimble connect you can see here you can see the point cloud in Trimble connect straight away you can make some to-do with precise location and send that information to the people in the field they know the location they know what they have to do exactly so that save a lot of time instead of sending a cruise somewhere voilà, try to do your best so that's something very very nice actually another example is a power line that got damaged after storm so mobile mapping here will be like overkill so better to like a scanner a bit faster to collect that data and then check the orientation of all the antennas and elements here and then the last slide to be on time almost last slide so it's again me in my street there was like after that storm like some port that collapsed I could go and collect the location with catalyst so very small antenna collect very precisely the location send a replacement all those things and then we can again upload to Trimble connect that's our cloud solution or we can also like upload to other cloud solution actually and then people can do all the analysis about what happened and send people where they need to be and the last story that the last slide is still on time about vegetation manager it's a solution we have in Trimble so once you know like the tree grow and you can see on top after you like some mobile mapping survey you get that LAZ file and we can make some simulation if a tree is falling down if it will eat the pole or the wires what is the distance for safety and same for the clearance between the ground and the lower cable so we can do all that analysis and sometimes unfortunately people cut trees depends if you love trees but unfortunately people cut trees could be also before a storm a tree could collapse before a storm so maybe it's safer to cut it before that storm obviously and thank you very much for your attention please come and see me there is many other people with that shirt on a big booth on all 27 but thank you for your attention thank you