Manufacturing is the production of merchandise for use for sale using labor and machines, tools, chemical and biological processing, or formulation. The term may refer to a range of human activity, from handicraft to high tech, but is most commonly applied to industrial production, in which farm materials are transformed into finished goods on a large scale. Such finished goods may be sold to other manufacturers for the production of other, more complex products, such as aircraft, household appliances, furniture, sports equipment or automobiles, or sold to wholesalers, who in turn sell them to retailers, who then sell them to end users and consumers. Manufacturing, engineering or manufacturing process are the steps through which farm materials are transformed into a final product. The manufacturing process begins with the product design, and materials specification from which the product is made. These materials are then modified through manufacturing processes to become the required part. Modern manufacturing includes all intermediate processes required in the production and integration of a product's components. Some industries, such as a mill conductor and steel manufacturers use the term fabrication instead. The manufacturing sector is closely connected with engineering and industrial design. Examples of major manufacturers in North America include General Motors Corporation, General Electric, Proctor & Gamble, General Dynamics, Boeing, Pfizer, and Precision Cast Parts. Examples in Europe include Volkswagen Group, Siemens, FCA and Michelin. Examples in Asia include Toyota, Yamaha, Panasonic, LG, Samsung and Tabu Motors.


band and orchestra teacher here at Butler High School and the middle school strings teacher at Mainstream Middle School. I am so excited for this evening. This is my first composition concert and this has been, okay, I'm not gonna cry, high schoolers. This has really truly been one of the most rewarding experiences of my teaching career. I have not taught kids how to compose before and learning about how students process music at different age levels, like I get to see them 5th through 12th grade. It's so interesting and so amazing to watch them create in a setting that we all love to do. And it's just so, especially for the youngest ones who are just learning how to read music and being so experimental and like have absolutely no bounds. It's very, very cool. So thank you for giving us this opportunity to share our pieces with you this evening. I'd love to introduce to you my friend Eric Nielsen. Eric is our resident composer and he has been working with this program for 10 years. Yeah, I think this is either 9th or 10th year. So huge shout out to Hilary Goldblatt for starting this program too. What an incredible story. So Eric and Hilary had a wonderful partnership for many years, bringing composition to our students and giving them this opportunity. And we also have, you know, an incredible composition program in Vermont called Music Comp, where Eric's a senior mentor and we have a lot of students who have participated in that. Jamie's about to create a piece with the Vermont Philharmonic. Callum just premiered a piece with the Vermont Symphony that had given them these amazing opportunities that high schoolers don't normally get. This is so unique. It's such a cool program. So Eric, I'm going to ask Eric to come up and tell you guys a little bit about the process and how we approach this approach to this session. Thanks, Allie. Every year is a little bit different when we're working, but the process in a lot of ways is the same. I come in to each class five times during the period of two months or so. And in between, the students get to work on a piece. There really aren't any boundaries except for the instrumentation. For the middle schoolers, they're writing for high school students to play. And so that's stream players. And then each year with high school, we're working with a different combination of instruments. So we have quite a variety this year, as you'll see. But in terms of the kid's inspiration, it's really interesting to watch because some kids just start right off and they have an idea and they run with it. And for other kids, it's a slower process. My job is to listen to what they're doing, to talk to them about it, to hear, to try and get inside the piece of music. That's my job, is to try and get inside what it is so that I can understand. And this may sound funny, but it's the way I think of teaching composition. I'm trying to hear where the piece wants to go, where it needs to go. And then I try and convey that to the student. It's not where I wanted to go. It's the piece talks to me. And as somebody who's been composing for a long time, I've learned to trust that. And the thing that is wonderful about this is that this is a potentially life changing experience. Because with the creative arts, these students are learning something they can't get anywhere else. They can point to a piece of music that they wrote, and they can say, this came from me. No one else created this. And if they realize that, that's a tremendous amount of empowerment that they can take with them everywhere, no matter whether they write more music in their lives or not. And so it's very humbling for me because I didn't really start composing until I was 20. So even the fifth graders here are ahead of me. And so it's, it's just wonderful to watch. It's wonderful to be part of. And to all your parents out there, thank you for sharing your children with us. And please encourage them to keep creating. So much Eric. So we'll go ahead and get started. So there is a little bit of transition time for those of you who have not been to a composition concert before. We have different groups of instruments that will come up and play each piece. So the middle school students were able to compose a piece for any combination of violin, viola, bass and cello and flute, because that is my main instrument. And so you'll see all these different combinations, duets, trios, quartets, and more tonight. So I'm going to invite our first composer up Charlotte Goodwin, who is going to introduce her piece. And it's a duet with the violin and viola. And I hope you And one thing I, I don't remember if I told everybody, I might have told the high schoolers, but at the end of our piece, or the end of your piece, when the audience claps, we use stands so the audience can clap for you composer. So everybody stands at the end of your piece. And Reagan maybe can give us a little light so we can see those composers that okay, can't do that. Oh, never mind. Just clap for the composer, give him a wave composers. All right, take it away, friends. This piece is called the subtle monkey by Harry Evans. Come on up, Harry. It's called the subtle monkey. I wrote this for a base and cello and so yeah, I hope you enjoy it. So what instrument do you play? She plays what I want. There's that. Oh, I should say, oh, sorry. Say Ali. Oh, right. Okay. Every kid in middle school orchestra composes a piece. And then because there are so many awesome kids, Eric and I have to make a tough decision to choose a couple of pieces that are completed. And we help them finesse those pieces in the end, then they come here this evening. Our next, we're gonna move on to sixth grade now. So I'm gonna ask Satya to come up and introduce her piece. Like I said, Eric can help us every day or over the past couple of weeks. Eric's granddaughter is a composer, Eden. And so Eden's piece was chosen as well this year. And I'd like to invite Eden to come up and introduce her piece. This is called fantasy and it's written for flute, cello and bass. I hope you like it. A cool opportunity to practice being a small ensemble together too and having a more soloistic role. Abram, would you like to come up and introduce your piece? Have to give a little pause there. And it's written for bass, cello and violin. Villain has arrived. Okay, our next composer is Winnie Lou. Winnie, come on and introduce your piece. This is called Spring Day and it's for violins. Enjoy. For your flexibility, Sirship. Sorry about that. Piece is Meek by Meekah Jacobson. Meekah, come on now. I want to go through all the process. It sounds like untitled title. I didn't want to leave it at that. This piece with the help of Mr. Nielsen and Ms. Tar Water. It's a duet between, I hope you guys enjoy. Dosti, come on, Charlotte. All right. Our last piece before our quick intermission is by Atticus Kamana. A seven minute intermission. So go use the map and get some water. Reagan, will you turn on those lights for us if you can? And we'll be back in just a moment with our high school compositions. Thank you. Shout out to Reagan Bridgeman. Thank you, Reagan, for being here. We're so grateful for having sounded like to us. Thank you, Orca Media, for being here for live streaming our events. We can watch it later too. Thank you. And thank Callum for being our sound guy today. Callum is testing out a really, really nice microphone. So we got some nice sound happening and some great lighting in this beautiful space. So lucky. So lucky. Okay, so we're going to go, I just told the high schoolers be on deck. We're going to move through these. So be ready before your piece.  So just come on up after the musicians have cleared. Does that make sense? But I will introduce our first person, who is Simon Reeves, who is a new student to band this year. And we're so glad you have joined us, Simon. Come on up. My piece is called Hopeful's First Journey. When I wrote it, all my friends told me it sounded like a sea shanty. So I asked Chad GVT what that would look like. And here it is. Our next piece is written by a freshman trumpet player named Maude Walker. And she says, Hello, my name is Maude Walker and I want this piece, Birds. It's a trio featuring the French horn, clarinet and flute. I chose this title because the flute part reminds me of Birds in Spring. I hope you enjoy it. For the violin, clarinet and bass in my piece, it's titled, it's a bit of a long one. I just want to say iPads for all.  It'd be pretty sick. It's pretty nice. While we are getting ready for this piece, I'm going to do a quick introduction up for my wonderful friends here who are playing music. So this is Joanna Al-Pazar, Michael Kose. And we've got Taylor Goodwin, Julia Bodewin, Erin Eberhardt and Jeremy Tawada. Our dear friend Jackson is in Austria skiing. Oh, poor Jackson. So I have a note from Jackson. So Jackson's piece is called Jackson Doesn't Know the Measure. We have a tally on our board in the orchestra room of how many times Jackson goes, Wait, what? Because he never knows what measure we're on. We love him dearly. So Jackson says, Hi everyone, my name is Jackson and my piece is named Jackson Doesn't Know the Measure because of my definitely very good ability to listen in class for what measure we're on. This is a short duet for violin and cello. This is an Austria skiing and Jacob is in Hawaii. I learned from Jacob for his piece, Tastes Like a Bagel, Without the Bagel. Hello, my name is Jacob Coffin, Elstrup and I wrote the piece, Tastes Like a Bagel, Without the Bagel. I started by just randomly putting notes down and writing a bunch of random phrases. Eventually I started to string together all those phrases into what is now Tastes Like a Bagel, Without the Bagel. I had no idea what to name this piece. So I asked my classmate, Indy Roberts, shout out. And she came up with a name, Tastes Like a Bagel, Without the Bagel. I don't know what caused her to conjure this name, but I liked it. So it's stuck. Enjoy. My name is Marie Vosian. Originally this piece was called A Stroll at Night, but this duet, Weeping Heavens, is for clarinet and cello. I originally named the piece A Stroll at Night, but the name didn't match the flow of the song. But when I came across the name, The Weeping Heavens, I believed that the name matched the piece. So here she is. The song, French Rise, is a solo for French War. I called it that because French Rise and the French War are both two things that we call French, or not, or are actually French. I hope you enjoy. C.C.'s duet, C for cello, C for clarinet. Hope you enjoy. The continuation is funny. It kind of, I've been told, sounds like the way my older brother Callum Rubacheck names his pieces, but that was non-tended. Anyway, this piece, when I wrote it, in the past, my pieces have been kind of a little weird, I don't know why, but this one I kind of tried to make a little less weird. So it's supposed to kind of be like simple and calming, with my goal. So anyways, to these great musicians. Crocs theme, my piece is called Crocs with Socks. Hope you enjoy. Okay, now I'd like to introduce my piece that I wrote this year. If you were here last year for chance, I must inform you that this is not another song along the lines of March of the Beans. No, I tried to give it an actual theme this time. I think it turned out well, even if I did have the trouble coming up with a name, and it took me a while. And but I found one that fits, and once I hear the piece, I think I'll understand what I was thinking. But further ado, here's Echoes. And I was writing the piece coming off of the back of this huge project for the B.S.O., which took me a few months to write, and I was like creatively, completely like I had nothing. And so I decided to just try and draw inspiration from somewhere that I like would never think to. And what I ended up doing was taking a big band chart from the forties that I'm playing with a big band and taking some voicings of jazz chords and sort of building up of that. And I really like it, so if you'll endure one more. Not being afraid to pick a little part. Thank you, Carter.  This was such a great experience for me. I hope you enjoyed it, too. Thank you so much, Eric, for teaching me a lot about composing and how to start these kids when they're so young, doing something that is really, really beautiful. And I am so excited to keep doing it. Thank you so much, composers. And I will see you all next year. Oh, I'll see you guys. Check your calendars in the program. There's some future events coming up. Oh, sorry. One more thing. Tiny two things.  First thing is that in March, March 17th, Jamie Maddox White is premiering a piece of the Vermont Philharmonic, which all of us except Michael and Erin play in. So we'll be there playing Jamie's piece, hooray. So come out and see that if you can. And Eric is also premiering a piece with Premierick. Is it a premier or is it just a piece? It's a piece that he's had premiered before in our on our April concert, which I wrote 28th, I think, 27th and 28th. So if you're interested in hearing some new music by living composers that are in our community, please come out and check that out. Happy February break, everybody. Have an amazing, amazing break. And I'll see you guys next time. Thank you.


Hi everybody. Good afternoon. I'm Nishchil. I'm the VP of Engineering and Data Science at Omnias. Omnias is a Berlin based company that's building AI product for the insurance industry to support their claims process. Today I'm going to be talking about document digitization and how we are rethinking this with machine learning. But what this talk is not about is it's not about OCR. So OCR does not mean you have digitized documents. OCR is just enabling your machines to be able to read documents. So what we are really solving at Omnias is to understand unstructured complex documents and identify semantic information in order to automate the claims handling process. So if you put Omnias in the picture, we are looking at PDFs, emails, scans, WhatsApp images, photographs, and a whole lot of noise that comes into our system for handling claims. And Omnias is making sense of this and using this to support claims handling. In the ideal world, and I would really like to thank Sandeer for covering a lot of things in the earlier session today. I would have loved to speak after her, because I am taking what she's spoken today about data and data sets and how you have accuracies for data sets and in the real world things fail. So in an ideal world, this is what you would expect. You have a curated data set, you have lots of different documents and you expect your models to perform well. What we see and receive once we've deployed the systems, is just one of the use cases is we see forms and documents coming in different ways. They're crumpled, they're short in different angles. People are taking them with shadows and prints and everything. And the expectation is that we identify what's important and how can we process this and provide this to a claims adjuster for him to make the decision. Can he settle a claim? Can he settle a claim? So the last two and a half years, I'll be going through the journey of what we've done at Omnias and at the very beginning, when we decided to get into the insurance industry, of course we were a team of five people on the tech team and we said, okay, we are smart and we know why we are smart. So let's get this rolling. So the coolest part is we wrote a lot of rules. Initially we said we didn't know what we wanted to solve. We said we'll write a lot of rules and we were comparing this with the data set that we had and we said, okay, looks great. So we've been able to extract a lot of information, understand the noise, classify, and then we evaluate this. Then we pushed it to our first, first, first customer. So we were in the 87% of failed projects that Sandhya was talking about and we started seeing real-world data. And one of the challenges that we have in the insurance, working in the insurance industry is, especially working with customers in the Europe and the US is there are a lot of regulations on how they can provide data and most of the times they don't because they don't exist on the cloud. Neither can they provide this data because it has a lot of private information. If they were to provide us medical insurance or claim, we would know what a person would have gone through. So which means the data sets that we work with a fragment of what is there in the real world and of course our systems are bound to fail. However, the beautiful thing of writing rules or starting with something very simple is you have a baseline. Now you can start working your way up from here. Now we got a baseline and we said, okay, if you want to solve this problem, the worst thing that we can get at is we can do only about 58% of accuracy I'm using accuracy here because things start to change a little bit on how we actually evaluate the solution. The next thing we did was we said, okay, do we have the courage to ask questions and if yes, what kind of questions do we want to ask? So we said, okay, how does a human solve this problem? How is the claim adjuster looking at this if he gets these documents? What is he trying to do? And these are all documents that he's familiar with or he's not familiar with, but he still gets to a conclusion. So we came to an understanding that it's a combination of what a person sees and with domain information what sense he makes of the text and the images that are present in them. So now once we started asking these questions, the next thing was do we have courage to ask more questions? So there's so much that you can do in any of these problem spaces, but given a small team and the focus and the impact that we wanted to have, we asked the questions, what algorithms do we want to use? What kind of data do we want to feed into these algorithms? What are we actually evaluating? Are we solving a data set here? Or do we want to really make an impact with our product? And if we want to do all of this, what kind of human resources do we need? Do we really rethink about how are we setting up teams?  Then we started to choose our battles. So the first thing that we did was we didn't have a lot of data. So given that we didn't have a lot of data, we could go into approaches. So we said we could use unsupervised learning to automate some of the training data, but we were very, very clear that we wanted to go with supervised learning from the very beginning. And one of the other learnings that we had initially was before we jumped on the NLP wagon, we did a lot of computer vision because our core team was a computer vision focus team and we tried to use object detection on documents and then we were trying to do message parsing networks. We did a lot of custom CNNs ourselves. And something that we realized was yes, to understand layouts, to understand that something exists as a table, even though you don't see lines and column identifiers and invoices that are present that have a lot of other information, it started to make sense. But if you do not know how to group them without understanding the information that exists in these documents, then we are bound to fail. We did a combination of natural language processing and computer vision and in this we were mostly looking at some of the sequence tagging framework, some of the domain adaptation, language modeling, combined with computer vision. So when you combine both of them, that's when we truly started seeing that we can solve a problem that the insurance industry can actually use. And to feed data initially, given that we didn't have a lot of training data, we used some of unsupervised learnings to actually generate the data set. So we did a little bit of work on something called as deep clustering that sort of become evident now with Facebook publishing a blog post recently where we used an already trained network, fed the documents as images to this network and just use the features to do a clustering to see what kind of different document spaces we have and how can we use them for further processing. Of course, the emphasis on supervised learning, I think everybody's aware of Richard Tucker here and we at Omnius too believe that a lot of work that we want to do is in the supervised learning space, although we see a bit of unsupervised learning coming up now that can have big impact. So the next question was, okay, we have some sort of data, we want to generate good training data that's well annotated. When we started about two and a half years ago, a lot of the systems that we were using did not have a mechanism to do annotations for both computer vision and text based at the same time. Everybody were dealing with autonomous cars, autonomous vehicles, autonomous drones. So a lot of companies build platforms to do annotation but mostly for computer vision. So what we decided to do was to combine both. So we built some tooling in-house where we could label the text inside, identify what text is important, build relationships between text, hierarchy of text and also group the layouts. So we sort of built in-house annotation system and we started using the RVL CDIP dataset that's publicly available and worked with an annotation company. So we gave them the tools, we gave them the data and we started getting a lot of training data to see what algorithms we can actually use. Now building a team. So I'm sure a bunch of you have already heard about this but from the very beginning, we've been a very ML Ops focused team. So something that we convinced the data scientists to do as well as if data scientists can write deep learning networks, they can definitely build Docker containers. So we were not thinking about building an engineering team around data scientists. So if that tends to happen, then taking things into production, running proof of concepts and running it on different environments would become a big issue. The other thing that worked out great for us is we started giving out master theses options to some of the students in Germany. So we said there are some core research problems and the master theses time duration is about six months and would also give us a way to identify some talent that we could build into the team eventually. So last year we had two master theses students one working on generative adversarial networks to actually generate training samples for us and the other student who did fantastic work on natural language processing and how we can actually use this. The cool part was not only was there research included into our product or actually saved our product to some extent, but the students loved to be part of the team. So once they finished their master theses, we actually were able to convince them to join the team. So the onboarding was absolutely not required because they knew what problems we were solving and they also knew some of the research problems that we had that we could take forward. Of course, if you're building a startup and if you're not aware, so a lot of cloud companies like Google, AWS and Microsoft are friendly enough to give you credits, especially I think initially Google helped us a lot because the credits that they gave us came with absolutely no attachment. So we could use multiple GPUs spin across different environments and you could do whatever you want to do. So this also saves you a lot of initial investment that you might have to do for infrastructure. So you should definitely get in touch with these teams and in case you don't know anybody, please feel free to reach out. I can connect you with a bunch of them. Now the next challenge for us was how do we evaluate the solution that we are building? So we had a pipeline of AI models. We had some natural language processing based classification. We had computer vision based classification. Then we had sequence tagging systems and then we had some other heuristics based system that were all put together to solve the problem and what we decided was we said individually, yes, it's good for us to understand how these models would perform, but for an end customer and especially for someone who's not a data scientist who's going to be using the system, how does he evaluate this problem? So we started evaluating doing end to end evaluations and the most important thing for claims adjustment or claims processing is the fact that in the end, how much of this could be automated. They do not really care about the fact that are using state of the art computer vision are using state of the art natural language processing. All they care about is if I have 100,000 claims that I need to settle in a week, how much of this can I do with as little human intervention as possible, which is why once we deployed some of the new changes that we had, our evaluation changed. So what we started looking at was a different dimension of evaluation. We said, can we do document automation given a certain confidence where the accepted errors are lesser than a certain percentage. Now this is for a business user something that he can understand because if you go to a business user and tell him I'm going to give you an F1 score of 95%. It makes no sense to him. If you go tell him that I'm going to give you an accuracy of 98%. What does it has no impact on him? So once we started rethinking about how we're doing evaluation and made it very transparent on what the customer could get as a return of investment or investing in AI and an entire product, it started making sense to them. Now to build this in the first year of course a lot of it was in Google Cloud because we're part of the startup program. Everything was containerized so that we could run it anywhere including our own infrastructure on our laptops and of course on the cloud. But of course once we were able to win a few customers with our AI results and what they could do, we had to go live. And I don't know how many of you work in enterprise world. When you say go live it's not the same as B2C where you deploy on the cloud, you have your cool DevOps team, everybody's monitoring, you can make changes on the fly. We were dealing with an industry that's probably one of the oldest in the world. Insurance industry has, you can see in Hammurabi's code of something that's coming to the insurance industry. So insurance industry has existed for a very long time which means that they're not on the cloud, they're completely on-premise and you have absolutely no way of understanding or knowing what's going on. So now you need to ship something that is quite new and you're building algorithms that you don't know how it's going to behave in the real world and the entire control is in the hands of a customer or a business user who also does not know what the system can do. So we have to go live or we were at a point where we had to go home. So then we started thinking about initially we could give them the capability that they could train their own models. We gave them, we had the annotation tools, we had the models, we knew what is the problem that we could solve. So we started bringing human in the loop where with AI assistance, initially they could solve their problem. If they were spending about 30 minutes on understanding what their claims process was with AI assistance they could do it with five minutes and we built a loop around which we said there are trained models, it predicts, it assists the human, the human corrects the errors and can be used for retraining. So it was a continuous improvement in the model or also the model could deteriorate which meant we had to focus on our domain. So we were very sure that we are not going to build a horizontal AI system. We are not going to engineer a system that can solve any problem. So we were very focused on what we can do in this domain and we had to educate our customers. We had to actually educate our own team. So when we were talking to our C-level team and we were talking to marketing sales you could understand their understanding of AI came from Google. So they would Google and ask, okay, what is AI, what is deep learning and all they could see was this beautiful fancy things where AI worked out of the box with 95, 98% accuracy, one AI tool that could do everything and of course your customers think the same way. So if you tell them your AI can do only so much the question they are going to ask is then why do we actually have to be a customer? If your AI cannot solve the problem they must be doing something wrong. Because Google, Facebook, LinkedIn, Microsoft do all of this so much better. So you have to educate your customers, make them understand what are the problems in the real world, how it's different from looking at papers and data sets and what is the true impact for them on a longer term. And the third thing that we had to also pay attention to was the fact that we need to solve this end to end. We need to close the entire engineering product around this where we were not selling AI. We were selling them a product that works with AI because if you sell AI then you're selling consultancy. You're not really selling a product. So this gave rise to the Omnis platform which consisted of three major components. One was the training platform where when we deployed for a customer they could use the annotation system, they could train their models on the fly, on their GPUs or CPUs, on the infrastructure that they had and then they could move these train models into a pipeline which could process documents and give them predictions. So they could validate the predictions and use them back for retraining. And of course when you're shipping enterprise software you need to ensure that at any given point people can understand what's actually happening in the software. Did the training job go well? How much of CPU, how much of memory can be used? Is our services going down? It's coming up. All the logs, all the infrastructure logs, the application logs, the monitoring. So to build all of this into our platform. So as I had already touched upon, so we had our annotation system and the challenge was what is the schema of the annotation system? So as we started playing around with insurance customers and their documents and we started seeing more and more problems, having a schema and defining what the schema should be is a very big problem. So initially of course we said, okay, let's put everything, convert. We initially had an XML model. We said, okay, XML is so old school. Why do we want to deal with XML? Nobody works with XML anymore. Let's do JSON. And during this sort of conflict of engineering and what we really wanted to do, one thing that I want to point out is we stayed with XML and it's a very important thing to have these sort of engineering discussions because choosing what's latest and greatest is not really going to help you solve a problem. If you don't understand what is the problem that you wanted to solve. So we had a lot of hierarchy of information. We had both computer vision algorithms and natural language processing algorithms writing to this or working with the schema and XML schema actually has a beautiful notation of having as many hierarchies as possible, connecting different sorts of annotation that you require which normally JSON does not allow you to do. And so we built our core annotation system with XML as our core schema and of course for visual representation we had a way to convert XML to JSON and back into XML. The next thing is for the ability to train models on the fly. So one of the architecture decisions that we made in the very beginning was to adapt Kubernetes and the reason why we started using Kubernetes was that it was not because it was one of the biggest projects from Cloud Native not because Google's side branch of Borg Kubernetes but the fact that was we were a small team and we wanted to support customers that were all across Europe and in the US and we could not build a service team or a service engineering team who could be on a pager service if something went down or came up. So Kubernetes for us was a choice that said can we use a system where we could use it on our infrastructure on any infrastructure including running it on your laptops and have the same capability of pushing your software and having someone to manage the software. So Kubernetes actually takes care of your services coming up and going down takes care of the fact that you can associate this much of CPU this much of memory for each of the services you can provide which services needs to run on GPU which service do not need to run on GPU so you can mount GPUs on the fly and this was actually one of the best decisions that we made because of the wide range of customers that we started seeing we did not shy away from implementing this on-premise because Kubernetes being open source you can deploy Kubernetes on-premise as is or you could also use Red Hat's system of Kubernetes for enterprise customers if they are willing to pay the license so it gives you the flexibility of having your cost at the minimum having someone or some system manage all your services and still capable of pushing code that does not have to be legacy in any manner. Then we started building a whole lot of prediction consoles so we started building async API in order to facilitate the throughput of the predictions that are coming through so if you had 100,000 documents coming in a day 10,000 documents coming in or 50 documents coming in it would not matter to the system because everything was throttled and giving the capability of async API gives the downstream system so I am talking when I say downstream systems I am not really talking about the new age systems that have callbacks I am really talking about mainframe systems systems that are from the 1970s, 1980s that have a lot of data and are connecting to our systems to work with them and microservice also played a huge role because choosing microservices gave us the flexibility of changing our pipelines changing and pulling and pulling out of our AI services as and when required and we didn't have to really build one pipeline or one product that does it all so it gave us a flexibility of depending on what the customer wants to solve giving them the capability of choosing what modules they really need and one other aspect that was quite important for us from the word go was how we looked at the data engineering part of our entire product so initially we built a Java workflow ourselves which was one of the first mistakes that we did it set us back for about two to three months because building something from scratch for a problem that's already solved just because you wanted to be proprietary does not make sense then we started evaluating a few data pipelines that you could use out of the box so there was Luigi from Spotify, there's Airflow from Airbnb and then there was Apache and ify and then we started evaluating a few of these frameworks and how we want to connect the dots of these microservices and we decided to go with Airflow for reasons that Airflow also supports a few features that are not currently present with Apache and ify and I would definitely suggest to use a lot of and evaluate a lot of open source systems before actually writing something of your own and now given the throughput that we want to work with we are also evaluating how to use Kafka instead of Airflow and that's probably one of the next updates that we're going to go push with our product now once all of this is running you need to be able to monitor them you need to see if your infrastructure is up your services are up are some of your services misfiring what's happening to the logs what's happening to the infrastructure logs can you do user management through a platform because a lot of these enterprise companies also have their own users they have their active directories they have their LDAPs now how can they connect to your system so for this and the biggest challenge in all of this was also for us to do configuration management because for every customer the configuration needs to be different no two customers have the same environment not the same configuration so how can we make all of this transparent to the customer and make it usable for them to work with so we used something called as key cloak for user management and for managing roles for configuration management we used something called as Helm with Kubernetes where you could inject configuration on the flight through your containers your services to bring your Kubernetes jobs and whatever you want to do to evaluate infrastructure logs we started using something called as Prometheus which visualizes with Grafana everything that your infrastructure uses at any given point in time setting up alarm setting up notifications that's required and of course for application logs we started shipping them to ELK that's also shipped with our product giving the customer the flexibility that if they already have their central logging server they could use something like Fluendee or Files or Forwarder to actually send all the logs to some of their central logging system now our tech stack exploded so we started using a lot of other tooling so okay so one thing that I forgot to mention was how many of you here have or are working with version control of your machine learning models okay so this is actually very very very important now think about the fact that you've given your customer or even your own team and you have a certain data set and you've changed hyper parameters you've changed your algorithm you've changed the architecture now you can version control your code which has been prevalent for quite some time but with machine learning deep learning and working with a lot of data you need to also version control your data you need to version control your models because you need to know which code generated what model and what was the output of this model along with what data that you're using because think about this so you do the retraining loop you're using AI in production and you do not know if your model deteriorated you do not know if this is the model that you actually want to use in your production and what if you want to roll back you can roll back code you can roll back services can you roll back on your models so when we were evaluating this of course Sainer minds did not prevail we spent about a month and a half writing tooling thinking that okay we're doing something great then I presented the tooling at a conference which is why I really like attending conference because someone in the audience said yeah but there's DVC this ML flow why are you building something of your own and we realized we were using the wrong Google search queries to identify what are the open source systems that are actually available there that we can use then we stumbled upon DVC there actually a cool team that's building fantastic stuff for model versioning and also ML flow by Databricks so currently we're using ML flow for a few reasons that it also gives us to do ML flow tracking and ML flow experiments so you could do whatever you're doing with your PyTorch models with your scikit-learn models with your Keras models, TensorFlow whatever and include the way to version control your model so you get a dashboard that says this model ran at this time with these hyper parameters and we could we had all the artifacts that got generated your confusion matrix, your reference course and for a business user you could actually go through a history of models for your entire to the entire lifetime the next thing that we did was also that we could not expect our customers to buy Tableau licenses we could not expect them to say now take our product and to understand business metrics which is also important use Tableau so Raghotam who's working currently at Ericsson suggested they were using Metabase when he was at Tivo which is a very cool BI visualization tool and we started shipping this with our platform for business users to get relevant metrics on how to understand what their systems currently doing the other part which I touched upon right now is data versioning so how many of you version your data version your databases version what exactly is going on okay a very big suggestions start doing it you need to version your data because if your data changes your model changes your hyper parameter changes so you've lost the fact that what is it that your models good in doing what is it that it's really learning so currently we're using because we deal with a lot of unstructured data and we're dealing with XMLs so the best thing that we thought was to use GitLab with Git LFS so a large file system that we could use to track all our XMLs if someone changed the data someone modified the data human got into the loop a system change something so we could track everything so it was a version control of our entire data history and before we ran an experiment we took a snapshot of it so we do releases of data we do releases of code and we do releases of models all tied together because there is nothing called as a failure of an experiment of an it's an experiment that gives you results so if you have to go back two years from now because you had a brain wave that said this experiment was important relevant for us with the current data set that we had you need to be able to reproduce this experiment without having to expect so GitLab or Git versioning gives you the capability to version control your data as well with Git LFS and of course if you're dealing with databases you could take a dump of your entire database release it push it to an object store and sort of build reports on this the next thing that we that I don't have here but that is something that also we do is we do releases of reports so it version controlling our reports of experiments of end to end evaluation and evaluation of our machine learning models because you need to know if you run the same experiment what's actually changed so some of the data scientists on the team are have been cool enough to do automation of reporting so there are reports that are generated on the fly after an experiment is run and they are pushed to our internal Git repository and we can we are version controlling this as well. So overall what I would emphasize on and when I was talking to Xenob about this I said I'm not going to be talking about deep learning architectures. I'm not going to be talking about what you have to do with math because there's so much content available out there. I would really like to talk about what it means to take AI into production and the ML ops that's non-privileged because people don't talk about what it means to take AI into production right. So please think about this and it's not just taking AI into production. So some of the learnings that we've had is the fact that if you think about engineering into AI you actually start making getting time which sounds very weird to run your experiments and a whole lot of experiments in parallel because once the framework has been set up you can run your experiments and expect and make sure that the results are something that you can evaluate also AI models in general if you're just trying to solve a data set. It's a very big problem. You need to think about what is the return of investment? Why are you building this AI model? What does it really solving for the customer? So which means it's important for you to focus on your domain focus on the problem at hand and please don't think about the fact that only deep learning or machine learning problems can solve or algorithms can solve your problem. So we use a lot of heuristic measures. So for example, a big thing that is very relevant for us to generate annotated data is we use something called as levitating distance. So there's some data that's present in an Excel file that we can use so expecting 10,000 documents to be annotated by 50 people over a span of six months is going to take you a lot of time to automate this with taking the data that's present in databases in Excel and sort of semi-automating the entire annotation jobs. So please, please, please think about or at least for us something that really helped is that the entire engineering team and data science teams are working together where they questioned each other on the idea that do we really require to write a deep learning network for this to be really required to write a classifier for this. Can we not use some of the heuristic measures or algorithms that are already tried and tested? One other thing that's important that also Sandeya touched on was the fact that at this point in time assisted AI is much more important than saying AI can solve everything. So think of the fact that people who are using your systems are the people who know what they're doing. So please bring in human in the loop initially when you're trying to test the models or someone's validating what's coming out and if they can use this it also builds trust because now they can see what your models can do and what it's not capable of doing. So which means you get an input of understanding if your model is running everything with related to correlation or you can actually understand what the model is capable of doing. Visualization is a very important feature. So we built a lot of visualization around some of the computer vision stuff that we did some of the natural language processing stuff that we did. So most of the times if you're working with data scientists they give you a graph that says confusion matrix. They give you a curve. That's a lost curve and they say the model is good. But do you know what exactly the model is learning? You can take even the sequence tagging the birds the language modeling that you can do you can actually visualize them in a very nice way and people have proved with writing open source utilities now on what you can do in the end you're giving probabilities to events that can happen. So which means if you're doing information extraction you're doing language modeling you can also show what the systems learning you can take the probabilities of what a certain thing can be and associated with what you're actually looking for. So visualization helped us a lot in understanding what our models were trying to do and is also helping us generate synthetic data that we can introduce for models to learn what it's supposed to be doing based on the data set that we have. Last but not least is something that we've introduced now and I know a bunch of you have been working in startups a few of you are working in enterprise companies in core AI teams is to bring in the idea of release process engineering. So when you bring in the idea of release process engineering what you're really trying to do is to automate or build tools for your engineering team for your DevOps for your ML Ops and data scientists to release their code as software that you can actually use and test across your entire infrastructure. So this gives you more time to build the new things. It gives you more time to improve your quality and you know what you're shipping to your customer. With that I'm done open for questions. So I think we have five minutes. Yeah. Hi. Hi. You mentioned you allow customers to do retraining of models. So is that I mean what are the safeguards there because there can be class imbalance label noise. Of course. Okay. So we have a lot of safeguards and we also do not have safeguards at all. So some of the safeguards are easy to build. So we have reports. We have data reports that are generated before the model is trained. So we actually tell the customer if you're training a classification algorithm, your data is imbalanced and we tell why the data is imbalanced and the same thing we do for complex natural language processing and computer vision models as well. So as part of the retraining loop before they could retrain and as an artifact that we generate out of the entire process, we actually have data reports reports that talk about data that tell the customer that your data is such and such and given that the models are versioned, they know if the model is deteriorated and tested against the right thing. Hi Nishal. Hi. This is Jain. Great talk. Thank you. So my question is regarding model versioning and data versioning, it is something we are doing, but still there is one caveat while doing it that mapping the model architecture, model and data. So updating the data doesn't mean adding new rows, but it also means adding new feature. But when the feature changes, the model architecture change and the hyperparameter tuning those features also will change. So how do you map the version between the model architecture, model, data and the source code? Okay. Wow. Okay. I'll try to keep it short. So one of the things that we do every time or we're trying to do is that when the data changes and when a version or a release is done, we actually generate a report of what's changed. And the same way when the model architecture changes, we have a report and artifact of what actually changed. So you can look at what happened in the last two versions and you can go all the way back in time and also keep track of the hyperparameters as well. So we document everything what hyperparameters went in, what architecture was changed, why it was changed and generate a report for the same.


does movies live 10 30 I'm starting on time which is nice I was almost delayed because I was really excited about the prospect of a sponsorship I get sponsorships from time to time and email form they come over they say hey Adam booby what are you thinking about promoting this razor or this 4k projector or whatever and I got one today and I was like wow they're up front with the price which is a little sketchy but fair they're up with the price they're putting themselves out there I responded saying yeah let's do this and then they sent over a zip file for me to open for the contract and some of the assets I opened the file I was a little a little nervous about it because it just something felt wrong you know usually that's not how this goes there's a bit more back and forth push and pull but I opened it and inside was a video of the company and a logo of the company and then a executable file a .dmg for mac users and so that was game over so I knew I knew at that point okay yep they're gonna install some malicious software on my computer this was too good to be true and I think I gave them like a like one final email it was like hey thanks thanks for being you know jerks so there's a lot of terrible people in the world they just I guess that's the the short of it there's a lot of bad people out there they're gonna try to take advantage so tread very lightly tread very lightly don't ever open a executable file dmg really even opening the zip was a kind of a stupid thing for me to do because at that point I'm already giving a command saying yeah like come on in water's warm you know I'm as I'm recording as I'm going live I see that I have very weak connection hopefully it's not terrible on your end that that's kind of unfortunate usually we have full bars and we do this but my neighbor is putting in a pool and they're doing a lot of digging they're they're they're tearing up grass they're they're whipping crap everywhere they're sadist all over my house it's really a fun experience mainly for me you know they're getting a nice pool for the whole fam and I'm getting my yard ripped up and a bunch of sadist and granite and stuff you know everywhere uh hello to everyone people are saying that it sounds okay so that's good the topic after this kind of long interlude this little rant is going to be on the top 10 most overrated movies of all time and that's that's for me that's for me I hear someone walking hello I'm okay okay Lindsay says I'm okay keep in mind Lindsay this does go up on audio version on a podcast so let's try to keep the interruptions at a bare min short for minimum she's not okay she's yelling across she says read the comments which I did so again you know I feel like every time Lindsay checks in to give an okay I've already kind of established it on my own but there's a delay on the stream that's fine this is what this is what a married couple does on a Friday night we go in different rooms and then we yell at each other through doors it's the most depressing Friday night I could ever imagine but at least you're sharing it with me and that's all that really matters speaking of sharing in a in a Friday night sadness or livestream I guess sadness uh before I get to the topic that I've now meandered through I did see dumb money the other night I did a back to back kind of a barbenheimer situation except for was what would we call it dumb x saw money I'm not sure how you combine saw 10 and dumb money but I saw them both back to back I put out youtube short reviews and I enjoyed them both saw 10 I enjoyed on a superficial level because it's really dumb but there's some gore there's some good kills and that's really all I was looking for the dumb money though was a genuinely good movie I have a review going out for that I don't know did I do a review for that yet no I didn't I'm gonna put the review out tomorrow hopefully I have a few things to shoot and put out my creator video review the long form one is delayed until tomorrow I have to shoot some more of it because I filmed it and realized I didn't talk about my favorite part of the movie and that was the gentleman four seats down that was on his phone the whole time so I gotta have a fun little you know a fun little post credit scene talking about him for a couple minutes that'll be fun okay now to the topic at hand we have top 10 most overrated movies of all time this is my personal list and I want to I always like to put a lot of little disclaimers on it I don't even outright hate some of these movies some of these movies are okay are pretty good it ranges from bad to pretty good and I just think they're overrated for a lot of perm is saying the video is randomly freezing now and then but audio is fine well okay I mean I guess the audio being fine is all we can hope for I can't help my internet right now the pool is going in though pools having no problems all the problems are on my side all the problems that he he did invite me to to swim in it when it's done I hope to not be here anymore but we'll see we'll see yeah I'll swim in your pool yeah I'll swim in your pool in hell doesn't make sense all right let's get on to it because I'm not gonna lie I was up till 2am the other day just just everything everything over at the Olinger house this is a complete shit show nothing is freezing on Lindsay Zen she says who's also using the same crappy hey uh sweetie honey honey pie Lindsay maybe you shouldn't be live streaming this on your phone and the internet won't have as much work to do if you don't have Netflix running which I'm guessing you also have going right now and you probably have something going on your computer and on your phone and so it just doesn't know it doesn't know what needs all of the assets and all the bandwidth just a just a thought throwing it out there okay on to the list let's begin and of course super chats are welcome through the name of the game I like to ignore the the main chat and just focus on supers because again it's a Friday night and this is what I this is what I'm doing for you I'm here for you so help me help you keep the live streams going Tuesday nights Friday nights 10 30 p.m. Eastern Central time Eastern standard time not central time those are two different times combined island twigs in the mix Shane it's good to see you dude I gotta I gotta give you a call we gotta chat got things to talk about okay let's go on to oh I'm gonna keep adding little things on top of it my first one's a doozy so strap in I'm gonna get some anger issues on this and people are gonna be mad let's do it yeah spider-man no way home for the audio only listeners out there is on my list this isn't ranked this isn't rated spider-man no way home I gave three and a half out of five stars so on letterboxed arbitrary review I don't really take a lot of stock in my reviews that I put like the scores don't mean much to me but um this is a dumb movie that has a lot of fan service which is fine I like fan service I'm fan but the story that the plot is is terrible it's atrocious and the fact that this is kind of like regarded as one of the MCU's finest just as kind of sad I guess about the MCU that's the state of things this isn't top 10 for me in the MCU it was great seeing the classic spider-man come together Andrew Garfield and Tobes Tobes McGuire bully McGuire and Tom Holland all under one roof doing their thing but I mean I feel like the bar has been set so low for the MCU under Disney now that it's it's just kind of sad like yeah great you got all three of them together for the last act of the movie we could have had a whole film with these guys we didn't need this really dumb ass plot point about Tom Holland's Peter Parker not getting his friends into the college they want because he's spider-man nothing about that even made sense to begin with but then you tack on Dr. Strange being incredibly incompetent and brazen with his spells and yeah it's just it was such a dumb storyline and I remember seeing the trailers and I thought okay this is a misdirect they're not really doing this with the plot where Dr. Strange is like doing a spell is craft and and Peter's like hey Mr. Strange I want Aunt me to remember me I want this person to remember me and Strange is like you're you're fucking up the spell kid shut up but he's like Mr. Strange like pulling on his robes and then the whole thing goes haywire no bad very bad regardless of the hang-ups there's enough small moments that do make the movie work in the end a lot of great cameo villains in the mix a lot of good action in the later half but the overall is not that great I'm sorry I tried rewatching this with my family my kids didn't see it when it hit theaters they didn't have that nostalgia bait like I did with the older spider-man spider-man's I don't know what the plural is of spider-man um so they they just thought it was kind of long and it took a long time to get going and even for me it was a chore to get through again so no spider-man no way home is very overrated let's go to the next one on the list this one takes us back a few years and I'm going to I'm gonna say this but I'm also gonna compliment this movie so the Blair Witch Project came out in this was the 90s right what year did 1999 this was an amateur film very very low budget shot on regular handheld cameras they wanted it to feel as raw and intense as possible by having the actors actually walk around the woods and film it themselves and they even had them add extra camera jerk and whatnot because regular folks don't know how to hold a fucking camera they think like they're dropping the camera and kicking it around it looks like it's filmed on the back of birds that they had clipped the wings of and we're just smashing into trees and stuff it looks terrible it's filmed horribly by choice by choice but no one no one films like like Terry Terry what's going on cameras just everywhere it's like super zoomed into the guy's nose comical that said I don't like this movie either by the way I had never bought the hype of Blair Witch my buddies dragged me out to the dollar theater to see it because I didn't want to go pay full price when it was in the main theater remember dollar theaters I think they still exist but the one by me you're more likely to get shot than you are to have a good time watching the movie um I was just bitching the whole time having to go to them it was kicking and screaming and I thought maybe there's a chance this wins me over but no it was it was actually worse than I thought it would be the only good thing about the movie is the final shot where the the woman's in the corner looking away from everyone that was creepy and that was it that that's the entire film right there it I give it some credit actually I give it a lot of credit for kind of pioneering the found footage style it might not have been the first but it was definitely the big one that went to theaters that they had a brilliant marketing campaign behind the people like this is real this actually happened and so people bought it and they went saw the movie and it was you know later revealed now that this is all bullshit this was all just a beautiful marketing ploy it worked though and then we got some much better I think found footage films like cloverfield or even paranormal activity I think is quite a bit better than this one and they continue to come out once in a while I enjoy chronicle quite a bit thought we were going to get another chronicle at some point but I guess that that ship has sailed yeah that that's uh that's my second pick Blair witch project here's one that's fresh and I know we're going to get a lot of disagreement on this one as well well barbarian came out 2022 I think the the director Zach was a Zach Crager he's kind of beloved in the community indie director he's done a lot of comedy stuff barbarian out of the gates was getting high high marks 90% raw tomatoes it's somewhere high like that still and I believe the hype I went in thinking okay this is a 24 they're hit and miss but when they hit they hit really hard and you know people are really talking this thing up it's scary it's intense it's it's different it's some of those things it's certainly different but not in the ways that I liked I fucking hate this movie I just do not like this film I don't get this film people I don't think anybody gets this film because they all they all have interpretations that are wildly different from each other someone will say oh no no no no it's supposed to be a parody well a parody of what no it's supposed to be a comedy thriller or it's subverts expectations you think it's going to go this way well yeah I get that it definitely subverted expectations I was expecting a good confidently made movie and instead I got this circle jerk I don't even know what to say it goes completely insane and for some people that's enough like yeah there's a giant male-nourished woman with their giant hits flopping around down in the cellar eating people throwing people into little dens that she can nurse back to health or I don't know the guy that made the tunnels is somehow still down there then it goes full-blown looney tunes she gets hit by a car she jumps off a water tower somehow manages to spin mid-air mind you while falling and gets up and leaves I just the movie drives me insane it drives me insane I don't want to think about it and yet I brought it up we have our first super chat of the night which I appreciate 999 Matt thank you Matt the sixth sense is his pick I know everyone loves the twist but that's all it's got horrible rewatch too many plot holes Matt I'm gonna be honest with you I've only seen the sixth sense one time I feel like I would not like it if I watched it again for probably the reasons you're saying I could kind of see the first time around when it was done thinking back how the wheels were coming off so I'm happy with the experience I had in the theaters I saw that with my mom and dad I was it was probably a sophomore in high school at the time I'm thinking around there and it was just a magical experience nobody saw this twist coming and yes it probably doesn't make any sense if you think about it but then but when we saw it it did make sense at least to an extent right it tricked our brains into thinking okay yeah maybe it is plausible that this boy's having conversations with himself and nobody seems any the wiser or that Bruce Willis doesn't realize he's you know whatever the movie's really old but for some reason I don't want to spoil it okay thank you Matt good pick the sixth sense I can't I can't defend it nor agree with you I'm in limbo on sixth sense it's been too long since I've seen it all right if I haven't been spicy enough yet let's get to the next one on my list this one might this might rub people the wrong way but they're overrated for me right they're overrated for me this is a movie devoid of laughs it's a film that's presented as a comedy I never laughed once the only time I think I chuckled into the god and that was about it just how he said the god and I don't think that does that actor even do anything anymore he they try to kind of make uh John is John Heeter they try to make John Header John Heeter a thing for a while I remember he was in Blades of Glory with Will Ferrell I think he was in a wrestling movie at one point and then just completely off the face of the earth after that maybe he's doing fine doing stuff I mean I hope so I hope he's doing acting and still keeping up but this was very much a huge disappointment and I felt like it was one of those movies where it was cool to like it it felt like it was this it was this indie movie that maybe was a little secretive at first where people knew of it but not mainstream knew about it so like have you seen Napoleon Dynamite nine times out of ten people say no and like oh man I remember specifically where I watched this film it was at my cousin's house uh it was a kind of a family get together so there was probably a baker's dozen of us sitting around the couches watching Napoleon Dynamite you could hear a pin drop in that room for almost the entire length of the movie nobody was laughing yet they all pretend like it's this funny ass hilarious movie from beginning to end and nobody's laughing nobody's laughing in the comedy it boggles my mind Napoleon Dynamite incredibly overrated quirky sure but I've seen quirky uh speaking of quirky this one's gonna piss my wife off my wife's a big Matilda fan I'm not I don't care for Matilda based on the uh role in doll role in doll book however you say the author this is directed by Danny DeVito came out in 1996 I just I don't I don't think I've ever finished this movie I hate how it's filmed and I get that DeVito's going for kind of this surreal child's eye view of the the happenings around her so everything has like a fish eye lens style to it it's very zany things are very frantic people are kind of like doing that like earnest thing where they're up at the camera and their nose is big and elongated and I just I don't like the look of the movie I find it atrocious I didn't find it funny I hate the cafeteria lady that became a thing and now we have a you know people just celebrate this film kind of like hocus pocus which is another one that I don't care for hocus pocus could be on my list as well hocus pocus should be on my list as well but it's not it's not Matilda is though Matilda is next up avatar the way of water not even the way of the way of water that doesn't even that's not even a sentence the way of water shut up James Cameron you failed me dude you failed me I hate this movie and it's not that I'm disappointed I legitimately hate this movie I uh I I'm nice on the first one right avatar state of the art tech it's doing new things has a very generic bland dances with smurfs plot fine I throw it out I forget it it's got Cameron's name on it I'm in it's got some cool action it's got some sci-fi appeal to it I have not finished that movie for the life of me ever again I watched avatar once in theaters blown away by the tech brought it home got it on Blu-ray no 3d no 3d no soup for you kind of a situate no 3d for you and it's um it's just unwatchable in my opinion it's far too long it's far too bland and Jake sully is maybe the lamest lead character ever to grace the silver screen now you take that and multiply it by two and you have avatar the way of water this movie is tonally ridiculous I don't understand the plot at all at one point we're resurrecting all the villains from the first film which kind of undercuts the entire point of the avatar program at another point they are extracting some sort of fountain of youth from whales we got a bunch of whalers is to turn into a green piece film listen I'm all for saving the animals but um avatar too what the fuck is the point make up your mind and the direction you're going and then you have these kids that somehow became infused with um like black urban culture they all they all have like black hairstyles they're all saying like they sound like they came out of the fresh prince of Bel Air all of a sudden I don't understand any of this film it's just a shit show um and that's just that's just some of the problems I have with avatar too as it stands the movie was praised by critics again uh I know a lot of people that enjoy this film they like it I don't I don't like it at all I think it's just bloated over long tedious stuff to watch and and there's really not that much story to tell there's not much there all right let's move on okay I'm not gonna have a whole lot to say on this movie other than this is a film Mystic River directed by Clint Eastwood 2003 that had a really good trailer that hooked me and I remember going to the movies and being so utterly pissed as I was watching this film play out how I was misled by those trailers I thought I was getting the story of Sean Penn as a a reeling father looking for his daughter and it would kind of be solving the mystery of who killed her but instead it's this bizarre kind of mob family who's who's taken out Tim Robbins and and kind of going on a tear and then he's like I'm gonna run this city this city is gonna be mine it turns into some bizarre gangster godfather shit I hated that I did not like the route this movie took and it's very overrated in my opinion I can't tell you much else about it because I tried to wipe it from my memory after seeing it in theaters but it haunts me still it comes back and it haunts me I still remember that awesome trailer where Sean Penn's like is that my daughter in there is that my daughter in there yes Sean it is you go to her you go you redeem what happened freaking mystic river okay this is the only one on my list the deer hunter 1978 that I believe was entirely created for an academy award and nothing else this is a film I was told over many decades in my life before seeing this movie that it's brilliant it's it's it was up for academy awards it's it's obviously got uh Willem Dafoe it's got Christopher oh no I'm sorry Christopher Walken Robert De Niro in that iconic scene I don't know if De Niro's in the scene I remember Christopher I know Christopher Walken's in it he's got the they do the Russian roulette they spin the chamber they put the the gun to their head if it's got a bullet they're dead if not they pass it on that's the only good scene in this entire bloated ass movie it's gotta be over two it's probably like two and a half hours long it feels like it's eight hours long they try to be like godfather with a really drawn out introduction but these characters are not interesting like the characters in the godfather and this movie is just a chore to watch it just reeks of award season schlock and that's what I say and this is my list so I can say it Jan Rose with a five dollar super chat you love to see it pretty woman oof why is anyone into this movie it's so ridiculous and unrealistic AF I can't stand rom-coms anyways okay random rant ended no Jan you're right to think that I don't care for I don't care for pretty women either pretty woman pretty women little women and pretty woman I don't care for it and I think you're right to be upset I think you're right to be upset by that you really all we got out of that entire film is the necklace snapping you know when he he oh he reveals the jewelry she tries to grab and he's Richard here smacks it down she's like oh that's it and I can watch Family Guy and see that and it's it's much better thank you Jan for the super chat let's move on past the freaking deer hunter bullshit okay this is another one that's not too old but the wounds are still fresh and I do not keep in mind I don't hate this movie I found Joker to be fine 2019 Todd Phillips Joker Joaquin Phoenix plays the titular clown the clown prince Robert De Niro's in this one too what do you what do you know this was just artsy fartsy with a little bit too much of a fight club twist for my liking and I saw it coming a mile away unlike with Fight Club which I did not see coming even 20 miles away Fight Club is awesome as a side tangent when I was coming up with my overrated list I did google what other people thought were overrated movies to get some inspiration see if I agreed I didn't agree with most of them and let me let me tell you I would say I clicked on probably eight different lists and they were 75 the same which just goes to show you how little creativity and free thinking there is on the internet when you can just plagiarize other people's stuff now obviously overrated movies are going to be super popular so maybe there was just a lot of crossover thought but I don't think so I think they just straight up ripped each other off because I saw Fight Club on most of those lists and pissed me off I saw Frozen on most of those lists I mean come on Frozen it's a very fine movie it's got super catchy music it's got lovable characters beautiful animation it's not overrated it's rated right where it needs to be it's iconic it's a timeless classic that it was a classic as soon as that damn song hit youtube let it go let it go oh man that song is brilliant it's bloody brilliant fucking overrated shut up oh someone had jaws on their list there was one list to be fair that basically was like what's every really popular good movie I'm gonna put it on my list just just because just because okay we got another one from Jeff Lucas for 999 thank you Jeff 100% agree with you about Avatar I remember when the first one came out people were leaving the theater shaking crying and saying things like I belong in Pandora felt like a weird cult I did not do that I should be there I should be in Pandora yeah that's hilarious but Jeff I will say Avatar has not aged well at all because of what we have with number two and just the fact that Avatar really was it's a roller coaster movie you paid you paid for the experience in in theaters you put on the 3d glasses you're in a chair you're surrounded with the plants and the animals and the music and the wonder the spectacle it's a ride and that ride does not translate when you get home so yeah that there you go Joker though yeah yeah this was beautifully shot I'll say that Phoenix does a great job as the character but he's not playing the Joker all right he's playing like a mentally disturbed individual that's going by the name Joker and that's a big problem I have with this movie it's just not connected in the slightest like Heath Ledger I get they go off book a little bit they take him very dark Nolan tries to ground him but he's still at his core a Joker type character he's still all about causing chaos he's still screwing around doing the magic trick with the pencil he's still laughing methodically this Joker though by Phoenix he's just mentally unwell and not in a and not in a fun crazy like haha um cousin chaos no he's just sick and he needs help psychological help maybe he'll get it from mrs quinn in the sequel maybe Harley can calm him down because we have the second one coming out with Lady Gaga playing Harley Quinn we'll see I think she can do a good job I just uh yeah this movie another thing about Joker I remember giving it you know my honest thoughts painting my picture and people were vicious in the comments on this one like fuck you you probably like all the Avengers Marvel stuff blah blah blah blah blah I'm not following you I'm not subscribing okay I don't care first of all secondly calm down it's a movie it's not that great either it's not that great you can joker another recent one 2021 is this my last one on the list there might be one more after this I lost count uh Ghostbusters afterlife this film I think was given pretty high marks by audiences and critics because it wasn't 2016's all female Ghostbusters reboot soft reboot there was some marketing issues going on with that movie I remember when it was considered a full blown reboot and then they added like the tagline Ghostbusters answer the call and they kind of tried to play at like it's a other world's movie or it's a companion piece to the original just just bad all around bad but Ghostbusters afterlife instead of kind of keeping true to the spirit of the original series where you have comedy and you have you know action and you got sci-fi stuff they went all in on this stranger things drama that was popular so we're following kids around which could have been fine but there's very little humor and then they go full bore into nostalgia again at the end of this one once again bring you back to stay pup characters once again bring you back fucking Zool and the gatekeeper and then they have the goddamn ghost of Egon Spangler there and that was the biggest insult to me the movie up until that point was fine it was it was okay but once that scene happened where Egon comes out as a ghost and it's all touching and magical and half the audience is crying I was livid about it because they weren't sad about Egon the character they were sad about Harold Ramis who died not that long ago before the movie came out they were just capitalizing on our actual what's a good way to describe it so every time there's a terrible disaster movie by Rowland Emmerich he did like Independence Day day after tomorrow 2012 I find him fun they're super schlocky and cheesy but he always uses major points of interest that everyone knows Statue of Liberty the White House the Empire State Building and he destroys the fuck out of them we're not sad like it elicits emotion that you're not really getting from the movie itself you're getting from the history of knowing what that thing is and that's why he does that and plus it looks cool but this is the same thing with the ghost of Egon they use this because we're not feeling sad that we're seeing Egon we're sad because we're seeing a herald and that's bullshit and I don't like that that's all that's my rant on it I thought that was super cheap a super blame cop out and not the way I want to see the ghost posters it also was incredibly funny that we find out herald's kind of a deadbeat dad and grandpa and none of this would have actually happened at all heading out contacted his granddaughter showed her where the proton pack wasn't stuff he's essentially the reason any of this even happened to begin with okay that's it wait I'm missing one on here I think one two three four five six seven eight nine okay we have ten I have one extra bonus one I'm gonna throw in one extra bonus one one I don't need a video for it I'm just gonna I'm just gonna say it okay the last one I want to point out and please feel free to super chat any over the movie you have I'll read it out we'll we'll give you a shout out and I'll comment if I've seen it I'll disagree with you if I disagree or I'll agree with you if I agree my last one is wrap Ralph breaks the internet the sequel to Wreck it Ralph it's atrocious the story is terrible everybody's chat on the emoji movie for being um unapologetic corporate greed product placement Ralph breaks the internet says uh hold my beer this movie has so much garbage it's got twitter placement it's got Pinterest plus all the disney stuff it's promoting and on top of that which I can overlook I don't really care about that so much the story is terrible it undercuts the message of the first film by Ralph accepting who he is and Vanellope becoming the the the driver that she always wanted to be by having her abandon her game which was the entire point of the first film don't abandon your post don't get your game shut down and then she does the same thing and Ralph's like yeah that's fine I'll do it too like what the fuck no no and then it just gets worse from there as it goes on the meme jokes are outdated it's tiring to sit through all around bad I've never thought about that movie outside of just now it's Ralph it's Wreck it Ralph and then nothing comes after that's that's the bottom line for me and that's the bottom of this list uh I hope you guys enjoyed it hopefully you know I didn't I didn't make people too upset again this is just personal preference uh movies are subjective at the end of the day and that's what makes them fun but these are some of the more popular higher rated reviews on rotten tomatoes see people seem to like these films and I I don't like most of them or I think that they're just a tad over overstopped overcooked Spider-Man no way home being the one that I think is the most most praised out of like the MCU and it's uh it's not that great it's not that great I'll give you guys a couple minutes to get any super chats in of movies you want uh you can give you a couple thoughts of mine before I shut this thing down we have an upcoming roast hopefully I want to say hopefully by the end of tomorrow maybe maybe not not the end of tomorrow I'm hoping mid afternoon like four or five o'clock we get a 20 25 30 minute roast of Street Fighter the movie that was a request right here actually someone super chatted and said Adam I want you to do there's like a it was a hundred dollar super chat I want you to destroy uh Street Fighter the movie and destroy I do destroy I shall I'm recording it tomorrow and then there's a pretty heavy editing process I also have to edit and get out my review for what was that movie that I just saw dumb money and the creator those ones are locked in the chamber I got to fire them off onto YouTube you have the shorts that you can watch in the meantime if you want next week I have a patreon mithril request that's been a long time in the work well not a long time in the works a long time me avoiding it I got to get to it he wanted me to watch Tomorrowland which I have seen it's a nice little Disney film that bombed at the box office no one gave it any time of day it's a good film directed by Brad Bird George Clooney's in it if that's your cup of tea but it's a nice little movie but he then decided hey Adam maybe don't just make it a review make it a rant about how Disney seems to be doing less of these original ideas or you know audiences are kind of changing how they're doing things or you know something around that and so I'm gonna have a podcast on that very topic how Disney and audiences are kind of playing each other playing each other and forcing each other's hands like hey here's some original content audience doesn't see it then Disney says here's a remake of something audience goes so that tells Disney okay let's just keep cash grabbing let's ignore the original content that we have it's actually pretty good it's a shame so I'll be going through kind of a history lesson of that outside of that um yeah I'm not sure what's on the docket I think that there's some decent stuff coming up Napoleon comes out next month I don't know what October brings as far as the scares go but I'll keep you informed well we'll do it again on Tuesday next Tuesday is the next live stream Monday is the podcast outside of that I'm tired I'm ready to go to bed I appreciate the super chats that came in thanks for watching and have a good night I'm out