Hello and welcome back. Now at some point in your life you're probably going to need to speak to a lawyer and if you're unlucky probably more than once. And if you already have spoken to a lawyer a few times I'm sure you've come across what we call legalese. It's that way lawyers have of speaking. If there's a way of making things more complicated they tend to find it don't they? Sometimes it's really hard to understand exactly what a lawyer says. For example in witness whereof the parties here on two have set their hands to these presence as a deed on the day month and year here in before mentioned which in plain English means the date. Lawyers spend a lot of time organizing and going over documents which is why the law is an excellent candidate for natural language processing or NLP and that is exactly what our next speaker is here to talk about. He is Álvaro Barbero chief data scientist and he's here with us right now. Álvaro great to see you thanks for joining us here and coming in person. How are you doing? Great a little bit nervous but well it's my fourth time I think I'll speak things so I'm really happy to be here again.  We're all very much looking forward to what you have to tell us so when you're ready I'll let you take it away. Okay great. So yeah I'm going to speak about natural language processing and in particular natural language processing in the law sector and well before starting let me just tell you a brief story. So this is going to be a story about going places and what I mean by going places is well since the beginning of mankind you have to solve this let's say transportation problem you want to go somewhere else you want to go from point A to point B so how do you solve this problem? Well the main solution or let's say the most common solution for most of the history of mankind has been well you want to go there well you just walk there right and it has been like that for most of the history since the advent of the Homo sapiens but fortunately like 5,000 years ago we developed new methods to solve this problem you will try to reduce the physical effort you need to do to go to somewhere else by putting that effort into a different being so for instance we invented horse riding well unfortunately for this animal it means that it will do the effort that we will normally do but fortunately for them this changed again in the 1886 where the first automobile was created and now we are placing that physical effort we need to move ourselves no not into another living being but onto a machine created by technology so this machine is powered by fuel and now the effort we need to perform the physical effort is almost zero now the interesting thing is that this trend is starting to change right now because in the near future what we will probably have in our streets will be self-driving cars and the change in the trend here is that now we don't need to we are not creating this technology to reduce physical effort but to reduce cognitive effort and that means that when I want to go somewhere else I don't have to think of all the little details of the plan I want to do the route I will just tell my car I want to go to this point and the car will do everything for me the physical effort the thinking of the driving all the decisions so this is the trend that we are seeing in most artificial intelligence applications right the point is of this technology is no longer to reduce physical effort but to remove the need to do this kind of menial task repetitive tasks tasks that are requirement of force but that not really so useful or interesting for us so this is the point and this is the application that we try to develop for the legal sector so this application was all about trying to create a map of all the information that pertains to a card case and I'm going to describe you what this system was about and all the let's say lessons we learned from this project so first let me give you a brief disclaimer so this system I'm going to talk about was a real system working with real card cases but of course I can't show you any real documents here because of legal reasons you know I everything you will see here are either public legal documents or mock-ups but you will still get a nice idea of the kind of documents we have to deal with right so this is the map of the expedient the project which is the Spanish name for car case map project this was a joint research and development effort between my company I I see and Rodriguez which is one of the biggest players in the legal sector and the main effort the main point here was to create an artificial intelligence solution which will remove this cognitive effort from the everyday lawyers work so let's build a system that will try with input we receive a lot of different documents from the same court case somehow organize that information extract the relevant data and plot a map of the whole court case so the lawyer can have a better day going through all the information and looking for the relevant data okay so this is the team at I I see that works in the project you can see it's a diverse team with different profiles we had people from data science computer engineering computational linguistics experts in graph visualizations so it's because of this diverse team and also because of the help we got from the rigorous lawyers that we were able to build this solution right so like every big project everything started out by drawing everything in a napkin right you know every big project starts like that you get an idea you drop it in the pub in a napkin and you start making the project out of that well the thing is I would like to say this is a true story and we had this napkin in our secret mission in our company well unfortunately it's not like that actually what we did is I draw this diagram in cheap application in my laptop but still you get the idea right we started with something very very small just a diagram of what we wanted to build you can see here a small graph in which you can see the relevant people in the court case the relevant companies how they are interconnected through different court case files and the kinds of files that we want to identify right so this was a core idea and well since we have been working natural language processing techniques for a few years already we identified which were the key tasks we needed to solve to achieve this so mainly they were first classify the documents into different kinds that will be useful for the lawyers also reading automatically through all these documents and finding out the people and companies that were mentioned in this documents and then after we gather all this information create a visualization that will mix everything and will be helpful for the lawyers so yeah if you think a little bit about this and you know a little bit about natural language processing you might realize like well this can't be so difficult right it's about document classification name entity recognition and then plotting everything together so we had a team of experts in all these fields so we said well this is gonna be easy peasy right we just need to apply all these fancy deep learning models to the data and everything will work out well let me tell you something reality happened so what this means is we found a lot of obstacles we had to overcome so let me tell you just a brief of them so first we expected the data to be as nice as this document you can see here again this is not a real document it's just a public document but you can get more or less an idea of what the real documents look like so we expected something like this right you could just take the text out of this nice PDF document and work with us that road text well unfortunately reality was more or less like that so you will have documents of let's say Vora in quality you will have tilt of pages markings somebody who wrote something in the margins post it and all this kind of stuff so we couldn't just get the text from there we needed to apply optical character recognition techniques to get the text out of these documents and now if you have ever worked with OCR methods you know that these are not perfect and we soon realized that even if we expected to get nice quality text out of the documents most of the times you will get something like this so you will have wrongly identified characters you will have text that should be in a margin and was embedded inside the main text so we had to build much learning models that were robust to these errors now the next hindrance we found might surprise you when we were just going through the pages of all these real documents we found out that sometimes you will get a page like this one so what the hell is this so there's a blank page but it's not entirely blank because you can see some markings there well actually what's going on here is that maybe somebody decided to print out this document as one-sided documents and then somebody else decided to scan back these documents as two-sided documents and of course you will get a blank page for each one real content page sometimes it was easy to identify like these ones but sometimes you will get other pages that had no real no real useful information like covers or some kind of document splitters and we had to develop a general technique to remove this data because they were noisy pages now going on with the problems this was another surprise for us we actually expected that when we get the data for a court case we will get one pdf file for each one of the documents that was involved in the case but actually what we found out that is that we will get some pdf files that were really long about a hundred or thousand pages even and we thought well maybe these are legal documents they are really so long but actually not what will happen is that somebody scanned a stack of different kinds of documents all together into the same pdf file so we have to develop some method to actually take a look at two pages and question ourselves well are these two pages part of the same actual document or it just so happened that somebody scanned them together so we have to develop a method to detect this and split documents and finally and this was probably the largest obstacle to apply machine learning techniques all this data we had was completely unlabeled so this means we cannot apply any supervised machine learning methods here and what you probably know about unsupervised machine learning methods but let me tell you these methods work sometimes and in this context they weren't gonna work because the problem was very difficult so the solution here was to create our own training and testing data set out of this unlabeled data so you can see here that we had many more tasks than what we initially planned but we still went out to the project and let me show you a brief overview of the whole system so the system works like this so you will get all the pdf files pertaining to a card case first we will run the optical character recognition methods then we run some kind of heuristics to remove those transcriptions for the OCR that were of poor quality because we actually realized that very poor transcriptions were adding noise to the system and it was better to just remove them completely then you will get a bundle of three machine learning models that will first remove non-informative pages from the data then split each one of those pdf documents into their real individual documents and finally classify each one of those actual documents now in parallel we have another machine learning pipeline that will take the only the useful information and apply an in an entity recognition model to find out persons and organizations appearing in the data now that was of course not gonna be so easy because you will often find that the same person will be spelled differently in the same document maybe because it really is a different spelling maybe we have the surname in some documents and not in others or maybe because you have these OCR mistakes I told you before so we had to run some heuristics again to group and clean those entities and then with all this extracted data we could finally build a visual extension showing all this information together so let me go into the details of each one of these pieces so first how did we build the training and testing data how did we build the corpus well Rodriguez was so kind as to provide us with all the data pertaining to six different court cases we kept one of those as the test data set and the rest for training and you might think that six cases are not so large quantity but let me tell you that these curses were really huge in amount of paperwork so you will have you will have more than a thousand documents and even around 80 gigabytes of data just belonging to these six court cases so we started with that nothing was labeled first we made some annotation guidelines to define which kind of document classes we wanted actually to annotate by our machine learning models so this was a joint work between our experts in computational linguistics and the lawyers from Garigas to find out which kind of automated classification will be useful for the lawyers and then look out in the documents if we could actually annotate the documents in that way that will be useful we started by a taxonomy of 13 categories of documents but in the end we realized that there wasn't enough representation from some of these classes so we merged some of them together and in the end we ended up with eight different classes so once we have this formally defined we started manually annotating the data so again we had two annotators in our team and they will independently go through a lot of different pages 28,000 pages annotate all of them manually to tell which kind of document was should be assigned to each one of those pages now they will mix their annotations and show the conflicting cases in which we have mismatching annotations but we found out that about 80% of the pages will get the same labeling by both experts so we think that was a good enough annotation it took a lot of time that you can imagine but we obtained a nice training and testing data set for this classification problem and then about the name entity coordination we had to repeat something similar we selected the kind of entities we wanted to detect at first we were interested just in people and companies organizations let's say but we also added the location entity to this annotation guidelines because this might seem trivial but it's not always so straightforward to identify whether something is a place or organization if I told you about the Spanish Ministry of Justice you might think well that's trivial right it's an organization but it's also actually a physical building in Madrid so you need to have some more details in the structure of how to do this and this is just one case there were many more conflicting cases so again we define these guidelines together with the lawyers from Guerrillas and then after having this formal annotation guidelines we went through all the job of annotating 500 different pages now this time the amount of pages was way smaller but just because annotating entities is way more difficult you can't just have a glimpse at the document and know what it is you have to read through all the words in the document still we'd got an annotator an inter-annotator agreement of about 83% which is still quite good and then we have our training and testing data for this problem so now we have our data set now let's talk about the machine learning models so focusing on what happens when you input one pdf file into the system what happens is first we run it through the OCR method then we will have a transcription let's say the rule text for each one of the individual pages of this document then we'll have the first machine learning model that we take a look at each one of those pages and remove them if the model decides that they contain not useful information now we have a first branching of the machine learning pipeline which will partition the document into the different sets of pages that perform the actual real documents remember those documents that were stuck together right we are going to split them and then we have the classification moment which we will assign a level to each one of those actual documents in parallel again we will have the name entity recognition model which will annotate the entities persons and organizations appearing in each one of those pages and then we will join this information with the output of the partitioning model to obtain entities at the document level after all these heuristics I told you about about cleaning entities grouping entities and so on so let's go into each one of these models in a higher level of detail so the first model informative versus non-informative it's actually quite simple conceptually it's just a binary document classification model so we can train it with the data we actually labeled manually and this will tell us which pages we need to remove now the partitioning model is not so simple because here we are solving a non-standard natural language processing problem so we have a very long document with hundreds or thousands of pages and we need to find out in which points we need to split the document into a new set of documents so the way we solve this was to transforming the problem into a binary classification problem we will create a model that will take a look at one page and the next page in the document and ask the following question so does this new page follows the current document we train them all to solve this question and when the model answers no it doesn't we will create a splitting of the document just at that point right after that we can run the document classifier model and this is conceptually more easy we just select the splits that were created by the previous model analyze all the pages there and assign a level in this taxonomy we created of eight pages and finally the name entity recognition model is also quite a standard just analyze our page and mark out where the entities are just burning mind all these heuristics I told you about but the core idea is that now what's inside these tiny little robots I have been showing you here so what are the real muscle learning models well of course if you know about natural language processing you might have already guessed they are language models so I have been talking about language models I think for the past two years at this conference so you might be bored already but if you don't know them let me tell you so the key idea here is that when you want to solve a very complex natural language processing tax you cannot just do it straightforwardly but you need to do is split the problem into different tasks so the first task is to create a model that kind of let's say understands the structure of your model but the model does actually is to learn what is the distribution of words that is most frequent in your in your language let's say the English language or the Spanish language or whatever language you're building your model for and then once you have this general language model for a for a particular model of a particular language you will perform the second task which is about fine-tuning that general language model into the tasks you want to solve it might be document classification or entity detection or whatever so this way of working has been very effectively for the last two or three years the first method to actually show that this was a very nice idea was the BERT model for the English language and this model is openly available so you can just take that model and download it it's freely available on the internet and just run the fine-tuning a step so half of the work is already done there is a lot of models that are available for the English language but unfortunately there are not so many models for the Spanish language so when we started this project there was only just a single language model for Spanish that was good enough this was the BERT model created by the University of Chile and we actually tried that model for this project for all the machine learning challenges we had here and these are the results so we compared more classic natural language processing methods against using this BERT model for the Spanish language you can see the classic methods in this slide as the blue columns and the new language model methods as the orange columns and you can already see that there is a significant improvement especially in those problems that are considered harder so finding out which pages are informative that's very easy there's not such a large difference but when we go to harder and harder and problems the difference between models is quite significant for the entity detection we didn't even try to use the classic methods because they were very hard to apply to this problem and the language will seem to work very well okay so you have seen all the path here right it identifying all the problems the new problems that appeared when we were solving the actual project and then solving each one of them using different techniques and applying the latest advances in natural language processing but now the question is and we might ask BERT this question so are these results good enough and what BERT might say to this question is they are never good enough we have to do better right how can we do vector so we develop the following idea this is the main way of applying language models I just described so you pre-train your language model for general data from data language and then you find you need to a particular problem we actually added a new step here because the legal domain is very specific in the kind of wording and expressions it uses even if you are talking about the same language and the key idea here is well let's take a general language model for Spanish and fine tune it to the legal Spanish which is a particular kind of Spanish and then we can apply that method to different tasks in in this project so how we did perform this so first we gathered a large data set a corpus for Spanish legal administrative documents we gather different kinds of open source data to to obtain this about eight gigabytes of documents we run significant processes to clean this data the duplicate this data and so on and in the end we ended up ended up with about three gigabytes of data about a half a billion words which is not such about corpus right and we've performed this domain adoption procedure in two steps so first we use all these corpus we gathered to adapt the beta language model for general Spanish into what we call legal beta so it's a beta language model specialized for the legal domain but we had another corpus we could use right all the court cases that Garregas provided us so we performed a second adoption step using all this data and with this we obtained what we call the Garregas beta language model and the key insight here is that this last model has seen the data with all the mistakes produced by the OCR so in this way we are making the model robust to these errors we will find in the real data and production so here are the results we got and you can see as a green bar either the legal beta or Garregas beta which ever worked best when we applied it to the task I described before so you can again see some advantage here of using this domain adoption step we also tried these problems with an openly available language model for the Spanish legal language which is was made publicly available this summer which is called Robert Alex and well unfortunately the results weren't so good so maybe because of this reason that we had to adapt the model to the OCR data so it seems that this custom made fine tuning and domain adoption step worked very well so as you can see here we can actually go to the further step of incorporating new techniques in the state of the art of natural language processing and they collaborate to produce a better solution so where are we standing right now well the journey still continues with this project and what we want to achieve is a simple system that will take all the pdf files from a court case and produce a visual like this one this is just a mocap but you can already get the idea so we would like to draw an actual map of all the people companies and documents that are related in the court case how they connect to each other through different documents so maybe the lawyers can use this to get some interesting information of course when you go to a real court case this graph will be huge so we are still defining with Garregas how we can present this in a way that is useful and actually we are incorporating this into the processor tool that the Garregas has this is a tool we created also together with Garregas some years ago which aims to allow the lawyers to search for data in instructor data files like pdf documents again but also audio transcripts and so on so this is the product I wanted to share with you you can see that going from the lab from the let's say from the lab to the law so going from the actual experiments we will do with language models to the real application in a actual project involves a lot of different hindrances but still all these fancy deep learning methods you can find around are useful and you can apply them in a real project so I hope you learned something from these lessons we have been sharing here and well maybe let's see you again in big things next year oh sorry so I forgot something really relevant this is the talk I wanted to give but since I have I think five minutes left or so yeah so I wanted to tell you something else so as I said before last year I was also present here at big things and I talked about an let's say interesting project we have been working here this project was called Rigoverta and the aim of this project was to produce our very old language model for the Spanish language so the aim here was to use these five key points to be able to produce this model so use a very large training corpus place a lot of focus in the cleaning of that corpus also use better training hardware better neural architectures from the latest advancements in natural language processing and apply this domain option ideas I described before and that we actually apply for the legal domain so the timeline of this project has been two years long we started in two years ago last year at big things I presented the alpha version of this which had some interesting results but we weren't still quite yet and today I can say that we have completed this project we have this language model in the 1.0 version let me share you very quickly some interesting facts about this so as I told you one of the key points here was to use a very large training data set we use the spawning and annotated corpora which is the same corpus used for the beta model but we also added two huge open sources for web crowds of the Spanish language oscar and mc4 and also our very own data set which we call for different media outlets we also try to incorporate the latest at the bottom end in new in deep neural networks for natural language models which were the usage of the divert architecture you can see here in this superglue challenge that the divert architecture was the first model to achieve a performance over the human performance and right now it's in the third position in this ranking but let me tell you when we decided to use it it was in the first position so this is the architecture we use for Rigoberta and of course we wanted to test how well this model will work so we compared against the three models that we think right now are most representative of the Spanish natural language community Beto is the one I just described we also have Maria which is a joint project between the national Spanish library and the Barcelona super computer center and also the Bertine project which is a community effort to create a language model I have to say the Maria version we use is the one that was released in the summer it's based on the Robert architecture just last week a new architecture was released so we were really had didn't have the time to do all the comparisons properly but here you can see some of the results so just to have a fast interpretation each line is a different pili and natural language processing task and you can see a star marking which model has the best performance so you can see that Rigoberta seems to be performing very nicely in many of well almost all of the data sets sometimes by small margin sometimes by a large margin but we are getting very interesting results and let me also say that we went to the extent of running what is called an MNG test for statistical difference this is a test that will tell you if there is enough statistical significance that a model is good better than another model and we have been able to check that Rigoberta seems to be better statistically than Bertine and Beto so I just wanted to share this with you very nicely this is interesting piece of news for us and well we plan to introduce this new Rigoberta language model in all the projects we are performing not just in this project by Garigas but also into different kind of fields in which natural language processing can be applied which is health maybe finance sector and so on so now for real I'm not tricking you anymore this will be the end of my talk and if you have any questions I will be happy to answer them if you don't have time to question right now you can see the links to my social media in the end of that talk and you can just ask me later so thank you for listening that was great Alberto thank you so much thank you isn't it ironic in a way that we're talking about natural language processing and lawyers don't speak in a very natural way at all so I think maybe what you're coming up with here is an unnatural language processing model well we can say that every domain has the different way of speaking the same goes through for a health sector I mean the way doctors speak is not so similar to lawyers not so similar to general Spanish so that's the reason that explains that if we can actually adapt these models from general Spanish to particular professions of Spanish let's say you will get an improved performance so it is still natural language but in a small piece of all the natural space for sure I was just just joking with you I loved your talk very nice analogy with transportation at the beginning which and your use of or explanation of reduction of cognitive effort and the trend that we're on so that helped us to to understand greatly what you were talking about and that was great and I and you didn't speak like a lawyer at all you were very clear so very good I congratulate you and it reminded me a little bit of the story of Van Halen I don't know if you remember that story of Van Halen and the M&Ms I'm not sure either okay you know Van Halen is the rock band famous rock band and they used to tour and do very very big tours and at the events for each show that they did required very lengthy contracts so that all of the security was in place they were very worried about health and security and so on and so they put into their contracts a clause which said that in each in each event backstage they wouldn't want a bowl of M&Ms the little sweets but there would be no at brown M&Ms in the bowl they didn't say why they just wanted to make sure there were no brown M&Ms which seems totally trivial a bit like a diva but actually the reasoning was that they would if they found a brown M&M in the bowl then they knew that people hadn't read their their contract properly and if they hadn't read the contract properly then maybe there would be some other health thing or security thing that they hadn't taken into account so I was reminded of that story and thinking well if they have maybe this natural language processing model then someone maybe could read these contracts very quickly and pick up on the brown M&Ms maybe we can train our models to find brown M&Ms yeah who knows so that's actually what I was going to ask you I mean do you think that this has a consumer usage in the future I mean could I for example scan a legal document and perhaps have that converted into a kind of plain English that I might actually understand could we get to that stage hmm well I know that has been some work in trying to let's say translate for general English to simple English for people that are not so proficient in language maybe we could try something like that but the main issue here is always the data so as you have seen this break also we have to manually annotate a lot of data to do make them all do what we wanted to do so here I guess I will be the same so we'll need a lot of lawyers and a lot of no lawyer let's say journal people to actually translate from lawyer English to plain English maybe it could work but yeah I don't know we will have to find applications we mentioned other sectors and how each one has its own idiosyncratic way of talking but would you say that when we talk about the law in particular we've really identified the greatest challenge for you or is there something that's even more difficult to to understand well about the wordings and so on I think the law sector is difficult but remember that I I told you we need to apply OCR methods to actually extract the the actual text from the document but we didn't go to the extent of grating handwritten text into the model that's another level of complexity and I don't want to name any professions but there are professions in which the handwriting is way more difficult to get yes I think we can all think of those I'm sure okay that's great well Alvaro we're almost out of time so I want to once again thank you very much for your time and I guess hopefully we'll be seeing you next year I hope so that's the trend we're on so Alvaro Barbero thank you very much indeed


I felt as an introduction to the town of Davis, the best place to start would be with an interview with a mayor and Evans. And what do you feel are the unique features about Davis that make it a wonderful place to live? Well, Lori, we've had a progressive leadership in Davis for over a decade now, which has introduced bicycle transportation, energy conservation features, cooperatives, and that's made a large difference in people's daily lives, roadside recycling. So we also have a town where we have very educated people. In fact, we're the third most educated in the country. And that increases people's receptivity to these new ideas, to innovation. And I think our geography makes a big difference. Aside from being just a valley town, we are not contiguous to any other urban area. And so we have been forced to have our own identity and our own history. And that's made us a stronger in character as a town. That's terrific. What do you hope to accomplish in your term as mayor? Well, I think people like Davis pretty much the way it is. And so first and foremost, my goal would be to preserve the qualities of our small town atmosphere, which people enjoy. But I'd like to do some innovation in economic development with our small, locally owned businesses. And I'd like to really concentrate on creating affordable housing opportunities for people here in town. That's a problem we haven't yet solved. And one which is a challenge ahead of us as people need to find cheaper places to live. That's very true. Looking ahead, I know you were instrumental in setting up the Davis 2000 Committee to plan what Davis might be like in the year 2000. How do you see Davis in the year 2000? Well, maintaining our strong city services. We have excellent police and fire. And I'd like to make sure that citizens get that high quality service regardless what decade we're in. But I think it's important also that we have a healthy, strong downtown. Well we have this video to do, guys. Does anyone have any ideas? Well actually, I did come prepared. I have an idea. My husband is a sociologist and he's doing an energy study on appliances. And one of the things that's really been fascinating is the study on refrigerators. He goes in and he looks at people's refrigerators and they talk about what's inside and they open the door. How many times do they open the door? And they make meaning out of what's inside of the refrigerator and the character of the people who own the refrigerators. I don't know how it would lend itself to video but I think it would be really neat if we could get into people's houses video their refrigerators and then interview them about it. What do you think? That's interesting. But what if we can't get any remote equipment that kind of concerns me? I had an idea. What if we do kind of a funny bit on like a day in the life of the chancellor? With all the funny things that happen to the union business and the teachers go on strike and there are no books in the library. We could make it funny. How's that funny? I don't know. It's sort of a cliche to me. I get kind of bored with Davis. Well, do you have an idea? Yeah, I have an idea. I think we ought to do a rock video and I have a song that would be perfect for that. It's called The New Mona Lisa and the audio is all done. It's been professionally recorded and you know it'll have a lot of scenes in Davis and you know it does utilize remote equipment but I think it'd be really creative and really anything to do. And if it's good enough it could end up on MTV basement tapes or something like that. It has a lot of vignettes so we could all be director, producer, talent. Well, yeah. The first thing you have to do is hear it though. I mean, if you're interested. I'd be willing to go listen to it. Sure. What about this weekend? Okay, we came up on the wrong camera. Okay, go. Hi there. How's it going? How's your weekend? Yeah, how was your weekend? Just great. Good chance to see the song. Yeah. Hear it, listen. What do you think? Oh God, I thought it was great. I thought it would really give us a lot of practice, you know, to do a lot of the vignettes. You know, to be perfectly honest, I really had a lot of reservations. It was just too rock for me but you know what, I had reservations and I called Tom and he said that he's willing to be flexible. He's willing to open it up again so we could look at alternatives because I just have some reservations. I don't know. Well, then we should talk about it because everybody's doing a class and we should all agree on what we want to do. Let's talk this one. Oh, great. Hi guys. How are you doing? Hi. Look, Lori was just telling me that she had some reservations that she expressed to you about the vignette video and I also wanted to know if you checked out cost. Yeah, I did. It would cost probably $200 which would be $50 a piece but great results. Kind of expensive I think. I'm sort of on a limited budget and I don't know if I can handle that. You know, I've got an idea that I was thinking about doing a performance idea and it's utilizing eggshells and street scenes and it would have film projection and there would be people coming up and handing trash cans in front of the film and that's sort of art creative type of... Eggshells and... Is that for sound? No, it's to create texture and... I don't know if I could believe this. What's going on here? I don't know if I could believe it. The eggshells and... Look, how about doing something? I'd still like to do something really based on Davis. What about a newscast but instead of like a network we'd film from Davis? You guys could be the anchors and you could do news stories based on Davis. You could be the weather person and do weather is always hot in Davis and sports do a master's thing. I think that's a little conventional. I think the idea about Davis is gone but the other thing that we're getting into here is we have, of course, four different point of views and what we really need to do is try to combine all of it. If we want to do something artistic or musical or Davis-like or if I want to do something sociological, I think since we're, you know, it's getting late, we've had class, why don't we all go home and think about it next class session. We've got to come with something, but come with something sketched out. Alright, that sounds like a good idea. I know, we've got to really think about this. I'm getting kind of nervous and we've got to get our interpersonal act together here.  Good idea. Who came with a great idea? Okay, I have an idea. Over the weekend I was flipping through some of the channels on the TV station and happened to pass by this really bizarre shot of this two women, women wrestlers. And I thought it would be fun to do a parody on women wrestlers. I kind of like it. I think that would be great. Have you ever watched them? I mean, they're amazing. We could interview them before or after this. It's so theatrical. We could get two things in. Are you talking about mud wrestlers or what are you talking about? No, no, no, not mud wrestlers. Just, I mean, same thing as regular wrestlers except they're women. During the same thing.  I mean, what are you talking about?    Hey, come on. I don't think this is a problem. We could go to the drama department or actually approach these women. And, you know, I'm sure that they actually approach these women. You do it. I can't believe it. Well, let's try it. What do you think, Tom? I don't like it. Okay. Did you come up with something better? Diane, we were talking on the phone and you had an idea. Okay. I had something pretty conventional. We could do a commercial. I mean, we could do something sort of Saturday night lifestyle, you know, where we do it real quick and funny and blast at people and just do simple well. Great. But funny. Cliché. Too old. It's all been done. Oh, you have a better idea. I have a better idea. Now, let's do this. Okay. Another rock video. But this time is a new song I just wrote. It's called Tupperware Party. Oh. The words are great. Tupperware Party Plastic. Do you bring lyrics? Hey, he's prepared, guys. No. This is great. So anyway. Thank you. This song is great. Tom, I love the song. Hey, I'm really glad. I love the rock video. It's my generation or something. You don't know Peter Paul and Mary's song. Okay. Let's can the rock video number. Okay. Let's just can it. I love you dearly. But we're getting nowhere. Okay. I have an idea. I have an idea. This is something kind of different. Okay. Oh, sure. I talked with a friend of mine. I said, we're working on a project and you have any ideas. And he said, why not take this opportunity? You know, we got the equipment and everything to do like a serious video on Davis as a town. Well, no, look, for people who are new here, in other words, like features of Davis, what we're seeing. And it probably could even run on cable TV or something. For instance, I know Ann Evans, the mayor, and I bet we could start. What do you guys think? I mean, I think you could really work. It's boring. I'm sorry, but it's boring. I don't know that you have the sense for these things. It just doesn't work. It just doesn't work. You know, we have a chance to do something creative and we all like take that opportunity and do it. You know, maybe something. I agree. I, you know, just an idea. I've been working with a dance company, catapult dance company here in Davis. And I've been using fabric and with a theory of movement and fabric and how they work together and it's spandex material. So it's real stretchy. And I like to utilize this tape as something a little more creative with the effects and that idea. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. You know, you guys, the more I think about it, the more I think we should do a video on us trying to decide how to do a video. That's what I mean, bro. That's what I'm gonna do.  I'm not, I want to do that. I'm not. You know what? I want to do that. Why do you want to? Why do you want to? So they're desks at school. Desks at school. Desks at school. Hands over head for the gold. About G.I.J. G.I. Joe. Everybody's worried about G.I. Joe. About G.I. Joe.


From the heart of the jungle comes a savage cry of victory. This is Tarzan, Lord of the Jungle. From the black core of dark Africa, land of enchantment, mystery and violence comes one of the most colorful figures of all time. Transcribed from the immortal pen of Edgar Rice Burroughs. Tarzan, the bronzed white son of the jungle. And now in the very words of Mr. Burroughs, the story of the trail of death. If it were possible for any man to know and to love jungle as much as Tarzan, that man was Captain Lawrence of the governmental police. And so it was natural that he disliked his new desk job in Tarak. The constant hubbub of the city jarned his nerves, the lusty odor of its rambling native section offended his nostrils, and the principal duty of his office tired his patience. It was now Captain Lawrence's responsibility to grant official permission to a constant stream of bloodthirsty hunters to go into the jungle to plunder and kill. And the two who sat opposite him now were among the worst it had been his misfortune to meet, the weak-looking, pasty-faced youth and the square-jawed, overbearing older man.  I'm very sorry, Mr. Fellows, but the questionnaires have to be filled out before I can grant you permission to leave on your safari. Perhaps I could take care of the details, Uncle. I wouldn't trust you to mail a picture postcard. I just thought if you wanted to go back to the hotel... Get on with the questions, Lawrence. Your full name and address? I've already told you that. George Anthony Fellows, 40 Cousin Street, Mayfair, London. And your name, sir? My nephew's name is Malcolm Fellows, same address. Captain, since he was 12, he hasn't the gumption enough to get out and get a job or a flat for himself. Really, Uncle? I don't think Captain Lawrence is interested in... Continue with the questions, Lawrence. We can't spend the rest of the day here. Your reason for wanting to go into the interior? Reason? You're going hunting, I suppose. Well, yes. Put down hunting. Your intended destination? See here, Lawrence, it seems to me I've answered enough questions. I'm a British citizen and I feel... I don't make the laws, Mr. Fellows. I merely enforce them. Regulations require that you answer all the questions on this form. Your intended destination. We're going to try to locate the Mutear Scarpment. The Mutear Scarpment? That's right. I can't approve your papers. And why not? Because I have absolutely no intention of giving you permission to commit suicide. To commit suicide? Why do you say that? Because not even the greatest of Africa's hunters ever attempted the escarpment. Livingstone? That's right, Mr. Fellows. Not even the great Livingstone was willing to risk his neck there. What's so very special about that section of the jungle? First of all, it lies just beyond the most savage tribe on the entire continent, one that hasn't even begun to become civilized. Secondly, it extends beyond a rampart of crags and precipices so steep they say not even the egos will have raised their height. And what about thirdly? There's always a thirdly. Yes, and this is no exception. Thirdly, this scarpment is inhabited by a roaming band of giant apes and a herd of wild elephants fiercer and louder than any elephants ever brought back to civilization. The elephants? They're there. Just like you said, Uncle Charles.   Not exactly. He wants to find those who are already dead. I told you to keep quiet. Oh, I might have guessed. You're out to find the secret graveyard where elephants go to die. So what? If I'm willing to risk my life, that's my business. If I can find that secret burial ground, I'll have the greatest treasure in ivory ever discovered by man. Tons of it. Just lying at my feet, waiting to be picked up. By whom? I won't approve your treatment. Without my approval, you won't be able to hire a guide or a safari member in Tarrak. Or anywhere else, for that matter. You civil servants in Africa make me sick.   Tarzan lived there most of his life, as a matter of fact. But you're not Tarzan, and you're not Livingston. And you're not my superior officer. So, unless you'd like to amend your application, leave our business together, is over, Mr. Fellows? Very well, Mr. Lawrence. Come on, Malcolm. I'll try to keep some people from killing themselves when they take it as a personal fun. I beg your pardon, that's where you're going next time. Come on, Malcolm, stop digging. I'm putting my signature on the table. I knew you were getting a bit older, Captain Lawrence, but I didn't know you'd begun talking to yourself. Tarzan! How are you, Lawrence? A little better now. The sight of you was a real tonic. I'm glad I came to Tarak. Oh, that's very flattering. You didn't travel all the way here to bolster my wavering morale. Perhaps I came to bolster my own. I'm giving up, Captain Lawrence. Giving up what? Trying to be the Robin Hood of the jungle, or whatever I've been aiming at. In the last half-dozen years, I haven't met a half-a-dozen people who proved worthwhile risking my life for. You sound very bitter.  So, at least for a little while, I'm going back home, away from people. Do you see Coast Cabin? No, no, really away to a place where no white man will come. That's why I traveled to Tarak to bid you goodbye, Captain Lawrence. You're my closest friend, and I wanted you to know, just in case I've never seen or heard of again, that I've decided to return to the land of the great apes and the giant elephants. I'm going back to the mutia escarpment. In just a moment, we'll return to our story of Tarzan. Count it, Malcolm. I can't hear myself think. Close that window. All right. Why were you gaping out the window anyway? Looking at Captain Lawrence's office across the street. I wanted to see if he'd come out yet. That jungle savage you gawked at as we left Lawrence's office? He's not really a savage, sir. I read all about him once. He's really an English nobleman by the name of Lord Greisto. Nonsense. It's time you learned to read something other than penny comics, Malcolm. But I read that in a Daily Mail. It said that in Africa, he was known as Tarzan. Are you sure? Yes, sir. They had his picture in the paper, too. Tarzan, the one man who knows the mutia escarpment. Malcolm, maybe we can turn the trick yet. How good an actor are you? Actor? If you can help me pull this off, you'll be rich, Malcolm. Half of what I get from the ivory will be yours. You can buy a business of your own. You can take your own flat. You can be your own man. Gosh, do you mean it, Uncle? I thought we've got to work fast. I've got a little tool kit in that top drawer over there. Find it for me. Yes, Uncle. And bring that bottle of mercuricrome from the top of the dresser. Found the tool kit, sir. Mercuricrome? Yes, yes, yes. And hurry. We've got to get this whole affair planned before Tarzan leaves that office. Here they are, sir. What are you doing with that pistol? I've got to rig up a blank cartridge and make a slight adjustment on the firing mechanism. Oh, but we'll get you fixed up first. Can you jacket? Yes, sir. Ah, there we are. The mercuricrome. All over my clean shirt. Close your jacket now. We don't want those blood stains to be seen until the right time. Now then, take some of that whiskey over there and sprinkle it liberally all over yourself. On my clothes? That's right. And put on your thinking cap. Maybe we won't have to resort to this little scheme, but if we do, you'll have to play your role to the teeth. I'm counting heavily on you, Malcolm.  Oh, Captain Lawrence, you've been most eloquent and a great deal of what you've said has been true, but I've decided to go home. Home. The heart of the wildest terrain on the continent. Is that still your concept of a home? I spent a great many happy years there, and there's a chance that my treehouse may still be intact. Oh, you know, there's not a chance of that. The more Tongers were glad to see their lifelong enemy leave the escarpment, and you can bet your last barb they've tried to erase every memory of you. Ah, perhaps. And they'll do their best to keep you from coming back, Tarzan. Don't you? I promise to be very, very careful. Now, it's time for me to head for the jungle and time for you to get back to that pile of papers. Blasted. Paperwork. Goodbye, Captain Lawrence. Goodbye, Tarzan. But take my word for it. You'll be back. If you don't come back, I may decide to brave the escarpment and come after you. Well, if anyone could reach the escarpment, it would be you, Lawrence. Goodbye. Lord Greystone. Lord Greystone? Sorry, I forgot you preferred to be called Tarzan here. It's been a long time since anyone called me Lord Greystone. I used to hear you referred to by that name at home. We have a great many friends in common. Oh, who? Well, I can't put my tongue on their names at the moment, but I've heard dozens of people speak intimately of you. Won't you join me for a drink? Oh, I'm sorry, but I don't drink. I feel very fortunate in having bumped into you like this. Perhaps you'll sit with me while I sip a cord. I don't like to be ungracious, but I am about to leave for the jungle. Surely you can spare five minutes for a fellow Britisher. I'd like to, but...  I haven't even observed the social amenities. Name's Fellows. George Anthony Fellows. Come along now. I won't take no for an answer. Yes, I can see that.  Well, I shouldn't have admitted it. Look, Mr. Fellows, the Murier escarpment is no place for white men. But you're white? Mr. Fellows, I am not a guide, and even if I were, no amount of persuasion would make me take you there. All right, Tarzan, skip the whole thing. I want it to go there, but if you advise against it, it's a closed matter. Let's drink on it. I told you I don't drink. A Britisher who doesn't drink? Why don't you? Because I've seen what liquor can do to men. Look at that youth over there, the one who's been staring at us. He couldn't be out of his teens, and he's reeling with whiskey. You can even smell it from here. See, that young chap has got a bit of a load on. Has he been staring at us? Yes, and now he's walking over this way. I think it's time for me to say goodbye. Oh, come now, Tarzan. It's just the shank of the evening. Are you Tarzan? Yes, that's my name. Tarzan, huh? The lord of the jungle? See, here, young man, Tarzan's my friend, and I don't like to see him annoyed by drugs. Tarzan couldn't be annoyed by anything. He's fearless. He can handle lions, and tigers, and cannibals, and everything. I couldn't even scare him with this gun.  Steady now, young fellow. Gun's a danger, isn't it? I'll say that, dangerous. You got twice my size, but I could unfasten the safety class and put my finger around the trigger. Tarzan, take the gun away from that truck. I won't let you take my gun. You shot him. I barely touched the gun. I didn't even have my finger near the trigger. He's bleeding terribly. We better get out of here. I've got to help him if I can. He's beyond help. He's dead. The police will be here in seconds, and you mustn't be found. But I can't run away. You don't know what you're saying. You're stunned. You're not thinking clearly. Let me do your thinking for the moment. We've got to get out of here right now. Where have you brought me, Mr. Fellows? This is a little shack no one else knows about. It's on the edge of town, and the jungle's right at our elbows. We can be miles away before morning. I can't do it, Mr. Fellows. I can't run away. You're not thinking very clearly yet, Tarzan. No, I'm not. I'm still dazed by what happened. Do you know what they'd do to you if you went back? I don't know. I suppose they'd arrest me and let it put me on trial. And you know what the outcome of the trial would be? You added up. A half dozen white men in the bar who'd identify you. Your fingerprints on the gun, a victim, a teenage boy who couldn't have had a gun permit.  But Captain Lawrence would tell them that I'd never carried a gun in my life. Oh, he tried to help you, Tarzan, but he couldn't do you any good. The verdict would be guilty. Guilty! And they wouldn't even give you the blessed release of the electric chair or the rope. What would they do? They'd wait patiently for life.  Is that what you want? Oh, no, no, I couldn't stand that. I've always been free. I can't even stand the restrictions of a city.  Just fall bare walls of stare day after day, year after year. What shall I do? Go beyond the reaches of man. You must flee to the most remote spot in the entire jungle, some place where you can never be found. Oh, yes. There's a mutia escarpment. I was going there. We must leave for there tonight. We? You're in no condition to go alone, so I'm coming with you. I'm your friend. Yes, you are my friend. Shall we leave now, Mr. Fellows? I'll be back in half an hour. I want to take care of her first. I want to go along, Uncle. I want to see the graveyard of the elephants.  If Tarzan saw you, he'd realize he hadn't shot you. That the whole thing had been a trick. But I could disguise myself. I'm a very good actor. Didn't I do everything just the way you said? You did very well, Malcolm, but it may take months to reach the escarpment and you couldn't fool Tarzan all that time. But what will I do until you get back? What do I care what you do? You're not coming back. But you don't really intend to give it to me. You never did. Now, take it easy, Malcolm. Why shouldn't I give you a share of the money? Because you're greedy, that's why. You took the money my mother and father left me. Every shilling of it. And you intend to take all the money from the ivory, too. I know it. Well, I won't let you get away with it. Hands off of me, Malcolm. Don't cheat me again. You think I'm a fool, but I'm not. I warn you, Malcolm. I've got the gun.  I won't let you cheat me. Don't stop this instant. You'll be sorry. I'll make you take me along with you. Make you take me to the graveyard of the elephants. I won't let you cheat me. I warn you, Malcolm. Maybe it was better that way. No one can ever clean the ivory I'll find with the help of my friend Tarzan. In just a moment, the exciting conclusion of the trail of death. As Mr. Fellows had predicted, Tarzan and he were far from the city of Tarac by dawn of the next day. And though Tarzan's heart was heavy, they continued to press on day after day and week after week until suddenly beyond the maze of riotous jungle growth, they loomed a mountainous palisade. Its summit lost in a haze of purple mist. This was the gateway to the mutia escarpment. Fantastic. I feel strange. I thought the sight of the escarpment would let me feel free again, but it doesn't. What do you mean? There's no peace for me there now. There or anywhere else. We're turning back. But you can't do that to me. To you. That's right. I'm in this just as deeply as you are. I helped a murderer escape. I'm in accessory to the crime, and if we went back, I'd face the same fate you do. Is that to be my payment for befriending you? No, I... I suppose I couldn't do that to you. Then let's get started again. This is it. My fear is though, I were on top of the world. You are, almost. Are you all right? Am I all right? You carried me halfway up those clips and literally dragged me the rest of the way. Don't you think you would better stop the rest for a while? We're almost to the spot where my treehouse was. I want to see if it's still there. Besides, it isn't safe to stop with the moatangas on our trail. I haven't seen them during the last hour or so. They aren't very far behind, but unless they've gained new bravery, they won't come this far. They always felt that I was some sort of a demon and that the land about my treehouse was full of dangerous jungle spirits. They didn't take very frightened of you, and we encountered them back in that clearing. Well, that was their part of the jungle, but look, it's still there, my treehouse. It is it that. But it's a long way up. I don't believe I... Hold on! You really gave me a start picking me up like that and swinging me up here. Hey, that's quite a house. Come inside. All right. It's still in perfect condition. They haven't touched it. Apparently, they're still afraid to come here. It's quite an amazing house. Much bigger than it looked from the ground. But it's still not a sanctuary for me. Having committed a murder, I'll never find peace again. Look, Taz, look, you'll be safe here until morning. I shall return. Don't leave me up here all alone. Mr. Fellows, I never should have brought you to the escarpment, but I've done so and I promise that I shall guard your life with my own as long as you're here, but I must leave you until morning. But where are you going? To find the companions of my childhood. The wild apes and the giant elephants who were once my friends. Perhaps with them, I shall find an answer. This might well have been the Tarzan of old, clamoring through the upper level, soaring like a bird as he swung from tree to tree, clinging to the topmost branch of a giant kayak as he raised his voice in the triumphant call of the bull-ape. And as his call reverberated through the escarpment, the denizens of the jungle listened and became aware that Tarzan had returned. Like a mighty army, they started toward the sound of his voice. For to them, Tarzan was still their leader. They undisputed, Lord of the jungle. And that night, while Tarzan roamed the forests with friends who asked nothing of him, Mr. Fellows deserted the treehouse, cautiously made his way to the ground and followed the trail back until he arrived at the camp of the savage Moatangas. I'd like to see your chief. Your headman, your leader. Leader. Look, look, there must be someone here who speaks English. I've come here to do you a favor. I'm your friend. Friend, friend. He co-attends awfully. I'm your friend, I tell you. I've come to warn you that Tarzan's going to drive you out. He's come back to kill the Moatangas, but I can bring him within your power. How? So, now you speak English, eh? Be quiet, man. I'll speak only to your chief. Here is chief. Oh, hello there. How you help us destroy Mandevil Tarzan? Not so fast. I've got a bargain to make. You show me the graveyard of the elephants and furnish me enough men to carry the ivory back to civilization and I'll cook up a little plan to bring Tarzan into your camp here. Tarzan, come now to save friend who is captive. The chief of the Moatangas had spoken the truth. Tarzan had already discovered the absence of his friend and even now he was entering the Moatanga forest of the escarpment. He traveled in the back of a giant elephant and the army he brought to battle the savage horde of natives consisted of a dozen similar elephants, their strong yellow tusks gleaming in the early light of dawn. The Moatangas had tied fellows by their council ring, but his guards fell back as Tarzan and his army entered the clearing. Spears flashed in the air. With Tarzan's knife, Greek vengeance, the yellow tusks like angry scimitaries tore through the ranks of the enemy. Then as Tarzan shouted orders, one of the elephants stressed fellows and swung them under his back. Now the herd of elephants, one bearing Tarzan, another bearing fellows, swarmed from the camp and turned back toward the heart of the escarpment. After them came the natives, their poisoned chip, Asagais, filling the air. It looks as though we're coming to some sort of a pass through the rocks. We can make a stand here and try to fight them off. Tarzan, the elephants, they're forming a barricade here. They'll keep the Moatangas from getting through the pass and we can go on.  Oh yes you will. A gun! You've had that all along. What good would it have done for me to kill two or three of them? I'm amazed they didn't take it from you when they held you captive. They didn't know what it was for, but I do. Now hurry before they get here. Hurry, or I'll shoot. No, no, no, don't shoot. I can't afford to die yet. I will go on, fellas, but you'll have to change mounts. Your elephant's been wounded and he's dying. I'm not getting off you. But you must! He has a journey to make before he dies. I know he has. A journey to the secret graveyard of elephants. Well, I'll be right with him. I'm going to find out where it is, and someday I'll come back. I'll come back and get that fortune and ivory.  So that was the reason you became my friend. That was why you came with me to the escarpment. Come on, Tangas, they're almost done. Get going, Tarzan, get going.   And so, with the balance of the elephant herd barricading the pass against the enraged Moatangas, Tarzan and fellows moved on. The great beast that the Englishman rode was dying. But he picked his way carefully along a narrow upward trail that would have challenged the footing of a mountain goat. And through it all, fellows kept his gun trained on Tarzan. It never wavered as they climbed steadily up the sheer slopes of the escarpment. Through the endless caves of the giant bats, along the winding underground passageway of the trail of death, and at last into the eerie silver-lighted cavern where tens of thousands of elephants had come to die. Look at them. Look at them, thousands of huge skeletons. Thousands of ivory tusks. Enough to make me rich as a king.  I was afraid they might not bring me here unless you were along. But I have no need for you now, Tarzan. I know it'll be hard to make my way back, but with his ivory as a girl, I... Watch out, fellows. Your elephant is dying. He's about... He's fully... All right, fellows. You can get up to your feet. And you won't be needing a miscarriage. What? What? But what are you going to do to me? Nothing. Nothing. But I'll keep an eye on you. We're leaving the escarpment at once. We're going to Tarrak. But I've decided that I must accept whatever fate holds in store for me there. I shall have to face the murder charge.  Captain Lawrence! I told you that if you didn't come back, I'd find my way to the escarpment. I didn't anticipate the things I've been through. What did you mean when you said I faced no murder charge? The lad you supposedly killed was more rugged than he looked. He even managed to survive a real bullet. I had at him my his dear uncle. Malcolm's alive. Well, enough not to testify against you, Mr. Fellows. You're under arrest. I wish I knew what this is all about. I'll tell you the whole story and return trip to Tarrak. Unless you're staying here. No. No, I'm ready to come back, Captain Lawrence. I've learned that freedom is not a matter of geography. A state of the heart. We'll return in a moment with a preview of our next exciting story of Tarzan. Africa is a violent place, used to the savagery of man and of beast, and yet the actions of one man cause a wave of horror to sweep over the entire continent. The government officials, the native police, and even crime hardened convicts joined in the human web that spreads through the Congo in a giant manhunt. And Tarzan becomes a central figure in the bizarre chase for there's a killer at large. Included in our cast were Jack Moyle's Walter White III, Shep Menken, and John Stevenson. Tarzan, a transcribed creation of the famous Edgar Rice Burroughs, is produced by Walter White Jr., prepared for radio by Bud Lesser, with original music by Albert Glasser. This is a Commodore production. Listen to our next story, Killer at Large, another thrilling episode of The Lord of the Jungle. Charles Arlington speaking.


What's going on? How are you guys this week? It is Wednesday and since I was not allowed to post for the past week I didn't end up filming a vlog on Monday, which I should have but I figured you guys wouldn't miss it this week So we're gonna do it anyway a little bit of a different day because we're not packing orders, but we've been pretty busy down here and I don't if you guys noticed I had the settings on my camera the white balance completely wrong for like the past three months So probably looks a little different for you guys today. Hopefully it's better as Usual something has to go wrong every single week to basically spend all of the business profits on this week the HVAC systems as I said haven't been working so the heat isn't on in the building and it's pretty cold in here today it's like 40 degrees in New York, so it's maybe 50 in here and The New York City heat law Might only be for residential so I might be stuck fixing this on my own was probably gonna cost one to two thousand dollars and The band saw that I purchased a month or two ago that I showed you guys We were cutting some steaks on it and it turned off after a minute every time so Although the guy said the machine was wired for 110 volt. It's not working So I got to have a technician come and look at this saw two Really every single problem is because I don't own the building and I don't have Correct three-phrase electrical on it and it has cost me probably tens of thousands of dollars at this point, so We do have some good news, however But I did want to announce that there are about 10 orders that we ran out of brisket on because my supplier Was delayed for about a month on the brisket and I do have the vitamin D3 in stock that is corrected So some of you guys had an issue where it was settling at the bottom Just send me an email and we'll send you guys out a new one before I show you guys anything today I will have something to eat. I have my water kefir and the Jerky is ready. We should have the jerky next week guys. It is Absolutely delicious and what's amazing about that is because it's only salt and meat Now there have been a couple of products from Whole Foods that I've liked a lot But this jerky just blows them all out of the water really really delicious I'm sure a lot of you guys have made jerky at home and it looks similar to this But when you're able to just use only meat a little bit of salt Dried at a low temperature instead of whatever these people are blasting it add in their commercial facilities Really really amazing products. I'm gonna have some of this maybe some apples and pineapple and Cliff bars, but we'll see you guys in a little bit So the dehydrator came with these what I'm assuming are aluminum trays that aren't too perforated so I spent about $900 on some new steel racks, which is definitely a good investment. It's a piece of equipment Probably have for a very long time. I have a little bit worried about the water kefir because since it's cold in here It's not gonna ferment at all So hopefully we don't get behind because you guys have been purchasing a lot of it. We go into the depths of the freezer Truly the depth on the lights off With the launch of best burger reviews we have plenty of our best burger custom blend, which is a secret blend I'll tell you guys there's some chuck roll in there, but it's pretty good I honestly wish I could have got it at a lower price point Unfortunately after charge ten dollars a pound for the best burger blend is it worth ten dollars a pound? You guys can try one or two out. Maybe we'll give it away one week. Just so you guys can try it What's exciting to me is since we did get that band saw working a little bit We have one of my favorite types of steak. We have a bone in Porter house tea bone steak Which is the New York strip on the right the filet mignon on the left. These are really really nice presentation for your family. So Check these out Porter house steak We're also going to have the bone in rib steak cut a little thinner once that saw is back up and running We do have some deal brains. We're getting more back in stock this week as well The bone in rib roast is back and this is kind of what Frankie Syringe Mean is offering for the holidays Do you guys want a nice centerpiece for your family? We have this beautiful four to five pound Bone in 100% grass-fed prime rib roast Let me know if you guys want something bigger and I can do it like a seven or eight pound roast But if not, it's not in the man. I'm not going to do it Organs looking okay These are the orders that are missing briskets. So as I said, there's about ten of them and guys I'll try to get it out to you Monday as soon as possible. I'm very sorry about the delay I've been jerking around a little bit. Did I show you guys the chicken feet the other week? if you guys like using chicken feet in your chicken broth gelatin stock soup for when you're sick or you just like chicken soup during the warmer months, we got some couple of pork skins back in stock these usually go really quickly People always email me about them forgot to mention. I've always had masticum with my meals and I probably Show a little bit after if you guys aren't able to like brush your teeth or something after a meal This is great for just cleaning your mouth show on it for a few minutes So that's really it on the meat end the the marrow bones were cleared from customs So now I just have to arrange the shipment of the marrow bones to here Problem is it's like two whole pouts and marrow bones and my bandsaw isn't working So this is basically the worst case scenario so hopefully this guy fixes the bandsaw tomorrow and then when the The bones come in we can cut them otherwise. I'm not so certain I mean thankfully we have a lot of space in the freezer right now So we can probably fit them But we really got to cut the bones down to a more manageable size So they don't take up so much bulk because the ends of the femur bones are very like large and The knuckle bones although normally we would boil them and sell them into a stop You might just have to Throw them out or give them away because they're too heavy to ship right now So let me show you guys what's going on down here this week Now I've done a video explaining every single one of these supplements and what I use them for but I'll give a quick rundown So vitamin K2 is the most important fat soluble vitamin that people are deficient in because the gut bacteria supposed to synthesize it But people generally don't have correct gut bacteria Incredibly important for magnesium and calcium regulation vitamin D3. We know The important thing to note about vitamin D3 supplements is it should only be used to fix a deficiency You should not be taking it above moderate blood levels Copper the antagonists to zinc Normally like especially stands in American diet people shouldn't supplement copper But when you come from carnivore and it's copper deficient, it's good to use for a month or two Magnesium the most important mineral the only mineral I consistently supplement, but I use our magnesium oil instead of the magnesium Liquid sometimes I use both, but I like spraying this on it gives me energy because it has MSM in it Then we have vitamin B complex Probably the only other thing I take regularly because B vitamins get very depleted Especially when you eat a lot of carbs and pasta like I do trace minerals manganese a boon molybdenum I might break this down into individual ones, but these are like the main minerals that aren't on the carnivore diet They're higher in certain plant foods like beans Antioxidant support if you have a headache it's glutathione, nac, vitamin C and ubiquinol. So You have like a super bed headache or you're trying to fight off a cold you can try this out iodine I Use this every day mainly for antiseptic purposes like I actually use it to rinse my nails rinse my mouth I even put some on my toothbrush and brush my teeth with it selenium Probably the most important mineral for the liver a lot of people are deficient in it And I usually take this like once every two weeks a couple times a month all the bullvine powders I mean they're corresponding to the organs and I'll link probably like the video for these at the end the macronutrient enzymes and the Carnivore enzymes if you're carnivore or just on a regular diet if you're having digestive issues I need help breaking it down and don't have a probiotic You can use those so I am experimenting with the pineal the third eye a little bit I'm not taking these are the ones right now, but we have basically every gland you would want You know immune strike like thymus and lymph nodes big brain the brain tissue pancreas you guys can try these out I was thinking of getting a whole like Supplement production line to do pills and capsules, but they're all on three phase electrics So I'm not gonna buy three machines and have them inverted and spend another 10-15 thousand dollars in electric bills in this building That I don't own so we got to figure that out sometime soon Wi-Fi shielding is nothing special I mean everything's just kind of hanging out here I was supposed to get those fabrics in but the the Chinese people increased the The price on the shipping and I'm kind of arguing with them So hopefully we can get the winter clothes as soon as possible and my tailor can make them Frankie's naturals as usual. I mean both of these companies that you know kind of stagnated I'm not getting that many orders and so Probably do some more video showing you guys the main thing I use every day is The deodorant I use the the truth powder if I'm going to see some dirty girl The hair pomade I put in my hair a couple times a week I don't use the black soap right now What I actually do is I just use the clay shampoo for my body and I'm everything else my hair The thing is like if you're really sweating and you actually smell It's gonna be kind of hard to get the smell out with the clay shampoo So that's where the black soap comes in so I don't really have anything else to talk about today as I mentioned earlier I didn't really want to film a vlog this week because it's like already Thursday And I'm gonna film another vlog next week And I kind of wanted to to try to get the heat fixed and the bandsaw fixed and have everything kind of set The jerky ready to sell and everything for you guys next week But next week is gonna be pretty big and I am I'm trying to stay happy and excited because these are the things that keep me going you know We launched we launched the best burger reviews this week. We got our blend We got the jerky available now, and I'm constantly just trying to add more and more and more and just make the business profitable and then Once once I'm in the slaughterhouse once I have my own facility The profit margin is drastically increased and I can do everything I want to do Which is basically help people improve their health and their lives through nutrition, but Unfortunately, it's like you have to play the same game as everyone else like make as much money as possible except instead of scamming people and buying Lamborghinis and mansions. I'm actually putting it back into the business and trying to help people So that's gonna be it. Thank you guys for joining me. I know a lot of you were very skeptical about me eating those burgers and the Burger reviews I'm going to address that in the day of eating that I will probably film tomorrow and explain how I deal with that and To basically sum it up. It's just like kind of yes I'm sacrificing my health to some degree just to hopefully market more on the on the like the normy Regular person and doing these burger reviews So if you guys can go to frank-to-found.com You can support me through all of my businesses if you watch the video we did two days ago on Tuesday you can see the link to the best burger review channel and check that out as well But if you guys could please drop a like on the video leave a comment down below Subscribe so that YouTube could unsubscribe you next week and be sure to check that notification bell so they don't notify you of my videos Thanks again for joining me guys frank-to-found.com. I'll see you for tomorrow We'll do a quick day of eating and talk to you guys a little bit