I'd like to share an experience I had with a game of Team Fortress 2. It was about 9pm on a Monday, I think two weeks ago, and I had decided to play a multiplayer match on TF2. I signed on, and started up the automatic server selection for play modes. I felt like playing capture the flag, so I selected that option. My computer liked a bit, while it was searching for a server, which isn't abnormal for me, as my laptop is fairly distant from my router, but eventually it found one. I've only been playing TF2 since a little after it became free, so I don't have a very big server history, nor experience with server mods or servers in general, but something seemed rather odd about this particular server. Once I gained access to the server, and the loading screen came up with the bar on the bottom right, as the bar got to about 70%, it jittered backwards and paused. It started going back and forth between 45-60%. The game froze after a few moments, and I decided to force quit the game. I went to get something to eat, and came back to the game, started it up, and decided to play from my server history. I noticed a new server added to the list, TFCTF Playdead 905, or something along that line, after the events I'm about to explain, the server disappeared from my list, so I'm going on playing memory here. I tried the server and, similar to before, the load bar jittered a bit, however, it managed to complete this time. Usually when you enter a custom server, there will be some sort of splash page to welcome you to the server, and advertise their site or briefly explain the rules. In the case of this server, the entire splash page was black. There was nothing on it. I started to think something was wrong with my graphics card, with all the strange things that was happening, but decided to continue on. The game loaded, I selected Pyro as my class, and I entered Two Fort. For those of you who have never played Team Fortress 2, here is a brief explanation. It's a team-based online multiplayer first-person shooter game, and in this particular capture the flight mode map, Two Fort, you have two fairly large mills, separated by a little lagoon and a bridge, and an underground network of pipes that you can enter. The objective is to, obviously, capture the opponent's flag, and return it to your base. For this server, there were two teams of six. Upon my joining, both teams became full. The game was already mid-progress, and everything was working normally. Our team, Blue, had already scored one point, and we needed two more to win the game. The red team had zero points. This particular game didn't have a time limit, and we seemed to be going back and forth for about 14 minutes, with little to no progress. Occasionally, members from our team would type angry messages to the opposing team, accusing them of being cheap by having like four engineers in their flag room with turrets, making it almost impossible to get their flag, and making the game seem endless. Up until now things have been relatively normal, until I started noticing that for about five minutes, I've been going around the level, and I have not seen a single one of my team members, nor any other players from the other team. People were still talking however, so it kind of took a while for it to sink in, that I haven't seen anyone at all for over five minutes. I thought that it had to be some sort of lag problem, so I asked the other players if they were having the same issue, and the general response was, nope. But one player said they haven't seen me for a bit either. About six more minutes passed, and I started to grow pretty bored. I decided to make one more round throughout the level, before I would find another server. I left our fort and fell into the lagoon in the middle, and proceeded to enter the red team's pike network. After the little room underground in the middle of the tunnel, there's a really long pipe that leads to a staircase going up into the base at the end.  A strange form of relief swept over me, that the game started to warp again, and I charged at him, and began to shoot him with my flare gun. The shots went right through him, and when I approached, he disappeared completely. Really bad lag, I thought to myself. I ascended the staircase, and as I entered the base, a strange static sound started to replace the other game sounds. I made my way throughout their base, and as I got closer to the flag room, the static got louder and louder, until I couldn't hear anything else in the game at all. This is when things started to get really weird. I asked if anyone else heard the static, and I did not receive any reply. No one has said a word, since I saw the enemy scout in the pipe tunnel. I managed to get to the room, adjacent to the flag room, with two halls going into it, and I could hear some sort of murmuring behind the static. As I went down one of the halls and turned the corner, I noticed that there was no flag room. It was plain black, like the hall leaded into the game's void, or all the textures and lighting were reduced to 100% black. I shot a couple of times and my bullets landed, as if there were walls there, so I entered the room anyway. I saw the flag and picked it up, and when I turned around to leave, there were no halls. Everything was black. The UI was still up on the screen, it said I had their flag, but there was nothing to the level, and all traces of the level disappeared. I tried again to get an answer after explaining my problem. No response. I stood in the room, or lack thereof, for about 3 minutes, until suddenly the game froze, and the game ended, but the message didn't say anyone won or lost, and the announcer's voice was all distorted. The scoreboard was broken. The loading screen quickly came up, and it said, downloading Playdead 0090, or something along that line. It took about 17 minutes to download, it was a really big file. When it was finished, the black server screen came up again, I went through the screens and selected Spy. The level loaded to something completely indescribable. I tried to save a screenshot, but every picture I tried to get said error, and print screen didn't work. The level looked rather black and white, and very glitchy, the textures on the walls were kind of like 8-bit blocks, but really distorted. It was almost like playing in a 3D version of a static snow screen on TV. I went through the level and asked everyone, what is this map? But there was no response again. I checked the player list and other than my name, everyone else was listed as, I and Dead 1, I and Dead 2, I and Dead 3, etc. I've never encountered anything like this before, other than some prank games people made for fun, and hacks along the lines of that Majora's Mask one, but this is an online game with, what I had thought, secure servers. I decided to give it a few more minutes and explore the level, which was a really large maze of very oddly shaped tunnels. Also, there were no sound effects whatsoever. Nothing I did caused a sound. I eventually came to a really long tunnel, with what I imagine was the middle of the level. The tunnel had darkness at the end, and it seemed almost endless, as I was walking for at least a minute in one direction with no change to the map. I decided to turn around and go back, thinking the map is glitched, and the moment I turned my camera around, I saw the enemy scout I saw earlier, except something was very wrong with it. His eyes were black, his left arm was rather deformed and bent in impossible directions, and his legs reminded me of a crab.  I stood there and stared down the strange scout. After several moments, the other players started talking, but all that appeared was corrupted garbage text. The scout quickly approached me in a sudden, though in regular fashion, and I heard a very very loud burst of static, and all of a sudden, it kicked me out of the server. I went to check the server, and it was gone from my history. Naturally, I immediately shut off the game, and haven't played it since then. I think it was some sort of prank, but it was pretty creepy, and game hacks don't usually creep me out. Update, July 14th, 2011. After posting this, I decided to play the game again. I went into my server history to find a king of the hill map, but I noticed that TFCTF played at 905 was back, but this time it had 0 over 16 players. I went into the server and, unlike before, it loaded normally without any issues. It was too fort, like when I entered it before, but there were no other players on the server. Nothing seemed really out of place, and I walked around the map for a while, finding nothing. The only thing I noticed that was strange was that I would hear the sound effects glitch out every now and then. When selecting a character, there was a static noise that kept repeating, and when I went to the tunnel area, I thought I heard footsteps.  Maybe I will be able to get some footage.


So hi everybody Today I will not only talk about or I will talk about specific topic that is rather not that Official in the data science called feature engineering because there is like no discipline that And you can learn in the university or find a course on this is Actually considered to be something that you should be able to do but it's not always that simple so and In the end of the presentation you will get by what I mean by saying data is not flat So the structure of this presentation will be as following I will just briefly mention what features the engineering is what is it is not and I Will concentrate on a certain example of feature engineering. There are like several types Then I will give you a classification example That's a really stupid project of mine and you probably will like it at least I hope So at the conclusion I will again tell you about how to use feature engineering what you can do almost automatically and What actually helps and works in most cases? And afterwards I hope we will still have time for QA session So what is a feature engineering according to the Wikipedia? It's pretty accurate definition This is a process of using domain knowledge of the data to create features that make machine learning algorithms work What does it mean? You probably all heard about concept garbage in garbage out Meaning if you have a garbage data that doesn't even reflect the situation you're trying to work on you Can try whatever model you can come up with it still won't produce any good results so feature engineering is an art of Preparing data the way that any algorithm can perform better So the feature engineering as It was seen in the description. It is an expert domain knowledge by that I mean that someone who is driving a taxi knows probably a bit more about the whole industry and how the things work rather than some college student Then these process can be both automated and Manual I won't talk about automated process. This is more of a machine learning approaches that Cover the feature learning like it's a some part of a knowledge transfer for instance Or there are a lot of also Python tools that if you have multiple data like real lot of tables you can concatenate them some way and Just run automatic Functions to create new features as some sort of transformation from the older columns. I Won't talk about that. I will talk explicitly about manual creation However, all of those approaches are quite expensive you need time to identify what you're working with you need time to identify what features might be useful and Sometimes it is important to First take a look at your model. Sometimes it doesn't mean anything at all and As garbage in garbage outstates data quality is probably way more important than the actual model Future engineering is not part of the data engineering process meaning data engineers are people who collect the data initially make some initial pre-cleaning things like Taking completely insane samples out Somewhat flattering it out if necessary. They make the data available They have no idea actually what for this data will be used and thus they are not responsible for certain specific knowledge search and It is not the part of pre-processing even if it's done by data scientists again these data science is a iterative process you Create a model you feed some data in if you are not even know how your model will behave you just watch what happens And then you started tweaking some parameters on the data side on the model side and this iterative will will repeat itself many times but future engineering comes in question if Your model does not perform well at all and even some additional models do not perform well, and you have a Kind of sense that the data is at fault So I said earlier that there are pretty much two main approaches manual and automated for automators like tons of articles just type in feature selection feature engineering automatic or feature creation Python and you will get all possible blog post GitHub repositories just play around with it it also pretty powerful but the Thing with those approaches you need to have quite a lot of data or you need to have high-dimensional data in this case They will work the best basically, you will try to reduce the amount of noise or Unuseful information to feed into your model Second approach is used when you don't have enough data. We are talking about Either very low amount of samples or very low dimensionality of the data or both so the Thing that I want to show you today will be concentrated on the case where I don't have data I don't not at all, but like thousand samples. It's nothing and At the same time, it's like one dimension So what can you do with this? Again, I quite like the article on the Wikipedia about the feature engineering It has quite a lot of resources and links to for further Usage and further sources to read and they mention as Process how to actually approach the feature engineering so brainstorming of testing features deciding what features to create creating features and Checking how the features work with the model Improving if needed improving meaning you probably have to scale they down or if it's a categorical feature You need to define categories the ways that the model actually will understand them and won't have this cute Distribution so you need to really know your data and Repeat it and repeat it and repeat it. So this is the process that we're gonna all do today For example, I said it's a free time project of mine in I have a friend. He is I don't know. He's a student informatic student and he has very weird sleeping cycles You know those typical informatic people who are going to bed at like seven in best case am and wake up at eight and Actually, he's a quiet big part of our friend like friendship community and everybody is always interested when is he awake? and on telegram everybody's asking is he awake is he awake and As a part of the meme or gag you can say I was given a dare Challenge to write a model that will actually try to learn his sleeping patterns and Instead of actually asking and waiting for response. We will get the approximate answer right away For that reason again, it was a dare project. So I write I wrote yet another wrapper around fully connected neural networks I did it because I needed it to be configurable my way It's nothing fancy. It's just NumPy with some features So About these an n generator it is available on Piper if you want to play around with it The feature that I needed was that the whole model should be configurable with Jason So that I will be able to run several different slightly different models Simultaneously or even have pure understanding. Okay, where I came from and where I am now So you can just give architecture It can be flat these currently there is only the sigmoid Meaning binary classification implemented This is the last layer and then you can have as many layers as you want to with the amount of nodes within you have activations for each layer and something like confidence meaning you have the Probability of something like the you don't get the zero one right away you get the probability and you need to interpret it So you can set it to whatever you want. You can have different learning rate all the hyperparameters pretty much So Originally I was given just thousand samples. These were timestamps And the state whether the guy was awake or not with a certain minutes Step so this is what we're gonna use pretty much We're gonna create input structure approachable for the building the model We're gonna create and train the model and just see what the prediction and the accuracy is in comparison to the Testing set I don't have a validation set because it's just ten Talbin samples and for the purpose of this presentation. It's not needed So originally I was so I thought what will happen if I just throw pretty much a time series inside and see what happens Well, not that good You can see that the model Performed at 30% accuracy, which is way worse than even a coin flip and I can wait just to see what the guy's awake or not and I Thought maybe it's not really a prediction problem. It's not a regression it might be a classification problem and in this case I obviously lack data, but the good thing I have time data and Time has certain attributes to it You can describe certain point on the moment by what date is it to date not even the date by what day of the week it is and For us people it is important because on the Monday to Friday We tend to work meaning we have certain behavioral pattern on the weekends our behavioral pattern might end Might not end but change and in this case this difference is important Also, it is important whether it's late in the evening early in the morning or it's a lunch time so you can see where I'm going there and I've tried to unwrap the timestamp into just Vector containing this feature describing each timestamp So basically I went through brainstorming or testing features Deciding what features to create this is the day and the hour and I created them and I also check them how they work By the way, I'm using two-layered fully connected neural network This is approved to be the final best model because I after I did some feature engineering I also played with different architectures and this one had the best performance So I take it from the very beginning so we run this one with two features which are date of the week and The hour the model suddenly jumps to 81% of the accuracy, which is like really not too shabby and That make me made me thinking What do I else know about this guy? I know that he's a student and He's a human. I know that he also lives in Hamburg where I come from and as any living being in Hamburg we are affected by the weather which is Better tend to be a little bit better in summer and it's completely disastrous in late winter early Spring so the season might also be important there And in this case I say, okay Let's throw some season into it as a third attribute and now we have as input data the three-dimensional vector Now the model performed 8% better than the original or the previous one Like the model is the same the date is just unwrapped so 89% is already way better than I've started originally with but at the same time 95% is something that should be considered to be production ready like 95 and above so I want to go further I want to Push the limits So what do I know about this guy again? Here's a student. Isn't it important? well actually yes because he has exam phase he has some lecture phase and he has vacation and I Know myself and I tended to behave completely different on exams and during vacation I slept like whole day through whole life through so why it should be different for him and In this case, this is really Artificial feature because it is static information taken from the website of our university where I and he are students But you probably cannot omit such information collection if you know how or if you trust the sources that can Provide you with this information anyhow, this is a little more or less static and now we have the four features model Which performed to 95% accuracy for me? It was like 95 industry ready. I'm done with it, but if I look if I step take a step back and look at it all I did is I try to reconstruct Behavioral attributes or time attributes of the behavior of this guy and I started originally with the only time stamp And I ended up with a pretty good results Only by feature engineering You should just believe me. You can obviously tried your tried out yourself, but Tweaking the model parameters taking different models didn't perform well at all I never got better than 45% accuracy on only timestamps. I tried STMs didn't perform. I tried some models from classic econometrics like Arima, but this is not exactly the problem. I was approaching So let's check out whether this guy is awake now No, he isn't it's like Three yeah, he will be probably awake in like two or three hours Ping me and the telegram or whatever just to ask whether he is really awake if you're interested So this is pretty much what I just wanted to show you that is very simple yet very powerful and I've only used my like I am as an expert. I know this guy. I Pretty much simulated my train of thoughts what I know about the guy what I should what information do I use and as a programmer I decide I try to implement or collect this data and Yeah, that's everything based only on timestamp Imagining if you have more data that you can unwrap another case. I can think of is for instance There is a competition on kegel about taxi. You have to compute the waiting time in the taxi based on the four or three years of taxi time from Mexico or Ecuador countries and There are like normal data you can see in the file they provide is Geo coordinates of pickup drop off timestamp of pickup on drop off the distance they actually cab drove and How long did it took how long did the whole ride lasted and stuff like that? but You can throw something like I don't know whether the time like for instance You can again can automatically unwrap the tame timestamps into date Into sorry date a day and an hour and based on day and hour you can for instance Decide whether it's a rush hour and you know that in rush hour cars tend to Have longer drive and waiting time than normally because the roads are full Also, you can define something like holidays national holidays This is where really expert domain knowledge comes in and this is purely manual I don't really imagine anything comes close to this Train of thought and performance by using automatic approach Because the data is really not there at least it looks like it so It is costly I to it took me about three days to like one day to build a model and One day I thought about okay. What what can I do? I only have this much data and only this data What should I do? What helped me originally? I've tried to again reconstruct my own Sots and I've tried pretty much the model why I choose the Connect fully connected neural networks is also reconstruction of my brain work For instance for me. It was important that today The feature importance is also took some place and the layers They are they reflecting my for instance Personal attributes that I value more for instance. I know that Exams for this guy are not that important But what our word is is way important and you could see that after adding just day and hour the We got 50% of accuracy and by adding only season. We got like five or seven percent It is very error-prone Because it's expert domain knowledge You mostly especially in literacy you will have to drag someone in who knows their stuff for instance some engineers or even taxi drivers and Between this communication or time between the expert and the programmer data engineer the data science Some information will be lost also again, it is not Really clear at what scale and what features should be Incorporated into the model or into the data set Sometimes you are So happy about the results about the progress that you do that you try to throw in some features that are based of it One each other meaning you'll land into multi-coloniality Situation where you have quite a lot of noise, but not that much meaningful data So sanity check is always a good thing just to create several features Think how they perform try to maybe fine-tune the model Try to get the best out of this Small scenario this local optimal and then think again because every time you need to create a brainstorming session You will be affected by the previous brainstorming session and at some point you will run out of ideas So feature engineering is not a knowledge transfer it can be used as a part of it Basically you train models on certain amount of data that has at the first glance nothing to do with your Task, but you have to train the first model somehow So it can be useful for that step as well By unwrapping features you create even more features obviously and You can use feature selection you can use automatic feature creation and whatever because you have way more space to do that I Think it's very powerful. You can argue with that, but I think it's powerful and It worked with all models. I tried some neural networks I tried classical approaches like decision tree and they all performed way better when they got more data It's like yeah, duh, but every time you see it in the code and their results. It's wow So this is pretty much everything I wanted to say about the feature engineering. Thank you for your attention So thanks for the great talk. There's time for questions Hi, thanks for the talk. I was just wondering have your tool compared automated feature selection to your Insightful feature selection and whether they perform better or worse Future selection like I won't use feature engineering when I have enough Data to actually use feature selection feature selection comes in first when you have to the data to search like to Take some features out to prepare for training Feature engineering is pretty much enabled feature selection. So I won't compare them on this level Thank you for the talk. How did you select the frequency about your? Your data, for example, I was given it I was pretty much just shoved a file with a textile file and I knew nothing more than this So in your case you want to know the at what hour your friend is awake not in what minute or second It will depend on the task obviously in this case It was enough like certain minutes for humans to know whether somebody is awake or not available or not Oh, the real-time application would be an Automated system for remote teams that will allocate slots for meetings or some discussions based on their Working partners where people tend to be available where I tend to you can use for instance multiclass Models where you put something like I want to do my correspondence. I am available for the talk I am focusing on new features and so on so forth. So in the real-time or real-world example You will probably need a different frequency of the data In my case it was more than enough again Based on the frequency of the data different features will be more or less Important for instance exams. I was just lucky that I had two University phases in the data sample. Otherwise, it won't make any difference Okay, some more questions Hi, thanks for the talk. You seem to be just advocating Adding more and more features until you've improved your model enough Is there some point that will start to become counterproductive and you'll hit the curse dimensionality? Adding more and more dimensions to your model in my case. It's rather hard because I And honestly for for me was already kind of what can I also add? But if you start not with one dimension, but more Basically, yes You can hit the problem where you're trying to get all the data simulating the whole real world Which is the point where you start noticing some? Partance or behavior if we are talking about classification that is not typical for your case But is more general for instance, you know, there are historical 100 cycles or whatever and There are not only the amount of data important but things that show exactly when it happens why it happens so you can learn from features and This is where it starts to be counterproductive. This is why I said Define not more than four features first test them out try to replace them Like it is a pretty small amount of work for the start of like four features unless you have like real giant model but in this case you probably already not Doing something wrong then you started with the future engineering and such a complex model and After a while, I personally tend to say that Depending on the case how complex is something you want to learn if you want to learn just Dislipping cycles of a dude then you don't need much data if you're trying to build a self-driving car Oh boy, you're gonna need quite a lot of sensors quite a lot of data quite a lot of features so basically on the Depending on the complexity of the task depends the complexity of the data and Directly amount of features needed you can estimate yourself How complex your data should be or could be? Reduces by two and then start working. Okay, any more questions? Then come on. I'm not biting Then maybe I have a question So I'm not a data scientist, so I don't have much clue about this, but I Heard that as you add more and more information to your model. There's a Risk of overfitting. Did you do some sort of cross validation? So because your your precision goes up, but yeah, that's sort of to expect it anyway, isn't it? Yeah, true in this case I didn't because I had like way too less data and I was in the territory of always underfitting and The amount of features was totally under control if I would start to throw something like Does this guy live with the parents does he has a pet and so on so forth then probably I will overfeed at some point so basically In order to not overfit or to have low risk of overfitting you need to not only keep your data You need to keep your data still low-dimensional To not over complicate the life or description of the situation okay Well, that's great. Oh, there's still time, but What if we come up with a feature that really is really not helping with the Accuracy, then we'll just drop or how do we how can we observe that? There was the thing When I initially started to creating features I also thought about I didn't sort at all at the first about the scale But this was okay because I had like four categories At some point I tried to scale them for instance and you saw here This three features to three features falls This is basically scaled and not like row version from zero to whatever and this is more of a normalized one from minus two to two or so and This models actually perform Not way better not always I had a thing like I tried to Define season like minus two minus one zero one and it decreases the performance by seven percent Also, some features as I said earlier are just a bit different form of already existing so they Won't really decrease your performance. They might have but they won't help either also always check your data whether this The feature you want to create will be present I had another case where I was really interested whether some national holidays are affecting taxi waiting time Have I don't know I spent not that much time like four hours by really finding out all the information and turns out They just don't have those sample points So time wasted Okay, one last question Thanks, so when you're dealing with these small data sets low-dimensional data How do you differentiate between the cases when? there just isn't enough information to build a useful model and When you just haven't found the right mainly engineered features yet Mostly gut feeling and common sense if honestly It might differ again in this case. It's was pretty obvious. I Simulated my own thoughts about the problem and I figure out what I personally call feature What my brain defined as a feature and this is pretty much where I stop in? more complex situations, huh, you probably would start with model first and not the data if I have at least like four features and after a while I will notice that no model tuning actually works and But the data is clean you I have I don't have missing values it is sane and then I will ask what my model wants to describe and As a human I am more smart. I'm smarter than the model because I knew more. I'm I've seen more and This is where I will start trying to dig Deeper and bring some features in that model might find sufficient might not Thank you very much.


Flags do a lot of traveling, mostly up and down the pole. Here at Saber Strike 2015, they visually demonstrate the bond between nations. Although it's not at the top of a pole, another flag travels horizontally and tells of a different kind of bond, the one between grandfather and grandson. I actually didn't even know that this existed until I got home from basic training in tech school. This is my Kentucky state flag from where I was born and raised. So what's a state flag doing at a national level exercise? I take it with me when I travel because Pawpaw gave it to me and it reminds me of home. He served 22 years in the Air Force, which I always thought was cool but I never really gave him much thought. And then I graduated college and then decided to join myself and then boom, we automatically had a common tie. A flag is tethered to the flagpole like this grandson is tied to his family's story. And this story continues here at Saber Strike 2015. So I'm here covering Saber Strike 2015 for AF in Europe where US troops and soldiers from all these different countries are coming together. We've got Germany and Poland even, that's just really interesting. They're coming together and building these partnerships that they started doing back when my grandpa was in and they continue to do so even into today. So of all the writing that I'm going to do while I'm here covering this exercise, this is probably going to mean the most to me, this writing right here. So this is the ledger that came with the flag that my grandpa wrote and it's just the last six years of his 22 year career that he took this flag.  We've got Vietnam, Korea, Okinawa, Langley, the Philippines. A family flag that's traveled across nations and time here at an exercise of Allied nations. And as all flags present write another story on the winds of history, another entry is added to the ledger. From Drosko Pomorsky Training Area, Poland, I'm Staff Sergeant Ben Woken.


brilliant audio presents at night all blood is black by David Diop translated by Anna Moskovakis performed by Dion Graham we embraced each other by our names one thing a friendship who thinks betrays Pascoginia Maria de Ponce I am two simultaneous voices one long the other short she come ado come ambiguous adventure one I know I understand I shouldn't have done it I offer India a son of the old old man I understand I shouldn't have God's truth now I know my thoughts belong to me alone I can think what I want but I want that the ones I might have told my secret thoughts to my brothers in arms who will be left so disfigured maimed eviscerated but God would be ashamed to see them show up in paradise and the devil will be happy to welcome them to hell don't ever know who I really am the survivors won't know a thing my old father won't know and my mother if she is still of this world we'll never find out the weight of shame will not be added to the weight of my day they won't imagine what I've thought what I've done the depths to which the word drove me God's truth the family honor will be spared the honor of appearances I know I understand I shouldn't have in the world before I wouldn't have dared but in today's world God's truth I allow myself the unthinkable no voice rises in my head to forbid me my ancestors voices and my parents voices all extinguish themselves the minute I conceived of doing what finally I did I know now I swear to you that I understood it fully the moment I realized that I could think anything it happened like that all of a sudden without warning it hit me brutally in the head like a giant seed of war dropped from the metallic sky the day mademba diep died mademba diep my more than brother took too long to die it was very very difficult it wouldn't end from dawn into evening his guts in the air his insides outside like a sheep that has been ritually dismembered after the sacrifice except mademba was not yet dead and already sample complete ready to continue


So let's talk about, we get so many questions about how the behavior panel started. Yeah. What happened, how did it come in, because it happened because of a few things that came together that normally in a normal world wouldn't happen. Let's talk about how that came about. Let's talk about how we all first connected. Well, you Greg connected first. Yeah, I think. Yeah, we connected first. And what happened was I was doing a TED talk and our TEDx talk in Nashville. And I'd read all Greg's books and there was one that said that he had a partner's book that said, if you don't pay attention to what you're talking about in body language, if you just start throwing things out there, you'll create a Frankenstein's monster of body language. So I thought, oh, there it is, body language Frankenstein. So that's what I made my TEDx talk about, right? Right. So I'm getting ready to go down there and do it. And the day before I said, and I could look at that picture of him on the back of the bucket, I said, I better get a hold of that guy and say, look man, here's what I'm doing.  I'm totally stealing your thing there. So I sent him an email and I said, hey, Greg Scott Rousson, sort of in the same business here. And I'm doing a TEDx talk and I have this thing that I came up with that I'm doing. It's called Body Language Frankenstein, but I totally ripped it from your thing. You'd have a, and I told him what was in the book. And I get back, which I know is okay now. I didn't get, I got an email back, but it wasn't an email. All it was, everything was in the subject line. And it just said, I'll call tomorrow at 12. I'm like, oh, and this isn't good. And I'll look at the picture again. Oh yeah, I was like, oh no, no, no, no, no. Cause I gotta do the thing tomorrow. So I said, okay, great, great, great.  I was at the entrepreneur at the center at the time. I was at the entrepreneur in residence there during this. And so I went out and sat in my car and I was like, okay, 12 o'clock, bang, 12 o'clock and phone rang, it was him. And I was like, hey man. He goes, hey, what are you doing? I was like, nothing, what are you doing? He's like, oh nothing. I think that's a great idea. And he was just like he is. I'm flattered. I was flattered. I mean, that's what I said. But I just, man, I had the worst feeling. I just knew I was going to say, where are you right now? Cause I'm in Nashville. Hold on, move over to the right. I'm sending my lives. I can't see your head, man.  Where are you in the car? No, it's flattered. That was cool for me because I'd written those books. I wrote books like that for a while. And then I took a break, you know, when things changed. So it was good timing. I'd forgotten how long ago that was. And then we met. The first time we met up was at this place called the Sportsman's Grill. Remember that? Oh yeah, yeah, yeah, yeah. Yeah. He said, I'm going to be in town. Well, let's, let's hook up and eat and kind of, and he said, be later in the night. I said, okay, great. It was eight or nine o'clock. I said, no, great place, my favorite place, Sportsman's Grill's right there at Vanderbilt's gone now. And I said, and he said, okay, we'll meet there. So we meet there and we're eating, right?  When you do, when you're eating with somebody, you're doing something, you start moving everything over into their space to kind of, and then you've got them blocked in a little box there just to see what they do. They're like, you come to see your glasses and all that stuff. He kept knocking my stuff off the table. You know, he kept knocking, I said, okay, good, I get it. And then he would actually spill something on the table. And I was like, and I knew what he was doing. I was like, and I want to say, dude, don't do that. You know what I'm doing? But then to this day, he says. No, no, it was nothing. Yeah. And I couldn't tell if he wanted me to call you out on it or what, so I didn't say anything because he was a lot bigger than me. So I didn't say much about that. Then he's like, then we got to talking. And I showed you all my slide decks, which are concepts of stuff, a lot of stuff you've done because you know how you do when you do these things, you take ideas from people's books and say, here's what I'm going to build. This is Eggman, this is Hartley, and this is Bowden, this is Hugh. And you tell what these things are. And so I said to you all those and you said, and we talked and said, we should do, I said, we should do a course together. And I said, yeah, we probably should. I think we probably should. How did, oh, so how did you guys come into this? And I'll bring it together with the... Well, so before we'd met though, I'd met, well, you guys had helped on Truth and Lies that Tracy Thompson and myself were writing. And so let me just explain that first of all before I go to our piece because I write all of my books based on the Huckleberry Finn Tom Sawyer method. We've tried to get other people to do all the work. That's brilliant. So, yeah, exactly.  And I'd seen your TED talk, which is great. And so I thought, okay, well, let's send Scott a message and see whether he'll do a page, because that's a page that I don't have to do, which is the main thing. And so I tell Scott, you know, here's what I'm up to and here it is, and I'd love you to do a piece. And Scott says, yeah, great, fantastic. And then he starts sending stuff back and I send him some stuff that we're writing. And he starts, and he's going, have you seen this article and this article? And there's this thing here. And I'm like, oh, okay, this guy's really quite nerdy and he's got a lot of, you know, seems like he's got a lot of kind of really good technical knowledge going on. And so I said, well, would you want to be like technical editor on this? Because I'm just thinking, I could probably get somebody to read the whole book. And work out whether it's any good or not and tell us what we've done, which is complete nonsense. Because that's something that I don't have to do. So, and Scott was like, yeah, that'd be great. So I'm like, this is fantastic. And he says, he says, I got this friend called Greg.  Like, let's all get involved. That was a corporate dude in those days. Yeah, yeah. And so, both of you guys are in Truth and Lies. It's fantastic. Now, I didn't know Chase at the time because Chase wasn't kind of out there at the time. You were still- I was active duty. You were still active duty at the time. So nobody knew that you were around at the time. So, why don't you take off as to how we met? Yeah. So I was in Toronto, Toronto. Toronto. Doing a, I don't know, three or four day seminar right when I was getting out of the military. Like this is the first seminar I've ever done in my life ever. It was horrible. Everyone said that they liked it, but I think it was. It was pretty bad, it was pretty bad. I want to refund them all or go, you know, now I want to go back and fix it, but. So I'm in town and I think I just reached out over email. So the guy, some guy who you were working with, Burt Henderson. Right, put, sent me a note and said, you shouldn't meet this guy, Chase. Yeah, so that's right. And it was snowing outside. And we met at this wine bar and I brought a copy of my book, Mark brought a copy of his. And it was incredible.   Like he's, his head is twice the size of my head. But then I got over that. And I think we talked for three hours. We did. We had a great time. A couple of bottles of wine. It was a great dinner. And the other guy paid for it, which was fantastic. He paid for it and didn't say a word. Didn't say a word. He was just like, I just want to see you. The thing that we were talking to each other. And I was sitting there going, we're just having to chat and this guy's paying for everything. It's always the best way. It's always the best way. Anybody else need a go? Yeah, it was great. It was a great evening. Wonderful guy. Wonderful guy. Fantastic. And I think that was it. You know, we took an Uber home.  Dropped you off at your house. Then a few months later, I'm doing a seminar. In Canada. It's in Toronto. Yeah. And then a few months later, I'm doing a seminar in Nashville. And I called this guy Scott Rouse. I was like, hey man, I'm doing a seminar way north up in Nashville. You want to meet at this little redneck bar somewhere. And so we're hanging out. I didn't know that Scott knew you. Right. I had no idea. So Scott, we're just sitting there and Scott goes, hey man, we should send a video to Mark Bowden. And I was like, oh, what kind of video? So Scott, I think it was your idea. It had to be your idea. Scott's like, well, I'm going to pretend like I'm completely wasted drunk. And then I'm going to come over the bar at you and then we're going to fist fight. But I want you to start greeting Mark to the camera and then we're going to get into a drunken fight. And maybe we can throw that clip somewhere around. That would be fine. This area here. Yeah. Mark, I hope you're doing well, man. Wanted to wish you and Tracy Happy New Year. Haven't spoke to you in a while. I'm in Nashville, Tennessee. Hey, I got some things working out over here. I'm trying to make a video for my friend here. I don't care what you're doing, Navy man.  You better get your half. And then COVID. Yeah. So, but hang on, before you go, before you go to have that, I want to add in some more stuff because I think I'd said to Scott, after meeting you, I'd said to Scott, so there's this guy Chase Hughes who I don't think anybody knows about and you should have a chat to him because I think he's really good. So I'd already thought that out there because when the guy who was buying his dinner had said you guys should get together, I kind of searched around the internet and I found a video of you. And I found a video of you. You'd gone to, I think it was Colgate, was it Colgate? Yeah, Colgate University. Colgate University and you were coming out of Colgate University and you'd been doing some research there into... MK Ultra. Yeah. And you were talking about the cubic feet of documents that you'd been through and you were saying some names of some people that I recognize and not everybody would really recognize. And you were talking about their documents in cubic feet. And I instantly went, oh, this guy is serious. Like this is a serious person. This is, I will go and meet this person because he's talking about documents in cubic feet. And this is the only video I can find of him.  I will go and go and look at him. Milton Erickson, was that the one you were talking about? Milton Erickson, Aldous Huxley, Margaret Mead. Yeah, okay. Yeah. And then the first time I talked to Chase, I think I texted you or sent you something on LinkedIn or something about your picture where you still had that big Navy picture. I was like, yeah, this guy looks like a...  I said, listen, I'm getting ready to go talk to like 21 people in a row. That thing where you... I was in Los Angeles, here in Los Angeles. Yeah. When you're talking about...   Any suggestions for that? You remember what I said? I said, yeah, man. Just go in there and read it one time. Yeah. You know, read technique it one time for each one of them. And which we all get an interrogation. But that's the first thing I remember you and you're going, okay, all right, that's what I'll do. That's what I'm gonna do. Now I remember thinking, I think you can probably do it.  I was like, yeah. So let me give you those results really quick. I don't know if I ever did. No. 29 interrogations, four days. So I had about 40 minutes per interrogation. All but one confessed to an over $10,000 felony crime. Nice. Wow. It was great. What was the commonality? Yeah, that's great stuff. What was commonality? What was your point? Corporate theft, but they had been using an AI screening system to detect stuff. And so they had a red list. So it wasn't like I just got false confession. They had a high risk list of people where this was this 29 people. So I went in there and. Oh, you could let you into it. That's screening. That's screening. I mean, what you're doing is AI screening versus a human doing it, which is more effective. Super effective. That's beautiful. That's a great thing about that kind of interrogations is you're working on a confession, not information, you're after a confession. Right. So back to this now. It's beautiful. So from, we have all that information right there. Now, one thing Greg and I always talked about, because we thought it was hilarious, he always wanted to do, he wanted to look at videos of people who've seen Bigfoot. Oh, I want to bring them in and interview them. Remember? Oh, that's right. That's right. That's right. He's like, bring them in. I want to talk to you. I was like, he's like, we need to do a show where we get people who've seen Bigfoot and we'll bring them in and talk to them and get them to tell us they haven't seen Bigfoot. And I was like, what? Right. I'm not a Bigfoot, ghost, whatever. Yeah, yeah. And I was like, okay. I was like, I'm down with that. We've got to talk to about ghosts too. We'll talk to my guys. He goes, all right, let's do it. And so then soon. So right after that, then Mark had said, yeah, make this new video. You'd said something like that. And she said, hey, make it. And you come and say, let's do a video. And Greg said, we need to do video. So I said, shoot, man, what are you thinking? Greg said, both gather everybody up and we'll start shooting. So I said, video to you guys. Which I'll show a part of it. It's a part of it right here. Politically, right. Hello, ladies. I haven't shaved for a week and a half. That's what's going on here. It's pitiful. Anyway, Greg and I were talking. We thought it might be cool if we did a zoom call with us four. And we did it. So where we recorded the screen, we had all four squares of us on the screen. And we talked about what we thought happened in the tiger king. If that woman killed her husband, did she really kill him? If she did, what'd she do with the body? Did she feed it to the tigers? Did she feed it to the lions? Did they dispose of it somehow into the sewage thing like that guy was talking about? It might be something that would be good, a video to give some heat anyway. It would be interesting. It'd be fun to do. And what are your thoughts on that? Let me know. And I said, hey, why don't we get together and do this and we'll do the tiger king. Remember that was the... You know where the tiger king came from is there's a guy, I know, who's a kid at the time, and he said, hey, there's this thing going on on social media that you should look into because everybody wants to know she did it. I was like, yeah, yeah, yeah, you know me.   But I went and look, yeah. This is what we gotta do, guys. It was so good. It was such a good one. So I put a video together and sent it to everybody. I was like, yeah, okay, we'll do it. Let's do that. That'd be great. And that's when we did. Remember, we all had our own channels. Yeah, yeah, yeah. Wow. Well, guys, honestly, I say this all the time. I did this a long time ago. I started this a long time ago. And I left and you brought me back to play in the body language game.  But when I was doing it a long time ago, there was not much of this going on. There's YouTube. I had to rewrite books about social media and that kind of thing because it was so long ago. And now that where we are, look how fast Greg pushed that, just pushed that train track right over into the humalians and alien investigators. But we didn't get bigfoot yet. We still, I'm still waiting for bigfoot. I got one right now. Let's do it. Let's do a bigfoot episode. Let's find this and without making fun because your thing was, well, it's over, we got to be able to save face and respect them. That's right. It's like anything else. When people leave, you can't, you got to have respect for them. There is a strong possibility there's some stuff out there. I mean, it's not impossible, but... Wait a minute. No, it's not. Have some more of this. Have some more of this. It's not impossible. Mark, I need more. Mark, I need more people telling some stories. I need more wine. The guys who are out in the woods smoking a little, enjoying a little, and they saw bigfoot. Okay, got it. And it's kind of like people who see it. We've interviewed people who've seen UFOs that we said, look, we can't say he didn't see a UFO. Let's go on the record right now. Do you believe in aliens? Possible. Do I believe they've been here? Well, my only, my logic equation says if you fly, you know, 10,000 light years, whatever it is to get here and then crash in a cornfield, something's wrong. That's my biggest problem. Yeah. Or you don't have, nobody detects you until you're here and you turn on the lights one evening. Come on, they're smarter than us if they can make that kind of stuff. Why would they suddenly have that happen? That's my only, it's always the redneck crashed aliens that we're talking about. Not, you know, some brilliant. For me, it's like past lives. Everybody's always some Egyptian king, never a peasant, never a peasant, you know, scrambling for potatoes in a pit. This is a way of mass-run Napoleon, you know. Michael Sharma would come in perfectly. But so what do you think? Yeah, I think yes. There has to be something else out there and it explains a lot of why it's so big out there. But do you think they've been here is the question? I do. And do you think what these people are seeing is that? I think what the Department of Defense has released is that. Yeah. I actually believe it. That's cool. None of us know the difference. None of us can... No, I think we can't be the only thing out there. Out here, out in the wild. I mean, there's got to be something else. I don't think they're coming down and I don't think they're living among us. I don't think any of that's happening. And people are going to say, you're being paid not to say that. We'll be paid if you give us enough of those Chick-fil-A cards, 40 bucks each, three times a year. All the $40 Chick-fil-A card. Or Cracker Barrel gift card. Ah, Cracker Barrel, he just left the gift card. Cracker Barrel's good. Yeah, heck yeah. So I think... Mark's going, they don't have those for you. I think you're right. I think there may be some, unless those tic-tac things are somehow some kind of weird drone or something that somebody's put together that we're...  Yeah, but I've never seen one. I'd have to see one. Or if you saw one and you told me, I believe, you said, dude, I saw one, then I would believe it. But I'd have to, because it's, you know, that's a tough call, but I think there's other stuff out there. We're not the only ones out there. We can talk off camera about that. Okay. For me, like I said with Tom Reed, I believe he believes what he saw. Yeah, when we told Tom, I was like... Totally lying. He should have told the same mother. Yeah. We also did the one on Homely's show.  Those guys are not lying to us. Whatever they believe.   Same thing about Bigfoot folks. I tell you guys a story. There's a kid when I was working at Seer who saw Bigfoot in the woods and told me about it and I went and looked and it was a deer standing on its back legs eating acorns. Look, if you've lived your whole life in the city and you see a deer walking on its back legs, it might look like that. Yeah, with the moon in the right direction and the shadows of all kinds of stuff. That's why I think we should do this Bigfoot alien thing.  That is honesty. We see honesty here. I think she was making up Humalians. They could possibly exist. Mark, how many years have you been doing that? Cut it off, G. So I'm, I think 98%ile in open to experience and 2% in skepticism. Get to 30 minutes later, Marshall. I love you guys, man. I love you guys, man. Yeah, I think you, we're probably in verse somehow. Yeah, yeah. I'm like, yeah, okay, whatever. But let's, let's, but I'm excited about any story we can come up with and folks listening can give us. I want to do, I think we should do, our original thing was Bigfoot. Let's, I, you know, I'd love to think there's something that intelligence tricking people and getting away from them, living out in the woods. But it's always Liberace unloading crates of Bigfoot's out of the back of the UFO. Yeah, it's, it's, it's really, yeah. So you never see somebody, you know? Let's schedule, like this was the, if this was the base of the pyramid for the behavior panel, let's schedule another trip like this. We'll book a suite. We'll bring in a couple of Bigfoot dudes. I have a better idea. Come to Georgia. There's the Bigfoot Museum. We'll fly them out of the car and say, come here, have you ever seen Bigfoot? Yeah. Hey, well, we'll talk to you right there. Come sign this release room. There's the Bigfoot Museum in Blue Ridge, Georgia. We should broadcast from there. That would be a great idea shot. No, no, we could, we're gonna be respectful. Well, thank you. You know what we do for a living? I think we could be respectful. A lot of people believe that they saw it. That's exactly what I think. We're not analyzing forensics or data. We're analyzing behavior. Some people are probably gonna be BS. Some people are gonna see it. Probably go BBS. I'm open to it. I don't think that's very skeptical at the same time. So am I. No, I'm gonna go ahead and say I'm gonna be biased. I don't think there is one. I'll tell them. I mean, not at the top.  I talk about interrogation. Here's the data point. I live in Georgia. If there was a Bigfoot walking around there. How about being in your backyard and knocking on the door? I've been to your house. Somebody would have it stuffed if it were in Georgia. Right. Anything they see that looks. They think it's good looking. That's a good looking deer. Boom. You know, this is where things go. And I'm from Arkansas. I would go into a garage freezer. Oh, yeah, they probably own it. They own all of it. It would have run to the governor at some point, wouldn't it? Yeah. The mayor probably shut out. He was governor last week. Mayor of the East. That's my aunt Martha. That's not the right. Yeah, I just. Well, I said we do it. So we're going to do Bigfoot. Yeah, let's do it. We've got Bigfoot, and that's one either way. So what's the plan? We're going to find some. I got one video already of two people that are fairly normal, you know, hunters who saw Bigfoot. And that's going to be our next start. All right. Let's get a variety of videos out here. I have three or four people. We'll spend two days at a lodge somewhere nearby these people so they don't have to travel very far. That's a great idea. This is North Carolina. There we go. Yeah. So I'm going to travel very far. Yeah, me either. Me either. I'll drive right down. Yep. We'll rent out an Airbnb lodge, get a film crew.  Yeah, for sure. So we'll get a film crew. We'll figure out how to do it. And we'll walk them through an interrogation of respectful interrogation. Yeah, you know, elicitation. Just get them to talk. Just, yeah, tell us what you saw. Sometimes this is the age old thing when you do debriefing and you're talking to somebody. They remember things they didn't remember before and they might even say, well no, it wasn't a big foot. It was a horse that I saw that, you know, I'd seen horses before out in that area, so it probably was. If you ask the questions the right way, you open the way they think. The thing we do with ops guys, you go and when the guys come back from the mission, you ask them about a building and say, hey, how many of these did you see in that building? And they'll say, I didn't see any. And you start walking. Okay, when you open this door, would you see? I saw four doors. Well, hold on a second. I did see that. It was on the right door. And it's just the way our brains store information. We store in big chunks and we skip over things sometimes. Yeah, maybe it's worth it. All right, well after those tiny little tacos we had today. Yeah, I'm sorry. We had going dinner. Yeah, yeah. Taquitos, we didn't have tacos. Those were tiny. They were babies. Yeah. So, all right, this is really funny guys. I'm glad we can do this. I'm glad we get to talk about this. Yeah, me too. Me too. And do this in a way we haven't done before. And cheers to all of us together guys. Cheers. What a change this is. This is just one guys. Cheers, cheers, cheers. Like separated children at birth or something. Let's get some food.