Hi, I'm Craig Young. I'm a researcher with Tripwire Avert and I'm presenting today Android Weblogging, Google Skeleton Key. So a little bit about myself. As I said, I'm a researcher with Tripwire's vulnerability and exposures research team, which means that I look for bugs, fix bugs, write bone checks for them. So like a lot of you, I like to break things. I also like to make things. One thing that I am definitely not is an Android developer. So please, if you look at the source code on the DEF CON CD, go easy on me. So the goal of this talk is raising awareness. I want to raise awareness about the fact that the Android ecosystem has made some compromises of people's security and their privacy in order to give a good user experience. So in order to reduce friction from using Google services, they've introduced these things called Weblogging tokens. These Weblogging tokens allow you to bypass password prompts so that you don't have to be entering your password on your mobile phone or your tablet or wherever else. I found that security tools that are out there for endpoint protection, they don't recognize this type of threat. They don't do anything to protect against it. And in the end, it only takes one token to fully compromise a Google apps domain, one token from the right user. So what is Weblogging? As I said, it's an Android token type. You see on the screen here, this is an example scheme for what a Weblogging token request would look like. What you get back from this is a URL. You browse to that URL and you get cookies that authenticate you to Google services, basically all of them. So this is a way of getting access without having a password. So there's some ways that I found that you could abuse this technology. For one thing, as you saw on the previous slide, it was the token type is requesting a specific service, but when you get the cookies back, these cookies are not limited to any services. It's just as if you had logged in from the web browser pretty much. So although you might be asked for permission to access your YouTube account, the app is also going to have permission or whoever gets the token to access your Gmail and your drive. Lots of other things we'll get into later. The permission prompt is only given once per app, per token type. So an application can generate as many of these tokens as they want if you've approved them. It's also some ways that you could get these tokens without asking permission, which we'll get into. And that's involving having root or physical access on the device. So the focus of this is about how to attack Google apps. So as I said, the first step is you need to retrieve a web login token for a domain admin. From there, you can continue on to the Google apps control panel and basically you have access to do whatever you might want to do. Some of the things that you can do, listed out here, you can disable two step verification, you can reset passwords for any users in the domain. You can add super users, although Google did attempt to fix this. We'll see how well my live demo goes. You can do things like adding privileges, removing privileges, deleting users, managing mailing lists, all sorts of good things. So also regular Google accounts are subject to some vulnerabilities from this as well or some exposure, I should say.  Initially when I started looking at this, you could also reset the user's password just by having this web login token. Of course, that did not work with two step verification because of the insistence on verifying a code that's sent to the phone. You could also do Google data dump. So Google take out would allow you to download all of the contacts from the target account. You could also download the drive documents, various other things. These, the ability to reset the passwords and the ability to get the Google data dump as I've called it, these have actually been addressed successfully. I'll get into that a little bit later. So some of the other things that I've been finding that you can do, once you have the web login token, you can go ahead and install apps from the Google Play Store onto the device you've already compromised but also other devices that are associated with that Google account. You can also access other websites which might use Google federated login and Google sites, you can make a website on behalf of the victim. These are all things that are applicable for Google apps and regular Google users. So how do you go about getting a web login token? The approach that I looked at or focused my research on is a more legitimate way using malware that's going to make a request using the account manager API to get one of these tokens and then egress it off to a server or use it on the device. You've also got some other options. If you can deploy a root exploit successfully, you've got to access the accounts DB, session identifiers are in there, everything you need to get uber auth and web login tokens. Also, of course, if you have physical access to the device, even if it's just momentary, the Chrome browser has this nice little insecure feature called auto sign it. What that does is generates a web login token for you and lets you get into basically any Google service from that device. And finally, if you have, if you're for example in law enforcement or something along those lines, you have access to a suspect's device that's been perhaps partially damaged, not really usable. But you can do chip off forensics, extract memory, and you get back to number two here of being able to get the accounts database. And then you're free to masquerade as the other suspect. Use their, view their emails, communicate as them, use Google talk as them, look through Google voice phone records. So the proof of concepts that I went through, my main goal was to be able to make a token stealing app that was going to do this without having to have root access to the device. I have an app that, or two apps that are on the DEF CON CD that you can take a look at along with source code. My secondary objective was to be able to publish this in Google play and see whether or not Bouncer would notice something suspicious about this. And then also, of course, I wanted to take a look at where the endpoint protection tools might fall or weigh in on this type of application. So privacy advisors and antivirus, that means. So making the app was not difficult at all. I'm not an Android developer but just reading some API documentation and some helpful blog posts was more than enough to get going and do this within a couple hours. You can see here there's the token type and then I'm using the get off token method from account manager, which generates a message like what you see on the bottom of the screen, which you can see it's requesting access or permission to web login with the service specified as finance and a continuation URL of finance.google.com. This is your stock portfolio stuff, which not particularly sensitive information for most people. So the revisions that I went through to get here, I started out with something I called TubeApp, which was intended to advertise itself as a YouTube downloader app, but then in actuality it was relying on that a Google Apps administrator would be using this application. It would go into the Google Apps control panel from your phone or your tablet, and it would fetch your OAuth secret for the domain. It did not do any kind of egressing with this. I just had it displayed on the screen, didn't post this to play or anything, but then my next step was to make the stock viewer application. So with this, I did post it onto Google Play. I gave it a very vague description saying that it's a testing application that shouldn't be installed, priced at $150 to avoid inadvertent or curious downloaders. And for this first revision of stock view, I made it so that all it would do is ask you for permission to access your web login token. If it's granted, it's going to request two tokens. One of them is going to be used to launch the browser and show you your stock view feed, and the other one is silently posted to my web server. So the next release that I did of this, I added S cell support because really I didn't like having my tokens go over in plain text, but I also updated the description in Google Play to make it much more blatant that this app is doing bad things. And in response to some things that I saw with Bouncer or lack of some things that I saw with Bouncer, I also added some code so that no matter what, when you run the application, it's going to enumerate your accounts list through the account manager API. This requires no permission request except for in the application manifest, and that would get immediately uploaded. Then you would be prompted for permission for the web login token. If accepted, that would get egressed over S cell. So here's what you see when you're installing the application. It's requiring the find accounts on the device to enumerate accounts and use accounts on the device so that you can use the get off token method from account manager. And of course, full network access for being able to egress off the device. And once again, you see the permission prompt that you get the first time you run it. If you allow access, you will never see that message again. So I published it in Google Play. They did not flag anything on submission. I didn't receive anything in my server logs indicating that Google Bouncer had actually executed the application in such a way that it would make my request. It brings up some questions. Namely, whether Google Bouncer is really running all of the apps that are being submitted or if they're running in some kind of limited environment that does not actually have support for Google accounts such that these account manager requests would just be skipped over and not recognize this potentially malicious. In my opinion, it should strike some red flags that my application is just requesting a token and immediately sending it up to a web server, but did not raise any flags with Google. This is what it looked like in the Play Store. I don't know how well you're able to read this, so I'm going to read it for you. This application provides quick access to your Google stock portfolio while completely compromising your privacy. If you prefer convenience over security, then this app is for you. This application is currently under testing and should not be installed by anyone ever. And of course, this was up, but a month later, probably somebody noticed this was a little bit suspicious looking and reported it. And my Google wallet account got suspended. I think it was up for about a month. I wasn't really paying much attention to it, but when I went back to start doing research for writing these slides now, I found that Google Apps or Android's Verify service or option on the device now would say, installing this app may harm your device. This app can be used to track or spy on you and that Google's recommending you do not install it. The only caveat about this is that if I rebuild my app and give it a slightly different name, this warning never comes up again because I guess it's just a blacklist. So looking at Endpoint Protection, I scanned five of the popular tools that I was familiar with. None of them found any risks. Looking at the privacy advisors, I found that Avast actually would recognize that the used credentials is potentially a privacy risk. Lookout Premium didn't seem to have a category for saying which applications have the permission to request tokens on your behalf. So demo time. And we'll see how this goes. First of all, this is a Chrome web browser that is in incognito mode, no cookies, no access to anything. I'll browse to Gmail. It'll ask me to log in. I'm going to unlock my tablet. Hopefully it will connect to the DefCon secure network. Oh, there we go. So switching over. This is just going to be tailing the logs from my web server. And as you see here, the tablet is slowly loading the Google finance page. Maybe some of you can see that. But more importantly, what we have here, this first request shows the accounts that were connected or configured on the tablet. And then the second one in this URL parameter, this is the URL that was returned as the token. This does have an expiration on it. If you don't use it quickly enough, it will not work. But I'll go ahead and load that. And now you can see we are authenticated as StupidAdmin at MindSculptors.net. You can then see that you can access their email. You can go through Drive. The contacts are accessible. But some things that used to be very interesting to me here, if you go into account, you have your download, your data. If you tried to do this two weeks ago, it would just let you download all the data. Now you have a security notice that's hopefully telling you that Google wants to make sure that you're really asking for this data download. And along those same lines, if I go back one more, you used to be able to add a recovery email address without having a password, which you could then use to reset the password on the account. This is now blocked off, but fortunately or unfortunately, depending on how you look at it, the Google Apps Control Panel is still completely accessible. So I've now browsed to Google.com slash A slash domain name. We can view the users in the domain. We can go ahead and do things like resetting a password. So I guess we'll spend a little more time with the demo here. So now I've just done an auto-generate password and you can see that I can show that password. I could also specify a password that I'm setting. I'm just going to go ahead and reset that password again without showing it this time. We could also do something like adding a new user, which is over here. So this is something that Google has actually tried to fix. The other day I recorded a demo because I noticed that they blocked off access to this vector. So we'll see if it works now. I've had mixed success since then. So yeah, you see we are unable to process your request this time. Please try again later. We'll try it one more time. Sometimes it works. But what we can still do, this is actually worked while I was in the speaker room 10 minutes ago, 20 minutes ago. But what we can still do is say take this user and add to it admin permissions. Super admin. Let's us do that. We can then reset the password for this user. I'm just going to use an auto-generated password. And now if we go and sign out from the web login token session, we can then just sign back in here. Hopefully remember the username that that was. Okay, so now we have admin console that is actually done through a password being manually entered. So in this context, you no longer have the stipulations that you're going to get that error that we had before when trying to create an account. So I can now add a user to the account. Hopefully this will still work and it does. So now we've added a new admin user to this domain. Some of the other things that I had mentioned that you can do, actually I haven't mentioned this yet, but let's say we want to transfer documents from a Google account to another Google account. The ability to say transfer from this user to the new admin 2 that we did. This has initiated transfer or initiated document ownership transfer. So now that new account that we just created has access to all of the data that was in the Google drive for that other user. There are some email notifications going out about that, but looking at some other interesting things that we can do, I'm actually going to get back in the context of the web login token. So let me sign out here. Let me see if we got a new token now. So that looks like the old token. Oh, there we go. So now the new token, as you can see here, if we copy this back into that, we're back here and we can go to play.google.com and pick a random app, use accounts on this device. That's good. Let's install that. And so now it's going in momentarily, actually already it's starting to download the new app. We can also use other sites that are relying on the Google federated login. So for example, I picked a random example, getsatisfaction.com. If I click Google, login with Google, it's now logged in showing Craig Young. If there was anything useful on here, somebody could do something with that. And since we have a few extra minutes, I'm thinking if there's anything else I want to show in here. Calendar is accessible. You can create appointments. Oh, here's an interesting one. You can go to history.google.com and see it says only you can see your history, but it's a little less than accurate. So I think let's jump back over to the slides. So how do you avoid being a victim? First of all, I used to do this ever since using Android with my Google Apps domain. I always was an admin user on the device. Not anymore. I've created other users that do not associate with any Android devices and my phone has at least some limited risk. You also want to be very skeptical if you're getting requests from an application to use a web login token or the LSID or SID session identifiers, which I've talked about in another talk, how you can use those to get other tokens. You want to stick with trusted app stores and vendors. I know that's a loaded thing, but I think still sticking with Play, for example, even that I was able to get malware in there pretty easily, it's better than some random Chinese app store. And although I found that antivirus didn't detect this threat, I do have faith that since it is signature based, it's most likely going to be able to pick up things like root exploits that are known about. So that's a little bit more protection there. If you've been pwned through this, you want to do some steps to recover. First of all, you want to invalidate all the sign-in cookies that's accessible through the Google Apps control panel, as well as in the Gmail account settings, I believe. You want to reset passwords for basically everything on your domain, or if it's just your individual account, just your individual account. You want to go through Gmail and make sure that nobody's added mail-forwarders or figured out how to add recovery email addresses, even though it seems now that Google has more or less protected against that. Of course, you want to look for new domain admins, that's something you don't want. And I found that Google Apps, the audit trail that it leaves, is very effective. It tells you which actions were unauthorized or it could let you know which actions were unauthorized, because it's recording everything that's done at a pretty granular level. And it is reporting IP addresses, so as long as the token's not being abused from your device, from your IP address, you are potentially going to get some information on how to track down who attacked you. So in the process of doing this, there's some links up here in the slides that you can check out. The first one is a blog post that explains all about account manager. They were looking at it for other purposes, but you can also check out, I did a talk at Beside San Francisco, and there's a duo security blog post. If anybody has any questions, you can find me, get me a beer and can talk shop, or I'll maybe be out in the hall for a few minutes. Thank you.


Good morning, John. As you may have heard, there's a lot of content in the world now, but I do sort of fancy myself a understander of media, and I think it's a really, like, important thing, because it's how we understand the world. Like, those stories that we are told, the stories we believe that we share, the ideas that move around inside and through people and on to others, that's what humanity is. We care about that a lot, and I care about it enough that when there's a new way that that's happening a lot, I almost feel an obligation to engage with it. I can't always do that, but I've been doing it with TikTok, and I will say up front, don't download TikTok just to follow me. It's a addictive platform for sure. It gathers a tremendous amount of data about you. Once that data is out there, it is very difficult to imagine what it might be used for in the future. Even if the platform doesn't want to use it for something bad, we see that that information can be compromised. But I have this theory that we now live in a world with functionally infinite content. That doesn't mean that all of the content that should be made is getting made, and it doesn't mean that all of the audiences that should be served are getting served. We need to be aware that the platforms through which we consume content really heavily affect what kind of content gets created. There's this old idea in media studies, Marshall McLuhan's The Medium is the message. The ways in which content is distributed and discovered influences what the content itself becomes in a way that is so profound that the medium itself is kind of the content. Like, we needed to make 22 minute long pieces of content for American television so we could have eight minutes of ads every half hour. And the situation comedy defined itself around that constraint. And now we live in a world where the medium, instead of being television or radio, is the platform. It's the algorithm. Those things so deeply control what kind of content gets made and for whom. Because another really important part of The Medium being the message is that content doesn't get created for everyone. Part of The Medium and legacy media is the people making the decisions about what content is going to be created. They get to decide. Humans, not very many of them. We don't have that as much, but we definitely have the fact that advertisers will pay more for some audiences than for others. This idea that I often sort of use as a touchstone that there is infinite content and we sort of have to prepare for that world isn't really true. There are still all of these constraints out there and those constraints are different for different media and they're different for different people creating different things for different audiences. But in the time I've spent on TikTok I've never felt more like we are getting closer to that infinite content world. Because the constraints on creation have been lowered. The constraints on discovery have been lowered. Like the story of quarantine has been TikTok being like cow and quibi which is a thing where it was like billions of dollars and trying to like interface with the best most talented creators in the world. Because here's another one of my anchors. Florence Italy during the height of the Renaissance when Machiavelli and Da Vinci and Michelangelo were all working. That turned out about the same number of people as Missoula Montana. It is not a lack of talent that holds us back. It is a lack of access. I'm not saying that TikTok is the Renaissance but the talent of the creativity is kind of astounding to the point where I just don't feel like I could ever find all of the good stuff and that's not a new feeling. I feel that way about television. I feel that way about books and this has something of a fracturing effect on society where we consume very different kinds of content and we don't have as many touchstones in common with each other. But it also means that people who didn't have access to that career didn't have access to any influence at all now get some access to that. And so when we have that sort of like off the cuff observation that like I can't consume all of the good content anymore. I think that that actually has some really deep societal implications that are really worth examining. John, I'll see you on Tuesday.


Hello everyone, thanks for coming to my talk. And even if it was not in the app, so I guess it was a bit challenging for you to know what's going on here. This, as the title says, this wants to be a gentle introduction to data science just to give an intuition. There are some formulas, some stuff, so I'm not sure how gentle is this being gentle. The goal is not that you really understand everything on that or really all the concepts. It's more to get an intuition or first, where data science come from or where artificial intelligence come from that it's quite related, it's my area. And second, just to see some applications, how it can be used. It's not really technical, but there are some parts that are a bit more about formulas and understanding some concepts. So, who I am, I'm working currently in a dating site, but I'm a data scientist there. I have a master in artificial intelligence to give some context that this is data science, but also I'll be talking about the human brain and things that are more considered artificial intelligence than data science. I'm also the non-focus ambassador here at EuroPython. Non-focus is a foundation that is sponsoring many of the PiData projects, or all the PiData projects, even the PiData brand is a non-focus thing. Non-focus is also organizing the PiData events. So, you can find me most of the time at the non-focus booth later on if you are interested in some of the projects to do data science in Python or even not in Python, some of them are also in other technologies like Julia or Stan. So, let's talk a bit about the human brain, and I would like to do a kind of a small experiment, but just try to make your visual eyes a bit to really be able to show what I want to tell. It's kind of a silly example. It's probably will sound to you more as a meditation exercise than as an artificial intelligence, but what I want you to think is that your own eyes are kind of like two cameras. Let's imagine that they are like 20 by 20 pixel cameras, and this image on the screen is just like the number five, there is nothing magic on it and it's not like anything that fancy, but it's just like 20, it's a famous, it's from a famous data set named NINST. It's quite common just as a benchmark in artificial intelligence in deep learning and other techniques.  So, what you arrive in your eyes in your retinas is just like this 20 by 20 information that can have values, let's say that zero for one and zero for white and one for black. Okay, if you imagine this, then hold information it goes into your brain. It's like it feels arrived to the eyes, these spheres you can see there. There are these channels of neurons, we'll talk a bit about neurons in a second, but there are these channels that what just do is propagate these 20 by 20 pixels, so imagine 20 by 20 neurons, just taking this to the end of the brain where the visual cortex is. The visual cortex is the part, as the name visual says that it's mostly focused on the understanding and the recognition of images. So, back to the example, we've got this number five, it's just like zeros and ones to say that get to your eyes and then at the end get to the visual cortex. So, some years ago, I just started to think this brain, I think it's something quite obvious but I think it's still worth mentioning that the human brain is just like a network of neurons and neurons basically are just like these cells that transmit electric information from one side to the other. The tricky part, the whole human brain, the whole human mind, everything we know or we think we know, it comes from these neurons just interacting among them in a massive scale and all the interactions are what are really making all our thoughts, all our perceptions, everything. It's probably not very intuitive when you think as a single neuron like having all these powerful but when you combine thousands of them, it's really like that. It's like if you imagine a switch, a switch is actually nothing intelligent but if you have a microprocessor of a computer, it's just like switch, it's just like zeros and ones, it's activating some signals, some electrical signals and at the end it's so powerful what you can do with a computer so it's exactly the same thing. So Hubble and Torsten were two researchers quite advanced for their period that they did an experiment with this poor cat. He did an amazing contribution but I think he wasn't really happy about that. They basically connected some sensors to his brain, to the initial part of the visual cortex. As I was saying in my example, you are getting these information, zeros and ones, it's like perceived by your eyes as if they were a camera, then they go to the visual cortex and what they were studying is this first layer of the visual cortex. So what happens when these neurons, instead of being just a channel that propagates the information, they start to mix one with another. So they start to build these networks. So the idea is like, okay, probably this cat in this visual cortex is recognizing circles like the one you can see in the experiment, it's probably recognizing squares, recognizing different shapes. So they took this cat, they had the cat just looking at shapes for many hours, looking for circles and waiting for neurons to activate. And the funny part of the story, I don't know if you can see in this, I think it can be seen in the screen, you can see these, those are these altes lights that it was just a transparent paper and they had like the shapes on that. The funny part is that the neurons of the cat, of the visual cortex of the cat, the first layer, actually activated not because of the circle, not because of the triangle, not because of any of the shapes, but because of the edge of the, like the border of this paper. You can see this line over here in diagonal. That was really what it made, it made the cat activate these neurons. So in the first layer, the takeaway of this is that in the first layer of these neurons, of these neurons connecting one with another, actually what is going on is that they activate for very, very small patterns, to say, very small, just like edges in different rotations and that. Another important experiment that Donald Hepp is based in the Heppian theory is that these connections on the brain actually are, besides some of them, are of course when you are born, you already get some connections in your brain, but some of them are created just because they get activated. So if you keep watching this edge we've seen before for many, many times, what this theory says, this theory is a bit complex and I don't expect you to even read that, so you have them in the slides. But the whole point is that if you keep activating, if you have like an impulse, you see like this person, this phase, this phase, this phase, one time and another time and once and once more, then at some point the neurons that are activating, when the first time you see them, they become stronger and stronger and they are kind of hard coding the information in the brain. So it's like something that you've never seen, one activate much neurons and something that you see often they will activate much more neurons. That's something very interesting because at the end, like memory or the learning, actually basically everything starts from this, everything is based on seeing something and then having it hard coded because some connections on the neurons are there and then being able to reproduce that. So we'll see, this is kind of like a bit of the history of the human brain, what's in the human brain, now I'll go to the counterparty like how computers are kind of cloning, cloning all those things. I'll start this, my son, I'll check it.  That's a linear regression and at some point you find if there is a correlation between these input fitters and that, you get that. So this is something quite simple, but at the end it's just like a way, if you think of this as neurons, it's a way that every neuron can have a weight, so every input you will have will have more or less importance. At the end, you pack all together, you will have like a single value and what's very important in this is that this activate function, what it does is binarizes the result. At the end what you have is that the output of this is a zero and a one. This is something similar, it's almost a logistic regression if you ever heard about that in data science. But it's still quite powerful if you start to do that. How has it been binarized? I won't talk much about that, but it's not usually taking like positive plus negative and making positive zero or positive one and negative zero. It's usually used in these sigmoid functions, just as a reference, that what they do is just do kind of the same exact thing, but you can take the derivative on that. And it is very key, because in optimization problems, when you want to build these networks on these and you need to see which weights are the good ones, being able to derivate that, it's really useful, it's how you really optimize. I'm going to talk about hopeful networks. I'm going to cover this briefly. Hopful networks are just like networks of these artificial neurons that everything is connected with everything. And the nice properties, everything is super simple. It's nothing really complex. Just a linear regression, you can implement this in a single line of code. And then you just connect them among them. The nice thing is that this has a property that is named an associative memory. What it does is like you show, as I said before with the number five, you keep showing images. And then this network is remembering the images in the same weights. In the weights of the linear regression, it's learning these weights. In this example, what you can see is like you have the original image. In the left is the image that you show to the network. And then at some point you show an image that is similar to this one. And the network kind of remembers and it optimizes in a way that after several iterations, what you get is the original image again. So it can be used, for example, to recover corrupted documents, corrupted images, because whatever you've shown to the network before, at some point it will be back to you. That's kind of the point. If you show all this to a neuron, and of course you need a proportionate number of pixels of neurons that are able to store all this information, but if you show all this at the end and you check the weights you represent the weights in a matrix, you have something like this. It's kind of subtle, but you can see that this is like the shape of the average number, and in every of the pixels at the same time, it has the average of the numbers that are activated, that have values at that pixel. So, for example, if you check on this side over here, on the top right corner, you will see the number seven, number five that go in that direction. If you see, for example, in the top left, you will see the number three, because the number three actually ends up in that dimension. This is, actually, it's kind of more a trivial thing, but this really shows how the weights of the linear regressions I was talking about before actually are keeping information on these images. They are able to recover it later. After hopeful networks, there are Boltzmann machines that start to be the basis of deep learning, if you've heard about that, that I guess you do. Boltzmann machines are the same with the difference that, in the previous example, all the networks are the same as the pixels you are getting. So, if you are giving, like, 20 by 20 pixels, you have these neurons and this is exactly what you have to store the information. In these cases, you have hidden neurons that they don't have a direct exposure. You won't have the initial perception, but you will have an indirect perception. And these are actually much more powerful because this work as a generative network. A generative network is something similar what we've seen before, but the idea is that you've got a model that it learns some parameters, in these cases, like the weights of the neurons, but if you think as a Gaussian, imagine that I want to generate data that it comes from the distribution of the height of the people in this room, or the ages. At some point I can have, like, a Gaussian distribution, I say, okay, the mean of the age and the people in the room is 30, to say, and the standard deviation is 5. So I can start generating samples and whatever samples I'm getting, it would be like the real samples, like if it was real people that I knew the age of the people, like the same as if I get the data from people entering the room. So what is quite important if you do this with image recognition, you can start making, like, these models, these networks, start to learn patterns. This is something I think it's named like the deep dream. I think it's an experiment by Google that they started to show images to one of these networks and what was happening is that then they say, like, okay, now tell me you about the data, give me these data, as I was saying with the ages, don't show me more data, don't show me more, don't tell me about the data you have already seen, generate new one. And this is what they got, I'm not sure exactly what they used to train this network, but this is something generated by a computer that just seen some images. I guess they saw some animals for what you see here, these capsules, I don't know exactly what they are, it looks like it also saw people, maybe a temple, I don't know exactly what it saw, but it can actually show again how much it's been seen. Just to emphasise a bit, on the example if you tell me the age and I start to assume that the distribution is this and then I start saying numbers, it's like I think a normal age would be like I don't know, 32 and 28. So this is basically what it's doing, but with images, which I think is quite an interesting thing. The problem with artificial intelligence usually is that you'll find these models, they are super cool, they are quite amazing, but at some point you'll realise que el computador és de la mateixa manera que el Big Bang per la present d'aigua per computar la informació per obter la solució optimal. En pràcticament, el que ha estat just són les màxines restrictives que tenen algunes propietaris que no tenen les connexions i que no tenen aquelles propietaris de la mateixa manera que es produeixen per les màxines de vols, però encara hi ha moltes coses. És una aproximació, però una bona aproximació, i això pot ser traïnt. Hi ha unes tecniques per optimitzar-ho i trair-ho més fàcilment, que és molt bo fer-ho en pràcticament. En pràcticament, no tens només una de les màxines, només tenen lliures de màxines i una connexió amb l'altra. I, com he dit abans, la primera màxina amb la cadua és justa. Vostè pot veure això a la esquerda. Vostè tenen aquestes pèrdues que, per a ells, no són tan fàcils, però quan comencen a convèncer-los en les màxines, en aquestes màxines, en la segona màxina, actualment, et comencen a tenir una màxina, et comencen a tenir una màxina, et comencen a tenir coses. És una cosa de sequència, una cosa de cascada que comencen amb una màxina no patent. Et comencen a crear petites màxines, en algun moment hi ha una màxina, i en algun moment hi ha una màxina i hi ha una fase, i hi pots construir el que vulguis, si hi ha una altra quantitat de màxines. Vull parlar molt de això, just per donar-li una intuïsió de com això pot ser fet, però el que necessites en data science és just de tenir l'error. Tu tens alguna data, quan tens alguna data, et diràs que això és una altra, o el que veus, per comparar l'answer amb la predicció de la teva màxina. I, si ets mismatxant, si ets tentant de predictar una màxina i no tens una màxina, computeu la diferència entre aquesta màxina i això. Això és per una regressió línia, és molt complex, però imagina't que tu just tens això. Tu pots comparar les imatges que el model genera amb les imatges que et vols veure. I, a base d'aquest error, just optimitzes-ho. I jo parlava abans de aquest derivatif, com aquestes funcions... Tu pots derivar-los perquè són justes regressions línies amb aquesta funcció segona que pot ser derivada. I, perquè el derivatif, el derivatif és demostrant com pots anar al mínim punt, al punt amb poc d'error. Aleshores, et comença a aquesta màxina i just dius que et derivaràs el resulte, la funcció que usas per estimar-ho, i després veus que és baixant en aquella direcció. Aleshores, et diràs en aquella direcció, et diràs que, a algun punt, aniràs amb un lloc en què l'error és mínim. Si l'error és mínim, és a dir que el model que es formi és expectat, si es poden reproduir càrregues, o el que pot reproduir. I tu facis això a les línies. Aleshores, et comences a fer això a l'última línia, et veus l'error, i després et veus en una recursiva manera. Aquesta és, actualment, d'una experimenta de Google. Vaig prendre, crec, 40.000 CPUs i traien aquestes sortes de neurons amb videoclips de YouTube, frames de videoclips de YouTube, per una segona setmana, per veure què era l'interna representació que aquests models tenien. I el que van fer, o el que van fer, és aquest càrregue, en un de les neurons, que sembla que és el que va fer l'error. I potser es pot fer sens, perquè en YouTube és full de càrregues. Aleshores, si pensem en aquesta cosa, en què el cervell és l'error, els neurons són canviant les transformacions a base de l'informació que veiem, pots veure el cervell com una cosa que és just un set de patterns que han estat estudiats, com aquestes shapes, que, si tens pares, pots reconèixer-ho. I, a un moment, tens aquestes âges, aquestes triangles, aquestes fases, i tu només pots reconèixer el que has vist abans. El teu cervell ha estat traient unes patterns certes, i només aquestes patterns són les que pots entendre. I és exactament el que el teu cervell és fent en termes de recognització. Aquest és un experiment molt interessant. El que van fer és que tens aquesta foto de Van Gogh, l'una a la mida, en una xarxa, i traien una altra neta neural amb aquesta informació. Aquesta informació, el que va fer és que la neta neural ha passat a la repressió del món. Les neurons tenien una manera que només podien veure pictures de Van Gogh. Aleshores, després de tot el entrenament i després de totes aquestes neurons que han estat codificades per activar. A certes estats, han vist aquesta foto. No sé si és Amsterdam o què és aquest lloc. I, quan es va ensenyar, em va ensenyar aquesta foto que he vist, perquè la realitat de la model, la representació interna de la model, és només sobre Van Gogh. El que retorna és la mateixa exacte foto, però només amb un patent de Van Gogh. És com el patent de Van Gogh. No hi ha una manera que es canta a una rècorda, una rècorda rècorda, perquè la neural ha never been trained for that. És molt, molt interessant com això funciona. Ara, jo just volia donar una intuïsió de què pot ser achievat o què és la direcció d'aquesta tota la rècorda d'artificial intel·ligència i què és el top trencament. Però ara vull parlar més de les aplicacions pràctiques, què ha de ser fet com una data científica en el lloc d'avui dia. La primera és la clasificació. Jo diria que és la més commona, una cosa que és una supervisa d'ajuntament, que hi ha alguna data. Jo treballo d'aquesta data d'aigua. Hi ha molts problemes amb espàmeres, persones intentant abusar el sistema, persones intentant conèixer-ho als nostres usos. I hem de detectar-los i bloquejar-los. Hem de bloquejar-los, com a alguns accounts. A un moment, en una data científica hi ha una data set en què moltes persones rebuten profiles i diuen que és una espàmera, no és una espàmera, no és una espàmera, no és una espàmera. I hi ha certes informacions. Imagineu aquí que la y-axis és l'aigua del profe i l'aigua és el que feta el que pots imaginar. No ho sé, potser és el temps que l'user ha registrat. I veus que hi ha patències que el profe que veus o el data que veus falla més en l'una d'aquestes sèries. Per això cal plotar una línia o un separator. Per això ho podem identificar. I això pot ser just per l'exemple clàssic de la espàmera, quan vas a l'inbox i veus una espàmera que ha estat classificada, et veus utilitzant la mateixa línia. Just enllà de tenir uns fitos, representar-los, plotar una línia o altres altres altres altres altres i, a un moment, s'haurien de distinguir quins són els bons del debat. Això és donat també a la prevenció de cancer. Per exemple, analitzen imatges de tumors i decideixen, basant-se en aquests models, si això és cancer o no, si és mal cancer o no, per païments de crèdit, si algú retornarà el seu crèdit. En aquest sort de projecte, que és l'únic i l'anàlisi de començar, vull dir, el que necessites és algú que realment labulti o, en alguna manera, que et faci labultar a la data. Vostè sap que certes users no pateixen, que certes coses així. I el que has de fer és replicar les mateixes decisions que haurien fet, i ho veus replicant en el mateix lloc. A la regressió, probablement s'haurien de saber exactament una cosa similar, però vull dir, vull saber el preu de l'anàlisi de l'anàlisi de crèdit, i, a base de les altres metres d'esquadra, ho hem fet. Aleshores, pots plorar tots aquests dots, i després et facis una regressió línica. També podem fer-ho en robòtics. Vull explicar un vídeo. T'has fet una idea. Aquest és l'estat d'art en robòtics. És una diferent tecnic. És una reforma d'enforçament, que és una manera que el robot segueix en l'experiència. El robot es utilitza al principi de l'estat d'enforçament. Es segueix fallant. I després, si et posa, no es falla. No em sorprèn. Una de les conseqüències en l'artificial intel·ligència és si els robots controlaran i es posen a l'estat d'esquadra. Jo crec que es farà senz després de veure com es tracta aquest robòtics. Més aplicacions, sistemes de recomanació, quan vas a Amazon, es utilitza totes aquestes dades d'artifices, artífics artificials, per veure què un customer, both, és el mateix que algú d'譑 increases que aquest producte que balloons, es compleixen aquestes dades de recomanació. I pak About classification és similar a Byt, però, en aquest cas, el lloc és que no els coneixes els labels. Tu has oir dos darrersproduction de slugs per a l'autodeterminació, el que prenegen per clar és que he d'anar els uns que estarm interessant a arr��ar res Calm發능 sexual i laireix français bak. Okay, and we really want to identify them, but I don't have labels. I nobody is gonna just review them and say, okay, we'll have this. So you have these techniques to train models and these models at the end are able to really split the data by itself on finding the distance between different groups, saying, okay, there is this group that is quite similar, but at the same time, another group that is quite similar among them, but they are very different groups. So it's about finding these patterns. Network analysis is quite another interesting topic. They are able to predict, for example, some problems with sicknesses like the flu. I think it was like some of the variants of the flu, that they started to detect based on the network of flight connections when it was gonna start the first cases in every single country. And they are predicting that with great accuracy. Also for the banking system, they check the transactions upon banks and they say, okay, if this bank is defaulting, this other bank is gonna default because they make so much transition together. Image recognition. It's quite challenging topics in many cases. In this case, the idea is distinguished. I think this is a Xihuahua, not Turkey, which kind of dog. And there is muffins. And deep learning is managing to identify which is a muffin, which is a dog. Even probably better than a person because in some cases it becomes quite tricky, I would say. Also self-driving cars is like the state of the art. Probably you've heard about them. They are already in the state in several places. They are already driving autonomously. They have these cameras, these sensors. And they are really being able to, even in cities, stop when there is a pedestrian who wants to cross and all that. Final, just briefly mention, I was mentioning before, there are many projects in Python that you can use. The more famous ones you probably know already about pandas, NumPy, which is like the internal representation of any of these software at the end. You have Jupyter, probably you already know about this one. It's just like a web application. It's like a Python terminal, but web-based. And it's very useful when you are in data science because you usually plot things. So also you have PyMC3s for Bayesian probabilistic models, quite advanced one. Gensim is a pretty cool one that it's being used for topic modelling. So you can really find the topics of conversations, what I was mentioning before, which are the topics that people is talking about. So that's it. I'm just a quick mention. We're thinking of organising something related to data on Saturday. Probably it would be partly a pandas sprint, so people who wants to contribute to pandas. But if people is new to pandas or new to data science, we can also do a tutorial and things like that. So please get in touch with me if you're interested. I'll be around on Saturday just in case anyone wants to contribute to these projects or just get a tutorial or discuss about that. Yeah, that's it. Thank you very much. We have a couple of minutes for questions. Have we have any? I guess no one really understood anything. I'm afraid it wasn't as gentle as I pretended. Maybe you both in the field. Hi. I don't know if my question is really related to mostly on computers, but on the other side of human brain. So you said that when there is, I mean, an image or a pattern that it's already... I mean, the user server has already seen more neurons activated than, I mean, a new image. So my question is, why do we feel much more tired when we learn new things? At least instead of, you know, my guess would be if there are more neurons that are activated, one should be like more tired. Why is the opposite? I didn't quite get the question. Why the number of neurons activated change? So my question is, you said that I was expecting that you get more tired, at least intellectually, where more neurons are activated, okay? But if I can... I mean, I would guess at the same time that when I'm learning new things, I get much more tired. Okay, but why is not the case, I mean... Well, I think there are two different steps in this process to say. One is about training. I would say that when you feel tired is because if you are seeing something new, you go to your new day at job and that, of course, you feel much more tired because everything is new, so your neurons are actually kind of training in this training stage. So all the connections need to be created. So you have new connections, some neurons that are not connected among them and they are going to get activated for the first time. So I would say it's related to that. And then when you are doing something repetitive, it's like just your neurons are just already being activated, the ones you are using, so it's just like the information just flows. I'm not a neuroscientist. I think it's kind of a question for more for a neuroscientist, but yeah, I think that would be probably the reason. And I have another question. Do you use any other languages than Python, like machine learning, like the language is designed for machine learning, like ProLoc or something? I think all the... I don't use much other languages than Python, actually. I think everybody is like using C in the... because Numpa is mostly programming C. There is also Cyton, that it's quite the standard for all those things. So I would say that I would use Pandas. I would use something that is not Python for... in terms of performance, because for coding, actually, I love Python, so I would never really like to have to use another language. In some cases, there is, for example, TensorFlow, Theano, all these things, some of them. I think Theano is partly writing in Python, but TensorFlow, it's not. But usually you have bindings. At the end, I think Python is very good with the interaction with the user, with the programmer, I think it's the most beautiful code you can read in Python, more than in C or anything else. So, yeah, I think it's like the trainee is moving there. You have a stand, for example, for ProLocistic programming. You have R, that it's quite good and quite used in this world, but in my case, I'm not using anything else, and I think you can really do a lot of data science just with Python. That's great, thanks. That's all the time we have for this talk, so thanks again.


Hello, I'm Congressman Ted Liu. It's very important to call your members of Congress and Senators to tell them about the devastation happening in Yemen and their unlawful airstrikes on civilians. And the United States should not be helping in that. We should not be selling weapons that are then being used by the coalition to drop bombs on civilians. I've been working on this issue and we do have bipartisan support. So please call your members of Congress and U.S. Senators and say do not sell weapons to the Saudi-led military coalition until they can demonstrate that they're not going to be killing civilians with those weapons.


Hi, today I will be showing you how to run through a Jupyter notebook. I will share my screen. There we go. So this is called Google Colab. It just lets us run the notebooks and visualize all the data and all the code we're running through. I'm going to scroll down. So these little gray boxes are called cells. That's where all the code is stored. And we call it running a cell for the code to compile and run and produce some sort of output. So the way we do that is we're going to click inside the cell and then click on this little play button to the left of it. You can also hit control enter and it will do the same thing to run the cell. So I'm going to click that. We usually get a warning the first time we run a cell and it will say something like this and we're just going to hit run anyway. And it should say done. There we go. The output of this cell has been produced and then we can go on to the next one. It's important to note here that the cell will only run if the cell before it has already compiled and run. So it's important to do all of this in order otherwise we might get some error messages which makes things a bit confusing. So you can do that and you'll get some dropdowns for some of the options for us to choose a facility by state or by country or congressional district. Here let's say I want to look at it by congressional district. Once I hit that I can just go ahead and run the next cell and do the same thing here. Let's say I want to look at California. I can choose California better and then run the cell here. This goes on for the entire notebook and you kind of do the same thing. You keep running the cells. You can copy images from this and any charts produced, things like that. I hope this helps.


Hi, welcome back to Rick and Corbin's black void take a whiff. Hey, welcome back to our Steve the director of Corbin I'm Rick you can follow the Instagram Twitter for more juicy content Thank you to everybody's for son patreon follows official Twitter account subscribe and hit the like button. Yeah, and Today we're doing a movie review We just came out of our second film of the day. We've been in the movie theaters all day long. It's true. Our families hate us But we just came out of the movie don't key our second film of the day We obviously we saw Salar first the Tolugu film and the new 2023 SRK Rajkumar Hrani Venture obviously the highly anticipated film very much a third film of the year. Yep And also starring obviously Tapsi Panu Boman Hrani Vickie's in there Many tickets basically the the known names there But obviously this just came out So we are going to do a non-spoiler and then if we want to get to some spoils We will tell you before we get to those spoils.  It's a skip it It's see it. You know you can't I always want the movies to do well But this one misses on a whole bunch of levels for me. We'll talk about it in a minute But yeah, this one this one did not come anywhere close to Meeting the expectations I had for it Yeah, I'm disappointed as well It's not like it's not like kgf2. It's not that that was a colossal Does that which is funny because we just came out of Salar, which we loved which is by the director as kgf2 It's not on that epic scale But it is a disappointment Absolutely, and if you were to ask me which one I was like which one do you think is going to be good and I would have told you I'm hoping they're both gonna be good, but I'm pretty sure donkey is getting a time Yeah, the betting odds for us going in just on past experiences would have been with that Expectation and it was completely the opposite which is No, I don't want to say fun. Yeah, but different Yeah, so though it was a and Yeah, there's a lot to unpack. There is we just came It was I feel like there was just too much. It's kind of it's kind of a hot mess with the messaging. Yeah, and also just Maybe he needs to go back to simpler stories Like the simple story of moon a by it's I mean the complex issue is that they think they have a hospital, right? I mean, that's the complexity of it, but then it's simple humans You know at that relation pk same thing with complex topics, right?   It was not our favorite right, but and then now this is just like I feel like you're trying to go So big when what people love you for is your small intimate Like I I think the biggest issue is that the screenplay I I didn't care about anybody. No, I mean Vicki Vicki was probably the main one that I cared about but he's a small supporting role in this as well I thought he did a phenomenal job in his small role But he was probably the extent of like I care for this character Other than that, I'm like and it's not I don't really think the actors fall either. It's just the story in the way they set it up I was like I don't This is like a awful situation that these people are supposed to going through but I'm not caring and it wasn't as awful as a Lot of people who have to go through the kinds of experiences They're trying to get to like this is they're going through an awful, right? It just it just didn't it didn't register and the other thing for me and I will I will Play this one note Banjo forever I Really would love I'm gonna keep wishing for the day that I get to watch Shahrukh Khan Bite into something as hefty as my name is Khan again He has what those in the acting world and who train actors and have been trained He hasn't he has a really good tool belt that he uses all the time And people who aren't trained in acting wouldn't notice it because his tool belts that good, but yeah He knows what we're talking about in that regard and and it's disappointing to watch an actor refer Just use what works rather than work on the incarnation of a character that is real flesh and blood And that that was disappointing for me as well. The biggest disappointment for me was the story I just the story is all over the place. It has some messaging. That's kind of infuriating And and it just it just it just didn't work and the comedy sections of it didn't work There was no laughter There's a maybe we had half a house I think I could count on one hand the number of times I heard some chuckles Well, no, I did hear people laugh more than we did there's a lady to the right of me That was well, she she was she was on cocaine. She was on cocaine And so I think some of the humor in this one and it wasn't like an insane amount of laughter But there was more people did laugh more than we laughed in certain circumstances So I think a lot of it was either cultural or Dialogue that that translation was just a lot of a lot of the humor for me was kind of like Three stooge's humor without the hilarity. Yeah. Yeah, and that was one of my concerns It was like does rush tomorrow Ronnie's humor work now, right? Because obviously a lot of times with old comedies you're like, okay, that's a dated joke We don't make those jokes in a joke. Yeah, just straight up insulting. Yeah, there's a really bad Like there's people are up in arms obviously we have our own opinions of animal But people open arms because of misogyny and we've talked about like that wasn't the issue for that film You know, we're fine with misogynistic characters give that's the character. Yeah, some of these jokes are way more insulting Than any then any joke I heard and well and some of the messaging we'll get into in spoilers Some of the messaging about immigration, which that's not a hit, you know, that's not a spoiler You know the film has to do with that. There's some messaging in there about immigration That's just that's just very problematic for me By reason of just it's a really important issue anywhere in the world. It's a really important issue here in America It's a really important issue for me who I am married to an immigrant and the process We have had to go through and the process. I know others have gone through we'll talk about that in the spoiler section But ultimately this is one of those no if you're gonna go to a movie this weekend I wish I could recommend both because you never want a movie to not do well I care about the people who make this film. We love the folks in the cast We don't ever want to wish a filmmaker to not do well But at the end of the day, I care more about as do you we care more about the art form and Cinema and this is just not one that I Unfortunately recommend. Yeah, I I just don't know if Rajkumar Hirani is the right person to make this film, you know, like It'd be interesting to see because I would love to know a story about the actual donkey like I'd like to see a real Like a real depiction of an actual story Yeah, because obviously people that immigrate to whatever country for whatever reason there's a lot of reasons, right? And obviously immigration is a really complex subject But it's like You know, it's almost like His style of filmmaking is almost making light of it at some point And it doesn't work in the way we've said this because we've recently seen what Nelson can do with a very serious subject matter Yeah, and actually make it work. I don't even know that Nelson I don't know that anybody should try to take this subject matter and weave it through in the context of this kind of Attempt at a comedy and then try some of the other things with the dramatic that just don't they just don't work Dramatologically and that means that it there's just no justification for some of the things that happen on the dramatic side other than We're going to do this for dramatic effect And that's never a good way to tell a story and I feel like there wasn't Even on that though, if he even if he did obviously he made this film I feel like there wasn't any Rajkumar Hirani magic.  Yeah, even there was some magic in in Sanju Like he has this element that was like, this is just I feel I feel good Correct, and I never got there with this and it was and I wanted to I'm looking for it It's like you're not expecting him to feed you the gold. I'm looking all over for it And it just it's just not there. Yeah It's sad But I mean we got one really good film and if you haven't seen our Salar review, please go watch our Salar Please go watch that movie if you like a big Actually with really good story actually Go see that film. But yeah, so that'll be the end of our non spoilers. We're gonna get to some spoils now And talk about certain things if you've seen it or you're just you've decided never gonna see it Yeah, you're gonna see it. Don't watch this till you've seen it. Yes so Go watch it come back and then they're now we're gonna start spoilers. So what were you gonna say about the non spoilers? We're in Africa now. No, yeah, that was there was actually a few of those. Yeah, there were yeah That one was just I sank down in my seat. That's what oh my goodness. Yeah, that was one of those Like if you put that in like moonabai back in the day, I'm like, okay. Yeah, it's a dated joke, right? I get it not like at the year of our Lord 2023 Shah Rukh Khan That's a strange one to make and then like that there was fat jokes and then there was like maybe he was thinking Okay, we're depicting this back in that time It didn't work. Yeah, that's just a strange one to put in there. Yeah, I was also also to put at the end after all of all of this the The notes and the statistics some of the US Census Bureau about immigration. No, I'm very very sorry There's a there's a fact that was put up there that didn't have anything on there It was just a statement and one of the statements was that the only people who have problems with immigration are the poor that is not true at all and that that there are so many things in this that did a disservice one of the things that was really hard for me to Wrap my head around was how a man like Shah Rukh Khan's character could have such a moral compass that he refuses to lie in front of the Magistrate to say he's doesn't need asylum, but he has no problem trafficking illegal immigrants The rationale behind that just was really weird for me to try to wrap My head around and and the the concept of illegal immigration. It's almost as if the film is saying You know, it's hard but do it because the system sucks So do if you need to get somewhere find a way to do it illegally if you need to and and no Because what that happens is take it from somebody who's going through the process with my wife now for the better part of four Frickin years the people who go about it illegally screw it up for those of us who are going about it legally And I personally know of people who have done it illegally and it's it really boggles my mind that people would Marry for money and do other various things and and there are parts of this that I could see people seeing Oh, they're the takeaway from this is if the system's not right rather than try to fix the system Which is complex like you said. Yeah, just do it illegally In certain instances like if you're actually in danger, that's what we're refugees and this island for them Like here in America We have a lot of people that want to get here from Mexico and from South America who come through Mexico Who they're dealing with cartels? They're dealing with horrific And I know this Experientially with people that I personally know as well as documentaries and things that I've seen living here in America and in LA You live this you're surrounded by illegal immigrants and and There are a lot of really bad players who take advantage of the system and they always will they always will I think the point about they the poor is is not so much about it It was just poor is that it it's more of that it only helps rich In terms of like them. I don't know another European one about if you donate a million dollars or whatever Whatever it was obviously the system everywhere set up for rich I think that that is what they were trying to say. I understand what you're saying. Yeah, and it and it does I mean, I've there are There's a reason why and Shahrukh Khan said this in the interview He said there that they did the round table kind of thing. He did with Tassi and And there are people that you know, they may not have a visa right now But I promise you if Tom Cruise wants them in a film here in America, they're gonna get their visa really damn fast. Yep Yeah, and that is not fair. Yeah And so obviously that so like that's why I don't know he's I was like I don't know if Rajkumar Ronnie's and his style of filmmaking if he wants to stick to his style And they were going for a family his normal styles Kind of film, right? But it also never got to his normal style. I feel like no it didn't and his other films don't include things that just for me We're like really She's she's gonna be dead in a month, and she's okay to just go take the donkey trail And then when when she she you know she does That that last moment there wasn't anything everything was really predictable in a bad way I knew she was gonna die and when he turned around I thought okay, really she's just she's showing No signs I mean she's on an IV in the hospital where she's gonna be dead in a month But now she can go and do all these other things she can pretend wrestle She's showing no signs of impending death, and then she's just sitting there talking with him and Those kinds of things just were like she died in love Yeah, so very problematic My the standout if I if I Vicki obviously is a standout for him, but he's always he's amazing He's an amazing actor. I'm very sad. We didn't get to see Sam that that same phone by the way No Because he's he's a very good actor The songs we heard a couple of them were we're not like the one there's one hook that I that I do really enjoy the Is it their love their love song? Yeah, I like that one a lot and then So but like It's a one-time watch at minimum, you know most I would not I wouldn't recommend it to anybody Yeah, it definitely this weekend. There's one. I would definitely recommend 100% I'd go see it again. Yeah Exactly, but anyways obviously we should hear obviously. I hope you've seen obviously or do you spoil everything for you? Oh, not everything, but Let us know what you thought about the film if you liked the film if you didn't like the film Why were idiots? Yeah, why you liked your opinions while you hated us. Why we're the white devils of YouTube? Yeah, I'm gonna change our intro to The white devils of you, too And we're not the I so so we're not the you guys don't like Bollywood films You're not the Bollywood bootlickers.  That is just the criteria for us as you know I don't even like anything in Hollywood anymore because it's not made by a South Indian Idiots any time we don't like a Bollywood film Yeah, you can't win them all anyways, let us know you thought about the film down below