Live from Austin, Texas, it's theCUBE. Covering DockerCon 2017, brought to you by Docker and support from its ecosystem partners. And we're back. Hi, I'm Stu Miniman, joined by Jim Kobielus and this is theCUBE worldwide leader in live enterprise tech coverage. Happy to have on the program Scott McCarty who is technical product marketing for containers with Red Hat, thanks so much for joining us. Thanks for having me. All right, so obviously this is the big container show. Red Hat, I saw when you talk about the number of contributors you're one of the top contributors there. But first tell us a little bit about your role at Red Hat, how long you've been there, some of your passions, what do you work on? Yeah, for sure. So I've been at Red Hat six years and I started actually as a solution architect six years ago, came from a startup before that. And so been in the operations space for a long time, did a lot of programming, background in anthropology and computer science. You're dating yourself, you're called programming because it's coding now. I used to program, but what is this coding stuff? I'm dating myself. Did you say anthropology? I did. Oh, you got to connect that with Red Hat at some point in our interview here. It matters in the culture of things. Yeah, the culture is important. So I did, so a very wide swath of our portfolio, I understand from being a solution architect. And then about two years ago moved into, well, but when Docker first started off, kind of got into containers and got pretty heavy in that and was excited about it. And then moved into just doing strictly technical product marketing for only containers, first or focusing on containers. Okay, so talk to us about how containers fit into the Red Hat portfolio. So containers is really something that touches every part of our portfolio because, whether at the lower levels, it's like the Linux layers, that's the actual nuts and bolts of what builds the containers and what the containers really are. But then at the other end of the stack, so if you look at our storage and our middleware, containerizing those applications and then figuring out how to package them in a cloud-native way and make them work in a cloud-native way so that they can operate inside of something like OpenShift, there's a lot of work to be done there. So there's a wide swath, I would say, across our entire portfolio of work around containers going on. Yeah, in the keynote this morning, I like there's the maturation of the use cases because it sounds a lot like, remember the early days of Linux or the early days of virtualization? Yes. Once again, they put together a bunch of use cases and like, oh, we're running applications and a wide variety of applications in containers. So what are your customers seeing? Any kind of cool use cases or things that people are doing and anything new that they're doing that they couldn't do before? Well, I'll give you a little take on that. So even for the last two years that I've been going out all over the world and talking to customers, I've noticed that there was a little bit of a disconnect between the industry and kind of only focusing on the app dev side of things. I think today, kind of here in Summit we talk about some of the other more traditional use cases, traditional or non-cloud native or we don't like to say the word legacy, but people say it. I would argue those have been a huge portion of what people were experimenting with and playing with, but we don't talk about them. Also, I think there's a little bit of a notion of this mode one, mode two kind of mentality, but that limits the way we think about it into only production workloads. So I have some really funny use cases. So give you some examples, network scanning. So like there are some vendors that provide network scanning software and I was a couple months back up in Canada talking to a telco and they mentioned that they were actually putting a commercial network scanning package in containers because when you think about, you say you have production Oracle database and you talk to the Oracle DBA and you say, hey, I'm going to install this giant network scanning package on your server and they're like, no, you're not doing that. So a container makes it very easy to just bring that application down, do this network scanning, troubleshoot something and then delete it and it's gone. That's just a tools use case, right? But it's something that people have been doing for a long time, but nobody's really talking about it. Another one is even affecting business more transformationally. So if you think about the way startups hire people, this happened to a friend of mine that's a CTO at a startup. They're interviewing a developer, it's very common to send him home with like a homework program, you know?  And there are two hiring managers, the one hiring manager and says, okay, and I'm sorry, also he says, by the way, I have a Docker repo, you can go out and pull it down if you want to just run my program and see if it works. The one hiring manager decides to try to rebuild it from scratch, takes about two hours messing around, trying to get the database schema to work because he used a newer version of Postgres than she had on her laptop. You can imagine the dependency chaos that is. The other hiring manager literally just said, okay, just Docker run this thing and then kind of ran the container and looked at the code. The one spent two hours, you know, getting it up and running, the other one spent five minutes. And so now if I can give you back, you know, the most valuable people in your organization, these very, very technical architects that are doing hiring decisions and trying to evaluate really critical core developers for your startup, if I can give you back two hours, if you have to interview 10 of those, that's 20 hours of your time. That's transformational, that's like digital transformation essentially, but for a startup, you know, like we don't want to have to spend all this analog time doing it. In addition to the traditional applications like databases and, you know, even typical web servers, all of those things, but not just mode two or cognitive, but also just traditional workloads. And we've been seeing that for a long time. I mean, this is similar to the virtualization journey. It's like you said, everyone said it wasn't possible and even two years ago I was saying, wait a minute, just wait for this, it'll happen. And we're seeing it happen. Yeah, anything particular, you know, we've made a lot of progress, but we're still working on storage, networking seems to be a little bit more mature than storage, you know, what are you guys helping to work on at Red Hat and, you know, what do you want to see going forward that, you know, we come here a year from now, we're going to say, oh, cool, we knocked down this barrier or we're doing something even better. So one of the things I'm excited about is kind of, if you look at the integration points between cloud infrastructure software like OpenStack and even the cloud providers, and then something like our OpenShift solution or Kubernetes, if you look at the storage and the network interactions, today the networking is pretty mature, but the interaction is pretty static.  In the future, we're investing in career and, you know, a year from now, I'd like to see some really dynamic interactions happening between OpenShift and OpenStack. I'd like to see an administrator say, oh, I need to provision a new project and that project needs its own network isolation. When that happens, OpenShift goes and talks to OpenStack, provisions a subnet that's encrypted with OVS and actually already is kind of set up, comes back and says, okay, cool, and then can provision a project inside that. On the storage side, we've actually already got that going. So we have what's called dynamic provisioning. So if you need storage inside of OpenShift and you have a persistent volume claim that needs access to storage, we actually have something called a dynamic provisioner that will actually go create that persistent volume and go talk to the storage and carve off a LUN of exactly the size you want or an NFS share of the exact size that you want. So I'd like to see more and more of that dynamic provisioning happening between the infrastructure and the container. Is that a capability that should be built into Kubernetes or entirely independent of that? So the current project is kind of neutral, but it will be kind of, think of it as almost like an interface that Kubernetes will be able to use as an interface to all the networking providers. So it's kind of a neutral third party thing. Really, it could be used by other things, other than Kubernetes. I'm going to take on Project Moby that was a really interesting announcement today. To what extent would Red Hat consider possibly using that as a tool to build custom container applications for your own product, family support? Probably the most interesting thing I found about the announcement was kind of a validation of, you know, already kind of a strategy that we had around Project Atomic and if you look at Origin and Project Atomic and Fedora, they mentioned Fedora, that model. I think it's a good model and we appreciate it. I think that there's some validation also around the idea of an immutable host and having control over the host. And honestly, I think it kind of validates that the Linux itself is not a commodity. There is actually something very technical there and you do need to actually be able to drive features in that kernel to actually support the containers because I think containers made the kernel hot again in a lot of ways. So I think it's validation of that and I think that's exciting. At the beginning, we talked about culture a little bit. We've interviewed Jim Whitehurst, I've read his book, The Open Organization. When you come to a show like this, where I mean today, we talked about the developer, we talked lots about open source and right, there's Linux Kit, there's the Moby Project, all these different things out in open source. What's your take on this ecosystem and what's going on in the industry? I think ecosystems are harder to build than what people first think. I don't think you can just, so if you look at certain, if I were to analyze the way open source works, there are sort of open core models which are like, let's give enough away to get free marketing. Then there's kind of open source models where we give away all the code but we don't really have a community. We don't really take patches, we just kind of put it out there, use it however you want, that's fine. And then I think there's truly community driven open source which is what Red Hat really tries to focus on. So if you look at Fedora, it's truly a community. I think building those and maintaining those takes a lot of nurturing and a lot of care and a lot of love and feeding. I also think it takes a lot of discipline around allowing these best of breed ideas to kind of happen the way they're going to happen and then also fail if they don't work. And so that can be tough. If you look at the model of a lot of startups, it's more kind of like unilaterally make decisions and then kind of release it and if it sticks and it's fail fast, the community driven model is a lot harder to handle because consensus is harder to build. And so you've seen Jim talk about this. I mean, one of the dangers in an open organization of our size is consensus, finding consensus and not going towards a completely consensus driven decision model. But that's hard because you have to satisfy everybody in the community and make sure everybody's getting something out and everybody's putting something in. And so it's tough. It's funny, I remember like an open stack for a couple of years. It's like, do we need the fanatical dictator of this ecosystem? Red Hat obviously is not a fanatical dictator of its community. Do you think Docker has a fanatical dictator of their community? Well, I think, or is that person a visionary?  Yeah, yeah, or the joking word in the community is the benevolent dictator.  I think some of the communities work that way. I think if you look at Python, you'll get the Linux, you know, it works that way. But if you look at bigger projects, and I'm going to date myself, but if you think about like Katie and Nome and some of those, there's no benevolent dictator. They're so big and so wide-ranging and such wide use case differences between what people do with them. But I think it's hard to have that. There are visionaries within the group and even that's true in the kernel.  I think that that takes, again, though a lot of discipline and maintenance to make that happen and keep that alive. All right, Scott, to take us on home, why don't you give us a little view as to what Red Hat has going on this week. Of course, you guys have your big show, Red Hat Summit, coming up in a couple of weeks. We'll have theCUBE there. I'm excited to be there also, but talk a little bit about this week and what you guys are doing. So this week, we're excited because we have kind of a bunch of 3.5 on the, I don't know if you guys have, if you guys heard about Atomic Image, we released Atomic Image. So it was not discussed in Brian's interview this morning. I'd love to hear a little bit about it. So Atomic Image, we've kind of looked at kind of some of the use cases around how people are consuming containers. And I've blogged on this a lot and talked in it. And honestly, it's pretty deep technically. You kind of get into it. It's about having, like, you know, Salman talked about today, you know, image size matters, and there is definitely a hunger for smaller images. You don't want to have stuff that you don't want. But that is also a very fine line balance. So the challenge being that the typical way that enterprises operate is they have a core build where they will add all the pieces of that core build that they think should be everywhere, right? Because you don't, like, say you need a fundamental core library like G-Lib C, you wouldn't add that to all of the different applications. You would add it once and then inherit it and all that. So it's kind of the dry model. Do not repeat yourself, right? So when you get into this dry model, you got to balance the size of that base image versus, you know, and its flexibility versus conciseness, you know, and how concise it is. Atomic Image, though, is meant for, we essentially released a very minimal image that matters for those very concise applications. So if you look at, like, a C binary that's very small, maybe all it needs is DNS resolution, some other service from the OS, from the user space. It doesn't need much. But it's a real small binary. It wants a really small image to live on. So we released something called Atomic Image, really targeting those use cases that are very specific. I remember when the project Atomic was launched, so it sounds a lot like what, you know, Docker announced with kind of the Linux kit today, too. So maybe you could compare and contrast a little bit. Yeah, so I would compare, you know, the Linux kit to Atomic host, which we've had for a long time, which is the kernel and system D and kind of what runs the containers, right? But now we've released a different user space set that's smaller for, to run on top of, you know, it's not like an agile, minimum viable product. This is a minimum viable container for a particular function.  And Scott, do you guys have their website or some documentation that you recommend people starting with on your sites? Yeah, absolutely. So I think Project Atomic's a great place to start. And that's in the blogs, I'm assuming, right? It is. If you blog for Atomic Image 2, you'll find a relblog entry. So relblog's a good place to kind of find some of that stuff. So relblog.redhat.com. And then also, if you look on just redhat.com, and also our container catalog is a good place to actually go and get started with that. So if you go to access.redhat.com slash containers, we'll get to that. Scott McCarty, it's great catching up with you. Next time we have you on, we've got to get the story behind Father Linux as your channel. Oh yes, yes, yes. All right, but we'll be back with more coverage here from DockerCon 2017. Thank you for watching theCUBE.


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And the passenger side seat is an excellent conditional rips or tears on that. You do get rear air controls and that headliner is an excellent condition as well. No rips or tears. This vehicle has never been smoked in and it does have your map lights up there. We'll take a quick look at the back seats. And then we'll check out under the hood as well. The back seats are just as clean as those front seats. No rips or tears back here.  These seats actually do slide forward and back. You do get in-floor storage back here. There's one on each side and those containers are removable for easy cleaning. And we do have the all weather floor mats back here as well. So to get this back seat up, pull that latch and you'll notice that that back, that seat part folds up. And then this seat slides forward so that you can get to the back seats. And you can see those seats are in excellent condition. No rips or tears back there. So that means in really nice shape as well. So when you get these seats down I'm going to slide it back here. So you got to lift this up to slide it back. But then these backrests also come down and they go down nice and flat. So when you have those third row seats flat, this is all flat back here. So really, really a nice setup. The other nice thing is that these doors go at a 90 degree angle. So it's easy to get in and out.  We'll take a quick look under the hood. I would personally like to thank you for checking out the video today. And my hope is that you were able to tell just how clean this journey is all the way around inside and out. So you can make a smart buying decision. Engine based very clean, runs very smooth. Like I said, this vehicle has been fully safety and inspected by our service shop. Has a fresh oil and filter change. All the fluids have been checked and topped off. 100% ready to go. I'd highly recommend it from a quality and condition standpoint. And to see more pictures of this journey or one of our other 450 new and used cars, trucks, SUVs, minivans, wranglers, you name it, we got to go to our website. www.summitauto.com. Full pictures and descriptions of every single vehicle from two locations all at summitauto.com. And if you'd like to check out more HD videos, you can go to youtube.com. Remember to like, subscribe, and share on this video and all the videos that you see there. In fact, in a second you will see links to subscribe to our YouTube channel on your left. It links more Dodge Journey videos like this one on your right. If you have not been to our website on the bottom link to this vehicle on our website, click those, check us out. And we really look forward to help you with this really clean 2019 Dodge Journey Crossroad All-Wheel Drive V6. Thanks again.


And they're trying to do that to the manosphere.   We're gonna get into in a minute, but I believe that the manosphere is the only real counterculture in America. You know, Apologious of Watson is a YouTuber I like. He's like two million subscribers. He's a good dude, I think. But he says conservatism is the new counterculture.   Tommy Larn is supposed to be this brave, conservative, pundit and the blonde and the blonde bitch, right?  She's pro-abortion, she went on a rant, all men are trash. Really? You're the counterculture is saying all men are trash? Smash the patriarchy. Really? You're the counterculture?  The manosphere is. It's not even like very political, but in general, culturally, the manosphere, this is what these Homeland Security people, what Kelly is looking at, the manosphere rejects all the woke garbage, right? Not just this tyrannical mandate shit, not just the trans crap, not just feminism, not just the woke garbage, not just communism, like all of it. Because masculine men don't like it. I've said fundamentally, as an attitude of life, as a point of view on the world, as a world view, I've said previously that our founding fathers, 1776, was the ultimate collision of masculinity and philosophy. Those men were super, super masculine, and they had good ideas to back them, right? They don't like that, it's a threat. Masculine men are a threat.  Again, with the conservatism, right? The manosphere is the real counterculture. This is a super thought. Tommy Lahren, I call her, I love the memes. The conservatism, political conservatism, I've been to a lot of conferences this year. I went to two CPACs, that's like the biggest conservative conference. I went to a Turning Point USA event, like a young college kind of conservative event, and I went even to a libertarian event, which they kicked me out of, by the way. They said my brand and fit with their brand, because I don't like feminism.  They can't handle that. I've been in these events, and I learned some things, Tim. I'm glad I went. I'm gonna go back next year. For example, I thought that Zoomers are more based than Boomers than I found the opposite to be true. Older Boomers that are conservative and stuff, they'll at least talk to you about masculinity and femininity and family issues, right? The Zoomer people are much more abrasive to it. They don't like it. They don't wanna talk about it. And I think the reason, not exactly why Boomers are, I think, more based than Zoomers, the opposite of what their current meme is, but I think it's because Boomers grew up in an age of America, and wherever they're from, but usually in America, these events, they grew up without the internet, right?  Like, wouldn't even have radical feminism anymore? We have hyper-radical feminism. The shit's like completely off the chain.  Anyway, this is like the conservative, a lot of conservatives there, right? Especially all the women.  Oh, I'm a conservative, I'm a Christian conservative.   But at least the men and some of the women will talk to us.  They wanted to buy them for me and stuff.  At least they'll talk to you. Zoomer is not so much. But again, this is an example of why the Manisphere is the real counterculture. The conservative stuff is not, that's BS, man.


The life here is really what I imagine as a student life, like you can find libraries all around and then you can say them study and then the campus is all around the city, yes. So there are students everywhere, yeah. It's really like a student city. What surprised me was the form of examination, because in China we don't have group exams, yeah like we don't do exams in a group and we don't write like really long papers like 10 pages or things like that, yes. Like in China we spend more time on classes, on taking the classes, but here like we spend more time on reading materials and study by ourselves. I think the most important thing is we will need to know how to be independent, because when you study abroad you don't only study but you also need to figure out how to live here and how to make friends here and all sorts of like other stuff than study. We also have like a really equal relationship with our teachers. In Denmark like I think it's a professor and that's why I'm more like friends because like also got some professors they say that if they just they have a little knowledge about something and then if we know more then it's that we can teach them something yeah and also they can teach us something and there will always be really encouraging students like sometimes I don't think I did something like really great but I will say you did really great like a lot of encouragement yeah and also I think here we're communicating a more direct way like if you have problems we just go direct directly to the professor and then ask them yes.


Okay, I think there's quite a few of you, so I will probably get started Again Thanks for coming My name is Karthik. I am an AI engineer. I live in Singapore and my my job on a day-to-day basis is to sort of lead projects that cater towards industry challenges that can be in the areas of finance in language processing in security in Healthcare, which is a key interest of mine I also did my master of engineering with the biomedical engineering department at NUS that's in Singapore And my research was based quite a bit around the use of AI for histopathology, which is a sort of subfield in medical imaging and histopathology is where we segment tissue from, you know, places where doctors do cancer resections and stuff like that in order to understand what the components of the diseases are I Didn't have a lot to show about my research previously until I think just two days ago You know we it was actually just published So if you're interested in the work that I was doing this was about three years of work I'm just going to put the link in the chat. So so that's the The sort of work that I did for my masters and stuff Not the most pleasant of experiences, but definitely I think something worth You know something that really taught me a lot about the way AI works and and how we use it properly and What its applications are in medicine? So of course Coming from You know with my domain expertise and in histopathology You'll find that a lot of the maybe the examples that I gave are heavily based around microscopic imaging That doesn't mean of course that these Techniques can't be adapted to other forms of imaging whether it's x-ray, CT, MRI And If you're interested in those techniques, I think there are some other science circle members who are working on on I think Some of these discussions I think sumo previously presented on magnetic resonance imaging MRI imaging so that's used to study for example diseases in the brain To study neurological function things like that and there's a lot of applications for AI in that sector Okay, so the thing about me is whenever I do these presentations. I Paul that's because your voice isn't on Well, oops, what's the point of me saying that if you Okay So so the thing about me is As an AI engineer, I often find that I Revisit the fundamentals a lot and so everything that I learn about what I do in the field Actually brings me back to some fundamental topic that I learned And I and these presentations I typically try to structure them so that we can go from something simple to something more complex and I am experimenting with this and I'm trying to use it to also You know ensure that I have my grounds well established and So in order for us to do that I think it's very important for us to try and look at a very simple example and to figure out how AI is applied from then then derive it to something like the field of computer vision So so this Example is something that I presented in a previous lecture. It's called deep learning if it feels like a repeat I apologize. I just hope it adds some value to what was presented before So let's talk about This concept of a neural network.  And these architectures are built to draw on mathematical properties of data to draw Relationships to then build a sort of classification. So this example for you, you know Let's talk about the predicting the probability of buying a house So we have a house. We want to know what are the chances this house is going to get purchased And so then there are two Factors that we have to let's say there are a few factors that we have to consider two of them could very well be One the number of rooms, right? We typically if you you want to buy a house if you have morbid rooms or something like that Maybe we take away price from the equation that could be something that makes the house more attractive The distance to the bus interchange nobody really likes to to you know travel and that sort of stuff So in that case these two factors could very well influence the probability that the house is going to get bought It's attractiveness Now a neural network sort of correlates these variables by multiplying things called weights And so these weights sort of talk about the contribution of each variable to an outcome So x1 the distance from the bus stop multiplied by weight number one that is Added to x2 the number of rooms multiplied by weight number two. That's a guess of course the weights And that sum is added to a bias bias Just think of it as something that is used to normalize those variables and that's used to determine the probability that someone is going to buy a house Now of course this doesn't mean anything if the network isn't trained So the network needs some Examples maybe we did a survey and then we showed a few people. Okay, this house The distance from the bus interchange is 200 Meters for another one. It's 100 meters one has three rooms another has four rooms So so what's the probability that you buy a house? Do you want to buy the house or not? So if it's a no, it's a zero if they do want to buy a house It's a one. So that's our ground truth information annotated information Now what the network is going to do of course, it's going to make a guess the first time about what the weights are It's going to randomize those variables It will add a bias to normalize the values And then what it will do is you will pass that into a function F And that function is called the activation function So this activation function sort of determines a probability between 0 and 1 the output of the network Whether or not something is true or false going to get bought or not. What's the probability? And that gets compared against the ground truth information So if they find that through these examples and these inputs of x1 and x2 the network got the variables wrong Right, it got the output wrong. It's going to calculate an error This error is also called a loss and that loss is going to be used to update the variables the w1 and w2 So in that sense what the network is actually doing is learning a bunch of features that contributes to a final outcome So it's trying to relate data and it's trying to make those relationships to come up with an outcome Imagine this just this segment of Predicting whether or not the house is going to get bought That could be a very small part of a bigger problem So whether or not the house is attractive could very well determine whether or not the house is going to appear in something like a magazine And so These are how sort of neural network architectures work They sort of apply some multiplications to some variables and then they come up with an outcome and Just to Continue explaining the architectures the input layer is where these variables go The output layer is where the prediction comes from the hidden layer could be anything that we sort of program So the thing about a neural network is it has no semantic idea about the properties involved It doesn't know what a house is. It doesn't know what distance from From a train station is or a bus interchanges. It doesn't know what the number of rooms are You as a human have an understanding of those variables But the computer everything is literally just mathematics and statistics And so it's really trying to make guesses and then it's updating that on the mathematical properties The error the you know the the factors and the variables And then it tries to make a guess about how the data is really related to each other The the data components are related to each other rather Um, of course the structure of a neural network architecture is something that we define That's not something that computers have the capability of understanding on their own That's of course still up to the programmer. So there is some human intervention involved at this stage Now in the field of computer vision, of course data isn't really one-dimensional You're not going to have um, you know, uh a single dimension or rows of data What you have is a two-dimensional Set it could be 3d, but you know, we'll just we'll just keep it simple for now And we'll talk about you know individual pixels in an image If we look really really really close into an image, um, of course we can see these pixels and we can see Um, we can sort of understand what sort of values they they contain or encompass So in an rgme image, you would have three different values for one pixel a red a green and a blue value Now we can call these red green and blue channels. So these are one of the um, the things we refer to This is one of the terms we use when we describe a color image We break it down into different channels. Of course, um, if you use the printer before, um, you don't necessarily see rgb You see cyan magenta yellow and black. So so there are different color representations Of course in computer vision mostly it's just red green and blue Now there are grayscale representations as well. These are very simple representations The typical values that we these range from in computer vision is to 0 to 255 0 to 255 because that's the range you can't distinguish with your human eye So if you looked at that color bar, the reason why it doesn't look grainy looks very smooth, right from 0 to 255 It's because the human eye can't distinguish color differences between those values at that level of precision Of course, there's uint 16 32 bits Those are a bit large for our computers to process. So, um, we won't really talk about those Um, and so, uh, let's talk about the diagnosing of an image Now if we think about it if we just studied the individual colors if we broke an image down into its individual components The red green and blue that tells us some level of a story about the image So it tells us how many red pixels there are how many green pixels there are And what's the intensity of those pixels in the range of the you know in in the context of the image itself Now the power of that of course Is we can use that to determine some property regarding the image. We can actually use that for image classification For example, let me just move away Uh Let's talk about differentiating cancer and non-cancer. So let's say a surgeon takes a tissue sample Um, he's trying to remove a tumor from a patient's body And that sample is then passed, uh for microscopic evaluation Now if it's not cancer, it's going to look a bit like the image on the top left There are not many cells in the tissue if it's cancer It's going to look like the tissue that's just next to that the one that says tumor call Tumor call because it's taken from the the central part of the tumor Now the concentration of cells is so much higher that the image looks a lot darker So if we studied the grayscale representation that effectively tells us that this is a tumor The problem is healthy tissue can also look very cellular by virtue of things like reactivity Reactivity meaning the way the brain responds to a disturbance. If you get an injury It's a very high chance that your cells will reproduce to Sort of make up for the loss of neurological function Cells in the part of the cerebellum, which is a part of your brain as well These also look quite granular and so the concentration is quite high So it's not enough for us to use colors alone to make that sort of classification What we need of course is morphology Mophology meaning shape and that's done through this mathematical operation called a convolution So in a convolution a filter that is maybe three by three in size of five by five pixels in size Is passed over the image for us to get a new representation of the image I typically use this for engineering You know students, but I don't think it really matters What it really what really matters is what are the effect of these convolutions on the image So let's talk about the image of a zebra and an image of a horse. We want to differentiate these two images I'm going to convert them to grayscale just to make it easier for us to visualize I am saying that I want to do of course, um the I want to do a classification I want to do a comparison and when I apply this filter this filter is called a sobel edge detection filter The resulting images look a bit like this So you can see the edges in the zebras, uh, you know lining the fur the surface All these things are really highlighted a lot more than they are with the horse because those features are not so distinctly represented and that alone allows us to make that diagnosis we've essentially Identified some morphological and shape aspect of the image or some textural aspect of the image and then we then made that classification So in computer vision if we think about it That use of a filter a two by two image, uh, two by two feature map to to sort of, uh, Derive some feature about the image some feature regarding the image It's almost the same as multiplying a weight to a variable to understand some outcome as we did with the house, of course And so these filters that that three by three filter those sets of numbers That's something that the computer can of course guess as well to determine some feature regarding the image to understand The textural the edge the shape components of the image and that's how neural network works When we talk about computer vision in the field of uh self-driving cars, for example, you know, these are the base The base principles that allow those networks to detect objects in the field and so um A neural network is essentially an expansion of those steps a neural network tries to identify the filters a cnn A convolutional neural network tries to identify those filters. It tries to guess those filters It makes some sort of diagnosis some sort of classification in the image And then it goes back it updates the layers and then it tries to train itself To better extract features from the images in order to better make a classification um, the power of this technique Is you know as we do these convolutions and as we update the weights based on the error The train neural network is able to do this The lines and the dots and the textures all these components are going to translate into high level features within the image What do I mean by this? These lines these dots and these edges will eventually tell us about the existence of things like wheels Things like wings things like a sail things like a nose a year an eye A fin uh, you know, um, and so so what they essentially allow the network to do is to identify salient features Within the images that people can use to make a classification And that just comes from mathematical operations that extracts these low level features the lines the dots and the edges And that's the power of the mathematics that goes on in computer vision systems Um, and how do we improve on those systems? How do we uh, apply these systems? Uh, when we have You know very little data to train on Sometimes it's a problem with a data set being small that doesn't allow us to extract these features easily And one of the biggest problems in medical imaging is is because of the policies that are involved in getting data sets It's very hard for us to get enough data to produce these features to train a model For it to identify and extract these features And so in that case we sort of apply certain techniques We cheat the model into thinking images are different by applying something called augmentation And augmentation simply means using the same image but a different representation of the image to extract more features We can adjust for example the orientation of the image We can flip it in the horizontal axis or the vertical axis We can adjust the contrast in the image the color of the image And so let's say a cameraman is moving closer towards an object when he takes a photo You know, we can apply an augmentation to train the model You know with an artificially enabled zoom in order for the model to learn those features that are important towards identifying this object this parrot Um in the context of medical imaging in microscopy, especially Um different labs that produce these microscopic images when they apply stains Chemical treatments sometimes even the temperature of the room that the lab operates in that could be different from day to day Or an hour to hour It can cause the image, you know, its contrast or its color features to change Uh and and in order for us to account for that sometimes what we can apply to that image is a it's an artificial color augmentation And so this forces the model to not learn from just the color representation of the image to pick up the shape components to pick up The shape representations the textural features that allow it to make a diagnosis Let's say we want to improve the model further and we don't have enough images One method for us to deal with that and one method that's really popular in medical imaging is this concept of transfer learning So transfer learning is where we apply and we learn representations from another image Or other another data set of images. Let's say you're looking at clouds, right? We like to say we sometimes what we like to do is when we look at clouds in the sky We like to think that oh, I see an elephant in the sky I see a tiger in the sky. I see a cat in the sky Some representation that we've learned something that we have in memory We use it to make an association between another object that we see transfer learning essentially works the same way A network learns on a multitude of images. It could be anything from cats versus dogs versus cows To to you know distinguishing classes of vehicles per se and these public data sets are available in massive amounts on you know The the internet and what we can do is we can train models on those data sets And we can then apply the weights the lines the dots the textures and all these features that it's learned from those images And we can apply them to other problems This is especially useful in medical imaging because when we don't have that large data set We can then apply these learn features and we can use them to diagnose medical images And so this is a powerful and commonly used techniques technique in AI And these networks that have been trained, you know, some of some of us we don't really have a large GPU at home We don't have multiple GPUs at home A graphic processing units. We don't have powerful computers to train these models on maybe 14 million images But what we can do is we can use models that were trained by companies like google or facebook Method these days, of course And these models that were trained on those large data sets the models that have learned features those lines those dots those Textures, they are also available on the public interfaces, you know public websites On github repositories And so that's the powerful thing and what I really love about the deep learning community You know a lot of AI researchers actually publish their models Publicly for many people to use them in their projects It doesn't really it doesn't really matter whether or not it's a commercial or non commercial projects these models That were trained by google and meta. They're available for anyone to use and so the accessibility of AI is really fantastic And that accessibility is actually allowed for you know, image analysis in the medical domain to really flourish A lot of models that were produced for different areas like gastric biopsies brain Pathology analysis, you know MRI imaging they were based on these models built by meta and facebook Maybe they were adapted in some way or another but essentially the principle is the same It's there to learn and then fine-tune on new data One example of a network that's used it doesn't really have to be used for classification Of course it can be used to derive a new representation of the image The idea is we feed the inputs of data that could be Again in the wine one dimensional space we could be talking about The the price of a house The number of rooms of course the distance from the train station and stuff The output feature is not really a classification. The output is the same data The goal is of course to make to derive a smaller representation of the image for those of you who work In mathematics, you are familiar. You are probably familiar with the concept of a PC a Principal component analysis. It's where we try and take the data from a high dimensional You know feature space to a smaller dimensional feature space and then try to reproduce the data such that we get a Minute representation a representation that takes the most important features of the data set If you don't understand this that's okay because What's really important is where this is used or where this is useful By learning a smaller representation of the image, we actually derive the most important features of the image Now we can choose to then decode the information to provide a new representation And how is this representation derived? It's derived from our annotation So let's say I have an x-ray image or I have a few x-ray images I basically draw a mask manually for some of the The x-ray images in order to identify the lungs within the image And then I pass that to the model and I say that okay for this particular x-ray image I want this output this representation What the model will do is it will learn to derive the important features from the image to then decode these features to provide that representation How does it learn that it's based on the error the first few times? It's of course making a guess the next few times It's of course updating the weights to fine tune itself in order to fit itself to your data to make sure that those representations are learned accurately And the usefulness of this You know in multiple domains is that we can use those representations to understand some underlying diagnosis In kovid-19 this this was really popular This sort of research regarding segmentation of the lungs because it helped us understand Whether or not a patient with kovid-19 whether or not his lungs are expended or they You know are contracted is there some sort of pneumonia property regarding the lungs? Is there a risk? And so this was really popular research at that point of time In the in my area of interest in brain brain tumor analysis The segmentation of the tumor for resections to remove the tumor accurately is something that is heavily discussed Why is it important in the case of the brain? If you don't delineate the boundaries of the tumor if you don't identify the location of the tumor Before surgery correctly if the doctor takes too much of the tumor away from the central part of the You know the the detected tumor if he takes too much if he cuts too much of the tissue What's going to happen is the patient is going to lose neurological function The patient might either go blind, you know, they may lose some hearing they may lose some motor skills And so that's the danger of not identifying the location of these tumors well enough And so segmentation allows us to artificially identify the boundaries of the tumor With so much precision, you know with the ability to understand features Mathematically that pathologies are not able to identify because they're not able to look at the pixels themselves closely enough By being able to do that diagnosis by being able to do that segmentation People are able to better identify resection margins for tumors They're able to identify the locations that they should cut and remove the tumor to make sure that it doesn't come back again After the patient has undergone surgery and to make sure that the healthy tissue in the brain is not accidentally removed One other area of learning that's really interesting for these imagerists is of course the use of self supervised learning A self supervised learning is a technique that's used again when there's not enough data to train these models If you think about an x-ray image or you know on the left is an is an image of the eye I think that's probably for oct optical coherence tomography Now these images like the x-ray images particularly there's a certain orientation. That's correct Right. You only stand You have an upright posture for the patient. So any tilting of the image is actually somewhat wrong What we can do is we can artificially augment the image by rotating it And then tell the model to predict the rotation How much is this this image actually rotated from its original ground truth? And when the model does these predictions it gets certain variables You get certain errors and those errors are used to update the weights Just by doing that, you know, we've not introduced any classification any annotation from the clinicians whatsoever But just by rotating the image and then telling the model that the image is wrong Tell me what's wrong with the image you make a guess and then it gets an output and then I tell it Oh, okay. You got this wrong You need to update your weights in order to fit the data the models capable of identifying and learning Some important parts of the image. So by rotating x-ray images The model is capable of learning features that are salient to x-ray images An x-ray image is supposed to have these features in this location per se And these models can then be used for classification tasks later on Because they've already learned some features features some textures some low level representations that can be adapted to tasks in the future Um, another really important use of uh, ai is generative ai chat gpt is a form of generative ai. Um What do you call it a stable diffusion is a form of generative ai? There is an input the model is trying to generate a fake image Um, when this was done early on The models that were used for this were called gans generative adversarial networks. Um, yes, exactly uh Actively communicating with each other and self-correcting each other is a perfect example of a way again works Basically, you have a generator a generator is taking some data some random data noise And then it's applying some sort of multiplication some mathematical Multiplication in order to try and generate a fake image of something in this case the picture of a dog Then you have this network called a discriminator The discriminator sees this fake image the discriminator also sees a real world image And its objective is to try and determine whether or not the image is real or fake So these two networks are essentially playing a game with each other They're trying to distinguish one is trying to distinguish whether or not the image is fake Another is trying to generate an image in order to trick the other model into thinking that it's real And these networks are then updated based on whether or not they got the outcome correct or wrong Whether or not the generator was able to trick the discriminator. Yes or no So by playing a sort of game with each other and by sort of improving with these outputs The models are able to get stronger at their individual tasks But the most important thing is that the generator that's output by this model is capable of generating fake images You know fake features and that's sort of the idea that's used you know in in the creation of models like stable diffusion One really interesting use for this that can be applied to the medical imaging domain is this idea of style transfer So style transfer essentially uses GANs to take a real image and then to apply some sort of Augmentation to make it look like another image in another style Let's say you wanted to see how your real world picture your photograph looks like in the eyes of van Gogh Right, so you would take a number of pictures That you've taken number of your own photographs You would have scans or maybe van Gogh's pieces, right? That captures his distinct artistic style and you could train a model to apply that style to your images Some of the simplest examples of these are you have pictures of zebras You want to show you want them to appear like horses instead So you apply a style transfer to make them look like horses and the powerful representations that these models learn Can then be applied in the medical imaging domain Previously we had to have matched images, which means let's say you wanted to apply Style transfer from a zebra to a horse your training images would have to consist of zebras and horses In the same position the same orientation so that the model can learn these representations by performing some sort of matching But now with advancements and new models, it's possible to do that with you know Images of zebras and horses in any positions So you could have three zebras in one image one horse in another image You could have two horses in one image three zebras in another image They could be standing at different locations The model is still capable of learning representations of zebras to pass that style onto images of horses The area in this where this is interesting of course is of course Microscopic imaging again my domain In for these samples that are being used by the way When they're taken during surgery Maybe to give the surgeon an idea of whether or not he's cutting tumor on a healthy tissue The samples are taken during surgery they brought to the lab and then they're stained But to provide a very quick output they are frozen And when they're frozen what happens is ice crystals start to form So these are not samples that go into the archive. There are samples that are used during surgery they're thrown away Samples that are straight Stained for archival analysis, you know for pathologies to look at maybe the next day or something like that They use a specific method of paraffin embedding In order to preserve the quality of the samples to see these images So for example, if you look at the top left Section right there are the four pictures on the top left the one at the top left the extreme left Those are the images that are frozen the one just next to that. That's the quality of an image that is unfrozen And you can see that the ice crystals have almost destroyed the image to such an extent that it's so hard for Pathologies to make a diagnosis for that image. So what if we could learn a style from the images? That are stained that are paraffin embedded and preserved with such quality that it's so easy to make a diagnosis What if it's possible to artificially transfer that quality? To the frozen sections so that we could train models to learn the quality from these separate images In order to apply a style transfer on future images And that's the power of style transfer In the medical imaging domain um This is one example of something that I tried personally for my for my master's thesis You can see that style transfer essentially allows us to perform Augmentation of the images in a way that we weren't able to before The one on the left the example on the left. That's the machenco normalization Which is the the standard method, you know the typical method that you use and all that does is provide a color transfer But if we look on the right we can actually see if you zoom in You can see that the sharpness of those individual nuclei those individual cells has actually improved To such an extent that it's easier to diagnose it. It's easier to look at this image It's easier to see the important features regarding the image and this was done with ai Another area that this can be used of course is uh in new medical imaging optical bioimaging rather So right now the traditional method of course is to take a biopsy It's to cut a piece of tissue from the patient and to then provide a stain and then to analyze it with the microscope But clinicians today Are exploring even more techniques using advanced laser systems for example In order to get these images to study the chemical markup of the tissue one example is a simulated raman spectroscopy So that's about using two lasers two laser beams with different timings You you direct these laser beams on the tissue you get the light the light that's reproduced And that tells you about the chemical composition of the tissue the lipids the proteins And these images actually look a bit like those images that are produced using staining The problem is they don't look exactly like them enough for it to be you know diagnosed It's not something that the clinician who works in the pathology department is used to Seeing so what if we could make their jobs easier? That's where we can use style transfer We learn features from the From the images that are stained and then we pass that style of staining that that appearance of the staining onto an optical bioimaging output So that's another popular Imaging domain and that's where one you know, that's where the future of medical imaging lies at the moment another area of course is the You know analyzing the genetic markup of images so that what we use for that is immuno staining So we we apply a stain that sort of identifies the genetic properties of the tissue In this case, it's ki 67. That's one gene that's used in tumor analysis for the brain So if that gene is very high in a particular area that stain will show up brown Uh, and so one area that we can do is we can identify the more morphological properties in another stain a simpler stain Because these stains are actually quite expensive a ki 67 stain is not cheap And there are many genes because of the heterogeneity in tumors, you know, you could have many genes in many places So one thing that people are studying is whether or not they can identify just the shape properties in the cells And whether or not that correlates to some genetic property in the tissue in that case You only have to use a hematose island and eosin stain a h and e stain and that's quite cheap So another area of stale transfer that's being explored Is that correlation the correlation between the genetic properties and the shape properties observed in the tissue Um This is one really, uh Advanced area of imaging and this is where imaging is going computer vision is going So, uh, the example that I like to use for this is actually a bit, um Localized What do I mean by that? Uh, I I basically used an example, um, that that's quite prevalent in Singapore Uh in Singapore when when kids are at the age of 12 They go for their primary school leaving examinations And during those examinations if they're taking english Uh, one of the exercises that they have to do you have your comprehension Of course you have your essay writing That's part of the exam another part of the exam is an oral examination So during the oral oral examination, they're basically given a picture like this And they're asked to then talk about the picture to the examiner just talk So their eyes go across the image they study the different things that are going on in the image And they then explain to the examiner that this is what it's going on I think this is why that's happening I noticed that this is happening and then they put they form those insights their ability to community communicate Um is then assessed for that examination And one of the things that people have been asking is whether or not computer vision models are capable of capable of doing that Because this image is not something that you can study as a whole They're very Various activities very different things occurring in different parts of the image And is the model able to identify these parts of the image very importantly In its individual patches for example And the other question that's asked is whether or not a model is capable of understanding the meaning of these things Or their relationships per se And that that actually brings us to natural natural language processing A part of ai a domain of ai that's heavily influencing computer vision Your chat gpk makes use of this quite a lot This idea was introduced quite recently in the past five years. It's called a transformer And it's based on this idea called attention So attention attention tells us how different elements of a piece of data is correlated That data could be a sequence of letters or work, you know a sentence for example So so in this case high i am a short sentence that's a sentence A sequence of tokens rather each token being high i am a short sentence so on that that then that then forms a numerical representation That's that's processed by the model Now could we understand whether or not these tokens are related to these other these components of the sentence the words For example, if we look at the the words in the bottom right hand corner a plain banks a grassy bank the bank of england They are the same word the same tokens But they mean various very different things in the context that they are used in right a plain banks refers to The context of aircraft An aircraft an airplane, you know, it could be related to an airport Uh the bank of england that's that's a finance financial context, right? So could we map these contexts that understand the the the use of the words in the sentence By understanding that it's very much possible for us to do things like classification or prediction We could predict the next word in the sequence. What's the next sentence that should follow after high? I am a short sentence. Maybe it's something like nice to meet you Right and that's essentially what chat gpt is doing You take your input sentence the stuff that you put in chat gpt draws these relationships It understands the context of the words through a transformer through the concept of attention It actually understands through attention the important words in your paragraph It learns to ignore the unnecessary stuff and it provides a prediction output that prediction output to you Looks like a very nicely response a very nice response Through your question It looks like an answer, but it's actually predicting the next sentence. What does the user want from this input? That can also be applied to images if we think about this feature space where we draw relationships with each other It's very much possible for us to put in images and correlate them to these words as well Right a picture of an aeroplane could very much go into the top right hand corner, you know for that image below A picture of a bank could go into that finance group So a transformer actually allows us to do this what it does is it takes that image and it breaks it down into individual patches And it processes processes them just as you would For a sentence so it's actually treating an image like a sentence like a story The image is the the paper for this is actually called an image is word 16 by 16 words It's actually a very powerful paper. It was introduced to to to to sort of treat images like sequences like sentences Um, and this vision transformer paper the the citation is below if you want to take a look later It actually projects these images. It understands the relationships between them But of course the way the model is trained again is dependent on some sort of output So was this image an image of a bird? Was it an image of a ball? Was it an image of a car? By updating the class by predicting the class it's able to understand the relationships the the mathematical weights that it should apply to the data in order to perform these associations And the usefulness of this in the context of medical imaging has to do with the use of attention Attention allows the computer to pay attention to or or rather to look closely and ignore areas of the image That are not really relevant to the classification In the case of a golden retriever the one with the golden retriever for example The picture of the dog is actually really a really small part of the image It's actually picking up from under the you know from the car So if you look at that image very closely You know the car the seats All those features are things that you don't want to pay attention to all you care about is the dog And if we want to make that classification the rest of the stuff in the image could get in the way of the model understanding the output And because of that we apply attention and attention helps us understand What's not important in the image and helps us focus on the important features for classification In the area of imaging There are you know, whenever you talk to a clinician, they will they will always tell you there are certain parts of the image that I I ignore I'm not really interested in Because these parts of the image will confound my dog by my diagnosis They will confuse me right by looking at these parts of the image like muscle tissue like white space like cells Maybe there's an ink stain on the image You know, maybe there's an artifact something like a scratch that was done during surgery that I'm not interested in And we can actually train transformer models to ignore those parts of the image Just as we can train transformer models to ignore parts of a sentence that we aren't interested in That don't mean anything at all to the context and the meaning of that sentence But one level up and this is where medical imaging is going in the future One level up The outputs from different transformer models. Remember how I said we could put a picture of a bank And into a feature space that talks about things like Finance Bank Interest Right, we could perform that mapping if we took a picture of a plane We could correlate it to picture to to words like airport Right and and so that that embedding that feature space allows us to do a lot with medical images That this previous annotation, you know, um where pathologies actually Draw draw lines around an image to indicate where a tumor is that's not really natural for clinicians It's more natural for them to write about the diagnosis What did I see in the images? Um, I saw this uh cancer the cells look very concentrated It's a very simple as simple example Of course the examples of the diagnosis are written Uh are on the top left hand corner of the images But that association allows us to then train models that are capable of Outputting this these diagnosis. That means if I put an image inside I'm capable of giving a text representation of the diagnosis A sentence that explains what the diagnosis is One example that this is being used in of course is radiological imaging And in really radiological imaging, it's really remarkable that a model is capable of learning Text diagnosis and it's capable of then looking at new images and providing a text output of a diagnosis So this is a really really really interesting example of where image image captioning That's what this is called where image captioning is being used in medical imaging This same correlation allows us to take different images from different modalities MRI CT histopathology To put them together and perform a more holistic diagnosis of diseases And that understanding of diseases that development of that understanding Will definitely help us in improving diagnosis for patients and I think that Yeah, from that base understanding of where neural networks come from that mathematical representation of images the mathematical manipulation of images of data in order for us to get a diagnosis and the learning of those Multiplications those variables those weights that contribute to some feature representation of images of text You know, those things are really powerful and not just helping us perform things like diagnosis in the medical imaging domain It's also helpful for us to obtain a numerical representation and understanding of disease You know, what what is the heterogeneity of the disease? How does this gene correlate to a patient's chance of getting cancer? How does this gene help us understand whether or not this drug Is going to be useful for the patient in reducing the tumor size By doing these correlations, I think medical imaging really has a chance to influence the development of Drugs in disease of treatments like radiotherapy In customized diagnosis for patients for example And I really think that AI as a statistic as a really just a powerful mathematical statistical method Is capable of helping us in the medical imaging domain improve outcomes for patients And so that's really all the all there is to it. That's that's why I'm interested with it And I hope that this presentation has sort of given you an insight Into what these techniques are where they're useful where they're going and what are sort of the considerations involved Um, yeah, and that's that Thank you very much


Now I feel like I'm closer to understanding how and why he is the person he is, kind of how people come to be this way. Part of it is just them. Part of it, like Holly said, this is a choice. This is a choice. People choose to be gross and awful sometimes, but part of it is the culture. In Illimation's most recent video, she said she had a better understanding of what her abusive ex-boyfriend Harris was thinking and doing and all of that. So in this video, we're going to see what lessons we can learn from this to help you overcome some of your previous trauma. What is up, everybody? This is Chris from the Rewired Soul, where we talk about the problem, but focus on the solution. And if you're new to my channel, my channel is all about mental health. And what I like to do is pull different topics from the YouTube community to try to teach you how to improve your mental and emotional well-being. So if you're into that, be sure to subscribe and ring that notification bell because I make a ton of videos and I've done recaps of all of Illimation's videos. And guess what? I have some very exciting news. Yesterday, I was tagged in a tweet by none other than herself, but Illi. And it says, please check out this whole series along with more of the Rewired Soul's content. He explains things so much further than how I could have in my videos. Heck, he's hitting on things. I didn't even know we're there. Amazing work. Thank you so much. Thank you, Illi. She's awesome. We actually talked a little bit in DM too. But yeah, it's so great like when, you know, the YouTuber who's referenced in my video, like, I always say like, I'm not making videos for the YouTubers. I never expect them to see it. I just use some of the things that are going on in their videos as context for you. So you have a better understanding of what you might be dealing with as well. But when somebody like Illi actually sees it and they like it and appreciate it and think it like, hmm, that is great. That helps validate what I'm doing a little bit of my imposter syndrome. All right. But anyways, Illi's tweet leads me into my next subject. So one of the videos I did, the truth about Illimation, it's been blowing up. It has like over 120,000 views as a time of recording those videos. And something that is absolutely amazing to me. Absolutely amazing is how many people in the comments are coming in there and thinking that I am Harris. Like, did any of you see this part of Illi's video? This kid was thinner than a string bean. Mother Trucker had a BMI of two, maybe. And look, I talk a lot of for someone who can barely hold a gallon of milk.  Like, I don't know if you got the memo, but I'm a little bit on the chunky side. All right. Now I feel like I'm closer to understanding how and why he is the person he is, kind of how people come to be this way. So yeah, based on that last clip, I want to talk about this because this is something that we need to discuss when it comes to healing our mental health, overcoming the past, especially when it comes to trauma. But this is also going to be very helpful for any of you who struggle with anxiety. All right. So our brains have this natural need, right? This natural need to create a story to help us understand the world. That is why our brain is going a million miles a minute. So there's a part of your brain towards the back up back here. It's called the posterior cingulate cortex. All right. This is when I tell you guys about being selfish and self centered, this part of the brain is responsible for that. But it also creates the stories about your life, right? It helps us make sense. And it links up with your memories as well as your, you know, fight, fight a freeze response and all of that. So we have this natural need to want to understand what happened to us. So when Ily's talking about how she had a better understanding of this, this is very therapeutic. All right. Now I'm going to talk about the other side of this later in the video. So make sure that you stay tuned. But yeah, this is one of the reasons why I tell you guys to journal, write stuff down. Okay. Like journaling is extremely, extremely important, whether it's just like a mood journal or just keeping track of what happened in your day. Like you need to be doing this. Like it will help your brain begin to calm down, chill and proceed with life when you start to have a better story of what happened and you make sense of this. But in Ily's situation, talking with Holly was very helpful to her, right? Like having somebody who went through a similar experience, you know, and with this, it was the exact same abuser, right? But just this is why I talk about being part of a support group and talking to other people. Like when you talk to other people who have been through this before, it will also help your brain process these things and make more sense of it. But lastly, one of the best things that you can do is work with a freaking therapist. All right. This is why we call it processing when you're working with a therapist because it's helping your brain process these things that you went through. It helps you make sense of these things because when our brain doesn't understand what happened and can't have any logical sense of the scenario, it's all scattered and just think about just your brain just bouncing all over the place. So when you have some kind of, you know, a story to go behind it, this will help you overcome your trauma. Now, part of that therapeutic process is learning how to let it go. Okay. So that is another huge thing and working with somebody can definitely help you out with that. But the other thing I want to talk about too before I move on to the next topic is this is extremely important for all of you parents out there. All right. There's an excellent book that I'll link down in the description and it's called the Yes Brain Child. Okay. From one of my favorite neuroscientists, Dr. Daniel Siegel. But part of it is they talk about when your child is very upset or anxious or scared or worried or whatever it is, help them create a story. This is one of the reasons why kids freak out because when they have such young adaptive minds, like they're trying to make sense of this world. So when you tell kids to just like forget about it or leave it alone or whatever, like this is actually messing up their mental health and it can screw them up later on as an adult too.  Okay. Because those of you who are parents out there, kids perceive things oftentimes in a much different way than they actually are. Heck, if I'm being honest, a lot of adults do that. One of my favorite responses to the comments is that's an interesting perspective, right?   So that's why it's our job as parents to sit down with our kids, ask them questions, say, okay, what happened? How did this happen? How did it make you feel? But again, this entire book, the Yes Brain Child is an excellent book. You should definitely check it out if you're a parent. So the last thing I want to talk about with this is trying to figure out what this other person was thinking. Now in the context of Elise's situation, you know, it's difficult. It is difficult. I'm going to talk about that in a second. But I just want to go on a topic that I know all of you can relate to. So many of us drive ourselves insane trying to figure out what other people are thinking, right? Like for example, if you have social anxiety, that is something that's driving you nuts, trying to figure out like do they like me?  Do I think I'm attractive? All these things that run through our heads a million miles a minute. But the other thing that really messes us up is when we're trying to ask ourselves what their motives, what were they doing behind this? Like I can definitely relate to that. I would sit and obsess sometimes I couldn't sleep at night trying to figure out what is this person thinking, right? What was going through their head? How do they feel about me? What do they think about me, right? And like I'm saying, that will drive you absolutely bananas. So like if you're in an Elise situation, I think the best thing you could do is be mindful. Like in every situation she's saying it helped her get a little bit more closure to understand a little bit more and all that. But if you find that it's causing more damage in your life, more chaos, more anxiety, more stress, bringing up past traumas, like what you need to do is start working on letting it go and understanding that you're never going to be able to read people's minds. Now, if I were to guess just being in the mental health field, learning a lot about the brain and trauma and all these other things, if I were to go inside the mind of Harris and assume things, right? Not knowing the guy, never meeting him.  Like there's a few things that are usually going on. One is that other person is dealing with their own mental illness. Now, again, this is in no way, in no way shape or form and excuse for any behavior. But I want you to understand there's a reason like you got to think about it. Some of the sickest, most twisted people in the world, they're struggling with some kind of mental illness. Like that's just the way it is. Now, the other thing is, is that many, many abusers are also the victims of abuse as well, right? But the last thing is, and in this scenario specifically with elimination, because I see this so much with young people like you guys, young jerks, young abusers turn into old abusers. Now, kids turn into who they're going to become. A lot of it is based on parenting. So like, we're always looking at who to blame, and I don't like even using that word blame, but so much of this comes down to parenting. Like when I'm watching Illy Mation's videos, and like when I was seeing this and seeing how young Illy was, and seeing that this guy Harris like lived with his family, I'm like, how did his family not seeing it see any red flags that this kid might be messed up, right? Like, there are definitely signs like this kind of thing doesn't go unnoticed. This is why I make so many videos and like I made a video about Tanimojo, but like the thing I'm trying to convey to all of you is don't be surprised. Like don't be surprised when certain things happen. Like I know parents who are clearly seeing the signs that their kid is probably going to turn up into a messed up teenager, which is then going to turn up into a messed up adult, but they're in complete denial of it, right? So like as parents, as parents, we have to be monitoring this stuff. We have to know what are healthy and unhealthy behaviors from our children, right? Because like there, I can't, I can't imagine in a world in any universe where my son was doing these types of things and I would not have any type of inkling that he's a little bit messed up in the head, right? Like when I look at somebody like Harris, like, and I see these like, you know, the extreme emotions, the threats of suicide and all these other things, like there's no way that that is just limited to speaking with, you know, women, right? I'm sure his family has seen these things too. So this is one of the reasons why I have this channel is to increase awareness and I get some like pushback on some of my videos, even though I always say that I'm not diagnosing anybody, I just want to teach you what symptoms look like. Like it is important that we know what certain symptoms look like so we can either A, get help for ourselves or B, help someone we know get help so they don't end up hurting themselves or others, all right? But anyways, anyways, I want to hear from all of you has, has processing things, whether it's journaling, working with a therapist, has any of that and helping create a story, has that helped you overcome some of the issues from your past. Let's have a conversation down below, all right? But anyways, that's all I got for this video. If you liked this video, please give it a thumbs up. If you're new, make sure you subscribe and ring that notification bell. And a huge thank you to everybody supporting the channel over on Patreon. We got some more new names up there and I got some really exciting stuff coming up for Patreon in the new year. So make sure that you sign up down below if you haven't yet, all right? Thanks again for watching. I'll see you next time.


Welcome to Toffy TV. It is the Everton News Daily. This video is sponsored by Unibet. Get over to the Unibet blog page. Check out Billy's blog there for Toffy TV talking about what Everton players we'd like in before a five o'clock deadline tomorrow. There's some other great writers on there as well writing all the transfer news to get on that blog. Check the link in the description below. Everton has still thought to be making another bid for Wilfred Zahar. It's been a mad day of stories. We had the story this morning that Crystal Palace wanted 80 million. Then reports of another bid rejected, but that's been refuted by some journalists, others are still going with it. Then there was an Everton bid for another player then Zahar handed the transfer request in and ultimately we're no clearer on the situation. Crystal Palace don't want to sell, fair enough. Everton still want the player. The player apparently wants to move to Everton. Other teams are reportedly circling. I don't know whether that's just sky trying to create a deadline day, but it seems according to some journalists that Everton will have to pay 80 million pounds to get Zahar out of Crystal Palace and it's whether or not Everton are going to pay 80 million pounds for the player. Quite clearly like him. Is there better value out there in the market? There is, but Wilfred Zahar is a proven Premier League player who would improve Everton's team and could be the difference between European football and not. So it remains to be seen that Everton will go back in with an acceptable bid for Wilfred Zahar. It's a watch this space as we count down to the five o'clock deadline tomorrow night. The player I was alluding to then in the Zahar conversation was of course Alex O'Wobee. Everton tabled a £30 million bid for the Arsenal player today. It was rejected by Arsenal, but again reports and reporters around Arsenal saying that a bid of around £40 million would be acceptable to Arsenal and O'Wobee would move to Gouldersham Park where it remains to be seen whether Everton go back at the moment he haven't for the player. I think it would be a good addition to Everton. He's fast, strong, got good feet, he's unpredictable, he's frustrating but all of those kind of players are.  Everton also made an attempt to take Chris Smalling on loan today from Manchester United that was rejected, the 29-year-old centre-back has dropped down the pechenorder with Harry Maguire's arrival at Old Trafford. Everton wanting an experienced centre-back to come in having lost of course Philip Jagielke and Kate Zuma. The Zuma deer looks dead in the water with David Louise looking like he could possibly join Arsenal tomorrow so there's no chance of getting Kate Zuma. But Everton have apparently turned their attention for Kaio Tamori and are chatting to Chelsea rather about a loan move for the young centre-back. So we'll have to see whether or not Everton get that one over the line. His highly rated play for the England under 21s was with Frank Lampard last season at Derby. Lampard waxed Liverpool about him, said he's got everything to get to the very, very top. And we'll have to see whether Everton do get Tamori on loan. James McCarthy is expected to complete his move to Crystal Palace today. Fe ranging between £3 million and £8.5 million has been quoted. We don't know, but one thing is, is wages will be welcome off the Everton wage bill. McCarthy on around £75,000 a week. The Blues will be happy to get that off their wage bill. It's been unfortunate for James McCarthy. He's only played about 16 games in three seasons. He's had some terrible time with injuries, but you know, was an excellent player when he was uninjured for Everton Football Club and I'm sure he'll do Crystal Palace a good job down there in South London. Everton are waiting to confirm Jibril Siddibi's move from Monaco. It's expected to be announced any time soon. It's done his medical today and Everton are just waiting on the announcement now from the club that he is an Everton player for the season. And finally, Matty Pennington is in talks about a loan move to Hull City. The defender has spent time, obviously, on loan with Ipswich Town. I see he picked up an injury before the end of the season and is now looking likely to follow Josh Bowler across to Hull City. We'll wait to see whether that one is finalised before tomorrow. And that is it for Trophy TV News Daily. Make sure you subscribe if you haven't already. Get over, join us on page and give this video a like. And don't forget we'll be live tomorrow with a Transfer Deadline Day special. Make sure you check that out. See you later.