Hello my name is Aaron Lee. It's a real privilege and an honor to give this Grand Rounds to you and I have to apologize for giving it virtually. There was a misunderstanding on my part on when I needed to be there in Salt Lake City and then by the time I realized it was it was too difficult to get call coverage for Tuesday night. So again I apologize for giving this virtually and if all is going well I should be arriving into Salt Lake City as you listen to this Grand Rounds. These are my financial disclosures. I'm going to break down this talk into four sections. The first is an introduction to some of the concepts that I'll be talking about and then the second part will be the beginnings of how we got started in this area of artificial intelligence and deep learning. The third will be about what I think is a very important study that we did around autonomous AI and then finally I want to end by talking about future directions. So I hope all of you know that we are firmly living in this era of big data and machine learning. We as human society are living in a very different time than we were even 20 years ago and what I mean by that is that we are living in a time where the information that is flowing all around us every day is being captured, harnessed, and then used to train and manipulate very powerful machine learning algorithms and these algorithms are indirectly or directly influencing things that we see and perceive in the world and it is really the first time that human society is being so heavily influenced by computer algorithms. And I always like to start these talks also by disambiguating some of these terms. AI, machine learning, and deep learning are unfortunately used interchangeably in the news media. They are used in a way where they appear to be synonymous but they actually have very formal definitions. So the field of artificial intelligence is actually very, very old. It has been around since really the dawn of modern computing and it is a field that describes the idea of intelligence being formed by something man-made. The field within artificial intelligence is machine learning and then there's a subfield within that called deep learning and the field of deep learning is relatively new. So what is deep learning? Well, to understand that you sort of have to understand what an artificial neural network is and this is these class of machine learning algorithms that were very popular in the 60s and 70s but at the time a traditional artificial neural network could not really be extended to more than three layers. So in my opinion there's four advances that led to the birth of what we call deep learning today. First it was a realization that computer graphics cards could be used to vastly accelerate linear algebra operations. It turned out that the mathematics behind rotating and rendering polygons in three-dimensional space for computer games was actually very similar to the math that was used to train deep learning models and so all of a sudden people had in their computers a mathematical co-processor that could be harnessed to use and train these very large deep learning models. The second was the use of these filters known as convolutional filters that could exploit the local coherence between pixels. These filters actually existed since about 1980s but they could never be implemented because they were too computationally complex and so when the GPUs were available to be used for accelerating these operations convolutional filters actually became feasible to implement computationally. The third was the use of non-linear activation functions and the final one was that our computer hardware and architecture had evolved to a point where the storage of large data sets became possible and so you had both the computational means and the capacity to store and process large data sets that could be used to train deep learning models and that all led to this very exciting time that we live in today where as we increase the size of the deep learning models and we increase the amount of data that they are fed the performance just seems to increase and increase and this is one of the reasons why even today every hour there's a new article about chat GBT or the large language models doing something that had never been possible before and this gets to you know this time that we live in which people are calling the fourth industrial revolution where now these AI methods are powering pretty much everything that we do every email that we write every search engine query that we type in there are very very powerful machine learning algorithms that are driving the content in terms of what we read and we see and on the biological side we've reached a point where in molecular biology and the omics world are able to generate huge data sets that could be used so we have this convergence of having oceans of data available to us and the AI methods coupled to analyze them so I want to walk you through our journey into how we got into deep learning and this goes back to when I joined the University of Washington one of the first things I did was I extracted all the OCT imaging that was available to us at our hospital and that was akin to about 5.5 million OCT B scan images on about 16,000 patients for more than one decade of time and then from the EHR I had access to all the clinical variables I could match up each OCT to the visual acuity the diagnosis whether they had laser or intravitual injection and the OCT expert interpretation and for a while I was sitting on this data set not really knowing what to do with it because at the time there weren't really scalable algorithms that could be used to collect and sift information from these OCT images so one of my friends told me that I should try this thing called deep learning and I said oh you know that's a great idea but I really don't think it's going to work I think it's just a fad it probably just passed and he said no no no you should really try it let me put you in touch with some of my friends at NVIDIA and NVIDIA I told NVIDIA about my data set and they said you know that sounds amazing why haven't you tried deep learning yet and I said well I don't have one of your fancy graphics cards and two days later they had shipped a one of their top end graphics cards to our lab from Taiwan and we constructed our first deep learning experiment and this was in my mind sort of the easiest problem to get started with and it was whether deep learning could distinguish between normal OCT images and those from AMD we constructed a data set of about 100,000 images and we trained this model at the time which was that was state-of-the-art called VGG 16 and we got these results so about a week after getting that graphics cards from Taiwan we were able to achieve these sorts of metrics and I was blown away to be perfectly honest with you I really did not believe or think that deep learning was capable of doing this and I thought I had made some sort of embarrassing you know beginner mistake in our experimental setup and I was very nervous about publishing this and so this sort of gets at the problem of these models being black boxes even today we struggle with this problem of trying to understand why or how these deep learning models are working I dug around the computer vision literature at the time and I tried to find some sort of way to understand what what the model was doing how he was able to achieve these sort of incredible you know AUROC's and sensitivities and specificities and I found this visualization method that even you know today I still use because I think it's very intuitive the way it works is if I give you this picture and I ask you do you think there's a ball in this picture you would say with a hundred percent certainty that yes there is a ball in this picture if I then cover up a very small part of the picture and I ask you again is there a ball you still say yes but if I keep moving this box around and at every possible pixel position in the in the picture I keep asking you is there a ball in this picture eventually the box ends up here and I will ask you is there a ball in this picture and you'll say I'm not sure anymore and so that's what we did we took OCT images that the model had not been trained on and we systematically occluded every possible position in the B scan and we watched this happen watch what happened to the probability of the model calling this macular degeneration and if the probability dropped a lot then we would highlight that region that pixel position with with with you know a high value and when we and so when we did that on these three B scan images we were able to generate these heat maps that showed that the model was actually looking at areas that were clinically relevant and that made me feel a million times better about the model that we had trained that it wasn't some silly mistake that I had made but the model had actually learned to try and distinguish the the relevant features between what was normal OCT B scans and what were abnormal OCT B scans. From there we went on to our second study and this was a study that we published in Biomedical Optics Express that has to do with the segmentation of intra retinal fluid on OCT scans and so here's an example of the deep learning model where on the left you see the original B scan images and on the right you see the areas actually the the confidence of the areas that are that the model called intra retinal fluid and at first you know I thought this these results are really exciting and so I went and showed you know some of the computer vision folks and said they said oh Erin this is great but this is actually a very simple problem all you have to do is find the ILM which you know there's tons of good methods of doing that and then you find this bright band here and everything in between that's dark is intra retinal fluid but if you follow that heuristic you know of course the shadow underneath vessels could erroneously be labeled as intra retinal fluid whereas in fact the deep learning models are actually able to not be fooled by these dark areas that are artifacts of the shadows cast by the vessels and instead correctly label the areas of intra retinal fluid. In the paper we showed that the model was as good as clinician to clinician variants but more importantly there was this video that was hard honestly to put into the paper and what we did here is we really slowed down the rate of the deep learning models learning to do this task and so if you remember this was this B scan that I just showed you that has this area of the of the area shadow underneath the vessel and if you watch this model learn to do this it will at first call this area underneath the vessel intra retinal fluid but as it is fed more and more examples and it's trained over a longer period of time you can see it getting better and better and better and you know it learns that even sub retinal fluid is not intra retinal fluid or over time but there's something that happened that was sort of hair raising at the beginning of this video now play play it again for you and and then it'll pause so here right when it's starting to you know go on this exponential growth in learning it it it actually learned to do something different than what we were trying to teach it to do if you look the areas that it's highlighting is not the intra retinal fluid at all but actually it's it's it learned the the organization of the retina it realized in order for it to learn where the intra retinal fluid was it had to understand these anatomic boundaries first and what I find amazing about that is that the model the deep learning model it essentially had identified a sub problem that it had to solve on its own in order to solve the bigger task of of intra retinal fluid and I kind of view this as sort of an example of emergent AI behavior so you know what do we take away from these first two experiments we learned that you know machine learning is always limited by the ground truth so what I mean by that is that if clinicians disagree on on whether you know a certain OCT scan has CSR versus you know wet AMD then the model will always be confused it will never do better than the average human confusion around the label of for the ground truth but it seems to also have this incredible potential like you saw in the video it it has this incredible flexibility to work with me you know different imaging types and and also identify sub problems that it needs to solve all on its own and you know our lab and others started to think about how you can push AI and deep learning to do more and that sort of led to you know the many many different experiments and papers that we published afterwards that showed everything from being able to predict in OCT a scan from regular standard structural OCT or you know predict you know outflow facility of the terbecular mesh work from the Myers of a Goldman Tynometer and so it's really has you know inspired the field to do quite a bit but I would love to spend this next section talking about a very important topic in our field and that has to do with autonomous AI and what I mean by autonomous AI is sort of if you think about the self-driving car industry there's sort of level zero or one level you know algorithms that help us drive our cars a little bit safer but the driver is doing everything all the way to level five self-driving cars that you get in the car and you can sleep or take a nap and it'll be fine because the car is doing everything completely autonomously and those same levels of you know tesla car automation or autonomous car self-driving cars exists in the medical domain and they're broken out sort of in these sort of ways where you know level one is sort of data presentation and level two is clinical decision support and those are the assistive AI algorithms and the autonomous AI algorithms are level three and level four level five where full automation is basically you know AI algorithms that are being used in you know populations where there's no human clinician in the loop at all and they're making you know a fully autonomous medical decisions and so what's kind of amazing to me and to many others in the medical space is that somehow in ophthalmology we went from level zero all the way to level five and we have very few AI algorithms in between whereas in the field of radiology it's the reverse they have many many AI algorithms that are up to level two but almost no algorithms that go beyond level three and so that led you know Eric Topol who's sort of a famous medical technologist to tweet this out about how you know most people think radiology is leading the AI movement but it's really ophthalmology that is leading the AI movement and that's because of what's happened with in the diabetic retinopathy space in the diabetic retinopathy screening space there are now today I believe three or four FDA approved algorithms that are fully autonomous and they are making clinical medical decisions at the population level with no clinical oversight so you know I think there's something known as this Gartner hype curve and it's really important to you know think about you know where we are on this hype curve it's really unclear to me whether we have reached the peak of inflated expectations or not with regard to AI and medicine if anything this this peak seems to be growing ever higher so we may still be on this ascending limb here but it's very important in this time space when they these algorithms are being deployed to understand you know where where the real plateau of productivity is so we embarked on a study a few years ago to compare the performance of seven AI models diabetic retinopathy screening models in a real-world screening setting and so the reason why we did this is because there were many many papers have been coming out by you know these the companies the commercial entities in this space claiming that they had you know amazing performance and even yeah even if you trusted them that they had you know done everything ethically and they had no no influence on the on the on the final test performance of their models you still had no way of comparing the the algorithms a versus b in a head-to-head fashion so we wanted to be able to compare them in a in a with equal sort of on an equal playing field so what we did is we reached out at the time to all the companies that we could find that were working in this space and we after discussing with them all five ultimately agreed to be part of our study we invited every company to submit up to two different AI models to for our study what we then did is we went to the VA tele-retinal screening system and from two different sites one in Seattle one in Atlanta we extracted all the imaging that was available from the tele-retinal screening system and we combined them together to create a full data set of about 311,000 images that had all been labeled by the original VA tele-retinal grader during routine clinical care in a subset of about 7,000 images we went through an arbitrated three retina specialist grading arbitration to arrive at a final final clinical grade for each of these diabetic rin hoppy images and this allowed you know basically us to compare the seven different AI models versus the original tele-retinal grader in against this arbitration set so these are sort of the headline results of our study we had these seven different algorithms and we showed that all of all the algorithms exhibited you know high overall negative predictive value and in a screening context this is absolutely what you want you absolutely want to make sure that your your negative predictive value is is as high as possible unfortunately most of the algorithms showed a low positive predictive value meaning that there were a lot of false positives being referred in if this was actually deployed you know in the clinic in my opinion you know this is one of the most important results from our project where we again use the arbitrated set to compare directly the VA tele-retinal grader in a pairwise fashion to each of the seven different algorithms and what we were very pleased to show was that the VA tele-retinal graders are actually doing an amazing job they had a hundred percent sensitive sensitivity for moderate or higher of npdr and then algorithms e f and g were statistically similar to the VA grader for moderate npdr or higher meaning that they would would not behaved almost statistically indistinguishable you obviously cannot do better than the VA grader since they had a hundred percent sensitivity but being indistinguishable to the VA grader in a large dataset with a pairwise fashion means basically that they made very few mistakes and so in my opinion these algorithms e f and g are safe for deployment in the VA tele-retinal screening system you know some of the limitations of this obviously it only really applies to the context of of the VA we did notice that the dilation it was important to reduce the rate of ungradable images and the algorithms varied very widely despite having regulatory approval or being clinically deployed somewhere in the world and this gets to this issue that I think it's actually very important to assess these models with an external independent validation especially if you're planning to deploy them and pull away the human clinician all together from from the medical loop so I you know I do believe that these models are very powerful and are ready for deployment but it makes sense to make sure in a small subset that they work in your clinical informatics system at your hospital one of the criticisms of the diabetic retinopathy AI models is that they are doing an amazing job for diabetic retinopathy but there's a lot more that is covered in a retinal color color fundus photograph of the retina than diabetic retinopathy and when you're using a tele-retinal screening system for diabetic disease the the readers the human readers are actually also screening for other conditions other incidental findings that would require you know referral and so there's a follow-up study that we're doing in partnership with the CDC there were two years during for the NHANES study where they had collected retinal images and they all of them were graded by the Wisconsin Reading Center at that time and the goal of this study is to do a more comprehensive screening for not just diabetic retinopathy but also AMD and glaucoma and to do this nine companies have agreed to send their models for evaluation and that means shipping a workstation that can work without the internet and being only connected to power to the CDC RDC where you know I'm not allowed to even bring in a cell phone to the to the room to do this study so this study is ongoing and we hope to be able to communicate some of the findings of this later on this year so I do want to spend a little bit of time talking about the future directions and in particular one study that we are starting up at University of Washington that I hope will you know be revolutionary for the field so if you take a step back and you ask yourself you know what are the datasets that are ideal for deep learning whenever I give these talks invariably people come up to me afterwards and they're excited because you know they have a data set that they've been collecting and working on for their for many years of their life and they think deep learning might be you know the perfect solution to their problems and so when I start talking to them about their data set I in my mind I'm constructing this graph where the number of measurements are on the y-axis and the number of subjects are on the x-axis and almost always the data sets I hear about are in this quadrant of the of the Cartesian coordinate plane and it's you know a couple hundred or even you know 500 patients where they have done a ton of different different different measurements on them and unfortunately in machine learning there's something known as the curse of dimensionality and this makes it very hard actually to do deep learning research with what you really want is almost the reverse of this you want a lot a data set where there's a lot of independent samples and relatively few key measurements of course if we had infinite time and if infinite money we'd all be living in the upper right right quadrant but but reality often precludes that from occurring so you know the data sets that characteristics are that one that it has to be large you want diverse pathology captured in your data set you ideally want your data set to be balanced for sex race and ethnicity to make sure that your AI models are not biased in some way for for or against any particular sex race or ethnicity and as I was explaining earlier the vast majority of deep learning advances have occurred in the field where either images or waveforms or or language is used and in those three sort of domains there's local spatial coherence that the models can exploit. One of the most successful data sets for doing machine learning and deep learning came from an effort in the United Kingdom called the UK Biobank and the UK Biobank was started in you know 2006 to 2010 where you know approximately half a million people they had asked them to come in and they started to collect all sorts of data over the years and it has really generated one of the most amazing resources for doing data science today. It's been a tremendous success 15 petabytes of data have been generated there are 2000 ongoing research projects around the world 1400 papers have been published and 200 of them have been published in the Nature Family journals so really really impactful and really the field of medicine took a substantial step forward because of the UK Biobank. One of the seminal pieces of work in our field came from the Google group where they used the UK Biobank and they showed that using from a color fundus photograph they could predict the age, the sex, smoking status, hemoglobin a1c, body mass index, systolic blood pressure and diastolic blood pressure all from a color fundus photograph and later on they showed even that they could predict you know the hemoglobin concentration and therefore the anemia status from a color fundus photograph so really amazing work and it sort of spurred this idea that you can you learn so much about the human body through the eye and the eye became sort of a center point for doing deep learning in the field of medicine because the images were easy to obtain they were they did not require radiation they were required instruments that were you know on the scale of 10 or attended to $20,000 but there's a couple really big problems with the UK Biobank and there is this is not really a criticism of the UK Biobank effort but an unfortunate reality of how the UK Biobank was set up first to reach the number of half a million they had to do convenient sampling where they set up recruitment centers in the United Kingdom and anybody who is willing to walk through doors they they took into the study and unfortunately that led to a situation where there was a healthy volunteer bias the people who are willing to walk through the doors were often much healthier than the normal participant in the in the UK population and unfortunately it was very heavily white British in fact it's about 95% white British and and to illustrate the healthy volunteer bias this graph I think speaks volumes where they showed the mortality rate for people in the UK population versus those in the UK Biobank and on average the being a UK Biobank participant your life expectancy was about 10 to 15 years longer than the same UK population that the participant came from so clearly you know everything that we've learned from the UK Biobank essentially has been on very healthy white British people the other sort of problem with the UK Biobank is that it has a fairly onerous path in order to get access to the data set when we try to get access to the data set at University of Washington it took us about three years before the data sets actually arrived on on our hard drives and so in the criteria that is known as fair or findable accessible interoperable and reusable I would say the UK Biobank does a fairly good job at being findable is decently interoperable and is obviously very reusable but is not as the most accessible data set in the world so that leads me to the the NIH so the NIH saw the success of the UK Biobank and similar efforts and they realized that there was a real need they realized that the US should set up something similar to the UK Biobank and they created the all of us program which is modeled in in spirit very similar to the UK Biobank and then they created the bridge to AI program the bridge to AI program is a common fund program and it's really charged with generating flagship AI data sets for medicine so Cecilia and I at UW during the pandemic marshaled our vision for this in about two months and we submitted about an 800 page application where we thought we had close to zero percent chance of of getting it somehow miraculously we ended up being one of the four data generation projects that was ultimately funded and the entire program has about 110 million dollars over four years to generate these flagships data sets for medicine so I would tell you a little bit about our project our project is called AI Ready and Equitable Atlas for Diabetes Insights and it is really targeting type two diabetes as a model disease for studying and generating a flagship AI Ready data set the ultimate goal of our data set is to create a multi-dimensional ethically sourced data set in diverse people for studying solutagenesis in type two diabetes if you're not sure what the term solutagenesis means hopefully this will make it a little bit more clear we spend a tremendous amount of time and and money trying to examine the concept of pathogenesis of or how you go from a healthy state to a disease state and we try to create interventions that either slow or halt the progression of going from healthy to diseased but we actually spend very little time thinking about the reverse of this and that's known as solutagenesis which is the the promotion of the of the human body back to a healthy state so to study this we assembled a very large team expanding across the United States the NIH broke up the British AI into these six different modules we have a teaming module being led by Mike Snyder who's the chair of the genetics department at Stanford as well as Sarah Singer who is a team science professor in the School of Medicine we have a skills and workforce module being led by Linda Zangwill and Sally Baxter at UCSD we have an ethics module being led by Alvin Lou and Kadicha Ferryman as well as Megan Collins from Hopkins and a team at UCSD we have a tools module being led by Bevesh Patel at Calmy as well as folks at OHSU and then a data module being led by Cynthia Alsley and Jerry McGuinn and you might notice that Joe Ricciata is at several different parts of this project and that's because he is leading an effort with American Indians on our project and finally we have a standards module that is trying to find and define representational standards for encoding the data set being led by myself as well as Christopher Shoot at JHU. So what we're trying to do is borrow an idea from the single cell RNA-seq to study soluted genesis and what happens in single cell RNA-seq is that there's a population of cells that is sampled and the transcriptome of each cell is characterized individually using single cell RNA-seq technology. Then if you apply machine learning and do a dimension reduction using something known as a UMAP you often can generate these graphs where one side of the graph might be these cells in an embryonic stage and then you have different branches as they go through a differentiation into a terminally differentiated state and they follow along these nice little lines where two dots close to each other are basically very similar to cells that are very similar to each other in transcriptome and two cells that are very far apart here are two cells that are very different in their transcriptomics. And so what we're proposing to do is collect a data set that is diverse and wide enough and captures different aspects of the human body in different states to allow the participants to hopefully fall along these manifolds where one axis will be pseudotime and then the other axis will be healthy versus diseased and then these manifolds will allow us to study both pathogenesis and pseudogenesis. In order for this technique to work it's very important that the data set is balanced with respect to the disease that you're interested in and so we broke up type two diabetes into these four bins. One is no diabetes at all. A second is lifestyle controlled or pre-diabetes and the third is oral medication controlled and the fourth is insulin and medication controlled. One of the big hopes and goals of our data set is for it to be balanced with respect to race and gender excuse me sex. And so what we hope to do is collect a data set where a thousand of the participants will be white, a thousand black, a thousand Asian American and a thousand Hispanic. Within that a thousand of them will be normal, a thousand lifestyle controlled, a thousand oral medication controlled and a thousand insulin dependent and within that they'll be one-to-one balancing of males and females. We'll have three data collection sites, one at University of Washington, one at UAB and one at UCSD and as I mentioned Joe is working with his team at Native Biodata and they'll be facilitating discussions with the Cheyenne River Sioux Tribe to collect a parallel data set that will be constructed in a similar fashion. The data set, the variables that we're actually collecting, well of course we'll collect a medical history, we'll perform vitals, we'll get a whole host of different blood work and urine, we'll perform surveys including social determinants of health and depression, we'll do a battery of cognitive function testing, we'll obtain a 12-liter EKG, we'll do different elements of visual function including visual acuity and low-contrast sensitivity, we'll obtain many different forms of ocular imaging including color fundus photography, OCT, OCTA and even hopefully FLIO. It's of note for these first three different imaging we're actually going to use devices from three different manufacturers in order to hopefully build a translational data set that will go in between these different devices. We're going to send them home with an Apple Watch to collect activity and heart rate and a continuous glucose monitor for 10 days and we've built a custom environmental sensor that can measure the pollution levels including PM1, PM2, PM4 and PM10, nitric dioxide and volatile organic compounds and the environmental sensor actually also has a spectrometer to measure the distribution of wavelength of lights that are present inside of people's homes to hopefully understand how that might affect circadian rhythm. And then we will bank a whole bunch of blood including plasma and serum and then we will store the blood in a way that will allow the generation of IPSC cells and organoids in the future. We hope to perform whole genome sequencing if the cost of whole genome sequencing comes down and most ambitiously we plan to open source our data set so we hope that we will be able to open source almost all the domains that you see here on the screen in a fashion where anybody with an internet connection anywhere in the world can click and download this data set and start using it for discovery. We have a lot of goals and challenges that we have to overcome in order for this project to be successful. We have a group of people who come from non-orthomology domains and have never worked together before and have a very tight timeline to deliver on this and so we'll need help with team science. We want to promote diverse perspectives, consider all the ethical consideration, engage the community. We need to build standards where they don't exist especially in some of the ophthalmic imaging domains. DICOM is ideally the format that all the imaging should be in. We are building a cloud-based platform for sharing and accessing the data and we want to train the next generation of AR researchers so that they can use this data set. So our project is really everything that you see in this sort of green box but what we hope that others will do as we release this data set is that they'll use this data set to do sort of hypothesis-generating type research or biomarker discovery and then if the manifolds are constructed then they can even do predictive AI modeling where they can study things like therapeutic targets and salutagenesis. We have this vision that any new data that's generated will be deposited back into the open repository. We have an ongoing engagement with American Indians and we hope that many other people will use this data set for their research and we really want to sort of accelerate the field of data science and medicine as a result. So this is our project. That's our website and that's our QR code. You're welcome to sort of follow along and if you're interested to be involved definitely let me know and I want to end on a very positive note that I sort of believe that the future of data science and eye care as well as you know vision science research is very bright mainly because of Michael Chang becoming the director of the National Institute and Michael Chang is known for many many different things but he is also an informaticist and well known for his work in bringing deep learning to retinopathy of prematurity and so I'm sort of hopeful that his vision for where the National Institute goes is towards tarnishing data science and I want to end by thanking all my funding sources as well as the members of my lab all of which you know none of this research would have been possible without them and I would be happy to take questions as my clean lands in Salt Lake City. Thank you very much and again I apologize for not being there in person. I will be on campus shortly if all goes well.


Hello everyone this is Amit Dan of amitdan.com. Today I'll have another episode of Chrome Browser Tips and Tricks video and I'll talk about how to clear cash, cookies and history in your Chrome browser. If this is your first time or a visit, thank you for your support on this channel. I'm always trying new and different ideas. If you're into thinking outside the box you may want to subscribe this channel. If you're looking for more tutorial videos there will be links in the description box below. So sometimes you need to clear your cash, cookies or history. The reason you want to do that just because of privacy or sometimes some websites just stop working if you have too much accumulation and clearing the cash or cookies or history might solve the problem. So today I'll show you how you can do that in three different ways and also these would end up at the same place but you know there are just three different ways to start with but at the end you're going to end up you know at the same section where you will have the option to clear cash, cookies and history. So the first step is if you take a look on your upper right corner so you're going to see three dots which is right here. Click on it and then you're looking for more tools scroll down a little bit and then here more tools and then if you choose clear browsing data so right here and then this is the section where you can control or clear your cash, cookies and history. So for example you see that this section is for cookies and this is for history and this is for cash. Now here this section time range it lets you control how long or how much further you want to go so you can just like choose which you want and if you'd like to choose even further then you can go to advanced option and there are more options right here but for most of us you can just you know do most of the part from here just by clicking what you're looking for and then click clear data and then that's going to do the job. So now let's go to second way. So the second way is going to be the same you're going to click on the upper right corner where the three dots are and then you're going to settings which is right here click on it and then here on the left you're going to see a panel and you're looking for privacy and security which is right here so click on it and now here you're going to be seeing the clear browsing data, clear history cookie and cash so click on it. So this is basically where we were before so it brings you to the same section but just a different way and here you can clear or delete your cash cookies and history. So now let's go to the third option which I would say is the most easiest option. So all you have to do is hit ctrl shift and delete. So let's see if it works ctrl shift delete and you can see that it's taking it to that section right away you don't have to go through any steps or anything else and from here you can just you know clear what you're looking for. Now this command which is ctrl shift delete would work with any browser not just Google Chrome but this is just a pro tip so if you use other browsers this should work as well. So that's how you can clear your cookies, cash and history in your Chrome browser. Thank you for watching this video there will be more videos coming up so I'll see you shortly thank you.


Hey guys, what is up, welcome to my channel. For today's video, I am so excited. I bought a few products specifically for this video, which is not something I normally do. With the exception of my reviews, I normally don't buy multiple products for one video because it's really expensive, especially from Sephora. But today we are going to go over the number one bestsellers at Sephora. Some of them I've tried before and I already own and the ones that I didn't own, I have purchased for today's video. So if you want to see my thoughts on all of these bestsellers and see me try out some new products as well, then just keep watching. Just so you know, the bestsellers at Sephora, they do kind of change daily. So we're going to go by what was number one on the day that I purchased these products. Like I know the eyeshadow palette isn't currently number one right now anymore. So things shift back and forth a little bit, but like I said, we're going after this one day. Okay, so I did my eyebrows first. I thought I was recording, but I wasn't, so you missed that. The number one bestselling eyebrow product at Sephora is the ABH Brow Wiz, which is not a surprise to me. I do think there are a lot of great brow pencils out there, but as far as high end, it's a tie between the ABH and the Benefit for me when it comes to the brands sold at Sephora. So not surprised, it's an amazing brow pencil. And it's been a staple of mine since I first got into makeup. So really happy I have that. I didn't have to buy a new eyebrow pencil. All right, let's go into face primers. So the number one bestseller was the Supergoo Unseen sunscreen. So I didn't have this and I am very, very bad about applying sunscreen. I know I work from home, I just don't think about it. So I was really excited to get to purchase this. I've heard a lot of good things about this product. And I don't necessarily know that it is a makeup primer, but we're going to count it as what it is. Oh, it's really, really oily, like, oh, okay. Is it supposed to be like that? Hold on, let me shake it up just in case. Cause that was really, really oily. It might be like that though. I don't know, be on the safe side. All right, let's see if it's, I guess. Okay, yeah, I guess it's supposed to look like that, but at least now it's more thick. It did need mixed a little bit. Ooh, it's weird. It feels really odd. I've never applied something like this to my skin. If you have dry skin, please moisturize before this cause this is not very moisturizing. Obviously it's to, you know, combat the sun, which is great. And I can see how this is good under makeup cause it feels very lightweight. Oh, it's so weird. This is a consistency that I cannot describe. But I guess if you have it, you know what it feels like. I'm really excited about this product honestly, just because I've been needing a sunscreen that I should start wearing every single day. And this feels really nice on the skin. Now it depends because my skin is picky. I break out easily from sunscreens. So we'll see about this, but it is 100% invisible. I will give it that. Okay, so let's move on to foundation. I did have the best selling foundation, which was the Good Old Essay, a lot of double wear. This has been a best seller for years as well. Looking at the list of the day that I had, there was the Fenty following it, which I don't like that one. The Armani, which is really great. And the Iliya, which I've been trying to push on you guys for like over a year now. So I'm really happy about the ones that are in the top. However, the Estee Lauder Double Wear is awesome. So I definitely know why it's the number one. It's probably my most recommended foundation. When I was doing bridal makeup, you know, a common question that I got asked a lot was what's a foundation that you would recommend for me? And I just think universally that double wear is a great one to recommend for something that's just versatile for a lot of events. You know, you can spread it out for a nice light coverage or you can really build it up for something more full for a more special event. And not only that, you guys, but it lasts forever. It's pretty darn bulletproof as a foundation. So I found that for the most part, people appreciate a multi-purpose foundation like this. And that is why it's currently Sephora's number one bestseller after all of this time. Now I have a video coming up where I talk about the Fenty foundation being in the bestsellers list though because I don't like it. So stay tuned for that video. It's almost like a commentary video on what's in the bestsellers, but really happy that this was the number one one because I already had it, so I didn't need to purchase it. And two, because it is legitimately a really fantastic foundation. So I'm gonna stop it here at about a light medium coverage. Let me put a little bit of extra coverage in the areas that I'm noticing some redness. And then we'll call it a day with this. And while this foundation doesn't impress me just on the initial application, it's the wear time where it really shines through for me. And also, you know, as the day goes on, it looks better and better. So that's why I really like this foundation. All right, let's move on to concealer. And it really was no surprise to me to see the NARS Radiant Creamy Concealer at number one. This is another concealer that's been a number one for years. Now, I was kind of hoping the Too Faced Born This Way would be number one. I'm shocked it's number four because it's my number one favorite concealer. I also have a couple others that are sold at Sephora that I like better than this. This for me, it's a good concealer. I use the shade Canal. It's a really good concealer, but I just feel like I have better. This is a bit drying for my under eyes. I find it's completely solid, you know? I don't have any complaints about it, but after all this time, I feel like some good concealers has come out since the time of the NARS Radiant Creamy Concealer. But this is still number one.  So it definitely deserves a best selling spot. I don't know about it being number one. It looks really good. It's not full full coverage, but it does give you some coverage. It's like great for everyday, but also for special occasions. Okay, let's talk powders. Now, I've literally never heard of this powder before, but it was number one. This is the KVD Beauty Lock It Finishing Powder. So I was like, okay, I guess I'll get it. I don't have it. The only thing I own from KVD Beauty are the liquid lipsticks, which I love. So I'm actually really excited about this powder because it's something that I normally would not have tried. So this is giving me a reason to try it. I just think it's weird that it's number one because I've never heard of it before. I've heard anybody ever talk about it, but I got the shade light. I was debating between this and the next shade up, but I figured I'd rather it be lighter for my skin tone so I can put it in the center of my face as opposed to being darker. So I'm using a Rapper number 19 brush and it says it's a finishing powder. So I don't think you're gonna get much coverage from it, but I'm just gonna put it in the center of my face, set my under eyes, let me lower my chair. Really hot. There we go. See how it looks on the forehead? I mean, it is kind of blurring a little bit. Something's not looking right. Like I'm looking very dry right here and I feel like the powder just emphasized that. Now part of that reason is my skin is dry. It's winter. But I feel like this powder just made my skin look very dry. So I'm gonna chill with the application of that and we're gonna put it to the side. But yeah, I don't think I'm a fan of this. I probably would like it better maybe in the summer or if my skin was in better condition, but I have other powders that wouldn't make my skin look like this. So it's not a big fan of that. Okay, let's move on to eyeshadow. And at the current time that I was on the best sellers list, Urban Decay, Naked Wild West was the number one best seller because it was new so people were buying it. I figured for sure I would have the eyeshadow palette that was the best seller, but I was wrong, wrong, wrong. I was thinking it would be maybe an ABH palette or I don't know, but yeah, I wasn't originally going to buy this palette but due to this video, I had to, which is great because I haven't been buying the Naked palettes that I've been releasing. This color story doesn't scream out to me. It looks kind of like any other palette on the market, honestly, just with a couple pops of blue, but whatever, we'll try it. I'll let you know what I think. And a lot of you guys have been asking me about this. So at the end of the day, I'm really happy that I ended up buying it so I could help you out. And by the way, the number one best selling eye primer was the Urban Decay primer potion, which is great because I already had it and I've been using this for years. I like Too Faced Shadow Insurance better than this, but this one is still really nice. And I feel like eye primers are coming back for a while. People tried to replace them with concealer and I think eye primers do a better job of oil control. I still always recommend a primer. I'm lazy though. I use a concealer a lot as my eye primer just added pure laziness, but if it's like a full blown day that I'm wearing makeup then I will go in with a primer. Lots of Urban Decay in this video. All right, here we go. I'm gonna start off with standoff. I'm gonna just kind of brush it underneath the eyebrow, line it up up there, give it a little bit of a natural highlight. It's kinda cool is there are some cool colors here so you can get a really cool toned look. I think I'm gonna do a little bit of a mixture of warm tone and cool tone. So we're using that nudie shade. We're just gonna blend it out. This is as basic as it gets for a transition color. And I haven't bought a Naked palette specifically since the Naked Heat came out. That is the last Naked palette that I bought. I have bought the Stone Vibes. So I bought a Urban Decay palette recently but I've been skipping out on the Naked palettes because I just, I'm so bored of them. I just need to come up with a new layout. Just drop the Naked, you know? These palettes aren't Naked anymore. They aren't nude and natural and wearable. So Urban Decay needs to move on but that being said, people are clearly still buying it. So I'm the one that's wrong. I'm gonna put a little bit of that along the lower lash line as well. It's an amazing how you can have creasy under eyes. Like you'll see my under eye wrinkles but when you put just a little bit of shadow down here it helps diminish the look of those wrinkles. We're gonna take a bit of spur. I'm gonna put this like right on the edge. Add a little bit of warmth to the look because it's gonna pull in some of the warmer tones I'm going to add. So we're gonna start off with just a touch of Ghost Town and I'm using the same brush so far for all of this. This is a BK Beauty 202. Just is so versatile. And I'm starting off building in the depth. I mean, to be fair, so far, all of the shadows are blending really, really nice. So I have no complaints about the quality. Gonna put a little bit of that down here as well. And then just a touch of Whiskey for some extra dimension but just a touch. And I got a lot of pigment from that and I barely dipped my brush in, which is not a bad thing. This has been my evil plan all along. I'm using a Ruffer 14 brush. We're gonna take this blue shade and I wanna see how we can incorporate this as a look and I'm getting a bit of fallout but nothing crazy. Impressed with the pigmentation. Now, to be completely fair to color theory, this isn't the best idea to do, you know? Because we have these warmer tones and the blue shade, so blue is cool, red represents warm. When you mix them together, you get mud and so they're not going to blend together. But this is me more so just testing the blendability of these shadows and honestly impressive and that added almost more of a smoky element. So when you mix these colors together, it's not necessarily this blue kind of color.  If you know what I'm saying, you can see what I mean. I guess gray, I don't know. I'm gonna take a bit of the peach color right here that we used. Again, I'm gonna soften these edges. I know you're worried about this look. It's gonna come together. I know this is so unoriginal but it's how I feel like I can really get to know this palette. We're taking some of bud, which is a blue. We're gonna pop this or wear. This is a decent blue. It's not knocking my socks off, but it has decent pigmentation, decent color. So I'm not mad at it at all. I'm taking a little bit more of Tex, which is that dark blue just to make it all make more sense. I didn't need to go in with all of the colors that I went in with just because I'm testing formulations. So I'm gonna put a little bit of that dark matte blue down here about halfway in. I'm not going the whole way. Hey, and now I'm gonna go in with cowboy Rick right here. And they had a shade called cowboy.  That's really pretty. It's like a gray, silvery color. That's stunning in the inner corner. I'm not mad at this palette. She's cute. Okay, and then last part of this look, we're almost done with the shadow. I'm gonna take a little bit of Holodom right here, like this. And I'm just gonna pop a little bit of gold right here. I don't know if I'll like this, but this shade's kind of a hard pan. I don't like the way it feels. It's okay, just blend it out some more. So far, just first use, first impression of this palette. I think it's nice.  The mattes blended really well. That for me was the most impressive part. The shimmers are kind of eh for me. Some feel a little bit inconsistent to me, but for the most part, I think it's a pretty solid formula. Again, I'm not in love with it and all of that stems from the fact that I'm not in love with the color story, but if you are in love with the color story, I don't think you'll be disappointed with it. All right, let's move on to eyeliner next. Now, the best-selling eyeliner was the Marc Jacobs High Liner Gel thing. I use that in my makeup kit, so it works. It's amazing, I love it. I know why it's the number one best-seller. I'm not gonna use the black one today. I'm going to use the, this is a glam glitter eye crown, which are not in the best sellers, but it's still a Marc Jacobs color and it's blue, so I wanna use it my waterline. My suggestion with these eyeliners is only to get the matte version. I have a couple of the shimmer versions and they're just not as good as the matte colors. See how perfect that is? So the Marc Jacobs eyeliners, as you can see, I do really like them, but I have a lot of the metallic ones because I'm attracted to metallic, but truly, the matte ones are better. So the matte black, you can't go wrong with and they have a lot of other fun matte colors in the line. Let's go back out to the face and we are going to do bronzer, blush, and highlight. A theme that I noticed for Benefit was that the Benefit Cheekstars Reunion Tour. This was the number one best-seller in the face palettes category, but the Benefit Hula Bronzer was the number one best-selling bronzer. Like, things have not changed in the last few years. The best sellers are still the best sellers. We do have Hula in here, so we're kinda gonna do a two-for-one deal over here since this is the most popular face palette and the most popular bronzer. So I'm gonna start off with my Hula bronzer. We're going to chisel the face a little bit. This is a great bronzer, honestly, and this palette is really great as well. If I'm correct, I don't know if they're planning on coming out with another one of these this year, but these palettes always come out this time of the year, and they're the best. I buy them every year. They just have the best colors. It's nice to refresh the colors, and I love them. They're kind of repetitive, and since I've been buying them every year, they're definitely starting to get more repetitive, but this is still a great palette overall, especially if you don't have a lot of face products and you're looking for the one and done kind of palette. This has it all for you. Now for blush, the number one best-selling blush individually was NARS Orgasm. Maybe not orgasm, but I believe it was the shade Orgasm. Surprise, surprise, again, y'all are boring. Number one best-seller for years, but I just didn't feel like using that, so I'm going to use one of the blushes from this palette, since this is the best-selling palette. Just to go with this blue eye, we're going to use Dallas here because it's like a muted peachy color, very, very muted, so I think it's going to go great with these kind of extra-looking eyes. You can almost not see this blush, which normally I don't like. Oh my gosh, why does it look like that? Do you see this like white patch? I don't know what is going on with my face today. I think I accidentally dipped into Georgia, which is shimmery, maybe? It looks like horrible in this one patch. Can't wait to do setting powder because, laudy, I'm having some trouble. Okay, let's do highlight now. And this one was probably the biggest shock to me. The Bobby Brown highlighting powder was the number one best-seller. I feel like I don't even know what's going on to make up anymore. This was the first individual highlighter I ever bought. This is probably old gross and disgusting, but never would've guessed, because I've been trying to tell people I love this highlighter for years, nobody bit, but this is in the shade Pink Glow. It's really, really pretty. It doesn't really go with my look today, but it's fine. Oh, pretty, I love this highlighter. I think it's really great quality, even though it's probably like five years old. It's a really great one, so. Not talked about, I think it's underrated, but I guess it's not if it's a best-seller, but it's a really pretty highlighter. And the best-selling face spray was no surprise to me, the Urban Decay All Nighter. Now, I don't like using this daily, but I'll use it if I know I have a full day of activities. So, and I really do feel like it makes my makeup last longer. I do. I just want to know what went wrong right here. Like, it probably doesn't look bad on camera, but in person, I'm noticing it. And I'm only noticing it because I'm looking really hard. By the way, normally I don't spray this much on my face, but I'm trying to fix some things here. Okay, mascara was the Too Faced Better Than Sex Mascara. And I'm sorry, guys, but I was not about to purchase that. I have samples everywhere, and I've used the mascara many, many times. It's just average to me. I don't get to judge on mascaras because I have like the worst lashes known to man. So I never like any mascara, so I'm just gonna quickly throw in a random one. But I don't know, people love Too Faced Better Than Sex and I don't get the hype about it. All right, we're gonna move to false eyelashes next. So the number one best-selling lash or the Sephora and House of Lashes eyelash collaboration. Now there were a couple styles. I picked this one randomly. The reason these were number one best sellers is definitely because they were on sale for $7. So I was happy about that because then I didn't have to spend like $30 for a lily lash. And this is a Stio Aqua Marine. I'm not really in love with it. It's too like thin, like it's just one layer of lash. Normally I like them to be a little bit more dimensional, but it was $7. So I'm just, I'm going to apply it now. Okay, one sec. Lash glue is still drying, but yeah, I don't like these lashes. I can see why they were on sale. I have no interest of like ever using these lashes. They're just like too straight down for me because I like a curl in my lash and they're too thin. Like there's just like one flat, tiny little band that holding them together. And I didn't like the band. The band was kind of hard to work with. It was like flimsy anyways, not a fan of that lash. But it is time to move on to lips. So we're going to start off with lip liner. The number one best-selling lip liner was my girl Charlotte Tilbury, one of my favorite lip liners of all time. I'm going to use the shade Iconic Newt. I think pillow talks probably the most popular color. It's like a little bit more pinky, but hold on. Let me do this. Kind of nude is kind of this nice natural brown color, which I really like. I think I use Iconic Nude more than I use pillow talk. Okay, let's move on to lips. This also was shooketh by, but the number one best-selling lipstick at the time was the Dior Rouge lipstick. And I was so excited. I don't own any Dior lipsticks. So I was so pumped that I had an excuse to buy a Dior lipstick. And I haven't tried this on yet, but this moment right here is going to dictate whether or not I buy more because their color range was it. I've never really heard anybody talking about Dior lipsticks, which is why I'm so excited because it looks so beautiful and untouched. I got this shade DeSante. DanSante. Why? Oh my goodness. I don't know how to pronounce anything. Anyways, peachy nude. Are we shocked? Here we go. Oh, she's smooth. Not a ton of color though. So this is a satin formula. So that's why I think it's not giving off a ton of pigmentation. Oh, but it feels so good and moisturizing. Can we talk about this lip combo though? These are made for each other. This lip combo, I love this lipstick. It feels so comfortable. I love the scent. It has like a powdery scent. It kind of reminds me of the, is it like the Gerlon balls? You know what I mean? Those ball things. Okay. I really like this lipstick. And the scent is just amazing. I need more Dior lipsticks in my life, I think. Where are these made? Made of France. Okay. Last product is lip gloss. And this was not a shock. The Fenty Gloss Bombs were the number one best seller. I'm just gonna put a touch on. Sure, let it a gloss. So I don't wanna ruin what I have going on my lips. But this is a bomb gloss formula. It deserves the number one place. Though I do like Pat McGrath lip glosses better, but I think obviously for the larger masses, I can see why the Fenty is the most popular. And here we are, y'all. Like, I don't know, my hair's like crusty today. I don't know what to do about it. Cause this Sunday, so I wasn't about to do anything about it. I need to just like tie my hair back. So here are my final thoughts about the best sellers. The newest products that I tried today. All of that. For the most parts, pretty much everything. I wasn't surprised by the fact that it was a best seller, but there were a few. But a lot of the best sellers were still long time favorites. And I have a video coming up soon where I question why some of these are still best sellers. But anyways, I guess that is the most surprising part is how long some of these have been in the best sellers. But again, I'm also not shocked they're in the best sellers. Now, here's what I disagree with. I think the worst product that I tried today, for me, it was the KVD Lock It Powder. It didn't do much for me. It just made my skin look really dry. But I don't want to knock it yet. Maybe it just might be my skin today and I didn't put it with the right products. That's what happens when you do a first impressions. So far though, I wasn't impressed by that. Also these lashes were awful, but they were on sale. So that's why they were a best seller. So they won't last their long, but I did not like that. Everything else I think was quite a solid choice. Even the Naked Wild West, a lot of you guys were curious about my opinion on it. I think it's good if you like the color story. I don't think it is a necessity, but I really like the way that the mats blended. I didn't really love the shimmer. So it's definitely not a love for me, but it's a decent palette. If you're interested in it and the packaging is really cool. So not bad at it. All right, you guys, that is all I have for today's video. I hope you enjoyed it. Thank you for taking the time to hang out with me while I play with some makeup. If you guys aren't subscribed to my channel already, I would love it if you would consider taking the time to do so. I will see you all in the next one. Bye guys, have a good one.


Welcome to Behind the Fence Line, I'm your host Victoria Pena, and on this episode we'll cover science, technology, engineering, and math, and how it relates to Naval Base San Diego and the environment.  The team I'm on is called an environmental team. Just like those sports teams, we all have different positions and perform different functions for Naval Base San Diego. So, come and take a walk with me and let's see what my teammates are up to today. Hi Tom, hi Kelly. Hi Victoria. Can you show what you're doing today in the middle of the pier? We're checking the storm drains for trash to make sure the trash isn't carried by rainwater into the bay. Oh, can I see through there? Yeah, come look. Get into the bay. We're also checking around trash bins to prevent trash from getting in the water. There's a lot happening on Navy pier and we want contractors and sailors to be aware. To check our areas for environmental compliance, we also create equipment on our trucks that allows us to show others how the Navy takes care of the environment. We can show you some of the equipment if you'd like to see it. Oh, I would love to see what you use. Well, besides a broom, we can use to sweep up small areas. We also have a special tool called a pier sweeper that we can use for larger areas. Would you like to see how it works? Yeah. Okay. Oh, that looks like a lawn mower. Navy based San Diego stormwater permit requires us to check the concentration of certain pollutants in the stormwater. Things such as oil and grease that may be getting down the drain will stick to plastic. So we use the special Teflon bag so that we get an accurate sample. Wow, that's a lot to learn about stormwater samples. Is there another way to collect stormwater samples? Yeah. Well, to collect the water that's under the storm drain beneath the pier, we use the Teflon line hose and a pump. What goes into the water affects all aquatic life, because everything that lives in the water. And it also affects all of us who use the water. So Navy, like a lot of people, spends a lot of time near the water here in San Diego. They're responsible for taking precautions to protect water quality. They're able to enjoy things like swimming, surfing, fishing and boating. Oh, thanks Tom and Kelly. I learned a lot today about how the Navy takes care of our water and how easy it is to take care of our community. Well, that's it for today's episode. I hope that's showing you a little bit about how the Navy works on their environmental responsibilities. You've seen how easy it is for you two to help our community. During all the trash in the proper bins, picking up trash, being aware of what goes down the storm dues in your school, where you live, can help make the water better for all. Well, tune in next time on Behind the Fence Live.


Two team keep it clean. I know there was so much to take away from that season ending press conference that the Baltimore Ravens had a couple of hours ago which featured general manager Eric DeCosta and head coach John Hallbaugh. They said a lot, they said a whole lot in that presser. And my thing, all right, you said a lot, you talked a good game, but for me, seeing is believing. Now I'm interested to see how their actions follow every single word that they said. And even if you can accomplish everything that you talked about, at least getting most of it done. Because some of the things that they talked about and they highlighted in this press are things that we've been highlighting for years that the Ravens should have been on a long time ago. So now, hey, better late than never, but hopefully it's not too late. So one of the things that we about to talk about right now, John Hallbaugh, he was asked about the next offensive coordinator, who it could be and whatnot. And if Lamar Jackson would have any say so in who that offensive coordinator was. And Hallbaugh said that Lamar Jackson, he will have input. He certainly will have input on whoever the next offensive coordinator is. Now, hopefully that input is significant enough to where Lamar Jackson, however he feels, can sway the decision whether Lamar Jackson is for it, great, well, whether Lamar Jackson is against it. So hopefully his voice will certainly be heard amongst the Ravens front office when it comes to the next offensive coordinator because it is such a huge decision for the Baltimore Ravens. And it's a risk, it's a risk, but it's a risk that I don't mind them taking. I like them taking the risk. I like them putting themselves out there for their quarterback or hopefully for who I hope their quarterback ends up being for the foreseeable future because obviously nothing is set in stone right now. Now, the way that it's risky, say for instance, Lamar Jackson's like, hey, I want him to be my offensive coordinator. And the Ravens hired him as offensive coordinator, Lamar Jackson ends up re-signing, great, amazing.  Ravens end up signing him as offensive coordinator, great, but Lamar Jackson doesn't re-sign with the Ravens. And what do you do? So that's one risk, but again, like I said, that's a risk that the Ravens should be willing to take if they really want to keep Lamar Jackson. Now, another thing with them allowing him to have input on this new offensive coordinator, whoever it ends up being, I think personally, this is just my personal opinion. I mean, everything I share on here pretty much a personal opinion, but I think this should have happened already. I really do. I think that this should have happened years ago. I think it should have happened, not even after the 2019 season now. That would be too early. Well, I think at the earliest, it should have happened after the 2020 season, but at the latest, it should have happened last year, last off season. That's why I think it should have went down. Because if you brought in somebody last year in Lamar's contract year, like, hey, what type of offense are you comfortable with the offense that we've been running? Do you still want to run that type of offense? Or do you want to go in a different direction? Hey, Lamar, we want to hear from you. Because what the Ravens are showing me right now, if Lamar truly has input on this offense, they're telling me that, hey, we want to invest in that guy. Because yeah, they've been talking about the contract for years. They've been talking about that. Obviously nothing has been done yet. Okay, cool. Like Eric DeCosta did say, it takes two. So it takes Eric DeCosta on his side, and obviously Lamar Jackson on his side to agree on a deal and move forward. But what they're telling me from this about him being able to have input is like, hey, we really want to invest in Lamar Jackson. So we want to invest in him with the contract, and we want to invest in him with the offense. Now, to take it up another notch, the Ravens also said, John Hopeall, he said that we want to completely like build up our wide receiver room. And I was like, yeah. Okay, and he even talked about how, yeah, we want Lamar Jackson to have more options. We want him to have more weapons. And I was like, huh. Oh, hold up now, Hopeall. Where you been that for the past couple years now? Where is all this coming from? But again, that's another indication that the Baltimore Ravens really want to invest in Lamar Jackson. Seeing is believing. So they still got to show action to back up all those words, but this is a good start. But those words let you know they want to invest in him. So my concern, my concern with this is how are they going to do this? What's going to be the chronological order of everything that happens? What I mean when I say that, what's going to happen first? Are you going to show Lamar like, hey, look, we invested in a wide receiver. We got this guy. We got that guy. We drafted this guy. We drafted that guy. We signed this guy. We brought him in. Are you going to show him first? And then as far as the offensive coordinator, because the offensive coordinator, I feel like that almost has to be the first domino that falls because that could really change everything. And even with whoever they get at offensive coordinator, I still feel like it's going to be really tough for them to bring in wide receivers, quality wide receivers that are free agents or even via trade too. Because I feel like they're either going to have to overpay. And I put that in quotation marks because, I mean, can you really put a price on great talent? But anyway, they will have to overpay. But then on top of that, they're really going to have to, throughout this entire year moving forward, especially if Lamar Jackson is here, they're going to really have to show to show the NFL that they're offense, that they can get receivers involved, that they care about the receivers. Like it's funny because if you go back a year in Hardball's presser, to believe it was a season ending one, he said, we're not begging for any receivers to come here. We're not doing that. But not that he necessarily changes tune, but it's definitely the script has been flipped. Because now before it was, oh yeah, we're not begging any receivers to come here. If you want to win, cool. But if not, hey, we're lying up against you. But now it's, hey, our offense is 75% intact. It's just that 25% that's not intact. And that 25% is a wide receiver room. So we're going to bring back some guys, but we're going to have a lot of new guys there too. I said, ooh. But then that made me wonder, like, why now? Where was this years ago? And then I thought, oh, wait a minute. They are saying all the right things now and it's contract time. So I just hope, my hope is that obviously I want Lamar Jackson to stay. I want the Ravens to re-sign Lamar Jackson to a long-term deal. But what I don't want to happen is the Ravens talk this good game. They talk a real good game. They end up re-signing Lamar Jackson to that long-term deal, which would be great. And then they'd be like, ah, we got him. We got him. Because that would be frustrating, but that would be business. That can happen in business, where people are negotiating. Then all of a sudden you're like, hmm, you know what? Stow out some incentives. Here, take that, here, take that. Hey, if you sign here, then we'll do this for you. If you sign that, we'll do that. For if you sign here, we got you with this. And then you sign there and then poof. Nothing. Nothing. So I just really hope Ravens deliver on all of this stuff for Lamar Jackson. And like we've been saying for years, this is stuff that they should have been delivered on. It's stuff that they should have been doing, stuff that they should have addressed a long time ago, years ago, literally years ago, but they hadn't. They didn't. And this is why I would always say that, hey, the Ravens, they showed us that they didn't really want to invest in Lamar like that. And we talked about it. We talked about that for years. Had the Ravens have shown us that they not invested in Lamar like that. We talked about it with the coaching staff, with how this offense that they've been running, they're not investing because that offense has just had such a lack of growth. We talked about that for years, man. Then we talked about the personnel, but how they, hey, they'll get like 50 tight ends. They'll get 60 running backs, but then they have like two, three receivers. And again, a lot of times it seems like with the Ravens, they only have receivers on the roster just because it's an NFL requirement. If they didn't have receivers on the roster, Ravens would just have, they have about 51 tight ends. 51 tight ends. But that's just something to watch out for, man. I just hope that, again, they deliver on all of this stuff that they're talking about. Lamar Jackson. He took to Twitter today, little after the Greg Roman announcement.  It was nothing new. So many people talked about how the Ravens, they revamped their offense, they rebuilt their offense all for Lamar Jackson, and that could not be further from the truth. The Ravens built their offense under Greg Roman for their philosophy, the philosophy that they had before Lamar Jackson, the philosophy that they have continued to have. And Greg Roman, the perfect offensive coordinator for that philosophy. We want to run the ball, run the ball, run the ball, run the ball, run the ball, play good defense. That's the Ravens right there in the nutshell. But Lamar Jackson, the tweet that he quote-tweeted, it came from fball game playing Emory Hunt. He said, let me just get this out here before folks start wrongly discussing the Ravens search for a new offensive coordinator. Lamar Jackson ran a pro-style offense in college at Louisville under Bobby Petrino. And Lamar Jackson quote-tweeted that and said, thank you. Thank you. And a lot of us were saying thank you to that same tweet because we talked about that too. We talked about that. This system, again, it wasn't for Lamar. It was for the Ravens. And they use Lamar in that system. And he's done a pretty good job in that system. Ravens have got a lot of wins, so credit the system and credit Lamar, great job working together. But they just been such a lack of growth. That's why with whoever the next offensive coordinator is gonna be, as long as Lamar Jackson continues to be the quarterback for the Baltimore Ravens, this is such a huge decision because the Ravens and Lamar Jackson, if he does really have input, they need to choose the best man for the job. Not a buddy, not a friend, not a family, no, none of that. They need to choose the best man for the job. You need to choose somebody who will not only bring out the best in Lamar Jackson, somebody who's really gonna challenge Lamar Jackson, really take him to a whole other level because the potential has just been sitting right there for years, it's been sitting there, but the Ravens haven't even tapped the potential, ain't even come close to tapping the potential. So you need somebody that's gonna challenge him to really bring it out of him, but on top of that, you need somebody who is going to challenge the talent and use, use the talent that the Ravens have at different positions and use guys for what they specialize in. They have to, cause Ravens have done such a bad job of doing that for years. They don't capitalize on guys' talents, on their playmaking abilities, on what they do well. They gotta get better at that. The situational play calling is of the essence. It's of the essence, cause that's been an issue for years as well. The accountability, that's a whole another topic though. So we talk about that another time, but Ravens certainly gotta do a better job. And Lamar, Lamar was definitely busy on Twitter today with the news, Lamar translator. Shout out to him. He said on a serious note, Greg is probably the best run coordinator in maybe NFL history. He's got the stats to back it up. The offense reached the ceiling and it was time for a move. Thank you for everything you did in Baltimore, including coordinating the best team rushing season ever. And yeah, shout out to Greg Roman. So I know so many people, they dump on Greg Roman. It's easy to dump on Greg Roman, but you gotta give him his credit as well. He had his issues, but he certainly had his good things as well. So shout out to Greg Roman, but Lamar Jackson, he liked that tweet. And then he liked this one from RG3, where it said, Greg Roman may be of the greatest run game tacticians I have ever been around at the NFL level. Running backs and tight ends love his system because they eat. Wide receivers absolutely hate it. That's why free agent wide receivers didn't want to go to or stay in Baltimore. It was never about Lamar Jackson. Wow. So RG3 said that Lamar Jackson liked that one too. That is partially true. That's not the whole truth though. That's part of it though. But the wide receiver thing with the Baltimore Ravens that's been going on way before Greg Roman was there. That's been happening. It's been happening. Even when Flacco was here, there was a receiver that he didn't put himself on the record. He said, leave me anonymous. He said that their scheme, he said their passing scheme is to just throw the ball up and hope for a passing offense. That's before Lamar was even a thought on the Baltimore Ravens. And we've seen the receivers that have come in and not come to the Baltimore Ravens. So, yeah man. But anyway, this is a step in the right direction for the Ravens when it comes to Lamar, when it comes to them keeping Lamar because you're giving him more power.  You value what they think on things.  And obviously Lamar Jackson, his one of his values seems to be that he wants to run a better offense. Obviously he wanna get paid. Like he want a valuable contract. Oh yeah, I mean, we all wanna get paid for our work. But I just really want the Ravens to show him, even though they should have been showed him. But I really want them to show him that they value him. And I just hope that it's not just so he can sign and then they be like, you know what, about that. No, follow through. It's my biggest thing. We'll talk more about the presser tomorrow. We actually already did the video covering the presser, but we'll drop that tomorrow morning for you. So I appreciate y'all team. Keep it clean. I love y'all and we out.


So YouTube team keeping clean. Wow, what Raven's just been super active. You see I tried to do a little New Orleans theme in the background for this video. Shout out to the same, shout out to New Orleans. You know, I've never been there before yet. I would love to go. I always hear so many great things about New Orleans minus everything from Eli Apple. But besides that, I hear so much great stuff about New Orleans. Anyway, the Ravens, they have requested to interview Chris Richard. Who is Chris Richard? Well, he's somebody that the Steelers actually requested to interview first. And he is the Saints DB's coach. So on the 24th, the Steelers requested to interview him for their vacant defensive coordinator opening. And then the Ravens, the very next day, the Ravens, they requested to interview Chris Richard for their defensive coordinator opening. So he's a very popular man amongst the AFC North. Oh, he used to be the Seahawks defensive coordinator. Now, was he that Seahawks defensive coordinator when the Seahawks was like that? If so, hey, can we Ravens get like that? To where they like are actually like tough. And I know defense is tough to play nowadays. You cannot play defense not nearly as you used to be able to, not even the same way it was like five, six years ago. And certainly not 10, 12 years ago, it's a completely different ball game. But the Ravens are just, they are missing something right now. But anyway, with Sean Payton leaving, whoever ends up being their next head coach, if they promote from within, and I know Ian Rappaport said, if Dennis Allen gets the job, then he probably like Richard to stick around. But whatever they do, if they promote from within, then somebody would probably want him to stay there. But anyway, another DB coach that the Ravens are looking at. Now, when we look at interceptions, the Saints were tied for one, two, three, fourth in interceptions this year with 18. They were tied with the Green Bay Packers with 18 picks. So they were up there, you know, and just to put it into perspective, the Ravens had nine interceptions. But anyway, and then when you just look at total turnovers, I know that sort of, that could be anything, that could come from the linebacker, it could come from the D line, it could come from the cornerbacks of course too, but total turnovers, they were tied for seventh, I believe at 25 with the Rams, Saints and the Texans. Whoa, Texans. Let me see where Ravens went on this list. Oh, Ravens, total turnovers, Ravens were tied for third from the bottom. So they were tied for third from the bottom. So yeah, we're 15. Anyway, it seems as if, this is very strange to me. So we keep talking about the Ravens they need, not even just that we wanna see, they need a philosophy change. They have to have, well, several philosophy changes. And I just find it so crazy that for a defense, and hopefully they're not just doing these interviews just to say that they did these interviews. Hopefully these are not blank interviews to where, cause you know some jobs will do it, they'll do it, and especially in the NFL, oh boy, especially with the whole Rooney rule. But anyway, you know some regular jobs will do it too. They will interview people just to say that they interviewed people. I've been through that before. I remember a job that I was working at, actually the last job that I was working before we started doing YouTube full time. In our department, they were like, all right, hey, we need somebody to be our team lead. Need somebody to be the team lead. If you wanna be the team lead, you can apply for it. Now, crazy part was everybody, all of us who worked in that department, we knew that the people at the top, they favored this one girl to get the job. But I knew, I knew just as much as she did, knew all the staff was friendly, happy, hey, what's up man? Not early in the morning, that ain't me, but early in the morning, be like, yo, what's good man. But anyway, I knew just as much as she did, but I was thinking like, oh man, like I'll apply for it and I'll go through the process of an interview, but I know at the end of the day, it's gonna go to her. Everybody knew at the end of the day that it was gonna go to her. And there were other people that applied too. And they knew some stuff too. But I knew I was more than confident and more than capable of doing the job. But I just felt like the department was just going through the interview process, just to say that they had done it. Just so it could be a fair fight, so to speak. So went through the interview process and guess who got the job? She did, not me, not anybody else. The girl who everybody expected to get the job, she got the job. And we knew it was coming. It was no shock, it wasn't no surprise. It wasn't like, oh whoa, she got it, great job. No, we all saw it coming. And hopefully this is not what the Ravens are doing. But anyway, I have just been really shocked that they've been going outside the organization. And they seem, at least early on, they seem to have a thing for DB coaches. Something's up there. So maybe they feel like, hey, if somebody can specialize in the secondary, then hey, maybe you could be the man for the job.  He used to be a secondary, I'm pretty sure he was a secondary coach before he ended up becoming a special teams guy. So Heartball, he got some experience in a secondary coach and he's probably like, look man, hey, I know, trust me, don't sleep on these secondary coaches because they got some potential. Look at me. But don't sleep on these secondary guys. But yeah, so we'll see what comes of this. So another interview, well, another request for an interview. We'll see if the interview goes down. But Ravens, they went from being hush-hush. They went from being super silent like, oh man, what's going on with these Ravens? I think Ravens actually probably watched a video that we did where we talked about the Ravens had been too quiet. Cause like a week after that video was done, maybe a week and a half. Oh, it's this, it's that, it's that. First it was old, they fired Winkle, they signed Ben Mason to a future deal. They expected to extend John Heartball to a contractor. Oh, they, they request to interview Joe Whitt. They request to interview Chris Ridgid. And it's like, whoa, Ravens got crazy active all of a sudden. But we hear now. Anyway, Team Keep It Clean, appreciate y'all. I love y'all so, so, so, so much. This off season, it's like, again, we can forget, we can easily forget during a week that, hey, it's still playoff games going on. It's still playoff games going on because there has been so much that has been going on. There has been so much that has been going on with the Ravens, man. And we ain't even, we ain't even into the new league year yet. We ain't even there yet. And these Ravens been getting super active, man.  It is going to be something. And a lot of, some of the Ravens moves that they've made have sort of changed how I feel as they approach this upcoming off season. They've, they've changed my expectations, obviously hoping for the best. But my expectations are different than what they were going into this season before a few moves were made. But we gonna see, man. We gonna see. I love y'all. Make sure you subscribe to the channel, turn your notifications on cause I haven't heard about anybody missing anything but I don't wanna hear about anybody missing anything.  I love y'all and for real this time, we out. Y'all stay up, man.