What I'm interested in is to use machine learning start from a lot of material candidates or crystal candidates that you would then screen with a DFT calculation to find thermodynamically stable crystal structures. There are actually a lot of databases already available for this such as materials, project and OQMD with both of which it has several hundred thousand structures already calculated. However, the problem with this approach of only doing DFT is that it's generally very slow or quite slow so you can take several hours to calculate the property for a single crystal structure. What I typically want to have is to have some initial filter to screen out structures that are actually relevant or likely to be relevant for further evaluation. How that is typically being done nowadays is that you basically use heuristics or element-based heuristics for substituting elements in already known crystal structures where you have some heuristics where you basically have the probability of changing one element to another would likely yield something that is stable. And then you do the DFT calculations for that. However, this tends to, it works, but it only has a generally a hit rate of finding around 10% of the 15% of the structures that are suggested are actually new stable structures. So what I'm going to talk a little bit instead about using energy-based machine learning models for accelerating the screening. So if you would use a machine learning model that is trained to predict formation energies for crystal structures and to see if such a model can help improve the screening. And the question is then what kind of machine learning model would you need, would you use for this kind of task? So there is a, you need to have the right tool for the job essentially. And, but before I go in a little bit further into what model you would use, I will quickly talk about the convex hollow stability, which is essentially one of the first or initial measures you use to assess whether crystal structure is thermodynamically stable or not. And the convex hollow stability is essentially we have one here for just a toy system where, or binary toy system where you have a consisting of two elements, one element A and one element B. And you basically then plot the formation energy of your crystal structure versus the ratio of elements in the crystal structure. And then what you do to get the convex hole is you basically take the lowest line, the crystal structures that has the lowest line energy, and then you draw a line between them, you do a linear interpolation between them. And this, this hall is then known as the convex hole. And basically, any crystal structure that lies above this convex hollow energy is very unlikely to be stable. So it will likely decompose to a linear combination of the two neighboring crystal structure that lies on the form. And if you would find something new that lies below the convex hole, then you you have a something that might be interesting, it could be actually made. And of course, you need to do further stability analysis such analyzing its phonospectro distorting the structure and so on but I won't go into any details on this in this talk. The question is then what is the best representation for this kind of job. So, in this talk I will not focus on any specific representation in the beginning, and rather talk about different levels of course grading of representations. So, with course scanning I mean how much information you include in the model and I will talk about anything ranging from full atomistic atomistic representations that contain full coordinate information to representations that contain essentially no structural information so only containing the composition of the system. If I go into crystals I will just briefly talk about representations for molecule because it's a slightly more developed field. So what we have here is essentially hierarchy of, sorry the pointer is a bit weird, and hierarchy of representations for molecules where the higher resolution you have for your representation. And in the top you have this coordinate base base representation that essentially encodes your system using the exact coordinates of the system, and typically encodes all atom positions. And on the second hierarchy you have this graph based representation which uses either something like molecular graphs or bombs or smiles to encode your structures and on the last, the lowest here you have these representations that doesn't contain essentially any chemical or any structural information directly so it only contains chemical properties such as the dipole moment of your molecule partial charges in the molecule or composition of the molecule. And yeah similar you can draw this kind of graph for materials where you would have on the top you would have similar coordinate base representations of your material. And on the second tier you would have something like a graph or prototype base so you would use some crystal prototypes to encode your structure. And in the bottom tier you have these composition based models. So the question is then which model would work best in high throughput screening, or which kind of representation would you use in a high throughput screening settings. So let's begin by looking at coordinate base representations. And, as you can see in this figure taken from a review paper by Felix misly at all. And they are a lot of different representation so in this paper they actually characterized a lot a lot of the the currently developed representation coordinate based representations for machine learning. And it's a very well developed field and there has anything ranging from. Sorry, at the center distribution functions to potential fields to just encoding internal coordinates of your system. And, and a lot of these tend to be very accurate, and they also most of these models coordinate based representations has a high structural resolution, which makes them great for predicting structural properties such as type of moments forces or energies. And they can be used to construct construct inter atomic potentials and force fields. So just some of the applications where these representations has been used are for example in simulating an amorphous materials, modeling grain boundaries in materials, and also for simulating polymers. And finally they have also been used to predict the comics all of stability for material structure, or for crystal structures. However, and yeah so so this is what we're interested in however, there is a slight caveat with using these coordinate based models for predicting crystal structures. Typically what people have done in literature is that when they predict the energy of a crystal is that they use the relaxed crystal structure to predict the energy that you can get very good, good accuracy with the model doing this, however, to use to get the relaxed crystal structure you would need to do the DFT calculation in the first place. And, as we can see from this high stroke with screening diagram, then you want to do the DFT calculation after the ML screening because otherwise the point is a little bit moot so you have like a circular problem where you need the structure. Sorry, you need a structure to predict the the energy, but that makes it. And for that you need a DFT calculation as well. So I would say that this is a little bit cheating. But what you could do instead is for example you could use a model that would have a good enough coordinate based machine learning model you could either relax your crystal structure back into the relaxed state with the machine learning model. However, I don't think the field is developed enough to have coordinate based models that can do this across a very diverse chemical space. And finally what you could do and people have tried is to use unrelaxed crystal structures to predict relaxed energies. However, as I will show later on this tends to reduce the performance quite a lot. So the, I don't think this coordinate based model are therefore really ideal for doing this kind of for using as a filter in this high throughput screening setups. And what about composition based models. I mean they're generally, since you typically just encode a composition of your, your crystal structure that makes them very easy to customize to specific tasks you're interested in they're easy to to interpret as well because they're so simple. And they're also very efficient in the low data limit because of that. However, yeah, and sorry there, and there has been a lot of models that has been developed for this task already in literature where you have model and several different machine learning frameworks and representations to that uses compositions to predict formation energies for crystal structures. However, it has some problems when you only use the composition and it's mainly that the representation itself is not unique. So, for example, as you can see here, you can have multiple crystals that has the same composition, but completely different energies. One way to solve it that people typically do is to just remove these crystal structures from that you just pick the lowest line crystal structure to train the model on. However, even if you do this you can have non unique compositions that lies very close to each other so you can get very you get a very rough surface to actually interpolate, which makes it difficult for the model. Nevertheless, as you can see in this learning curve here where you essentially train this model train on the OQ MD data set that I mentioned earlier, where you have the mean absolute error plotted against the number of training sample. The model has been trained on for several different composition based model, you can see that the error consistently goes down when you increase the training set size and they achieve actually quite low errors. So, how do these models then perform for actually classifying structures that lie below the comic soul. So, as was found in the review paper by Bartlett et al, where they try to do a critical assessment of how well these models actually work. So what we found that they are quite good at classifying structures that lie above the comic soul but actually very poor at predicting structures that lie below the comic soul so they could barely find any new interest in crystal structures or new stable structure sorry. So what we have, so what they did is they, in these figures they excluded a lithium manganese transition metal oxides from the training of the models and then they try to use these models to predict the comic soul energies. And so what we have in these figures here is that you have the models predicted energy from the models predicted distance from the comic soul plotted versus the actual distance from the comic soul. And you would basically want your model to the models predictions to lie, either in this, the top box, or the bottom box. And because that's when gives a correct classification whether it's stable or not. And as you can see that all the models correctly classify structures above the whole, but at the same time all of them are almost failing for almost all crystal structures to classify ones that correctly lies below the hall. So, and in addition to this that even if you would have a machine learning model that is composition based that could correctly classify structures that lie on the comic soul of stability, you have a uniqueness problem that you have to realize the crystal structure in after you have the from the composition but many compositions can have many different crystal structures corresponding, many different corresponding crystal structures. So you would then have to screen a bunch of different crystal structures actually find the one that lies on the comic song. Yeah, so I would say that what you would need to do this kind of task is some kind of graph or prototype based representation, which, and you would need this representation to have a sufficient resolution to actually be able to reconstruct the crystal structure from the representation itself. And also you want the same time to be coarse grained enough to avoid. So, so that it has the same representation when you train it on on relax and relax crystal structures. And yeah so but there is barely any focus on this in. There are barely any representations for that for materials that lie in this class, so I think this class of representations needs a lot more focus. So yeah, I'm, I think I can take some questions now if there are any because then I will move on to talk about my research. Nice talk. The question that I have actually, I mean, I'm not quite sure how you came up with the number of 10 to 15% heat rate. When you talk about the suitable substitution in the slide with this like when you have some structure and then you replace with some kind of silicon to Germany was things like that.  Very initial one. Yeah, yeah, this one. Yeah, so 10 to 15% heat rate what you mean by that. This is just when they use these heuristics for. I will get to that a little bit later because there is a data set that we will talk about in the research where they actually use this kind of heuristics to simulate the materials workflow the same materials workflow that materials project is using, and they find that with this workflow they have 15% of the structures that they find lie on a new convex all of stability. And, and I think there has been some other research in literature as well on it so so they typically these models has this kind of performance from what I know. And something else if I'm allowed to ask. So you talk about the reason reason actually when it's about this like representing of the molecules, or, or about I mean like coordination smiles or chemical properties and in case of the other one grassy graph or composition based. My question is, what do you say when in case of a database, somebody's combining both. It's like, in some part of the data I'll be using the structure, atomic structure, and in some of the cases, or mix match of them, so that it's not to demanding on the data set. Am I clear what I'm asking. I'm sorry I'm not really following. The resolution increases depending on what you choose from atomic structure to the composition based. Yeah, do you think it's a possibility that if somebody wants to mix match between them. So it's use a mix between coordinate based and non coordinate based. I mean, of course, it can be done, I guess but I'm not really sure I haven't thought about really how you would do it. Because like what would you, how, how do you suggest and that you wouldn't mix match it. Okay. I thought I would get an answer. Sorry, I shouldn't be asking you questions. Yeah, we can talk about it. Yeah, we can talk about it. Thanks. Kevin to have a question. I'm online, whether you could specify whether this set of techniques is just for high throughput screening, or could be applied more in general. Specifically which set of techniques molecular systems or compounds in general well the questions. You, you mean, I'm not still not sure exactly which technique you're talking about. Is it the representations or the DFT workflow. I mean, yeah, I think it's the same for molecules as well you suffer from it has the same issues when you train and predict on relaxed structures as well. Fantastic. One question here. Yeah, thank you for the presentation.  So, comparing the prediction on structures on the convex cell and above the convex cell. So, this looks like the classic problem when you have many more structures above the convex cell than on the convex cell so it's a classic imbalanced data set problem. Can you comment a bit on this like, how did they address this in the, because when in your data itself there's many more negative cases than positive cases, it's a bit natural that the model tends to go that way. I mean, I guess that I'm not sure if they've actually done any rebalancing of the model to to weight it by its distance from the comic so probably not in this case, which could maybe improve it slightly. But I don't know exactly how how they did it in this paper. Hello. Thank you very much for the talk. My question is regarding all the representations. So, is the uniqueness problem solved when we use a graph based representation. So the second step of your pyramid, let's say, I mean, it depends on what you mean by uniqueness because like you wouldn't be able to use a coarse grain representation to create a force field for example right because it doesn't have there. So in my mind is, for example, for materials you mean. Yeah, I mean if I have a molecule I can have different conformers of the same molecule. Yeah, in that case, I should take in account, I don't know, do exist graph based representation that is in account. So it hasn't. There are a few, I will get to it as well and my next talk will talk about such a representation. And as well so perhaps I will, I will answer your question soon. Okay, I look forward to that. Yeah, other questions. No, but you're making me run guys. Thanks for the presentation. So, I have one point that I didn't understand when you're talking about the coordinate based method. So, could you go back to that slide. Yeah, this one. So I didn't get, I can understand that you need to do DFT to fulfill the circular but why you need to the energy of the relax the structure. Because when you relax the structure with DFT you get the energy off it anyway. So, but if you, you just need the self consistent calculation to get the energy of a random structure to fit your machine on everything. So, why do why does it need to be relaxed, because when you relax it goes to the ground state. But typically, how you do these DFT calculations for crystal structures is that you do symmetry constraint calculation so you relax it only along the symmetry. And there you can even an offer in addition to that you can be in a local minimize you relax it so it won't relax into the ground state in most cases. Okay, thank you. So other questions right now. So I think you can continue with the second part and then you take more questions in the end. Yeah, so just to reiterate very quickly. That on what representation you would use that the coordinate based representations are not unique, which makes them unsuitable and full structure based representations. They don't suffer from this circular problem that you need to use to relax structure to. In order to predict the energies. And we would want to have something. Wait, I don't think I know it's working okay. You would want to have something like a graph or prototype based representation. However, there hasn't been that much work on it in literature. So you would use representations like using Baroni tessellations to encode your crystal structure. And, but I'm going to talk a little bit about using the symmetry operations of your crystal to represent so you would use something called like of sequences. So we're in in this we have it just a toy crystal structure here. We're to the toy crystal where with different atoms placed at different positions. You essentially can encode your structure using which symmetry lines they lie on so the cross yellow crosses would correspond to for example one symmetry position or one wake of position and one. These blue pentangons would correspond to another symmetry position and the red circles would correspond to another yet another symmetry position so you could then take these symmetry operations of your crystal structure and put them into a regressor to predict your energy. And these tend to be very stable towards relaxing the crystal structure because generally the only way is for these crystal structure these representations to increase in, sorry, to change symmetries it goes up into higher more symmetric crystal structure and that's relatively rare. So that's to go into a little bit more detail of how we actually do this representation, or how we encode the atoms. Is that we use a way encode the element type using just a math scholar embedding. So some embedding vector that encodes the elemental structure, and then we use the part which encodes the atomic position we have the crew we encode the crystal system as well as it's brought it the bravi centering of the crystal to encode the lattice itself, and then we use a multi holiday embedding to encode the actual atoms position on this lattice on the crystal lattice. And then what we do is we pass this. These wike of representations into a graph attention network, which basically updates the embeddings for each of these atom, based on all the other atoms around them and we call this this framework, I'm sorry, a wike of representation network. The output of this to predict the actual energy of the system. And what is very nice with this is that you can quite easily realize or get an unrelaxed crystal structure back from this representation and it's completely innumerable so it's very easy to search through your space. And in most cases, as I said, these representations are unique, they are we found some edge cases where you can construct symmetries or crystal structures that you multiple crystal structures from one wike of sequence, but in practice, this seems to only happen for very high energy crystal structures that lie but far above the convex home, which we're not very interested in any way. And then, yeah, finally, you can do a DFT, because DFT relaxation on the crystal structure to get the final structure you want. And so our word discovery workflow would then be that you start from a huge library of these enumerated wike of positions. You then put the, oh, this got weird when I moved this line of apparently, but anyway, so. You then you put the these crystal structures through this brand model to get the formation energy of the crystals. And then you compare them to the current convex hollow stability, select the ones that lie below the convex hollow stability. So you can reconstruct them in the crystal. And then finally, validate whether they actually lie below the current convex hole using DFT. And just the first we looked at two different data sets to to bed to see how this model performs. So, one of them is this is materials project which is a very large database of crystal structures. And the other is this DBM data set, where they essentially try to mimic this mimic materials project discovery workflow but using a heuristic, the heuristic base substitution method that I discussed earlier in the, the prior talk. If we just look at the learning curves. First, where we have the mean absolute of the error of the model for out of sample data plotted against the number of number of training points we can see that the error goes down. So the reason why we have two plots here is one is for just a single model and the others we use an ensemble of between multiple models simultaneously using ensemble predictions we take the average prediction to get the actual energy. But you can get very low errors. And what is perhaps more interesting than the error itself because as you saw from these composition based methods that they can look like they get good error but they actually don't perform very well so what we instead look at is the error of the model plotted against its distance from the convex home. So here we have four different models plotted against the model error for different models where they plot against the distance, their distance to the comic soul so we have the top, which is the brand model which is this model that we previously talked about. And then we have this Verona tessellation which is one of the other few course grain representations for crystal structures and CG CNN which is a full structure based neural network for crystals. And then we have this cone here. Basically corresponds to the difference between the mean absolute error and the comics hold distance. And the, you essentially want your model to be good inside this cone because the better it performs inside this cone, the more likely, or the better you will be able to classify structures that actually lies above or below the comics hold, and then it doesn't matter that much if it performs poorly outside of or far away from the cone because you will have structures that will not be stable or very unlikely to be stable anyway. And what we can first see is that all the models essentially have their lowest error inside this cone, which is, which is good, which is what we want. And the second thing is that we have the CG CNN model performs better quite a bit better than the other models by quite quite a substantial margin. However, if you then this model is trained on relaxed structures. If you would then train it on the pre relaxed structures. Then the model error deteriorates quite substantially to this red curve here. And it actually becomes one of the worst performing models. Whereas if you look at the base model which is which is coarse grain, while it performs poorly compared to the other models initially doesn't change in performance much when you train it on pre trained structures. And we actually also tried this for the rent model, but there were essentially no difference in performance when you trained it on pre trained or relax structure so that's why we only see one curve for it. So how well does the model then perform it actually classifying stable structures. So what we have here is basically a histogram to histograms. And I'm sorry three histogram for histograms, where the red and green histograms corresponds to predictions that the model has predicted to lie below the convex hole. So these two, and the orange and blue corresponds to what the model predicts to lie above the convex hole. So then you had it the, the green ones is that is true positive so it correctly classifies them to lie below the convex hole, yellow is false negatives. So it misses to classify them to lie below the convex hole, and read this false positive so the model things they lie below the convex hole. But they actually don't lie below the convex hole, and the blue is true negative so that model things they lie below the convex hole but they actually don't. So you can see here that unlike these stochometric based models which managed to barely capture any structure to lie below the convex hole, we do manage to catch a substantial portion of structures that actually life. We actually managed to capture a bunch of structures that do lie below the comic so and it correctly classifies 38% of the structures to lie below the current comic so which is more than 2.5% better than these heuristic models. And finally what we did is that we wanted to see how would how would this workflow work in a more prospective manner. So we essentially started from bunch of initial structures, and we did substitution of the elements in these crystal structures, and then we use the model to pre screen the energies and then actually calculated that the with DFT validated the components that the model predicted to be stable. So we started with roughly 415,000 structures, and then the model predicted 37,000 to actually lie below the comics for those stability. And since we used an ensemble model, we could actually get an uncertainty from the model. So if we would added that we want the convex hole distance plus the uncertainty of the prediction to be below the comics hope, we ended up with around 5,700 structures. And after validation with the DFT. And for several reasons we only managed to finish around 4,700. We found that 33% of these structures. 33% of the completed calculations lie below the comic so which is still a big improvement. And finally I just quickly say that we were working now to see if we can do even more prospective model where we're working to see if we can find. We can optimize several properties simultaneously by by looking for interesting dielectric compounds compounds. So, which which has applications in for example flash stores CPUs and rams. Well, what we're trying to optimize then is both the band gap of these models or band gap of the crystal structures as well as their total dielectric compounds and we also collaborating with experimental is to see if we can actually synthesize some of the compounds that the model suggests. And with that I would just like to summarize that I given us a very cursory overview over of the representations. Well, it works in high screenings I think, and I think there needs to be a lot more focus on course greatness models for materials screening. And I also discussed one of these course grain models that we have developed that can be used for these high throughput tasks. And finally, I would like to thank. First of all, Reese Goodall, who is the PhD student who did most of this, this work I just talked about. And then I would also thank Alpha Lee, which is my group leader in Cambridge. And then finally, Richard Armiento and a beat to our collaborators from Linköping University, and you can find the paper preprint on the paper through this QR code. Okay, thank you. So we have plenty of time for questions if you have any see. We're here for us. Thanks Felix very nice. Can I just ask about the generator step. How do you deal with the fact that most we call sites have one or two degrees of freedom right so there's like a line you can put things on or a plane. How do you choose where on that line or plan to put things. So, typically, like what we found in practice is that they, there is only one minimum along these lines so we will relax into the correct one. However, there were there were some edge cases where it doesn't. We can have multiple minimas, but this seems to be very rare and generally we found them for mostly high, high energy structure so it doesn't seem to form a big problem in practice. Can I just ask could you go back a few slides to yeah one of the keep going back. Yeah, okay stay there so you see the bottom left picture there say the yellow cross right yeah so that could be along that line, all four of them obviously move together but you. Yeah, for example, the yellow cross here would be would be along this line the, the red circle would be along this line and then you can even have plain symmetry planes that the Athens could line so this. This one would would lie in in this plane. So exactly so that the bottom left picture the yellow cross could move inside the circle of blues for example, and I'm pretty sure that would then no no if you would move in into the blue part then it would break the symmetry so you would correspond to a different structure. Okay that's no longer on that way plus I can move inside the circle of blues but still on the diagonal. Yeah. Okay. Thanks. So we have a couple of questions from the zoom chat. So one was on data set sizes and if there's a lower limit on the data set or the data size that you need. Yes, that's hard to answer. It's hard to say I mean I think depends really on your own what you want to solve with it but I think the more data is always better for, especially for these neural network based models are generally quite data hungry. If you would use something like a random forest they typically you can get away with using less data. And the second question was on. So you talked about energy models but what about models where you need a derivative like strain or stress how would you proceed then. That's a good question. And I'm not sure by hand, I can come up from my head exactly how we would solve it, because, but you might be able to encode it into the symmetry positions themselves but I'm not sure how exactly. But what I've been mostly concerned is actually predicting whether crystal structures are stable or not, which for which you need only deformation energy, since if it's not going to be stable. You're very, very unlikely to be able to make the crystal structure in the end. Nice talk again. And if I if you can go back to this, maybe we can ask him here. So, can you just clear me one thing actually what the symmetry representant of white clock is adding over or what is the advantage of doing this way over coordination methods, the slide number 32 I guess, so what it adds extra doing this symmetry way of other than coordinate. Because you, you mean instead of using a coordinate based representation. I mean, because you don't have to relax or crystal structure. Most of the cases you have the same representation. When you use your, your for the relaxed and unrelaid structure with these symmetry operations. Whereas if you use a coordinate based model you would have the representation can change quite substantially if you do relax the structure. Okay, so then to iterate over it. Sorry. Yeah, just to iterate this question. I mean, when you have this coordinate based one when I'm encoding this and also the symmetry one when you are encoding this. So, is there any kind of generalize ability that gets added when we are doing this symmetry one when we're encoding this. I mean, depends on what you mean by generalize ability. So, I mean like different structures and only if the model performs better in general.  So I showed the non coordinate based model actually performs a lot better. But if you train them on the and relax structures to predict the relaxed energies, they, the model performance of these coordinate based models goes down quite a lot. And on the slide actually what you mean by pre relaxed is like and relaxed ones. So you've used I don't remember exactly how we did it but it's a very cheap initial relaxation of the crystal structure just to make sure that it's not something completely nonsensical. And if you just go to the database one where you were like moving down and the number of the database and the novel. No, no, no. The one with the database where you are like 33% of the one you will have at that. Sorry, the slide with the data. Yeah, we're moving down and all this one. No, no, next one. Yeah, this. Okay, so this one question may not make a lot of sense. I'm just asking is like, when you have start from these noble candidates like 415420, then you end up with 1558. And since you talk about the new of JNC data set, and then you have substituted a new ones. So, I just want you to comment over if we delete some of the data set that already existed and do this experiment. So, what are the predicted the novel materials that you say in this case, in that way, will it be predicting the already existing one. Sorry, pretty say that again. Just say it's like we have we take the data set up to 2012. Yeah, but we're living in 2020 and we do this same thing, and will it be able to predict some of the ones that we already knew in 2020. What, what will you retrain it and try to do predictions again. I mean, I think the model, or we did these experiments actually and the model performance becomes worse, simply because there are less structures left that lie on the convex hole. So each time you would iterate this out while the model gets better. There is also less possible candidates to to find thanks. A quick question from zoom. So you showed a model for the band cap right and for the total energy. Is there anything else machine learning models can predict or these type of models can predict. I mean, this is what we have tried so far but in principle you should be able to apply them to predict any property you want. And I mean I cannot say to how well the model will actually perform because that's generally something you just have to try to figure out. And in this like workflow in this slide and there's this like Sigma value that you added to like decreased the number of candidates. So did you like really decide the value of Sigma or so this is basically we since we for for this workflow we used an ensemble of neural networks so we trained several models. And the mean is then the energy prediction that we use, but you can also get a standard deviation, which is the Sigma so it's basically some measure of how uncertain the model so how much the different neural models disagree with each other. You shifted it to like just by one. Yeah, so we will send that deviation of that. Yeah. Okay. One more question. Hello. So I have a question about DFT part. When you say DFT you mean what you mean a periodic DFT or I don't know. This is standard, like, DDA PV, I think. Oh, okay, okay. We use VASC for these calculations. And do we have any other question on zoom. No. Good. So we're a little bit early for the coffee break that's good. And it was a 20 minutes of an hour. And then we have one more lecture this afternoon. So please come back for that. Our lecture is already ready. Okay, so coffee break now.


Once upon a time, Britney Spears was the most talked about pop superstar on the planet. Back in those days, whether she was dressing in All Gene next to Justin Timberlake, having a drunk wedding with a high school ex or locking lips with Madonna, whatever she did was always front page news. But that was the mid-2000s and in more recent years, things have settled down for Britney. According to a recent interview with Entertainment Tonight, Britney has spent the last year prioritizing herself and her well-being, something she didn't have a chance to do as much during her Las Vegas residency. She has had less time with her sons but got really close with her mom Lynn and sister Jamie Lynn again recently. Britney spends most of her time at home with her boyfriend Sam Esgari. He brings her so much joy and is a source of stability in her often chaotic life and she's been with him for almost 4 years now. But of course, there have been plenty of other Bachelors over the years. Some of you remember for better or worse like K-Fed who at the time of this recording has 28k followers on Instagram. Out. Also, who knew she had a thing with Eminem and Jared Leto. In this video, I've got everyone from her childhood sweethearts up to her random flings and the hot model man she currently has in her life. What's going on guys? It's Kara back at it with a new series titled Who Dated Who.  I'm looking at you Lindsay Lohan. As we do with our other series, I need you guys to let me know who to do next in the comments down below. How about Ariana Grande or Paris Hilton? You let me know, you know where and I'll see you guys after the intro. Next up we got Reg Jones whom she met back in 1986 and the couple dated for two years. He was four years her senior and it looks like he even took her to his prom. I actually found an old love letter between the two dated back to 1999 where she is begging him to come visit her in Atlanta during her baby one more time concert tour. Someone has paid 1200 bucks for this piece of Britney history. There is rumored to be one more dude from her hometown named Jason Gettert but besides info about her being into him, no one has info or a photo. Next up we got Robbie Carrico. He was another pop star on The Rise as a member of Boys and Girls United and the pair dated for six months throughout 1999. Just like his group, Robbie would disappear never to be heard from again. Today he looks totally different with long hair and was an American idol wannabe. Alright now we get to one you all know, the couple that made matching jeansuits famous. I'm talking Justin Timberlake from 1999 to 2002. Although the two knew each other from back in their Mickey Mouse club days they made the perfect queen and king of pop for the early 2000s. They broke up not once but twice in 2002 before it was finally over which then gave Justin albums justified where he got the ultimate revenge on Britney with the song and music video for Crimea River. So Britney cheated on JT. Who was the man? Well the internet seems to believe that it was backup dancer Wade Robson who she dated briefly while the internet seems to believe that it was backup dancer Wade Robson who she dated briefly following her split from Justin. Last, anything anyone's heard about Wade is that he was part of the documentary Leaving Neverland where he went back on past statements and stated that he was sexually molested by Michael Jackson. From there Britney Spears was all over Hollywood hooking up with everyone. She apparently hooked up with Eminem, the New England OG Tom Brady before dating Fred Durst briefly. There were more Hollywood hookups with Colin Farrell, Scott Storch, Gloomba Short and Jared Leto before she decided to settle down. That's right Britney Spears married Jason Alexander for a total of two days. Let's breathe, let's breathe through this, okay and it's not gonna be that bad but here we go.  What was your headspace? Honestly, I really wanted to see what it was like to be married. Maybe that sounds silly but in that moment I was just, I was with a friend and that I loved dearly and I wanted to do something wild and crazy and I wanted to get married. Next up was the real deal with Kevin Federline. After dating for three months the two were engaged and married which lasted close to three years. Don't worry, there was a prenup. Everything overnight, all the adjustments, everything and then I got to wear the dresses and be in the wedding. That was pretty. But she was just like well I wanted it to be a surprise. All the guys were downstairs and all the ladies and the girls ended up upstairs and it was just the most fun. Everybody at first was very shocked but then the shocked went into excitement. We probably had as much fun getting ready for the wedding as we had during the wedding in the ceremony. Actually seeing my little sister looking really grown up, that really touched me a lot. So I mean it worked out. It worked out pretty good. Perfect. I'm like sitting in the back getting dressed, changing, you know, I got butterflies in my stomach and everything, palms and feet are sweating. He got a big heart, you know what I mean? Kevin's a cool dude. Kevin's still Kevin dude, that's what's good about him. He's still Kevin. And then we got my brother Chris, we got my other brother Curtis and then we had her brother in there too. Those were my best men. I mean just when we was getting ready to go out before the ceremony, I was really, you know, just congratulating him. He's ready for that step.  Just letting him know we was there with him, that's it. More recently, K-FIT has been on Celebrity Fit Club in the Australian weight-loss show Excess Baggage. When the marriage ended, Brittany went back to her old ways bouncing from man to man. First up, there was record producer JR Rodham who she was with in 2007 for two months. Then there was male model Isaac Cohen, the two hooked up in 2007 before she went to rehab. A place he later told the tabloids he felt was a good decision for her. In rehab, she met singer Howie Day whom she dated for four months in 2007. Then there was a rumoured hookup with Chris Angel that same year. She met pilot John Sundall in AA and they became dating sober mates for six months in 2008. Sadly, the man passed away serving in Afghanistan. The next one has to be the strangest one on this list and the creepiest. For whatever reason, Brittany began to date one of the men who stalked her on the daily. We're talking about paparazzi member Adnan Ghalib. She dated him for 10 months between 2009-2010 but now has a restraining order against him. Adnan had shacked up with her in multiple hotels and gone on secret jaunts to Palm Springs and Mexico with her.   Another strange rumour is Mariel Lopez who she was said to have hooked up with in 2008. It didn't take that long for Brittany to start thinking third times a charm. After dating Hollywood agent Jason Trawick for over two years, the two were engaged but then called it off in 2013. Then there was regular Joe David Lucatum she met at a party. It was said that he had cheated on her after the pair dated for one year and five months so it's safe to say that was over. Next up, TV producer Charlie Ebersol for eight months in 2015. Then CEO of Hollywood's Pink Taco and the co-owner of the Viper Room Harry Morton for five months in 2016. And finally, we have Sam Asgari who she's currently been dating for the past three and a half years. He's 26 years old at the moment and is a personal trainer turned model and even aspiring actor. They met on the set for her music video shoot for Slumber Party. Sam told Men's Health about the gig that, I was excited that I get to meet one of the biggest artists of all time. I had butterflies. Sam said he cracked Joe's to Brittany on set pretending he didn't know her name and flirted. He then gave Britt his number and five long months later she actually reached out. Brittany said in a radio interview, I was like he was really cute. This guy was really cute so I called him. He's just a really fun, funny person. Afterwards, they went out for sushi together and it wasn't long before the paparazzi was snapping pics of the good looking couple. They became Instagram official on New Year's Day 2017 with this photo Brittany posted. Then it was his sister. In fact, Britt recently posted an early birthday tribute on Instagram to her main man and it's super sweet. Happy early b-day to this man. I adore and love him so much. Happy birthday Sam as Garry. She captioned a snap of the two of them with this. Two workout together a lot whether it's outside, they're indoor gym, dancing and more. Not to mention Brittany is 13 years his senior but Sam doesn't mind at all. In an interview with Entertainment Tonight, he said absolutely when asked whether he saw marriage in his future with Brittany and added, this is something that every couple should do. That's the whole point of a relationship. We're a family, trust me if we ever do get married everybody's going to know. How cute is that? Alright guys, so that about wraps it up for our list of who the queen Brittany Spears has dated over the years. Although she's had a pretty long list, the woman's been in Hollywood since she was a kid so it's no wonder all of them have been publicized. From Justin Timberlake to K-Fed and everyone in between, all those aside she seems to be happy now. Brittany is in a steady relationship with her personal trainer slash model boyfriend Sam and the pair seems content as ever. Now of course I want to hear from you guys in those comments down below. Did you know about all the guys Brittany's dated or were there some that surprised you? Now of course I want to hear from you guys in those comments down below. Did you know about all the guys Brittany's dated or were there some that surprised you? What do you Brittany fans think about Sam? If you like these who dated who videos, let me know whose list you'd like to see next. Make sure you're subscribed and follow me on Instagram if you want to chat more and I'll see you all in the next video. Bye!


I just appreciate you, man, for letting us come down here in this eight-minute dope establishment, man. We can't wait to get the tour. Yeah, we're getting the tour here in a minute.  We're doing a lot of stuff in here, so well, you know, we got a lot of success out of this. How long you been here? We got finished around about 2018, I think.  I had to do something right with it.  It's dope, man. Just going up and just looking at what I've already seen. I know I'm going to see more in a minute, but man, dope, man. I love the records, man. The records are real, bro. Like, the work is real, you know what I mean? And that's the thing about it, man. You're doing real work and you're really putting it down, man.  Yeah.  I try to work everybody in and just try to bring as many of these plaques back to the city of Houston as we can. Hey. But even though I put something in Dallas, too, because I gave some of those DJs plaques, too, I think, because they really had a big part in breaking Eric and making them. Yeah, and making. That's cool. Yeah. Did you ever think that you do it back to back? I know you don't want to. It's bad for that. I know. I know. You know, I just didn't really think about it to be honest with you. I just knew it had to be. It had to be done, you know? Do you think you can do it again? Oh, of course. I think I can do it again. With who? I don't know. Yeah. With all of them. You know what I'm saying? With all my orders. But, you know, it's always that one that just gonna, you know, work as hard as another one and stand out more. You know, I noticed that. That's what it's all about. Like, like at the end of the day, you know, being able to sniff out that talent. You know what I mean? Being able to understand when it's time to apply the pressure. Right. Being able to keep consistently working. Right? Like you already know this from being in the sports. Yeah. If you don't, if you don't work, you ain't, it ain't going to pay off. Yeah. You got to, you got to be consistent. You know what I'm saying? You're going to practice how you play all the rules that we learn in the baseball. I mean sports, first one, the last one to leave, you know, apply all those simple rules to the music and the work for me. So how we got like Spooty who plays sports to kind of understand that, you know, really makes it easy on their time. But how draining is this industry? Because I see it just like when you were doing those press runs, you were on the go constantly. How, um, what do you do to savor your, your energy? I just try to stay, sit still in one spot and sleep whenever I can, you know, pretty much try to rest as much as possible because the night time is even worse than the day time. You know, so it's like that really takes me out. So trying not to do too much at night and then, um, rest, rest during the day. Do you still work out? Not too much. Like I do like body stuff. Like I don't go to the gym with that. Every way you're going to hurt yourself. You know what I'm saying? Like I'm like, my age, I'm 40 years old. Right. So I don't want like muscles and bulk. So I want my clothes to look right on. But I be thinking about cardio. Okay.  No, I believe that because I have a track in my house. So I walk a lap or something to keep my heart rate going. Right. But like for as like just heavy weights and stuff like that, I stopped that. Yeah. I do like band weights or push ups or something like that. Resistance weight is way better. Yeah. That's why I do resistance. Yeah. That's the most dope is saying, man. So I mean, you know, um, just, um, just seeing your movement, man, stun a bam and all these guys, man, what's the next project coming out for you? Well, we actually just put out a D-Raw project today. Okay. Okay. D-Raw, man. D-Raw, man. So we've been waiting to put that out for a while. It's a little new style where he's kind of like mellow. You know what I'm saying? Singing to the girls and being a cool kid. I love the cover. Yeah. That cover is dope. And then we put smoothie single out chat butler today. And, um, you know, he got a project coming soon and stun a bam. Music is actually coming out as well. We put Martina music out for the EP through songs. Yeah. I see that. That was a little something to work with. So we got a lot of music coming out through 15-01. We just hope, you know what I'm saying? Some catch. We keep going. Keep it going, man. Hey, man. It's just a wonderful thing to be able to sit down and just hang out with you, man. And just, uh, you know, um, just get to see the movement, man.  We had some dope people to come through the day. Thanks to you, man. We came down here to Houston as we spoke on the show that we was going to do. And the good thing about it is to be wanting to do what you, you know, say that you're going to do. And most people, you know, they don't keep their word. So that's the most important thing. I hate when people don't keep their word.


Alrighty. I have a bit of an opinionated video to do today, but before I get started, I did wanna stress before you listen to any person's opinions on the matters going around in the world right now, another personal opinion I have is you should always listen to the healthcare professionals and take advice from your government officials and people like me on YouTube don't know any more than your doctors and your health professionals in your country. So definitely take their advice over anyone on YouTube and everything I say is just an a personal opinion and just a reflection of what's going on. So I just wanted to say that before I got into the video because I don't want anyone to listen to me and to take what I'm saying as, oh, everything must be good or everything must be wrong. So I'll get right into it now. Obviously for obvious reasons, a place like Canada's Wonderland isn't open right now and that makes sense, that makes a lot of sense. So places like Canada's Wonderland and Marine Land in the Toronto area and the Niagara region currently are closed for obvious reasons to keep everyone safe. But as we progress through with what's going on, I think we're getting to a much safer place to start thinking about again, using the keywords thinking about these places opening up. So obviously I use this video here right now to show the last season. So this is Winterfest at Canada's Wonderland back in 2019. It's super crowded and things like this aren't gonna happen for a while and that's unfortunate. When I reflect back on it, it's really gonna miss stuff like this. We were walking through here complaining about how busy it is. Now I only dream about walking through a place this busy and just being around people in general again. I'm really looking forward to that. It's funny how what once used to be super annoying is now, wow, I really missed that time. But nonetheless, parks like Canada's Wonderland and the chain that owns Canada's Wonderland and other chains like Six Flags and the parks in Orlando have all presented measures in order to keep people safe and measures so they can open up. And honestly, upon review, every single park out there seems to have a really good game plan. And I honestly, again, as I always say, trust my life with the company that operates Canada's Wonderland, that is Cedar Fair. I trust them with any decisions they make. They're a very intelligent company. And they definitely know what they're doing in terms of safety. Again, Canada's Wonderland, as you saw on a recent documentary, it wasn't a documentary, sorry, a thing by CBC, I think it was. Canada's Wonderland has one of the best track records out there in terms of theme parks. So I trust them. And I know that when they do open, if they open this year, everything will be extremely safe. It'll be extremely clean. And the processes that they're presenting seem very viable in terms of opening up the park. I think it's time. I'm not saying open the gates tomorrow. I'm just saying with what happened today, so for those of you that don't know, the Toronto Zoo started selling tickets for a drive-through option. And to get a ticket, as you see right here, your spot in line is 4,861. And from what my personal experience, I had multiple tabs. They went all the way back to 6,000, which means there's probably like 30,000 people in Toronto right now, trying to get tickets to the Toronto Zoo. And they're only selling tickets for a week period. And that being said, they're probably gonna sell over 100,000 tickets today and tomorrow alone. What that tells anyone paying attention is we are really bored. We are really bored. We are looking for things to do. And we need things to do. If we don't start keeping ourselves occupied, you're gonna end up with a lot of people starting to get frustrated, starting to be unsafe, again, keyword unsafe, not following the rules and recommendations presented by health officials. And you're gonna end up with spikes in that sense in terms of what's going on. However, if you open things up safely and you enforce things like wearing a mask and you have security guards that are enforcing the mask policy and you're doing the testing at the front gate, again, these were just options presented by Cedar Fair, but they were discussing even doing quick blood tests to ensure no one had what was going around coming into their park. They were gonna do temperature checks. They're gonna do social distancing, an app to even line up to get onto our ride. So no one will be lined up close together. So you just click a ride and it'll tell you when to come ride it. Mobile ordering for food and an extremely limited capacity in terms of attendance, people allowed in the park. I'm super excited. I think that with those options presented, we also just learned from the CDC that in terms of spread, it is not as easy. Again, keywords not as easy. A lot of people were sharing that article being like, oh, it doesn't spread on surfaces. That's not true. It just spreads not as easily as they originally thought. So in my personal opinion, I think now might be the right time to start thinking about opening up parks in Canada. We see theme parks in Florida opening up and they get more cases than we do, almost double the cases we do. I'm not sure about the population difference there. I should have looked that up before this video. I just didn't think I was gonna go on that topic, but parks are opening up in America with more cases. And I think that Ontario, with the things that Canada's Wonderland will have in place to handle this, I think that they could honestly open up by July 1st. I would like to see Canada's Wonderland open up by July 1st. I'm saying July 1st because they obviously still have training and getting the park ready to do. And yeah, but I do know that they would be ready. I know that they would be safe. And I do trust the systems that they have presented as options to keep people separated, social distance and still enjoying and having fun. Now, with that being said, seeing what happened at the Toronto Zoo today, getting into Canada's Wonderland once they do open is going to be extremely difficult. And that is the part that I'm afraid of, but at least it'll be open. But I guess the enthusiasts won't be able to go every day because getting a ticket's gonna be extremely hard. But I'm excited. I think that the Toronto Zoo and opening up today, well, it opens up tomorrow, but you could get tickets today shows that people are really looking forward for something to do. And what a lot of people don't understand is a lot of people really do wanna go back to work. You know, I see online the 40 plus in age people are accusing the younger generation of enjoying being unemployed and enjoying being on Serb. And in fact, that's not true at all. Every single person I've talked to is dying to get back to work. Everyone wants to get back to work. Everyone wants to start enjoying their time again. And again, it is moving too quickly for the people that expect it to open up today or things to move day by day. No, this is an every two-week thing. We have to watch what's going on in terms of charts is how I'll word it. And yeah, you can't just magically snap your fingers and everything's open. You have to watch it. And I really do trust how the government and health officials have been handling things. And you have to, they know what's going on. They're not lying to us. The whole world isn't working together in terms of lying to us. But nonetheless, we're here. And I do think with everything that's going on in terms of North America, I think that a July 1st opening for Canada's Wonderland isn't out of the picture. And I would really like to see that. And I think that they're ready, Cedar Fair and Canada's Wonderland. And yeah, I'm curious to hear what you guys have to say about this matter. Do you think Canada's Wonderland should open around July 1st? Do you think that's too early? Do you think that's too late? Do you think that's an appropriate time? Again, you guys have opinions just like me and I would love to hear them down below in the comments section. Don't be afraid to call me out on anything I've said in this video. Again, as I said, always trust your healthcare professionals any more than any guy or girl on YouTube. They know a lot more than we do. And again, this is just an opinion. I personally just think it's time. And I would like to see more things open.  And if a park or a business isn't following the guidelines set forth by the government officials and the health professionals, then they should be shut down. Or as it was said, our government in Ontario is up to a 92% tracing rate within a 24-hour period. That's a really good number to have. I think that ultimately we're ready. I'm ready personally. I'm ready. I would like to go walk out and hang out with a group of even just five friends, social distancing together, all wearing masks. And obviously abiding by all the guidelines set forward by our health professionals, Canada's Wonderland Cedar Fair and any business that chooses to open. And yeah, again, it's a difficult topic to discuss because I know there's gonna be some people that watch this video and get upset. And I know there's gonna be some people who overly agree with my opinion on this subject. And then there's a few of us like me who's in the middle like, yeah, I know we need to be safe and yet we need to open up. We need to open our economy back up but we need to do so safely. Wearing masks, washing our hands, being clean and social distancing. Anyways, this video was not meant to offend anyone again. Hopefully you took this just as an opinion and feel free to counter my opinion down below in any form. And hopefully you're staying safe, wear those masks and listen to your healthcare professionals, guys. And hopefully we'll see each other at the park someday soon. Thanks so much for watching, guys. Have a good one. Bye.


Hey, John Crestani here and I'm going to be giving you an affiliate marketing tutorial for beginners. If you just don't know what niche to start selling products in. I've made tens of millions of dollars selling products as an affiliate marketer. You don't need any inventory to get started. You don't need any money to get started and anybody can do this. It's very simple and I'm going to go over exactly how to decide where to start in affiliate marketing if you're just a complete beginner novice and have no idea where to get started. Let's get into it. John Crestani here affiliate marketer making tens and tens and tens and hundreds of thousands of dollars every single month doing this since 2012. It's a great way to make a lot of money online especially if you have zero capital and zero skills to start with. Now I'm going to be giving you a little bit of a tutorial of what niche to start in if you're just kind of just completely green a total noob with two zeros in it and let's get right into this. Now the first piece of advice I give you having made tens of millions dollars is actually to start with a mass market niche. Now a lot of folks in affiliate marketing start with some sort of make money niche but there are many other niches other than that. Keep in mind almost everything is bought over the internet now. I mean 2020 has been a turning point. People are buying more products than ever through the internet as opposed to in real world and you could be a part of that and make some of that money in all of that transactions that are floating around. Now the big three niches in affiliate marketing are health, wealth and relationships. Now I have a product called the super affiliate system which you can make money with and through the Clickbank or the DigiStore affiliate networks and that's in the wealth niche right but health and relationships are even bigger. Health is probably the biggest niche of them all because most people actually aren't concerned with necessarily making more money. Most people tend to be fairly comfortable with their incomes and they just want to look better or they want to feel better or they want to be skinnier or more beautiful and those kind of fall under the health niche right. Fitness, weight loss, beauty all kind of fit under that general headline. Then you have relationships which is probably the thing that people care most about and there are a lot of products out there where people can you know learn how to be better in relationships also about dating websites which is a very very large niche online and it's always bad and what's great about health, wealth and relationships is these are niches which are always going to be around. These are problems that people always are going to want to have solved. People are always for the next hundred years, for the next thousand years, for a thousand years in the past will need to be and desire to be in relationships. People for thousands of years and for thousands of years moving forward have desire to look better and be healthier until we get uploaded into like a computer program like Elon Musk is trying to do with his Neuralink. People are still going to need to be healthy and look good and for thousands of years and for thousands of years moving forward people are going to need to be able to produce money or some sort of currency whatever that is to sustain their lives. So ultimately these are niches that are not going to go away. I suggest you start off in them because they're always is going to be this need for them. So you know that if you are promoting something to help people make more money to help people be healthier or to help people get in relationships or be in better relationships that really you're marketing to a problem that is important and if you can't sell products in that niche then it's not that people don't need what you have to sell it's that you're marketing it wrong so you can eliminate that variable right away. Now one of the things I do in my own business is this is something I learned from David Rusenko co-founder of Weebly is you always want to optimize your business to minimize unknown unknowns. There are four quadrants there known knowns unknown knowns known unknowns and unknown unknowns and as much as you can optimize for the unknown unknowns and you know that you aren't going to have a problem selling products in either of these three monster niches as long as you go into one of them. Now the second tip I have for you is to interact with the audience in your niche before you start marketing it so before you start your YouTube channel where you're talking about how people can make money before you start your Instagram where you're motivating people to get fit or before you start your Pinterest where you're displaying images or advice of how to get in better relationships or to get on more dates interact with that audience talk to people again many marketers tend to be introverts many people become marketers and affiliate marketers in particular because they don't want to actually talk to people they just want to make money online and type in that's me if you are an introvert and you just don't want to talk to people and make money online that's why you're doing this but I'm telling you the most valuable data and the best way to get good at marketing and selling to these folks is to actually talk to them and hear their pain points in person hear what their problems are of why they believe they're not making any money here why they don't believe they're getting in the right relationships talk to them and hear what's holding them back from sticking to their diet or their fitness programs and you'll get very valuable information that you can use and talk to your audience on a very personal one-to-one level in order to help them succeed and that in line will help you build your audience on Instagram YouTube Facebook or help you do your advertising on the same sorts of networks now I'm a constant question asker in fact it actually annoys a number of people by the amount of questions I ask because I'm always drilling down to get information I'm always asking people about their lives what they believe is holding them back and I try to do it in as non-invasive way as possible but this gives me an advantage over other folks that just scroll on their Facebook news feed looking at what their friends are saying and living in a bubble again I keep expanding my bubble I keep expanding my circle of influence all the time and you know I'm teaching people how to make money so I'm always asking people about their side hustles about what they're doing what's holding them back why they don't believe they are where they think they should be and a very personal one-to-one understanding of how to sell products in my particular niche which is called make money online so I've niched down within the wealth niche to online money but there are a number of other niches such as real estate money stock trading money MLM money etc and my third piece of advice would be to give away some sort of free report or white paper guide or calendar whatever it is to help people in your particular niche many marketers go into any of these niches and just try to push affiliate products and remain undifferentiated and it's very easy to become just another marketer in a particular niche but you can differentiate yourself by giving away some sort of report or guide or step by step or you know video or calendar that is jam packed with value that will attract people to you for example let's say you want to sell products in the sleep niche right helping people sleep good which is falls under the overall banner of the health niche what you could do is you could give away a calendar that helps people chart their sleep or you could give away a free ebook that goes over advice on how to help people get better sleep and in this ebook or in this calendar or guide or whatever you want to call it you could have links multiple affiliate offers that help people get better sleep there are a lot of sleep related products on Amazon there's a ring that you can wear there's apps you can buy there are pillows there are mattresses there are all sorts of bedsheets that are supposedly help you get better sleep there's essential oils that help people get better sleep there's supplements that help people get better sleep and what you can advise in your guide or ebook or calendar is you could have links to all of these products every step of the way so that you can make money from a dozen sleep related products and you could do this through Amazon's affiliate program just to give an example now if you've created a good guide that people draw value from and people are relying on they'll buy from your links now just like PewDiePie which is a popular video gaming youtuber has a number of affiliate links to the chair he sits in and the headset he wears and the games he plays in his video description you could also start a YouTube channel and just talk about sleep related activities and let that be your free value and then put affiliate links underneath your description and check out one of PewDiePie's videos and you'll see affiliate links you'll see how he does affiliate marketing I hope you got some value from this and check out my other videos if you want more in-depth or step-by-step information on affiliate marketing I also encourage you to subscribe to the channel hit the notification button and join my community on discord I have a lot of good content on there thousands of people are in the community and I encourage you to be one of our members it's all free have a good day talk to you soon and remember I give away thousands and thousands of dollars every single month to my subscribers who have their notifications enabled so you can just see you know tons of gift cards I scratch these live and then I show you the winnings you know whoever basically enters in the gift card codes first gets the money so enable notifications subscribe and talk to you soon have a good day yeah but within the first a few exercises I did the exercises immediately and I got immediate sales and I just couldn't believe it and I just made my first commissions pretty freaking crazy because I had no clue what I was doing hi I'm Shalinda and I just made my first online commission how does it get any better than that I'm truly grateful for this program and I look forward to learning what it is that I need to know in order to create what is a brand new in business involvement for me so thank you so much for making this available to everyone and I just want to take a moment so I'm so grateful for me for choosing this by me choosing this allows me to create a reality that is completely different from the one I have at the moment


But no one can actually ever tell you exactly how much work goes into it. You know, it's not what you knew you'd be doing that's really a tough. It's the stuff you didn't know you'd be doing the whole time. I think it wasn't until the first day of filming when we saw the crew pull up and all the equipment and the weird old barrel's worth of supplies that just kept coming that we were like, hey, wait a second, this is not exactly what we were expecting. Because there's so many parts moving and people involved that you can't be prepared for it. I think even if I were to do this a second time, there'd probably be things that will pop up. I'm basically a, believe it or not, a shy person. And for me to get up in front of a group of people and a lecture is a little scary. It was just, it was kind of madness. It was organized chaos. And I remember feeling bad for you because you were the one who was in charge. Roll camera, please. Good evening. I guess the main story is an introduction to this organ inside of our skull that defines who we are. It is the one organ that gives us our personality, that helps us navigate the world, helps us understand the world, helps us interact with the world. It defines who we are. It defines our humanity. And it's really that, that I guess is the story. And every single component, every single video is one building block to understanding the whole. This explains why you can hold food or liquid in your mouth and breathe at the same time. And if we laugh about something, we tend to remember it better. The first round of videos was produced in late 2013 and it was actually part of the first flexible learning initiative here at UBC funded through the Teaching and Learning Enhancement Fund. And so we got this money to flip the classroom and we focused it solely on the neuroanatomy labs.  Students have come to believe that, you know, the words educational and video are synonymous with this is going to be really bad. And I want to take them away from that. I want to show them that educational video can be something really compelling and really good.  And he said, you know, it all needs to fit together and we need to have a certain look. We need to have a certain color palette to go with it. Okay, I was just worried about the content. And one of the unique things that we have here at UBC are medical illustrations that were made in the 50s. And it was from those images that we extracted the entire look of the videos. So the lighting, the way we were dressed, the lecture theater that we chose to film in was like a time capsule from the 60s. At faculty medicine we tend to have people that aren't used to writing video scripts, writing them because they're the subject matter experts. And that's terrific and they bring a wealth of knowledge. But they haven't written a script before and my first piece of advice to them is write it quickly. Write it right away. Don't worry about it. Let's get a quick deadline in and let's finish that script as quickly as possible. And rarely, if ever, does this actually happen because detail oriented people tend to worry about the details. So about six weeks before we were supposed to go to camera we received the scripts which were essentially two to three hundred page PowerPoints, unwatchable and unreadable. And for me the alarm bells went off. Script writing is another thing that we don't usually do. We're basically writing textbooks to be read to the audience. As much as we were talking about we had these lofty goals to be innovative and make it fun and interactive. It was we kind of recreated what we set out to not do. You know we met with the group and what did we do? We had them read the scripts out loud and discovered very quickly that they had not done this before. And when you're reading something out loud you get a sense of how it feels, how it sounds, you know what it's going to be like. It's very different to type something out and think about it. Oh yeah that sounds perfect in my head because I've fixed it, fixed all the problems that might be there. But to hear it out loud as an audience with other people in the room. And I could see by the middle of the second script being read out loud I could see the panic in their eyes and I knew that we had been effective. You can't just do your typical content spiel. You have to make this different and exciting or why should I care, that sort of thing. And we were all kind of like, oh okay how do we do that? We're not used to that. They gave us a template that said what is the person saying and there's the next column, what are they doing? The next column, what is on screen? What are we seeing? What are the actions? And the last column is sort of like what's the point? What are the extra notes? And seeing it kind of compartmentalized in that way changed the way that we wrote the scripts because like oh you can't just have, you know, 6,000 words. So after we had the team go back to rewrite the scripts you know everything changed. The tone changed. The story changed. It became a really interesting story about why we do the things we do as human beings. Especially in the second series we had a lot more fun with it because we were allowed to be a lot more creative because we knew the medium. There wasn't this learning curve of trying to understand what video is like. Now knowing what video is like we were allowed to play with it. And so we started the conversation saying what is amazing about the brachial plexus? What is amazing about the pterigopolitide fossa? What is the hook? What is interesting? How can we best summarize this topic in a way that would be engaging? That would capture people's interest and imagination. So one of the areas we wanted to teach about is called the impertemporal fossa here in the head. And there's a number of nerves that go through there and a number of fibers and it's complicated. So as we were developing this I kept on asking the team what's cool about this region? Why do we care? And so I threw in I like that taste goes through there. And so then Wayne said well I think the most important thing about this region is the way the parasympathetic fibers go through there which will supply your salivary glands. And then I said well now we're salivating and we're tasting. What else are we doing? And so we came up with this idea that we should just Complex flavors blend it into something absolutely delicious. The nerves that cause salivation and that carry taste as well as sensation from your mouth. The project was fairly challenging to film and it's challenging to film because you only have so much time, you only have so much budget and that equals crew. That equals the amount of time you have of rental equipment, the amount of hours you have with people in reality, the amount of food you can buy. And so your sequences tend to be limited by what you can spend on those things. In science that's not usually as big of a problem if something takes longer you just start again, you pick another day but this was very much like this is all the time you have so you better get it. And like that was just a different kind of stress that I hadn't experienced before. Videos are very different than a lecture in that every single thing that happens during the video is premeditated. And the thing about filming such dead scientific material is you actually require teleprompters so that the performers can read what they have to say. They know it very well but it's very difficult to perform exact scientific jargon on the spot. You know if I were to choose my favorite sequence in the whole thing it would have to be the opening to brachial plexus. So there was a circular track around the violinist that encircled the violinist and the camera was on the track and the camera went around the violinist as she was playing and I had to walk in and keep walking around the track. Into this scene walks you know Wayne looking like a million bucks who then begins to teach. The way the video was captured, the lighting, the music, the choice of the music and everything is perfect. 100,000 views like that project has been seen so many times that's very gratifying. Both of us felt that we wanted to get material into the public domain. I think that UBC is one of the best universities in the world has a social responsibility to reach out. We can't stay on our ivory tower and just keep our knowledge and our expertise to ourselves. Something like this is really easy to share and I think we have to. Now when you go and Google neuroanatomy the first thing that comes up is our website. Well they say the proof is in the pudding, right? So you have to go and see have you had an impact and one of the ways that we measure impact is of course through the assessment and so we had had the assessment of the neuroanatomy labs over many years the neuroanatomy lab exam and we had statistics on how students perform in that lab and it's pretty much the same year after year there's no statistical difference. When we flipped the classroom we were able to shift the bell curve to the right which means that we were able to help the students who would perform more poorly and shift them into the better performing cohorts. That was for us the proof that we'd had a real impact on student learning. The exam didn't change but the student knowledge changed and they performed much better. We did take these resources for granted to really understand how much work went into making a quality product. We started writing I think a first script in May of this year and we finished shooting it was in October I think yeah and yeah I think the movie is going to be out in January but there's a lot more time you know left me need to kind of figure out how to put animations in so it's a lot of works. Start early create deadlines and then deadlines for your deadlines and deadlines for those deadlines and you have to be really really proactive because it's always harder than you think it's going to be. It's awesome to have this on my CV as far as like a teaching portfolio thing it's anybody can go and teach a class anybody can go cover a lecture or give a presentation I've been a part of creating digital media for a website that's being viewed over a hundred thousand times it's kind of nice.