[User] Please transcribe this text

[Assistant] So you want to go to a lymph node when you're naive. You don't want to go to a lymph node anymore when you're an effector cell. You want to go kill things. You can tell I did a PhD on CD8s. My cell is always a CD8.


[User] Please transcribe this text

[Assistant] Maybe you want to go back and look for those initial infections.


[User] Please transcribe this text

[Assistant] We can also see things like BCL2, which is important for cell survival. One of those apoptosis proteins or anti-apoptosis proteins you learned about from Dr. Dunaway. You want naive cells to live, but you really want memory cells to live. Um, and so memory cells will have more of that protein.


[User] Please transcribe this text

[Assistant] Uhm, we also see more of this receptor.


[User] Please transcribe this text

[Assistant] That allows memory cells to get survival cytokines, particularly IL-7. This is the IL-7 receptor.


[User] Please transcribe this text

[Assistant] That same old survival cytokine we saw way back during T cell and B cell development. Memory cells use that too to help them survive.


[User] Please transcribe this text

[Assistant] Genes turned on in a naive cell, partially because that chromatin is closed.


[User] Please transcribe this text

[Assistant] Well, in either a memory cell or an effector cell, the chromatin is much more open, as you can see here. We have a lot of our signaling proteins pre-made, and so that cell has a much lower trigger. Uhm, it's going to, uhm, not take as much to be activated.


[User] Please transcribe this text

[Assistant] Uhm, once they've gotten their first activation, they're good. They don't need signal 2 anymore.


[User] Please transcribe this text

[Assistant] We're also going to see a lot more general cytokine production from a memory cell to help those memory cells survive.


[User] Please transcribe this text

[Assistant] When we think about different memory cells, uhm, like frankly when we think about immunology,


[User] Please transcribe this text

[Assistant] Uhm, we can further divide up the cells. And so there are different types of memory cells.


[User] Please transcribe this text

[Assistant] So this shows three different types of memory cells that sometimes we think about.


[User] Please transcribe this text

[Assistant] Sometimes we think about cells that are called central memory cells.


[User] Please transcribe this text

[Assistant] Central memory cells mostly spend time in the secondary lymphoid organs, like the lymph node.


[User] Please transcribe this text

[Assistant] They are going to go back to the lymph node and look for their initial antigen, just like the naive cells were. They're sort of.


[User] Please transcribe this text

[Assistant] Trying to do the job of naive cells of catching that first antigen in the lymph node. And then they're just going to do their job faster.


[User] Please transcribe this text

[Assistant] There are some memory cells that are called effector memory cells that


[User] Please transcribe this text

[Assistant] We'll circulate both through the secondary lymphoid organ, but we'll also spend some time outside in tissues. So you might imagine after a SARS-CoV-2 vaccination,


[User] Please transcribe this text

[Assistant] You might get some central memory cells, or say, we'll say, we'll say SARS-CoV-2 infection.


[User] Please transcribe this text

[Assistant] You might get some central memory cells that go to your lymph nodes that look for that virus again.


[User] Please transcribe this text

[Assistant] But you're gonna get some other ones that are gonna go a little long from time to time.


[User] Please transcribe this text

[Assistant] Because the lung is where they found their first antigen. So they're going to say, maybe the lung is a good place to look for this antigen. We're going to look in the lymph node, but we're also going to look in the lung. We also see resonant memory cells that just spend time in the tissue.


[User] Please transcribe this text

[Assistant] They say, okay, I found my antigen in the lung. The place I should be looking for my antigen to come again is the lung.


[User] Please transcribe this text

[Assistant] Uhm, these are, uh, we have understood kinda central and effector memory for a while. We are starting to understand resonant memory more.


[User] Please transcribe this text

[Assistant] I'm going to show you a couple of things briefly and then we're going to come right back to this slide.


[User] Please transcribe this text

[Assistant] We can distinguish all of these different memory subtypes.


[User] Please transcribe this text

[Assistant] As well as other ones that people talk about based on their expression of different markers by flow cytometry. And so that's how many of them have been described. That was also something that was listed here.


[User] Please transcribe this text

[Assistant] In terms of the difference between a central memory or an effector memory cell, uhm,


[User] Please transcribe this text

[Assistant] The central memories, which are the ones that go to secondary lymphoid organs mostly.


[User] Please transcribe this text

[Assistant] Have the trafficking molecules to go to secondary lymphoid organs.


[User] Please transcribe this text

[Assistant] The ones that don't go to secondary lymphoid organs as much don't have those trafficking molecules.


[User] Please transcribe this text

[Assistant] And the amount of differentiation, how much signal they need to be triggered, things like that are different.


[User] Please transcribe this text

[Assistant] That we need to think about when thinking about this slide.


[User] Please transcribe this text

[Assistant] And just sort of in general. One thing that I mentioned to you is that we've known


[User] Please transcribe this text

[Assistant] And we know more details about central and effector memories than we do about resident.


[User] Please transcribe this text

[Assistant] We think of some infection you had, it can be whatever infection you want to imagine. Any infection that you personally have had.


[User] Please transcribe this text

[Assistant] And you want to know if you have memory T cells for that, uh, particular pathogen. Let's say that first you want to measure some central memory slash effector memory cells.


[User] Please transcribe this text

[Assistant] Well, I haven't had SARS-CoV-2, we'll pretend it's a SARS-CoV-2 infection.


[User] Please transcribe this text

[Assistant] How, how would I find out if I have central or effector memory cells against SARS-CoV-2? Yeah, Michael. So you're going to take a blood sample from me, and then what are you going to do?


[User] Please transcribe this text

[Assistant] And then you're going to look and see if you can find T cells with the right proteins on the surface using flow cytometry to be called central memory T cells or effector memory T cells. Yes, and in fact that is exactly what we do.


[User] Please transcribe this text

[Assistant] Which one do you think you might find more of in that situation, in that experiment?


[User] Please transcribe this text

[Assistant] You can see more of their time is in the blood. The central memories are spending a fair amount of time in the lymph node. Right? If you really wanted to measure


[User] Please transcribe this text

[Assistant] Central memory cells, you'd probably have to take a lymph node biopsy to at least get a full view of what's up with central memory cells.


[User] Please transcribe this text

[Assistant] In knowing about your central memory cells to have a lymph node biopsy? That sound like a good time? Not really. So most of the time we kind of use the blood sample. Go with that. It's easy. What if you had 50,000 people?


[User] Please transcribe this text

[Assistant] Who you had to study. Say you were, I don't know, Pfizer doing a vaccine trial.


[User] Please transcribe this text

[Assistant] 50,000 people, which is about how many people they had in their vaccine trials.


[User] Please transcribe this text

[Assistant] It's faster in terms of, if you got to do 50,000 of them, you're not going to do something that's, something that's super involved. You're going to do the sort of simpler one, right? Where you can get okay info, right?


[User] Please transcribe this text

[Assistant] Okay, so what if I want to know now how many resident memory cells I have? For, again, we'll say SARS-CoV-2, but.


[User] Please transcribe this text

[Assistant] Could be whatever. How do I figure out how many resonant memory cells I have?


[User] Please transcribe this text

[Assistant] So what tissue sample? Lungs. So I would actually have to give a lung sample.


[User] Please transcribe this text

[Assistant] So we have to take a long sample to look to see how many T cells there are.


[User] Please transcribe this text

[Assistant] I will tell you from having done this by experience, when you start looking at tissues, remember how when you had a lab, in the lab you looked at the spleen and you had to smoosh up the spleen and put it through a filter and all that kind of stuff?


[User] Please transcribe this text

[Assistant] Some other tissues are really annoying to get the cells out of. And they take like protease digestion and all sorts of work.


[User] Please transcribe this text

[Assistant] Uhm, I used to, I was doing some vaccine, vaccinations into the muscles of mice. I was putting vaccines into the quadriceps of mice. And so I would sometimes have to prep the T cells out of a quadricep of a mouse. Honestly, it looks like a milkshake.


[User] Please transcribe this text

[Assistant] But like, it's no matter how much protease you use, no matter how much you try to just, it's, it's, bleh. And that is not good flow psychometry. End case.


[User] Please transcribe this text

[Assistant] That's your lung sample. So it's going to be a sample that's not going to be that easy to work with. Do, does, does anyone want to give a lung sample right now?


[User] Please transcribe this text

[Assistant] Now, sometimes we know how to do it where we don't actually have to cut out a piece of your lung. We can just, uhm,


[User] Please transcribe this text

[Assistant] Make you snort a bunch of saline, and then try to pipette the saline back out. Doesn't that sound fun?


[User] Please transcribe this text

[Assistant] Yeah, exactly. Do you see why we know less about resonant memory cells?


[User] Please transcribe this text

[Assistant] You think resident memory cells are high on their list of things that they were gonna look for?


[User] Please transcribe this text

[Assistant] Especially if they wanted to get that vaccine trial done fast so that they could vaccinate people and all the people of the world and get that vaccine out fast.


[User] Please transcribe this text

[Assistant] Okay, we're gonna come back to this, but remember that. So remember that


[User] Please transcribe this text

[Assistant] Some ish, some things with the memory cells can be tricky because they have technical issues in terms of how you would measure them, some of them.


[User] Please transcribe this text

[Assistant] Immunologists in general have done a lovely job characterizing what's in the blood. But there are an awful lot of things not in the blood. Uhm, and those can be really hard to characterize.


[User] Please transcribe this text

[Assistant] So the key thing about this response that is happening as a result of these memory cells is that we're going to see something called a secondary immune response.


[User] Please transcribe this text

[Assistant] And the secondary immune response is much more potent than the primary immune response. So you can see there is an improvement. First of all, we started with more cells than we had before. So we were already at a better place quantity wise. But those cells increased really rapidly.


[User] Please transcribe this text

[Assistant] And we're better at their function so that.


[User] Please transcribe this text

[Assistant] Instead of, say, this virus replicating for a long period of time, we stop that virus from replicating right away.


[User] Please transcribe this text

[Assistant] Perhaps to the point where we don't have any symptoms. And so we have prevented disease even if we have not prevented infection.


[User] Please transcribe this text

[Assistant] And this is generally how we say, how we talk about, you know, you can't get whatever, chicken pox or whatever, twice.


[User] Please transcribe this text

[Assistant] Uh, because if you were to ask somebody a second time, they would say, oh no, I never had that infection again. I only had it the first time.


[User] Please transcribe this text

[Assistant] If you did super sensitive testing and sequencing, you would find that yea, they did have it a second time. It was just that it never got severe enough for them to be symptomatic. I point this out because I made this slide in 2009.


[User] Please transcribe this text

[Assistant] You can see in the secondary immune response, there was a little bit of infection, but no disease.


[User] Please transcribe this text

[Assistant] Now, the press likes to call that a breakthrough infection, and they're shocked that it happens.


[User] Please transcribe this text

[Assistant] That oh my gosh, you could have been vaccinated with SARS-CoV-2 and you could get infected again. Yup.


[User] Please transcribe this text

[Assistant] That's exactly how it's supposed to work. But the idea is you're going to get a less severe infection because you have such a good memory response. The difference is that in 2009,


[User] Please transcribe this text

[Assistant] We weren't testing people all the time for certain viruses and doing super sensitive sequencing.


[User] Please transcribe this text

[Assistant] And so the only way you knew if you had gotten infected a second time was if you had symptoms.


[User] Please transcribe this text

[Assistant] Now we're actually looking beyond the symptoms because of new technology and we can actually start to detect this infection. We always kind of knew it was there, but now we're actually measuring it.


[User] Please transcribe this text

[Assistant] If we had decided to like, spend our time doing hardcore sequencing,


[User] Please transcribe this text

[Assistant] Uhm, so if we actually look specifically at some of the differences, uhm, with the primary response we had a small number of specific cells.


[User] Please transcribe this text

[Assistant] There are more of them at the start in the secondary response. There was that delay.


[User] Please transcribe this text

[Assistant] Uhm, in the primary response that was not present, in the secondary response, things are already present.


[User] Please transcribe this text

[Assistant] Uhm, in the primary response, we haven't done class switching yet. We haven't done affinity maturation yet.


[User] Please transcribe this text

[Assistant] Uhm, we've already done those things by the time of the secondary response, so we're going to have really good antibodies present.


[User] Please transcribe this text

[Assistant] In the primary response it takes a lot to activate our cells, in the secondary it takes less. We've also got this delay, uhm,


[User] Please transcribe this text

[Assistant] With, uh, T cells in the primary that we don't have with the secondary. And then in the primary response, uhm, the innate and the adaptive responses are a little bit more separate time-wise. While they're happening at the same time and can work together more in the secondary response.


[User] Please transcribe this text

[Assistant] Uhm, so just thinking about some details with regards specifically to secondary antibody responses.


[User] Please transcribe this text

[Assistant] Uhm, what you'll see is that in a primary response, uhm, particularly at the beginning, you can see our lag. And then we see mostly IgM.


[User] Please transcribe this text

[Assistant] Uhm, and then we start to really get more of an IgG response later because we needed the time for class switching. Remember all of our naive B cells start out making IgM.


[User] Please transcribe this text

[Assistant] IgG as our predominant isotype, uh, not so much IgM being made.


[User] Please transcribe this text

[Assistant] We can also see that we have much better affinity of that IgG, um, than we did of the IgO. Um, so those antibodies are great. You can see this from your textbook here.


[User] Please transcribe this text

[Assistant] Uhm, so we have, it's really the same thing I've been saying, secondary response, we have more cells.


[User] Please transcribe this text

[Assistant] Secondary response, we're making our class switch isotypes. We have high affinity, we have a lot, we've already had somatic hypermutation.


[User] Please transcribe this text

[Assistant] First time we haven't had those types of things. And oh my gosh, it's the same thing again.


[User] Please transcribe this text

[Assistant] Uhm, though the one thing I will note is that we really are going to be seeing a lot of that secondary response.


[User] Please transcribe this text

[Assistant] With protein antigen, because remember to get that good B cell response and to get, help B cells become a memory B cell, differentiate into memory B cells, they need T cell help.


[User] Please transcribe this text

[Assistant] And so we need to get the T cells to provide help, which uhm, is something that we largely see with, uh, protein antigen.


[User] Please transcribe this text

[Assistant] We can also see a little bit of an effect in terms of different mutations or different genes.


[User] Please transcribe this text

[Assistant] I told you a little bit about, uhm, a disease called hyper IgM syndrome, where patients just make IgM, and hyper, too much IgM.


[User] Please transcribe this text

[Assistant] Really the reason why they're making too much IgM is because they're making not enough of anything else. They make like 100% IgM and 0% anything else. Uhm.


[User] Please transcribe this text

[Assistant] And there are, we now know, we know of three different genes that can be mutated in those patients. One of them is CD40, one of them is CD40 ligand, where you can't get help from your T cell to the B cell. The other gene that was mutated in that disease is AID.


[User] Please transcribe this text

[Assistant] So we can look at IgM levels and IgG levels in response to either first or second dose here in mice that either have AID or do not. So if we look at IgM, it goes up.


[User] Please transcribe this text

[Assistant] And then it goes up a little bit more upon the second immunization. And like, surprise, surprise, that's what it should look like. And if we have the AID deficient mice, it kind of looks the same.


[User] Please transcribe this text

[Assistant] Because remember, AID is important for somatic hypermutation in class switch. So we shouldn't really see a big issue with IgM. If we look, however, at IgG,


[User] Please transcribe this text

[Assistant] You can see, and we don't have AID, you can see we have absolutely no AID, uh, IgG being made, while we get this great increase in IgG in our normal mice.


[User] Please transcribe this text

[Assistant] One other slide that I've already shown you.


[User] Please transcribe this text

[Assistant] And I want you to look at the IgM data after the second immunization.


[User] Please transcribe this text

[Assistant] Is there anything that you notice about the IgM data after the second immunization?


[User] Please transcribe this text

[Assistant] So, so if we compare the amount of IgG versus IgM, we do see less. I mean, you can see it's way less than it is for IgG. Certainly. There's something else that you can notice about these data.


[User] Please transcribe this text

[Assistant] Yeah, the one that can't class switch, the one with the ID actually has more IgM.


[User] Please transcribe this text

[Assistant] Than does the right normal. Like there's extra IgM. That's kinda weird.


[User] Please transcribe this text

[Assistant] If you look at this image that I'm showing you.


[User] Please transcribe this text

[Assistant] When you look at a normal secondary response, it actually shows, during the secondary response, you make less IgM than you did the first time around.


[User] Please transcribe this text

[Assistant] You actually, the IgM went down and you made less. So there's something weird going on about IgM levels during secondary responses.


[User] Please transcribe this text

[Assistant] Almost like the IGG is suppressing the IGM.


[User] Please transcribe this text

[Assistant] And when you can't make the IgG, you don't get that suppression anymore. Are you noticing that?


[User] Please transcribe this text

[Assistant] Yeah, so this is really more of a naive B cell, right? So the naive B cells are making IgM. The primary response, that's all you got.


[User] Please transcribe this text

[Assistant] Yeah. Mature or activated B cells.


[User] Please transcribe this text

[Assistant] You know, it's only here later on do you have some of them to start contributing IGG. Here you've got a lot of them.


[User] Please transcribe this text

[Assistant] We're, we're, let's sort of think about this situation. We're in the lymph node. This room is the lymph node. Okay.


[User] Please transcribe this text

[Assistant] We don't have infinite amount of antigens.


[User] Please transcribe this text

[Assistant] We don't have infinite amount of glucose. We don't have infinite amount of space. We don't have infinite amount of cytokines.


[User] Please transcribe this text

[Assistant] Right? So we have to kind of pick and choose who we're going to give them to.


[User] Please transcribe this text

[Assistant] Right? That's kind of what we would ideally want to have happen. And so in fact,


[User] Please transcribe this text

[Assistant] We do suppress the IUB cells during the secondary response.


[User] Please transcribe this text

[Assistant] So that we can make sure that there are plenty of resources for those activated B cells.


[User] Please transcribe this text

[Assistant] And we can see this happening here.


[User] Please transcribe this text

[Assistant] It has an FC receptor on its surface.


[User] Please transcribe this text

[Assistant] The FC receptor is specifically an FC gamma receptor. What does an FC gamma receptor bind?


[User] Please transcribe this text

[Assistant] An FC gamma is going to bind the FC region of IgG.


[User] Please transcribe this text

[Assistant] At the beginning, the first time you see that antigen, there's no IgG around. So that receptor does nothing.


[User] Please transcribe this text

[Assistant] Later, when you actually have some IgG around, that IgG will also bind to the pathogen and will bind to that FC receptor on the naive B cell.


[User] Please transcribe this text

[Assistant] Conserve resources for the memory cell, maybe not turn on those naive cells. The memory cell, it doesn't have the FC receptor.


[User] Please transcribe this text

[Assistant] This is just showing you that same negative signal coming from the FC receptor.


[User] Please transcribe this text

[Assistant] I actually was really worried when I was putting together these slides because I touch this issue in two different ways in this slide, in these slides. And both times I touch it, I'm like, oh god, do I really have to go there?


[User] Please transcribe this text

[Assistant] Um, because it's, it's so messy right now and it's so murky. And so I'm going to point this out and then I'm going to say, I don't really know what's up and the field doesn't know what's up and.


[User] Please transcribe this text

[Assistant] I feel bad that this comment is going to end up on YouTube because it's probably going to come back and bite me. Anybody who says they know what's up on this, I don't think has looked it up.


[User] Please transcribe this text

[Assistant] I think it's a big mess and it's not super clear. And that is this question of uhm, something called immune imprinting. Whether the first


[User] Please transcribe this text

[Assistant] Antigen you see influences the second antigen you see.


[User] Please transcribe this text

[Assistant] I mean again, show you some slides on another part of this later. But this might make you say, well yeaah, if I get Omicron, I'm going to


[User] Please transcribe this text

[Assistant] Turn off naive B cells that might be specific to Omicron.


[User] Please transcribe this text

[Assistant] And just turn on the memory B cells responding to the original.


[User] Please transcribe this text

[Assistant] This phenomenon that I show you here is very important. It is clearly something that happened. There is a disease state I'm going to tell you about in a week or two that we can treat and basically cure based on this. So this is a hundred percent a thing.


[User] Please transcribe this text

[Assistant] But when we get into real life and real microbes and things, it gets a bit messy. So,


[User] Please transcribe this text

[Assistant] What I'm telling, what I wanted you to see is if you have heard or read or thought anything about this idea of


[User] Please transcribe this text

[Assistant] A previous immune response impacts a later one. This is one of the phenomena that plays a role. But there's a whole lot of phenomena that play a role and it's really messy.


[User] Please transcribe this text

[Assistant] Uhm, we can also think a little bit about, uhm, memory T cells as opposed to thinking about memory B cells.


[User] Please transcribe this text

[Assistant] Some effector cells turn into memory cells or do some naive cells turn into memory cells and other ones turn into effector cells? Uhm, I think this is actually...


[User] Please transcribe this text

[Assistant] More the way it goes, uh, as I look from my read of the literature.


[User] Please transcribe this text

[Assistant] Uhm, actually this is paper that's crazy cool. That shows that when the naive T cell interacts with the dendritic cell,


[User] Please transcribe this text

[Assistant] It turns its microtubules so that it can divide exactly in a plane like that.


[User] Please transcribe this text

[Assistant] Uhm, related to the dendritic cell and the close cell becomes the effector and the far cell becomes the memory cell.


[User] Please transcribe this text

[Assistant] So uhm, we do think that the cells are actually memory cells from the start.


[User] Please transcribe this text

[Assistant] And we often think about memory cells as almost being like stem cells, where they can give you more effector cells, they can give you more memory cells, they can give you more of lots of different things.


[User] Please transcribe this text

[Assistant] Where, as the effector cells are the most differentiated cells.


[User] Please transcribe this text

[Assistant] Uhm, and have less and less of an ability to, uhm, become other things. They can just be effectors and die. Whereas, uh, memory cells, particularly more like the central memory cells are, uh, more stem cell-like and can make more memory cells, make some effector cells, make whatever you need. They're great.


[User] Please transcribe this text

[Assistant] We also know that there are a lot of cytokines involved in allowing memory cells to develop.


[User] Please transcribe this text

[Assistant] So, in order to get memory cells to become memory cells, those cells need to receive certain cytokines.


[User] Please transcribe this text

[Assistant] And in order for those cells to live for a long time, they need to get certain cytokines. Uhm, here you can see roles for IL-2, IL-15 and IL-7.


[User] Please transcribe this text

[Assistant] Uhm, in particular, IL-15 is helping those memory cells become memory cells. IL-7 is helping those cells survive. Uhm, and it is just that general survival cytokine that are developing T cells and developing B cells wanted.


[User] Please transcribe this text

[Assistant] All of the cytokines on this slide.


[User] Please transcribe this text

[Assistant] Are cytokines that are part of the common gamma chain family.


[User] Please transcribe this text

[Assistant] So I mentioned the common gamma chain family, this family of cytokines that all use one chain, the gamma chain, paired with other


[User] Please transcribe this text

[Assistant] Uhm, separate chains. So for example, IL7 uses gamma chain plus an IL7 specific chain.


[User] Please transcribe this text

[Assistant] IL-9, which is not shown here, uses the IL, uh, the gamma chain plus an IL-9 receptor.


[User] Please transcribe this text

[Assistant] IL-2 actually uses the gamma chain and two other things.


[User] Please transcribe this text

[Assistant] So uhm, this family seems to be really important in terms of thinking about memory in general. Uhm, it has been shown by me, uhm, that another member of this family


[User] Please transcribe this text

[Assistant] Uhm, this is an experiment that I did for my PhD. Uhm, and I infected mice with adenovirus. Uhm,


[User] Please transcribe this text

[Assistant] At day 0 and then at day 56, so 8 weeks.


[User] Please transcribe this text

[Assistant] Uhm, and I was looking for, uh, the percent of virus-specific CD8 T cells. And if you look just at the purple, you can see I got a nice primary response. It contracted down. And then you can see I got a secondary response. Uhm,


[User] Please transcribe this text

[Assistant] And it eventually contracted down. And I actually did a whole bunch of math to show that uhm, the slope here is different, so it is both a, not just that we started with more,


[User] Please transcribe this text

[Assistant] The cell is actually divided faster too, and they have better functions and all that good stuff. Hooray! Immunology is true.


[User] Please transcribe this text

[Assistant] Uhm, but I also used some mice that were missing the receptor for IL-21.


[User] Please transcribe this text

[Assistant] IL-21, just like all those other ones, it has the gamma chain, and the IL-21 receptor part!


[User] Please transcribe this text

[Assistant] Uhm, and so I use mice that were missing that IL-21 receptor part.


[User] Please transcribe this text

[Assistant] And in those mice you could see I actually got the same peak.


[User] Please transcribe this text

[Assistant] For the primary response. Hard to see that here. Uhm, though the cells seem to die a little bit quicker. But when I gave the mice another dose, they made a primary response again.


[User] Please transcribe this text

[Assistant] And these mice kept making primary responses instead of making a secondary response because those cytokines are so important for helping to make sure that the cells can actually become memory cells as they differentiate. So this is how you might examine something like this.


[User] Please transcribe this text

[Assistant] Alright, so for the rest of the time today,


[User] Please transcribe this text

[Assistant] I wanna talk through some kind of example situations. I don't know how many of my example situations I'm gonna get through. We'll get through some. It'll be fine.


[User] Please transcribe this text

[Assistant] Where we think about what we've seen with immune response kinetics and kind of try to apply that to some real world situations, some real world things that you might have seen. So we're going to be thinking about sort of this, but moving forward. So.


[User] Please transcribe this text

[Assistant] Uhm, I showed you a piece of data on Monday. Friday. Whatever the last time I saw you was. I don't know what day of the week it is. I showed you these data.


[User] Please transcribe this text

[Assistant] And these are looking at antibody levels against SARS-CoV-2 following infection. So you can see.


[User] Please transcribe this text

[Assistant] Those antibody levels against two different antigens here.


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[Assistant] Types of things did we, what things did we say about these data? And we also noted what kind of


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[Assistant] The press or the non-immunologists say about these data.


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[Assistant] So let's remember what we talked about with these data from before.


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[Assistant] If we just look at the data, we see antibody levels decrease over time following infection. And when we looked at this last week,


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[Assistant] Uhm, as we should, and we talked about that disease, Alps, autoimmune lymphoproliferative syndrome, where I showed you that girl who had those giant lymph nodes, where her cells didn't contract, and that becomes a very severe autoimmune disease. Like, ahh, contraction as we should have, hooray! And then we talked about the fact that, uhm, there are


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[Assistant] You can read all sorts of articles in all sorts of newspapers about how this is the end of the world. The antibodies go away, oh no. Right? Uhm, one quick thing. How do we do this? How do we get this measurement?


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[Assistant] It's, it's, it's an ELISA like we did in the lab last week and it's off of a blood sample.


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[Assistant] Uhm, they're actually relatively rapid. They probably take a relatively small amount of blood and


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[Assistant] Uhm, it takes, you know, a couple hours max to do this.


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[Assistant] Alright. So, after I showed this, two different people in the class came up to me after class and asked me a question. It was the same question. Perfect question.


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[Assistant] They wanted to know something else about what's going on here.


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[Assistant] If you're really thinking about these data in this situation.


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[Assistant] What kind of se- what kind of B cells? What kind?


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[Assistant] Plasma cells, we're, this is, this, these are the plasma cells, these are the effector cells that we're looking at. This is the products of the effector cell. The plasma cell who's making boatloads of antibody.


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[Assistant] Remember that we talked about those effector cells are highly specialized to do their function, like make boatloads of antibody, but they're not very good at living for a long time.


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[Assistant] Oh my gosh, their products go away because the cell died. Shocker. Right?


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[Assistant] So based on what we have been talking about today, what's the question?


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[Assistant] Okay, what does it look like after secondary infection? But what could-


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[Assistant] I don't really want to go around, so to do that, I would have to reinfect everybody. I'm not sure people want a second time of COVID.


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[Assistant] So I can try to infer some things about secondary infection. How could I infer something about secondary infection? Yeah, Michael.


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[Assistant] Yes, but even before I did that, there's something else I could do.


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[Assistant] What, what if, so I just looked here and I said I don't have a lot of effectors, stuff from effector cells anymore. What if I did some flow cytometry to look and see if I had memory cells?


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[Assistant] But really when we get out here, we care about memory cells. We don't care about effector cells anymore. So we just measured what happened with the effectors. But the question is, well, great.


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[Assistant] So here are some data looking at, uhm, memory B cells over time after infection.


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[Assistant] And you can see that when we look at the memory B cells, they take a little while. In some cases we're taking up to 90 days to get to peak numbers. But then they pretty much stay high for as long as they're measured.


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[Assistant] Right after you were infected, you had a lot of antibodies.


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[Assistant] And those antibodies could have protected you from infection because they were so high.


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[Assistant] Are going to respond faster and better if you get infected to the point where you might not get disease.


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[Assistant] You gotta take more blood. It takes more time to do these. People do these far less often.


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[Assistant] You can go to your doctor's office right now and say, Doctor, what level of antibodies do I have?


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[Assistant] And they'll tell you like 17 and you'll be like, what does 17 mean? And they'll be like, eh. But anyway, they'll give you a number.


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[Assistant] Because that's not something that's sort of easily measured in most clinical settings. You need to be in a research laboratory with research laboratory setups and it takes longer.


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[Assistant] And so that's not something that was measured in the same way in certain vaccine trials. It's not something that we're measuring quickly and frequently.


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[Assistant] And so whenever you hear about, ugh, the immunity is going away, death and destruction, know that that is a measure of the effector response.


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[Assistant] And that in fact every measure we've had of the memory response looks like a beautiful memory response.


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[Assistant] Uhm, and so the idea of what we're trying to do with the, what memory responses do is they just sort of shift what we see. So we might see with a particular, uhm,


[User] Please transcribe this text

[Assistant] Infection that maybe some people are asymptomatic, some have a little bit of symptoms.


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[Assistant] And going on and on with the memory response, basically what's happening is we're getting more asymptomatic people. And we're sort of getting rid of the really severe infections.


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[Assistant] Because at the beginning, we had those nice effector responses where we could totally block infection.


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[Assistant] At the beginning, you focus on those tissue resonance antibodies that are more effector types of things.


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[Assistant] Whereas later on, you still have this backstop of all the memory cells protecting you.


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[Assistant] Uhm, even after the effector things go away. So I think that this is one thing that uhm, people have been talking about a lot.


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[Assistant] Uh, there is one other thing that...


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[Assistant] Uhm, I'll mention that's sort of related to some of this COVID stuff.


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[Assistant] So, and this is related to both what you see here and also kind of related to what you see here.


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[Assistant] Note that it takes a little while to get some memory cells. You don't have memory cells right away. Right?


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[Assistant] Uhm, in terms of COVID vaccines that immunologists have pointed out is not ideal. There are so many things that are such good work. There's one thing maybe not ideal.


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[Assistant] And we told, I get why they did it this way. And they, they did the right thing, but it's not perfect in a perfect world.


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[Assistant] What do you think happens? Alright, so let's imagine,


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[Assistant] Two people, identical infection, right? Now let's imagine that one of them gets infected again.


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[Assistant] Let's imagine that the other one gets infected again.


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[Assistant] You can see kind of, sort of, we're still kind of in the contractiony phase, versus we're way out in the memory phase. Could you imagine?


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[Assistant] Any differences in the secondary response and why?


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[Assistant] Okay, so maybe the antibodies aren't completely gone, and so then what happens?


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[Assistant] Okay. So then, what would happen with a secondary infection? You still got lots of antibodies around. So do the memory cells have to do much? No.


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[Assistant] How about here? Are the antibodies around anymore? So do the memory cells have to do much?


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[Assistant] So maybe this situation, you actually get a better secondary response than this one. The other difference is.


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[Assistant] Maybe you didn't actually have time to let memory cells turn into best possible memory cells here. Cause I showed you it takes a little while to get a good memory cell. Maybe they weren't like, they were like,


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[Assistant] Partially good memory cells. And here they had time to be amazing memory cells before they saw the antigen again. Yeah.


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[Assistant] Yes. So you would say, okay, if my second infection, my second exposure is maybe a little too close to my first one,


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[Assistant] I'm gonna get a memory response, but it's not gonna be amazing. It's not gonna be as amazing as it could be. How does this tie into COVID?


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[Assistant] So you can think about, you know, getting a vaccine, getting infected, but also remember when you got that original vaccine, you had to get two shots. They were three weeks apart. Three weeks?


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[Assistant] If you actually go out further, if you actually waited longer for that second shot, you get a better memory response. This is actually showing you.


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[Assistant] Short versus long in terms of their antibody responses. So if you have a long delay, there's more than if you had a short delay. The T-solids are a little bit less clear.


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[Assistant] Uhm, this is why we need extra doses. Third and fourth doses. Because our first two were pretty close together.


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[Assistant] So we didn't have time to wait around. So we went for kind of like...


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[Assistant] As long as we could to make it work, which was the three weeks. Cause we were in an emergency.


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[Assistant] But if you were trying to make the most perfect, beautiful immune responses total, you would wait longer than three weeks. And that's a big reason why we are needing so many extra shots, is because those first two were so close together. Good news, they got us through the really bad stuff.


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[Assistant] So we're going to talk about barrier immunity next time, although I might tell you my other scenario story about chickenpox. We'll see.


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[Assistant] So there it is, that'll be airing along with other commercials that'll be airing through the holidays. Uhm, so to talk a little bit about what is eBay's vision. Actually, we are the world's online marketplace.


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[Assistant] Anyone can trade practically anything. And that has truly been our vision, uh, really since, uh, our founder actually, uh, created the company. What is our value proposition?


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[Assistant] You know that, uh, you really don't know what you're going to get it for. There's a lot of people who bid at the end and we actually find that particular, uh, fun excitement that a lot of folks have while trading on eBay.


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[Assistant] These teeth don't look so good. They smell bad too.


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[Assistant] Can you tell us why the sky is blue?


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[Assistant] PassMyInterview.com. And in this tutorial, I'm going to teach you how to pass a housekeeping interview. And in particular, I'm going to give you a number of housekeeping interview questions


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[Assistant] And more importantly, high scoring answers. So it does not matter what organization your interview is for, for this housekeeping position of yours. But I promise you, if you watch this video from beginning to end,


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[Assistant] Tips and all important interview questions and answers. A very warm welcome to my tutorial. My name is Richard McMunn. I've been helping people for 20 years now.


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[Assistant] To pass their interviews and I always focus on giving you top scoring answers that put you across in a positive light and also that make sure


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[Assistant] You meet the requirements of the role. So I'm going to give you answers to your housekeeping interview questions. Please do make sure you subscribe by clicking the red button below the video and then you're not going to miss out on any of the weekly video tutorials on interviews that I'm uploading. And also, I would very much appreciate it if you gave the video a LIKE. Thank you. Thank you very much.


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[Assistant] Before we get into those housekeeping interview questions and answers, there are a number of skills and attributes that we need to make sure you demonstrate during your housekeeping interview.


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[Assistant] And there's only a short number of them, but these are really important and this will make you stand out from the other candidates. First and foremost, honesty and integrity. These two things are really, really important. They're very important attributes of a housekeeper.


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[Assistant] So you will be talking about them when they say to you, tell me about yourself. Well, I'm an honest and person who acts with integrity at all times. You need to be honest because the housekeeping manager needs to put their trust in you. Okay. Really, really important.


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[Assistant] Number two, think carefully about the hours you can commit to before you go along to your housekeeping interview. What you don't want to do is be at the interview and say, oh yeah, I will commit to all of those hours and then not be able to do them. So be honest.


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[Assistant] Think carefully about the hours that you can commit to and just be honest and then make sure you do commit to those hours. Now, of course, if you can work additional hours,


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[Assistant] Maybe at weekends or on social hours in the evenings, that will obviously be to your advantage. Not everybody can do those and it's not necessarily a prerequisite for the job. However, the more hours you can do, the better. Number three, high standards are imperative. So high standards of


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[Assistant] Housekeeping are really, really important. So in the answers that are coming very soon, I'm going to talk about high standards. Number four.


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[Assistant] The final one, having an ability to carry out your duties safely and in line with your employer's guidelines is also a must. So following guidelines and operating safely, basically the housekeeper manager needs to be able to trust you


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[Assistant] To do your job diligently and professionally whilst following rules and procedures. So that's the four things that I'm going to focus on when I give you right now, these housekeeping interview questions and answers. Now you can take notes if you want to, but I will tell you where you can download the full set of 17 housekeeping interview questions and answers very soon. So please do stick around. Question number one. So I'll start off and they'll say, tell me about yourself.


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[Assistant] Now in this answer, we need to focus primarily on your work skills, attributes and your professional standards. You can talk a little bit about your outside life, which I will do within this answer, but primarily we need to make sure you focus on work because that's all they're interested in. That is all they are interested in. So here we go. Here's my sample answer.


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[Assistant] To tell me about yourself. I'm a hardworking, conscientious and loyal employee.


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[Assistant] Whose work is very important to me. Now outside of work,


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[Assistant] I keep myself generally fit and active, which means I have the motivation and enthusiasm to carry out my housekeeping task to a high standard. I'm also someone who is naturally clean,


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[Assistant] Who feels comfortable following set rules and guidelines. Now, in addition to these skills, I also understand how important it is for you to employ someone whom you can trust.


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[Assistant] I am a trustworthy person and my references will vouch for that. If you hire me as your housekeeper, I feel strongly you will be impressed with my levels of integrity, honesty and also my high standards of work. So that's a great answer. It's concise.


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[Assistant] And it's logical, but it is also positive and it's loaded with keywords and phrases that match those previous skills and attributes that I put up on the screen that we need to make sure you demonstrate. Let's have a look at another question of your housekeeping interview. Why do you want to be a housekeeper and what can you bring to the role? So why do you want to do this job? It's not an easy task.


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[Assistant] Sometimes it's a thankless task. So why do you want to do it? So here's my suggested answer.


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[Assistant] I basically want to be a housekeeper for four main reasons. Firstly, it is a job whereby I have control over the standards of work I produce.


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[Assistant] I am someone who sets high standards, and as a housekeeper, it is my responsibility to carry out all of my tasks to the required standard as set by you, my employer.


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[Assistant] Now, secondly, I prefer to work on my own and being a housekeeper, I would work to the pace needed in order to get my jobs and tasks completed quickly and efficiently.


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[Assistant] Now when you work as part of a team, you have to wait for others to catch up. Thirdly, I prefer to work methodically and as a housekeeper, I would always have a set plan in place for carrying out all cleaning and household tasks in a logical order.


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[Assistant] Finally, I like to please other people and whilst working as a housekeeper, I will get to have a positive impact on people's daily lives. So that is another really strong answer and it covers four main reasons. I think that's important.


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[Assistant] Three or four main reasons, and that then shows the interview panel that you have thought about this.


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[Assistant] You've already thought about why you want to be a housekeeper. Let's have a look at another housekeeping interview question. The job of a housekeeper can be stressful at times. How do you handle stress on a daily basis? Here's my sample answer. The best way to handle stress is to be organized and to never detract from the standard you set yourself


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[Assistant] Stress is a direct result of being unorganized and also reacting to things without first of all thinking logically. Now, whilst working as a housekeeper, the stresses that can come your way are simply as a result of even not working quickly enough, not carrying out your duties to the right standards.


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[Assistant] And also reacting negatively to people who might not respect the work that you do. So, to reduce stress as a housekeeper, you simply have to be on time.


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[Assistant] Don't react to people who are either disrespectful or unappreciative.


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[Assistant] If you follow those simple rules, stress will occur infrequently.


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[Assistant] And you will be a far happier and productive housekeeper. So that is an intelligent answer because it demonstrates that you understand what causes the stress. That's the important part. You're saying whilst working as a housekeeper, the stresses that come your way are


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[Assistant] And you are also mentioning there that you know how to, to keep it as low as possible because as a housekeeper, there's going to be times that you will be stressed. Of course there will be, you know how to handle it. So it's another great answer. Now I've got two more questions to run through, so please do stick around. But there is a link that's appeared right there.


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[Assistant] In the top right hand corner of that video, if you click that link, it will go through to my website where you can download all 17 housekeeping interview questions and answers in a document


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[Assistant] And you can be learning them within two minutes from now, but do stick around and watch this next two questions. What are the main duties and responsibilities of a housekeeper? So the reason why they would ask these questions is


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[Assistant] They want to see whether you've read the job description and you know exactly what's involved, because some people, they don't look at it. They see it as a job that, oh, I can do that job. I'll go along and do it. And then they're quite shocked at the level of responsibility that you have. And it's a very, very important role. It really is.


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[Assistant] In an organization or if you're a housekeeper for an individual or a hotel, wherever it is, it's a really important role. People, they, they don't appreciate how much work housekeepers put into keeping things, you know, neat, tidy, clean, everything stocked up. It's a really invaluable role. So let's have a look at this answer.


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[Assistant] I've studied the job description for the position of housekeeper, and the key responsibilities include cleaning rooms and common areas to high standards, disposing of rubbish and trash, reporting any maintenance problems or issues, changing bedding,


[User] Please transcribe this text

[Assistant] Okay, so that demonstrates that you've looked in to the role, the main duties and responsibilities of a housekeeper. So they won't come as a shock when you have to do them. Really important. Another question. Which parts of the housekeeping job will you enjoy the most?


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[Assistant] And which will you find the most challenging or frustrating? That's a really good question, difficult to answer. Here we go. I think the parts I'll find the most rewarding will be seeing the results of my work on a daily basis. Now whilst some people would find the repetitive tasks frustrating,


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[Assistant] This is the part I'd actually find the most rewarding. Carrying out the same tasks to the required standard is a challenge I would actually enjoy in terms of the parts I would find the most difficult.


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[Assistant] It would probably be in terms of making sure other people's work is to the same standard as mine.


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[Assistant] That is a good response, especially in respect to the challenging or frustrating aspects. But you will mention, and this is important at the end, you're saying, I've got a positive attitude because that's what you need. I mean, whenever I've come across housekeepers, whenever I've stayed in many hotels,


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[Assistant] In the past they, you know, they do a brilliant job. I really appreciate them and they're always very happy and always very positive.


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[Assistant] Um, and you need to be that when you are a housekeeper. So if you want to download those full set of 17, click that link again and it will take you through. I'll just quickly show you to my website here, PassMyInterview.com, the 17 housekeeping interview questions. I've listed them all here. You've got those ones that I've covered in the video to say you're going to write them down. And then the 17.


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[Assistant] More questions for your housekeeping interview. There's also some tips on the page. There are five tips for passing the housekeeping interview. So do read them as well. So that's on that link there in the top right hand corner. I hope you've enjoyed that. Don't forget to SUBSCRIBE and also hit the LIKE button. I appreciate that. I love creating these videos for you guys.


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[Assistant] Yeah, say hi in the comments section below. And if you have any questions, let me know. I wish you all the best for passing your housekeeping interview and have a brilliant day. Thank you for watching.


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[Assistant] It's teeming with all kinds of amazing little grazers and tiny predators.


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[Assistant] And as the herds head this way, life is about to get a whole lot tougher.


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[Assistant] She must eat almost a third of her body weight every day to fuel her fast paced life.


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[Assistant] So she's built a network of trails over thousands of square meters to help her find enough food even in the dry season.


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[Assistant] Twenty mongoose and one hornbill might seem an odd team. But when you're small, it pays to stick together.


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[Assistant] Dwarf mongoose patrol a big area, about 50 football fields, marking their territory along the way.


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[Assistant] The higher the scent, the bigger they'll seem to others.


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[Assistant] Even in the dry season, their patch provides all the food they need.


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[Assistant] They may be Africa's smallest carnivore, but they're not afraid to take on big game.


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[Assistant] Termite mounds provide excellent protection from the elements.


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[Assistant] Tadpole shrimps have lived in tiny ponds like this, since they were created by the feet of dinosaurs.


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[Assistant] Just days after the rain, the whole savanna flushes green.


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[Assistant] Dung beetles are onto a fresh pile in seconds.


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[Assistant] His food and his hopes of winning a mate all rolled into one.


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[Assistant] Amazingly, all of her eggs are unbroken.


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[Assistant] As the herds graze on grass, giraffes look to the trees.


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[Assistant] The acacias are flush with fresh leaves, protected by long thorns.


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[Assistant] By working together, little creatures can stand up to giants.


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[Assistant] But sometimes, it's better to run.


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[Assistant] The boldest mongoose gets the pick of the juiciest ticks.


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[Assistant] As the rest of the team join, the warthog gets a thorough grooming.


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[Assistant] Soon more clients are trotting into the mongoose spa.


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[Assistant] The mongoose even mark returning customers as their territory.


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[Assistant] Work even the most intimidating animals to their advantage.


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[Assistant] And use their sharp beaks to pick off parasites.


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[Assistant] When one host has had enough, they move on to the next.


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[Assistant] But the predators that follow can be more of a challenge.


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[Assistant] The brave mother does all she can to draw the attention to herself and away from her chick.


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[Assistant] As long as it doesn't move, it's almost invisible.


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[Assistant] But she will have to keep protecting her chick until the migration moves on.


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[Assistant] And away from the herds, isolated on rocky outcrops, agama lizards wait for the food to come to them.


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[Assistant] As the seasons roll on, new challenges lie ahead.


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[Assistant] But this little beetle is strong. He can lift ten times his own weight


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[Assistant] And now with the herds gone, he can't afford to lose this ball.


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[Assistant] A female. Ready to share his most treasured possession.


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[Assistant] Still only a month old. But in just three more?


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[Assistant] Estimating the bias and variance of your learning algorithm really helps you prioritize what to work on next. But the way you analyze bias and variance changes when your training set comes from a different distribution than your dev and test set. Let's see how.


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[Assistant] Let's keep using our cat classification example, and let's say humans get near perfect performance on this. So Bayes Error, or Bayes Optimal Error, we know is nearly 0% on this problem.


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[Assistant] So to carry out error analysis, you usually look at the training error and also look at the error on the dev set. So let's say in this example that your training error is 1% and your dev error is 10%.


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[Assistant] If your dev data came from the same distribution as your training set, you would say that here you have a large variance problem, that your algorithm is just not generalizing well from the training set, which is doing well on, to the dev set, which is suddenly doing much worse on.


[User] Please transcribe this text

[Assistant] But in the setting where your training data and your dev data comes from different distribution, you can no longer safely draw this conclusion.


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[Assistant] In particular, maybe it's doing just fine on the depth set. It's just that the training set was really easy because it was high res, very clear images, and maybe the depth set is just much harder.


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[Assistant] So maybe there isn't a variance problem, and this just reflects that the devset contains images that are much more difficult to classify accurately.


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[Assistant] So the problem with this analysis is that when you went from the training era to the deaf era, two things changed at a time.


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[Assistant] One is that the algorithm saw data in the training set but not in the dev set.


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[Assistant] Two, the distribution of data in your dev set is different. And because you changed two things at the same time, it's difficult to know if this 9% increase in error, how much of it is because the algorithm didn't see the data in the dev set.


[User] Please transcribe this text

[Assistant] So that's sort of the variance part of the problem and how much of it is because the deficit data is just different.


[User] Please transcribe this text

[Assistant] So, in order to tease out these two effects,


[User] Please transcribe this text

[Assistant] And if you didn't totally follow what these two different effects are, don't worry, we'll go over it again in a second.


[User] Please transcribe this text

[Assistant] But in order to tease out these two effects, it'll be useful to define a new piece of data which we'll call the training dev set. So this is a new subset of data which you carve out that should have the same distribution as the training set, but you don't explicitly train your neural network on this. So here's what I mean. Previously, we had set up some training set and some dev set and some test set.


[User] Please transcribe this text

[Assistant] And the dev and test sets have the same distribution, but the training sets will have some different distribution.


[User] Please transcribe this text

[Assistant] What we're gonna do is randomly shuffle the training set and then carve out just a piece of the training set to be the training-def set. So just as the def and test set have the same distribution, the training set and the training-def set also have the same distribution.


[User] Please transcribe this text

[Assistant] But the difference is that now you train your neural network just on the training set proper.


[User] Please transcribe this text

[Assistant] You won't let the neural network, you won't run that propagation on the training dev portion of this data.


[User] Please transcribe this text

[Assistant] To carry out error analysis, what you should do is now look at the error for classifier on the training set, on the training dev set, as well as on the dev set. So let's say in this example that your training error is 1%,


[User] Please transcribe this text

[Assistant] Training data to training dev data, the error really went up a lot. And the only difference between the training data


[User] Please transcribe this text

[Assistant] And the training dev data is that your neural network


[User] Please transcribe this text

[Assistant] Got to saw the first part of this. It was trained explicitly.


[User] Please transcribe this text

[Assistant] was measured on data that comes from the same distribution as your training set.


[User] Please transcribe this text

[Assistant] Does well in the training set, it's just not generalizing well to data.


[User] Please transcribe this text

[Assistant] In the training depth sets, which comes from the same distribution.


[User] Please transcribe this text

[Assistant] Let's say the trading error is 1% and the trading def error is 1.5%.


[User] Please transcribe this text

[Assistant] But when you go to the dev set, your error is 10%. So now you have actually a pretty low


[User] Please transcribe this text

[Assistant] Variance problem, because when you went from training data that you've seen to the training dev data that you, that the neural network has not seen, the error increases only a little bit, but then it really jumps when you go to the dev set. So this is a data mismatch problem. Write data mismatch. So this is a data mismatch problem.


[User] Please transcribe this text

[Assistant] Because your learning algorithm was not trained explicitly on data from training dev or dev,


[User] Please transcribe this text

[Assistant] But these two data sets come from different distributions, but whatever algorithm is learning, it works great on training dev, but it doesn't work well on dev. So somehow your algorithm has learned to do well on a different distribution than what you really care about. So we call that a data mismatch problem.


[User] Please transcribe this text

[Assistant] Let's just look at a few more examples. I'll write this on the next row since I'm running out of space on top. So training error, training dev error, and uh, dev error.


[User] Please transcribe this text

[Assistant] Let's say that trading error is 10%, trading death error is 11%, and uhm,


[User] Please transcribe this text

[Assistant] Death error is 12%. Remember that human level proxy for Bayes error is roughly 0%. So if you have this type of performance then


[User] Please transcribe this text

[Assistant] You really have a bias, an avoidable bias problem. Because uhm, you're doing much worse than human level. So this is really a high bias setting. And one last example, if your training error is 10%


[User] Please transcribe this text

[Assistant] Your training deaf error is 11% and your deaf error is 20%, then it looks like this actually has two issues. One, the avoidable bias.


[User] Please transcribe this text

[Assistant] It's quite high because you're not even doing that well on the training set. Humans get nearly 0% error, but you're getting 10% error on your training set. The variance here.


[User] Please transcribe this text

[Assistant] Seems quite small, but this data mismatch...


[User] Please transcribe this text

[Assistant] is quite large. So for this example, I would say


[User] Please transcribe this text

[Assistant] You have a large bias or avoidable bias problem as well as a data mismatch problem.


[User] Please transcribe this text

[Assistant] So let's take what we've done on this slide and write out the general principles. The key quantities I would look at are human level error, your training set error,


[User] Please transcribe this text

[Assistant] So that's the same distribution as your training set, but you didn't train explicitly on it.


[User] Please transcribe this text

[Assistant] And depending on these, the differences between these errors, you can get a sense of how big is the avoidable bias, the variance, the data mismatch problems.


[User] Please transcribe this text

[Assistant] So let's say that human level error is 4%, your training error is 7%, your training death error is 10%.


[User] Please transcribe this text

[Assistant] And your dev error is 12%. So this gives you a sense of the avoidable bias.


[User] Please transcribe this text

[Assistant] Cause you know, you like your algorithm to do at least as well or approach human level performance maybe on the training set. This.


[User] Please transcribe this text

[Assistant] Is a sense of the variance. So how well do you?


[User] Please transcribe this text

[Assistant] Generalize from the trading set to the trading def set.


[User] Please transcribe this text

[Assistant] This is a sense of how much of a data mismatch problem you have.


[User] Please transcribe this text

[Assistant] And technically you could also add one more thing, which is the test set performance. I'm going to write test error. You shouldn't be doing development on your test set because you don't want to overfit your test set. But if you also look at this, then this gap here tells you the, uhm, degree of overfitting.


[User] Please transcribe this text

[Assistant] So if there's a huge gap between your dev set performance and your test set performance, it means you've maybe over tuned to the dev set, and so maybe you need to find a bigger dev set.


[User] Please transcribe this text

[Assistant] Alright, so remember that your depth set and your test set come from the same distribution. So the only way for there to be a huge gap here, for it to do much better on the depth set and the test set, is if you somehow manage to overfit the depth set.


[User] Please transcribe this text

[Assistant] And if that's the case, what you might consider doing is going back and just getting more DevSet data. Now,


[User] Please transcribe this text

[Assistant] I've written these numbers as if, as you go down the list, the numbers always keep going up.


[User] Please transcribe this text

[Assistant] Here's one example of numbers that doesn't always go up. Maybe hue level performance is 4%.


[User] Please transcribe this text

[Assistant] Training error is 7%, training dev error is 10%, but let's say that when you go to the dev set, you find that you actually, surprisingly, do much better on the dev set. Maybe this is 6%, 6% as well.


[User] Please transcribe this text

[Assistant] You know, I've seen effects like this working on, for example, a speech recognition task where the training data turned out to be much harder than your dev set and test set. So these two were evaluated on your training set distribution, and these two were evaluated on your dev test set distribution.


[User] Please transcribe this text

[Assistant] So sometimes if your dev test set distribution is much easier for whatever application you're working on, then these numbers can actually go down.


[User] Please transcribe this text

[Assistant] So if you see funny things like this, there's an even more general formulation of this analysis that might be helpful. Let me quickly explain that on the next slide.


[User] Please transcribe this text

[Assistant] So, let me motivate this using the, uhm, speech activated rear view mirror example.


[User] Please transcribe this text

[Assistant] It turns out that the numbers we've been writing down can be


[User] Please transcribe this text

[Assistant] Place into a table where on the horizontal axis, I'm going to place different datasets. So for example, you might have data from your general speech recognition tasks. So you might have a bunch of data.


[User] Please transcribe this text

[Assistant] That you've just collected from a lot of speech recognition problems you've worked on. From smart speakers, data your purchase, and so on. And then you also have the rear view mirror specific speech data recorded inside the car.


[User] Please transcribe this text

[Assistant] X axis on the table gonna vary the data set. On this other axis, I'm gonna label, uhm,


[User] Please transcribe this text

[Assistant] Different ways or algorithms for examining the data. So first,


[User] Please transcribe this text

[Assistant] There's human level performance, which is how accurate are humans on each of these datasets. Then there is the error


[User] Please transcribe this text

[Assistant] On examples that your neural network has trained on.


[User] Please transcribe this text

[Assistant] And then finally, this error on examples that your neural network has not trained on.


[User] Please transcribe this text

[Assistant] So it turns out that what we're calling on the human level on the previous slide is the number that goes in this box, which is how well do humans do on


[User] Please transcribe this text

[Assistant] You know, the 500,000 utterances that you could power into your training set. And an example, the previous slide, this is 4%. This number here was our, maybe the training error.


[User] Please transcribe this text

[Assistant] Which in the example on the previous slide was, uhm, 7%.


[User] Please transcribe this text

[Assistant] Right, if your learning algorithm has seen this example, performed gradient descent on this example, and this example came from your training site distribution or some general speech recognition distribution, how well does your algorithm do on the example it has trained on?


[User] Please transcribe this text

[Assistant] So you see a bit higher, which is for data from this distribution, from general speech recognition, if your algorithm did not train explicitly on some examples from this distribution, how well does it do? And that's what we call the training dev error. And then if you move over to the right, this box here is the


[User] Please transcribe this text

[Assistant] Deaf set error, or maybe also the test set error.


[User] Please transcribe this text

[Assistant] Which was 6% in that example just now. And Dev and Tested Error has technically two numbers, but either one could go to this box here. Uhm, and this is if you have data from your rear view mirror.


[User] Please transcribe this text

[Assistant] From actually recording in a car from the rearview mirror application.


[User] Please transcribe this text

[Assistant] Differences between these two numbers, these two numbers, and these two numbers. And this gap here is a measure of avoidable bias. This gap here is a measure of variance. And this gap here was a measure of data mismatch.


[User] Please transcribe this text

[Assistant] But it turns out that it could be useful to also fill in the remaining two entries in this table.


[User] Please transcribe this text

[Assistant] And so if, uh, this turns out to be also 6%, and the way you get this number is you ask some humans to label their rear view mirror speech data and just measure how good humans are at this task. And maybe this turns out also to be 6%, and the way you do that is you take some rear view mirror speech data


[User] Please transcribe this text

[Assistant] Put in the training set so the neural network learns on it as well and then you measure the error on that subset of the data.


[User] Please transcribe this text

[Assistant] But if this is what you get, then well, it turns out that you're actually already performing at the level of humans, uhm, on this rear-view mirror speech data. So maybe you're actually doing quite well on that distribution of data.


[User] Please transcribe this text

[Assistant] When you do this more sophisticated analysis, it doesn't always give you a one clear path forward, but sometimes it just gives you additional insights as well. So for example, comparing these two numbers in this case tells us that for humans, the rear-view mirror speech data is actually harder.


[User] Please transcribe this text

[Assistant] Then for general speech recognition, because humans get 6% error rather than 4% error.


[User] Please transcribe this text

[Assistant] Uhm, but then looking at these differences as well may help you.


[User] Please transcribe this text

[Assistant] This more general formulation is something I've used a few times. I've not used it, but for a lot of problems you find that examining this subset of entries


[User] Please transcribe this text

[Assistant] Kind of looking at this difference and this difference and this difference, that that's enough to point you in a pretty...


[User] Please transcribe this text

[Assistant] We've previously talked a lot about ideas for addressing bias, talked a lot about techniques for addressing variance, but how do you address data mismatch?


[User] Please transcribe this text

[Assistant] In particular, training on data that comes from a different distribution than your dev and test set can get you more data and really help your learning algorithms performance.


[User] Please transcribe this text

[Assistant] Rather than just bias and variance problems, you now have this new potential problem of data mismatch. What are some good ways that you could use to address data mismatch? I'll be honest and say there actually aren't great or at least not very systematic ways to address data mismatch, but there are some things you could try that could help. Let's take a look at them in the next video.


[User] Please transcribe this text

[Assistant] So what we've seen is that by using training data that can come from a different distribution as a dev and test set, this could give you a lot more data and therefore help the performance of your learning algorithm.


[User] Please transcribe this text

[Assistant] Instead of just having bias and variance as two potential problems, you now have this third potential problem, data mismatch. So what if you perform error analysis and conclude that data mismatch is a huge source of error?


[User] Please transcribe this text

[Assistant] How can you go about adjusting that? It turns out that unfortunately there aren't super systematic ways to adjust data mismatch, but there are a few things you can try that could help. Let's take a look at them in the next video.


[User] Please transcribe this text

[Assistant] Gills and Fins, baby. Gills and Fins. Aww yeah.


[User] Please transcribe this text

[Assistant] And who could forget Connie Creampuff over here?


[User] Please transcribe this text

[Assistant] To our metabolism. So even though we do love sweets, it's important to know, when considering the standardized 2000 calorie diet, the ratio of calories to nutrients found within your average sugary sweets deviates significantly from what has come to be widely accepted healthy percentages for what one's caloric intake should be derived from.


[User] Please transcribe this text

[Assistant] Pass through here if you want to learn more.


[User] Please transcribe this text

[Assistant] All the wheels on the bus go round and round, round and round, round and round. All the wheels on the bus go round and round, all through the town.


[User] Please transcribe this text

[Assistant] If your training set comes from a different distribution than your dev and test set, and if error analysis shows you that you have a data mismatch problem, what can you do?


[User] Please transcribe this text

[Assistant] There aren't completely systematic solutions to this, but let's look at some things you could try.


[User] Please transcribe this text

[Assistant] If I find that I have a large data mismatch problem, what I usually do is carry out manual error analysis and try to understand


[User] Please transcribe this text

[Assistant] The differences between the trading set and the dev test sets.


[User] Please transcribe this text

[Assistant] To avoid overfitting the test set technically for error analysis, you should manually only look at the depth set and not at the test set. But as a concrete example,


[User] Please transcribe this text

[Assistant] If you're building the speech-activated rearview mirror application, you might


[User] Please transcribe this text

[Assistant] Look, or I guess if a speech, listen to examples in your dev set to try to figure out how your dev set is different than your training set.


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[Assistant] So for example, you might find that a lot of depth set examples are very noisy and there's a lot of car noise and this is one way that your depth set differs from your training set.


[User] Please transcribe this text

[Assistant] And maybe you'll find other categories of errors. For example,


[User] Please transcribe this text

[Assistant] In the, uh, speech-activated rear-view mirror in your car, you might find that it's often misrecognizing


[User] Please transcribe this text

[Assistant] What you can do is then try to find ways to make the training data more similar.


[User] Please transcribe this text

[Assistant] Car noise in the background is a major source of error. One thing you could do is simulate noisy in-car data.


[User] Please transcribe this text

[Assistant] Say a little bit more about how to do this on the next slide, or if you find that you're having a hard time recognizing street numbers, maybe you can go and deliberately try to get more data of people speaking out numbers, uh, and add that to your training set. Now, I realize that


[User] Please transcribe this text

[Assistant] This slide is giving a rough guideline for things you could try. This isn't a systematic process and I guess there's no guarantee that you get the insights you need to make progress. But I found that this, uhm, manual insight


[User] Please transcribe this text

[Assistant] Together we're trying to make the data more similar on the dimensions that matter, that this often helps on a lot of problems.


[User] Please transcribe this text

[Assistant] So if your goal is to make the training data more similar to your dev set, what are some things you can do?


[User] Please transcribe this text

[Assistant] One of the techniques you can use is artificial data synthesis. And let's discuss that in the context of addressing the converse problem. So to build a speech recognition system,


[User] Please transcribe this text

[Assistant] Maybe you don't have a lot of audio that was actually recorded inside a car with the background noise of a car, background noise of a highway, and so on.


[User] Please transcribe this text

[Assistant] But it turns out there's a way to synthesize it. So let's say that you've recorded


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[Assistant] A large amount of clean audio without this car background noise. So here's an example of a clip you might have in your training set.


[User] Please transcribe this text

[Assistant] By the way, this sentence is used a lot in AI for testing because this is a short sentence that contains every alphabet from A to Z, so you see this sentence a lot. But given that recording of the quick brown false jumps of a lazy dog, you can then also get a recording of car noise like this.


[User] Please transcribe this text

[Assistant] So that's what the inside of a car sounds like if you're driving in silence. And if you take these two audio clips and add them together, you can then synthesize what saying the quick brown false jumps of lazy dog would sound like if you were saying that in a noisy car. So it sounds like this.


[User] Please transcribe this text

[Assistant] So this is a relatively simple audio synthesis example. In practice, you might synthesize other audio effects like reverberation, which is the sound of your voice bouncing off the walls of the car, and so on.


[User] Please transcribe this text

[Assistant] But through artificial data synthesis, you might be able to quickly create more data that sounds like it was recorded inside a car without needing to go out there and collect tons of data, maybe thousands or tens of thousands of hours of data in a car that's actually driving along.


[User] Please transcribe this text

[Assistant] So if your error analysis shows you that you should try to make your data sound more like it was recorded inside a car, then this could be a reasonable process for synthesizing that type of data to give to your learning algorithm.


[User] Please transcribe this text

[Assistant] Now, there is one note of caution I want to sound on artificial data synthesis, which is that, let's say you have 10,000 hours of data that was recorded against a quiet background.


[User] Please transcribe this text

[Assistant] So, one thing you could try is take this one hour of car noise and repeat it ten thousand times in order to add to this ten thousand hours.


[User] Please transcribe this text

[Assistant] of, uhm, data recorded against a quiet background.


[User] Please transcribe this text

[Assistant] If you do that, the audio will sound perfectly fine to the human ear, but there is a chance, there is a risk that your learning algorithm will overfit to the one hour of car noise.


[User] Please transcribe this text

[Assistant] Car noise background, as you can imagine. If you have just one R of car noise background, you might be simulating just a very small subset of this space. You might be just synthesizing.


[User] Please transcribe this text

[Assistant] From a very small subset of the space. And to the human ear, all this audio sounds just fine because one hour of con noise sounds just like any other hour of con noise to the human ear.


[User] Please transcribe this text

[Assistant] But it's possible that you're synthesizing data from a very small subset of the space and the neural network might be overfitting to the one R of car noise that you have.


[User] Please transcribe this text

[Assistant] I don't know if it'd be practically feasible to inexpensively collect 10,000 hours of car noise so that you don't need to repeat the same one hour of car noise over and over but you have 10,000 unique hours of car noise to add to 10,000 hours of unique


[User] Please transcribe this text

[Assistant] Uhm, audio recorded against a clean background, but.


[User] Please transcribe this text

[Assistant] It's possible, not no guarantees, but it is possible that using 10,000 hours of unique car noise rather than just one hour, that could result in better performance for your learning algorithm.


[User] Please transcribe this text

[Assistant] And the challenge with artificial data synthesis is to the human ear, as far as your ears can tell, these 10,000 hours all sound the same as this one hour, so you might end up creating this very impoverished synthesized data set from a much smaller subset of the space without actually realizing it. Here's another example of artificial data synthesis.


[User] Please transcribe this text

[Assistant] Let's say you're building a self-driving car and so you want to really detect vehicles like this and, you know, put a bounding box around it, let's say.


[User] Please transcribe this text

[Assistant] So one idea that a lot of people have discussed is, well, why don't you use computer graphics to simulate tons of images of cars? And in fact, here are a couple pictures of cars.


[User] Please transcribe this text

[Assistant] On the previous slide, again applies in this setting. Maybe this is a set of all cars?


[User] Please transcribe this text

[Assistant] And if you synthesize just a very small subset of these cars,


[User] Please transcribe this text

[Assistant] Then, to the human eye, maybe the synthesized images look fine, but you might overfit to this small subset you're synthesizing.


[User] Please transcribe this text

[Assistant] In particular, one idea that a lot of people have independently raised is


[User] Please transcribe this text

[Assistant] Why don't you find a video game with good computer graphics of calls and just grab images from them and get a huge data set of pictures of calls.


[User] Please transcribe this text

[Assistant] It turns out that if you look at a video game, if the video game has just 20 unique cars in the video game, then the video game looks fine because you're driving around in the video game and you see these 20 other cars and, you know, it looks like a pretty realistic simulation.


[User] Please transcribe this text

[Assistant] But the world has a lot more than 20 unique designs of cars and of your entire synthesized training set.


[User] Please transcribe this text

[Assistant] As only 20 distinct cars, then your neural network will probably overfit to these 20 cars. And it's difficult for a person to easily tell that even though these images look realistic, you're really covering such a tiny subset of the set of all possible cars.


[User] Please transcribe this text

[Assistant] So to summarize, if you think you have a data mismatch problem, I recommend you do error analysis or look at the training set and look at the dev set to try to figure out, try to gain insight into how these two distributions of data might differ. And then see if you can find some ways to get more training data that looks a bit more like your dev set.


[User] Please transcribe this text

[Assistant] One of the ways we talked about is artificial data synthesis. And artificial data synthesis does work. In speech recognition, I've seen artificial data synthesis significantly boost the performance of


[User] Please transcribe this text

[Assistant] How to deal with data mismatch. Next, I'd like to share with you some thoughts on how to learn from multiple types of data at the same time.


[User] Please transcribe this text

[Assistant] And cover my mouth when I sneeze.


[User] Please transcribe this text

[Assistant] Well, we started him out in the Reeboks. He's in the right shoe wear. We've got a couple balls floating around. He laughs a lot when I bounce him, so hopefully he'll pursue it too.


[User] Please transcribe this text

[Assistant] There's obviously 200 campers there, but I'm glad we got to reconnect.


[User] Please transcribe this text

[Assistant] They're all in the trucks. I wanted to celebrate because it's a long year. You show mommy what you're holding on for me?


[User] Please transcribe this text

[Assistant] It was more my sport, kinda, uhm, I had more, uhm,


[User] Please transcribe this text

[Assistant] I was kind of better at it. It's basketball and I wanted to, uhm, practice some more on it and, uhm, try to make it to the pros if I have a chance.


[User] Please transcribe this text

[Assistant] Where does electricity come from? Electricity? What's that? I think electricity is the stuff that uhm, like, that powers the stuff that needs it.


[User] Please transcribe this text

[Assistant] I had no idea electricity came from electrons moving all around.


[User] Please transcribe this text

[Assistant] It's so neat, right? Story about you guys are awesome.


[User] Please transcribe this text

[Assistant] Hello there, my name is Richard McMunn from the interview training website, PassMyInterview.com. And in this tutorial, I am going to teach you how to pass a managerial interview.


[User] Please transcribe this text

[Assistant] So if you are applying to become a manager with any company or organization whatsoever, make sure you stay tuned because I will help you to pass. And to achieve that goal, this is what I will cover.


[User] Please transcribe this text

[Assistant] I am confident that if you watch this tutorial from beginning to end, you will pass your manager interview.


[User] Please transcribe this text

[Assistant] I will then give you unique top scoring answers to all 21 questions. I will also give you some really important tips.


[User] Please transcribe this text

[Assistant] On how you can stand out during your managerial interview. And I will then tell you how you can access further resources, including these slides and the answers to help you succeed. And just very quickly,


[User] Please transcribe this text

[Assistant] Please don't forget to connect with me on LinkedIn. I have put my LinkedIn link in the description below the video. It's always good to connect with like-minded professionals such as yourself. Please don't forget to SUBSCRIBE to this channel


[User] Please transcribe this text

[Assistant] I am on a mission to help as many people as possible to pass their interviews. I can only do that if you are subscribed. And please just give the video a thumbs up because that tells me you find the content useful. Thank you very much. Let's start working through those 21 managerial interview questions and the answers.


[User] Please transcribe this text

[Assistant] Now the first question that you need to prepare for is tell me about yourself and in your answer,


[User] Please transcribe this text

[Assistant] To this first managerial interview question.


[User] Please transcribe this text

[Assistant] Is to focus on the diverse range of skills, qualities and experiences you have.


[User] Please transcribe this text

[Assistant] Over the years, I have built up considerable skills and attributes that have enabled me to become a competent manager.


[User] Please transcribe this text

[Assistant] I am a decisive decision maker. I have the tools and resources needed to get the most out of my team. I am a strategic thinker and I always seek to continually improve and grow within any managerial role that I occupy. For example,


[User] Please transcribe this text

[Assistant] In my last managerial position, I helped the organization implement a new set of operational policies and procedures that enabled the company to not only improve operational efficiency, but also increase turnover by 20%. Now, there comes a significant amount of responsibility with being a manager


[User] Please transcribe this text

[Assistant] And in the work that I do for your company, I will always put your commercial and financial objectives above everything else. That is a brilliant answer and it will set you apart from the other candidates.


[User] Please transcribe this text

[Assistant] Question number two of your managerial interview. Why do you want to be a manager?


[User] Please transcribe this text

[Assistant] Now becoming a manager is a big step in terms of the level of responsibility you will have.


[User] Please transcribe this text

[Assistant] And perhaps the best way to answer this common managerial interview question is to say that you will relish the challenge the role presents and that you genuinely believe you can make a significant impact within the role. So here's another great answer to help you pass your interview. Why do you want to be a manager? Here we go.


[User] Please transcribe this text

[Assistant] I want to be a manager primarily because of the level of responsibility that comes with the position. We spend a lot of time at work and I want to be in a role where I can influence the success of the organization that I am a part of. Being a manager is challenging.


[User] Please transcribe this text

[Assistant] And you have to constantly think on your feet. You must always try to be one step ahead of everything. And you have to use effective motivational and leadership skills to persuade your team to believe in your vision. Over the years, I have studied other managers to see how they make the decisions they do.


[User] Please transcribe this text

[Assistant] A positive impact within the organization and the team I am responsible for. That is another.


[User] Please transcribe this text

[Assistant] Rock solid positive answer that is going to help you to pass your interview. Now, don't go anywhere because I still have another 19 managerial interview questions and answers to give you. But when you're ready, if you want to CLICK THAT LINK, that is in the top right hand corner of the video.


[User] Please transcribe this text

[Assistant] It takes you through to my website, PassMyInterview.com and you can download


[User] Please transcribe this text

[Assistant] Here is the next question that I want you to prepare for.


[User] Please transcribe this text

[Assistant] What are the most important qualities needed to be a manager?


[User] Please transcribe this text

[Assistant] Now, instead of giving a textbook answer to the common managerial interview question, give a response that demonstrates you have considered carefully the qualities needed to help their organization thrive from a committed manager's perspective. So here's another great example answer to help you pass your interview.