[User] Please transcribe this text

[Assistant] Any chance you can do the math and uh, quantify the increased risk of uh, of this 410 cycle?


[User] Please transcribe this text

[Assistant] Uh, well, thank you so much for sharing this idea, Yuri.


[User] Please transcribe this text

[Assistant] I'm feeling more like home right now, just because I'm used to being in this kitchen. I'm used to, eh, the space around myself.


[User] Please transcribe this text

[Assistant] Today I'm making Manona's meatballs. That's the way that I'm using the bread in the meatballs. And then I'm gonna use fresh bread, toss it in the oven. That's gonna need to soak it up the sauce of the meatballs. This is a childhood dish. That's the dish that Manona does.


[User] Please transcribe this text

[Assistant] So when the judges eat them meatballs, they gotta just have a mightful, all nice and fluffy meatballs. And also the sauce has to be that delicious and strong that they just gotta bring back.


[User] Please transcribe this text

[Assistant] That's my approach now. Be relaxed, enjoy. And cook Italian, the things that are no best.


[User] Please transcribe this text

[Assistant] To give extra flavor to the sauce, what my nonna does normally is just put a few fresh basil leaves and then the pork skin.


[User] Please transcribe this text

[Assistant] I leave the meatballs just simmering away in the tomato sugo. You got the pork, the beef, the mozzarella, the parmesan, it's gonna melt through. It's got the parsley, the extra garlic as well. It's all gonna combine all together.


[User] Please transcribe this text

[Assistant] This dish is just a simple dish. And I hope to get the same level of perfection in the meatball test. Mind on that.


[User] Please transcribe this text

[Assistant] So to clear it up, I scooped the meatballs in the ramekin and then I'll top it out with the sauce, little bit of fresh parmesan, little bit of fresh basil and a drizzle of olive oil.


[User] Please transcribe this text

[Assistant] Those meatballs are so beautiful. They are unbelievable. And I hope to make Manona proud.


[User] Please transcribe this text

[Assistant] Gold ingredient that's so versatile on that bench, so I'm in heaven. Beautiful.


[User] Please transcribe this text

[Assistant] Every pasta dish for me, it's a feast. I love pasta. And there's an Italian app that I'm gonna deliver a great dish today for the judges. I don't want to disappoint them. So fingers crossed it will work out.


[User] Please transcribe this text

[Assistant] Breads and garlic and some almonds.


[User] Please transcribe this text

[Assistant] I really love the flavours of this dish. You get the nice charriness of the leeks and they're beautiful and sweet and tender. The romesco sauce is bold and punchy and it has a bit of spice to it.


[User] Please transcribe this text

[Assistant] Once I get my leeks nice and tender, I move on to my capsicum. The skin's looking nice and blistered.


[User] Please transcribe this text

[Assistant] For this dish, I'm gonna make an onion puree, some beautifully rare, thinly sliced beef, and a clear tomato tea with some parsley oil over the top. There we go. Now I cook the cass.


[User] Please transcribe this text

[Assistant] Inspiration for this dish came from pecanha, which is rump cap and it's a Brazilian dish. You serve it with some tomato kind of salsa salad on the side. I know the tomato tea, it's really, really subtle.


[User] Please transcribe this text

[Assistant] But I'm hoping that the flavours that I add are going to pair with the beef.


[User] Please transcribe this text

[Assistant] My onions are pureed, the parsley oil has come together and is really nice and vibrant and green. Yum.


[User] Please transcribe this text

[Assistant] My tomato tea is on the stove and infusing with different herbs. I think it'll complement my rum.


[User] Please transcribe this text

[Assistant] A tomato tea with a celery and parsley oil. I'm gonna do some rum and some onion.


[User] Please transcribe this text

[Assistant] So anxious, I'm so nervous. I know that flavour is such an important thing and I know they have doubts about the flavour of this dish, but hopefully when they eat it all together, you know, the flavours will work.


[User] Please transcribe this text

[Assistant] Rump with a onion puree, a tomato tea and some parsley oil.


[User] Please transcribe this text

[Assistant] I had fair warning, I knew that this dish might not have worked.


[User] Please transcribe this text

[Assistant] But I kind of stuck to my guns and I tried to force it and unfortunately, it didn't work.


[User] Please transcribe this text

[Assistant] I've been trying to get a hold of you for hours.


[User] Please transcribe this text

[Assistant] I was waiting for you in the restaurant for three hours.


[User] Please transcribe this text

[Assistant] This is the fifth time you've stood me up in two weeks. You need to get your priority straight.


[User] Please transcribe this text

[Assistant] I'm tired of you putting your job first all the time.


[User] Please transcribe this text

[Assistant] You know what? Maybe we should just take a break.


[User] Please transcribe this text

[Assistant] I need some time to think about where this relationship is heading.


[User] Please transcribe this text

[Assistant] I just broke up with my boyfriend. Don't you have any idea how sad I am?


[User] Please transcribe this text

[Assistant] So, Casey, how are things going with the photos for the press kit?


[User] Please transcribe this text

[Assistant] Soooo, Casey, how are things going with the photos for the press kit?


[User] Please transcribe this text

[Assistant] Soooo, Casey, how are things going with the photos for the press kit?


[User] Please transcribe this text

[Assistant] Very often, a few simple plots can give you a better sense of what the algorithm is doing and just sanity check that everything is going okay and the algorithm is doing what it's supposed to.


[User] Please transcribe this text

[Assistant] For example, in an earlier video I talked about how plotting the cost function J of theta can help you make sure that gradient descent is converging.


[User] Please transcribe this text

[Assistant] Often plots of the data or of the learning algorithm outputs will also give you ideas for how to improve your learning algorithm.


[User] Please transcribe this text

[Assistant] Fortunately, Octave has very simple tools to generate lots of different plots and when I use learning algorithms, I find that plotting the data, plotting the learning algorithm, and so on,


[User] Please transcribe this text

[Assistant] Are often an important part of how I get ideas for improving the algorithms. And in this video I'd like to show you some of these Octave tools for plotting and visualizing your data.


[User] Please transcribe this text

[Assistant] Here's my octave window. Let's quickly generate some data for us to plot. So I'm gonna set t to be equal to, you know, this array of numbers. Here's t, set of numbers going from 0 up to 0.98.


[User] Please transcribe this text

[Assistant] The sine function is very easy, I just type plot t comma y1 and hit enter, and uhm,


[User] Please transcribe this text

[Assistant] Up comes this plot where the horizontal axis is the t variable and the vertical axis is y1, which is the sinusoid function that we just computed. Let's set y2 to be equal to the cosine of 2 pi 4 t, like so. And uhm, if I plot t comma y2, what Octave will do is it will take my sinusoid plot


[User] Please transcribe this text

[Assistant] And it will replace it with this cosine function, that now, you know, cosine of x starts at 1. Now, what if I want to


[User] Please transcribe this text

[Assistant] We have both the sine and the cosine plots on top of each other. What I'm going to do is I'm going to type plot t comma y1, so here's my sine function.


[User] Please transcribe this text

[Assistant] And what Hold On does is it calls this octave to now plot new figures on top of the old one.


[User] Please transcribe this text

[Assistant] And uhm, let me now plot t, y2. I'm going to plot the cosine function in a different color. So let me, uhm, put the r in quotation marks there.


[User] Please transcribe this text

[Assistant] And instead of replacing the current figure, I'll plot the cosine function on top and the r indicates that I want this in red color.


[User] Please transcribe this text

[Assistant] Here are additional commands, x label time, say, to label the x axis or the horizontal axis, and y label value, say, to label the vertical axis value. And, uh, I can also


[User] Please transcribe this text

[Assistant] And I can also label my two lines with this command, legend, sign, cosine, and this puts this legend up on the upper right, showing what the two lines are. And finally, title, my plot, puts the title at the top of this figure.


[User] Please transcribe this text

[Assistant] Lastly, if you want to save this figure, you type printd-dpng myplot.png


[User] Please transcribe this text

[Assistant] So PNG is a graphics file format, and if you do this, this will actually save this as a file. If I do that, let me actually change the directory to, uhm, let's see, like that, and then I will print that out. So this will take a while, depending on


[User] Please transcribe this text

[Assistant] How your Octave configuration is set up, this may take a few seconds, but change the directory to my desktop and Octave is now taking a few seconds to save this.


[User] Please transcribe this text

[Assistant] Myplot.png, which Octave has saved, and you know, that's the figure saved as a PNG file.


[User] Please transcribe this text

[Assistant] Auto can save files in other formats as well, so you can type help plot if you want to see the other file formats rather than PNG that you can save figures in.


[User] Please transcribe this text

[Assistant] And lastly, if you want to get rid of the plot, the close command, uhm,


[User] Please transcribe this text

[Assistant] Altive also lets you specify a figure number. So you type figure 1, plot t, y1. That starts up first figure and that plots t, y1. And then if you want a second figure, you specify a different figure number. So figure 2, plot t, y2.


[User] Please transcribe this text

[Assistant] Like so, and now on my desktop I actually have


[User] Please transcribe this text

[Assistant] Two figures, figure one and figure two, that's one plotting the sine function, one plotting the cosine function.


[User] Please transcribe this text

[Assistant] Here's one other neat command that I often use, which is the subplot command. So I'm going to use subplot 1, 2, 1. What that does is it divides, subdivides the plot into a 1 by 2 grid. That's what the first two parameters are. And it starts to access the first element. That's what the final parameter 1 is, right?


[User] Please transcribe this text

[Assistant] Divide my figure into a 1 by 2 grid and I want to access the first element right now. And so if I


[User] Please transcribe this text

[Assistant] Uhm, type that in, it's brought up this figure that is on the left and if I plot t,


[User] Please transcribe this text

[Assistant] Y1, it now fills up this, you know, first element, uh, and if I now do subplot 1, 2, 2, I'm going to start to access the second element, and plot T, Y2 will fill up.


[User] Please transcribe this text

[Assistant] Throw in y2 in the right hand side or in the second element.


[User] Please transcribe this text

[Assistant] And last command, you can also change the axis scales and change axes to 0.51, minus 1, 1, and this sets the x range and y range.


[User] Please transcribe this text

[Assistant] Uh, for the figure on the, on, on, on the right and concretely it sets the horizontal range of values and figure on the right to range from 0.5 to 1 and the vertical axis values to range from minus 1 to 1.


[User] Please transcribe this text

[Assistant] And, you know, you don't need to memorize all these commands. If you, uh, ever need to change the axis, all you need to know is that, you know, there's an axis command, and you can really get the details from the usual octave help command. Finally, just a couple last commands. CLF. Clear is a figure.


[User] Please transcribe this text

[Assistant] Trick that I sometimes use to visualize a matrix, which is I can use image SC of A


[User] Please transcribe this text

[Assistant] I'll take my matrix, I'll plot this as a 5x5 grid of colors.


[User] Please transcribe this text

[Assistant] Where the different colors correspond to the different values in the A matrix. So, uh, concretely, I can also do color bar.


[User] Please transcribe this text

[Assistant] Let me use a more sophisticated command. ImageSCA color bar color map gray.


[User] Please transcribe this text

[Assistant] This is actually running three commands at a time. I'm running image sc, then I'm running colorball, then I'm running colormap gray.


[User] Please transcribe this text

[Assistant] And what this does is it sets a color map to a gray color map and on the right it also puts in this color bar.


[User] Please transcribe this text

[Assistant] And uhm, so this color bar shows what the different shades of color correspond to.


[User] Please transcribe this text

[Assistant] Concretely, the upper left element of the A matrix is 17, and so, you know, that corresponds to kind of a mid-shade of gray. Whereas in contrast, the, uhm,


[User] Please transcribe this text

[Assistant] Second element of A, so the 1, 2 element of A is 24, right, so it's A, 1, 2 is 24.


[User] Please transcribe this text

[Assistant] So that corresponds to this gray up here, which is a, you know, nearly a shade of white. And, uh, the small value, say, uh, a, what is that, uhm, a45, uh, you know, is the value 3 over here.


[User] Please transcribe this text

[Assistant] That corresponds, you can see on my color bar, that corresponds to a much darker shade in this image.


[User] Please transcribe this text

[Assistant] So, here's another example I can plot to be larger.


[User] Please transcribe this text

[Assistant] You know, here's a magic 15 that gives me a 15 by 15 magic square. And this gives me a plot of what my 15 by 15 magic square's values look like. And finally, to wrap up this video,


[User] Please transcribe this text

[Assistant] What you've seen me do here is use a comma chaining of function calls. Here's how you actually do this. If I type A equals one, B equals two, C equals three, and hit enter, then this is actually carrying out three commands at the same time, or really carrying out three commands one after another.


[User] Please transcribe this text

[Assistant] Uhm, and it prints out all three results, and this is a lot like a equals one, b equals two, c equals three.


[User] Please transcribe this text

[Assistant] Except that if I use semicolons instead of a comma, it doesn't print out anything. So this, you know, this thing here we call comma chaining of commands, or comma chaining of function calls. And, uh, it's just another convenient way in Octave to put multiple commands like, uhm,


[User] Please transcribe this text

[Assistant] MHSC color bar color map to put multi-commands on the same line.


[User] Please transcribe this text

[Assistant] So, that's it. You now know how to plot different figures in Octave. And in the next video, the next main piece I want to tell you about is how to write control statements, like if, while.


[User] Please transcribe this text

[Assistant] The choice you are given through medicine from a lot of your mentors even is like, you have to choose, like being a really good person or a really good doctor.


[User] Please transcribe this text

[Assistant] I didn't feel like the stories I had were worth telling or that they mattered.


[User] Please transcribe this text

[Assistant] The hardest thing was coming up against that voice that was telling me no one wants to hear the right stories, like why invest the time in this thing that doesn't really mean anything in the grand scale of life. Maybe the biggest thing is like maybe it does.


[User] Please transcribe this text

[Assistant] Now that you know how to load and save data in Altair, put your data into matrices and so on, in this video I'd like to show you how to do computational operations on data. And later on we'll be using these sorts of computational operations to implement our learning algorithms. Let's get started.


[User] Please transcribe this text

[Assistant] You can also do element-wise operations and do a dot times b. And what this will do is it will take each element of a and multiply it by the corresponding element of b. So that's a, that's b, that's a dot times b.


[User] Please transcribe this text

[Assistant] And in general, the period tends to, is usually used to denote element-wise operations in octave.


[User] Please transcribe this text

[Assistant] So, here is the matrix A, and if I do A dot carrot 2, this gives me the element-wise squaring of A. So, you know, 1 squared is 1, 2 squared is 4, and so on.


[User] Please transcribe this text

[Assistant] e to the v is, you know, base e, exponentiation of v's, uh, elements. So this is e, this is e squared, e cubed, because this was v, so.


[User] Please transcribe this text

[Assistant] And, uh, I can also do abs v to take the element-wise absolute value of v. So here, you know, v was all positive. We did abs, say, minus 1, 2, minus 3. The element-wise absolute value gives me back these non-negative values.


[User] Please transcribe this text

[Assistant] Negative V gives me the minus of V. This is the same as negative 1 times V, but usually you just write negative V instead of negative 1 times V. And uhm,


[User] Please transcribe this text

[Assistant] What else can you do? Uh, here's another neat trick. So, let's see. Let's say I want to take v and increment each of its elements by 1. Well, one way to do it is by constructing a 3 by 1


[User] Please transcribe this text

[Assistant] Vector, there's all ones, and adding that to V. So if I do that, this increments V by, from 1 to 3 to 2, 3, 4. The way I did that was length,


[User] Please transcribe this text

[Assistant] Alright, and what I did was v plus 1's 3 by 1, which is adding this vector of all 1's to v, and so this increments v by 1.


[User] Please transcribe this text

[Assistant] Transpose, this gives me the, you know, transpose of my matrix A. And of course A transpose, if I transpose that again, then I should get back my matrix A.


[User] Please transcribe this text

[Assistant] Some more useful functions, uh, let's say lowcase a is 1, 15, 2, 0.5. So it's a, you know, 1 by 4 matrix.


[User] Please transcribe this text

[Assistant] And this returns thou an end, which are


[User] Please transcribe this text

[Assistant] Going to be the maximum value of a, which is 15, as well as the index, so it's the element number 2 of a that was 15, so n is my index into this.


[User] Please transcribe this text

[Assistant] So using this example, the variable lowercase a, if I do a less than 3, this does the element-wise operation, element-wise comparison.


[User] Please transcribe this text

[Assistant] The third and fourth elements of A are, I meant less than 3, third and fourth elements are less than 3, so this is 1, 1. So this does the element-wise comparison of all four elements of the variable, lowercase a to 3, and it returns true or false, depending on whether or not it's less than 3. Now, if I do find a less than 3, this will tell me which are the elements of A


[User] Please transcribe this text

[Assistant] The variable a is less than 3. In this case, the first, third, and fourth elements are less than 3.


[User] Please transcribe this text

[Assistant] The magic function returns, let's type health magic. The magic function returns functions called magic squares, returns these matrices called magic squares. They have this


[User] Please transcribe this text

[Assistant] convenient way, you know, to generate a 3 by 3 matrix. And these magic squares have the property that each row, each column, and the diagonals all add up to the same thing. So it's kind of a mathematical construct.


[User] Please transcribe this text

[Assistant] Uhm, I use magic, I use this magic function only when I'm doing demos or when I'm teaching octave like this and I don't actually use it for any, you know, useful machine learning application.


[User] Please transcribe this text

[Assistant] But, uh, let's see, if I type rc equals find a greater than or equals 7, this finds, uhm,


[User] Please transcribe this text

[Assistant] All the elements of A, they're like greater than or equal to 7, and so RC stands for row and column. So the 1, 1 element is greater than 7, the 3, 2 element is greater than 7, and the 2, 3 element is greater than 7. So let's see. The 2, 3 element, for example, is


[User] Please transcribe this text

[Assistant] A2, 3 is 7, is this element out here, and that is indeed greater than equal to 7.


[User] Please transcribe this text

[Assistant] By the way, I actually don't even memorize myself what these find functions do and what all of these things do myself. And whenever I use the find function, sometimes I forget myself exactly what it does, and you know, I type help find to lock up the document.


[User] Please transcribe this text

[Assistant] Okay, just two more things I'm going to quickly show you. One is the sum function. So here's my a. I'm going to type sum a. This adds up all the elements of a. And if I multiply them together, I type prod a. Prod sends a product, and this returns the product of these four elements of a.


[User] Please transcribe this text

[Assistant] Uh, floor A rounds down, these elements of A, so 0.5 gets rounded down to 0, and seal, or ceiling A gets rounded up, so 0.5, rounded up to the nearest integer, so 0.5 gets rounded up to 1. You can also, uhm,


[User] Please transcribe this text

[Assistant] Let's see, let me type rand3 to generate a 3x3 matrix. If I type max rand3,


[User] Please transcribe this text

[Assistant] This is my magic number, this is my magic square, 3x3a. Let's say I type max a 3x3a.


[User] Please transcribe this text

[Assistant] And, uh, if you want to, and, uh, remember, it defaults to a column-wise element. So if you want to find the maximum element in the entire matrix A, you can type max of max of A.


[User] Please transcribe this text

[Assistant] Like so, which is 9, or you can turn a into a vector and type max of a.


[User] Please transcribe this text

[Assistant] And this treats this as a vector and takes the, uhm, max element of that vector.


[User] Please transcribe this text

[Assistant] So let me do sum a1. So this does a per column sum. So I'm going to take each column of a and add them up. And this, you know, let's just verify that indeed for a 9 by 9 magic square, every column adds up to 369. That's the same thing.


[User] Please transcribe this text

[Assistant] Now let's do the row-wide sum, so the sum a comma, uhm, 2.


[User] Please transcribe this text

[Assistant] And this sums up each row of A and indeed each row of A also sums up to 369.


[User] Please transcribe this text

[Assistant] Now let's sum the diagonal elements of A and make sure that A, that also sums up to the same thing. So what I'm going to do is


[User] Please transcribe this text

[Assistant] Construct a 9 by 9 identity matrix, that's I9, and we're going to take A and construct


[User] Please transcribe this text

[Assistant] Multiply A elements-wise. So here's my matrix A. I'm going to do A dot times I nine. And what this will do is take the element-wise product of these two matrices, and so this should wipe out everything in A except for the diagonal entries.


[User] Please transcribe this text

[Assistant] And now I'm going to do some sum of a of that. And this gives me the, uhm,


[User] Please transcribe this text

[Assistant] So you can sum up these diagonal elements and indeed it is 369. Uh, you can sum up the other diagonal as well. So instead of top left to bottom right you can sum up the opposite diagonal from bottom left to top right.


[User] Please transcribe this text

[Assistant] The commands for this is somewhat more cryptic. You don't really need to know this. I'm just showing you this in case any of you are curious. But uhm, let's see.


[User] Please transcribe this text

[Assistant] Flip ud stands for flip up down, but if you do that, that turns out to sum up the elements in the opposite, the other diagonal of A, and that also sums up to 369. Here, let me show you. Whereas I9 is this matrix, flip up down of I9,


[User] Please transcribe this text

[Assistant] This is indeed the identity matrix with essentially ones on the diagonals and zeros on the off diagonals up to a numerical round off.


[User] Please transcribe this text

[Assistant] And in the next video I'm going to very quickly show you how again with one or two lines of code using Octave you can quickly visualize your data or plot your data and use that to better understand what your learning algorithms are doing.


[User] Please transcribe this text

[Assistant] Like we have so many risks in our life, like as we said, if you go into a labor market during a recession or basically if something bad happens, like you need to find a way to hedge it. We have stuff for like hedging against oil risk, for agricultural risk. Are there any innovative financial products for hedging against say labor market risks?


[User] Please transcribe this text

[Assistant] This episode is brought to you by the Things You Don't Need to Know podcast. Each weekly episode will teach you about something you wouldn't normally learn, like how to land a plane or perform open-heart surgery. Find it now!


[User] Please transcribe this text

[Assistant] Lately, we've been talking a lot about new variants of SARS-CoV-2 that arise through mutation, the small genetic changes that accumulate over time. But early in February, scientists in the US said they'd found a different type of variant.


[User] Please transcribe this text

[Assistant] That's a hybrid of sorts, with chunks of genetic information from two variants that came together in a host


[User] Please transcribe this text

[Assistant] Now, this recombinant virus is a preliminary finding, and there is zero evidence that it's circulating in the population. It came up in a sample from one patient, and it could even be an artifact that occurred after the sample was taken. But it's notable.


[User] Please transcribe this text

[Assistant] of a recombination event happening in the wild. And, well, a lot of people are talking about it. But again,


[User] Please transcribe this text

[Assistant] It is not a huge surprise. Scientists have been looking for recombinant viruses throughout this pandemic, like some of the first concerns were published in March 2020. But while there's been the occasional paper, the general consensus is that there just hasn't been enough evidence for researchers to agree these recombinations were happening.


[User] Please transcribe this text

[Assistant] One reason they've been monitoring this so closely is because of what they've seen with other coronaviruses that infect livestock.


[User] Please transcribe this text

[Assistant] Recombination of a couple of these viruses has led to big changes that let them escape vaccines. That's a term that scientists use when a vaccine isn't as effective.


[User] Please transcribe this text

[Assistant] Offspring, if you will, has key genetic features that could mean its


[User] Please transcribe this text

[Assistant] This type of recombination can happen when someone is infected with two different SARS-CoV-2 variants at once.


[User] Please transcribe this text

[Assistant] Because coronaviruses do something a bit weird while they're reproducing. See, inside an infected cell, specific viral proteins move along the length of the virus's RNA genome to make copies of it, some of which later get wrapped up and exported as new virus particles. But the proteins usually don't make these copies all in one go.


[User] Please transcribe this text

[Assistant] So if there are two different viral genomes floating around, they can start copying one genome, and then hop over to the other one when they continue. And that can lead to new viruses with chunks of genetic information from both variants. But the real takeaway here is that researchers have been expecting this sort of thing.


[User] Please transcribe this text

[Assistant] So there could be things happening that don't get recorded, and when variants don't have a lot of genetic differences, recombination events may be harder to detect. There's also new evidence that recombination in coronaviruses isn't just common, it's a fundamental part of their life cycle.


[User] Please transcribe this text

[Assistant] In a study published in January, researchers infected cells in the lab with coronaviruses and looked closely at the genome copies they made. And they saw evidence


[User] Please transcribe this text

[Assistant] And fortunately, it looks like it's still pretty rare for someone to be infected with two variants at once.


[User] Please transcribe this text

[Assistant] Prevention measures like distancing, handwashing, masking, and vaccination should make it even less likely for that to happen. But also, the encouraging news here…


[User] Please transcribe this text

[Assistant] What the researchers saw suggests recombination is essential to coronaviruses. The jumping on and off while copying seems to be a key part of how they make the instructions that tell the cell they're infecting to build new viruses.


[User] Please transcribe this text

[Assistant] Then the thing that makes these viruses seem so scary may also be a giant weak point. We may be able to develop drugs that specifically target the proteins involved in recombination to mess up their ability to replicate.


[User] Please transcribe this text

[Assistant] You might enjoy a brand new podcast called Things You Don't Need to Know.


[User] Please transcribe this text

[Assistant] It's hosted by Ari Kagan, an eighth-grade dropout who spent his life learning all sorts of fascinating, if not so everyday relevant things. And now, by listening to his weekly podcast, you can also learn these new and unconventional things, like how to defuse a bomb or invent a new food. And hey, even if you never use the skill in your daily life, it's sure to spark some fun conversations. You can find it by searching Things You Don't Need to Know on the podcast platform of your choice, or by clicking the link in the description.


[User] Please transcribe this text

[Assistant] I finally got a Nintendo Wii! Come on, help me hook it up to the TV.


[User] Please transcribe this text

[Assistant] I'm going to find that guy and nothing is going to stand in my way.


[User] Please transcribe this text

[Assistant] Hey, what are you doing? I'm helping this man of course.


[User] Please transcribe this text

[Assistant] She said be quiet. Be quiet and help. She said it again. Go and call an ambulance. Me? Yes, you. And be quick. But be quick.


[User] Please transcribe this text

[Assistant] Now you, go and get some water.


[User] Please transcribe this text

[Assistant] Where from? From that house over there. Don't look at me like that. Go! I'm going.


[User] Please transcribe this text

[Assistant] Feel his pulse. But I- Go on, feel his pulse.


[User] Please transcribe this text

[Assistant] You've got the water? Good. Give him one of the pills with some water.


[User] Please transcribe this text

[Assistant] Come with us, Mr Scott. We'd like to take you to hospital.


[User] Please transcribe this text

[Assistant] In this video, we'll talk about how to fit the parameters theta for logistic regression.


[User] Please transcribe this text

[Assistant] In particular, I'd like to define the optimization objective


[User] Please transcribe this text

[Assistant] Or the cost function that we'll use to fit the parameters.


[User] Please transcribe this text

[Assistant] Here's the supervised learning problem of fitting a logistic regression model. We have a training set of M training examples.


[User] Please transcribe this text

[Assistant] And as usual, each of our examples is represented via a feature vector that's n plus one dimensional.


[User] Please transcribe this text

[Assistant] And as usual, we have x zero equals one, uh, first feature or zero feature is always equal to one. And because this is a classification problem, our training set has the property that every label y is either zero or one.


[User] Please transcribe this text

[Assistant] This is a hypothesis, and the parameters of the hypothesis is this theta over here.


[User] Please transcribe this text

[Assistant] And the question I want to talk about is, given this trading set,


[User] Please transcribe this text

[Assistant] How do we choose or how do we fit the parameters theta?


[User] Please transcribe this text

[Assistant] Back when we were developing the linear regression model, we used the following cost function.


[User] Please transcribe this text

[Assistant] I've written this slightly differently, where instead of 1 over 2m, I've taken the 1 half and put it inside the summation instead.


[User] Please transcribe this text

[Assistant] Of h of x comma y to be equal to this. It's just equal to this one half of the square error. So now we can see more clearly that the cost function is a sum


[User] Please transcribe this text

[Assistant] Over my training set was 1 over m times the sum over my training set of this cos term here. And uhm,


[User] Please transcribe this text

[Assistant] To simplify this equation a little bit more, it's going to be convenient to get rid of those superscripts. So just define


[User] Please transcribe this text

[Assistant] Cos of h of x comma y to be equal to one half of the squared error.


[User] Please transcribe this text

[Assistant] And the interpretation of this cost function is that this is the cost I want my learning algorithm to, you know, have to pay if it outputs that value, if its prediction is h of x, and the actual label was y. So let's just cross off those superscripts, right?


[User] Please transcribe this text

[Assistant] And uhm, no surprise for linear regression, the cost we've defined is that, well the cost for this is that it's one half times


[User] Please transcribe this text

[Assistant] The square difference between what I predicted and the actual value that we observed for y.


[User] Please transcribe this text

[Assistant] Now, this cost function worked fine for linear regression, but here we're interested in logistic regression.


[User] Please transcribe this text

[Assistant] If we could minimize this cost function that is plugged into j here, that would work okay. But it turns out that if we use this particular cost function, this would be a non-convex function of the parameters theta.


[User] Please transcribe this text

[Assistant] Here's what I mean by non-convex. We have some cost function j of theta, and for logistic regression, this function h here


[User] Please transcribe this text

[Assistant] And if you take the sigmoid function, plug it in here, and then take this cost function and plug it in there, and then plot what J of theta looks like.


[User] Please transcribe this text

[Assistant] You find that J of theta can look like a function that's like this. You know, with many local optima.


[User] Please transcribe this text

[Assistant] And the formal term for this is that this is a non-convex function, and you can kind of tell if you were to run gradient descent on this sort of function, it is not guaranteed to converge to the global minimum.


[User] Please transcribe this text

[Assistant] Whereas in contrast, what we would like is to have a cost function J of theta that is convex, that is a single bow-shaped function that looks like this, so that if you run gradient descent, we would be guaranteed that gradient descent, you know, would converge to the global minimum.


[User] Please transcribe this text

[Assistant] And the problem with using the squared cost function is that because of this very nonlinear sigmoid function that appears in the middle here, j of theta,


[User] Please transcribe this text

[Assistant] Ends up being a non-convex function if you were to define it as the square cos function.


[User] Please transcribe this text

[Assistant] So what we'd like to do is to instead come up with a different cost function that is convex.


[User] Please transcribe this text

[Assistant] And, uh, so that we can apply a great algorithm like gradient descent and be guaranteed to find a global minimum.


[User] Please transcribe this text

[Assistant] If it outputs a value h of x, so if this is some number, like 0.7, but if it predicts a value h of x, and the actual class label turns out to be y, the cost is going to be minus log h of x if y is equal to 1, and minus log 1 minus h of x if y is equal to 0.


[User] Please transcribe this text

[Assistant] This looks like a pretty complicated function, but let's plot this function to gain some intuition about what it's doing. Let's start off with the case of y equals 1.


[User] Please transcribe this text

[Assistant] If y is equal to 1, then the cost function


[User] Please transcribe this text

[Assistant] Is minus log h of x. And if we plot that, so let's say that the horizontal axis is h of x, so we know that our hypothesis is going to output a value between 0 and 1.


[User] Please transcribe this text

[Assistant] Alright, so h of x, that varies between 0 and 1. If you plot what this cost function looks like, you find that it looks like this.


[User] Please transcribe this text

[Assistant] One way to see why the plot looks like this is because uhm, if you were to plot log z,


[User] Please transcribe this text

[Assistant] With z on the horizontal axis, then that looks like that, and it sort of approaches minus infinity, right? So this is what the log function looks like. And this is zero, this is one.


[User] Please transcribe this text

[Assistant] Right, just flipping the sign. Minus log z. And we're interested only in the range of when this function goes between 0 and 1. So get rid of that. And so we're just left with, you know, this part of the curve. And that's what this curve on the left looks like.


[User] Please transcribe this text

[Assistant] The hypothesis exactly, you know, predicts h equals 1 and y is exactly equal to what it predicted.


[User] Please transcribe this text

[Assistant] Then the cost is equal to zero, right? That corresponds to the curve doesn't actually flatten out. The curve is still going.


[User] Please transcribe this text

[Assistant] First, notice that if h of x equals 1, if the hypothesis predicts that y is equal to 1, and if indeed y is equal to 1,


[User] Please transcribe this text

[Assistant] Then the cost is equal to zero. That corresponds to this point down here, right? If h of x is equal to one, and we're only considering the case that y equals one here, but if h of x is equal to one, then the cost is down here, is equal to zero. And that's what we'd like it to be because, you know, if we correctly predict the output y,


[User] Please transcribe this text

[Assistant] H of x approaches zero, so as h, as the output of a hypothesis approaches zero, the cost blows up and it goes to infinity. And what this does is this captures the intuition


[User] Please transcribe this text

[Assistant] So we thought that if our hypothesis, you know, output zero, that's like saying, our hypothesis saying the chance of y equals one is equal to zero. It's kind of like going to a medical patient and saying the probability that you have a malignant tumor, the probability that y equals one is zero. So it's like absolutely impossible that your tumor is malignant.


[User] Please transcribe this text

[Assistant] But if it turns out that the tumor, the patient's tumor actually is malignant, so if y is equal to 1, even after we tell them, you know, that the probability of it happening is zero, it's absolutely impossible for it to be malignant.


[User] Please transcribe this text

[Assistant] But if we told them this with that level of certainty, and we turn out to be wrong,


[User] Please transcribe this text

[Assistant] Then we penalize the learning algorithm by a very, very large cost. And that's captured by having this cost go to infinity.


[User] Please transcribe this text

[Assistant] Uhm, if y equals one and h of x approaches zero.


[User] Please transcribe this text

[Assistant] This slide considered the case of y equals one. Let's look at what the cost function looks like for y equals zero. If y is equal to zero, then the cost looks like this, looks like this expression over here.


[User] Please transcribe this text

[Assistant] And uhm, if you plot the function, minus log 1 minus z,


[User] Please transcribe this text

[Assistant] It, what you get is the cost function actually looks like this. So it goes from 0 to 1, and like that.


[User] Please transcribe this text

[Assistant] And so if you plot the cost function for the case of y equals zero, you find that it looks like this.


[User] Please transcribe this text

[Assistant] And, uh, what this curve does is it now


[User] Please transcribe this text

[Assistant] It blows up and goes to plus infinity as h of x goes to 1, because uhm, it's the same that if y turns out to be equal to 0, but we predicted that y is equal to 1 with almost certainty, we're probably 1, then we end up paying a very large cost. And conversely,


[User] Please transcribe this text

[Assistant] If h of x is equal to zero and y equals zero, then the hypothesis nailed it. The predicted y is equal to zero, and it turns out y is equal to zero. So at this point, the cost function is going to be zero.


[User] Please transcribe this text

[Assistant] The topic of convexity analysis is beyond the scope of this course, but it is possible to show that with our particular choice of cost function, this would give us a convex optimization problem, as our cost function, our overall cost function J of theta will be convex and local optima free.


[User] Please transcribe this text

[Assistant] In the next video we're going to take these ideas of the cost function for a single training example and develop that further and define the cost function for the entire training set.


[User] Please transcribe this text

[Assistant] And, uh, we'll also figure out a simpler way to write it than we have been using so far. And, uh, based on that, we'll work out gradient descent, and that will give us our logistic regression algorithm.


[User] Please transcribe this text

[Assistant] In our model, the optimal portfolio decision of all investors in equilibrium will be symmetric or identical.


[User] Please transcribe this text

[Assistant] Given that, we can conclude that no single investor will ever short a stock in equilibrium.


[User] Please transcribe this text

[Assistant] Because then everybody in our model would be sorting that stock. Which...


[User] Please transcribe this text

[Assistant] Brings up the question, who is providing the stock for you to short?


[User] Please transcribe this text

[Assistant] I think a great sweet treat's obviously the balance of flavors. The enjoyment that goes with it, so whether it's a bit of nostalgia, the story you tell from why you created it. So, you know, always try and throw an experienced flavor in there. You know, whether it's a savory or something like recreating a meal. Those classic flavors versus those experienced flavors, you know, creates a little balance.


[User] Please transcribe this text

[Assistant] Chocolate. 70% chocolate. Key to some great chocolate desserts. What's that? So that's yuzu juice.


[User] Please transcribe this text

[Assistant] Licorice, one of the toughest flavours, yeaah, I mean it's delicious, but can be quite polarising to a lot of people. But used well, can be one of the most amazing desserts you'll ever have.


[User] Please transcribe this text

[Assistant] That's it, they're in there. And then something to create with those, salt and vinegar chips, they're acidic, they're salty, they've got great texture, it just depends on how you use them.


[User] Please transcribe this text

[Assistant] Be your best everybody. Your time starts now.


[User] Please transcribe this text

[Assistant] You've got some really good core skills here. Get those layers right and you'll be a gun.


[User] Please transcribe this text

[Assistant] I think that for me the mousse could have been just a touch lighter. Crumbs delicious, really well made. And that gel, just a bit more on the plate. I think it would have really taken that dessert to another level. Well done.


[User] Please transcribe this text

[Assistant] I love the sound of it. It's right my alley. I mean, as a kid I ate chip sandwiches every day after school.


[User] Please transcribe this text

[Assistant] That's taken me right back. I mean, I grew up in a supermarket, sitting there eating chips and a sandwich, but in an ice cream version. Thanks, Sarah.


[User] Please transcribe this text

[Assistant] Yeah, oh look, for me that was bang on. You know, I think textural, flavour, a little bit of difference, you know. You put that little bit of eucalyptus and the caramel. It's all you need to make a dessert pop and take it to the next level. I loved it. Hats off for that one.


[User] Please transcribe this text

[Assistant] You all done an amazing thing well today. I think it showed that like, getting that mystery box and all those tricky ingredients and you really put together some amazing desserts. I saw some great textures, flavours, creativity and all in different ways. So, you know, you should be all very proud. There was one standout dessert for me today.


[User] Please transcribe this text

[Assistant] Amazing work. Good core flavours. Nailed them. And had a surprise. And you did that. Really well done. Well done, Bruce. Very proud.


[User] Please transcribe this text

[Assistant] For our second choice today, there was one standout dish that just sang. And that second dish today was you, Ben.


[User] Please transcribe this text

[Assistant] As simple as it was, your little meringue kisses were as bright and powerful as your shirt.


[User] Please transcribe this text

[Assistant] I think it looks beautiful. You know, the colours pop and it matches the theme that you've chosen. The shoe, well filled. Good bit of custard. I always like it juicy. Done it well. Well done. Good effort. Well done guys.


[User] Please transcribe this text

[Assistant] The first thing for me, nicely made in the way of like getting that nice hollow inside to field well. That filling, the texture of it was beautiful.


[User] Please transcribe this text

[Assistant] That rosemary, piquet and caramel, the flavour is just not there. You just got some work there to do with your texture.


[User] Please transcribe this text

[Assistant] I think the texture of the macarons are nice. He did that well. Uhm, he's a lot of fennel.


[User] Please transcribe this text

[Assistant] I mean, I love fennel, but fennel's like one of those flavours. It's gotta be something to make it sing.


[User] Please transcribe this text

[Assistant] I commend you for getting something up and I think that's, you know, showed the spirit that you didn't give up and, you know, you kept going, which is amazing. I think it looked beautiful. It's just the flavours got lost.


[User] Please transcribe this text

[Assistant] That filling of caramel was on point by itself was an amazing filling but inside that it literally just gets overtaken by the sesame seeds.


[User] Please transcribe this text

[Assistant] I think you all did a fantastic job. I mean, it's one thing to get a recipe and replicate it, but it's another create your own version of it. To make such a monster of a dish, you should be very proud.


[User] Please transcribe this text

[Assistant] Making a passion fruit curd tart with Swiss meringue.


[User] Please transcribe this text

[Assistant] If you've been around the internet long enough, you might have discovered that wombat butts are pretty cool. I mean, they produce cube-shaped poop. You may have also heard that wombats can use their rear ends to crush the skull of any predator who wanders into their burrow. Unfortunately, the stories of wombat combat seem to be unconfirmed, but the idea of a tactical butt


[User] Please transcribe this text

[Assistant] Dragonfly larvae. Dragonflies are master predators. They're sometimes called the most successful hunters in the animal kingdom because they can catch up to 95% of their intended prey. And their babies are no joke either. Dragonfly larvae are also called nymphs.


[User] Please transcribe this text

[Assistant] They live underwater, and to breathe, they don't use their mouths. Instead, they draw in water through their anus.


[User] Please transcribe this text

[Assistant] Into a large muscular hindgut chamber where they extract oxygen from it.


[User] Please transcribe this text

[Assistant] This butt kind of does everything. It breathes, it digests, it poops.


[User] Please transcribe this text

[Assistant] And it even acts like an engine. By suddenly squeezing water out of their hind guts,


[User] Please transcribe this text

[Assistant] Dragonfly larvae can create powerful jets that rocket them around underwater and help them avoid danger. They can even use their hindguts to supercharge their mouths. See, these little assassins have specialized mouthparts that can shoot out in a fraction of a second and grab fast-moving prey.


[User] Please transcribe this text

[Assistant] Kind of like a chameleon's tongue. But the thing is, their mouths don't have a muscle large and powerful enough to shoot their jaws out like that. So instead, the nymphs use hydraulic pressure


[User] Please transcribe this text

[Assistant] Discharging some of the internal pressure and rocketing their mouth forward. This method allows the insects to capture quick-moving prey even through the drag of water. Plus, they don't have to load up their mouths with giant muscles that would weigh them down.


[User] Please transcribe this text

[Assistant] Another critter with a remarkable rear is the scorpion. Now, scorpions are famous for their stingers, which inject venom into their prey. But stingers are not butts. In fact, they're not even part of the digestive tract.


[User] Please transcribe this text

[Assistant] Some scorpions do have a legitimate rear-end-related skill, and they use it for defense. Specifically, they have disposable, sacrificial butts. Researchers have found that 14 species in South America can break their tails off to avoid capture.


[User] Please transcribe this text

[Assistant] Sacrificing part of yourself can let you distract predators or escape from them. I mean, it's a super costly strategy, but it's better than dying. So when threatened, these South American scorpions can drop their stingers and other segments of their tails for a quick getaway. But here's the catch.


[User] Please transcribe this text

[Assistant] Now, if there's a silver lining here, it's that scorpions don't poop that much. In fact, in the lab, it took up to eight months for constipation levels to become critical. The researchers suggest this may be more than enough time for the animals to mate and carry on their genetic line.


[User] Please transcribe this text

[Assistant] Which might explain why this trait hasn't evolved away. After all, natural selection generally only cares whether you successfully have babies. And if the anus-less scorpions can still reproduce…


[User] Please transcribe this text

[Assistant] Well, from an evolutionary perspective, it doesn't matter what happens after that. Speaking of bum deals, sea cucumbers have to put up with some problems that are literally a pain in the butt. In general, these odd critters may seem pretty unassuming, but they have a number of tricks up their posterior sleeve. Like, when in danger, sea cucumbers may eviscerate themselves,


[User] Please transcribe this text

[Assistant] Throwing their intestines out their anuses at predators. This is a kind of autotomy, like what scorpions do. And while intestine hurling may be a distraction, it can also drive predators off.


[User] Please transcribe this text

[Assistant] Since sea cucumbers' guts may contain noxious chemicals or adhesive tubules, luckily sea cucumbers can regenerate those bits. And that's also not the weirdest thing about their backsides. Some species also come equipped with spines called


[User] Please transcribe this text

[Assistant] To hide from predators. Like, sometimes, several pearlfish shelter in one sea cucumber's caboose. In many cases, the sea cucumbers aren't really harmed by the fish. But sometimes, while the pearlfish are hanging out there, they do pick out a nice snack for themselves.


[User] Please transcribe this text

[Assistant] The sea cucumber's gonads. Like we said, sea cucumbers can regenerate their organs, but having your gonads eaten


[User] Please transcribe this text

[Assistant] Probably isn't pleasant. Now, you might think sea cucumbers could just, like, clench up if suspicious fish are hovering around their hindquarters. But here's the thing. Like dragonfly nymphs, many species of sea cucumber also breathe through their butts, which means they have to open up at some point


[User] Please transcribe this text

[Assistant] Giving fish an opportunity to wriggle where they're not wanted. So these spiky anal teeth allow sea cucumbers to breathe easy.


[User] Please transcribe this text

[Assistant] Many species use them as deterrents, think skunks or bombardier beetles. And if we went through each of them, this list show would be hours long. But, rove beetles take backside chemical bombs to the next level. These beetles can use their butts for both attack and defense. For example, one water-skimming beetle group uses its butt


[User] Please transcribe this text

[Assistant] But when a predator appears, they need to make a quick escape. You might expect them to just paddle their little feet as fast as they can, but instead, they secrete a hydrophobic liquid from their butt


[User] Please transcribe this text

[Assistant] And drop it onto the surface of the water. The secretion breaks up the water tension, which is the force that causes water to naturally stick together. Normally, the tension would pull on all sides of the bug at the same time, making it stand still. But by disrupting the tension behind them,


[User] Please transcribe this text

[Assistant] The only tension acting on the beetle is toward the front. This unequal force causes the bugs to rocket forward, like a surfer who makes their own waves.


[User] Please transcribe this text

[Assistant] And speed away from danger. Escaping danger is the typical reason for producing nasty butt secretions, but what's special about rove beetles is that there may be at least one species that uses secretions as a lure. Now, we should caveat that this part seems to have come from a single report. That's to say,


[User] Please transcribe this text

[Assistant] One group described something without another group following up on it. That doesn't mean they're wrong. When we reached out to a scientist about this, they pointed out that these kinds of rare observations can be hard to follow up on. But it is a kind of gap in the system.


[User] Please transcribe this text

[Assistant] So sometimes, the beetle hides near the fly's favorite foods, like rotting fruit, poop, and dead animals.


[User] Please transcribe this text

[Assistant] Then, when the flies come to feast, the beetles ambush them. However, if no obvious fly lures are around, the beetles seem to make their own. In the 1990s, a pair of scientists reported that when these rove beetles couldn't stake out any poop or dead animals or whatnot, they perched on leaves or rocks and lifted their butts in the air.


[User] Please transcribe this text

[Assistant] Finally, we leave you with an honorable mention in this rump-tastic roundup. Like the rove beetle lures, it's based on a single report that has yet to be repeated, but it's pretty remarkable if true. In the 1980s, there was a report that the larvae of beaded lacewings could use their butts to attack and paralyze prey.


[User] Please transcribe this text

[Assistant] By farting on them. In that paper, researchers reported that they saw termite-eating lacewing larvae approach termites, then lift up their butts and wave them past the termites' face. After a few minutes, the termites became paralyzed,


[User] Please transcribe this text

[Assistant] Whereupon the larvae began to feed. Investigating a bit more, the researchers concluded that the weapon must be a gas. A truly toxic fart. And this could be a useful tactic, because the termites are actually much larger than the larvae.


[User] Please transcribe this text

[Assistant] So it's kind of a David and Goliath situation, and the larvae needed to get creative. This would represent the only known case of a chemical being used to attack like this. But again…


[User] Please transcribe this text

[Assistant] These examples show that scientists need to do a lot more research into the remarkable, versatile, and underappreciated butt. Not because it makes a good joke, but because rear ends can genuinely have some extremely clever, unexpected adaptations that teach us more about life in the animal kingdom. And that…


[User] Please transcribe this text

[Assistant] If you liked this episode, well, we've got a special treat for you. Every month on our Patreon, we release a…


[User] Please transcribe this text

[Assistant] Let's just call it a potentially not safe for work podcast. It's called SciShow After Hours, and it's where Hank and one of our editors get together to talk about some fascinating science we just couldn't put on this channel. If you want to learn more about it, you can head over to patreon.com slash SciShow.


[User] Please transcribe this text

[Assistant] Uhm, what does it mean to be rational? I'm married to a psychologist by the way. You can ask my wife. We're economists. We have a very simple model of people.


[User] Please transcribe this text

[Assistant] But what does it mean to be rational? Sometimes I think rational people.


[User] Please transcribe this text

[Assistant] They don't pay attention to financial markets at all. They're into more important things like making friends. But on the other hand, there is some basic, simple notion of human rationality that, uh, uh, is, is, I think it's partly correct. People are partly rational.


[User] Please transcribe this text

[Assistant] But they often make mistakes, and this is something we're going to talk about in the semester, behavioral finance is an important, uh, field. Uh, and, uh, we mostly think of ourselves, do you think of yourself as rational?


[User] Please transcribe this text

[Assistant] I don't know if we have agreement on this, but who's... I never thought of myself as particularly rational. I have trouble with self-discipline. Do you ever get lazy?


[User] Please transcribe this text

[Assistant] That's part of your rationality. Or do you ever find that you're just following entertainment? You're just bored with getting the facts. You want the entertainment?


[User] Please transcribe this text

[Assistant] So these are, these are human traits. Uh, the capital asset pricing model, the heroic assumption that is often, uh, taken uncritically is that everybody is investing according to that model.


[User] Please transcribe this text

[Assistant] Now, on the face of it, that is absolutely absurd, because if you asked...


[User] Please transcribe this text

[Assistant] The population to describe the capital asset pricing model, it wouldn't be more than one in a hundred who would know what you're talking about. To assume that they're just doing it is, uh, is pretty extreme.


[User] Please transcribe this text

[Assistant] On the other hand, I still like to go through the mathematics and see what would it be if everybody were extremely rational and logical and how would the markets behave. It's interesting.


[User] Please transcribe this text

[Assistant] So let's just think about one risky asset and one riskless asset. Suppose I put X dollars into a risky asset 1.


[User] Please transcribe this text

[Assistant] Uh, now I'm going to invest one dollar total. I'm just normalizing it on one dollar. So the money I have left over after I invest X is one minus X, so I'll put that in the second asset.


[User] Please transcribe this text

[Assistant] Which is sure, earning a sure but low return, ar sabbath.


[User] Please transcribe this text

[Assistant] So what is the expected value of the return on my portfolio? Well, I have X dollars in the first asset, so it's going to be X times the expected return on the first asset, the risky asset.


[User] Please transcribe this text

[Assistant] Uh, and so, uh, the total expected return is xr1 plus 1 minus x times rf. Uh, and so, what is the variance of my return? The variance is just equal to x squared times the variance of the return on the first asset.


[User] Please transcribe this text

[Assistant] So if x is 1, that means I put it all in the first access, then it's just the portfolio variance is the variance of the return on the first asset.


[User] Please transcribe this text

[Assistant] Uh, what if I short it? I put minus one. If I short it, that wouldn't be, it doesn't sound like a smart move, generally, on average, shorting the stock market and investing in the riskless asset. But I could do that. I could make x equal minus one. And then I would have two.


[User] Please transcribe this text

[Assistant] Uh, dollars, uh, invested in the riskless, riskless asset.


[User] Please transcribe this text

[Assistant] My portfolio variance would be the same, but I'd be on the wrong end of it, right? I would be, assuming that I've got my numbers right, I would be shorting the high return asset and investing in the low return asset. So, uh, and having the same risk anyway.


[User] Please transcribe this text

[Assistant] You can compute the portfolio of standard deviation, which is just the square root, uh, of, uh, of this portfolio variance.


[User] Please transcribe this text

[Assistant] So it's linear, the standard deviation of the portfolio is linear in, uh, the expected return on the portfolio. I can make the, see the real insight here is, I've tried to convey that last time, I, you want an expected return? I can give it to you. On stocks, on anything you want.


[User] Please transcribe this text

[Assistant] I'll do it by exposing you to risk. I can leverage you up. You want 100% expected return next year? Great. I'll leverage you to the hilt and then you'll have that expected return which will probably get wiped out because you've taken on too leverage on investment.


[User] Please transcribe this text

[Assistant] Uh, but we're not going to worry about being wiped out here. We're just worrying about, uh, what your return will be. Now, suppose we move from just one risky asset to two risky assets.


[User] Please transcribe this text

[Assistant] Uh, now I want to put X1 dollars in risky asset 1 and 1 minus X1 dollars in risky asset 2.


[User] Please transcribe this text

[Assistant] What is the portfolio expected return? Now I'm not putting anything into the riskless asset. So now I have two risky assets.


[User] Please transcribe this text

[Assistant] Uh, then what is the variance of this portfolio? Uh, it turns out this is the formula. Uh, the variance of the portfolio is x1 squared times the variance of the return on the first one plus 1 minus x1 squared times the variance of the return on the second risky asset.


[User] Please transcribe this text

[Assistant] Plus 2x1 times 1 minus x1 times the covariance of the return. Now the covariances matter because if they covary positively that makes


[User] Please transcribe this text

[Assistant] That makes them interact in a positive way, increasing variance. So positive covariance is bad for, uh, your portfolio.


[User] Please transcribe this text

[Assistant] It raises the variance, uh, of your portfolio. Negative covariance is good. If you can find two risky assets that move opposite each other, or tend to move opposite each other, then their covariance is negative, and, uh, this thing reduces the portfolio variance.


[User] Please transcribe this text

[Assistant] Mr. Crumb's day. We now have statistics. Okay, so I'm imagining here that you are managing a portfolio.


[User] Please transcribe this text

[Assistant] And you have historical data on the returns of the different assets that you can put into the portfolio. So you've calculated what the expected return is for the first asset, the average historically for this return.


[User] Please transcribe this text

[Assistant] You've calculated what the expected return on the asset, second asset is, you've taken an average.


[User] Please transcribe this text

[Assistant] You've, you've also computed the variances of their returns, so you know how variable they are, and you've even calculated the covariance of the returns.


[User] Please transcribe this text

[Assistant] Now you might question whether covariances, variances, expected values that I estimated historically will continue to be true in the future. Maybe things are different now.


[User] Please transcribe this text

[Assistant] But at least as a first exercise, it sounds like this is something we should understand, doesn't it?


[User] Please transcribe this text

[Assistant] As a first guess, we'll assume that these variances are going to be stable through time. So I want to know what I should do if I assume they are stable through time. That's the key idea. And that had not been worked out until Harry Markowitz developed the capital asset pricing model in the early 1950s.


[User] Please transcribe this text

[Assistant] Sashi's using his immunity pin today and I'm so happy for him. But I'm also so nervous.


[User] Please transcribe this text

[Assistant] I've only seen it on Instagram. It looks so nice. Yet so complicated at the same time.


[User] Please transcribe this text

[Assistant] For my mom's birthday, I made her this beautiful kind of mint green cake and it was inspired by Catherine Sabbath's designs. She's such an inspiration. Back at home, I was learning how to recreate her cakes.


[User] Please transcribe this text

[Assistant] This is probably the best pressure test ever and I'm happy to be in the pressure test today.


[User] Please transcribe this text

[Assistant] Now I've got a piping bag full of the pink buttercream and another with a raspberry buttercream.


[User] Please transcribe this text

[Assistant] To make the white buttercream, I add a bit of white food coloring, give it a quick mix, and hopefully get the really nice white color.


[User] Please transcribe this text

[Assistant] I am having fun at the moment. It hasn't come to a stress yet. I think everything is going well so far, I think.


[User] Please transcribe this text

[Assistant] I get my eggs, I'm cracking it into the bowl and...


[User] Please transcribe this text

[Assistant] To make that really nice pink swirl in the chocolate, I have to put dollops of the pink on top of the white and give it a little bit of a shake and swirl with the skewer.


[User] Please transcribe this text

[Assistant] I really wanna cook these cakes longer but I don't have the time. I need them to cool down.


[User] Please transcribe this text

[Assistant] Once I've got my layers, I do a thin coating of a crumb coat on the outside.


[User] Please transcribe this text

[Assistant] Oh my god! I should stop scraping this cake.


[User] Please transcribe this text

[Assistant] My white tempered chocolate's on the table. It's quite soft. I'm worried that it's not tempered now. I feel really embarrassed. I should know how to temper chocolate.


[User] Please transcribe this text

[Assistant] I should have stopped smearing it cause I'm a little bit unhappy with all the little bubbles.


[User] Please transcribe this text

[Assistant] Walking my cake in, in my heart, I know the presentation is right, but...


[User] Please transcribe this text

[Assistant] It all comes down to that vanilla cake as well as that chocolate being tempered.


[User] Please transcribe this text

[Assistant] I am pleased with it, but I think I smudged it quite a lot, so I'm a little bit devastated about that.


[User] Please transcribe this text

[Assistant] It's an increasingly common sight in hospitals around the world. A nurse measures our height, weight, blood pressure, and attaches a glowing plastic clip to our finger. Suddenly, a digital screen reads out the oxygen level in our bloodstream.


[User] Please transcribe this text

[Assistant] How did that happen? How can a plastic clip learn something about our blood without a blood sample?


[User] Please transcribe this text

[Assistant] Here's the trick. Our bodies are translucent, meaning they don't completely block and reflect light. Rather, they allow some light to actually pass through our skin, muscles, and blood vessels. Don't believe it? Hold a flashlight to your thumb.


[User] Please transcribe this text

[Assistant] Light, it turns out, can help probe the insides of our bodies.


[User] Please transcribe this text

[Assistant] Consider that medical finger clip. It's called a pulse oximeter. When you inhale, your lungs transfer oxygen into hemoglobin molecules, and the pulse oximeter measures the ratio of oxygenated to oxygen-free hemoglobin.


[User] Please transcribe this text

[Assistant] It does this by using a tiny red LED light on one side of the finger clip and a small light detector on the other.


[User] Please transcribe this text

[Assistant] When the LED shines into your finger, oxygen-free hemoglobin in your blood vessels absorbs the red light more strongly than its oxygenated counterpart, so the amount of light that makes it out the other side depends on the concentration ratio of the two types of hemoglobin.


[User] Please transcribe this text

[Assistant] But any two patients will have different sized blood vessels in their fingers. For one patient, a saturation reading of 95% corresponds to a healthy oxygen level. But for another with smaller arteries, the same reading could dangerously misrepresent the actual oxygen level.


[User] Please transcribe this text

[Assistant] This can be accounted for with a second infrared wavelength LED.


[User] Please transcribe this text

[Assistant] Light comes in a vast spectrum of wavelengths, and infrared light lies just beyond the visible colors.


[User] Please transcribe this text

[Assistant] All molecules, including hemoglobin, absorb light at different efficiencies across this spectrum. So contrasting the absorbance of red to infrared light provides a chemical fingerprint to eliminate the blood vessel size effect.


[User] Please transcribe this text

[Assistant] Today, an emerging medical sensor industry is exploring all new degrees of precision chemical fingerprinting using tiny light-manipulating devices no larger than a tenth of a millimeter.


[User] Please transcribe this text

[Assistant] This microscopic technology, called integrated photonics, is made from wires of silicon that guide light, like water in a pipe, to redirect, reshape, even temporarily trap it.


[User] Please transcribe this text

[Assistant] A ring resonator device, which is a circular wire of silicon, is a light trapper that enhances chemical fingerprinting.


[User] Please transcribe this text

[Assistant] When placed close to a silicon wire, a ring siphons off and temporarily stores only certain waves of light— those whose periodic wavelength fits a whole number of times along the ring's circumference. It's the same effect at work when we plug guitar strings.


[User] Please transcribe this text

[Assistant] Only certain vibrating patterns dominate a string of a particular length to give a fundamental note and its overtones.


[User] Please transcribe this text

[Assistant] Ring resonators were originally designed to efficiently route different wavelengths of light, each a channel of digital data in fiber optics communication networks.


[User] Please transcribe this text

[Assistant] But someday, this kind of data traffic routing may be adapted for miniature chemical fingerprinting labs on chips the size of a penny.


[User] Please transcribe this text

[Assistant] These future labs on a chip may easily, rapidly, and non-invasively detect a host of illnesses by analyzing human saliva or sweat in a doctor's office or the convenience of our homes.


[User] Please transcribe this text

[Assistant] Human saliva, in particular, mirrors the composition of our body's proteins and hormones and can give early warning signals for certain cancers and infectious and autoimmune diseases.


[User] Please transcribe this text

[Assistant] To accurately identify an illness, labs on a chip may rely on several methods, including chemical fingerprinting, to sift through the large mix of trace substances in a sample of spit.


[User] Please transcribe this text

[Assistant] Various biomolecules in saliva absorb light at the same wavelength, but each has a distinct chemical fingerprint. In a lab on a chip, after the light passes through a saliva sample, a host of fine-tuned rings may each siphon off a slightly different wavelength of light and send it to a partner light detector.


[User] Please transcribe this text

[Assistant] Together, this bank of detectors will resolve the cumulative chemical fingerprint of the sample. From this information, a tiny on-chip computer containing a library of chemical fingerprints for different molecules may figure out their relative concentrations and help diagnose a specific illness.


[User] Please transcribe this text

[Assistant] From globetrotting communications to labs on a chip, humankind has repurposed light to both carry and extract information. Its ability to illuminate continues to astonish us with new discoveries.


[User] Please transcribe this text

[Assistant] Curious how a hidden network of light and glass make the Internet work? Check out this video.


[User] Please transcribe this text

[Assistant] Myron Gordon, uh, half century ago, uh, nearly told what, uh, gave a formula for the present value of a growing quantity.


[User] Please transcribe this text

[Assistant] Suppose we have an asset, let's call it land, that is producing revenue for you every year, and the revenue is growing in value.


[User] Please transcribe this text

[Assistant] So it produces, uh, x the first year, then it produces x times 1 plus the growth rate the next year, and then it produces x times 1 plus the growth rate squared the next year, and then it does that forever.


[User] Please transcribe this text

[Assistant] And then we have time 1, time 2, times 3, time 4, that would be 1 plus g cubed, etc. I say this might be land, because land, assuming that it's managed properly and doesn't get depleted, will yield a crop every year.


[User] Please transcribe this text

[Assistant] And as time goes on, the crop will be worth more, partly because demand for it probably goes up in a growing economy, and partly because of technical progress. Uh, and we're going to assume this land goes on growing like this forever.


[User] Please transcribe this text

[Assistant] Uh, so, uh, the question is, what do you pay for this land at time zero? Okay, what is, so, uh, Myron Gordon, uh, he's famous for this formula, principally, is that the present value


[User] Please transcribe this text

[Assistant] Equals x all over r minus g, where r is the rate of discount.


[User] Please transcribe this text

[Assistant] What he's saying is that, uh, it equal, the present value is x all over 1 plus r.


[User] Please transcribe this text

[Assistant] That's this first term here, plus x times 1 plus g, all over 1 plus r squared.


[User] Please transcribe this text

[Assistant] If you calculate, you can show that infinite sum reduces to this simple formula.


[User] Please transcribe this text

[Assistant] As long as g is less than r. See, if g, if g is less than r, then each term is smaller than the one before it. And it sums to a finite number. If g equals r, then every term here is x over 1 plus r and they're all the same. And so the sum would be infinite.


[User] Please transcribe this text

[Assistant] So as long as g is less than r, this is a formula for pricing the value of a, of a asset that actually yields an infinite amount, but in the future.


[User] Please transcribe this text

[Assistant] And even not, it's actually growing forever. Actually, G can be negative also. It doesn't matter whether it's positive or negative.


[User] Please transcribe this text

[Assistant] Yeah, it could be that the land is being depleted. And so the growth rate of the value is negative.


[User] Please transcribe this text

[Assistant] So then this becomes r plus something because g is negative. You still have a present value. And this is an important thing to recognize that even assets whose payments are running down to zero, there still is a price for them today. That's really important to recognize.


[User] Please transcribe this text

[Assistant] So some people think that businesses that are growing are the only ones I should invest in. That's bad investing.


[User] Please transcribe this text

[Assistant] You can make a fortune investing in businesses that are declining. You can fill up your portfolio with declining industries.


[User] Please transcribe this text

[Assistant] Uh, doesn't matter, it's a question, can you buy them for less than the present value? And if you're buying them for less than the present value, it's a good investment.


[User] Please transcribe this text

[Assistant] Yeah, I'm taking that as, as given. Okay. The growth rate. It might be like 2% a year. Right. Uhm, and the interest, R might be 5% a year. So, uh, the value would be X divided by


[User] Please transcribe this text

[Assistant] 5% minus 2%, or x divided by .03. And this is a very useful formula because a lot of possible investments have a growth rate.


[User] Please transcribe this text

[Assistant] Uh, in fact they usually do. Usually you don't expect the earnings of a company, for example, to stay exactly where they are today. Some companies are expected to grow through time and some are expected to decline through time. So you end up using this formula.


[User] Please transcribe this text

[Assistant] All the time to ju- to judge. You look at their earnings today and you think, or uh, you think, well what are the, uh, what is it worth? What is this stream of future earnings worth? And you can plug it into this formula.


[User] Please transcribe this text

[Assistant] Now I, I haven't talked about risk in this equation. I was saying the land is going to do this. We just know this for certain.


[User] Please transcribe this text

[Assistant] But we don't, uh, in fact, often we don't know the future with certainty. So there's amount of risk. So if this growth rate is more uncertain than you thought, that should lower the price.


[User] Please transcribe this text

[Assistant] Uh, right, uh, so if the asset is riskless, if there's no risk, if we actually know all the future


[User] Please transcribe this text

[Assistant] Payouts from the asset, then this R would be the riskless interest rate.


[User] Please transcribe this text

[Assistant] Uh, and that would be measured by beta in the capital asset pricing model, then R would be increased, reflecting that risk.


[User] Please transcribe this text

[Assistant] You still use the same Gordon formula, but you, you have a higher R. R is no longer the riskless rate.


[User] Please transcribe this text

[Assistant] So, I like to bring up the example of railroads. When the first railroads came out, well it was in the 1830s, but by the 1840s, there was a big bubble in railroad stocks. Lots of people thought, wow, railroads are growing. They were right, railroads were growing.


[User] Please transcribe this text

[Assistant] Uh, but people paid too much. Even though they're growing, you can pay more than the present value. So lots of famous people in the very beginning of the railroad era lost fortune.


[User] Please transcribe this text

[Assistant] Even including Charles Darwin, the great biologist,


[User] Please transcribe this text

[Assistant] He couldn't figure out present value, he was a smart guy, but he couldn't figure out what the real value of these railroad stocks were.


[User] Please transcribe this text

[Assistant] But then as time went on, railroads stopped being glamorous and exciting and they became old hat. And then we got airplanes and, uh, and, uh, well trucks and cars and


[User] Please transcribe this text

[Assistant] All these other alternatives to, uh, to railroads. So railroad stocks then became underpriced relative to this formula.


[User] Please transcribe this text

[Assistant] So one of the best investments to make in 1929 was rail, railroads. Already people were thinking about Charles Lindbergh flew across the, uh, lost the Atlantic Ocean. Everything is moving fast for airlines.


[User] Please transcribe this text

[Assistant] And they just got overpriced. The same thing happened in the year 2000.


[User] Please transcribe this text

[Assistant] In 2000, that was the peak of the dot com bubble. Everybody was investing in


[User] Please transcribe this text

[Assistant] Uh, computer, software, social media stocks, uh, and, uh, because they saw the growth rate, uh, but they made a mistake. You have to use this formula, uh, because they made a mistake in thinking that they're worth more than


[User] Please transcribe this text

[Assistant] They really were, and the whole dot com bubble collapsed.


[User] Please transcribe this text

[Assistant] Uh, and, uh, it may not even be a declining industry. Uh, uh, it, it wasn't living up to expectations initially in terms of earnings growth.


[User] Please transcribe this text

[Assistant] But they made good investments sometimes, and not every time, depending on how the price compares to the present value of their, uh, earnings.


[User] Please transcribe this text

[Assistant] Some serious faces, and I'm not surprised. It was a big night last night. Ben, you were the captain yesterday.


[User] Please transcribe this text

[Assistant] You were cooking for immunity earlier on this week and now you find yourself in a black open.


[User] Please transcribe this text

[Assistant] Well, best of luck. Your fate in this competition is going to be determined by...


[User] Please transcribe this text

[Assistant] The rules, you have 45 minutes to bring us a delicious prawn dish inspired by your cuisine. Remember what's at stake. The contestant with the least impressive dish will be eliminated. Your 45 minutes starts now.


[User] Please transcribe this text

[Assistant] Pantry's jam packed full of ingredients that are specific to all those cuisines. So if they're clever, they can pick a couple of those and just run with it. With this much at stake, it better be good. I wouldn't want to be cooking Greek for George.


[User] Please transcribe this text

[Assistant] Because there's too many things to, to find fault in.


[User] Please transcribe this text

[Assistant] Listen up you lot, only 20 minutes to go. You do not want to be eliminated. Come on!


[User] Please transcribe this text

[Assistant] We should remind ourselves more often, give you 45 minutes and give you a theme to cook for and it focuses the mind.


[User] Please transcribe this text

[Assistant] It's very fresh and the prawn certainly is the hero. Delicious to eat, the prawns are beautifully cooked.


[User] Please transcribe this text

[Assistant] Well, you know what, we could chat to you all day, but at the end of today, someone will have cooked their way back into this competition. You're gonna have one hour.


[User] Please transcribe this text

[Assistant] To bring us your best dish. Sweet.


[User] Please transcribe this text

[Assistant] Or savoury. You're gonna have an open pantry, gardens in play. All the odds are stacked in your favour.


[User] Please transcribe this text

[Assistant] Blow our minds and you will win your way back into this competition. Your 60 minutes starts now.


[User] Please transcribe this text

[Assistant] What are you all doing in here? You've forgotten how stressful this competition is. You've been in here for four minutes already. Come on, hustle.


[User] Please transcribe this text

[Assistant] There is a lot of really good food in here. The time is running out to win your way back into this competition. Ten minutes to go.


[User] Please transcribe this text

[Assistant] What a great atmosphere in the kitchen. A lot of fun, a lot of good cooking, but there's also serious sides to this challenge. Someone will get back in this competition. Let's find out who it's going to be. First up, Aldo.


[User] Please transcribe this text

[Assistant] I like the look of it. I think it's really pretty. I like the simplicity. I like the colour on the broth. Should we dig in and see how the fish is cooked?


[User] Please transcribe this text

[Assistant] It's nice. Very firm, textured fish, that's for sure.


[User] Please transcribe this text

[Assistant] Above all, it's just very well balanced. And it's got a lot of comfortable flavours going on there. It could be anything from lasagna to bolognese to just a lovely tomato pasta that it is. Obviously being out of this kitchen is giving you some clarity. And that's a wonderful thing.


[User] Please transcribe this text

[Assistant] Wow, look at the color. Yeah, it looks amazing. That's amazing. Lisa, you gotta be happy with that. Yeah.


[User] Please transcribe this text

[Assistant] I can't think of a place near me that does a bowl of broth like that. That's gorgeous.


[User] Please transcribe this text

[Assistant] I think that is the best cooking you've given us in the kitchen. I mean, that is very clearly defined. It's a beautiful idea. It's delivered beautifully and perfectly in the bowl. And that broth is rock and roll. And it's the kind of broth, really, that in a 60 minute challenge is very hard to do.


[User] Please transcribe this text

[Assistant] That is a dream to eat. That is a beautiful dish. And the lobsters are beautifully cooked. The strength of that sauce. The way it pins that whole dish down. Very modern, but super classic when you kind of dig into it.


[User] Please transcribe this text

[Assistant] But that's what? I love it. Really, really like it. Thank you, thanks.


[User] Please transcribe this text

[Assistant] For somebody that just cooked a dish that'll...


[User] Please transcribe this text

[Assistant] Be their ticket back into the competition. But before we get to that, we just want to say how impeccably you've all carried yourselves today. What fun you've had.


[User] Please transcribe this text

[Assistant] Thanks guys. Commiserations because your dish was really beautiful, uh, it was very delicate.


[User] Please transcribe this text

[Assistant] You have been a dynamo in this kitchen. You've been a wonderful ray of sunshine. Very funny. And of course, you know, we can't wait to see what happens when you get out there and do your own thing, because we're coming, alright? As long as we get good service, yeah. Of course. Good service. Dory's open.


[User] Please transcribe this text

[Assistant] Today is a massive day. It's illumination. We're cooking with prawns and we have 45 minutes with our randomly chosen cuisine. I've got Mexican. What are you making?


[User] Please transcribe this text

[Assistant] I'm going to grill the prawns and put it with a green tomato coriander like salsa.


[User] Please transcribe this text

[Assistant] I've done grilled prawns with a tomato and coriander salsa.


[User] Please transcribe this text

[Assistant] And what about my food? I asked for a burger and french fries fifteen minutes ago. I'm sorry, but there are a lot of people eating here today.


[User] Please transcribe this text

[Assistant] What about my burger? You can do it! What did you say?


[User] Please transcribe this text

[Assistant] Do we have butts? It might seem like a cheeky question, but if you look around the animal kingdom, even at our closest relatives, big butts are pretty uniquely human. So why would evolution grant us such an asset?


[User] Please transcribe this text

[Assistant] We have a few hypotheses. So, our butts are made primarily of two things. Fat and muscle.


[User] Please transcribe this text

[Assistant] And each likely has its own evolutionary role to play. Like, in general, fat is a resource that's useful for our brains as they develop. A 2016 study proposed that development for both our large brains and our fat stores


[User] Please transcribe this text

[Assistant] Or so the hypothesis goes. There's still plenty we don't know about the evolution of fat and


[User] Please transcribe this text

[Assistant] Bum fat in particular. It's tough to track because it isn't preserved very well in fossils. However, we're in slightly firmer territory with the muscle part of all this.


[User] Please transcribe this text

[Assistant] The gluteus maximus. It's the biggest muscle in our bodies, which is only the case for humans, not our ape relatives. We're also the only primates that habitually walk on two legs, so for nearly 200 years, scientists have thought that these two observations are probably related. Now, your gluteus maximus isn't actually used very much when you walk on flat ground,


[User] Please transcribe this text

[Assistant] But it sure is used when you run. As your heel hits the ground with each stride, your body angles forward, and it's that big gluteus maximus that's partly responsible for stabilizing your body and pulling it back. Like fat, muscles aren't usually preserved in fossils, but the bones they attach to are.


[User] Please transcribe this text

[Assistant] And the functions of bones and muscles are closely related, so we can make inferences about the evolution of one from the other. We know that, of our various ancestors, Homo erectus had a pretty human-looking pelvis around two million years ago.


[User] Please transcribe this text

[Assistant] Which could mean a human-looking butt. This came along with a bunch of other features that make us good runners. Things like long Achilles tendons, short toes, and long legs. Humans are the only primates that can run long distances, and scientists think that distance running helped us evolve new strategies for obtaining food by chasing prey to exhaustion or by quickly getting to carcasses to scavenge them.


[User] Please transcribe this text

[Assistant] Butts would have been a key advantage for hunting. In support of this idea, the evolution of the butt roughly corresponds to a time anthropologists think our diet increased in proteins.


[User] Please transcribe this text

[Assistant] Possibly because we were better at running after our food. And in turn, all that protein was once again helpful for developing those big brains of ours. So the next time you're out for a run, or even thinking through a tough problem, remember, thank your butt.


[User] Please transcribe this text

[Assistant] Thanks for watching this episode of SciShow! If you enjoy completely earnest discussions of the science of butts, you'll probably like the podcast with a dedicated butt-related segment, SciShow Tangents. It's a lightly competitive knowledge showcase where the hosts try to outdo each other with esoteric science knowledge.


[User] Please transcribe this text

[Assistant] Be sure to stay until the very end to hear but one more thing, the weekly but fact that ends each episode. Get it wherever fine podcasts are available.


[User] Please transcribe this text

[Assistant] A ghost in love. Scene 1. Brad sees a ghost. Brad and Jerry come into the hotel room and put their bags down. It is a hot day and they are wearing summer shirts. Brad sits on a bed.


[User] Please transcribe this text

[Assistant] I was never 19. I died at 18.


[User] Please transcribe this text

[Assistant] Oh Matthew, don't say that. Oh Matthew, I'm so happy to see you again.


[User] Please transcribe this text

[Assistant] Scene two. Brad and Jerry want to know about Ellen. Maud is a very old woman. Her hair is white. Brad and Jerry


[User] Please transcribe this text

[Assistant] A bad, bad girl. When she looked at someone, the next day they were ill.


[User] Please transcribe this text

[Assistant] So, she was a witch? Yes. She looked at animals too.


[User] Please transcribe this text

[Assistant] Then all the cows in the village had no milk in them.


[User] Please transcribe this text

[Assistant] All the books. Our village is famous because of bad Ellen Bannister. Scene 3. Was Ellen a witch? Brad and Jerry are in Mrs Morgan's village shop.


[User] Please transcribe this text

[Assistant] There are two men and three women in the shop. And Mrs Morgan.


[User] Please transcribe this text

[Assistant] Her boyfriend was the bad one, that Matthew. It wasn't her.


[User] Please transcribe this text

[Assistant] He stole things from rich people.


[User] Please transcribe this text

[Assistant] Then she sold them and he took all the money. He took all her money too. But she loved him.


[User] Please transcribe this text

[Assistant] Scene four. Where is Ellen now? It is night time. Jerry is sitting on the bed. Brad is walking up and down.


[User] Please transcribe this text

[Assistant] We sold things, you and me. Rich people's things. You stole them, remember?


[User] Please transcribe this text

[Assistant] Witch people didn't like me. They said bad things about me. Ellen Bannister is a witch. They said that, but it's not true.


[User] Please transcribe this text

[Assistant] Then one day, they came for us. Twenty men. What happened? We ran away. You found a ship. A ship going to America. You said, see you at one fifteen. At the harbor. We can go away. You and me.


[User] Please transcribe this text

[Assistant] The ship went early. I wasn't there in time. You went on the ship. You didn't wait for me.


[User] Please transcribe this text

[Assistant] When I arrived, there were a lot of ships there. I didn't know the name of our ship. I looked for you. I waited and waited and waited. Ellen.


[User] Please transcribe this text

[Assistant] I love you too, Matthew. What is Brad going to do? It is dark. Brad is standing at the window of the room. Jerry comes in.


[User] Please transcribe this text

[Assistant] Brad always has his back to Jerry in this scene.


[User] Please transcribe this text

[Assistant] Scene six. In love with a ghost. The next day. Brad and Jerry are in the street. Jerry has two bags. Mrs Finch is there too. You must leave then.


[User] Please transcribe this text

[Assistant] That's right, uhm, we're talking about another business topic, aren't we?


[User] Please transcribe this text

[Assistant] Yep. Yeah. I guess lead time is the time in between point A and point B on a production schedule, right?


[User] Please transcribe this text

[Assistant] So that's the time between when you make an order, uhm, and when the product is finished.


[User] Please transcribe this text

[Assistant] So the time between when you place an order and when, uh, the product gets delivered to you.


[User] Please transcribe this text

[Assistant] And we at EnglishPod have a two week lead time for our podcast, right?


[User] Please transcribe this text

[Assistant] Yes. Yeah, I guess they're putting in some, some cost control measures.


[User] Please transcribe this text

[Assistant] So, so Marco, there's some great language in here. Why don't we look at it now in Language Takeaway. Language Takeaway.


[User] Please transcribe this text

[Assistant] Uhm, yeaah, maybe when you, you say something that's already been said.


[User] Please transcribe this text

[Assistant] Right, and it comes from the base word redundant, right?


[User] Please transcribe this text

[Assistant] Uhm, meaning extra, more than what's needed.


[User] Please transcribe this text

[Assistant] So when a person or position is made redundant, uhm, basically it means they get laid off, right?


[User] Please transcribe this text

[Assistant] Or I could say, uhm, I want to move my workstation closer to my employees.


[User] Please transcribe this text

[Assistant] Exactly, uhm, yeaah, given here is operating exactly the same as, here the word given is operating exactly the same way as any.


[User] Please transcribe this text

[Assistant] Right, so for example, maybe you produce chairs and you make one and this chair is missing a wheel.


[User] Please transcribe this text

[Assistant] So what you're saying is they're producing goods, uhm, just for the time they need it, right?


[User] Please transcribe this text

[Assistant] So in the dialogue, they want to implement the just-in-time strategy as a way of avoiding redundancies, but it sounds to me like actually this might cause redundancies sometimes.


[User] Please transcribe this text

[Assistant] Yes, have you guys worked in a place where uhm, where they use just-in-time?


[User] Please transcribe this text

[Assistant] So visit our website, uh, englishpod.com and Marco and I are always around to respond to your comments and answer your questions.


[User] Please transcribe this text

[Assistant] Yeah, I especially like the story about the rabbits.


[User] Please transcribe this text

[Assistant] Yeaah, some phrases you can use when you welcome someone to your house. Alright, well, we've got some language to preview, so let's turn now to vocabulary preview.


[User] Please transcribe this text

[Assistant] When you have an appetite, you, you're hungry or you, you have a desire to eat food, right?


[User] Please transcribe this text

[Assistant] Exactly. So when you poison someone, uhm, you put something in their food or maybe in their drink, uhm, that makes them really sick and maybe even makes them die.


[User] Please transcribe this text

[Assistant] Yep, and the noun is actually the thing that you put inside the food or drink.


[User] Please transcribe this text

[Assistant] Or maybe this perfume has the aroma of roses and lavender.


[User] Please transcribe this text

[Assistant] So chocolate, when it has no sugar, is bitter.


[User] Please transcribe this text

[Assistant] Yeah, and sometimes red wine can be quite bitter.


[User] Please transcribe this text

[Assistant] Uhm, it means faint or become unconscious.