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[Assistant] And triggers a chain reaction that revs up your appetite and gets your digestive system ready to receive food. And then once you've gobbled down enough food to stretch your stomach a bit, the ghrelin tap turns off and you feel satiated for a while. But if you've ever been tempted by the dessert menu, even after a big meal, you know that food cravings are different from straight-up hunger. In some cases, a food craving can be a sign that you need more of a specific nutrient. If you're very low on salt, for example, you might crave potato chips or pretzels. But if you need something more specific, say magnesium,


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[Assistant] There's no evidence that you'll start craving chocolate, even though chocolate has magnesium in it. Often, cravings are more about a psychological hunger than a physiological one, and they can be wrapped up in emotions like stress and anxiety. In the past, we've talked about how humans tend to be drawn to fatty, high-carb foods, in part because they're loaded with the energy we need to think and move and do stuff.


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[Assistant] Sure, that's a physiological need, but the pull toward junk food is often a psychological one as well. Eating a butter-frosted cupcake or a bag of salty french fries releases an opioid typhoon that lights up the brain's pleasure center and makes us feel awesome. At least for a while. Celery just...


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[Assistant] Cravings are also tied to your brain's memory center, which explains why you might also crave a food that isn't full of fat or sugar. Your brain could be tying that food to a happy memory, or a feeling of reward. And thinking about a memory associated with a food...


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[Assistant] Another psychological cause of cravings can be a boring or restricted diet. In studies where subjects had to drink only meal replacement shakes, they ended up with a lot more cravings than usual.


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[Assistant] These cravings probably have more to do with mood. So if you're really jonesing for that chocolate, it's probably just because your brain remembers that it feels good to eat it.


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[Assistant] I mean, is anyone here going to deny that Taco Bell is delicious? Next, you might be confused by some of the seemingly useless extra parts that your human body shipped with, like your appendix. Except, well, the appendix was dismissed as pointless for years until researchers discovered the point. Here's Stefan with more.


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[Assistant] That's sweet, it's doing its best. Speaking of extra bits and bobs, our human bodies also ship with considerable variation from unit to unit. And, in general, these differences are great. Sometimes, though, there's even more going on than we realize, like the fact that we have hundreds of blood groups beyond the four that we usually learn about. Once more, let me explain what that means.


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[Assistant] Like if you've donated blood, you might have been asked about your blood type. And even if you don't know what yours is, you're probably aware that you could have A, B, AB, or O blood.


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[Assistant] And that your blood can be positive or negative. But that's not the whole story because there are potentially millions of blood types out there. There are so many possible blood types because of the way blood types are defined. You see, your blood type is defined by the antigens present in your blood. Antigens are anything that can elicit a response from your body's self-defense system.


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[Assistant] And the list hasn't stopped growing yet. Many of these antigens fit into blood group systems, like ABO, each of which is defined by a gene at a single site or by multiple genes that are closely related. And all of us have these genes.


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[Assistant] Your type for a particular group depends on how your genes translate into the antigens that end up on the outside of your blood cells. There are 36 blood group systems currently recognized.


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[Assistant] So your full blood type, if written out, would include all 36 of these groups and the variants you have or don't have for all of those 600-plus antigens. Which is why we can say that there are millions of potential blood types. Of course, there are only 8 common ones, and that's because many of these antigens are found in practically everyone.


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[Assistant] While others are present in only a few individuals. For example, the SARA antigen has only ever been seen in two families, while 99.96% of people have the VEL antigen. And blood groups can get really complicated too.


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[Assistant] Just look at the Rh blood group. That's the group that gives you a positive or negative blood type. Like, if you're AB positive, the positive part generally means you have the Rh antigen called RhD. But to make things more confusing, whether your blood is considered positive or negative may depend on how much of the antigen you have in your system.


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[Assistant] Since that can impact what sorts of antibodies your immune system makes to protect you. And D is just one of over 60 known antigens in the Rh group.


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[Assistant] So positive or negative doesn't even begin to capture your overall Rh blood type. In fact, one of the rarest blood types in the world occurs if you have none of the antigens in the Rh group. If you're one of the roughly 50 people with this blood type, which is known as the Rh null,


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[Assistant] Your body will reject the blood from practically anyone else. And the Rh group isn't the only blood group where having no antigens can be a matter of life or death. Another example is the Diego group.


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[Assistant] It's antigens or proteins that help the lungs and kidneys perform essential functions. Like with the ABO group.


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[Assistant] There are two primary antigens, A and B, that determine a person's Diego blood type. But unlike the ABO group, where O or the lack of antigens is most common, there has only been one documented case of someone lacking both of these key Diego antigens. Sometimes, though,


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[Assistant] Since the cells you receive could have Duffy antigens on them that your body sees as foreign. The good news is that despite all the potential blood types you could have, for the most part, the ABORH blood typing we're using does a pretty good job of matching people's blood. With this system, if you receive blood from someone with the same ABORH type,


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[Assistant] There's a 99.8% chance your blood will be compatible with your donor's. For some reason, your body's immune system doesn't go after every antigen equally.


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[Assistant] So you don't usually need to know what version of every single known antigen you have. And if you do want to be extra sure, there are ways doctors can tell if you have a rare blood type. For example, they can screen for unexpected antibodies that could potentially target donated red blood cells, or perform cross-matching, where your blood is mixed with the donor's to see how the two react.


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[Assistant] Doing these two steps racks your safety margin up to 99.95%. And if you do happen to have a rare blood type like Rh null, don't fret.


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[Assistant] Efforts like the International Rare Donor Panel work hard to make sure you can get the blood you need, no matter where in the world you are. One person needing a transfusion had a rare blood type delivered from the UK to Cameroon.


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[Assistant] That's about 4,000 miles away. So even if your blood is literally one in a million, you can be pretty confident that you'll be able to find someone whose blood matches yours.


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[Assistant] And finally, it's important to realize that our bodies sometimes require the use of carefully designed chemicals to maintain optimal function. I'm talking about medicine. But the dose of these chemicals can vary a lot depending on the unit's age. In other words, you can't give the same amount of medication to a child as you do to an adult. But how the dose varies might surprise you. Stefan can tell us more.


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[Assistant] So remember, if your human body experiences technical issues, it's best to consult a professional. Thanks for watching this SciShow compilation! If you'd like to keep going, we have a user's guide to the human brain as well. And if you'd like to help us make more videos, you can support us at patreon.com slash SciShow.


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[Assistant] I've just got a bad feeling about him. Really? Why? Well, yesterday I brought over a housewarming gift but Armand started acting really weird and then he practically kicked me out.


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[Assistant] I tried to sort of peek into his house but everything was so dark inside that I couldn't really get a good look. The whole thing really creeped me out.


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[Assistant] I don't like Kelly's new boyfriend. I've got a bad feeling about him.


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[Assistant] I've got a bad feeling about this trip. I feel like something's going to happen. Maybe we shouldn't go.


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[Assistant] Yeah, I know. I met the owner of the house yesterday as he was moving in. His name is Armand. Really? What's he like? You have to fill me in.


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[Assistant] Well, yesterday I brought over a housewarming gift, but Armand started acting really weird, and then he practically kicked me out.


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[Assistant] I tried to sort of peek into his house, but everything was so dark inside that I couldn't really get a good look.


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[Assistant] The whole thing really creeped me out.


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[Assistant] I've just got a bad feeling about him. Really? Why? Well, yesterday I brought over a housewarming gift but Armand started acting really weird and then he practically kicked me out.


[User] Please transcribe this text

[Assistant] I tried to sort of peek into his house, but everything was so dark inside that I couldn't really get a good look. The whole thing really creeped me out.


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[Assistant] If you're in moose country in winter, you might glimpse a strange sight.


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[Assistant] A moose licking a car. Yes, this is an actual thing, and they get really into it. This might seem a bit odd, but it's not that they're confused or trying to express their love of a slick paint job. They do it because the car is covered in salt.


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[Assistant] But in the places where moose usually live, salt is pretty hard to come by. Plus, their plant-based diet is especially low in sodium. That's because sodium is actually sort of bad for most plants, and they actively exclude it. So to meet their needs, moose have to look elsewhere. In summer, they may be able to gorge themselves on aquatic plants that are much richer in sodium. Or they can find themselves a salt lake.


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[Assistant] These are muddy, wet places with a high concentration of minerals. And moose love them. Once they know where a lick is, they'll visit the spot regularly to socialize and fulfill their salt craving. But in the winter, most licks are buried in snow.


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[Assistant] Also in the winter, humans dump literally tons of salt on roads to de-ice them. So roads get covered in salty water, which splashes up onto cars and dries there.


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[Assistant] And presto, you've got mobile salt licks. Now, if you don't see the problem here, let me remind you that adult moose weigh several hundred kilograms.


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[Assistant] And most of that weight is balanced on top of their very long legs, like a full-grown male would have to duck to get his antlers through your front doorway. So when moose meet traffic, well, that's just really uncomfortable for the moose and drivers alike. Most of the time, moose avoid roads because the noise and the lights just kind of freak them out. But salt is so important to them that they'll go out of their way to get it, even if it means getting into an uncomfortable situation. And that's why it's important to avoid teaching them that roads and cars are good sources of salt.


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[Assistant] Wildlife managers and other folks can do to help with this. Like, in the spring, leftover road salt can be dumped far from roads.


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[Assistant] This gives the moose a safe place to get their fill, and it looks more like a natural salt lake. Other tricks are to drain salty puddles that form along the side of the road and fill them with rocks.


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[Assistant] Or to clear vegetation away from the roadside to make moose feel a little less comfortable there. We could also consider de-icing our roads with a chemical that doesn't have sodium, like calcium chloride.


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[Assistant] But for now, those substances are too expensive for widespread use. In the meantime, if you see a moose approaching your car, please resist the urge to pull out your selfie stick. Moose are incredibly dangerous, and carlicking is a bad habit that should not be encouraged.


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[Assistant] Thank you for watching this episode of SciShow, and a special thank you to our patrons on Patreon. You're helping make videos like this free for everyone, because we couldn't make SciShow without our patron support. And if you're not a patron, but supporting educational science videos sounds like it might be your jam, you can learn more about joining our patron community over at patreon.com slash SciShow.


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[Assistant] I feel like I could run right up this mountain. Good!


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[Assistant] William Golding was losing his faith in humanity. Serving aboard a British destroyer in World War II, the philosophy teacher turned Royal Navy lieutenant was constantly confronted by the atrocities of his fellow man.


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[Assistant] And when he returned to England to find Cold War superpowers threatening one another with nuclear annihilation, he was forced to interrogate the very roots of human nature. These musings on the inevitability of violence would inspire his first and most famous novel, Lord of the Flies.


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[Assistant] After being rejected by 21 publishers, the novel was finally published in 1954.


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[Assistant] It takes its title from Beelzebub, a demon associated with pride and war, two themes very much at the heart of Golding's book.


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[Assistant] The novel was a bleak satire of a classic island adventure story, a popular genre where young boys get shipwrecked in exotic locations.


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[Assistant] Golding's satire even goes so far as to explicitly use the setting and character names from R.M. Ballantine's Coral Island, one of the most beloved island adventure novels. But while Ballantine's book promised readers pleasure, profit, and unbounded amusement, Golding's had darker things in store.


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[Assistant] Lord of the Flies opens with the boys already on the island, but snippets of conversation hint at their terrifying journey.


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[Assistant] Their plane had been shot down in the midst of an unspecified nuclear war.


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[Assistant] The boys, ranging in age from 6 to 13, are strangers to each other. All except for a choir, clad in black uniforms and led by a boy named Jack.


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[Assistant] Just as in Ballantine's Coral Island, the boy's new home appears to be a paradise with fresh water, shelter, and abundant food sources.


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[Assistant] But even from the novel's opening pages, a macabre darkness hangs over this seemingly tranquil situation.


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[Assistant] The boy's shadows are compared to black, bat-like creatures, while the choir itself first appears as something dark, fumbling along the beach.


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[Assistant] From these ominous beginnings, Golding's narrative reveals how quickly cooperation unravels without the presence of an adult authority. Initially, the survivors try to establish some sense of order.


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[Assistant] The boy named Ralph blows into a conch shell to assemble the group and delegate tasks.


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[Assistant] But as Jack vies for leadership with Ralph, the group splinters and the boys submit to their darker urges. The mob of children soon forgets their plans for rescue, silences the few voices of reason, and blindly follows Jack to the edge of the island and the edge of sanity.


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[Assistant] The novel's universal themes of morality, civility, and society have made it a literary classic, satirizing both conventions of its time and long-held beliefs about humanity.


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[Assistant] While island adventure stories often support colonialism, Lord of the Flies turns this trope on its head. Rather than cruelly casting native populations as stereotypical savages, Golding transforms his angelic British schoolboys into savage caricatures.


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[Assistant] And as the boys fight their own battle on the island, the far more destructive war that brought them there continues off the page. Even if the boys were to be rescued from themselves, what kind of world would they be returning to?


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[Assistant] With so few references to anchor the characters in a specific place or period, the novel feels truly timeless— an examination of human nature at its most bare.


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[Assistant] And though not all readers may agree with Golding's grim view, Lord of the Flies is unsettling enough to challenge even the most determined optimist.


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[Assistant] Can you preserve your mind in a society where free will, self-expression, and curiosity are punishable offenses?


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[Assistant] Continue exploring this classic conundrum with our video on Fahrenheit 451.


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[Assistant] Actually, we're all headed there, so I'm sure somebody would be happy to show you.


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[Assistant] Huh, uh, it's in Hauser, over there, second building, on the left.


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[Assistant] You know, I don't think dogs are exactly allowed in class.


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[Assistant] Uh, no, your academic roster.


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[Assistant] Everyone take your seats. Professor Callahan should be here any second.


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[Assistant] You know, three years ago I was sitting right where you're sitting and I've heard all the same rumors, I'm sure you've heard too. Callahan's Ruthless.


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[Assistant] Hey! Woods, comma L. Listen, I got kicked out of class once first year too. It's awful. But trust me, your law career is not over.


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[Assistant] Yeah, come back tomorrow and make sure you've done your reading.


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[Assistant] You came out here to follow a man.


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[Assistant] Harvard Law was just part of that plan.


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[Assistant] I grew up in the Roxbury slums with my mom and a series of bums, guys who showed me all the ways a man can fail. I got through law school by busting my ass, worked two jobs in addition to class, so forgive me for not weeping at your tail.


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[Assistant] There's a chip on my shoulder, and it's big as a boulder. With a chance I've been given, I'm gonna be driven as hell. I'm so close I can taste it, so I'm not gonna waste it. Yeah, there's a chip on my shoulder. You might wanna get one as well.


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[Assistant] You drink a lot of Red Bull, don't ya?


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[Assistant] You do study, don't you? Where are those law books?


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[Assistant] Hi. Hey there. Uhm, for you. Not as good as going home for Christmas, but...


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[Assistant] It's a real time saver. Shampoo and conditioner in one.


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[Assistant] I don't know if you've noticed before, but each time Warner walks through the door, your IQ goes down to 40, maybe less.


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[Assistant] L. They lived together for ten years.


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[Assistant] Ms. Wyndham, I'm Emmett Forrest, your co-counsel from Stidwell, Zaskowski, Fox and Callahan. These four interns are the cream of the crop at Harvard Law. We're here to, uh, whip up your legal defense. Incidentally, my mom is a big fan of your DVDs, credits you with her Nutcracker butt.


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[Assistant] Her words. Anywho, we'd love to discuss your case and go over a few choices. We wanna free you as soon as possible so that you can bring your message back to your fans.


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[Assistant] Callahan briefed me on your meeting and there is a significant amount of evidence against you.


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[Assistant] To free you, the jury is going to need to hear an alibi.


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[Assistant] This is not a lifetime original movie, Elle. I'm not interested in nobility right now, I'm more interested in saving books' life!


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[Assistant] Give me a chance to crack this guy. I have an idea I'd like to try. The floor is yours. So, Mr. Archie Tacos.


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[Assistant] Well, I, I have to share this victory with Elle.


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[Assistant] He's wrong. Hey. We'll fix it. We'll fight it.


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[Assistant] I never mentioned love. The timing's bad, I know. But perhaps...


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[Assistant] If I made it more clear that you belong right here, you wouldn't have to go, cause you know that I'm so much in love.


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[Assistant] Yes, she does. I'm licensed, Your Honor, I'll gladly supervise. Well, like hell you will. You work for me, remember? No, I work for myself. And I don't have to hit on interns, Professor.


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[Assistant] I think someone needs to talk to you. That's okay.


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[Assistant] A mother and her son trek across an endless desert, wearing special skin-tight suits to dissipate heat and recycle moisture. The travelers aren't worried about dying of thirst. Their fears are much greater.


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[Assistant] The pair try to walk without rhythm, letting the vibrations of their footsteps blend into the shifting sands.


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[Assistant] But soon, the sound of the desert is drowned out by a louder hissing.


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[Assistant] As a mound of sand races towards them, the pair's unnatural gait turns into a sprint.


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[Assistant] Written by Frank Herbert and published in 1965, Dune takes place in a far-flung future, where humanity rules the stars in a giant feudal empire.


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[Assistant] This medieval motif goes beyond just the government. Unlike most interstellar sci-fi, Herbert's humans conquered the stars without any computers. Following an ancient war with robots, humanity has forbidden the construction of any machine in the likeness of a human mind.


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[Assistant] But rather than stifling their expansion, this edict forced humans to evolve in startling ways, becoming biological computers, psychic witches, and prescient space pilots.


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[Assistant] Members of these superpowered factions are regularly employed by various noble houses, all competing for power and new planets to add to their kingdoms.


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[Assistant] But almost all these superhuman skills rely on the same precious resource— the spice.


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[Assistant] This mystical crop, also known as melange, is essential for all space travel, making it the cornerstone of the galactic economy. And it only grows on the desert planet Arrakis, a dangerous and inhospitable world whose native inhabitants have long rebelled against the Empire.


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[Assistant] Arrakis, also called Dune, is the setting for Herbert's novel, which follows Paul of the noble house Atreides.


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[Assistant] The book begins with Paul's family being assigned control of Dune as part of an elaborate plot by their sworn enemies, the sadistic slave drivers of House Harkonnen.


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[Assistant] The conflict between these houses upends the delicate political balance on Arrakis. Soon, Paul is catapulted into the middle of a planetary revolution, where he must prove himself capable of leading and surviving on this hostile desert world.


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[Assistant] But Arrakis is not simply an endless sea of sand. Herbert was an avid environmentalist who spent over five years creating Dune's complex ecosystem.


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[Assistant] Paul's mother Jessica is a member of the Bene Gesserit, an ancient cult of spice-assisted psychics. Sometimes called witches for their mysterious powers, the Bene Gesserit have operated as a shadow government for millennia in an effort to guide society towards enlightenment.


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[Assistant] However, no group is more central to Dune than the Fremen.


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[Assistant] All these factions have deep histories that pervade the text, and Herbert also incorporates that sense of scale into the book's structure. Each chapter begins with a quote from a future history book, recalling elements of the events that are about to unfold.


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[Assistant] The book also contains in-universe appendices that further explore the Empire's history, alongside a glossary of words like gam-jabar and shai-halud.


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[Assistant] Dune's epic story continues to unfold over a six-book saga that spans millennia. But every story of Arrakis' future begins here, as Paul pursues a path that is dangerous, demanding, and always on the verge of being consumed by the oncoming storm.


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[Assistant] For all our science fiction fans out there, we think you'll love Octavia Butler. And if you're inspired enough to put pen to paper, check out this video on building fictional worlds.


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[Assistant] But please forgive me. The journey took much longer than I expected. There's no need to apologize.


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[Assistant] Oh yeaah? Better hope you're right.


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[Assistant] Hello world, this is CS50 and this is Web Programming with Python and JavaScript with CS50's own Brian Yu. This course picks up where CS50 itself leaves off, diving more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap.


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[Assistant] What? You want to sell bikinis in Alaska? That'll never fly.


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[Assistant] Uhm, in fact, we're the most expensive in the market, so maybe we need to lower our prices to match the competitors.


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[Assistant] The minute she said it, the temperature in my classroom dropped. My students are usually laser focused on me, but they shifted in their seats and looked away. I'm a black woman.


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[Assistant] Who teaches the histories of race and US slavery.


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[Assistant] I'm aware that my social identity is always on display. And my students are vulnerable too, so I'm careful.


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[Assistant] I try to anticipate what part of my lesson might go wrong. But honestly, I didn't even see this one coming.


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[Assistant] I was in my first year of teaching when the students said the N-word in my class. She was not calling anyone a name. She was bright-eyed and bushy-tailed.


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[Assistant] She came to class with her readings done, she sat in the front row, and she was always on my team.


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[Assistant] When she said it, she was actually making a point about my lecture by quoting a line from a 1970s movie, comedy, that had two racist slurs. One for people of Chinese descent, and the other the N-word.


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[Assistant] But she assured me, it's a joke from Blazing Saddles. And then she repeated it.


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[Assistant] Uhm, I'm a professor of US history. It's, it's in a lot of the documents that I teach. So I had to make a choice.


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[Assistant] After consulting with someone I trusted, I decided to never say it. Not even to quote it. But instead to use the euphemistic phrase, the N-word. Even this decision was complicated.


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[Assistant] I didn't have tenure yet, and I worried that senior colleagues would think that by using the phrase, I wasn't a serious scholar, but saying the actual words still felt worse.


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[Assistant] The incident in my classroom forced me to publicly reckon with the word, the history,


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[Assistant] Uhm, the history, the violence, but also anytime it was hurled at me.


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[Assistant] It all came flooding up in that moment, right in front of my students. And I had no idea what to do.


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[Assistant] If you've ever been stumped or provoked by the word,


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[Assistant] Uh, whether as the result of an awkward social situation,


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[Assistant] And uncomfortable academic conversation. Something you heard in pop culture.


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[Assistant] Or if you've been called the slur, or witnessed someone getting called the slur, you have experienced a point of encounter.


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[Assistant] And depending on who you are and how that moment goes down, you might have a range of responses. Could throw you off a little bit, or it could be incredibly painful and humiliating. Uhm.


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[Assistant] I've had lots of these points of encounter in my life, but one thing is true. There's not a lot of space to talk about them.


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[Assistant] That day in my classroom was pretty much like all of those times I had an uninvited run-in with the N-word. I froze. Because the N-word is hard to talk about.


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[Assistant] Part of the reason the N-word is so hard to talk about is usually only discussed in one way, as a figure of speech. We hear this all the time, right? It's just a word.


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[Assistant] The burning question that cycles through social media is who can and cannot say it.


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[Assistant] Black intellectual Ta-Nehisi Coates does a groundbreaking job of defending the African American use of the word. On the other hand, Wendy Kaminer


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[Assistant] Race in America 2019, researchers asked US adults if they thought it was okay to, uh, for a white person to say the n-word.


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[Assistant] 70% of all adults surveyed said never. And these debates are important.


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[Assistant] But they really obscure something else. They keep us from getting underneath to the real conversation. Which is that the N-word is not just a word.


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[Assistant] It's, it's not, uhm, neatly contained in a racist past, a relic of slavery.


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[Assistant] The N-word is an idea disguised as a word. That black people are intellectually, biologically, and immutably inferior to white people. And, and I think this is the most important part.


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[Assistant] Speaking of the word only as racist spew or as an obscenity in hip hop music makes it sound as if it's a disease.


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[Assistant] Located in the American vocal cords that can be snipped right out. It's not, and it can't. And I learned this from talking to my students. So next time class met,


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[Assistant] Uhm, I apologized and I made an announcement. I would have a new policy.


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[Assistant] Uhm, students would see the word in my PowerPoints.


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[Assistant] In film, in essays they read, but we would never ever say the word out loud in class. Nobody ever said it again. But they didn't learn much either.


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[Assistant] Afterwards, what bothered me most was that I didn't even explain to students why, of all the vile, problematic words in American English, why this particular word had its own buffer, the surrogate phrase, the N-word. Most of my students


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[Assistant] Didn't even know that the phrase, the N-word, is a relatively new invention in American English. When I was growing up, it didn't exist. But in the late 1980s,


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[Assistant] Black college students, writers, intellectuals,


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[Assistant] More and more started to talk about racist attacks against them.


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[Assistant] They felt that every time the word was uttered, it opened up old wounds, so they refused to say it.


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[Assistant] They knew their listeners would hear the actual word in their heads. That wasn't the point.


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[Assistant] The point was, they didn't want to put the word in their own mouths or into the air. By doing this,


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[Assistant] They made an entire nation start to second-guess themselves about saying it.


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[Assistant] Those of us who use the phrase the n-word or people who become outraged


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[Assistant] You know, just because the word is said, of being over principled, politically correct,


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[Assistant] Or as I just read a couple of weeks ago in the New York Times, insufferably woke. Right?


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[Assistant] So, so I bought into this a little bit too, which is why the next time I taught the course I proposed a freedom of speech debate.


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[Assistant] The N-word in academic spaces for or against.


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[Assistant] I was certain students would be eager to debate who gets to say it and who doesn't. But they weren't.


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[Assistant] A white student from New Jersey talked about standing by.


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[Assistant] As a black kid at her school got bullied by this word. She did nothing and years later still carried the guilt. Another from Connecticut talked about the pain of severing


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[Assistant] A very close relationship with a family member because that family member refused to stop saying the word.


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[Assistant] One of the most memorable stories came from a very quiet black student from South Carolina. She didn't understand all the fuss. She said everyone at her school said the word.


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[Assistant] She wasn't talking about kids calling each other name in the hall. She explained that at her school,


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[Assistant] When teachers and administrators became frustrated with an African-American student, they called that student the actual N-word. She said it didn't bother her at all. But then a couple of days later, she came to visit me in my office hours and wept. She thought she was immune. She realized that she wasn't. Over the last 10 years,


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[Assistant] I have literally heard hundreds of these stories from all kinds of people, from all ages, uh, people in their fifties remembering stories from the second grade and when they were six, either calling people the word or being called the word, but carrying that all these years around this word, you know.


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[Assistant] And as I listen to people talk about their points of encounter,


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[Assistant] What the pattern that emerged for me as a teacher that I found most upsetting is the single most fraught site for these points of encounter is the classroom.


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[Assistant] Most U.S. kids are going to meet the N-word in class.


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[Assistant] One of the most assigned books in US high schools is Mark Twain's The Adventures of Huckleberry Finn in which the word appears over 200 times. And this isn't an indictment of Huck Finn.


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[Assistant] The word is in lots of US literature and history. It's all over African American literature. Yet I hear from students that when the word is said during a lesson, without discussion and context,


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[Assistant] It poisons the entire classroom environment. The trust between student and teacher is broken.


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[Assistant] Uhm, often with the very best of intentions, still say the n-word in class.


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[Assistant] They want to show and emphasize the horrors of US racism, so they rely on it for shock value, invoking it


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[Assistant] Brings into stark relief the ugliness of our nation's past, but they forget the ideas are alive and well in our cultural fabric.


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[Assistant] The six-letter word is like a capsule of accumulated hurt.


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[Assistant] It releases into the atmosphere the hateful notion that black people are less.


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[Assistant] My black students tell me that when the word is quoted or spoken in class, they feel like a giant spotlight is shining on them.


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[Assistant] One of my students told me that his classmates were like bobbleheads, turning to gauge his reaction.


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[Assistant] A white student told me that in the eighth grade when they were learning to kill a mockingbird,


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[Assistant] Uh, and reading it out loud in class. The student was so stressed out at the idea of having to read the word.


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[Assistant] Talk about switching majors and dropping classes because of poor teaching around the n-word.


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[Assistant] has reached such a fevered pitch it's led to protests at Princeton, Emory,


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[Assistant] The New School, Smith College, where I teach, and Williams College, where


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[Assistant] Just recently, students have boycotted the entire English department over it and other issues. And these are just the cases that make the news. This is a crisis.


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[Assistant] And while student reaction looks like an attack on freedom of speech, I promise this is an issue of teaching. My students


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[Assistant] They want to learn about James Baldwin and William Faulkner and about the civil rights movement.


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[Assistant] feature of their lives as young people in the United States.


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[Assistant] It's in the music they love, and in the popular culture they emulate, the comedy they watch.


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[Assistant] In, in the hallways at school and the chat rooms, the video games they play. It is all over the world they navigate.


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[Assistant] I didn't even really understand what the word meant until I did some research. I was astonished to learn.


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[Assistant] Not in the 1970s or 1980s, but as far back as the 1770s. And I wish I had more time to talk about


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[Assistant] Uhm, I understand the virulent roots of this word. It's slavery. They're only partially right.


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[Assistant] This word, which existed before it became a slur.


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[Assistant] But it becomes a slur at a very distinct moment in US history, and that's as large numbers of black people begin to become free, starting in the North in the 1820s. In other words,


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[Assistant] Black mobility and black aspiration.


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[Assistant] Even now, nothing so swiftly unleashes an N-word tirade.


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[Assistant] As a black person asserting their rights or going where they please or prospering, think of the attacks on Colin Kaepernick when he kneeled or Barack Obama when he became president. My students want to know this history, but when they ask questions, they're shushed and shamed.


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[Assistant] By shying away from talking about the N-word, we have turned this word into the ultimate taboo, crafting it into something so tantalizing that for all you as kids, no matter their racial background,


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[Assistant] We treat conversations about it like sex before sex education. Uhm, we're squeamish, we silence them. So, they learn about it from misinformed friends and in whispers.


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[Assistant] We're all connected by our inability to talk about this word.


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[Assistant] But what if we explored our points of encounter and did start to talk about it?


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[Assistant] Today I try to create the conditions in my classroom to have open and honest conversations about it. One of those conditions, not saying the word.


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[Assistant] Another important condition is I don't make my Black students responsible for teaching their classmates about this. That is my job. So I come prepared. Uhm, I hold the conversation with a tight rein.


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[Assistant] Uhm, and I'm armed with knowledge of the history.


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[Assistant] I always ask students the same question. Why is talking about the N word hard? Their answers are amazing. They're amazing. More than anything though,


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[Assistant] I have become deeply acquainted with my own points of encounter, my personal history around this word. Because when the n-word comes to school, or really anywhere, it brings with it


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[Assistant] All of the complicated history of US racism, the nation's history, and my own, right here, right now, there's no avoiding it.


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[Assistant] Being the final three is very exciting. What has driven me so far in this competition.


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[Assistant] And I want to continue making them proud. And uh, hopefully win the competition. Family is the inspiration for my dishes today. Warm and generous. It's gonna bring me back to my home, Singapore. Fingers crossed, I want to do well today and go to the finale.


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[Assistant] Sasha, what are you starting there? I'm starting with my dessert first.


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[Assistant] I'm prepping the pineapple, uh, puree first for the pineapple part. Because that takes a long time to cook down.


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[Assistant] This is a dish I add, uh, while I was a kid at home and it's my favorite. My mom does this every time at home for us. So this is something that is close to my heart and hopefully I make my mom proud today. Because this is her recipe. If anything goes wrong, I might get a smack on the head.


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[Assistant] The past Master Chef Contestants, they know a lot about food and, uh, it's pretty stressful.


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[Assistant] Smells good. My ice cream is in the churner. I'm very happy with the texture of it.


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[Assistant] But now I'm gonna start with my sweet crust pastry. Uhm, on track according to my plan, but you know, in Masjid Jaffa kitchen, things can change very fast. SASHI!


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[Assistant] I'm starting with the curry paste for the chicken lemak. I'm cooking chicken lemak with jasmine rice wrapped in bitter leaf. I'm putting some ginger, garlic, turmeric and chilli on it. Blitz them up together.


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[Assistant] I'm cooking chicken lemak with jasmine rice wrapped in bitter leaf.


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[Assistant] My dish represents generosity. Shannon, he is right. I'm serving two litre chicken.


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[Assistant] I want my place in the finale so badly. I don't want to go back home just because of the portion size. I will get more chicken and uh, increase the portion size. Let's go Sashi.


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[Assistant] Yeah, I'm feeling the time pressure. I have a lot of more things to finish up.


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[Assistant] This is gonna stretch my limit, but I just have to work even harder.


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[Assistant] At the same time, I'm also cooking off the skin side of the chicken. Here I have a lot of more things to finish up.


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[Assistant] The tart for the dessert looks beautiful. It looks amazing.


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[Assistant] I want a little bit more neatness on the plate here, okay?


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[Assistant] Bright yellow from the lemak. A bit of redness from the cherry tomato. And the green from the bitterleaf.


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[Assistant] I know that the flavours have come through very well. Hopefully the judges love it.


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[Assistant] I want the judges to feel very homely with a pineapple tart. What I want to give them. My home, in a blade.


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[Assistant] I never stay in one country for a long time. It gets boring. I like to move on, see new places, meet different people. It's a good life, most of the time. When I need money, I get a job. I can do most things. Hotel and restaurant work, building work,


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[Assistant] Picking fruit? In Europe you can pick fruit most of the year.


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[Assistant] You need to be in the right country at the right time, of course. It's not easy work, but the money's not bad.


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[Assistant] I like to go south in the winter. Life is easier in the sun.


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[Assistant] And northern Europe can get very cold in the winter. Last year, 1989 it was, I was in Venice for October.


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[Assistant] I did some work in a hotel for three weeks. Then I began slowly to move south. I always go by train when I can. I like trains. You can walk about on a train. And you meet a lot of people.


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[Assistant] I left Venice and went on to Trieste. There I got a cheap ticket for the slow train to Sofia in Bulgaria.


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[Assistant] This train goes all down through Yugoslavia and takes a long time. A day and a half. But that didn't matter to me.


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[Assistant] The train left Trieste at nine o'clock on a Thursday morning. There weren't many people on it at first, but at Zagreb more people got on. Two girls went along the corridor past my carriage. They looked through the door, but they didn't come in. Then an old woman came in, sat down, and went to sleep.


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[Assistant] And I looked out of the window for about ten minutes. Then I went to sleep too.


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[Assistant] When I opened my eyes again, the two girls were in the carriage. They looked friendly. So I said, hello. Hi, they said. You're American, I said. Or Canadian, right? American, the taller girl said. She smiled. And you're 23? Your name's Tom Walsh?


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[Assistant] You've got blue eyes. And your mum lives in Burnham-on-Sea, UK. Right? How did you know all that? I asked. The second girl laughed. She looked at your passport. It's in your coat pocket. Oh, right. My coat was on the seat next to me. I took my passport out of my pocket.


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[Assistant] And put it back in my bag. Who are you then? I asked. They told me. Melanie and Carol from Los Angeles, USA. They liked Europe, they said. They knew a lot of places. Britain. Holland. Denmark. Germany. France. Spain. Italy. Yugoslavia.


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[Assistant] Bulgaria, Greece. I'm going to Bulgaria now, I said, for about a month. Then I'm going south for the winter. Cyprus, or perhaps North Africa. Oh yes, they said. We love Bulgaria. Sofia is a great town, wonderful. What do you do about money, I asked.


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[Assistant] You know, Carol smiled, sometimes we get a little job. This and that. But what about you? Yeah, come on, Melanie said. Tell us about you.


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[Assistant] Tom Walsh with the blue eyes, and the mum in Burnermontsea. What are you doing with your life, hey? So I told them. They were nice girls. They were older than me, perhaps twenty-seven or twenty-eight, but I liked them. We talked and laughed for hours.


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[Assistant] I told them a lot of stories about my life. Some of the stories were true, some weren't


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[Assistant] But the girls laughed and said I was a great guy. I asked them about Bulgaria because I didn't know the country. They knew Sophia well, they said. Hey Carol, Melanie said.


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[Assistant] But let's go over to Sophia this weekend and meet Tom there.


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[Assistant] We can meet him on Saturday night at the Hotel Marmara. Yeah, it's a good hotel, Carol told me. Cheap, but good. What do you think, Tom? Great, I said. Let's do that. The train was very slow.


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[Assistant] We got to Belgrade at six o'clock in the evening and a lot of people got off. There were only me and the girls in the carriage then. The guard came and looked at our tickets and went away again.


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[Assistant] Carol looked at Melanie. Hey Mel, she said.


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[Assistant] Why don't you and Tom go along to the restaurant? I'm not hungry and I want to sleep for an hour.


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[Assistant] Food's very expensive on the train, I said. I haven't got much money just now. I'm going to get a job in Sofia. Oh, Tom, Melanie said. Why didn't you tell us? Look, you're a nice guy, right? We're okay for money this week. We can buy you a meal. Of course we can, Carol said.


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[Assistant] We can take you to the best restaurant in town. It's a great place. We love it. What could I say? I was hungry. They had money. I didn't.


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[Assistant] So, Melanie and I went to the restaurant and had a meal. When we came back, Carol was still alone in the carriage.


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[Assistant] Melanie put her feet on the seat and went to sleep. At Nis, some more people got on the train and two old men came into our carriage. They looked at Melanie's feet on the seat and talked in loud voices. Carol laughed and Melanie opened her eyes and sat up. Are we nearly there? She asked Carol.


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[Assistant] And looked out of the window. Yeaah. About half an hour, I think. Why are you getting off at Bela Palanca? I asked. What are you going to do there? Melanie smiled. Find a cheap hotel. Meet people. Take a look at the town. You know. Just for a day or two, Carol said. But there's nothing there.


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[Assistant] Oh well, you never know, Melanie Loft. See you in Sofia, right? On Saturday night.


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[Assistant] The Hotel Marmara, okay? Eight o'clock, Carol said. Don't forget now. Okay, great, I said. See you there.


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[Assistant] The train came into Bella Palanca and stopped. The two girls got off and stood on the platform. They smiled at me through the window. Saturday, eight o'clock, Melanie shouted. Okay, I called.


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[Assistant] They smiled again, picked up their bags and walked away. Nice girls.


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[Assistant] I'm gonna have a great time in Sofia, I thought.


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[Assistant] The police are taking some people off the train. Then two policemen came into our carriage. A tall, thin one and a short, fat one. They looked at everybody carefully. And then they looked at me again. Come with us, please. The fat policeman said in English. What, me? I said.


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[Assistant] But policemen never like questions from young men with long hair. So I stayed quiet, picked up my bag, and went with them.


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[Assistant] In the station building there were a lot more policemen and some people from the train. They were all young people I saw. Some were afraid, some were bored. The police looked in everybody's bags and then the people went back to the train. My two policemen took me to a table. Your passport please.


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[Assistant] The fat policeman said, and opened your bag. They looked at my passport and I opened my bag.


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[Assistant] There was a young policewoman with red hair at the next table. She had a nice face, so I smiled at her and she smiled back. Aha! the tall policeman said suddenly.


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[Assistant] And a lot of police ran up to our table and stood behind me. 50,000, 100,000, 150,000,


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[Assistant] There's two hundred thousand dollars here, the toll policeman said.


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[Assistant] What an interesting bag, Mr. Tom Walsh. I found my voice again quickly. But it's not my bag! I shouted.


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[Assistant] There was a big happy smile on that policeman's face.


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[Assistant] And yes, of course, it was my bag.


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[Assistant] So how did two hundred thousand US dollars get into my bag?


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[Assistant] You cannot bring a US dollars into this country, the fat policeman said. He had very short grey hair and little black eyes. He didn't smile once. But I didn't bring them, I said quickly. They're not my dollars. I never saw them before in my life and... There was a lot of noise in the station.


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[Assistant] I looked out of the window and saw my train. Slowly it began to move. Hey, I shouted, that's my train. The tall policeman laughed. It was a great day for him. Oh no, he said, you're not getting back on that train. You're staying here with us.


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[Assistant] In our beautiful country he smiled happily. So I never got to Sofia on Saturday. I was very unhappy about that.


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[Assistant] I wanted to have a little talk with Melanie and Carol. Ask them one or two questions, you know. You're a nice guy, Tom. See you in Sofia, okay? Take you to the best restaurant in town. Yeah, great.


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[Assistant] And I never got down to Cyprus or North Africa that winter. Oh well, I live and learn. It's not an easy life in prison, but it's warm in winter and the food's not bad. And I'm meeting some interesting people. There's a man from Georgia, USSR. Boris his name is.


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[Assistant] He comes from a place by the Black Sea. He's a great guy. When we get out of here, he and I are going down to Australia. Brisbane perhaps, or Sydney. Get a job on a ship. Start a new life. Yeaah. Next year's going to be okay.


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[Assistant] Your last chance to get your hands on one of those aprons.


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[Assistant] How wonderful is that? A rousing round of applause. It makes you feel good, puts a smile on your face. Welcome.


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[Assistant] To the second day of judges auditions. In fact, the last day of judges auditions. And your last chance...


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[Assistant] To get your hands on one of those aprons.


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[Assistant] If your loved ones were here today, what would they be telling you?


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[Assistant] Has to be absolutely bang on. The best food that you can possibly cook.


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[Assistant] We're still at that round of applause stage, aren't we? How did they go? Tell you what, I hope you're hungry.


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[Assistant] That sauce tasted absolutely delicious. The duck was beautifully cooked. Uh, the skin was crispy and well rendered. No question, in any of our minds, you're in this year's Top 24.


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[Assistant] And it doesn't stop there, does it? Who cooked the chicken curry?


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[Assistant] And I'm afraid that wasn't the only one. Who cooked the flan? Patissiere, I think.


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[Assistant] Fiona, it- I think, or we think,


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[Assistant] That maybe you had a different intention for that dish. Something happened in the kitchen today. Because what we got were a tumble of deconstructed ingredients that really weren't in balance. So I'm afraid you're not getting an apron today either.


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[Assistant] Bonnie, you know your ice cream was absolutely wonderful. And we knew that it was an orange dish. There were just too many flaws in the other elements.


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[Assistant] And that's why we can't give you the apron either.


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[Assistant] A good plan B. It's the difference between either getting an apron and joining that lot or going home.


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[Assistant] What you've got is some very good elements in that bowl. Panacola is absolutely bang on it's soft.


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[Assistant] Question is, do they go together well enough to get you an apron?


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[Assistant] Hi everybody. My name is Matt Cutts and I worked at Google for almost 17 years.


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[Assistant] As a distinguished engineer there, I was pretty close to the top of the Silicon Valley ecosystem.


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[Assistant] Then I decided to follow some inspiring folks and do a short tour at the US Digital Service. That's the group of geeks that helped rescue healthcare.gov when that website went down hard in 2013.


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[Assistant] Yeah. So I signed up for a three- to six-month tour, and almost three years later, I'm still in Washington, DC, working for the federal government, because the government really needs technologists right now.


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[Assistant] At my old job, every room had video conferencing integrated with calendars, power cables were built right into the furniture. When I moved to a government agency,


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[Assistant] I had to call a person to set up a phone conference. And when we moved to a new office, we didn't have furniture for a while, so we set up the phone on a trash can.


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[Assistant] One of the things that surprised me whenever I moved to DC is how much the government still has to deal with paper.


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[Assistant] Paper has some downsides. Here's a pop quiz. If your last name starts with H or higher, H or higher, would you raise your hand?


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[Assistant] I have some bad news. Your veteran records might have been destroyed in a fire in 1973. Yeah.


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[Assistant] Paper processes are also slower and more prone to errors.


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[Assistant] If you're a veteran and you're applying for your health benefits using a paper form, you might have to wait months for that form to be processed.


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[Assistant] We replaced that with a web form, and now most veterans find out if they can get access to their health benefits in 10 minutes.


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[Assistant] Here's another launch that I'm proud of. We worked with the Small Business Administration to move one of their systems from paper to digital.


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[Assistant] So this is a picture from before, and this is afterwards.


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[Assistant] Same cubicles, same people, just a better system for everyone.


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[Assistant] And the grocery store got really weirded out by that request. They wanted a letter on official government letterhead.


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[Assistant] Well, we worked for the government, so we wrote a letter that said you can use this public domain form on a cake for celebratory purposes.


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[Assistant] Which led to bad jokes about filling forms out in triplicate. Yes, bad jokes in government.


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[Assistant] Now, I've talked a lot about paper, but we also bring up computer systems that go down. We bring in modern technology practices like user-centered design and the cloud, and we also help improve procurement.


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[Assistant] It turns out, government buys software the same way that it buys chairs and brownies and tanks from government regulations that are over a thousand pages long. So yes, there's some stuff that's messed up in government right now.


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[Assistant] Some of the best and brightest minds in technology are working on meal delivery startups and scooters and how to deliver weed to people better.


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[Assistant] Silicon Valley likes to talk about making the world a better place. But you feel your impact in a much more visceral way in government. This is somebody whose dad passed away.


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[Assistant] He hunted me down on Twitter to say that a system that we had improved worked well for him during a tough time. Those tough times are when government needs to work well and why we need innovation in government.


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[Assistant] Now I have a confession to make. When I came to DC, I sometimes used words like bureaucrat.


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[Assistant] These days, I'm much more likely to use words like civil servant, like Francine, who can make you cry.


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[Assistant] Or at least, she made me cry. Because she's so inspiring.


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[Assistant] I am also deeply, fiercely proud of my colleagues. They will work through illogical situations and put in late nights to get to the right result.


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[Assistant] The government can't pay huge salary bonuses, so we ended up making our own awards.


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[Assistant] Our mascot is a crab named Molly, and so that award is actually a crab-shaped purse, screwed into sheet metal.


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[Assistant] These days, I believe less in silver bullets that are going to fix everything. I believe more in the people who show up to help.


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[Assistant] If you're looking for something deeply meaningful and full disclosure, sometimes incredibly frustrating, here's what you need to know. There is something difficult and messy and vital and magical.


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[Assistant] happening when civil servants partner with technologists at the city and state and national level. You don't have to do it forever, but you can make a difference in public service right now. Thank you.


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[Assistant] In 1974, I was a senior lecturer in forensic medicine at the University of Leeds.


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[Assistant] Wilma McCann had been struck twice on the back of the head. She had three groups of stab wounds, two in the breasts, the bra had been pulled up, and one in the abdomen. The unusual thing about the stab wounds was that whoever has done it has


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[Assistant] Taking time to push that instrument around and then time to stand back.


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[Assistant] You'd have to draw on your experience and say that this wasn't done by somebody who is disturbed to some considerable degree.


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[Assistant] There was little in the way of scientific identification evidence in the case of Wilbur McCann. Nothing in the way of fibres, trace evidence. So the cards were stacked on this one right from the beginning.


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[Assistant] Wilbur McCann's body was found sufficiently near to the Chapel Town red light area.


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[Assistant] For it to be an assumption almost from minute one amongst the attending police that this could well have been prostitution related.


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[Assistant] Would you references like Good Time Girl?


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[Assistant] That sort of thing, but you could easily read between the lines.


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[Assistant] The coroner's officer phones and says, can you please come to the scene? This needs looking at.


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[Assistant] She was up this rather muddy alleyway, lying with her head away from the road and her feet towards the road, so that when somebody came round the corner,


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[Assistant] The first thing they would see would be Emily Jackson laid out, legs splayed, with the feet pointing towards them.


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[Assistant] You cannot help speculating of the victim. How on earth must they have felt in the last few minutes of their life while this was happening?


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[Assistant] The body was found near to the Chapeltown red light area, only a short distance from the gaiety.


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[Assistant] Gradually, it became clear that Emily Jackson was working as a prostitute.


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[Assistant] Into Leeds and use that as her place to do business. She had small children.


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[Assistant] And her husband in danger of being declared bankrupt and prosecuted, and they had to earn some money somehow.


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[Assistant] There were two obvious circular depressed fractures of the skull at the back of the head, very accurately placed.


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[Assistant] So, from the forensic pathologist point of view,


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[Assistant] Some of the stabbed wounds were cross-shaped. Others were rounded.


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[Assistant] The possibility was immediately raised that this instrument was a Phillips screwdriver rather than a flat blade.


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[Assistant] I think it was James Bond who said once is happenstance, twice it's coincidence, but three times it's enemy action.


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[Assistant] Was found by a dog walker on Sunday morning in Rounday Park.


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[Assistant] With her feet towards the road and her head away from it.


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[Assistant] Her boots were laid neatly on top of the back of her legs.


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[Assistant] And the contents of the handbag were next to the handbag, but they'd not just been tipped out of the handbag, they'd been laid out fairly neatly.


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[Assistant] A pattern is now definitely emerging of blow, death, mutilation, no evidence of penetrative sex, no evidence of seminal fluid either.


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[Assistant] And display to maximum effect afterwards. The similarities were there.


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[Assistant] During that period, Dennis Hoban was promoted and so he left the inquiry and Jim Hobson, who had been his deputy in Leeds, was moved upwards.


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[Assistant] There were various car tracks which were of interest to the police.


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[Assistant] But tens of thousands of vehicles could have been fitted with those tyres.


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[Assistant] Patricia Atkinson was the first victim who was killed in her flat.


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[Assistant] There were boot prints and footprints in blood on the carpet leading to the door and you could see from the distribution of the prints that whoever had done this had walked to the door


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[Assistant] But there were so many boots of that sort that they decided that that would not have been a particularly profitable area to go down.


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[Assistant] On blood and body fluids. Basically relied on blood grouping.


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[Assistant] You've got over 60 million people in the country, so it's a pretty wide field.


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[Assistant] And of course in all these cases, there was no semen, no saliva. So, there was nothing at all to go on.


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[Assistant] I could see why the police were getting quite desperate.


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[Assistant] In July 1977, the news broke that a woman had been attacked in Bradford. Her name was Maureen Long.


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[Assistant] She had been struck on the back of the head. She had been stabbed four or five times.


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[Assistant] And was capable of talking about it.


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[Assistant] The Gaiety was one of those strange anomalies that every city has. And the main feature of it would seem to be that striptease went on from dawn till dusk. Not just striptease, we're talking about people with whips and god knows what.


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[Assistant] It obviously was a gathering place for people who were interested in that sort of thing.


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[Assistant] The mills never stopped running 24 hours a day. There was vast wells.


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[Assistant] It turned a local home for a prostitute killer into a massive national story.


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[Assistant] Tim's letting you start rest. The clock is ticking. So much pressure.


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[Assistant] I'd like to see all those people. I shall make inquiries to trace them.


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[Assistant] And I would think that it might be helpful to some people if they came forward to see me rather than have me making inquiries about some of the facts that I know.


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[Assistant] In this video I'm going to define what is probably the most common type of machine learning problem, which is supervised learning. I'll define supervised learning more formally later, but it's probably best to explain or start with an example of what it is, and we'll do the formal definition later.


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[Assistant] Let's say you want to predict housing prices. A while back a student collected data sets from the city of Portland, Oregon, and let's say you plot the data set and it looks like this.


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[Assistant] Here on the horizontal axis, the size of different houses in square feet, and on the vertical axis, the prices of different houses in thousands of dollars.


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[Assistant] Given this data, let's say you have a friend who owns a house that is say 750 square feet and they're hoping to sell the house and they want to know how much they can get for the house. So how can a learning algorithm help you? One thing a learning algorithm might be able to do is


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[Assistant] Put a straight line through the data, also fit a straight line to the data, and based on that, it looks like maybe the hulls can be sold for maybe about $150,000.


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[Assistant] Instead of filling a straight line to the data, we might decide that it's better to fill a quadratic function or a second order polynomial to this data. And if you do that and make a prediction here, then it looks like, well, maybe they can sell the house closer to $200,000.


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[Assistant] So, this is an example of a supervised learning algorithm.


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[Assistant] And the term supervised learning refers to the fact that we gave the algorithm a data set in which the quote right answers were given.


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[Assistant] That is, we gave it a data set of houses in which for every example in this data set we told it what is the right price or what is the actual price that that house sold for.


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[Assistant] And the task of the algorithm was to just produce more of these right answers, such as for this new house that your friend may be trying to sell.


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[Assistant] To define a bit more terminology, this is also called a regression problem.


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[Assistant] And the term regression refers to the fact that we're trying to predict this sort of a continuous values attribute. Here's another supervised learning example. Some friends and I were actually working on this earlier.


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[Assistant] Let's say you want to look at medical records and try to predict of a breast cancer as malignant or benign.


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[Assistant] If someone discovers a breast tumor, a lump in their breast, a malignant tumor is a tumor that is harmful and dangerous, and a benign tumor is a tumor that is harmless. So obviously people care a lot about this.


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[Assistant] Let's say you collect a data set and suppose in your data set you have on your horizontal axis the size of the tumor.


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[Assistant] And on the vertical axis I'm going to plot one or zero, yes or no. Whether or not these are examples of tumors we've seen before are malignant, which is one, or zero, or not malignant, or benign.


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[Assistant] So let's say your dataset looks like this, where we saw a tumor of this size that turned out to be benign, one of this size, one of this size, and so on.


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[Assistant] And sadly we also saw a few malignant units. So one of that size, one of that size, one of that size.


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[Assistant] So in this example I have 5 examples of benign tumors shown down here and 5 examples of malignant tumors shown with a vertical axis value of 1.


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[Assistant] And let's say you have a friend who tragically has a breast tumor and let's say her breast tumor size is maybe somewhere around this value.


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[Assistant] The machine learning question is, can you estimate what is the probability, what is the chance that the tumor is malignant versus benign?


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[Assistant] To introduce a bit more terminology, this is an example of a classification problem. The term classification


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[Assistant] Refers to the fact that here we're trying to predict a discrete valued output, 0 or 1, malignant or benign.


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[Assistant] As a concrete example, maybe there are three types of breast cancers, and so you may try to predict the discrete value of 0, 1, 2, or 3, where 0 will mean benign, benign tumor, so no cancer, and 1 may mean no cancer.


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[Assistant] Type 1 cancer, there's three types of cancer, whatever type 1 means, and 2 may mean the second type of cancer, and 3 may mean the third type of cancer. But this would also be a classification problem because there's sort of a discrete value set of outputs corresponding to no cancer, or cancer type 1, or cancer type 2, or cancer type 3.


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[Assistant] In classification problems, there is another way to plot this data.


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[Assistant] Let me show you what I mean. I'm going to use a slightly different set of symbols to plot this data.


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[Assistant] So if tumor size is going to be the attribute that I'm going to use to predict malignancy or benignness, I can also draw my data like this. I'm going to use different symbols to denote my benign and malignance or my negative and positive examples. So instead of drawing crosses, I'm now going to draw O's for the benign tumors.


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[Assistant] Okay, I hope this figure makes sense. All I did was I took, you know, these, my data set on top and I just mapped it down to this real line, like so, and started to use different symbols, circles and crosses to denote malignant versus benign examples.


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[Assistant] Now, in this example, we use only one feature or one attribute, namely the tumor size, in order to predict whether tumor is malignant or benign.


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[Assistant] In other machine learning problems, we may have more than one teacher or more than one attribute. Here's an example.


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[Assistant] Let's say that instead of just knowing the tumor size, we know both the age of the patient and the tumor size.


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[Assistant] In that case, maybe your dataset would look like this, where I may have a set of patients,


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[Assistant] With those ages and that tumor size, it'll look like this. And a different set of patients that look a little different.


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[Assistant] Uh, causes of tumors and, you know, with this, hopefully we can decide that your friend's tumor is more likely, so if it's over there, that hopefully the learning algorithm will say that your friend's tumor falls on this benign side and is therefore more likely to be benign to malignments.


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[Assistant] In this example we had two features, namely the age of the patient and the size of the tumor.


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[Assistant] In other machine learning problems, we will often have more features. And my friends that work on this problem, they actually use other features like these, which is clump thickness, clump thickness of the breast tumor, uniformity of cell size of the tumor, uniformity of cell shape of the tumor, and so on, and other features as well.


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[Assistant] And it turns out one of the most interesting learning algorithms that we'll see in this class is a learning algorithm that can deal with not just two or three or five features, but an infinite number of features.


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[Assistant] On this slide, I've listed a total of five different features, right? Two on the axes and three more up here.


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[Assistant] But it turns out that for some learning problems, what you really want is not to use like three or five features, but instead you want to use an infinite number of features, an infinite number of attributes, so that your learning algorithm has lots of attributes or features or cues with which to make those predictions.


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[Assistant] So, how do you deal with an infinite number of features? So, how do you even store an infinite number of things on a computer? I mean, your computer's going to run out of memory.


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[Assistant] It turns out that when we talk about an algorithm called the support vector machine, there will be a neat mathematical trick.


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[Assistant] That will allow a computer to deal with an infinite number of features.


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[Assistant] Imagine that I didn't just write down, you know, two features here and three features on the right, but imagine that I wrote down an infinitely long list. I just kept writing more and more and more features, like an infinitely long list of features. Turns out we'll be able to come up with an algorithm that can do that. So just to recap,


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[Assistant] In this class we'll talk about supervised learning and the idea is that in supervised learning in every example in our data set we are told what is the quote correct answer that we would have quite liked the algorithm to predict on that example. Such as the price of the hulls or whether a tumor is malignant or benign. We also talked about the regression problem.


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[Assistant] And by regression, that means that a goal is to predict a continuous value output. And we talked about the classification problem where the goal is to predict a discrete value output. Here's a quick wrap up question.


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[Assistant] And you want to develop learning algorithms to address each of two problems.


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[Assistant] In the first problem, you have a large inventory of identical items. So imagine that you have thousands of copies of some identical items to sell.


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[Assistant] In the second problem, problem two, you like, you have lots of users and you want to write software to examine each individual of your customer's accounts, each one of your customer's accounts, and for each account decide whether or not the account has been hacked or compromised.


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[Assistant] So, for each of these problems, should they be treated as a classification problem or as a regression problem?


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[Assistant] When the video pauses, please use your mouse to select whichever of these four options on the left you think is the correct answer.


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[Assistant] So hopefully you got that, uh, this is the answer.


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[Assistant] For problem one, I would treat this as a regression problem, because if I have, you know, thousands of items, well, I would probably just treat this as a real value, as a continuous value.


[User] Please transcribe this text

[Assistant] And, uh, cheat there for the number of items I sell as a continuous value.


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[Assistant] And for the second problem, I would treat that as a classification problem because uhm, I might say set the value I want to predict to be zero to denote the account has not been hacked.


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[Assistant] And set the value 1 to denote an account that has been hacked into. So just like, you know, breast cancer is right, 0 or benign, 1 is malignant. So I might set this to be 0 or 1, depending on whether it's been hacked, and have an algorithm try to predict.


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[Assistant] That's it for supervised learning. And in the next video, I'll talk about unsupervised learning, which is the other major category of learning algorithms.


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[Assistant] And here is your first surprise.


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[Assistant] And this between you and us is our favourite moment. Feeling your excitement. The look in your eyes. Wondering which one of you


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[Assistant] Take your time. We just want you to soak up the atmosphere.


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[Assistant] Uhm, and these, they're just, they're lovely and tender, they're full of flavour.


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[Assistant] Reluctant. I think, you know, the presentation, the idea of the splat. But what I'm knocked out about is the flavour. I mean, I actually love that little crumb.


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[Assistant] I mean, you've got the chat and you're very confident, but I want to kind of, you know, kind of fish through that a bit and find out whether or not, you know, you've got what it takes. Are you happy with the dish? I'm happy with the dish, yep.


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[Assistant] Uhm, the lamb's a little undercooked. And the sweet potato rosti, it's soggy, so it's not crisp. And I'm kind of after that crispness.


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[Assistant] The lamb's a little undercooked. It just needs finesse.


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[Assistant] So basically what you've got is a hot dish that's gone cold. Another great day in the kitchen.


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[Assistant] It's a beautifully balanced dessert. Moose is light, delicious, ice cream, perfect texture, lovely little wave of whiskey through it. And that caramel sauce is just absolutely spot on.


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[Assistant] Always the tough thing is balancing how many aprons we've got to give away. It's a game changer, isn't it?


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[Assistant] Gutsy, very real. Lots of stuff shaped with the fingers. Like proper cooking, you know, you know, shaping with the hands. Soulful food. But I always get a bit nervous because I think, are we giving too many away too early? But if the dish deserves it, then I think we have to, don't we?


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[Assistant] That piece of beef is amazing. And you've cooked it beautifully.


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[Assistant] You've rendered the fat, you've caramelized it well, you've seasoned it well, and it's cooked and rested to, for me, absolute perfection. So I don't know what the boys think, but, pfft. Definite. Definite yes from me. Definite. Take care.


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[Assistant] Done. Wow. There you go. We look forward to seeing you cook.


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[Assistant] They get to have another go and we get to eat hopefully some really delicious dishes.


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[Assistant] Hello, how are you? I'm good, how are you guys? Very good, the first tentative steps into the MasterChef kitchen. Wow.


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[Assistant] Okay, so massive change, prison officer in SA to wanting to be on MasterChef, why, why?


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[Assistant] Wow. I find it a little bit kind of, um, aggressive on the flavors.


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[Assistant] So exciting this year. Can I tell you something? They're so light, they're healthy.


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[Assistant] Let's see. I'll tell you though, it looks amazing. I wanna get just the right amount. Do you wanna jump in first? Our resident octopus expert. The Greek one. You know, I'm watching him.


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[Assistant] You know what I love about it is there's basically three ingredients there that just work in absolute perfect harmony. Absolutely delicious. I was just eating that octopus there and thinking to myself,


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[Assistant] I take that over something like lobster, for example, any day of the week, just because it's cooked so beautifully. It's tender, but it's got enough texture. It's got enough bite in there to make it interesting. Uhm, just delicious.


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[Assistant] I'm great, I'm really happy to be back, ready to deliver another great podcast for all our listeners.


[User] Please transcribe this text

[Assistant] Right, and they're actually looking at their profit and loss statements, so that's kind of interesting.


[User] Please transcribe this text

[Assistant] Uh, yeaah, sure. Well, all companies come out with a profit and loss statement every year or every three months. And it's basically a document that shows all the money that the company is spending and all the money that the company is earning.


[User] Please transcribe this text

[Assistant] Right, so in the end basically it'll tell you we're making money or we're losing money.


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[Assistant] Okay great, so today on our vocabulary preview we have two words for you. So let's take a look at the first one.


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[Assistant] Right, so for example, in a company, regular company expenses are salaries or office supplies or the phone bills, internet bills.


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[Assistant] Expenses, great. Let's take a look at our second word. Through the roof. Through the roof. Through the roof. Through the roof. So when something is through the roof, it's... Really, really, really, really high. Really high.


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[Assistant] So, for example, we can talk about gas prices are through the roof lately.


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[Assistant] So when we talk about through the roof, it means that the number is really high.


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[Assistant] Yep, let's listen to the dialogue. As always, the first dialogue is gonna be a little bit fast, so it might be a little bit difficult, but...


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[Assistant] Well, we'll talk about that in a little while. Why don't we take a look at our language takeaway for today?


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[Assistant] It's pretty similar, but it has a little bit of a difference, so why don't we listen to some examples and see if we can understand.


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[Assistant] Right, that's the total amount of money versus expenses is the things that you spend money on.


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[Assistant] Out of control. Out of control. So when something is out of control, it's... It's crazy. Crazy. You can't control it.


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[Assistant] You can't stop it. You can't stop it. Out of control. Okay, great. Now let's take a look at our last word for today. Go over. Go over.


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[Assistant] Go over. Go over. So go over is a pretty simple word. Why don't we listen to some examples so we can understand go over.


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[Assistant] Yeah, that's, and that's a more natural way of saying something like that.


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[Assistant] And this time the dialogue will be slower so we can understand and listen to all the words we just talked about.


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[Assistant] Great, so today on Fluency Builder we have three words for you. Let's start by looking at the first one.


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[Assistant] So this word is very similar to the root.


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[Assistant] Right, so let's listen to some examples of how we can use off the charts in a different situation.


[User] Please transcribe this text

[Assistant] Yeaah, you can. Definitely. You can use off the charts or through the roof. Both of them mean the same thing.


[User] Please transcribe this text

[Assistant] Exactly, so I will do something about it. I will investigate, and I'll do it.


[User] Please transcribe this text

[Assistant] Okay, so now let's listen to the dialogue a third time. It's gonna be at its normal speed, but now you have all the tools you need to understand it a lot better.


[User] Please transcribe this text

[Assistant] Yes, when you are a manager or a CEO of one of these big companies, you get a lot of great things like, I don't know, a private jet maybe?


[User] Please transcribe this text

[Assistant] Or uhm, the company often pays for your house.


[User] Please transcribe this text

[Assistant] Or for your car, they'll give you a company car.


[User] Please transcribe this text

[Assistant] Uhm, they'll pay for your kids or your children's, uh, tuition for high school.


[User] Please transcribe this text

[Assistant] And also, I've heard about entertainment bonuses.


[User] Please transcribe this text

[Assistant] What crazy expenses you have. For example...


[User] Please transcribe this text

[Assistant] Well, for example, I spend a lot on DVDs each week. I usually buy two or three movies.


[User] Please transcribe this text

[Assistant] That is off the charts for DVD spending. So we want to know what you spend your money on. Be sure to go into our community website and tell us what you spend money on.


[User] Please transcribe this text

[Assistant] Great, so, we'll see you there, and until then, it's... Goodbye!


[User] Please transcribe this text

[Assistant] You see a lot of people who are very, very nervous, but there's also a real excitement.


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[Assistant] Um, dumplings stuffed with lemons, and I'm serving them with saffron broth.


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[Assistant] Somewhere hot. Somewhere far away.


[User] Please transcribe this text

[Assistant] That's 28,000 pounds for the two of us for a week!


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[Assistant] That's interesting. I like camping. Are they far away? The Scot here.


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[Assistant] The first major challenge. Cook something for us as if we were coming round to your house.


[User] Please transcribe this text

[Assistant] The 24 of you are standing here today because you're great home cooks and probably the best time in your own home kitchens is that time when you get to cook for friends or for family, but who you invite over maybe for a special occasion. That's some of the most rewarding meals that you can cook ever.


[User] Please transcribe this text

[Assistant] And today, what we want you to do is cook something for us as if we were coming round to your house for a dinner party.


[User] Please transcribe this text

[Assistant] You can cook anything that you want. Sweet or savory. A single plate or shared style. But what it's gotta do is it's gotta wow us. It's gotta represent you as a cook.


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[Assistant] An open pantry and the garden is in place, so you've got loads of opportunity to bring us something that is delicious. We're gonna taste every dish.


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[Assistant] And the contestants that cook the three least impressive dishes will go into tomorrow's elimination.


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[Assistant] Problem 3. Possible going home. First person out of the competition. Never good.


[User] Please transcribe this text

[Assistant] Uuuh, a little bit of porky goodness. I do love a little bit of pork belly. With pickles, rice, sounds like not a bad place to come to New York place.


[User] Please transcribe this text

[Assistant] So, share with me the secrets of your father and his true love.


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[Assistant] So Aldo, this looks like something the fisherman down by the egg castle might have knocked up. What is it?


[User] Please transcribe this text

[Assistant] But it's honestly paired with precision. You've taken real care with the seafood. You've been bold with your sauce.


[User] Please transcribe this text

[Assistant] This is a great example of the sort of good home cooking that belongs in great restaurants.


[User] Please transcribe this text

[Assistant] The delicate silkiness of that custard, the beautifully cooked fish, all goes beautifully together.