[User] Please transcribe this text

[Assistant] Keep that story going and help them know that even if something has happened traumatic to them, that their life is still worth telling. Their story is worth telling.


[User] Please transcribe this text

[Assistant] Now, in order to do that, I really feel like we have to embrace our own personal stories as educators.


[User] Please transcribe this text

[Assistant] Many of you might be sitting there and thinking, yeah, that happened to me. But I'm not ready to share. And that's okay. The time will come when you will feel it inside your soul.


[User] Please transcribe this text

[Assistant] That it's time to turn your past pain into purpose for the future. These children are our future. I just encourage you to take it day by day. Talk to someone. Be willing. And just open. My life story came full circle.


[User] Please transcribe this text

[Assistant] In the spring of 2018, where I was invited to speak to a group of beginning teachers and mentors. I shared my story, much like today, with you. And afterwards, I had a lady approach me


[User] Please transcribe this text

[Assistant] She had tears in her eyes and she quietly said, thank you. Thank you for sharing. I cannot wait to tell my dad everything that I heard today.


[User] Please transcribe this text

[Assistant] She must have seen the perplexed look on my face because she followed up by saying, Mr. Randolph is my dad.


[User] Please transcribe this text

[Assistant] And I wanted her to know I have advocated for more funding for guidance counselors, for school social workers, for psychologists, for nurses, because they are so vital to the mental and physical health of our children. I'm thankful for Ms. McFadden.


[User] Please transcribe this text

[Assistant] In order to find your way out of the darkness, you have to find the light.


[User] Please transcribe this text

[Assistant] Today, I hope that you leave this place and you seek opportunities to be the light


[User] Please transcribe this text

[Assistant] For not only students, but for adults in your classrooms, in your schools, in your communities.


[User] Please transcribe this text

[Assistant] Captain, the parade's about to start. Don't wanna be late, sir.


[User] Please transcribe this text

[Assistant] Ready to rock? Do you even have to ask?


[User] Please transcribe this text

[Assistant] We were his prisoners, me and my mom, Ifalna. He kidnapped us because she was a sutra. A specimen for his experiments. And after what he did to her...


[User] Please transcribe this text

[Assistant] I imagine the things I'd do to him if I got the chance.


[User] Please transcribe this text

[Assistant] Can't say when you'll meet, but trust me, you'll love her.


[User] Please transcribe this text

[Assistant] Hmm, and one resulting from fusion no less. The superior genetics producing a far stronger being.


[User] Please transcribe this text

[Assistant] How I envy your opportunity to be present for this epochal event.


[User] Please transcribe this text

[Assistant] So if you could get them out of here, we'd be so grateful. Speaking of clothes, they'll never let you on the beach looking like that.


[User] Please transcribe this text

[Assistant] And to help you with that, here's lucky ticket number seven! Take it!


[User] Please transcribe this text

[Assistant] We had to go to Hollywood Boulevard and find three characters that were played by Australian actors.


[User] Please transcribe this text

[Assistant] We were 100% confident that all the photos that we'd had taken were actors that were Australian-born. I read enough trashy magazines to know who they are.


[User] Please transcribe this text

[Assistant] Because you realise as the boys get here, they'll just go bang, bang, bang, bang, bang. Jared and I had built such a strong lead. We're not far away from a quarter of a million dollars. Have a crack, Jared. It's just a hot dog with


[User] Please transcribe this text

[Assistant] We had to head to Moorabbin Airport. We knew already where Moorabbin Airport was.


[User] Please transcribe this text

[Assistant] Once Jared and I get flustered, it never ends pretty. Where is it?


[User] Please transcribe this text

[Assistant] And this could just be anywhere. Why does it have to be when we're bloody lost? We had to find a hidden treasure inside a sand castle.


[User] Please transcribe this text

[Assistant] Really? Yes you do. I instantly went, damn, why did I do this one?


[User] Please transcribe this text

[Assistant] I sort of feel like I should have maybe done the, uh, roadblock.


[User] Please transcribe this text

[Assistant] We've seen so many different parts of the world that we probably wouldn't have had an opportunity to see. But it just makes us want to see more.


[User] Please transcribe this text

[Assistant] Welcome back. Today we're going to take a look at a number of interesting applications of symbol tables in the binary search tree data structure, uh, to address problems with processing geometric data.


[User] Please transcribe this text

[Assistant] So let's take a look at it. The idea is that we're going to be talking about geometric objects, not simple keys like strings and numbers.


[User] Please transcribe this text

[Assistant] So, here's an example. So, say your geometric objects are points in the plane, and you specify a rectangle that's oriented with the horizontal vertical axis. And you might want to ask which points are inside the rectangle, or how many points are inside the rectangle.


[User] Please transcribe this text

[Assistant] Or maybe what you're processing is rectangles. You have a set of rectangles and we want to know which of these rectangles intersect or how many rectangle intersections are there.


[User] Please transcribe this text

[Assistant] Uh, these are, uh, interesting problems that have lots and lots of applications, from computer-aided design to games and movies, uh, and also in, uh, abstractions such as, uh, databases, uh, and other situations where, uh, you might have multiple keys or multiple dimensions.


[User] Please transcribe this text

[Assistant] Uh, and it's a very interesting extension of the ideas that we've looked at for symbol tables for all sorts of familiar applications.


[User] Please transcribe this text

[Assistant] Uh, and su, surprisingly, uh, binary search trees and the associated algorithms that we've looked at are going to provide very efficient solutions to a number of important problems in this area and really have, uh, enabled, uh, new developments and new technology, uh, in all of these kinds of applications.


[User] Please transcribe this text

[Assistant] So to get started we're going to look at a simple problem called one dimensional range search. And it really forms the basis for what we're going to do.


[User] Please transcribe this text

[Assistant] It's a little bit of an extension of the ordered symbol table API that we gave before.


[User] Please transcribe this text

[Assistant] Uh, and we're going to have the operations, uh, range search and range count. So, you know, one dimensional just means we have one key. So we'll insert a key value pair as before. Uh, and what we want to do is be able to search for a key and the value associated with it. We want to be able to delete.


[User] Please transcribe this text

[Assistant] Uh, but then we want these operations range search and range count. So, find all the keys that are between two given keys, or give how many keys are there between two given keys. So for this example at right,


[User] Please transcribe this text

[Assistant] Uh, we have, uh, insert a number of keys, uh, and, and we're just showing them, uh, in sorted order. Uh, but then you might say, well, how many keys are there that are between, uh, G and K? In this case, there's just two.


[User] Please transcribe this text

[Assistant] Uh, and this is a very common operation, say, in databases. Uh, you wanna return, uh, how many, uh, taxpayers have salaries, uh, between 1 million and 10 million, uh, and then which ones are there and so forth. Uh, so range searching is a very, uh, important, uh, fundamental operation.


[User] Please transcribe this text

[Assistant] Uh, now in geometric interpretation we just think of the keys as points on a line.


[User] Please transcribe this text

[Assistant] Uh, and so the key values, uh, well, are just specified as points on a line. We might convert the letters to numbers or we might, keys might be numbers. And then what we're looking for is to find or count the points in a given interval in one dimension.


[User] Please transcribe this text

[Assistant] So, uh, how are we going to implement that? Well, uh, this is a basic problem that is very similar to our symbol table problem. Uh, we might consider keeping the things in an unordered array.


[User] Please transcribe this text

[Assistant] Uh, just put them in an array and then while insertion is, is fast, we just add it to the end of the array. Uh, we might have to use resizing to make the array grow.


[User] Please transcribe this text

[Assistant] Uh, but this is unattractive because for large numbers of keys, in order to count the keys that fall within a given range, you have to go through all the keys, uh, and test whether they're in the range or not.


[User] Please transcribe this text

[Assistant] Uh, and to return them the same way. So take linear time, uh, for a large number of keys.


[User] Please transcribe this text

[Assistant] Then, uh, to insert, uh, in order to keep it in order in an array, you might need to move the larger ones over one position and so forth. Our elementary implementation of binary search when we did symbol tables, uh, did this.


[User] Please transcribe this text

[Assistant] So the insertion time might be linear, but then you can use binary search.


[User] Please transcribe this text

[Assistant] Uh, to look for the two, uh, endpoints, it's only gonna take time proportional to log n, uh, and then from that you could figure out, uh, how many keys there are, uh, or, uh, return them all between the index of the lowest one in the range, index of the highest one in the range.


[User] Please transcribe this text

[Assistant] Uh, so those elementary, uh, implementations are, uh, not acceptable for large numbers of keys because, uh, they have the linear time operation.


[User] Please transcribe this text

[Assistant] Uh, so what we really want is to have, uh, time proportional to login, uh, for, uh, insert and, and for counting. For range search, of course, we, we have to touch every key that we return, so the running time's going to be proportional to the number of keys that match.


[User] Please transcribe this text

[Assistant] Well, what we're going to do is just, uh, keep the, uh, keys in a binary search tree. And we looked at the implementation of the rank function for binary search trees where, uh, for every key we can compute how many keys are there that are strictly less than that key.


[User] Please transcribe this text

[Assistant] Uh, so, uh, in this case the rank of E is 2 and H is 3 and so forth, so in a binary search tree those rank numbers.


[User] Please transcribe this text

[Assistant] Uh, go, uh, in increasing order, uh, as we do an in-order traversal. Uh, and that's, uh, easy, uh, to compute. Either keep that rank tree as a field or keep a field which has the size, uh, of the tree, uh, and, uh, it's easy to compute the rank from that.


[User] Please transcribe this text

[Assistant] Entry in the range query is in the table, uh, and not plus one over it. So there's the same number of keys between E and S as there are between E and T. Uh, five, uh, between F and T there's only four.


[User] Please transcribe this text

[Assistant] The number of nodes examined when we do a search is the length of the search path to low plus the length of the search path to high, uh, to, uh, find their ranks, uh, and that's going to be time proportional to log n.


[User] Please transcribe this text

[Assistant] So, uh, in range search, uh, well, we just, uh, do a recursive search, uh, and to find all the keys between low and high, uh, you, uh, look in the left subtree if any of them could fall in the range.


[User] Please transcribe this text

[Assistant] So if we're looking for all the keys between, uh, f and t, uh, then we have to look at both sub-trees of the root s. Uh, but we don't have to look at the left sub-tree of e because all of those are less than e and therefore are less than f. So we don't have to.


[User] Please transcribe this text

[Assistant] Okay, dude. If that's how you roll, it's all good. Ready? Set! Let's do this thing!


[User] Please transcribe this text

[Assistant] At one o'clock, the judge left the court for an hour.


[User] Please transcribe this text

[Assistant] Anna Harland talked to Sarah for ten minutes. Sarah cried at first. I don't like that police lawyer, she said. Those questions were very difficult. I couldn't answer them. It doesn't matter, her mother said. Your lawyer, Mr. Cheng, is very good.


[User] Please transcribe this text

[Assistant] And Stephen is here too now. We're all going to help you. Sarah was angry. Don't talk to me about Stephen, she said. I don't like him. I don't want to see him here.


[User] Please transcribe this text

[Assistant] But Sarah, he was your boyfriend for two years. He wants to help you.


[User] Please transcribe this text

[Assistant] Sarah began to cry again. Perhaps he does want to help me. I don't know. But he's different now, Mother. You don't understand. His eyes are different. They're... I don't know.


[User] Please transcribe this text

[Assistant] And he can't sit quietly and talk to people now. His body is moving all the time.


[User] Please transcribe this text

[Assistant] He came to see me in our hotel the night before we came to this country and I didn't like him, mother. Why is he here? He doesn't love me now and I don't love him.


[User] Please transcribe this text

[Assistant] And then she went to see Mr. Cheng and Inspector Aziz. Stephen went with her.


[User] Please transcribe this text

[Assistant] Who made that phone call to the police at the airport? She asked. We need to know that. Yes, Mr. Cheng said. That's very important. Can the police tell us? Perhaps, the inspector said. But it's very difficult. It was not a long telephone call.


[User] Please transcribe this text

[Assistant] Perhaps he put the heroin there. And he wanted the police to find it.


[User] Please transcribe this text

[Assistant] Perhaps someone doesn't like your daughter, Mrs Harland.


[User] Please transcribe this text

[Assistant] I don't know, Anna said slowly, but perhaps


[User] Please transcribe this text

[Assistant] But then the judge came back into the courtroom and everybody stopped talking.


[User] Please transcribe this text

[Assistant] And those shoes are very expensive.


[User] Please transcribe this text

[Assistant] Hasan stood there, tall and very quiet. He waited for the questions. He did not look afraid. But when Mr. Cheng looked at his papers and asked the first question, Hasan closed his eyes.


[User] Please transcribe this text

[Assistant] Did you know about the heroin in those tubes of toothpaste? No sir. Did Sarah know? No sir. Who bought the toothpaste?


[User] Please transcribe this text

[Assistant] Hassan closed his eyes again for two or three seconds. Then he answered, I did sir. You did? You're sure of that? Yes sir. I'm sure. I clean my teeth a lot, you see. Hassan smiled for a moment. Well, he does have very white teeth.


[User] Please transcribe this text

[Assistant] Sarah! Hassan began. Then he stopped. Yes.


[User] Please transcribe this text

[Assistant] Again Hasan closed his eyes and waited for a second.


[User] Please transcribe this text

[Assistant] Then he looked at the jury and said very loudly, Sarah did not buy the toothpaste. I bought it. I bought all three tubes of toothpaste. It was my toothpaste.


[User] Please transcribe this text

[Assistant] Anna sat up in her chair and looked carefully at her son.


[User] Please transcribe this text

[Assistant] That's interesting, she thought. Perhaps he does love Sarah. I see, Mr. Cheng said.


[User] Please transcribe this text

[Assistant] And when did you first meet Sarah Harland? About two months ago. I was in Australia. We were in the same hotel. She had a difficult time with her boyfriend. And I helped her.


[User] Please transcribe this text

[Assistant] Anna looked at Stephen. He was very angry. And he hit the chair in front of him with his hand. I see. Mr. Cheng said again. Then he asked his next question.


[User] Please transcribe this text

[Assistant] How much money did you have at the airport? About 80 pounds, I think. That's okay. When I need more money, I get some work for a week or two. We don't need much money.


[User] Please transcribe this text

[Assistant] And do you sometimes take heroin? No sir, never. Thank you. Stay there please.


[User] Please transcribe this text

[Assistant] Mr. Cheng sat down, and the police lawyer stood up. He smiled at Hasan, but it was not a nice smile.


[User] Please transcribe this text

[Assistant] Now, Hassan, you bought the toothpaste, but it was in Miss Harlan's bag. Why? Why did she carry it for you?


[User] Please transcribe this text

[Assistant] Or do you always ask your women to carry things for you? He smiled. Hasan said nothing.


[User] Please transcribe this text

[Assistant] The lawyer began again. You had 80 pounds, you say.


[User] Please transcribe this text

[Assistant] But 80,000 pounds is better than 80 pounds, I think. What do you think?


[User] Please transcribe this text

[Assistant] Of course it is. But I don't sell heroin. It's wrong to sell heroin.


[User] Please transcribe this text

[Assistant] You are a very good young man, with very clean teeth, but no money. You met a young English girl.


[User] Please transcribe this text

[Assistant] She was unhappy with her boyfriend, so you helped her and took her away with you. Is that right? Oh dear.


[User] Please transcribe this text

[Assistant] It's not a very good story, you know. I don't believe it.


[User] Please transcribe this text

[Assistant] And I don't think the jury believes it, young man.


[User] Please transcribe this text

[Assistant] Then he looked at Ahsan and said loudly, You don't love Sarah Harland, and she doesn't love you.


[User] Please transcribe this text

[Assistant] You went with her because she could help you. And she went with you because she wanted the money.


[User] Please transcribe this text

[Assistant] She carried the heroin for you to sell. That's right, isn't it?


[User] Please transcribe this text

[Assistant] You put the heroin in the toothpaste tubes, and she knew about it. Is that the true story, young man? I think it is. No, I- Hasan began angrily. But the lawyer did not listen. He sat down. I have no more questions, he said to the judge.


[User] Please transcribe this text

[Assistant] Here you are. A companion card and a memento. What we give all our concert goers. Now, as for what we owe you for your performance.


[User] Please transcribe this text

[Assistant] You've become one with the mountain. Say what?


[User] Please transcribe this text

[Assistant] Welcome to the stunning Mount Lofty house in the Adelaide Hills. And welcome to Shannon.


[User] Please transcribe this text

[Assistant] It's hard to believe that we're only 20 minutes away from Adelaide. And actually, if you stand on your diptoes, I think you can just about see it.


[User] Please transcribe this text

[Assistant] This part of South Australia, the rolling hills, green fields.


[User] Please transcribe this text

[Assistant] Pretty picture postcard towns like Arndorf. Anybody been to Arndorf?


[User] Please transcribe this text

[Assistant] If you don't know, some of the farms that grow and rear that beautiful produce were in the pantry at the start of the week. Figs from Glen Ewen, apples from Ashton, and of course those strawberries from Berenberg.


[User] Please transcribe this text

[Assistant] No strawberry picking today, I'm afraid. Today is all about that immunity challenge. And of course, that immunity bin.


[User] Please transcribe this text

[Assistant] It's been in the pantry every single day of the competition so far.


[User] Please transcribe this text

[Assistant] It's not a juniper berry, it's a muntry, which is a little native berry.


[User] Please transcribe this text

[Assistant] Congratulations, Khan. You are the winner of the first round, which means you go into the second round and you get your chance at that immunity pin by competing.


[User] Please transcribe this text

[Assistant] The two of you, this is how it's gonna work. Khan, you'll have 75 minutes.


[User] Please transcribe this text

[Assistant] Jin, because you're the professional here, you're gonna get 60 minutes. Nice.


[User] Please transcribe this text

[Assistant] You also won't know what you're cooking with until your time starts.


[User] Please transcribe this text

[Assistant] You have to know too that this is a blind tasting. So Matt, George and I will have absolutely no idea who's cooked which dish.


[User] Please transcribe this text

[Assistant] So Jin, are you ready for this? Yes, I'm ready. Time to head round to the front of the hotel until it's your time to cook.


[User] Please transcribe this text

[Assistant] Adelaide Hills is stunning. Mount Lofty House is beautiful. And the contestants had a bit of fun this morning, I think, in that first round. And of course, Kalm ends up with two


[User] Please transcribe this text

[Assistant] Because there's so much going on, because you've got nori, because you've got prawn, because you've got pickled ginger, then every single one of those flavours is a distraction and I can't taste any Vegemite. So in terms of heroing Vegemite, then it's lost on me. It's a challenging dish.


[User] Please transcribe this text

[Assistant] Professional chef and amateur cook and one very curly, if not delicious ingredient. Shannon?


[User] Please transcribe this text

[Assistant] Down to business, the serious stuff. Who cooked the risotto? I cooked it. So Khan, you cooked the...


[User] Please transcribe this text

[Assistant] And Jin, I scored you a 6 out of 10.


[User] Please transcribe this text

[Assistant] Jim, difficult challenge. Yes.


[User] Please transcribe this text

[Assistant] And we struggled in all honesty to make sense of that dish.


[User] Please transcribe this text

[Assistant] That means you've scored 17 out of 30. Yeah. Khan, you need 18 or more to beat Jin and win that pin.


[User] Please transcribe this text

[Assistant] Right, Jenny! Can I please have 28?


[User] Please transcribe this text

[Assistant] Can I please have number eight?


[User] Please transcribe this text

[Assistant] But I get a really spicy finish, almost like a wasabi finish.


[User] Please transcribe this text

[Assistant] How do you explain when things don't go as we assume? Or better, how do you explain when others are able to achieve things that seem to defy all of the assumptions? For example, why is Apple so innovative?


[User] Please transcribe this text

[Assistant] And yet, they're just a computer company. They're just like everyone else. They have the same access to the same talent, the same agencies, the same consultants, the same media. Then why is it that they seem to have something different?


[User] Please transcribe this text

[Assistant] Why is it that Martin Luther King led the civil rights movement?


[User] Please transcribe this text

[Assistant] And why is it that the Wright brothers were able to figure out controlled powered man flight when there were certainly other teams who were better qualified, better funded, and they didn't achieve powered man flight and the Wright brothers beat them to it? There's something else at play here.


[User] Please transcribe this text

[Assistant] About three and a half years ago, I made a discovery.


[User] Please transcribe this text

[Assistant] And this discovery profoundly changed my view on how I thought the world worked and it even profoundly changed the way in which I operate in it.


[User] Please transcribe this text

[Assistant] Some know how they do it. Whether you call it your differentiating value proposition, or your proprietary process, or your USP. But very, very few people or organizations know why they do what they do. And by why, I don't mean to make a profit. That's a result. It's always a result. By why, I mean what's your purpose? What's your cause? What's your belief?


[User] Please transcribe this text

[Assistant] Well, as a result, the way we think, the way we act, the way we communicate is from the outside in. It's obvious. We go from the clearest thing to the fuzziest thing. But the inspired leaders and the inspired, inspired organizations, regardless of their size, regardless of their industry, all think, act, and communicate from the inside out.


[User] Please transcribe this text

[Assistant] Let me give you an example. I use Apple because they're easy to understand and everybody gets it. If Apple were like everyone else, a marketing message from them might sound like this. We make great computers.


[User] Please transcribe this text

[Assistant] We believe in challenging the status quo. We believe in thinking differently.


[User] Please transcribe this text

[Assistant] The way we challenge the status quo is by making our products beautifully designed, simple to use, and user friendly. We just happen to make great computers. Wanna buy one?


[User] Please transcribe this text

[Assistant] And they make great quality products, and they can make perfectly well designed products, and nobody bought one.


[User] Please transcribe this text

[Assistant] In fact, talking about it now, we can't even imagine buying an mp3 player from Dell. Why would you buy an mp3 player from a computer company? But we do it every day.


[User] Please transcribe this text

[Assistant] People don't buy what you do, they buy why you do it. The goal is not to do business with anybody, with everybody who needs what you have. The goal is to do business with people who believe what you believe. Here's the best part. None of what I'm telling you is my opinion.


[User] Please transcribe this text

[Assistant] It's all grounded in the tenets of biology, not psychology, biology.


[User] Please transcribe this text

[Assistant] If you look at a cross section of the human brain looking from the top down, what you see is the human brain is actually broken into three major components that correlate perfectly with the golden circle. Our newest brain, our homo sapien brain, our neocortex,


[User] Please transcribe this text

[Assistant] Corresponds with the what level. The neocortex is responsible for all of our rational and analytical thought and language.


[User] Please transcribe this text

[Assistant] The middle two sections make up our limbic brains, and our limbic brains are responsible for all of our feelings, like trust and loyalty. It's also responsible for all human behavior, all decision making, and it has no capacity for language.


[User] Please transcribe this text

[Assistant] In other words, when we communicate from the outside in, yes, people can understand vast amounts of complicated information like features and benefits and facts and figures. It just doesn't drive behavior.


[User] Please transcribe this text

[Assistant] When we communicate from the inside out, we're talking directly to the part of the brain that controls behavior, and then we allow people to rationalize it with the tangible things we say and do. This is where gut decisions come from.


[User] Please transcribe this text

[Assistant] But if you don't know why you do what you do, and people


[User] Please transcribe this text

[Assistant] Respond to why you do what you do, then how will anybody, how will you ever get people to, to, to, to vote for you or buy something from you, or more importantly, be loyal and want to be a part of what it is that you do?


[User] Please transcribe this text

[Assistant] Again, the goal is not just to sell people who need what you have, the goal is to sell to people who believe what you believe. The goal is not just to hire people who need a job, it's to hire people who believe what you believe. I always say that, you know, this, uh, if you, if you, if you, uhm,


[User] Please transcribe this text

[Assistant] Hire people just because they can do a job, they'll work for your money. But if you hire people who believe what you believe, they'll work for you with blood and sweat and tears. And nowhere else is there a better example of this than with the Wright Brothers.


[User] Please transcribe this text

[Assistant] Most people don't know about Samuel Pierpont Langley. And back in the early 20th century, the pursuit of powered man flight was like the dot com of the day. Everybody was trying it. And Samuel Pierpont Langley had what we assume to be the recipe for success.


[User] Please transcribe this text

[Assistant] I mean, even now, you ask people, why did your product or why did your company fail? And people always give you the same permutation of the same three things. Undercapitalized, the wrong people, bad market conditions, it's always the same three things. So let's explore that. Samuel Pierpont Langley.


[User] Please transcribe this text

[Assistant] He was given $50,000 by the war department to figure out this flying machine. Money was no problem. He held a seat at Harvard and worked at the Smithsonian and was extremely well connected. He knew all the big minds of the day.


[User] Please transcribe this text

[Assistant] The difference was Orville and Wilbur were driven by a cause, by a purpose, by a belief. They believed that if they could figure out this flying machine, it'll change the course of the world.


[User] Please transcribe this text

[Assistant] Samuel Pierpont Langley was different. He wanted to be rich and he wanted to be famous. He was in pursuit of the result. He was in pursuit of the riches.


[User] Please transcribe this text

[Assistant] In the Wright Brothers' dream, worked with them with blood and sweat and tears. The others just worked for the paycheck.


[User] Please transcribe this text

[Assistant] And they tell stories of how every time the Wright brothers went out, they would have to take five sets of parts because that's how many times they would crash before they came in for supper. And eventually on December 17th, 1903, the Wright brothers took flight.


[User] Please transcribe this text

[Assistant] And no one was there to even experience it. We found out about it a few days later.


[User] Please transcribe this text

[Assistant] And further proof that Langley was motivated by the wrong thing, the day the Wright brothers took flight, he quit.


[User] Please transcribe this text

[Assistant] He could have said, that's an amazing discovery, guys, and I will improve upon your technology. But he didn't. He wasn't first, he didn't get rich, he didn't get famous, so he quit.


[User] Please transcribe this text

[Assistant] People don't buy what you do, they buy why you do it. And if you talk about what you believe, you will attract those who believe what you believe.


[User] Please transcribe this text

[Assistant] Well, why is it important to attract those who believe what you believe?


[User] Please transcribe this text

[Assistant] And if you don't know the law, you definitely know the terminology. The first 2.5% of our population are our innovators. The next 13.5% of our population are our early adopters.


[User] Please transcribe this text

[Assistant] Is that if you want mass market success or mass market acceptance of an idea, you cannot have it until you achieve this tipping point between 15 and 18 percent market penetration and then the system tips.


[User] Please transcribe this text

[Assistant] And I love asking businesses, what's your conversion on new business? And they love to tell you, oh, it's about 10% proudly. Well, you can trip over 10% of the customers. We all have about 10% who just get it. That's how we describe them, right? That's like that gut feeling. Oh, they just get it. The problem is how do you find the ones that just get it before you're doing business with them versus the ones who don't get it. So it's this here, this little gap.


[User] Please transcribe this text

[Assistant] That you have to close, as Geoffrey Moore calls it, crossing the chasm. Because you see, the early majority will not try something until someone else


[User] Please transcribe this text

[Assistant] And by the way, they didn't do it because the technology was so great. They did it for themselves. It's because they wanted to be first.


[User] Please transcribe this text

[Assistant] People don't buy what you do, they buy why you do it. And what you do simply proves what you believe.


[User] Please transcribe this text

[Assistant] In fact, people will do the things that prove what they believe.


[User] Please transcribe this text

[Assistant] As we said before a second ago, the recipe for success is money and the right people and the right market conditions, right? You should have success then. Look at TiVo.


[User] Please transcribe this text

[Assistant] Who likes to have total control over every aspect of your life, boy do we have a product for you.


[User] Please transcribe this text

[Assistant] People don't buy what you do, they buy why you do it, and what you do simply serves as the proof of what you believe.


[User] Please transcribe this text

[Assistant] Now let me give you a successful example of the law of diffusion of innovation. In the summer of 1963,


[User] Please transcribe this text

[Assistant] He didn't go around telling people what needed to change in America. He went around and told people what he believed. I believe, I believe, I believe, he told people. And people who believed what he believed took his cause and they made it their own and they told people.


[User] Please transcribe this text

[Assistant] It just so happens that the civil rights movement was the perfect thing to help him bring his cause to life.


[User] Please transcribe this text

[Assistant] Listen to politicians now with their comprehensive 12-point plans. They're not inspiring anybody. Because there are leaders and there are those who lead.


[User] Please transcribe this text

[Assistant] Leaders hold a position of power or authority, but those who lead inspire us.


[User] Please transcribe this text

[Assistant] With other individuals or organizations, we follow those who lead, not because we have to, but because we want to. We follow those who lead, not for them, but for ourselves. And it's those who start with why that have the ability


[User] Please transcribe this text

[Assistant] To inspire those around them or find others who inspire them. Thank you very much.


[User] Please transcribe this text

[Assistant] You're tired of traveling too? No.


[User] Please transcribe this text

[Assistant] Thanks to Brilliant for supporting this episode of SciShow. Go to Brilliant.org slash SciShow to learn how you can take your STEM skills to the next level.


[User] Please transcribe this text

[Assistant] Before scientists test new drugs, diagnostic procedures, and therapeutics on humans, they usually start by evaluating how those things perform in animals. But sometimes, their choice of animal seems… maybe not that representative of human biology?


[User] Please transcribe this text

[Assistant] One common test subject is a fruit fly. Except, fruit flies have been behind some of the most groundbreaking discoveries in physiology and medicine. And they're helping us understand everything from inheritance all the way to cancer and sleep. In some ways, the reason we use fruit flies in the lab are pretty basic.


[User] Please transcribe this text

[Assistant] Like, they're cheap to keep and maintain, for example. But there are also bigger reasons. For one, the flies are much easier to study than complex vertebrates, especially when it comes to genetics. Their whole collection of genetic material consists of just four pairs of chromosomes, compared to hour 23. That means it's easier to study what genes do and how they interact.


[User] Please transcribe this text

[Assistant] Which allows us to test what happens when you break things. And of course, their short life cycle is useful in a research setting, too. But like, here's the key thing.


[User] Please transcribe this text

[Assistant] Equivalent genes for 60% of the human genome. They also have a similar gene for 65 to 75% of the human genes that have been linked to diseases like Alzheimer's, diabetes, and many cancers.


[User] Please transcribe this text

[Assistant] And thanks to those similarities, they've shown us quite a bit. Fruit fly research officially began in the early 1900s, when the now-famous biologist Thomas Hunt Morgan was studying how genes are inherited. By this point in history, we knew inheritance was a thing, but we didn't quite understand how it worked.


[User] Please transcribe this text

[Assistant] So Morgan was looking into it. In his research, he found a male fruit fly with white eyes instead of the typical red.


[User] Please transcribe this text

[Assistant] And he observed how this trait was passed on by breeding different fruit flies together. Ultimately, he discovered that only male flies ever had these white eyes, and that the genetic factor that determined eye color was on the same chromosome that determined sex. In doing so,


[User] Please transcribe this text

[Assistant] He was the first to link the inheritance of a trait to a specific chromosome. And it not only earned him a Nobel Prize, but it opened the door to most of what we understand about inheritance in humans today.


[User] Please transcribe this text

[Assistant] Because we also have traits controlled by our genes on our sex chromosomes. One example is red-green colorblindness, which is passed down on the X chromosome. That's why people who are XY and only have one copy of this chromosome are more likely to be colorblind.


[User] Please transcribe this text

[Assistant] People with two X chromosomes would need to have the gene for colorblindness on both of them, which is less likely.


[User] Please transcribe this text

[Assistant] Meanwhile, one of Morgan's students, named Herman Muller, made another revolutionary discovery using fruit flies that earned him a Nobel Prize. He found that X-rays and other ionizing radiation can cause genetic mutations by breaking apart chromosomes. This can cause DNA to get deleted, duplicated, swapped around, or just inserted into the wrong place.


[User] Please transcribe this text

[Assistant] All of which could be passed on to offspring. Thanks to his work, we now take precautions when taking X-rays, like using those heavy lead aprons you might wear at the dentist. But this work had implications beyond inheritance, too. Since they had to keep track of all these rearrangements, scientists ended up creating records of what sorts of genetic information lived in different parts of the chromosome. And that led us to broader discoveries. Like, it taught us more about how embryos develop.


[User] Please transcribe this text

[Assistant] Specifically, it let us find one of the signaling pathways that controls development within an embryo. A signaling pathway is how cells communicate with each other. This one is called NOT.


[User] Please transcribe this text

[Assistant] Because researchers found it while trying to figure out what made certain fruit flies develop notches in their wings. But lots of other animals have it, including mammals like us. Through a lot more research spanning decades, scientists not only learned how it communicates with other cells


[User] Please transcribe this text

[Assistant] But that this pathway is critical for the development of things like neurons, blood cells, the heart, bones, and skin. Through this pathway, cells are told what to become during differentiation, the process where they transition from one type of cell.


[User] Please transcribe this text

[Assistant] are far from exhausted. They're being used to study wound healing, new bioengineering technologies, the effects of new pharmaceuticals, how the brain works, and cancer as a whole.


[User] Please transcribe this text

[Assistant] In the 2010s, the number of cancer studies using fruit flies increased exponentially. And they're teaching us about everything from how tumors spread, all the way to why cancer cells can resist medication. Fruit flies have even taught us about sleep. It turns out that fruit flies need to rest just like we need to sleep.


[User] Please transcribe this text

[Assistant] As well as the regions of the brain responsible. So while these results don't have direct applications to humans yet, they could lead to a better understanding of the differences in sleep habits. Really, fruit flies may look nothing like us on the outside, but because of their biology and their genes,


[User] Please transcribe this text

[Assistant] There's a practically endless number of things that they could teach us. So the next time you see a bunch of them hovering around your fruit bowl, you may want to thank them before getting out the fly swatter. A huge thing in research these days is the existence of neural networks.


[User] Please transcribe this text

[Assistant] Brilliant's Introduction to Neural Networks course can help you do that. Brilliant's math, science, computer science, and engineering courses are always interactive and hands-on, and this one is no exception. It makes it shockingly easy to learn about the internal machinery of neural networks, and by the end of the course,


[User] Please transcribe this text

[Assistant] You'll be equipped to make one of your own. If you want to check them out, you can go to Brilliant.org slash SciShow, and the first 200 people to sign up at that link will get 20% off an annual premium subscription.


[User] Please transcribe this text

[Assistant] Do not be deceived. You know the truth. Trust in me.


[User] Please transcribe this text

[Assistant] And they're gonna give it that real roasty caramel flavour. I'm really confident in the flavours. I know that the roasted fennel, the fronds, the grapes, all go so beautifully well with that melty pork. The thing I am worried about is that it's not gonna be melty pork.


[User] Please transcribe this text

[Assistant] I'd be happy if we could cook it for at least two hours, but I think we're gonna get about an hour and a half out of it, so I'm a bit worried. We need to get this pork belly into the oven as soon as possible.


[User] Please transcribe this text

[Assistant] I don't cook with a lot of pork fillet. It can dry out quite fast.


[User] Please transcribe this text

[Assistant] Fennel's looking beautiful, the grapes are looking beautiful. With the pork fillet, you've got to be careful not to overcook it. But if we can just bring this dish together. Now I think we can still win this today. Rhys, I'm going to turn this down now because it's hot. Okay? It's, yep.


[User] Please transcribe this text

[Assistant] Well get your bird prepped for you. Just sit tight in the jockey lounge.


[User] Please transcribe this text

[Assistant] You there, dangly man. Would you mind checking one more time?


[User] Please transcribe this text

[Assistant] The offer still stands. A fortune or two to steer you true.


[User] Please transcribe this text

[Assistant] Oh, Sephiroth, you say?


[User] Please transcribe this text

[Assistant] Alright, hold on to your underpants. Meow, meow, meow, meow, meow, meow, meow, meow, meow.


[User] Please transcribe this text

[Assistant] What, indeed? Give me another chance! I'll get it this time! Come on!


[User] Please transcribe this text

[Assistant] As the sun shines on their last day in South Australia. Welcome to Maggie's Eatery in the beautiful Barossa Valley. The dark cloud of elimination looms. It's a gate lift chicken.


[User] Please transcribe this text

[Assistant] I'm sorry for not believing you when you warned us about this place.


[User] Please transcribe this text

[Assistant] Today, round one is a classic skills test.


[User] Please transcribe this text

[Assistant] Sashi, congratulations. You now have a chance to make MasterChef History and win a second pin. Yes. But...


[User] Please transcribe this text

[Assistant] Standing in your way is a professional chef.


[User] Please transcribe this text

[Assistant] And this is a professional chef with a fearsome reputation.


[User] Please transcribe this text

[Assistant] He's worked in great restaurants overseas. Dom, in Brazil. Les Trayères, in France. And here in Melbourne, he was the executive chef at The Point. And also, View de Monde. Oh no. Oh no. Oh no. So, please welcome, from QT Melbourne, Andy Harmar!


[User] Please transcribe this text

[Assistant] Uuuh, Andy, welcome to the MasterChef kitchen. You've worked with Shannon for, what, four or five years. How does it feel knowing that Shannon is going to be mentoring your opposition today?


[User] Please transcribe this text

[Assistant] So that tastes like roast chicken, Gary, that sauce. Wow.


[User] Please transcribe this text

[Assistant] The lamb, the lamb's under for me. Love, love, love the peas. I love the way he's treated the pods and also the peas. But the one thing I would love would be the lamb to be cooked always.


[User] Please transcribe this text

[Assistant] Smells good, doesn't it? It smells good. And look, certainly the side of the lamb that we can see is, that looks pretty darn perfect. Yeah, and nicely caramelised. Look at the, look at the caramelisation.


[User] Please transcribe this text

[Assistant] Beautiful, beautiful looking dish. Great sauce. I especially loved the treatment of the peas. But the lamb, unevenly cooked, George's and mine were both under. I scored your dish, eight out of ten.


[User] Please transcribe this text

[Assistant] So Andy, that gives you a total of 24 out of 30.


[User] Please transcribe this text

[Assistant] Sashi, you need 25 or more to make history. 24 points is a very good score.


[User] Please transcribe this text

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


[User] Please transcribe this text

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


[User] Please transcribe this text

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


[User] Please transcribe this text

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


[User] Please transcribe this text

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


[User] Please transcribe this text

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


[User] Please transcribe this text

[Assistant] Garry's worried that my tomato tea is not going to go with the rump. That the rump is too intense a flavour to pair with something so delicate.


[User] Please transcribe this text

[Assistant] If I can't prove the judges wrong, I could be going through to the bottom three.


[User] Please transcribe this text

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


[User] Please transcribe this text

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


[User] Please transcribe this text

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


[User] Please transcribe this text

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


[User] Please transcribe this text

[Assistant] What keeps us healthy and happy as we go through life?


[User] Please transcribe this text

[Assistant] If you were going to invest now in your future best self, where would you put your time and your energy?


[User] Please transcribe this text

[Assistant] There was a recent survey of millennials asking them what their most important life goals were.


[User] Please transcribe this text

[Assistant] And over 80% said that a major life goal for them was to get rich.


[User] Please transcribe this text

[Assistant] And another 50% of those same young adults said that another major life goal was to become famous.


[User] Please transcribe this text

[Assistant] We're given the impression that these are the things that we need to go after in order to have a good life. Pictures of entire lives, of the choices that people make and how those choices work out for them, those pictures are almost impossible to get.


[User] Please transcribe this text

[Assistant] Most of what we know about human life, we know from asking people to remember the past.


[User] Please transcribe this text

[Assistant] We forget vast amounts of what happens to us in life. And sometimes memory is downright creative. But ...


[User] Please transcribe this text

[Assistant] What if we could watch entire lives as they unfold through time? What if we could study people from the time that they were teenagers all the way into old age to see what really keeps people happy and healthy? We did that.


[User] Please transcribe this text

[Assistant] The Harvard study of adult development may be the longest study of adult life that's ever been done.


[User] Please transcribe this text

[Assistant] Year after year asking about their work, their home lives, their health, and of course asking all along the way without knowing how their life stories were going to turn out.


[User] Please transcribe this text

[Assistant] Because too many people drop out of the study, or funding for the research dries up.


[User] Please transcribe this text

[Assistant] Or the researchers get distracted, or they die, and nobody moves the ball further down the field.


[User] Please transcribe this text

[Assistant] But through a combination of luck and the persistence of several generations of researchers, this study has survived.


[User] Please transcribe this text

[Assistant] About 60 of our original 724 men are still alive, still participating in the study, most of them in their 90s.


[User] Please transcribe this text

[Assistant] And we are now beginning to study the more than 2,000 children of these men. And I'm the fourth director of the study.


[User] Please transcribe this text

[Assistant] The first group started in the study when they were sophomores at Harvard College. They all finished college during World War II, and then most went off to serve in the war.


[User] Please transcribe this text

[Assistant] And the second group that we followed was a group of boys from Boston's poorest neighborhoods.


[User] Please transcribe this text

[Assistant] Boys who were chosen for the study specifically because they were from some of the most troubled and disadvantaged families in the Boston of the 1930s. Most lived in tenements, many without hot and cold running water.


[User] Please transcribe this text

[Assistant] All of these teenagers were interviewed, they were given medical exams. We went to their homes and we interviewed their parents.


[User] Please transcribe this text

[Assistant] And then these teenagers grew up into adults who entered all walks of life.


[User] Please transcribe this text

[Assistant] Climb the social ladder from the bottom all the way to the very top. And some made that journey in the opposite direction. The founders of this study.


[User] Please transcribe this text

[Assistant] Every two years, our patient and dedicated research staff calls up our men and asks them if we can send them yet one more set of questions about their lives.


[User] Please transcribe this text

[Assistant] Many of the inner city Boston men ask us.


[User] Please transcribe this text

[Assistant] To get the clearest picture of these lives, we don't just send them questionnaires.


[User] Please transcribe this text

[Assistant] We interview them in their living rooms. We get their medical records from their doctors. We draw their blood. We scan their brains. We talk to their children. We videotape them talking with their wives about their deepest concerns.


[User] Please transcribe this text

[Assistant] What are the lessons that come from the tens of thousands of pages of information that we've generated on these lives?


[User] Please transcribe this text

[Assistant] Well the lessons aren't about wealth or fame or working harder and harder. The clearest message


[User] Please transcribe this text

[Assistant] Good relationships keep us happier and healthier. Period.


[User] Please transcribe this text

[Assistant] We've learned three big lessons about relationships.


[User] Please transcribe this text

[Assistant] The first is that social connections are really good for us and that loneliness kills.


[User] Please transcribe this text

[Assistant] It turns out that people who are more socially connected to family, to friends, to community are happier.


[User] Please transcribe this text

[Assistant] They're physically healthier and they live longer than people who are less well connected.


[User] Please transcribe this text

[Assistant] So the second big lesson that we learned is that it's not just the number of friends you have, and it's not whether or not you're in a committed relationship, but it's the quality of your close relationships that matters.


[User] Please transcribe this text

[Assistant] It turns out that living in the midst of conflict is really bad for our health.


[User] Please transcribe this text

[Assistant] High conflict marriages, for example, without much affection turn out to be very bad for our health, perhaps worse than getting divorced. And living in the midst of good, warm relationships is protective.


[User] Please transcribe this text

[Assistant] Once we had followed our men all the way into their 80s, we wanted to look back at them at midlife.


[User] Please transcribe this text

[Assistant] And to see if we could predict who was going to grow into a happy, healthy octogenarian and who wasn't. And when we gathered together everything we knew about them at age 50,


[User] Please transcribe this text

[Assistant] It was how satisfied they were in their relationships. The people who were the most satisfied in their relationships at age 50 were the healthiest at age 80.


[User] Please transcribe this text

[Assistant] And good, close relationships seem to buffer us from some of the slings and arrows of getting old.


[User] Please transcribe this text

[Assistant] But the people who were in unhappy relationships on the days when they reported more physical pain, it was magnified by more emotional pain. And the third big lesson that we learned


[User] Please transcribe this text

[Assistant] About relationships and our health is that good relationships don't just protect our bodies, they protect our brains.


[User] Please transcribe this text

[Assistant] It turns out that being in a securely attached relationship to another person in your eighties


[User] Please transcribe this text

[Assistant] Is protective that the people who are in relationships where they really feel they can count on the other person in times of need, those people's memories stay sharper longer.


[User] Please transcribe this text

[Assistant] And the people in relationships where they feel they really can't count on the other one.


[User] Please transcribe this text

[Assistant] Those are the people who experience earlier memory decline.


[User] Please transcribe this text

[Assistant] And those good relationships, they don't have to be smooth all the time. Some of our octogenarian couples could bicker with each other day in and day out. But as long as they felt that they could really count on the other when the going got tough, those arguments didn't take a toll on their memories. So this message that


[User] Please transcribe this text

[Assistant] Good, close relationships are good for our health and wellbeing. This is wisdom that's as old as the hills.


[User] Please transcribe this text

[Assistant] Well, we're human. What we'd really like is a quick fix. Something we can get that'll make our lives good and keep them that way.


[User] Please transcribe this text

[Assistant] Relationships are messy and they're complicated and the hard work of tending to family and friends, that's not sexy or glamorous. It's also lifelong. It never ends.


[User] Please transcribe this text

[Assistant] The people in our 75 year study who were the happiest in retirement were the people who had actively worked to replace workmates with new playmates.


[User] Please transcribe this text

[Assistant] Just like the millennials in that recent survey, many of our men, when they were starting out as young adults, really believed


[User] Please transcribe this text

[Assistant] That fame and wealth and high achievement were what they needed to go after to have a good life.


[User] Please transcribe this text

[Assistant] But over and over, over these 75 years, our study has shown that the people who fared the best were the people who leaned into relationships with family, with friends, with community.


[User] Please transcribe this text

[Assistant] Let's say you're 25 or you're 40 or you're 60. What might leaning into relationships even look like?


[User] Please transcribe this text

[Assistant] Well, the possibilities are practically endless.


[User] Please transcribe this text

[Assistant] It might be something as simple as replacing screen time with people time.


[User] Please transcribe this text

[Assistant] Or livening up a stale relationship by doing something new together. Long walks or date nights.


[User] Please transcribe this text

[Assistant] We're reaching out to that family member who you haven't spoken to in years because those all too common family feuds take a terrible toll on the people who hold the grudges.


[User] Please transcribe this text

[Assistant] No, I don't really do Bolognese at home. My kids don't like Bolognese. They find it a bit, uh, a bit mushy, the mince. But steaks and, uh, lamb I cook a lot at home. I'm making pan-fried rum with anchovy butters, smoked carrot puree, and caramelized onion.


[User] Please transcribe this text

[Assistant] The sashish touch is always flavors. So, I'm using goncalo to render the fats to give the extra flavor to my caramelized onion.


[User] Please transcribe this text

[Assistant] On my anchovies butter, I put heaps of anchovies. Anchovies give the umami to the butter.


[User] Please transcribe this text

[Assistant] Today, I use a rum with anchovy butter, carrot puree and caramelized onion. The rum start to have nice grill marks on it. It looks very beautiful. The color is amazing. I'm sharing the sides also very well. It looks very good. I hope the judges like it all together.


[User] Please transcribe this text

[Assistant] Fan-fried rum with anchovy butter, smoked carrot puree, and caramelized onion.


[User] Please transcribe this text

[Assistant] Welcome back. Today we're going to look at hashing, which is another approach to implementing symbol tables that can also be very effective in practical applications.


[User] Please transcribe this text

[Assistant] Here's our summary, uh, where we left off with red-black BSTs, where we could get guaranteed logarithmic performance for a broad range of, uh, symbol table operations.


[User] Please transcribe this text

[Assistant] The question is, can we do better than that? Is logarithmic performance the best we can do?


[User] Please transcribe this text

[Assistant] Uh, and the answer is that actually we can, uh, but it's a different way of accessing the data, uh, and also doesn't support, uh, ordered operations. But there's plenty of applications where, uh, the extra speed for search and insert that we can get this way, uh, is worthwhile.


[User] Please transcribe this text

[Assistant] Uh, the basic plan is, uh, to think of the symbol table, uh, as really try to reduce the problem to being like an array. Uh, and what we do is use a function known as a hash function.


[User] Please transcribe this text

[Assistant] It takes the key that, uh, our symbol table key, uh, and reduces it to an integer, uh, an array index. And we use that array index to, uh, store, uh, the key and the value in an array, maybe the value in a parallel array.


[User] Please transcribe this text

[Assistant] Now there's a lot of issues in doing this. First thing is we need to be able to compute the hash function. Uh, that, uh, is easy for some types of data, but it can get complicated for more complicated types of data.


[User] Please transcribe this text

[Assistant] Uh, then, uh, the other thing is that, uh, instead of doing compare-tos, we're going to be doing equality tests. So we have to be sure we've got the method that we want for checking whether two keys are equal.


[User] Please transcribe this text

[Assistant] All we're gonna do is look in the table and try to see if the key that's there is equal to the key we're looking for.


[User] Please transcribe this text

[Assistant] Uh, where, uh, it's, since there are so many possible values, uh, for a typical data type, we're gonna get the situation where two values hash to the same array index. And we need a collision resolution strategy to try to figure out what to do in that case.


[User] Please transcribe this text

[Assistant] Uh, and these things are not difficult, but they're all worth articulating as separate issues that we have to deal with, uh, in order to, uh, get an effective symbol table implementation.


[User] Please transcribe this text

[Assistant] The hashing really, uh, at its core is a classic space-time trade-off.


[User] Please transcribe this text

[Assistant] If we had no limitation on space at all, uh, then we could have a very huge array, uh, with space for, uh, every possible key, uh, and just use the key itself as an index. If our keys are 32-bit integer keys and we've got a table of size 2 to the 32nd, then we're just fine.


[User] Please transcribe this text

[Assistant] If there were no time limitation at all, then I would just hash everything to the same place and then do sequential search.


[User] Please transcribe this text

[Assistant] Uh, but sequential search can be slow if we have, uh, lots of keys. So, uh, when hashing is kind of in the real world where we're trying to trade off this, uh, idea that we don't have unlimited space and we also don't have unlimited time, so we're trying to find something in between.


[User] Please transcribe this text

[Assistant] So we'll look at, uh, hash functions, separate chaining, uh, and then two collision, uh, resolution methods called separate chaining and linear perving.


[User] Please transcribe this text

[Assistant] So, idealistically, what we'd like is to be able to take any key and uniformly scramble it to produce a table index. We have two requirements. One is that we have to be able to compute the thing efficiently in a reasonable amount of time.


[User] Please transcribe this text

[Assistant] Uh, and the other is that, uh, it should be the case that every table index is equally likely for each key.


[User] Please transcribe this text

[Assistant] Mathematicians and computer scientists have researched this problem in a lot of detail and there's quite a bit known about it. But in practice, this is something that still we have to worry about somewhat.


[User] Please transcribe this text

[Assistant] Probably a bad idea to use the first three digits of the phone number as a hash function because so many phone numbers will have the same area code. It's not equally likely that each phone number has the same first three digits.


[User] Please transcribe this text

[Assistant] You have a better chance using the last three digits, uh, but actually, in most cases, you want to find a way to use all the data.


[User] Please transcribe this text

[Assistant] Another example is social security numbers. Again, it's not too good to use the first three digits because they're associated with some geographic region and it's better to try to use the last three digits.


[User] Please transcribe this text

[Assistant] And the real practical challenge with hashing is that with developing a hash function is that every type of key needs a hash function and you need a different approach for every key type.


[User] Please transcribe this text

[Assistant] Now for standard keys like integers and strings and doubles and so forth, we can count on the designers and implementers of Java to implement good hash functions.


[User] Please transcribe this text

[Assistant] Uh, if we're gonna be implementing a symbol with our own types of data, uh, we're gonna have to worry about these things in order to get a hash function that's effective, that leads to an effective symbol table implementation.


[User] Please transcribe this text

[Assistant] Uh, so hashing is, uh, widely used for systems programming and applications, so, uh, some conventions for hashing are built into Java. In particular, all Java classes, uh, inherit a method called hashCode, uh, which is, returns a 32-bit int value.


[User] Please transcribe this text

[Assistant] Uh, and it's a requirement that if x and y are equal then their hash code should be equal. Uh, so, uh, that's, uh,


[User] Please transcribe this text

[Assistant] Uh, something that, uh, uh, is, uh, a convention that's built into Java, and that enables the hash code to be used for hashing. Uh, also, of course, uh, if they're not equal, then you'd like it, like it to be that their hash codes are not equal, but you can't always get that.


[User] Please transcribe this text

[Assistant] Now the default implementation for hashing is the memory address of the object. For hashing an object is the memory address of an object.


[User] Please transcribe this text

[Assistant] So that kind of meets these two requirements for Java. The one that it doesn't maybe meet is the idea that every table position should be equally likely. So usually we'll do some more work to try to make that one happen.


[User] Please transcribe this text

[Assistant] As far as the algorithms go, as far as the rules go, you could always return 17. That's legal. It doesn't have this highly desirable attribute. But everything would compile. So you have to be a little careful that somebody isn't in there doing that.


[User] Please transcribe this text

[Assistant] So Java has customized implementations for the standard data types that people would use for symbol table keys and that's the sweet spot for hashing.


[User] Please transcribe this text

[Assistant] Uh, where, uh, someone expert has done, uh, implementation of, of the hash code, uh, and also, uh, your application does not need, uh, ordering.


[User] Please transcribe this text

[Assistant] Uh, but for user defined types, uh, you're on your own. Uh, and we'll talk a little bit about how to implement hash code.


[User] Please transcribe this text

[Assistant] They are what they are, uh, and what we'll do is, uh, with knowledge that that's what the hash code is, uh, we'll do some extra work to try to get this extra property that, uh, every table position should seem to be equally likely.


[User] Please transcribe this text

[Assistant] So if it's an integer, the hashed code's supposed to be 32 bits, the integer's supposed to be 32 bits, so they just return the value. If it's a boolean, they pick out a couple particular values that they return.


[User] Please transcribe this text

[Assistant] Uh, for, uh, double value, uh, uh, this is the code. They, uh, uh, convert to 64 a bit and XOR the most significant 32 bits with the least significant 32 bits. Uh, this illustrates something that you want to do if you have a lot of bits. You want to try to involve all the bits somehow in the hash function.


[User] Please transcribe this text

[Assistant] Uh, and for strings, uh, it kind of treats the string as a, a huge number, uh, and then, uh, uh, really computes the value of that number, mod 32.


[User] Please transcribe this text

[Assistant] Uh, it uses an arithmetic, uh, a way of evaluating a polynomial or a number, uh, so called Horner's method, uh, where for each digit, uh, you just, uh, multiply. So it treats it as a base 31 number.


[User] Please transcribe this text

[Assistant] Uh, and that's called the Horner's Rule, and if you're familiar with it, fine. If you're not, you can look at this little example and decide what it is. And again, it involves all the characters of the string in computing the hash function.


[User] Please transcribe this text

[Assistant] And actually, since strings are immutable, what Java does is keep the hash value in an instance variable so it only gets computed once.


[User] Please transcribe this text

[Assistant] Uh, and that, uh, is, uh, going to be very effective for, uh, performance in, uh, lots of applications. So once it computes the hash code, it stores it as an instance variable, and the next time you ask for the hash code of that string, uh, it'll just provide it. And that works because strings are immutable.


[User] Please transcribe this text

[Assistant] And so we might have a, our transaction type might have a couple of instance variables, a string, a date, and a double. And we need to compute a hash code, so return a 32 bit value.


[User] Please transcribe this text

[Assistant] And again, we want to try to make use of all the pieces of data that we have. And we also want to make use of the hash code implementations for the types of data that we're using.


[User] Please transcribe this text

[Assistant] So, one thing to do is start out with some small prime number.


[User] Please transcribe this text

[Assistant] And this kind of mimics, uh, uh, Horner's method, uh, to, uh, just add in more data as we get it. So we pick some other small prime number, uh, and for each field, uh, we multiply by 31 and then add the hash code for that field.


[User] Please transcribe this text

[Assistant] If it's a reference type, you just use the hash code. So, uh, who is a string? So string has a hash code method, so we add that in. And dates, when's a date? So we add that hash code. Multiply by 31 and add that hash code in.


[User] Please transcribe this text

[Assistant] Trying to take all the bits and scramble all the bits and use them. And for primitive types, take the wrapper type and use the hash code.


[User] Please transcribe this text

[Assistant] So that's the standard recipe. Use the 31x plus y rule to combine all the fields. If it's a primitive type, use the wrapper hash code.


[User] Please transcribe this text

[Assistant] If the field is null, return zero. If it's a reference type, use that hash code and apply it recursively. And if you have an array, you have to apply it to each entry, or actually Java implements that in the array, in its arrays library.


[User] Please transcribe this text

[Assistant] So this recipe works pretty well in practice and it's used in several, in Java's libraries. Now in theory, it's possible to do something that has the property that


[User] Please transcribe this text

[Assistant] That, uh, all, uh, uh, positions are equally likely. It's called universal hash functions. These things exist, but they're not, uh, so widely applied, uh, in practice.


[User] Please transcribe this text

[Assistant] Uh, so the basic rule is, uh, if you're computing your own, try to use the whole key, uh, but consult an expert, uh, if you're, uh, seeing some performance problems or you really want to be certain, uh, in some performance critical situation.


[User] Please transcribe this text

[Assistant] Now, uh, what we get back from a hash code is, uh, a, uh, int value that's between minus 2 to the 31st and 2 to the 31st minus 1.


[User] Please transcribe this text

[Assistant] Uh, now what we need is, if we have a table of size m, uh, an array of size m that we're going to use to store the keys, we need an int value between 0 and m minus 1.


[User] Please transcribe this text

[Assistant] Uh, in, uh, the value of m is maybe a power of two, uh, or, uh, sometimes we pick, pick a prime, uh, because of the way that we normally would, uh,


[User] Please transcribe this text

[Assistant] So to get the big hash code value down to be a number between 0 and M minus 1 is to just do mod M. And if mod, if M is a prime, then, uh, then from math, uh, uh, modular arithmetic, we know that we're using all the bits in the number, uh, in that point too.


[User] Please transcribe this text

[Assistant] But even if you take the absolute value, uh, there's, uh, gonna have minus 2 to the 31st, uh, is possible.


[User] Please transcribe this text

[Assistant] Uh, so you have to just take the 31 bits.


[User] Please transcribe this text

[Assistant] Get the hash code out, make it positive, and then modem is the way to go.


[User] Please transcribe this text

[Assistant] Math doesn't quite work out right. So anyway, that, uh, code down at the bottom, uh, is, uh, you can, uh, use that as a template for, uh, what you might want to do.


[User] Please transcribe this text

[Assistant] Uh, and that's what we do in order to, uh, get the hash code, uh, to be a number between 0 and M minus 1. And, uh, if M is prime, it gives us some comfort that, uh, we have, uh, uh, some possibility of each table position, uh, appearing with equal likelihood.


[User] Please transcribe this text

[Assistant] And this, uh, assumption again, uh, it's, uh, with work, uh, it's possible to come close to this, uh, lots of researchers have, uh, done good work to show this. Uh, and so we'll, we'll assume that as a starting point.


[User] Please transcribe this text

[Assistant] Uh, and that allows us to model the situation with the so-called bins and balls model that directly relates the study of hash functions to classical probability theory. So we've got the m bins, uh, that's our, uh,


[User] Please transcribe this text

[Assistant] Uh, that's our correspondence to our hash table, uh, and we get m balls, uh, and we have some number of balls, however many keys we have, uh, and we throw them uniformly at random into m bins.


[User] Please transcribe this text

[Assistant] And, uh, these things are studied in classical combinatoric analysis. For example, there's the birthday problem, uh, which how many balls do you throw, uh, before you find two hitting the same bend? When do you get the first collision?


[User] Please transcribe this text

[Assistant] Uh, when does all the bins fill up? That's called the coupon collector problem. After about m natural log m tosses, every bin has at least one ball.


[User] Please transcribe this text

[Assistant] And those are just examples of classic results from combinatorial analysis that help us understand what happens when we do this, which is what we're doing with hashing. And we'll look at more advanced versions of these problems when we want to study hashing.


[User] Please transcribe this text

[Assistant] Uh, so this is just, uh, an example, uh, showing, uh, all the words in a tale of two cities, uh, using, uh, the modular hashing function for strings, uh, like the one that Java uses, uh, and they're pretty uniformly distributed. That's the, uh, summary for hash functions.


[User] Please transcribe this text

[Assistant] You'll be cooking my Autumn Harvest.


[User] Please transcribe this text

[Assistant] We're at this point where there's 13 people now and everyone's bloody amazing. So I'm going to give it everything today.


[User] Please transcribe this text

[Assistant] Follow the recipe, but at the same time you also need to use a little bit of your intuition.


[User] Please transcribe this text

[Assistant] Like it's fallen from the tree, that there's really nice leaves of the garnish on top. It just reminds me of autumn.


[User] Please transcribe this text

[Assistant] It's so intense and there's like a depth of flavour.


[User] Please transcribe this text

[Assistant] Hey, I'm a surgeon. My strategy today is to be quick, be clean and be efficient and just move. I'm gonna fight so hard because I don't want to be my last cook today.


[User] Please transcribe this text

[Assistant] Just rip the skin. Moving along.


[User] Please transcribe this text

[Assistant] She's got a motor on, come on Liz, come on. I'm cutting the skin off the breast, but then I start to poke some holes through it.


[User] Please transcribe this text

[Assistant] So I'm kinda struggling and it's kinda worrying me because it needs to be perfect.


[User] Please transcribe this text

[Assistant] Lisa, how you going? Uhm, I've put a few holes into my skin. I've poked a few holes. Have you, have you read the recipe right to the end?


[User] Please transcribe this text

[Assistant] Um, no, I've been just trying to do one step at a time. Lisa. Yep. Stop.


[User] Please transcribe this text

[Assistant] I'm reading the recipe, going through the steps and making sure and understand how it all works together. And it makes me realise.


[User] Please transcribe this text

[Assistant] You know, I'm just gonna chop it up into rough pieces, so I have no time to waste on it anymore. I'll quickly put it straight into the oven so it can crisp up.


[User] Please transcribe this text

[Assistant] I'm just concerned, uh, with time, like I need to make sure I push and get it done, cause I feel like I'm a bit behind, but I'm not gonna give up. Never gonna give up.


[User] Please transcribe this text

[Assistant] Time is going by so quick. I'm feeling really frazzled. I have so much to do. I still need to put the pasta into the fridge. Get cracking on my carrots. My crispy elements. Two purees. My veloute and my confit egg.


[User] Please transcribe this text

[Assistant] So, I'm rushing, I'm a bit stressed. It's probably not as perfect as Alice. But I just need to get it all done if I'm gonna catch up. Keep going, make that pasta. Come on.


[User] Please transcribe this text

[Assistant] The meat falls right off the bone and it's really juicy from all that duck fat.


[User] Please transcribe this text

[Assistant] This will stop the cooking process. I crack the egg and I carefully move away the egg whites and keep just the yolks.


[User] Please transcribe this text

[Assistant] I'm really happy with my egg yolks and my duck confit is done. So I need to move on to the celeriac puree.


[User] Please transcribe this text

[Assistant] The celeriac puree sits at the bottom of the duck and that's what gives it this creaminess.


[User] Please transcribe this text

[Assistant] So I just need to get the celeriac and milk onto the stove and cook it until it's nice and soft. Ah, thank you.


[User] Please transcribe this text

[Assistant] Cause I just wanna do a great dish and Ella's a Nikon and I wanna make sure I do her proud.


[User] Please transcribe this text

[Assistant] I'm definitely feeling really nervous because there's so many elements that need to go on the plate. So I'm thinking how am I going to get this all up?


[User] Please transcribe this text

[Assistant] I'm going through my mind trying to remember all the elements and how it tasted when I first tasted it. But it's really hard to remember.


[User] Please transcribe this text

[Assistant] It's just frantic. I'm trying to get as much as I can on the plate. So whilst I might have all the elements, I'm not sure it looks or tastes exactly like Ella's dish.


[User] Please transcribe this text

[Assistant] I have this thinking feeling because I don't feel like I've done Alice de Justice.


[User] Please transcribe this text

[Assistant] I had a great time. I just don't want that to be my last cook in the kitchen and the competition. Uhm, yeaah. So that's how I'm feeling right now.


[User] Please transcribe this text

[Assistant] I don't feel like I've done Alice. Do you stress this?


[User] Please transcribe this text

[Assistant] I got flustered and I don't know if I got the balance right. So I'm really worried at this point.


[User] Please transcribe this text

[Assistant] That's what pushes me through. It's only because the family support that I have that I've been able to do this without them. There's no way I could go on this journey. So, they're at home for me.


[User] Please transcribe this text

[Assistant] Hi girl. I'm really disappointed that I'm going home, but I think I've done well to get to this point, so I'm proud of myself.


[User] Please transcribe this text

[Assistant] Whether I left the first day or whether I left today, They'll be proud of me no matter what.


[User] Please transcribe this text

[Assistant] Gave it my best shot and this is definitely not the last you've seen of me.


[User] Please transcribe this text

[Assistant] Rat put some coins into the mouse's paw. Great, said Rat. Go buy us some good food.


[User] Please transcribe this text

[Assistant] The rest of the mice sat down by the fire. Mole was opening his empty cupboards. Oh dear, Mole said, shaking his head. Now, cried Rat. What shall we do for fun? Mole turned and looked at Rat.


[User] Please transcribe this text

[Assistant] Samuel should do it, said one, pointing. Yes, Samuel will do it, said the others. One young mouse stood shyly. Say something from the play, said Rat. Ahem, said Samuel. Go on, said Mole. Don't be shy, said Rat. Uhm.


[User] Please transcribe this text

[Assistant] The young mouse repeated. Here now, little one, said Rat. Everyone is waiting.


[User] Please transcribe this text

[Assistant] Here's the thought. I just want to pull my weight. Help my pals out when I can. I swear.


[User] Please transcribe this text

[Assistant] Free Flyers couldnae care less though, and go wherever they please.


[User] Please transcribe this text

[Assistant] As we walked towards the judges, I can smell firewood and smoke and then I see the ovens and the grills and I think I know what's happening today.


[User] Please transcribe this text

[Assistant] Service starts to desert and we're not really ready. Service is literally now. I know. I get the first mousse out of the tin. It comes away beautifully and I'm hoping it's set. I cut my knife into it. This is not set properly.


[User] Please transcribe this text

[Assistant] When I take that first slice out, it just collapses. There's runny mousse everywhere. We're having sex, guys. It's really bad. It's okay. It's alright.


[User] Please transcribe this text

[Assistant] I can only hope that the flavors will carry.


[User] Please transcribe this text

[Assistant] I did, yeaah. I just wanted to drop by and say thank you. If it wasn't for you, I doubt I'd be standing here today.


[User] Please transcribe this text

[Assistant] Think you could at least tell us what it was all about? They were talking like you sold them out for money, but I know that isn't true. That's not you. So how'd they get it so wrong?


[User] Please transcribe this text

[Assistant] It's a normal morning in a MasterChef house. We're all reading our books. And next second, bang!


[User] Please transcribe this text

[Assistant] I have never used tandoor oven before. Now I have to do a testa.


[User] Please transcribe this text

[Assistant] It looks great. I've never cooked in a tandoor oven. It's exciting to see how quickly the bubbles appear on a dough. I'm running out of time. It's really tricky, really hot. It's a lot of pressure, but they make none in this oven. I'm sure I can do it as well.


[User] Please transcribe this text

[Assistant] Woah, easy there. I can spare a bite.


[User] Please transcribe this text

[Assistant] Not after our fathers and grandfathers risked their lives to maintain them.


[User] Please transcribe this text

[Assistant] My carrot halwa is looking really good. It tastes nice. I can taste the carrot, I can taste the sultanas, but I don't want it overly sweet.


[User] Please transcribe this text

[Assistant] A little dollop of whipped cream, a couple of samosas, and a drizzle of caramel sauce with flaked almonds on top. I think we did that brief really well.


[User] Please transcribe this text

[Assistant] I've baked this dish before, so I'm quite confident. But today I'll be using a wood-fired oven, and yeah, it's so much different. Chloe, what do you think of this one? The smaller got a slight wobble in it.


[User] Please transcribe this text

[Assistant] We did a tiny tester in just a small pot and it cooked really, really quickly. We're gonna have to keep a good eye on these ganaches.


[User] Please transcribe this text

[Assistant] Time is just flying by. I've still got a few things to finish off with our dessert.


[User] Please transcribe this text

[Assistant] Hey, up ahead. Is that our jungle? Indeed it is!


[User] Please transcribe this text

[Assistant] See how they dry their mushrooms here? Bet they'd make for a mean stock. Doubt there's much it wouldn't improve. Soups, pilafs, maybe even a pot roast. What sort of thing would you go for?


[User] Please transcribe this text

[Assistant] I remember mom saying how Claudio's was the best she'd ever had. If you tell me what it was like, I could try and make it for you.


[User] Please transcribe this text

[Assistant] That's right Sue! Would you like to be our navigator and show us the way?


[User] Please transcribe this text

[Assistant] Welcome to our nature nesting site. Mmm, smell that piney air.


[User] Please transcribe this text

[Assistant] Alright everybody, close your eyes and take a pebble.


[User] Please transcribe this text

[Assistant] Okay cadets, it's time to build your nests. Go find some twigs!


[User] Please transcribe this text

[Assistant] It's always tough being in elimination. At this stage we're all producing amazing food, so you really need to step up your game.


[User] Please transcribe this text

[Assistant] I've been working so hard trying to learn more, practice every day.


[User] Please transcribe this text

[Assistant] My parents came over to Australia from Vietnam on a leaky boat.


[User] Please transcribe this text

[Assistant] I felt like I didn't really fit in. I was a bit of an outsider.


[User] Please transcribe this text

[Assistant] And so that's where my love for food really started to grow.


[User] Please transcribe this text

[Assistant] It's really important that I do well today and put up an amazing dish because I want to be in that top 10. So I'm just going to have to fight and push so that I don't go home today.


[User] Please transcribe this text

[Assistant] As soon as I saw a cinnamon, I thought of a cinnamon whiskey. Yeah, nice. I'm gonna do that into a caramel, I think. Do it with an ice cream. And like, you know how sticky date has cinnamon in it? I'm just gonna boost that.


[User] Please transcribe this text

[Assistant] Today, the dish that I'm cooking is called My Drunken Sticky Date.


[User] Please transcribe this text

[Assistant] I'm really happy with the whole concept behind this dessert, but my biggest concern is not getting the ice cream to set in time.


[User] Please transcribe this text

[Assistant] Today's elimination is massive. We're cooking for Kirsten Tibbles and we need to make a dessert either harrowing coconut, cinnamon, banana or almonds.


[User] Please transcribe this text

[Assistant] I have my sticky day pudding in the oven. My ice cream is churning. I need to get started on my whiskey caramel now.


[User] Please transcribe this text

[Assistant] It's silky, it's smooth, it tastes awesome and I wanna keep going back for more. Which kind of is what I want the judges to think when they're eating it.


[User] Please transcribe this text

[Assistant] I feel really happy. Like I know that it's in the judges hands right now, but I'm proud of the dish that I put up. I really would hope that I'm in the top 10. I have a good feeling about this, so I'm just keeping my fingers crossed.


[User] Please transcribe this text

[Assistant] It's my drunken sticky date. It's a sticky date pudding with a cinnamon ice cream, a salted crumb and a cinnamon whiskey caramel.


[User] Please transcribe this text

[Assistant] I think so. I hope so. I'm really happy with everything that I've presented to you guys here. I'm really proud of what I'm putting up today.


[User] Please transcribe this text

[Assistant] Come on, Brendan. Come on, guy. Damiro!


[User] Please transcribe this text

[Assistant] Khan, hey, how are you going? Well done.


[User] Please transcribe this text

[Assistant] There you go. What does he get in return? He gets to cook with both pantries. Okay.


[User] Please transcribe this text

[Assistant] Knowing that Khan's just put a dish up in 10 minutes that knocked the judge's socks off means he can obviously cook, so I'm gonna have to bring the A game. He's running, maybe I should be running.


[User] Please transcribe this text

[Assistant] There's a pretty broad selection of ingredients in front of me and what I'm trying to do is pin them together somehow.


[User] Please transcribe this text

[Assistant] I think I'm gonna do fish. Yeah, easy. So my strategy now is to get the bones of a really good dish together. So I take the John Dory, I take some great vegetables, and I take the pistachios.


[User] Please transcribe this text

[Assistant] The inspiration for my dish comes from a memory, eating a French dish, croissant allemande, which is fish with an almond crust.


[User] Please transcribe this text

[Assistant] What can go wrong with my dish today is if any of the flavours overpower each other. So what's going through my mind is just try and find balance.


[User] Please transcribe this text

[Assistant] Pan roasted fillet. Okay. I'm gonna use these vegetables. Kohlrabi, sweet, squash, anything you're worried about? Not really, just time.


[User] Please transcribe this text

[Assistant] Little piece of seafood, and I think that this could really add something.


[User] Please transcribe this text

[Assistant] So there's 30 minutes to go and I get on to just starting to cook my fish and the abalone.


[User] Please transcribe this text

[Assistant] What I wanna do is to get a little bit of colour to set the protein so that the pistachio will stick to it.


[User] Please transcribe this text

[Assistant] So I cooked the fish for about two and a half minutes on the skin side down.


[User] Please transcribe this text

[Assistant] No, I'm gonna just part cook it, turn it over, put the crumb on and then we'll flash it in the last minute.


[User] Please transcribe this text

[Assistant] The abalone we're going to serve practically raw. I just want it to be warm inside and the fish is going to go back in the oven. Very, very small fillet, huh? No, no, we're good. Okay.


[User] Please transcribe this text

[Assistant] So I take the fish now and then I start to cover it with those freshly chopped pistachios. Pistachios are going to add a slight little bit of texture but also this beautiful nuttiness that I think works so well with seafood. Ashley, hello, how long are you going to have the fish in the oven?


[User] Please transcribe this text

[Assistant] It's only gonna take probably two minutes. Oh yeah, yeah.


[User] Please transcribe this text

[Assistant] We're feeling good, I'm just gonna build this sauce and start heating up the vegetables. So I season the sauce with salt, pepper, olive oil.


[User] Please transcribe this text

[Assistant] And a little bit of sherry vinegar. And I just hit it with a little bit of butter. The butter's gonna start to emulsify and thicken the sauce. I wanna get the balance right in my sauce.


[User] Please transcribe this text

[Assistant] Yeah, yum. Abalone liver's gonna help to thicken the sauce, give it a real hit of the sea, but I want to use it sparingly.


[User] Please transcribe this text

[Assistant] So my timer goes off and the fish is nowhere near. I shut the oven and leave it in there for another minute or so.


[User] Please transcribe this text

[Assistant] I'm happy with the flavours of the dish. I think it's got a nice balance. I hope the judges agree.


[User] Please transcribe this text

[Assistant] Congratulations again, this guy did a really good job. Thank you.


[User] Please transcribe this text

[Assistant] I am now. Thought I was a goner.


[User] Please transcribe this text

[Assistant] Uh, this is Cloud. He's a jockey for Gramps' ranch. Oh, he is, huh? Perfect. Billy here needs some help. You can pick up the


[User] Please transcribe this text

[Assistant] One of my chocobos ran off when we put her out to pasture. Need you to find her. Bring her back.


[User] Please transcribe this text

[Assistant] Well, I underestimated ya. Takes real skill to tame this girl. Skills like those of... Hold on. I know this. I do. Why can't I remember?


[User] Please transcribe this text

[Assistant] You get your hands on that choco grass!


[User] Please transcribe this text

[Assistant] Hold your chocobo, son. There's still one thing you need to do for me first.


[User] Please transcribe this text

[Assistant] So you haven't realized you've been training this whole time? Tracking down Selena forced you to get in touch with a Chocobo's feelings.


[User] Please transcribe this text

[Assistant] And all that running across the water you did helped hone your sense of balance and your bird handling. Key skills for any aspiring jockey.


[User] Please transcribe this text

[Assistant] What do you say, Cloud? Bet you feel like a real Choco Pro by now. Can't say I do. Sounds to me like you need more training.


[User] Please transcribe this text

[Assistant] When you're ready, give me a holler and we can hit the track.


[User] Please transcribe this text

[Assistant] If I didn't know better, I'd say this wasn't your first rodeo. Surprise. Well shoot, you got me good.


[User] Please transcribe this text

[Assistant] And once you start tearing up the track out there, the racing world will blame me for letting you


[User] Please transcribe this text

[Assistant] Hmm, alright then. What do you wanna know?


[User] Please transcribe this text

[Assistant] Let's see. They couldn't have been much older than you are now. The two of them came and asked me to take them under my wing. Turned them down, seeing as I'd already retired. But they hung around the grounds anyway.


[User] Please transcribe this text

[Assistant] Since they had nothing better to do, I put them to work. And that's how it all began.


[User] Please transcribe this text

[Assistant] Well, wasn't nothing out of the ordinary, I'd say.


[User] Please transcribe this text

[Assistant] And in their downtime, they took it upon themselves to dig through my library, learning about chocobos and how to handle them. Came to differing conclusions. They used to argue something fierce.


[User] Please transcribe this text

[Assistant] I can't say the two of them got on terribly well. I figured.


[User] Please transcribe this text

[Assistant] Will and Sam had mighty strong opinions about how to best raise chocobos, and it drove them to fight with their words and with their fists.


[User] Please transcribe this text

[Assistant] That said, only reason those two fought as hard as they did is cause they both cared deeply about the cause. As far as passion is concerned, the two of them were birds of a feather.


[User] Please transcribe this text

[Assistant] When we first met, I saw the way your eyes were brimming with anger.


[User] Please transcribe this text

[Assistant] I knew right then and there that what you needed wasn't teaching or training. What you needed was time. Time to look deep inside yourself.


[User] Please transcribe this text

[Assistant] But that being said, you should never use your birds as tools for revenge.


[User] Please transcribe this text

[Assistant] The Wind in the Willows Episode 37 Badger Gets Mad


[User] Please transcribe this text

[Assistant] Badger, Rat and Mole arrived at Toad Hall. Badger was right, said Mole. Look at that!


[User] Please transcribe this text

[Assistant] A shiny car was parked at the house. It was painted red, Toad's favorite color. It sure is big, said Rat.


[User] Please transcribe this text

[Assistant] I'll knock on the door, said Badger. But before he could, the door burst open.


[User] Please transcribe this text

[Assistant] You are just in time, said Toad.


[User] Please transcribe this text

[Assistant] It's time for a ride in my new car. The three friends stared at him. What a silly outfit, said Rat. What's wrong, said Toad. These are my driving clothes. Take him inside, said Badger. Rat and Mole grabbed Toad.


[User] Please transcribe this text

[Assistant] Boys, take off his things, Badger said. The three friends went right to work. I'll sit on him, said Rat.


[User] Please transcribe this text

[Assistant] Here, I've got his gloves, said Mole. I've got his goggles, said Badger. They pulled at his cap and coat. Toad kipped angrily. Hey, hey! screamed Toad. What are you doing?


[User] Please transcribe this text

[Assistant] Get your seeds on a stick here. Best seeds in town.


[User] Please transcribe this text

[Assistant] But I basically only eat two chocolate and pistachio. 21 please.


[User] Please transcribe this text

[Assistant] There's a lot of creamy flavours that look exactly the same. 30.


[User] Please transcribe this text

[Assistant] I definitely know the flavor, but it's just not coming to me.


[User] Please transcribe this text

[Assistant] There's just a little bit of musty flavour. Also musty.


[User] Please transcribe this text

[Assistant] I'm definitely gutted, but you know, I'm leaving all my girl power to Kristen today. Come on, Kay.


[User] Please transcribe this text

[Assistant] Kristen isn't taking Shannon's advice. He tells her not to add any more lime to the mousse, but she does.


[User] Please transcribe this text

[Assistant] Now we follow the cave where it leads, deep as it will take us.


[User] Please transcribe this text

[Assistant] That spectre you fought was a fallen key. Even in death.


[User] Please transcribe this text

[Assistant] That I cannot answer. Only the Ki themselves could. But enough about them. You are here to learn about yourself, Nanaki.