[User] Please transcribe this text

[Assistant] All those points have very low probability, and so we're gonna flag those points as anomalous. And so it's gonna define some region that maybe looks like this, so that everything outside this, it flags as anomalous.


[User] Please transcribe this text

[Assistant] Whereas the things inside this, uh, ellipse I just drew, if it considers okay or non-anomalous, not anomalous examples. And so this, this example, X2 test, you know, lies outside that region, so it has very small probability, and so we consider it an anomalous example.


[User] Please transcribe this text

[Assistant] So again, when someone buys a futures contract, one agrees with the exchange to a daily settlement procedure. Now on the floor of the exchange, uh, they traditionally used hand signals.


[User] Please transcribe this text

[Assistant] Because, and this goes back to Dojima. It shows that Dojima is really the source of the whole idea of futures trading.


[User] Please transcribe this text

[Assistant] So you get all these people together in a so-called pit where traders talk to each other, but they, there's so much noise that you can't hear someone standing right next to you. They're all shouting. So they developed a sign language. So if you want to buy futures, you put up your hand like this, meaning


[User] Please transcribe this text

[Assistant] Buy. And then you had ways of signaling the number, the price. Uh, and if you sell, I guess you have your hand out, meaning I want to sell.


[User] Please transcribe this text

[Assistant] Oh, the Dojima contract also had some funny features. They wanted to impose a trading hours in Japan, uh, back then, a long time ago. Uh, and so they didn't have clocks, so they didn't, you couldn't put up a nice clock on the wall in those times. So they had a burning fuse.


[User] Please transcribe this text

[Assistant] And at the, just before the closing of trading, they would light the fuse and everyone would see the fire. And when the fire went out, that was a warning, you know, like a five minute warning.


[User] Please transcribe this text

[Assistant] The market will be closed as of that time. And they wanted to stop trading after the market closed. And so they had what were called watermen who, uh, came with buckets of water and they walked around the trading floor and they looked for someone who was still trading and they splashed them with water to stop trading.


[User] Please transcribe this text

[Assistant] We don't do that anymore. In fact, now it's all largely electronic.


[User] Please transcribe this text

[Assistant] There still are. You can still go to the CME in Chicago and see people on the floor trading with their hand signals, but it's, uh, last time I heard, I was there on the floor a few years ago. It looked awfully quiet. It's dying down because so much more is electronic.


[User] Please transcribe this text

[Assistant] So as mentioned before, there's daily settlement. Every day until expiration, a margin account is credited or debited with an amount equal to the change in the settle price times the contract amount. Oh, by the way, the settle price, you might think of that as the last trade of yesterday.


[User] Please transcribe this text

[Assistant] But the problem is that they're worried about people trying to manipulate the last trade of yesterday because it could be a small trade and then there would be big settlements based on the trade.


[User] Please transcribe this text

[Assistant] So, in order to protect that, they have a settlement committee that looks at the final trades and asks whether they're representative. And they might choose a settled price, which is not the same as the closing price, the last price.


[User] Please transcribe this text

[Assistant] Alright, so the only difference is the last day. So if it's a physically settled commodity, physical delivery futures market, the last day is different. The last day, if you haven't closed out by the last day,


[User] Please transcribe this text

[Assistant] If you're a seller, you'd better be there with your trucks of grains. And if you're a buyer, you'd better be there with empty trucks to load it in.


[User] Please transcribe this text

[Assistant] The only people who do that, everyone else has closed out before the last day. The only people who do that are arbitrageurs.


[User] Please transcribe this text

[Assistant] So a farmer in Iowa, who's planting a crop, expected to yield 50,000 bushels, is essentially long 50,000 bushels, uh, and so sells, uh, let's say 10 Chicago September corn contracts for the price on that date, and post-margin.


[User] Please transcribe this text

[Assistant] A corn produced products manufacturer plans to buy a corn at that time and buys the 10 contract. But again, they never talk to each other or meet each other or worry about each other because they're both doing this with the exchange. So they locked in the price for both of them.


[User] Please transcribe this text

[Assistant] Okay, good question. So let me get back to the core idea of a futures market. Let's start with, as you described, physical delivery. So let's talk about a corn futures market. We have that at the Chicago Merchant CME Group.


[User] Please transcribe this text

[Assistant] Uh, one contract is five thousand bushels. You can buy corn, uh, for a year in the future.


[User] Please transcribe this text

[Assistant] Uh, what does it mean to buy corn a year in the future? What it means is I put up margin today. So 5,000 bushels might cost something like $50,000 depending on the price. So if I agree to buy


[User] Please transcribe this text

[Assistant] Uh, 5,000 bushels in one year at $10 a bushel.


[User] Please transcribe this text

[Assistant] Do I have to put up $50,000 today? No. You only have to put up margin today.


[User] Please transcribe this text

[Assistant] Uh, and then, then your margin account is credited or depleted as the price changes. Uh, and at the final, there's a final settlement at the end where you either have to deliver the corn and take, uh, the, uh,


[User] Please transcribe this text

[Assistant] At the then futures price, uh,


[User] Please transcribe this text

[Assistant] And what tends to bring the futures price in line with the corn price in the future is, as you say, the fact that if the futures price differed a lot from the price of corn, if the futures price were, uh,


[User] Please transcribe this text

[Assistant] Let's say, uhm, below the, uh, uh, spot price of corn on that date, uh, then people would be buying the futures and bringing the corn for delivery and selling it, which would tend to make the two convert. That's what you understand already. But the other side is, what if the contract is cash settled?


[User] Please transcribe this text

[Assistant] Then, uh, you have the same daily resettlement. So if I buy S&P 500 index futures for delivery in one year, uh, what it means is that the price today, if it never changed, would lead me to, uh,


[User] Please transcribe this text

[Assistant] A settlement at the end, uh, well there's two S&P 500 index features. There's one, the regular conventional one, which is $250 times index, and there's the e-mini, which is $50 times the index.


[User] Please transcribe this text

[Assistant] Now there's nothing you can deliver. You can't deliver the S&P 500 index. It's just a number.


[User] Please transcribe this text

[Assistant] Daily resettlement of your, based on changes in the futures market, will raise or lower your margin account.


[User] Please transcribe this text

[Assistant] But when you come to the end, there's a final settlement.


[User] Please transcribe this text

[Assistant] Now this is maybe what is a little bit tricky to understand. The final settlement does not involve you coming with 5000 bushels of corn and arriving at the facility that receives corn or empty trucks to receive 5000 bushels of corn.


[User] Please transcribe this text

[Assistant] There's no such thing. But on the last day, there's a final settlement, which is the difference between the last futures price and the actual index. So it's as if you had, it represents the profit or loss you would have made if you actually delivered the index.


[User] Please transcribe this text

[Assistant] So that means that if the futures price is below the S&P 500 index on the day before the final settlement,


[User] Please transcribe this text

[Assistant] You can be an arbitrator just the same. You don't have, all you have to do is buy a contract. And that's all you need to do because it will automatically settle at the end as the difference between the index and the uh,


[User] Please transcribe this text

[Assistant] So it's even easier to arbitrage the financial futures contract. So it happens quite reliably. That is, what's quite reliable is on the last day, when the final, when the, when the delivery day


[User] Please transcribe this text

[Assistant] That corn price future is going to be exactly the same as the price of a five, well, of a bushel of corn. And the S&P 500 index future is going to be, I mean, within a tiny margin of error, the same as the S&P 500 index.


[User] Please transcribe this text

[Assistant] So it's a, it's a clever system. I hope I made that clear. But it's, but, uh, cash settlement opens up the possibility of doing many more kinds of, uh, I'm mentioning my home price future. That's a cash settled contract.


[User] Please transcribe this text

[Assistant] We can't, uh, deliver homes. Uh, that would be kind of tough. How would you deliver a home? Uh, and also homes are not standardized. They're all different. Uh, when you have a physical delivery as a settlement procedure, it has to be possible to define precisely what it is that's delivered.


[User] Please transcribe this text

[Assistant] But homes are so variable and some of them are beat up and run down and some of them are beset by crime and some of them are


[User] Please transcribe this text

[Assistant] Wonderful neighborhoods, I mean, it's all so intangible. So you need cash settled futures. It's kind of remarkable that cash settled futures didn't really occur until a few decades ago.


[User] Please transcribe this text

[Assistant] Cause it's a great idea. It's one of those inventions in finance that I like to talk about.


[User] Please transcribe this text

[Assistant] Imagine you're asked to invent something new. It could be whatever you want, made from anything you choose, in any shape or size.


[User] Please transcribe this text

[Assistant] That kind of creative freedom sounds so liberating, doesn't it? Or does it?


[User] Please transcribe this text

[Assistant] If you're like most people, you'd probably be paralyzed by this task. Without more guidance, where would you even begin? As it turns out, boundless freedom isn't always helpful.


[User] Please transcribe this text

[Assistant] In reality, any project is restricted by many factors, such as the cost, what materials you have at your disposal, and unbreakable laws of physics. These factors are called creative constraints, and they're the requirements and limitations we have to address in order to accomplish a goal.


[User] Please transcribe this text

[Assistant] Creative constraints apply across professions to architects and artists, writers, engineers, and scientists.


[User] Please transcribe this text

[Assistant] In many fields, constraints play a special role as drivers of discovery and invention. During the scientific process in particular, constraints are an essential part of experimental design.


[User] Please transcribe this text

[Assistant] For instance, a scientist studying a new virus would consider, how can I use the tools and techniques at hand to create an experiment that tells me how this virus infects the body's cells?


[User] Please transcribe this text

[Assistant] And what are the limits of my knowledge that prevent me from understanding this new viral pathway?


[User] Please transcribe this text

[Assistant] In engineering, constraints have us apply our scientific discoveries to invent something new and useful.


[User] Please transcribe this text

[Assistant] Take, for example, the landers Viking 1 and 2, which relied on thrusters to arrive safely on the surface of Mars.


[User] Please transcribe this text

[Assistant] The problem? Those thrusters left foreign chemicals on the ground, contaminating soil samples.


[User] Please transcribe this text

[Assistant] So a new constraint was introduced. How can we land a probe on Mars without introducing chemicals from Earth?


[User] Please transcribe this text

[Assistant] The next Pathfinder mission used an airbag system to allow the rover to bounce and roll to a halt without burning contaminating fuel.


[User] Please transcribe this text

[Assistant] Years later, we wanted to send a much larger rover, Curiosity. However, it was too large for the airbag design, so another constraint was defined. How can we land a large rover while still keeping rocket fuel away from the Martian soil?


[User] Please transcribe this text

[Assistant] With each invention, the engineers demonstrated an essential habit of scientific thinking, that solutions must recognize the limitations of current technology in order to advance it.


[User] Please transcribe this text

[Assistant] Sometimes this progress is iterative, as in, how can I make a better parachute to land my rover?


[User] Please transcribe this text

[Assistant] And sometimes it's innovative, like how to reach our goal when the best possible parachute isn't going to work.


[User] Please transcribe this text

[Assistant] In both cases, the constraints guide decision-making to ensure we reach each objective.


[User] Please transcribe this text

[Assistant] Here's another Mars problem yet to be solved. Say we want to send astronauts who'll need water.


[User] Please transcribe this text

[Assistant] They'd rely on a filtration system that keeps the water very clean and enables 100% recovery.


[User] Please transcribe this text

[Assistant] Those are some pretty tough constraints, and we may not have the technology for it now.


[User] Please transcribe this text

[Assistant] But in the process of trying to meet these objectives, we might discover other applications of any inventions that result.


[User] Please transcribe this text

[Assistant] In fact, many scientific advances have occurred when serendipitous failures in one field address the constraints of another.


[User] Please transcribe this text

[Assistant] When scientist Alexander Fleming mistakenly contaminated a Petri dish in the lab, it led to the discovery of the first antibiotic, penicillin.


[User] Please transcribe this text

[Assistant] The same is true of synthetic dye, plastic, and gunpowder. All were created mistakenly but went on to address the constraints of other problems.


[User] Please transcribe this text

[Assistant] Understanding constraints guides scientific progress, and what's true in science is also true in many other fields.


[User] Please transcribe this text

[Assistant] Constraints aren't the boundaries of creativity, but the foundation of it.


[User] Please transcribe this text

[Assistant] I've been thinking, and well, I think we need to start seeing other people.


[User] Please transcribe this text

[Assistant] Tim, you are too good for me.


[User] Please transcribe this text

[Assistant] You deserve someone who can make you smile and make you happy the way that you made me happy. I could say that I'll be all you need, but that'd be a lie. I know I'd only hurt you. I know I'd only make you cry.


[User] Please transcribe this text

[Assistant] I've been thinking, and well, I think we need to start seeing other people.


[User] Please transcribe this text

[Assistant] That's just it. I'm not happy anymore, Tim. It's not you, it's me.


[User] Please transcribe this text

[Assistant] I know that I can be hard to deal with, and you're a great guy. You're the type of guy that anyone one would kill for.


[User] Please transcribe this text

[Assistant] Tim, you are too good for me.


[User] Please transcribe this text

[Assistant] You deserve someone who can make you smile and make you happy the way that you made me happy. I could say that I'll be all you need, but that'd be a lie. I know I'd only hurt you. I know I'd only make you cry.


[User] Please transcribe this text

[Assistant] I can't, Tim. I hope someday you can find some way to understand. I'm only doing this for you.


[User] Please transcribe this text

[Assistant] I don't really wanna go, but deep in my heart, I know this is the kindest thing to do.


[User] Please transcribe this text

[Assistant] Here are your keys. I'll send my sister to pick up the rest of my things next week. I'm sorry, Tim. I wish you all the best and I hope that one day we can meet again. I'll always love you. Goodbye.


[User] Please transcribe this text

[Assistant] I've been thinking, and well, I think we need to start seeing other people.


[User] Please transcribe this text

[Assistant] So here's the problem. Imagine that you're a website or a company that sells or rents out movies and what have you. And so, you know, Amazon and Netflix and I think iTunes are all examples of companies that do, are all examples of companies that do this.


[User] Please transcribe this text

[Assistant] And let's say you let your users rate different movies, uh, using a 1 to 5 star rating. So use a rating or something, 1, 2, 3, 4, or 5 stars.


[User] Please transcribe this text

[Assistant] Love at Last, Romance Forever, Cute Puppies of Love, Uhm, and I'll start car chasin' and swords with a karate.


[User] Please transcribe this text

[Assistant] So let's say Alice really likes Luck at Last and Raced at Five Stars, really likes Romance Forever, Raced at Five Stars. She did not watch Cute Puppies a lot, but did not rate it, so we don't have a rating for that. And Alice really did not like Non-Stop Car Chases or Souls vs Karate.


[User] Please transcribe this text

[Assistant] And, uh, different user, Bob, user 2, maybe raced a different set of movies. Maybe he likes Love at Loss, did not watch Romance Forever, just had a rating of 4, a 0, a 0.


[User] Please transcribe this text

[Assistant] And uh, maybe our third user raised the zero, did not watch that one, zero, five, five. And you know, let's just burn some of the numbers.


[User] Please transcribe this text

[Assistant] And so just to introduce a bit of notation, this notation we'll be using throughout, I'm going to use nu to denote the number of users. So in this example, nu will be equal to 4. So the u subscript stands for users.


[User] Please transcribe this text

[Assistant] are giving high ratings to these, uh, romantic comedies or movies about love and giving very low ratings about the action movies. And for Carol and Dave is the opposite, right? Carol and Dave uses 3 and 4 really like the action movies and give them high ratings, but don't like the romance and love type movies as much.


[User] Please transcribe this text

[Assistant] And whenever our ij is equal to 1, whenever user j has rated movie i, we also get this number yij, which is the rating given by user j to movie i. And so yij would be a number, you know, from 0 to 1.


[User] Please transcribe this text

[Assistant] So the recommender system problem is, given this data set, that is given these, um, R ij's and the Y ij's, to look through your data and look at all the movie ratings that are missing and to try to predict what these values of the question mark should be.


[User] Please transcribe this text

[Assistant] But look in this data, you have Alice and Bob both like the romantic movies, maybe we think that Alice would have given this a 5, maybe we think Bob would have given this a...


[User] Please transcribe this text

[Assistant] 4.5 or some high value, whereas we think maybe Carol and Dave would have given these very low ratings and Dave, well, if Dave really likes action movies, maybe he would have given Swords and Karate a 4 rating or maybe a 5 rating.


[User] Please transcribe this text

[Assistant] Okay, and so our job is developing a recommender system.


[User] Please transcribe this text

[Assistant] is to come up with a learning algorithm that can automatically fill in these missing values for us so that we can look at say the movies that the user has not yet watched and recommend new movies to that user to watch to try to predict what else might be interesting to a user.


[User] Please transcribe this text

[Assistant] After an hour, I finally worked out the answer to this math problem.


[User] Please transcribe this text

[Assistant] Let's say you're on a game show. You've already earned $1,000 in the first round when you land on the bonus space.


[User] Please transcribe this text

[Assistant] Now you have a choice. You can either take a $500 bonus, guaranteed, or you can flip a coin.


[User] Please transcribe this text

[Assistant] If it's heads, you win a thousand dollar bonus. If it's tails, you get no bonus at all.


[User] Please transcribe this text

[Assistant] In the second round, you've earned $2000 when you land on the penalty space.


[User] Please transcribe this text

[Assistant] Now you have another choice. You can either take a $500 loss or try your luck at the coin flip.


[User] Please transcribe this text

[Assistant] If it's heads, you lose nothing. But if it's tails, you lose $1,000 instead.


[User] Please transcribe this text

[Assistant] If you're like most people, you probably chose to take the guaranteed bonus in the first round and flip the coin in the second round.


[User] Please transcribe this text

[Assistant] But if you think about it, this makes no sense. The odds and outcomes in both rounds are exactly the same.


[User] Please transcribe this text

[Assistant] So why does the second round seem much scarier?


[User] Please transcribe this text

[Assistant] The answer lies in a phenomenon known as loss aversion.


[User] Please transcribe this text

[Assistant] Under rational economic theory, our decisions should follow a simple mathematical equation that weighs the level of risk against the amount at stake.


[User] Please transcribe this text

[Assistant] But studies have found that for many people, the negative psychological impact we feel from losing something is about twice as strong as the positive impact of gaining the same thing.


[User] Please transcribe this text

[Assistant] Loss aversion is one cognitive bias that arises from heuristics, problem solving approaches based on previous experience and intuition rather than careful analysis.


[User] Please transcribe this text

[Assistant] And these mental shortcuts can lead to irrational decisions. Not like falling in love or bungee jumping off a cliff, but logical fallacies that can easily be proven wrong.


[User] Please transcribe this text

[Assistant] Situations involving probability are notoriously bad for applying heuristics. For instance, say you were to roll a die with four green faces and two red faces 20 times.


[User] Please transcribe this text

[Assistant] You can choose one of the following sequences of roles, and if it shows up, you'll win $25. Which would you pick?


[User] Please transcribe this text

[Assistant] In one study, 65% of the participants, who were all college students, chose sequence B.


[User] Please transcribe this text

[Assistant] Even though A is shorter and contained within B. In other words, more likely. This is what's called a conjunction fallacy.


[User] Please transcribe this text

[Assistant] Here, we expect to see more green rolls, so our brains can trick us into picking the less likely option.


[User] Please transcribe this text

[Assistant] Heuristics are also terrible at dealing with numbers in general. In one example, students were split into two groups.


[User] Please transcribe this text

[Assistant] The first group was asked whether Mahatma Gandhi died before or after age 9.


[User] Please transcribe this text

[Assistant] While the second was asked whether he died before or after age 140. Both numbers were obviously way off.


[User] Please transcribe this text

[Assistant] But when the students were then asked to guess the actual age at which he died, the first group's answers averaged to 50, while the second group's averaged to 67.


[User] Please transcribe this text

[Assistant] Even though the clearly wrong information in the initial questions should have been irrelevant, it still affected the students' estimates. This is an example of the anchoring effect.


[User] Please transcribe this text

[Assistant] And it's often used in marketing and negotiations to raise the prices that people are willing to pay.


[User] Please transcribe this text

[Assistant] So, if heuristics lead to all these wrong decisions, why do we even have them? Well, because they can be quite effective.


[User] Please transcribe this text

[Assistant] For most of human history, survival depended on making quick decisions with limited information.


[User] Please transcribe this text

[Assistant] When there's no time to logically analyze all the possibilities, heuristics can sometimes save our lives.


[User] Please transcribe this text

[Assistant] But today's environment requires far more complex decision making.


[User] Please transcribe this text

[Assistant] Affecting everything from health and education to finance and criminal justice.


[User] Please transcribe this text

[Assistant] We can't just shut off our brain's heuristics, but we can learn to be aware of them.


[User] Please transcribe this text

[Assistant] When you come to a situation involving numbers, probability, or multiple details, pause for a second.


[User] Please transcribe this text

[Assistant] And consider that the intuitive answer might not be the right one after all.


[User] Please transcribe this text

[Assistant] I hope you've all got a fire in your belly. Good luck. Your time starts now.


[User] Please transcribe this text

[Assistant] I actually cooked the potatoes for long enough.


[User] Please transcribe this text

[Assistant] I love the scallops and the puré and the corn.


[User] Please transcribe this text

[Assistant] Taking the onion skin, the garlic skin, the sage mixture, turning that into powder, and then burning it as well to get the aroma. It was clever.


[User] Please transcribe this text

[Assistant] There's an awful lot of chefs that when they watch this should take a big leaf out of your book because I just feel this


[User] Please transcribe this text

[Assistant] Part of the competition, you know it. The ups and downs, the challenges that we throw at you, it's all a rite of passage. We've said it before.


[User] Please transcribe this text

[Assistant] You know what's good too is he made the most of the equipment that he had, you know, and he was able to, you know, utilise all of those things to, you know, make a delicious dish.


[User] Please transcribe this text

[Assistant] Hey look, duck loves pickles, it loves cherries, you know, and uh, caramelized shallots loves them too. Ducks cook nicely though, don't they?


[User] Please transcribe this text

[Assistant] That meringue is so much worse than I thought it was going to be. I have to start again.


[User] Please transcribe this text

[Assistant] No, but we're gonna use it when we explain some of these words, so you need to know it.


[User] Please transcribe this text

[Assistant] Okay, so a golf club is the tool you use to hit the golf ball.


[User] Please transcribe this text

[Assistant] I sure hope so because it would be a huge loss to the world of golf.


[User] Please transcribe this text

[Assistant] Alright, so the announcers were talking about the game, and they, and they, they're talking about the course, and they, and they're here on the eighth hole, and they said that last time, Jack Nicklaus hit a hole in one.


[User] Please transcribe this text

[Assistant] So when you tee off, uhm, you begin playing.


[User] Please transcribe this text

[Assistant] Yes, it's the person who carries your golf clubs.


[User] Please transcribe this text

[Assistant] Yeah, he tells you which way you should hit the ball and maybe, you know, gives you a nice cold drink and...


[User] Please transcribe this text

[Assistant] Okay, so the next thing they were talking about were some different types of scores that you could get on a whole. And the first one they mentioned was par.


[User] Please transcribe this text

[Assistant] Okay, or the par of the course can be four.


[User] Please transcribe this text

[Assistant] Right, so it's like, it's like the, the, right, so when the, the, when the, when a hole is par four, you should only hit the ball four times.


[User] Please transcribe this text

[Assistant] Alright, so this is actually a lot of weird, crazy, strange language. Birdie, bogey, caddy, driver. Why don't we listen to the dialogue one more time to help us understand?


[User] Please transcribe this text

[Assistant] Okay, well one of the phrases I want to talk about is something that Bob Copeland said. He said, as you can see, ladies and gentlemen, we're here at beautiful Pebble Beach.


[User] Please transcribe this text

[Assistant] Yeah, it's a way to say, I know you can see this. I'm telling you something you know.


[User] Please transcribe this text

[Assistant] Yes, so this is actually a really helpful phrase, it can help you sound really native-like, so why don't we hear some examples of how it's used.


[User] Please transcribe this text

[Assistant] Alright, so when it's not your day, uhm, you're having a bad time or a bad day.


[User] Please transcribe this text

[Assistant] Yeaah, so for example, today, uhm, I had a headache and I ripped my pants and I spilled coffee on myself, so it really wasn't my day.


[User] Please transcribe this text

[Assistant] Now the last phrase, uhm, similar to it's not your day, we also heard that Tiger was having a hard time.


[User] Please transcribe this text

[Assistant] Of course, you can talk about anything like this. So, oh, I'm having a hard time understanding what this word means.


[User] Please transcribe this text

[Assistant] Alright, so Marco, I know that in uhm, the Western business world, golf is actually super important for your career, isn't it?


[User] Please transcribe this text

[Assistant] So maybe you negotiate a deal out on the golf course.


[User] Please transcribe this text

[Assistant] Aha, okay. Well actually I know that it is so important for people, uhm, who are wanting to do well in business and many people who don't speak English as their first language take golf English lessons.


[User] Please transcribe this text

[Assistant] Like how to talk about golf, how to, uhm, you know, talk to other people and make deals while you're on the golf course.


[User] Please transcribe this text

[Assistant] That's right. So if you have any questions or comments about today's lesson, why don't you visit our website at EnglishPod.com?


[User] Please transcribe this text

[Assistant] Dating back at least to the time of Socrates, some early societies decided that certain disputes, such as whether a person committed a particular crime, should be heard by a group of citizens.


[User] Please transcribe this text

[Assistant] Several centuries later, trial by jury was introduced to England.


[User] Please transcribe this text

[Assistant] Checking the government and involving citizens in decision making.


[User] Please transcribe this text

[Assistant] Juries decided whether defendants would be tried on crimes, determined whether the accused defendants were guilty, and resolved monetary disputes.


[User] Please transcribe this text

[Assistant] While the American colonies eventually cast off England's rule, its legal tradition of the jury persisted.


[User] Please transcribe this text

[Assistant] The United States Constitution instructed a grand jury to decide whether criminal cases proceeded.


[User] Please transcribe this text

[Assistant] ...required a jury to try all crimes except impeachment and provided for juries in civil cases as well.


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[Assistant] Yet, in the US today, grand juries often are not convened. And juries decide less than 4% of criminal cases and less than 1% of civil cases filed in court.


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[Assistant] That's at the same time as jury systems in other countries are growing. So what happened in the US?


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[Assistant] It's permitted plea bargaining, which now occurs in almost every criminal case.


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[Assistant] The way it works is the prosecutor presents the accused with a decision of whether to plead guilty.


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[Assistant] If they accept the plea, the case won't go in front of a jury.


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[Assistant] The risk of a much greater prison sentence after a trial can frighten even an innocent defendant into taking a plea.


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[Assistant] The proportion of guilty pleas has increased from around 20% to 90%, and the numbers continue to grow.


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[Assistant] The Supreme Court has permitted the use of another procedure that interferes with a jury called summary judgment. Using summary judgment, judges can decide that civil trials are unnecessary if the people who sue have insufficient evidence.


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[Assistant] That's a difficult thing to determine. Yet usage of summary judgment has stretched to the point where some would argue it's being abused.


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[Assistant] For instance, judges grant fully or impart over 70% of employers request to dismiss employment discrimination cases. In other cases,


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[Assistant] Both the person who sues and the person who defends forgo their right to go to court.


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[Assistant] Arbitration can be a smart decision by both parties to avoid the requirements of a trial in court.


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[Assistant] But it's often agreed to unwittingly when people sign contracts like employment applications and consumer agreements.


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[Assistant] That can become a problem. For example, some arbitrators may be biased towards the companies that give them cases. These are just some of the ways in which juries have disappeared. But could the disappearance of juries be a good thing? Well, juries aren't perfect.


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[Assistant] They're costly, time-consuming, and may make errors. And they're not always necessary, like when people can simply agree to settle their disputes. But juries have their advantages.


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[Assistant] When properly selected jurors are more representative of the general population.


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[Assistant] And don't have the same incentives as prosecutors, legislators or judges seeking re-election or promotion.


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[Assistant] The founders of the United States trusted in the wisdom of impartial groups of citizens to check the power of all three branches of government.


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[Assistant] And the jury trial itself has given ordinary citizens a central role in upholding the social fabric. So will the jury system in the US survive into the future?


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[Assistant] It was. Until I fell in love with a jerk!


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[Assistant] Dennis' dish is described as a tart. It looks really beautiful, but very difficult.


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[Assistant] Ah, I got the, it was tasting absolutely beautiful, I've gone through all the process, put it onto whip, and didn't realise it would split. Oh no, so you've gone too far.


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[Assistant] I definitely know I can caramelise bananas. Reading that in the recipe feels really good because I can see it's the last element that I've got to do before assembly.


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[Assistant] But the final decision came from the judges. It's a close decision today. And Samuel's green team ended up in elimination. Tonight, seven will arrive.


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[Assistant] It's great to see some familiar faces, isn't it? I'm sure they've been out in the big wide world doing some very interesting things. Brian, we've heard you've been doing a little bit of work experience. I'm with Reynolds, too, at Coit.


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[Assistant] Fantastic. There's nothing like going home, leaving the competition behind to really focus the mind.


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[Assistant] Garden is in play. We're looking for the six best dishes to go through to round two. Are you ready?


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[Assistant] To the six of you, we're sorry. Great to have you back in the MasterChef kitchen. You don't have to win this competition to prove that you can do amazing things. So, it's up to you. Do amazing things. But, for now, it's time to say goodbye.


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[Assistant] What an absolutely brilliant cook. You had 75 minutes to take your chosen ingredient and make something sweet and delicious. So.


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[Assistant] Wow. Mistakes don't get any higher than this. Cooking to win your place back in the competition. And as we expected, it was a very, very close fall thing. Boy, did you make it hard for us to make a decision.


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[Assistant] In the next few videos, we'll talk about large scale machine learning. That is, algorithms for dealing with big data sets.


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[Assistant] In these next few videos, we'll talk about algorithms for dealing with when we have such massive datasets.


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[Assistant] So why do we want to use such large data sets? We've already seen that one of the best ways to get a high performance machine learning system is if you take a low bias learning algorithm and train that on a lot of data.


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[Assistant] And so one early example that we've already seen was this example of classifying between confusable words. So for breakfast, I ate.


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[Assistant] So long as you feed the algorithm a lot of data, it seems to do very well. And so it's results like these that has led to the saying in machine learning that often is not who has the best algorithm that wins, it's who has the most data.


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[Assistant] And this is actually pretty realistic for many modern data sets. If you look at the US Census data set, if there are 300 million people in the US, you can easily get hundreds of millions of records. If you look at the amount of traffic that popular websites get, you easily get training sets that are much larger than hundreds of millions of examples.


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[Assistant] In order to compute this derivative term and to perform a single step of gradient descent


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[Assistant] Because of the computational expense of summing over a hundred million entries in order to compute just one step of gradient descent, in the next few videos we'll talk about techniques for either replacing this algorithm with something else or to find more efficient ways to compute this derivative.


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[Assistant] By the end of this sequence of videos on large-scale machine learning, you know how to fit


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[Assistant] Models, linear regression, logistic regression, neural networks, and so on, even to datasets with, say, 100 million examples.


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[Assistant] Of course, before we put in the effort into training a model with a hundred million examples, we should also ask ourselves, well, why not use just a thousand examples? Maybe we can randomly pick a subset of a thousand examples out of a hundred million examples and train our algorithm on just a thousand examples.


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[Assistant] So before investing the effort into actually developing the software needed to train these massive models, it's often good to sanity check if training on just a thousand examples might do just as well.


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[Assistant] The way to sanity check of using a much smaller training set might do just as well, that is if using a much smaller m equals 1000 size training set might do just as well, is the usual method of plotting the learning curves. So if you were to plot the learning curves and if your training objective were to look like this, that's J train theta, and if your cross-validation set objective


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[Assistant] Jcv of theta were to look like this, then you know, it looks like, this looks like a high variance learning algorithm, and we would be more confident that adding extra training examples would improve performance. Whereas in contrast, if you were to plot the learning curves, if you're


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[Assistant] If your training objective were to look like this and if your cross-validation objective


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[Assistant] What's it look like that? Then this looks like the classical high bias learning algorithm. And in the latter case, you know, if uhm, you were to plot this up to say m equals one thousand, right, so that's m equals five hundred up to m equals one thousand, then it seems unlikely that increasing m to a hundred million


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[Assistant] We'll do much better and then you'd be just fine sticking to m equals a thousand rather than investing a lot of effort to figure out how the scale would be out of them.


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[Assistant] Of course, if you were in the situation shown by the figure on the right, then one natural thing to do would be to add extra features or add extra hidden units to your neural network and so on so that you end up with a situation closer to that on the left.


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[Assistant] Maybe this is up to m equals a thousand and this thing gives you more confidence that trying to, uhm, add infrastructure or change the algorithm to use much more than a thousand examples that that might actually be a good use of your time.


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[Assistant] So in large scale machine learning, we'd like to come up with computationally reasonable ways or computationally efficient ways to deal with very big data sets. In the next few videos, we'll see two main ideas. The first is called stochastic gradient descent and the second is called MapReduce.


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[Assistant] Hello, welcome back to your basic English lesson.


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[Assistant] Today we will start lesson seven. So get your paper and your pencil.


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[Assistant] First, let's learn some new words. I'm gonna draw the pictures here.


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[Assistant] Now remember to write all of this down in your notebook.


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[Assistant] Draw the pictures too. That would really help a lot. Okay? This is a cap. A t-shirt. A jacket.


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[Assistant] Okay, now let's ask a question. What are you wearing? Hm? Wait a second. Wearing?


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[Assistant] No clothes is not wearing. Okay, and a dress or wearing clothes is wearing. You see that?


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[Assistant] Alright. Let's try again. What are you wearing?


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[Assistant] Draw some people here. I'm wearing shorts.


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[Assistant] I'm wearing a t-shirt.


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[Assistant] Oh, there we go, two. I'm wearing a t-shirt and shorts.


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[Assistant] I'm wearing a pink t-shirt, jeans, and sandals.


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[Assistant] I'm wearing a pink t-shirt, jeans, and sandals. We can add color to our sentences.


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[Assistant] And, what are you wearing?


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[Assistant] I'm wearing a t-shirt and shorts. That's okay.


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[Assistant] I'm wearing a grey t-shirt and brown shorts. I'm wearing a grey t-shirt, brown shorts and blue shoes. That's much better. What is he wearing?


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[Assistant] He is wearing a black t-shirt and jeans. Well, what is she wearing?


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[Assistant] She is wearing a brown shirt, brown shorts, and brown shoes.


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[Assistant] He is wearing a grey jacket, blue shirt, black jeans, and black shoes.


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[Assistant] Remember, take your notes. Great job today.


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[Assistant] You are really starting to get good at English. Keep practicing with your friends and your family.


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[Assistant] Sorry I'm late everyone. There was a huge traffic jam on the highway this morning.


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[Assistant] I think I'll just stay here and maybe rest all my feets.


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[Assistant] Don't worry, it's nice to have a little bit of alone time sometimes.


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[Assistant] So you've got a little gift, haven't you? Yeah, I certainly have, and I love my vegetables, and I know your team does as well, so keep eating your vegetables, make sure they take centre plate, and have a great competition.


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[Assistant] This video was made possible by Skillshare. Start learning for free for 2 months by signing up at skl.sh slash RealLifeLore33.


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[Assistant] Okay, so if you're watching this, there's been a big problem. You've touched something that you didn't realize was a time machine, and you've inadvertently been transported back through time, and you have no idea where or when you are. Understandably, this is a highly stressful situation for you, but have no fear.


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[Assistant] Through this quick and simple tutorial, you'll be able to quickly and hopefully safely be able to figure out what year you're actually trapped in, and from that, you'll hopefully be able to survive. Alright, so first things first, let's start from the very beginning. Has the Big Bang happened yet? If not, that's uh...


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[Assistant] Pretty weird, and you're stuck sometime before 13.8 billion years ago. I can't really help you very much with that, so hopefully the answer to that question was yes, it has happened. So if so, the next important question to ask is, is there a planet where the Earth is supposed to be? If there isn't, well, you're trapped sometime between 13.8 and 4.54 billion years ago, and you're probably pretty screwed unless you have a space suit. If Earth does exist, proceed to question three.


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[Assistant] Unfortunately for you, a protoplanet is going to collide with Earth during this time eventually, so you're gonna violently die no matter what. On the other hand, if you do see a moon, it means that you're still drowning in lava, but at least you get to watch the moon rotating around between 4.5 and 3.8 billion years ago. On to question four now.


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[Assistant] If you're not immediately melting, are you immediately suffocating and unable to breathe? Because if you are, you're unfortunately between 3.8 billion and 850 million years ago when the earth was full of carbon dioxide and it didn't have enough oxygen yet for you to breathe.


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[Assistant] So basically, if you aren't immediately dying, you're sometime after 850 million years ago, which is great. So, question 5. Do you see any animals wandering around on the land?


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[Assistant] It won't be necessarily pretty but at least you've got some pretty quirky little animals to hang out with. Okay, next up.


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[Assistant] If yes, it still kinda sucks, but you're somewhere between 232 million and 65 million years ago. The other question that ties into this one though is, do you see any mammals? Mammals and dinosaurs evolved alongside one another, so if you don't see any mammals, you


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[Assistant] Do you see any humans or creatures that look like humans lurking around?


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[Assistant] Well, we already know that you're sometime after 65 million years ago if you only see other mammals. So do you happen to know what continent you're standing on?


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[Assistant] So if you don't see people, you're between 65 million years ago and any of these times depending on where in the world you are.


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[Assistant] Next up, if you do see other humans, do you only see Homo sapiens, Neanderthals, or a mix of various human species? Because if you only see Neanderthals and no Homo sapiens, you're between 250,000 and 200,000 BCE. And you're stuck with them, so have fun.


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[Assistant] If you see humans and Neanderthals together, or other human species on the other hand, it means that you're somewhere between 200,000 and 25,000 BCE, which is the upper end for our estimates when Neanderthals went extinct. If you happen to be in Papua New Guinea though, you might be up until 15,000 years ago because the Denisovan human population may have survived there up until then.


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[Assistant] Alright, so if you only see humans that look like modern humans, the next big question is this. Are they acting relatively normal?


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[Assistant] Like, do you see evidence of culture, burials, or language? Because if not, you're still probably trapped between 200,000 and 50,000 BCE, and you just haven't met the Neanderthals yet. However, if the humans are acting normal to what you would expect, then you know you're after 50,000 BCE. So to narrow it down even further, you probably need a quick astronomy lesson.


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[Assistant] Depending on which hemisphere you time travel to, the brightest constellations that are easiest to spot are the Big Dipper in the north and the Southern Cross in the south.


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[Assistant] These constellations are the brightest because the stars that make them up are located closer to Earth than most. Which means that their movements over time are observable.


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[Assistant] So out of these three choices, which one is the closest to what you're currently seeing in the night sky?


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[Assistant] Option 1 means you're indeed somewhere around 50,000 BCE. Option 2 means you're sometime around 25,000 BCE. And option 3 means you're sometime around the modern era.


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[Assistant] So if you see option 3, do you see any evidence of these humans lurking about farming, or is it only hunter gatherers still?


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[Assistant] So if you do see farming, do you also see people riding around on horses? Because if you don't, you're before 4000 BCE, and if you do, you're after it. Next question, do you see people using wheels? If you do, you're sometime after 3500 BCE. Arguably even more important though, do you see any evidence of people riding?


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[Assistant] Because writing was first developed in the Middle East and Egypt around 3100 BCE. In China around 2000 BCE and Mesoamerica by 650 BCE.


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[Assistant] So if you do see writing, you're sometime after these dates based on where you're at. It's also important that you pay attention to the alphabets people are using.


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[Assistant] Because they came into existence at different times. This, for example, is ancient Phoenician, which came into being around 1200 BCE. It gave birth to the Aramaic alphabet that looks like this around 600 BCE, which further gave birth to the Arabic script around 100 CE.


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[Assistant] Finally, the Cyrillic alphabet didn't come into existence until the 9th century in Bulgaria. So if you don't see any Cyrillic in Eastern Europe, you're probably before then.


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[Assistant] Gunpowder wasn't invented until the 9th century in China, and it wasn't used in warfare any time prior to the year 900.


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[Assistant] So if you see guns or cannons being used, you're definitely sometime after this. In regards to our previous discussion on alphabets and writing, do you only see scrolls or books that are handwritten, or do you also see printed books?


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[Assistant] The printing press and mass produced books weren't invented until 1439 in Germany. So if you see a book or a script that looks like it's printed, the odds are pretty good that you're sometime post 1439. And that means you're getting pretty close enough at this point to simply being able to ask someone what year you're in.


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[Assistant] If you're an average English speaker, any year post-1500 would probably be possible for you to vaguely understand what people are saying in England. But it's fairly complicated to say for certain.


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[Assistant] This is why you'd probably be capable of asking someone what year it is and finding out an answer any time post 1500, so long as you're in England and can speak English.


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[Assistant] But English any time prior to 1450 is just unintelligible gibberish to most modern English speakers. For example, this is the same Lord's Prayer in Middle English between 1066 and 1450, and this is the same prayer in Old English


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[Assistant] English was a completely different language back in those days, so effectively, if you can't understand what anybody is saying in England, you're probably sometime before the year 1500. And if you can understand people, then just ask them what year it is, and you'll hopefully figure it all out from there.


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[Assistant] Unfortunately, you probably won't end up alone in an ice age or stranded with dinosaurs anytime soon. Unfortunately, you might be stuck at home, like I am, and feel like you may as well be in a Hadian volcano. If you can relate, then I've got the perfect suggestion.


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[Assistant] This class on cinema-style filmmaking using an iPhone by Niles Gray and Caleb Babcock that's available right now on Skillshare. And if you've already tackled that home movie project, you can also learn to illustrate your time traveling adventure with this incredible


[User] Please transcribe this text

[Assistant] Skillshare is, of course, an online learning community with millions of members who want to further their own creative goals. When you're a member, you can take these classes I mentioned as well as thousands of other classes on any subject you can imagine.


[User] Please transcribe this text

[Assistant] By clicking the link in the description, or by going to skl.sh slash RealLifeLore33, you get access to their thousands of courses for just $10 a month when you sign up for an annual subscription afterwards. It's a great way to learn new skills and support RealLifeLore at the same time, and as always, thank you for watching.


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[Assistant] So you kind of act as like a carbohydrate delivery system.


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[Assistant] Yeaah. Lapping used to be so easy.


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[Assistant] No. Every day there's something new and interesting to discover.


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[Assistant] Ah, the Toyota Corolla. The most sold vehicle in human history, and based on the comments that you'll see on this video and on my other videos, you probably also think that it's my favorite car ever made.


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[Assistant] The Corolla does have a special place in my heart, but so too does every other car.


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[Assistant] We're uploading new videos with new cars all the time, at least once every week. I think it's pretty cool, and I hope you do too, so thanks.


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[Assistant] Any idea what they are? Callum? Looks a bit like meat fruit from here.


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[Assistant] So it's a frozen pumpkin cream, there's roasted pumpkin and a bit of chocolate crumb. I believe you've got a bit of magic to do. A little bit.


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[Assistant] And I wanted to include that some way or another, so I included it in the scent.


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[Assistant] Asa's got a lot to teach and I can smell a couple of tricks coming today, so hopefully I'll learn something.


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[Assistant] And now here they lie, poised at the edge of the unknown.


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[Assistant] Bottle 1, like hundreds of millions of tons of his plastic brethren, ends up in a landfill.


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[Assistant] This huge dump expands each day as more trash comes in and continues to take up space. As plastics sit there being compressed amongst layers of other junk, rainwater flows through the waste and absorbs the water-soluble compounds it contains. And some of those are highly toxic.


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[Assistant] Together, they create a harmful stew called leachate, which can move into groundwater, soil, and streams, poisoning ecosystems and harming wildlife.


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[Assistant] Bottle 2's journey is stranger, but unfortunately no happier. He floats on a trickle that reaches a stream, a stream that flows into a river, and a river that reaches the ocean.


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[Assistant] After months lost at sea, he's slowly drawn into a massive vortex where trash accumulates, a place known as the Great Pacific Garbage Patch.


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[Assistant] This is one of five plastic-filled gyres in the world's seas, places where the pollutants turn the water into a cloudy plastic soup.


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[Assistant] Plastic makes them feel full when they're not, so they starve to death and pass the toxins from the plastic up the food chain.


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[Assistant] For example, it's eaten by lanternfish. The lanternfish are eaten by squid. The squid are eaten by tuna. And the tuna are eaten by us.


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[Assistant] And most plastics don't biodegrade, which means they're destined to break down into smaller and smaller pieces called microplastics, which might rotate in the sea eternally.


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[Assistant] Okay, this sounds pretty bad too, but hang in there. It gets better. The blocks are shredded into tiny pieces, which are washed and melted so they become the raw materials that can be used again.


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[Assistant] Lullaby, lullaby, while your daddy goes for a fly. Go to sleep, you little birdies. One, two, three, four, five. One, two, three, four, five.


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[Assistant] So given this image patch, we'd like to recognize that that's a T. Given this image patch, we'd like to recognize that that's an S.


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[Assistant] And if you use a word processing software, depending on what word processor you use, it might have all of these fonts and many, many more already stored inside. And in fact, if you go to different websites, there are again huge free font libraries on the internet where you can download many, many different types of fonts, hundreds or perhaps thousands of different fonts.


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[Assistant] So after some amount of work, you know, it is a little bit of work to synthesize realistic looking data, but after some amount of work, you can get a synthetic trading set like that.


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[Assistant] And then apply, you know, maybe little blurring operators, little affine distortions, affine meaning just sharing and scaling and little rotation operations. And if you do that, you get a synthetic training set, like the one shown here.


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[Assistant] And so if you use this sort of synthetic data, you have essentially an unlimited supply of label data to train a supervised learning algorithm for the character recognition problem.


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[Assistant] But what you can do is then take this alphabet A or take this image and introduce artificial warpings or artificial distortions into the image so that you can take the image A and turn that into 16 new examples. So in this way you can take maybe a small label of trading sets and amplify your trading set to sort of suddenly get a lot more examples out of it.


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[Assistant] And for the specific example of character recognition, introducing these warping seems like a natural choice. But for a different machine learning application, there may be different distortions that might make more sense. Let me just show one example in a totally different domain of speech recognition.


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[Assistant] So in speech recognition, let's say you have audio clips and you want to learn from the audio clip to recognize what was, what were the words spoken in that clip. So let's say you have one labelled training example.


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[Assistant] Alright, so there's some counting from zero to five, and so, you know, you want to try to apply a learning algorithm to try to recognize the words said and done.


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[Assistant] So how can we amplify the data set? Well, one thing we can do is introduce additional audio distortions into the data set. So here I'm going to add background sounds to simulate a bad cell phone connection. When you hear beeping sounds, that's actually part of the audio track. There's nothing wrong with speakers, but let me play this now.


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[Assistant] Here's another one, and so taking the original clean audio clip, so taking the clean audio of someone saying zero, one, two, three, four, five, we can then automatically synthesize these additional training examples and thus amplify one training example into, you know, maybe four different training examples. So let me just play this final example as well.


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[Assistant] And by synthesizing additional distortions by introducing different background sounds, we've now multiplied this one example into many more examples without much work. By just automatically adding these different background sounds to the clean audio.


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[Assistant] Just one word of warning about, uh, synthesizing data by introducing distortions. Uhm, when you, if you're trying to do this yourself,


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[Assistant] In contrast, usually it does not help to add purely random meaningless noise to your data. Uhm, not sure if you can see this, but what we've done here is taken the image and for each pixel in each of these four images just added some random Gaussian noise to each pixel.


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[Assistant] But if you're trying to decide what sorts of distortions to add, you know, do think about what are the meaningful distortions you might add that will cause you to generate additional training examples that are at least somewhat representative of the sorts of images you expect to see in your test set.


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[Assistant] Finally, to wrap up this video, I just want to say a couple words more about this idea of getting lots of data via artificial data synthesis.


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[Assistant] So what you really want to avoid is to, you know, spend a whole week or spend a few months figuring out how to get a great artificially synthesized dataset only to realize afterward that, you know, your learning algorithms and performance doesn't improve that much even when you give it a huge training set.


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[Assistant] So that's our usual advice of testing that you really can make use of a large training set before spending a lot of effort going out to get that large training set.


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[Assistant] So one useful calculation that I often do is, uhm, ask, you know, how many minutes, how many hours does it take to get a certain number of examples. So I should sit down and figure out, you know, suppose it takes me, uh, 10 seconds to label one example. Then, uhm, and suppose that, uh, my, suppose that for our application currently we have a thousand labeled examples. So 10x as much of that would be an n would equal to 10,000.


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[Assistant] So what I mean by this is I will often sit down and do a calculation to figure out how much time, you know, like just how many hours will it take? How many hours or how many days will it take?


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[Assistant] Then what we'll do is sit down and ask, well, how long does it take me really to collect and label one example? And sometimes maybe it'll take you, you know, 10 seconds, uh, to label one new example.


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[Assistant] And so if I want 10x as many examples, I do a calculation. If it takes me 10 seconds to get one training example, if I want to get 10 times as much data, then I need 10,000 examples, so I do the calculation.


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[Assistant] How long is it going to take me to label, to manually label 10,000 examples if it takes me 10 seconds to label one example?


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[Assistant] So when you do this calculation, often I've seen many teams be surprised at, you know, how little work, sometimes a few days of work, sometimes a small number of weeks of work, but I've seen many teams be very surprised at, uh,


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[Assistant] Sometimes how little work it could be to just get a lot more data and let that be a way to give you learning out from a huge boost in performance. And necessarily, you know, sometimes if you've just managed to do this, you will be a hero in whatever profit development, whatever team you're working on, because this can be a great way to get much better performance.


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[Assistant] Uh, if you want to try to hire many people to fairly inexpensively on the web, label marshmallows and data for you.


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[Assistant] Not always, but sometimes you, you, you may be surprised by how easy that turns out to be. Uh, maybe a few days or a few weeks of work and that can be a great way to give you learning out from a huge boost in performance.


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[Assistant] Four iconic English gardens. Each is the product of one moment in history.


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[Assistant] And each gives us a fascinating window into the century in which they were made and the people who created them. Much more than just a history of gardening, these are extraordinary tales of escape, social ambition, heartbreak, downfall and disaster.


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[Assistant] In unraveling these remarkable stories, we reach back over the centuries to see these four great gardens through fresh eyes.


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[Assistant] Nestled in the West Sussex countryside is one of the most celebrated English gardens of the 20th century. Great Dickster.


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[Assistant] Created at the beginning of the century, it was the final flowering of a movement that looked back to the past to escape industrial Britain.


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[Assistant] Great Dixter was made famous by Christopher Lloyd, the celebrated plantsman and writer, who used this garden as a living laboratory.


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[Assistant] The garden at Great Dixter contrasts rigid formality with wild exuberance. It's a garden that thrives on contradiction. Traditional but experimental. Rooted in the past, but reaching to the future.


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[Assistant] Christopher Lloyd died in 2006, but his garden continues thanks to Great Dickster's head gardener, Fergus Garrett, who's taken on the daunting challenge of keeping Christo's legacy alive.


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[Assistant] Fergus came to work at Great Dix to 21 years ago in 1993.


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[Assistant] He oversees every aspect of the garden, from designing the planting schemes for each area, to directing his team of full time staff and students.


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[Assistant] Christopher Lloyd made Great Dixter world famous, but this unique garden was created a decade before he was born.


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[Assistant] And it was shaped and inspired by a remarkable group of men and women.


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[Assistant] Christopher's father, Nathaniel, who built the family fortune and great dickster by pioneering color printing.


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[Assistant] His driven mother, Daisy, who proudly claimed descent from Oliver Cromwell, and brought that same zeal to everything she grew.


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[Assistant] A visionary writer called William Robinson, whose revolutionary ideas let nature loose in the garden.


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[Assistant] Whose radical sense of colour transformed the English flowerbed.


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[Assistant] At the center of the garden is an extraordinary house.


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[Assistant] In part dating back to the 15th century, it's a classic example of the arts and crafts movement, which celebrated the skills and materials that had been swept aside by the Industrial Revolution.


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[Assistant] Just like the garden, the house bears the mark of the Lloyd family.


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[Assistant] Nathaniel and Daisy arrived from London in 1910 with the dream of creating a unique home for them and their family.


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[Assistant] But the house and garden that's here today is very different from what Nathaniel and Daisy saw when they first came to Dickster.


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[Assistant] What they started with was a dilapidated medieval hall with 450 acres of farmland that was called simply Dickster.


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[Assistant] To help them transform it into the home of their dreams, Nathaniel's masterstroke was to employ the brilliant architect Edwin Lutyens.


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[Assistant] He was famous for his sympathetic restoration of medieval houses, and as the champion of the arts and crafts style.


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[Assistant] ...is trying to discover how a run-down farmhouse was transformed into the centerpiece of one of England's most brilliant gardens.


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[Assistant] Having made his fortune as a lithographic printer, Nathaniel was ready to abandon London for a life in the country, where he could spend his time on his great passions of golf and photography.


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[Assistant] But he was also searching for an outlet for his other great obsession. Medieval architecture. And Dickster was the perfect project.


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[Assistant] Daisy, 14 years younger than her husband.


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[Assistant] Came from a family immersed in horticulture and literature. She'd always envisaged a large family and wanted to create the perfect rural idyll.


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[Assistant] Nathaniel and Daisy Lloyd's dream was to keep the harsh realities of a new century outside their garden gate. But in the real world, a terrible conflict was brewing.


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[Assistant] Historian and writer Andrea Wolff has been searching through the family archives to try and discover how their new refuge, Great Dixter, would face up to the challenge of a world war.


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[Assistant] Daisy was perfectly suited to the role of family matriarch.


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[Assistant] She relished the responsibility and power that came with being a wife and mother writing


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[Assistant] When I was twelve or thirteen, I conceived a secret ambition to be the best mother in the world, and have the most beautiful children. If I hadn't married, I would have been a school mom. Instead, I had a class of six.


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[Assistant] And in an age when the battle for sexual equality was just brewing, Daisy stood toe to toe with her older successful husband.


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[Assistant] Ideally, an arts and crafts house and garden were designed by the same person, and Dickster's architect, Edwin Lutyens, managed seamlessly to integrate the two into a single entity.


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[Assistant] To try and understand how we achieved this, garden designer Chris Beardshaw has got his hands on Lutyen's original designs.


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[Assistant] Lutyens garden wraps itself around the old manor house, embracing the sloping terrain and the old buildings.


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[Assistant] To the right there are two walled rooms, and to the left


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[Assistant] There's a series of stepped compartments with U hedges. All these spaces are secluded and look inwards, but never lose touch with the house.


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[Assistant] At the back and outside of these formal rooms is one of Dixter's most famous features, the celebrated long border, which runs almost the entire width of the garden.


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[Assistant] Below there are rolling meadows and orchards that open up the garden to the countryside, a walled rose garden, and a topiary lawn.


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[Assistant] Lutyens didn't actually prescribe much detail for these spaces, but he didn't need to.


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[Assistant] His brilliant design gave the garden its bone structure, and the loids a perfect canvas to express their personality and ideas.


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[Assistant] Alan Power has been examining the role that you hedgers play in the garden.


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[Assistant] Walking through this garden is like attending an extraordinary performance.


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[Assistant] The U-Hedges can seem overpowering, directing where you go and what you see.


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[Assistant] But in high summer, even they struggle to contain the exuberant planting and colors that flood through Great Dixter, as Chris Beardshaw's been discovering.


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[Assistant] Stepping out of the high garden is like coming up for air.


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[Assistant] For the first time you can see open sky and the rolling countryside that surrounds Great Dixter.


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[Assistant] You're suddenly in a different world, dominated by one of the most beautiful and unique features. The wild meadows.


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[Assistant] Unrestrained and overflowing with wild flowers and insects. The meadows are a perfect counterpoint to the claustrophobia of the upper gardens.


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[Assistant] Who rejected the formality of Lutyen's plans for traditional lawns in favor of something wilder and less conventional.


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[Assistant] But her inspiration came from a hugely influential gardener and writer called William Robinson.


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[Assistant] Born in Ireland in 1838, Robinson moved to London as a young man, quickly earning a reputation as a brilliant botanist and gardener. By his mid-twenties


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[Assistant] Outspoken, opinionated, and fiercely energetic.


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[Assistant] By the age of thirty he was publishing his own gardening magazine.


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[Assistant] Which he followed with his first book, The Wild Garden.


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[Assistant] The Wild Garden became a best-seller, helping Robinson earn enough money to buy a country house in West Sussex called Grave-Tie Manor. In the opening chapter, he set out his stall.


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[Assistant] The gardener must follow the true artist, however modestly, in his respect for things as they are, in delight of natural form and beauty of flower and tree. If we are to be free from barren geometry, if our gardens are forever to be true pictures,


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[Assistant] He spent the next forty years transforming the grounds and gardens into a living illustration of his ideas.


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[Assistant] At Gravetide, Robinson encouraged untidy edges to allow the garden to blend into the larger landscape.


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[Assistant] He grew wild meadows where the garden appeared to merge into the surrounding woodland and lake.


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[Assistant] Robinson was a fierce critic of carpet bedding, a flamboyant, high Victorian style of gardening, which was typified by densely packed, brightly coloured planting set in geometric patterns.


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[Assistant] In stark contrast in his garden he used hardy perennial plants.


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[Assistant] Which were encouraged to express their true nature and personality.


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[Assistant] But under the subtle and almost invisible hand of the gardener.


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[Assistant] His book's popularity was largely due to Robinson's promise that wild gardening could be easy and beautiful.


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[Assistant] and that it followed nature which he considered the source of all true design.


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[Assistant] Robinson's philosophy really struck a chord with Daisy Lloyd.


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[Assistant] And at Great Dickster, the influence of his radical ideas is still very clear.


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[Assistant] The wild meadows are Daisy Lloyd's greatest legacy at Dickster.


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[Assistant] Her husband Nathaniel was drawn to something much more structured and architectural.


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[Assistant] His great passion was box hedging and topiary Nathaniel filled the garden with his obsession


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[Assistant] Creating some of its most distinctive characters, and even writing a book about it that's still in print today.


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[Assistant] This remarkable garden had been taking shape for ten years by the time that Christopher Lloyd was born in 1921.


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[Assistant] Christo, as he became known, was the sixth and last child of Daisy and Nathaniel.


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[Assistant] And he would make Great Dickster world famous.


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[Assistant] His first sight of what would become his life's greatest work was from the crawling window in the nursery.


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[Assistant] This had been specially designed by Edwin Lutyens to allow the Lloyd infants to look out on the garden as soon as they could crawl.


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[Assistant] Right from the start, Daisy seemed intent on sharing her passion for gardening with her youngest son.


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[Assistant] Daisy Lloyd taught her son embroidery and letter writing, but more importantly, she taught him the language of plants.


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[Assistant] Under her loving gaze, he began a lifelong study of their colors, shapes, and textures, their habits, likes and dislikes.


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[Assistant] Andrea Wolfe has been searching through the Great Dixter Archives to discover more about this formative relationship between Christopher and his mother.


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[Assistant] In later life when Christo became a celebrated writer, he would credit letter writing and his mother for giving him his gift for words. But from the very beginning,


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[Assistant] At the age of nine, Christo was sent away to prep school, and for the next 70 years he remembered that day as one of the most traumatic in his life.


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[Assistant] Christo may have been out of Daisy's sight, but their daily correspondence made certain that she and great Dickster were never far from his mind.


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[Assistant] This little boy's instructions to his mother hint at the powerful gardener Christopher would eventually become.


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[Assistant] But he would struggle for decades to make the garden his own.


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[Assistant] After school, Christo studied modern languages at Cambridge, but World War II intervened.


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[Assistant] Christo was sent to Africa and India, while his beloved great Dixter became home for evacuee children.


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[Assistant] After a non-eventful war, he finished his degree and came home. By now in his mid-twenties.


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[Assistant] Christo was struggling to come to terms with his sexuality.


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[Assistant] Homosexual in orientation. He lived in an age when same-sex relationships were not just a social taboo. They were illegal. He was equally unsure about his path in life.


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[Assistant] To a young upper middle class man, gardening was not considered a proper profession.


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[Assistant] But Christo decided to abandon propriety and try and make a career from the one thing he was genuinely passionate about.


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[Assistant] By his late twenties, he'd got a degree in horticulture from Wye College.


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[Assistant] Christopher was ready to begin transforming Great Dixter and the world of gardening.


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[Assistant] But first, he would have to wrest control of the garden from the most powerful figure in his life.


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[Assistant] Christo's first great battleground was the famous Long Border. Dividing the upper garden from the meadows below, it ran almost the entire width of the garden.


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[Assistant] Under Daisy's iron rule, it had become one of the garden's most celebrated features.


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[Assistant] It drew its inspiration from one of the greatest gardeners of all time, Gertrude Gickel.


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[Assistant] Jigla's impressionistic use of color and texture revolutionized garden design, as seen here in her garden at Munstead Wood.


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[Assistant] By studying in great detail the shapes and forms of plants she began to combine them in subtle new ways, breaking away from the brashness and rigidity of the popular Victorian planting style.


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[Assistant] She was a prolific writer, contributing to country life, and writing a hugely influential chapter on colour in William Robinson's bestseller The Wild Garden.


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[Assistant] She commissioned the architect, Edwin Lutyens, to design a home for her garden at Monstead Wood.


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[Assistant] It began a collaboration that produced some of the most famous houses and gardens of the time.


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[Assistant] Jekyll's philosophy shaped Lutyens and his designs, and she created planting schemes for many of his most famous commissions. But not Great Dickster.


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[Assistant] It seems that Daisy Lloyd wanted to put her own stamp on the garden, so Giggle was never actively involved, though the Lloyds bought plants from her and drew inspiration from her ideas.


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[Assistant] However by the time that Christopher came into his own, a rebellion was brewing.


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[Assistant] Christopher's design still had a direct relationship with Jekyll, but he moved them on to a new level.


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[Assistant] This adventurous approach to combining plants is largely what has made Christopher Lloyd and Great Dickster famous.


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[Assistant] Today, Fergus encourages his team to have that same analytic lie and apply that spirit of experiment throughout the garden.


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[Assistant] One of Dixter's most famous features that provides the perfect opportunity to hone those skills is the pot displays.


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[Assistant] Two of Fergus's team, Rachel and Yannick, are remodeling the display around the medieval porch.


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[Assistant] Christo's growing mastery of colour gave him the confidence to step out of his predecessor's shadow.


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[Assistant] And begin to reshape the garden to his own tastes.


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[Assistant] The neatly clipped putting lawn and toe-perry at the back of the house had always epitomized his father Nathaniel.


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[Assistant] Christo decided to convert the lawn into a wild meadow.


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[Assistant] Transforming this part of the garden by counterpointing his father's traditional formality with a new freedom.


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[Assistant] As he grew even bolder and more confident, Christo took on other parts of his father's legacy.


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[Assistant] In 1921, Christopher's father Nathaniel created his own signature section of the garden.


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[Assistant] On Lutjen's plans, this area was set out as a simple rectangular croquet lawn.


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[Assistant] But drawing on his design experience and passion for architecture, Nathaniel created what became known as the Sunk Garden.


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[Assistant] This had been softened by Daisy, but it was Christo who really transformed this part of the garden.


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[Assistant] As great Dickster and Christo went from strength to strength, word of his growing virtuosity spread fast. Christo had always been comfortable with pen and paper.


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[Assistant] So when the magazine Country Life approached him to write a weekly article, he didn't hesitate. With Great Dickster as his muse and laboratory, it was the beginning of a brilliant new career.


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[Assistant] If a plant bores you, something must be done about it.


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[Assistant] The simplest course, if it belongs to you, is to throw it out. If it's someone else's, look the other way.


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[Assistant] If it belongs to someone you rather dislike anyway, don't be ashamed to let it confirm you in an inclusive repulsion.


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[Assistant] Christopher's writing raised his profile and brought him to the attention of some of the most brilliant gardeners around.


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[Assistant] By the time he wrote his classic book, The Well Tempered Garden,


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[Assistant] Beth Chatto had already carved a reputation for herself as a designer and writer, but his words made a remarkable impression.


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[Assistant] Beth's personality and garden couldn't have been more different from those of Christopher and Digster. Unlike him, she hadn't inherited a garden, but created it from scratch just a decade before they first met.


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[Assistant] Inspired by environmental ideas and her husband's botanical expertise, she transformed a couple of fields on the family fruit farm into one of the most unique and innovative gardens in the world.


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[Assistant] Working with difficult terrain and land that was too dry to farm, she used her brilliant knowledge of plants to create a garden that required no watering and very little maintenance.


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[Assistant] Called the Dry Garden, it became famous all over the world.


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[Assistant] And over the next decade she extended her garden to include a range of innovative and environmentally sensitive designs.


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[Assistant] In sharp contrast to Christo and Dickster, Beth Chateau's gardens are serene and delicately composed, but despite the contrast, or maybe because of it, they became firm friends.


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[Assistant] Daisy had always maintained that people made too much of money and death. So there was no funeral.


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[Assistant] And the day after she passed away, Christopher drove up to Scotland to give a lecture on hardy perennials. For more than half a century she had dominated his world, so it must have been a huge loss. But, for the first time in his life, Christopher was truly the master of his own destiny.


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[Assistant] Added to his hectic social life, Christopher continued to write books and articles as well as lecturing all over the world.


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[Assistant] Great Dixter continued to be a source of inspiration, but by the time Christopher reached his seventies, the relentless intensity of the Dixter style of gardening was beginning to exhaust even him. He and the garden needed fresh blood.


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[Assistant] In 1993, Christo invited a brilliant young gardener called Fergus Garrett to come to work at Great Dixter.


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[Assistant] Their extraordinary collaboration would raise the garden to new heights.


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[Assistant] Freeing Christo to express his talent in a way that had never been possible.


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[Assistant] With his new protege, Fergus, by his side, Christopher was ready to finally break free from the past.


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[Assistant] He began by turning his gaze to one of the most traditional parts of the garden.


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[Assistant] For 80 years, the old rose garden had been a celebrated feature in Great Dijkstra.


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[Assistant] Designed by Lutyens and surrounded by Nathaniel's new hedges, it's ten beds filled with Daisy's favorite roses,


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[Assistant] The Rose Garden epitomized the people that shaped Christo and Dickster. With the help of his new partner in crime, he ripped it out.


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[Assistant] Christo described the moment in country life. The rending noise of huge old roots.


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[Assistant] Each year since then, the exotic garden is replanted.


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[Assistant] For over a decade, Fergus and Christo worked together to make Great Dixter into the most breathtaking and exciting garden in England.


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[Assistant] Their remarkable collaboration ended in 2006.


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[Assistant] When Christo died at the age of 84 after a short illness.


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[Assistant] Great Dijkstra is now managed by a charitable trust, and under Fergus's creative leadership,


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[Assistant] The garden grows more vibrant and beautiful every year.


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[Assistant] Christo's signature act of rebellion, the Exotic Garden.


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[Assistant] Epitomizes the spirit of adventure that still pulses through great digster.


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[Assistant] Next time, one of the grandest landscape gardens, Stowe. Radical, provocative, monumental. It broke free from centuries of formality and put English gardens centre stage.


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[Assistant] In this section, we will take a look behind the scenes of JavaScript and learn how the language actually works.


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[Assistant] So, throughout this section, we will take your basic knowledge of JavaScript and elevate it to a completely new level, where you will then understand exactly how everything that you learned before actually works under the hood.


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[Assistant] And this is actually extremely important, because this knowledge will really empower you to write better code and to understand other developers' code. And so in the end, it will make you a better, more confident and more successful developer.


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[Assistant] Now, I'm telling you all this because some people find this kind of theory lectures boring for some reason.


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[Assistant] But they are really not. And I actually invested well over a month into building this section alone.


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[Assistant] And so that might show you just how crucial this information is. And finally, knowing how JavaScript really works will also set you apart from the competition, because many other junior developers simply don't know about much of this stuff.


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[Assistant] If you do leave today, what's the first thing you're gonna cook when you get home?


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[Assistant] First up, we have to commend the three of you. That was one tough, long, incredible pressure test.


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[Assistant] That guy is making me nervous. He's really creepy looking.


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[Assistant] And we also spent quite a lot of time talking about unsupervised learning algorithms like k-means clustering, principal components analysis, or dimensionality reduction, and anomaly detection algorithms for when you have only unlabeled data XI. Although anomaly detection can also use some labeled data to evaluate the algorithm.


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[Assistant] We also spent some time talking about special applications or special topics like recommender systems and large scale machine learning systems including parallelized like map review systems as well as some special applications like sliding windows object classification for computer vision.


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[Assistant] And finally, we also spent a lot of time talking about different aspects of sort of advice on building a machine learning system. And this involved both trying to understand what is it that makes a machine learning algorithm work or not work. So we talked about things like bias-variance and how regularization can help with some variance problems. And we also spent a lot of time talking about how


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[Assistant] Question of how to decide what to work on next or how to prioritize how you spend your time when you're developing a machine learning system.


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[Assistant] And we also spend a lot of time talking about debugging learning algorithms and, uh, making sure that your learning algorithm is working. So we talk about, you know, diagnostics like, uh, learning curves and also talked about, uhm,


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[Assistant] Things like error analysis and ceiling analysis and so all of these were different tools for helping you to decide what to do next or how to spend your valuable time when you're developing a machine learning system.


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[Assistant] And so in addition to having the tools of machine learning at your disposal, sort of knowing the tools of machine learning, like supervised learning, unsupervised learning and so on, I hope that uhm, you now not only have the tools, but that you know how to apply these tools really well to build powerful machine learning systems. So that's it.


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[Assistant] Those were the topics of this class, and if you've worked all the way through this course, you should now consider yourself an expert in machine learning.


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[Assistant] As you know, machine learning is a technology that's having a huge impact on the science technology industry.


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[Assistant] You're now well qualified to use these tools of machine learning to create a factor.


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[Assistant] I hope that many of you in this class will find ways to use machine learning to build cool systems and cool applications and cool products and to hope that you find ways to use machine learning not only to make your life better but maybe someday to use it to make many other people's lives better as well.