[User] Please transcribe this text

[Assistant] Where we take trillions of tons of carbon from underground and put it in the atmosphere and oceans. This is an insane experiment. It's the dumbest experiment in human history. Why are we doing this? It's crazy.


[User] Please transcribe this text

[Assistant] The things like oil, oil, coal, gas, it's the easy money. It's the easy money.


[User] Please transcribe this text

[Assistant] You know, it's very difficult to put that CO2 back in the ground. It doesn't like being in solid form. It takes a lot of energy.


[User] Please transcribe this text

[Assistant] Is that possible? No, no, it doesn't, it's not possible. No. Nope. Nope. Nope, definitely not. It's not fucked. No, we're not fucked.


[User] Please transcribe this text

[Assistant] I mean, this is quite a complex question. Right.


[User] Please transcribe this text

[Assistant] The more carbon we take out of the ground and add to the atmosphere, and a lot of it gets permeated into the oceans, the more dangerous it is. Like, I don't think right now, I think we're okay right now. We can probably even add some more.


[User] Please transcribe this text

[Assistant] But the momentum towards sustainable energy is too slow. There's a vast base of industry


[User] Please transcribe this text

[Assistant] Vast transportation system, like there's, there's two and a half billion cars and trucks in the world.


[User] Please transcribe this text

[Assistant] So and the new car and truck production, if it was 100% electric, that's only about 100 million per year. So it would take, if you could snap your fingers and instantly turn all


[User] Please transcribe this text

[Assistant] All cars and trucks electric, it would still take 25 years to change the transport base to electric.


[User] Please transcribe this text

[Assistant] Makes sense, because how long does a truck, car, truck last before it goes into the junkyard and gets crushed? About 20 to 25 years.


[User] Please transcribe this text

[Assistant] The thing that is going on right now is that there is an inherent subsidy in any oil-burning device. Any power plant or car is fundamentally, uh, consuming the carbon capacity of the oceans and atmosphere.


[User] Please transcribe this text

[Assistant] I'll just say AppSphere for short. Uhm, so like, you can say, okay, there's a certain probability of something


[User] Please transcribe this text

[Assistant] Bad happening past a certain carbon concentration in the atmosphere.


[User] Please transcribe this text

[Assistant] And so there's some uncertain number where if we put too much carbon in the atmosphere, things overheat, uh, oceans warm up, ice caps melt, ocean real estate becomes a lot less valuable, you know, things are underwater.


[User] Please transcribe this text

[Assistant] And, and, but it's not clear what that number is, but it's definitely a scientist's little, it's really quite...


[User] Please transcribe this text

[Assistant] Overwhelming. I mean, I don't know any serious scientists, actually zero, literally zero, who don't think that.


[User] Please transcribe this text

[Assistant] You know, that, that we were, have quite a serious climate risk that we're facing. And so.


[User] Please transcribe this text

[Assistant] There's fundamentally a subsidy occurring with every fossil fuel burning thing. Power plants, aircraft.


[User] Please transcribe this text

[Assistant] Car, frankly, even rockets. I mean, rockets use up, you know, they burn fuel. But there's just, you know, with rockets, there's just no other way to get to orbit, unfortunately. So it's the only way. But with cars, there's definitely a better way with electric cars.


[User] Please transcribe this text

[Assistant] And to generate the energy, do so with photovoltaics because we've got a giant thermonuclear reactor in the sky called the Sun.


[User] Please transcribe this text

[Assistant] So if you can generate energy from solar panels stored with batteries, you can have energy 24 hours a day.


[User] Please transcribe this text

[Assistant] And then you can, you know, can send it to the poles or near to the north, uh, with the high voltage lines. Also the northern parts of the world tend to have a lot of hydropower as well.


[User] Please transcribe this text

[Assistant] Uhm, but anyway, all fossil-fuel powered things have an inherent subsidy, which is their consumption of the carbon capacity of the atmosphere and oceans.


[User] Please transcribe this text

[Assistant] So, people tend to think like, why, why should electric vehicles have a subsidy? But they're not taking into account that all


[User] Please transcribe this text

[Assistant] Fossil fuel burning vehicles fundamentally are subsidized by the cost, the environmental cost, to Earth.


[User] Please transcribe this text

[Assistant] But nobody's paying for it. We are going to pay for it, obviously. In the future, we will pay for it. It's just not paid for now.


[User] Please transcribe this text

[Assistant] Yeah, I've gotta scale up production. Gotta make the car compelling.


[User] Please transcribe this text

[Assistant] Make it better than gasoline or diesel cars.


[User] Please transcribe this text

[Assistant] No, we could totally make one right now that would do 600 miles, but the thing is too expensive.


[User] Please transcribe this text

[Assistant] Yeah, they're like 30 miles an hour, 30. It's like on level ground with...


[User] Please transcribe this text

[Assistant] You pump the tires up really well and go on a smooth surface and, and you can go for a long time. Um, but you can like definitely comfortably do 300 miles. This is fine for most people. Uh, usually 200 or 250 miles is fine. 300 miles is, you don't even think about it really.


[User] Please transcribe this text

[Assistant] Well, the surface area of a car is, without making the car like really blocky, uhm, or having some- Like a G-Wagon.


[User] Please transcribe this text

[Assistant] Yeah, like, and, and just like, I've been looking at a lot of surfaces where like maybe like solar panels fold out or something.


[User] Please transcribe this text

[Assistant] Well at the beginning of Tesla I did want to have this like unfolding solar panel thing that you'd press a button and it would just like unfold these solar panels and like charge, recharge your car in the parking lot.


[User] Please transcribe this text

[Assistant] Yeah, we could do that, but I think it's probably better to just put that on your roof.


[User] Please transcribe this text

[Assistant] Yeah, I mean, I think it could like recharge like two miles a day or something.


[User] Please transcribe this text

[Assistant] Yeaah, there was a guy in Kazakhstan that, uh,


[User] Please transcribe this text

[Assistant] I think it was Kazakhstan that he, he just boated through a tunnel, underwater tunnel, like a flooded tunnel, and just...


[User] Please transcribe this text

[Assistant] Turn the wheels to steer and press the accelerator and it just floated through the tunnel and he's wow, steered around the other cars. You're like, that's amazing. It's on the internet.


[User] Please transcribe this text

[Assistant] Generally won't go sideways. It won't. No. Why not? It will correct itself before it goes sideways. Even in black eyes?


[User] Please transcribe this text

[Assistant] Yeaah, there's videos where you can see the car, the, uh,


[User] Please transcribe this text

[Assistant] That alone. Traction control system is very good. It makes you feel like Superman. It's great. You like, feel like you can, like, it will make you feel like this incredible driver.


[User] Please transcribe this text

[Assistant] We do our testing on like an ice lake in Sweden. Oh really? Yea, in like Norway and Canada and a few other places.


[User] Please transcribe this text

[Assistant] Well, electric cars have really great traction control because the reaction time is so fast.


[User] Please transcribe this text

[Assistant] So with a, with a gasoline car, you've got a lot of latency. Uh, it takes a while for the, the engine to react and for the, you know, but for electric motors, incredibly precise. Uhm, that's why, like, you can imagine, like, if you had like a printer or something,


[User] Please transcribe this text

[Assistant] Uh, you would only, you wouldn't have a gasoline engine printer. That would be pretty weird.


[User] Please transcribe this text

[Assistant] Uhm, or, or like a surgical device. It's gonna be an electric motor on the surgical device on the printer.


[User] Please transcribe this text

[Assistant] The gasoline engine's gonna be just chugging away. It's not gonna have the reaction time.


[User] Please transcribe this text

[Assistant] But to an electric motor, it's operating at the millisecond level. So it can turn on track, on and off traction within like inches of getting on the honest, like let's say driver patch of ice. It'll turn traction off and then turn it on a couple of inches right after the ice. Like if there's a little patch of ice.


[User] Please transcribe this text

[Assistant] Because in, in the frame of the electric motor, you're moving incredibly slowly. You're like a snail, you're a snail. You're just moving so slowly. Because it's, it's, it can see at a thousand frames a second.


[User] Please transcribe this text

[Assistant] Yes. I'm on board. Fuck motors. Fuck regular motors. The S, X, and 3 have the lowest probability of injury of any cars ever tested by the US government.


[User] Please transcribe this text

[Assistant] Yeah, but it's pretty funny, it's pretty crazy, like we, you know, people still sue us, like they'll have like some accident at 60 miles an hour where they've like twisted an ankle and they slew, they like, they'll be dead in another car. They still sue us.


[User] Please transcribe this text

[Assistant] So yeah, I've got quite a lot of respect for the justice system. Judges are very smart and they see they as like, I haven't so far. I've found judges to be very good at justice because I like, like, like what, like, and jewelry is a good too. Like they're actually quite good. Um, you know, people.


[User] Please transcribe this text

[Assistant] You know, you read about, like, occasional errors in the justice system. Let me tell you, most of the time, they're very good. Um, and, and like the, you know, the guy mentioned that, who fell asleep in the car and he, he rode over a cyclist.


[User] Please transcribe this text

[Assistant] And, you know, and that was what encouraged me to get autopilot out as soon as possible. That guy sued us.


[User] Please transcribe this text

[Assistant] Yes, he, he, I'm not kidding, he blamed it on the new car smell.


[User] Please transcribe this text

[Assistant] Yes, he got a lawyer and he sued us and the judge was like, you're, this is crazy. Stop bothering me. No.


[User] Please transcribe this text

[Assistant] Is this an elected judge or a, or, cause like some people say they have a judge that's like actually a politician. No he's elected.


[User] Please transcribe this text

[Assistant] I do think it's this private prisons thing is creating a bad incentive.


[User] Please transcribe this text

[Assistant] I mean, that judge is in prison. Thank God. Yes. For people who think perhaps the justice system consists entirely of judges like that, I want to assure you, this is not the case. The vast majority of judges are very good. I agree. And they care about justice, and they could have made a lot more money if they wanted to be a trial lawyer.


[User] Please transcribe this text

[Assistant] And instead they cared about justice and they made less money because they care about justice. And that's why they're judges.


[User] Please transcribe this text

[Assistant] No, most, most police are, are, are, are very honest. Yes. Uhm, and, uh, and, and, and, and, like, the military, the military personnel that I know are very honorable, ethical people, and much more honorable and ethical than the average person. That's my impression.


[User] Please transcribe this text

[Assistant] But, and that's not to suggest that we be complacent and assume everyone's honest and ethical.


[User] Please transcribe this text

[Assistant] And obviously if somebody is, is given a, a trusted place in society, such as being a police officer or a judge, and they are corrupt, then we must be extra vigilant against such situations. Yes. And take action. Uh, but, but we should not think that this is somehow broadly descriptive of people in that profession.


[User] Please transcribe this text

[Assistant] Yes, I mean, if, you know, if you're a prosecutor, you should not always want to win. There are times when you should like, okay, I should not want to win this case, uhm, and then...


[User] Please transcribe this text

[Assistant] Man, there's some, there's some terrible human beings out there. Uhm, and he was like, yep. And he was like dealing with some case, which consisted of a couple of old ladies that would


[User] Please transcribe this text

[Assistant] Run people over somehow for insurance money. It was rough. I was like, wow, that's pretty rough.


[User] Please transcribe this text

[Assistant] It's like, it's like hard to maintain faith in humanity if you're a district attorney, but, but you know, it's only a few percent of society that are actually bad. And then if you go to the worst, say 0.1% of society, although


[User] Please transcribe this text

[Assistant] Like how bad is the millionth worst person in the United States? Pretty damn bad. Like damn evil. Like the millionth, like the millionth, well one in a million of evil is so evil, evil cannot even conceive of it.


[User] Please transcribe this text

[Assistant] But there's 330 million people in the United States, so that's 330 people out there somewhere.


[User] Please transcribe this text

[Assistant] By the same token, there's also 330 people who are incredible angels and unbelievably good human beings.


[User] Please transcribe this text

[Assistant] It is. It is a very dangerous... I really think...


[User] Please transcribe this text

[Assistant] You know, people should give other people the benefit of the doubt and assume that they're good until proven otherwise. And I think really most people are actually pretty good people. Nobody's perfect. They have to be.


[User] Please transcribe this text

[Assistant] Yeah, uh, you know, we got a big, uh, test site in central Texas near Waco. Oh, beautiful. Yeah, SpaceX, uh, in McGregor, uhm, it's about 15 minutes away from Waco.


[User] Please transcribe this text

[Assistant] You know, we have lots of fire and loud explosions and things and people are cool with it.


[User] Please transcribe this text

[Assistant] You know, I wish, I wish politicians were better at science. That would help a lot.


[User] Please transcribe this text

[Assistant] Yes. There's no incentive for them to be good at science. There isn't.


[User] Please transcribe this text

[Assistant] Uhm, actually you know they're pretty good at science in China, I have to say.


[User] Please transcribe this text

[Assistant] Yeah, the mayor of Beijing is, I believe, an environmental engineering degree, and the deputy mayor is a physics degree. I met them. And, uh,


[User] Please transcribe this text

[Assistant] Well, I think, like, I guess if you're, if you're, uh, if you have like


[User] Please transcribe this text

[Assistant] Give a rat's ass what porn you watch. They do not care.


[User] Please transcribe this text

[Assistant] So, like, what secrets does a national spy agency have to learn from the average citizen? Practically nothing.


[User] Please transcribe this text

[Assistant] Not really. I don't think we should worry too much about Huawei phones.


[User] Please transcribe this text

[Assistant] You know, our national security agencies shouldn't have Huawei phones. Maybe that's a question mark. Uh, but, uh, I think for the average citizen, it doesn't matter.


[User] Please transcribe this text

[Assistant] I'm pretty sure the Chinese government does not care about the average American citizen.


[User] Please transcribe this text

[Assistant] Do people want privacy? Cause they seem to put everything on the internet. Well, right now they're-


[User] Please transcribe this text

[Assistant] Yeah, I think we really need to worry about security in that situation. For sure. That's like a security reparamount.


[User] Please transcribe this text

[Assistant] But, there's actually, like I think people, there's like not that much that's kept private that people, that is actually relevant. Right. That people, other people actually care about. I mean, I think other people care about it, but they don't really care about it, and certainly governments don't.


[User] Please transcribe this text

[Assistant] The things that we think are important now will probably not be important in the future, but there will be things that are important. What will be more important? There might be some war of ideas, potentially. I don't think Darwin's going away.


[User] Please transcribe this text

[Assistant] It would just be a different, a different arena. Different arena.


[User] Please transcribe this text

[Assistant] Different arena. Darwin's not going away.


[User] Please transcribe this text

[Assistant] Well, it's quite hard to run companies. Especially car companies, I have to say. It's quite challenging.


[User] Please transcribe this text

[Assistant] Not that SpaceX, SpaceX is no walk in the park, but, but a car company, it's very difficult to keep a car company alive. It's very difficult. You know, there's only two car companies in the history of American car companies that haven't gone bankrupt and that's Ford and Tesla. That's it.


[User] Please transcribe this text

[Assistant] 2008 is not a good time to be a car company, especially a startup car company and especially an electric car company. That was like stupidity squared.


[User] Please transcribe this text

[Assistant] Yeah, we had like, uh, he was a highly modified Elise chassis. The body was all completely different. By the way, that was a super dumb strategy that we actually did.


[User] Please transcribe this text

[Assistant] Based on two false premises, uh, one false premise was that we'd be able to, uh, cheaply convert the Lotus Elise, uhm, and use that as, uh,


[User] Please transcribe this text

[Assistant] The AC propulsion technology did not work in production and we ended up using none of it in the long term. None of it. We had to redesign everything.


[User] Please transcribe this text

[Assistant] And then the, once you add a battery pack and electric motor to the car, it got heavier, got 30% heavier, invalidated the entire structure, all the crash structure, everything had to be redone.


[User] Please transcribe this text

[Assistant] Nothing, like, I think it had less than 7% of the parts were common with any other device, including cars or anything.


[User] Please transcribe this text

[Assistant] Yes, the, the, the windscreen. The windscreen. No, I think the windscreen is the same. Yes, I think, I think we were able to keep the windscreen.


[User] Please transcribe this text

[Assistant] Every body panel was different. The entire structure was different. The, uh, we couldn't use the H, like the HVAC system, the, you know, the air conditioner. It was a belt driven air conditioner. So now we needed something that was electrically driven. We needed a new AC compressor.


[User] Please transcribe this text

[Assistant] Yeah, we need a small, highly efficient air conditioning system, uhm, that fit in a tiny car and was electrically powered, not belt driven. It was very difficult.


[User] Please transcribe this text

[Assistant] Uhm, it was about 50, well, there were different versions of the car. Uhm, so it was about 55 on the rear. That's not bad.


[User] Please transcribe this text

[Assistant] Yeah, the 9-11, like the joke, is like they managed to do it despite Newton not being on their side. If you're fighting Newton, it's very difficult. It's like you've got those, the moments of inertia on a 9-11 don't make any sense. They do once you understand them.


[User] Please transcribe this text

[Assistant] With, with something that's, where the engine is mounted over the rear axle or off the rear axle towards the rear, is that your portal moment of inertia is fundamentally screwed. You cannot solve this. It's unsolvable. You're screwed. Portal moment of inertia is, you're screwed. Right. Like, essentially, if you spawn the car like a top, that, that's your portal moment of inertia. You're just, I promise I wouldn't swear on this show, by the way.


[User] Please transcribe this text

[Assistant] Yeah. But anyway, like, like the Porsche, it's kind of incredible how well Porsche handles given that, uh, it's the physics, the moments of inertia aren't so messed up.


[User] Please transcribe this text

[Assistant] To actually still make it work well is incredible.


[User] Please transcribe this text

[Assistant] That was really great. The typical lag was, is like, you know, if you floor it, like phone home, call your mom.


[User] Please transcribe this text

[Assistant] I mean, you should try Model S P100D. Go blow your mind out of your skull.


[User] Please transcribe this text

[Assistant] No, it's super easy. It's like, yeaah. It's like a dryer plug. It's like a dryer outlet.


[User] Please transcribe this text

[Assistant] Yeah, yeah, we're, I have it on my roof right now, actually. I'm just trying it out. But it's like, it's the thing, it's, it takes a while to like test roof stuff because roofs have to last a long time. Right. So like you want your roof to last like at least 30 years. Could you put it over a regular roof? Now, so there's two versions. There's like the solar panels you put on a roof. So like, it depends on whether your roof's new or old. So if your roof's,


[User] Please transcribe this text

[Assistant] Roof's new, you don't want to replace the roof, you want to put like, solar panels on the roof.


[User] Please transcribe this text

[Assistant] Uhm, so that's like retrofit, you know. And then we're trying to make the retrofit panels look real nice. Uhm, and then, but then the new product we're coming out with is if you have a roof that's either you're building a house or you're gonna replace your roof anyway, then you make the tiles have solar cells embedded in the tiles. Uhm, and then,


[User] Please transcribe this text

[Assistant] It's quite a tricky thing because you want to not see the solar cell behind the glass tile, uh, so you have to really work with the


[User] Please transcribe this text

[Assistant] See, if you look closely, you can see if you're zooming, like you can see the, so you can see the cell. But if you zoom out, you don't see the cell.


[User] Please transcribe this text

[Assistant] See? That's hard. That's really hard. Because you have to have sunlight go through, but when it gets reflected back out, it hides the fact that there's a cell there.


[User] Please transcribe this text

[Assistant] It depends on your energy on how efficient... Expansion, sure. Yeaah, yeaah. So, uhm...


[User] Please transcribe this text

[Assistant] Generally, yes, I would say it's probably for most, it's gonna vary, but anywhere from


[User] Please transcribe this text

[Assistant] More than you need to, uh, maybe half. Like, call it half to 1.5 of the energy that you need.


[User] Please transcribe this text

[Assistant] Are you air conditioning rooms when they don't need to be air conditioned, which is very common cause there's a pain in the neck, you know, it's like programming a VCR, it's like...


[User] Please transcribe this text

[Assistant] You know, it's just the blinking 12, so people are just like, the hell with that, I'm just gonna make it this temperature all day long. Right.


[User] Please transcribe this text

[Assistant] Yeah, it should predict when you're gonna be home, uh, and then cool the room, the rooms that you're likely to use with a little bit of intelligence. We're not talking about like,


[User] Please transcribe this text

[Assistant] Genius home here, we're just talking like elementary basic stuff. Right.


[User] Please transcribe this text

[Assistant] Yeaah. Yeaah, we do. But we need to hook it into the air conditioning to really make the air conditioning work.


[User] Please transcribe this text

[Assistant] Yeah, well, you know, uhm, like there's the self-winding watches where, you know, it's just got a weight in the watch and as you move your wrist, the weight moves from one side to the other and it winds the watch up. That's a pretty cool thing.


[User] Please transcribe this text

[Assistant] Yeah, you could do the same thing and create a little charger that's based on, uh, wrist movement. It really depends on how much energy your watch uses.


[User] Please transcribe this text

[Assistant] Okay, it seems like they should really, uh, recalibrate that because if it's always fast, you can just, you know, delete those minutes.


[User] Please transcribe this text

[Assistant] It's kind of amazing that you can keep Ty mechanically on a wristwatch. With these tiny little gears.


[User] Please transcribe this text

[Assistant] I like gears. I love them. I think they're beautiful.


[User] Please transcribe this text

[Assistant] Okay, so it's like Posh, Posh, Tobacco, Posh. You never had that? Yeah, I think I tried one once.


[User] Please transcribe this text

[Assistant] Uh, well the wheels work pretty well. The wheels do. Yeah, yeah. They work pretty well. Decelerated at, you know, 1.1 to 1.2 G's. That kind of thing.


[User] Please transcribe this text

[Assistant] Yeah, I think it'd be sort of like, you know, it was a time of transition where there were horses and gasoline cars on the road at the same time. It's been pretty weird.


[User] Please transcribe this text

[Assistant] Yeah. And horses were tricky. You know, back in Manhattan had like 300,000 horses. You figure like a horse lives 15 years, got 20,000 horses dropping dead every day or every year I should say. Every year has 20,000 horses if there's 300,000 horses and 15 year lifespan. Back in the gangs in New York days.


[User] Please transcribe this text

[Assistant] Yeah. It's a lot of dead horses and you need a horse to move the horse. Right. They'll probably get pretty freaked out if they have to move a dead horse.


[User] Please transcribe this text

[Assistant] Yeah, it's really cool. My study is filled with weird devices.


[User] Please transcribe this text

[Assistant] That would result in very rapid technology innovation, that's for sure.


[User] Please transcribe this text

[Assistant] I don't think, I don't think you'd necessarily want to be me.


[User] Please transcribe this text

[Assistant] No, uh, I'm getting text messages from friends saying, what the hell are you doing smoking weed? Is that bad for you? It's legal.


[User] Please transcribe this text

[Assistant] I mean, it's not, you know, uh, I'm not a regular smoker of weed. How often do you smoke it? Almost never.


[User] Please transcribe this text

[Assistant] You have a latte. Yea, it's more like the opposite of a cup of coffee. It's like a cup of coffee in reverse.


[User] Please transcribe this text

[Assistant] I like to get things done. I like to be useful. Uhm, that is one of the hardest things to do. Is to be useful.


[User] Please transcribe this text

[Assistant] Yes, doing something useful for other people that I like doing.


[User] Please transcribe this text

[Assistant] I don't know what would happen if I got into a sensory deprivation tank. Sounds concerning. It's like running the engine with no resistance.


[User] Please transcribe this text

[Assistant] Maybe it's not. Maybe it's fine. I don't know. I'll try it. I'll try it.


[User] Please transcribe this text

[Assistant] And we just sort of sit there and be quiet and then repeat some mantra, which acts as a focal point. It does still the mind. It does still the mind.


[User] Please transcribe this text

[Assistant] I think when I was, I don't know, five or six or something, I thought I was insane.


[User] Please transcribe this text

[Assistant] Because it was clear that other people do not, their mind wasn't exploding with ideas all the time.


[User] Please transcribe this text

[Assistant] It was just strange. It was like.


[User] Please transcribe this text

[Assistant] When you were little. Yeah, and then they put people away. What if they put me away?


[User] Please transcribe this text

[Assistant] Yeah, that'd be pretty weird. Don't I agree? Real weird. Definitely.


[User] Please transcribe this text

[Assistant] Try to maximize the probability that the future is good. Uhm, make the future exciting. Something you look forward to, you know. You know, with the...


[User] Please transcribe this text

[Assistant] You know, with Tesla, we're like trying to make things that people love. You know, it's like not, like, how many, how many things can you buy that you really love, that really give you joy? So rare. So rare. I wish there were more things.


[User] Please transcribe this text

[Assistant] That's what we're trying to do. Just make things that somebody loves. That's so difficult.


[User] Please transcribe this text

[Assistant] Yeah, like, what are the set of things that can be done to make the future better?


[User] Please transcribe this text

[Assistant] You know, like so, I think that a future where we are a space-faring civilization, and out there among the stars, this is very exciting. This makes me look forward to the future. This makes me want that future.


[User] Please transcribe this text

[Assistant] You know, there are things, there need to be things that make you look forward to waking up in the morning. You wake up in the morning, you look forward to the day, look forward to the future.


[User] Please transcribe this text

[Assistant] In a future where we are a space-faring civilization and out there among the stars, I think that's very exciting. That is a thing we want.


[User] Please transcribe this text

[Assistant] Whereas if, if you knew we would not be a space-faring civilization, but forever confined to earth, this would not be a good future. That would be very sad, I think. It would be sad in terms. We don't want a sad future.


[User] Please transcribe this text

[Assistant] It's probably, if you're saying, when does the sun boil the oceans?


[User] Please transcribe this text

[Assistant] There are two futures, and one future is we're out there among the stars, and the things we read about and see in science fiction movies, the good ones, are true. We have these starships, and we're...


[User] Please transcribe this text

[Assistant] We're going to see what other planets are like, and where a multi-planet species and the scope and scale of consciousness is expanded.


[User] Please transcribe this text

[Assistant] Across many civilizations and many planets and many star systems, this is a great future.


[User] Please transcribe this text

[Assistant] This is a wonderful thing to me, and that's what we should strive for.


[User] Please transcribe this text

[Assistant] We can definitely go to the moon and Mars. And we can go to the asteroid belt and we can go to the moons of Jupiter, Saturn, we can even get to Pluto.


[User] Please transcribe this text

[Assistant] That wouldn't be easy, but yes, you could just warm, you could warm it up.


[User] Please transcribe this text

[Assistant] No, but uh, probably spend more time with your friends and less time on social media.


[User] Please transcribe this text

[Assistant] I think probably something like that, yeaah. It's about right. Yeaah. Good 10%. Yeaah.


[User] Please transcribe this text

[Assistant] I mean the only thing I've kept is Twitter because I kinda like need some means of getting a message out, you know? Right. Uhm, well that's about it.


[User] Please transcribe this text

[Assistant] It's mostly, I think it's unbalanced more good than bad, but there's definitely some bad. So.


[User] Please transcribe this text

[Assistant] I mean, there are a vast number of negative comments, uh, so the vast, for the vast majority of them, I just ignore them. The vast majority. Yeah. Like every now and again, you're drawn in. It's not good. It's not good. Make mistakes.


[User] Please transcribe this text

[Assistant] Yeah, I'm not sure why people think that, that anyone would think that deleting a tweet makes it go away. It's like hello, been on the internet for a while. Yeah.


[User] Please transcribe this text

[Assistant] Yeaah. I mean, it's way easier to be mean on social media than it is to be mean in person.


[User] Please transcribe this text

[Assistant] Yeah, likewise. It really has been. It's been an honor. Thank you for having me.


[User] Please transcribe this text

[Assistant] Nah, I think, you know, I think people should be nicer to each other and give people, and give, give more credit to others and don't assume that they're mean until you know they're actually mean.


[User] Please transcribe this text

[Assistant] You know, just, it's easy to demonize people. You're usually wrong about it. People are nicer than you think.


[User] Please transcribe this text

[Assistant] Remember, this is your last chance to get an apron. One hour to go!


[User] Please transcribe this text

[Assistant] Listen up everybody. It's all about the apron.


[User] Please transcribe this text

[Assistant] Recently, just because it happened to be what was around the house and it worked so well and it became one of my favourites. When I cook, I'm in my creative element. I love to experiment with new and traditional flavours. I'm always thinking about, you know, what meal's next and what I'm gonna make. I've worked in front of house for the last ten years.


[User] Please transcribe this text

[Assistant] I studied education support, sign language, business admin. I didn't finish any of them, nursing or I finished some of them. But when I'm cooking, I feel happy. I can express myself in a creative way, which I can't do any other way. Hey Matt, what have we got on the board today?


[User] Please transcribe this text

[Assistant] I love managing the cafe, but one day I really want to be in that kitchen. This is my future. I want a career in food.


[User] Please transcribe this text

[Assistant] I have so much to give and I have so much to put into it that I can't wait for that day.


[User] Please transcribe this text

[Assistant] Having an ice cream on the dish is kind of essential, uhm, to bring that creamy element.


[User] Please transcribe this text

[Assistant] If I can nail this ice cream, I've got a good chance of getting an apron.


[User] Please transcribe this text

[Assistant] My cardamom ice cream is a bit of a feature of my dish and I don't know whether it's ready to be put into the freezer or not.


[User] Please transcribe this text

[Assistant] If it doesn't go to plan, then this could cost me an apron.


[User] Please transcribe this text

[Assistant] I'm flying by the seat of my pants here. I've never made an ice cream before. Okay hurry.


[User] Please transcribe this text

[Assistant] It's just over 20 minutes left on this cook and I'm only just getting my ice cream into the freezer. I'm hoping it will set in time.


[User] Please transcribe this text

[Assistant] If I don't, I'm not gonna get a dish up and I'm not gonna get an apron and that pressure is really getting to me.


[User] Please transcribe this text

[Assistant] On the dish I've got the ricotta, orange and walnut cake, I've got the blood orange curd, I've got the pepita praline, the syrup on the side.


[User] Please transcribe this text

[Assistant] I gave everything in this first challenge. I'm exhausted. My head is all over the place. I cannot think clearly. My mind is just blank.


[User] Please transcribe this text

[Assistant] I don't know what to cook. I'm really gonna have to get back in that headspace of just, just get it done and rely on adrenaline to get me through it. Go Bonnie!


[User] Please transcribe this text

[Assistant] Uhm, still deciding, maybe vanilla and passion fruit kind of pot over the raspberry and nin-cooley or something. Breathe.


[User] Please transcribe this text

[Assistant] At home, I've got more than enough time to put it in the fridge and let it cool on its own.


[User] Please transcribe this text

[Assistant] I definitely didn't cool it down before I put it into the fridge. So I'm, I'm worried it's not gonna set.


[User] Please transcribe this text

[Assistant] If this panna cotta isn't set, there's no apron.


[User] Please transcribe this text

[Assistant] I've got my panna cotta in the blast chiller. I'm really hoping that is going to set in time.


[User] Please transcribe this text

[Assistant] You have, both of you, Michelle and Bonnie, 15 minutes. I really need to have something just in case this panna cotta doesn't work. Think about the elements you've already got. My whole future rests on this.


[User] Please transcribe this text

[Assistant] I'm a fighter. There's something that I want. I won't give up. I'm gonna get this apron. I'm gonna fight for it. My plan B is to whip up a chocolate mousse and have it with the raspberry coulis and the praline. And just hope it works.


[User] Please transcribe this text

[Assistant] The pressure's unbelievable.


[User] Please transcribe this text

[Assistant] Okay, so I've got the buttermilk and vanilla panna cotta, raspberry coulis, macadamia, praline, chocolate mousse and a mint dust.


[User] Please transcribe this text

[Assistant] I'm worried that the panna cotta and the moose might clash. I'm worried that all the elements combined is maybe gonna be my letdown.


[User] Please transcribe this text

[Assistant] I've given everything that I could. I want that apron.


[User] Please transcribe this text

[Assistant] I'll walk away with my head held high. I do believe I gave everything in that cook. I'm not gonna give up. I want a future in food.


[User] Please transcribe this text

[Assistant] Did you have it specially made, Khanh? Just for you guys. Thank you. George is usually in charge of the knife block, but today it's mine. Don't hurt yourself. One, two, three.


[User] Please transcribe this text

[Assistant] George looks so over the moon about my ingredient. Hopefully he puts up a dish that I'm gonna be really happy to eat.


[User] Please transcribe this text

[Assistant] Gary's won like every year, but I would be worried if I was in his position, because I think George has a strong chance of winning today.


[User] Please transcribe this text

[Assistant] The beetroot is so refreshing and then you get this sweetness from the shallots. I'm obsessed.


[User] Please transcribe this text

[Assistant] So, what I loved was the different textures and temperatures in George's dish. Gary's though, every time I went for it, I wanted to go back, I wanted to have more, I wanted the garlic, I wanted the toastiness of those sesame seeds. So, we've decided that uhm,


[User] Please transcribe this text

[Assistant] For desserts, I'm making pears and fennel, which is basically a pear and cardamom sorbet.


[User] Please transcribe this text

[Assistant] I've got, uhm, my cider going. Uhm, I'm poaching my, uhm, fennel in that with a little bit of sugar. Uhm, just to tie it back into the pears. Uhm, I've also got my sorbet going. Do you want to try the sorbet?


[User] Please transcribe this text

[Assistant] It'll be pretty cool to beat the judges today. Uhm, how many people can say that they went head to head against Gary and George and have won? So the three of us take it out, that's pretty awesome.


[User] Please transcribe this text

[Assistant] When I got in, I just said to the policeman before they could say anything, I said, Jane's dead, isn't she?


[User] Please transcribe this text

[Assistant] So, they just look a bit taken aback, you know, and, uh,


[User] Please transcribe this text

[Assistant] But I presumed she'd probably, you know what young people are.


[User] Please transcribe this text

[Assistant] The opportunity that it's given me to do things that I really, really love. I think Master Jeff can definitely make dreams come true.


[User] Please transcribe this text

[Assistant] And it all started with one of these.


[User] Please transcribe this text

[Assistant] I put, you know, time with my husband and starting a family on hold. So look, this means a lot to me and I really want it.


[User] Please transcribe this text

[Assistant] Oh my gosh. Oh, look at that. That's so pretty.


[User] Please transcribe this text

[Assistant] Okay, so now the last point on our agenda. Jill, let's go over the profit and loss statement.


[User] Please transcribe this text

[Assistant] Let's see. These numbers are off the charts! What's going on here?


[User] Please transcribe this text

[Assistant] Thank you, that will be all, I'll take care of it.


[User] Please transcribe this text

[Assistant] Okay, I get it. Thank you for your very thorough analysis.


[User] Please transcribe this text

[Assistant] Okay, so now, the last point on our agenda. Jill, let's go over the profit and loss statement.


[User] Please transcribe this text

[Assistant] These numbers are off the charts. What's going on here?


[User] Please transcribe this text

[Assistant] Thank you. That will be all. I'll take care of it.


[User] Please transcribe this text

[Assistant] Okay, I get it. Thank you for your very thorough analysis.


[User] Please transcribe this text

[Assistant] I'll take care of it. I'll take care of it.


[User] Please transcribe this text

[Assistant] Okay, so now the last point on our agenda. Jill, let's go over the profit and loss statement.


[User] Please transcribe this text

[Assistant] Okay, I get it. Thank you for your very thorough analysis.


[User] Please transcribe this text

[Assistant] Did you just ask that? After 10 years of tasting together, can you double dip?


[User] Please transcribe this text

[Assistant] I love making preserves and jams and chutneys. I'm not really sure what I'm going to go with, but I've got my own fridge, I've got my own pantry, so really the rest is up to me.


[User] Please transcribe this text

[Assistant] We're all so very proud of you and excited to see how far you can take this adventure. All our love, the Kerr boys. Andrew, Simon and H.


[User] Please transcribe this text

[Assistant] So as soon as I read the letter, a dish came to me straight away. I'm gonna make chicken dumplings. The boys love chicken dumplings. They could never get enough of them. Quite often we'll have them Friday night. So that's what I'm gonna do today.


[User] Please transcribe this text

[Assistant] I'm starting to not be so nervous now. I don't want to go home. I don't want to be the first to go home. I know someone has to, so we'll just see what takes place. Today I'm making chicken dumplings with a vinaigrette and a chilli oil. My dough for my dumplings is nice and smooth. I now just do my little donut.


[User] Please transcribe this text

[Assistant] I'm gonna do a tester of my chicken dumpling filling until I know that I've got the flavours right before I put them together.


[User] Please transcribe this text

[Assistant] The first tester I taste, the balance isn't there. Oh, it needs a bit more sugar. So I adjust the filling and then try again and keep going until I get it right.


[User] Please transcribe this text

[Assistant] I want to serve the judge as a big batch of dumplings. That's what I normally do at home when I'm making them for the kids. So I've taken a lot of care to make as many dumplings as I can.


[User] Please transcribe this text

[Assistant] Next is to cook the dumplings. I put them in the fry pan with a little bit of oil just to crisp up the bottom. And then I need to add some water.


[User] Please transcribe this text

[Assistant] Immediately I think, I'm gone. What am I going to serve? I've got nothing. I've got about 30 minutes left. I'm thinking of the kids at home and I don't want to disappoint them. I've just got to keep fighting. So I'm just going to have to make some more.


[User] Please transcribe this text

[Assistant] I'm fighting hard. I, I wanna stay in the competition. I've just gotta, just keep pushing.


[User] Please transcribe this text

[Assistant] So with the filling I had left, I could only make four dumplings. It's nowhere near as much as what I would normally make at home, but at least I'm gonna be able to get something up.


[User] Please transcribe this text

[Assistant] I had to dish my first lot of dumplings because I set them on fire but I managed to get four dumplings up based on the scraps that were left over. All I can hope for now is that they cook in time.


[User] Please transcribe this text

[Assistant] I would normally serve a big batch of these to the family, but with all the problems I've had, I could only get three on the plate.


[User] Please transcribe this text

[Assistant] I think so. I uhm, I think I was inspired to do these after reading the note from the boys. Because I know how much they love their dumplings. So uhm, I hope I've done enough.


[User] Please transcribe this text

[Assistant] You know, we got through and it was tough but now it's, it's good.


[User] Please transcribe this text

[Assistant] I cannot believe I made top 24. You know, those cartoons that you see the heart just pumping out. That's what I feel like. I love Sage. How good is that? I'm in MasterChef Season 10.


[User] Please transcribe this text

[Assistant] I'm Gina, I'm a nona and I'm from New South Wales.


[User] Please transcribe this text

[Assistant] Becoming a nona has been one of the greatest gifts that I've been given in my life. My ultimate goal would be to open a nona's cooking school. To be able to pass on the knowledge of simple basic cooking the same way I was taught.


[User] Please transcribe this text

[Assistant] So I've tweaked it to become a little bit lighter, more airy, a little bit more fancy.


[User] Please transcribe this text

[Assistant] It's going to be the best blow your mind tea cake. It's going to surprise them.


[User] Please transcribe this text

[Assistant] I'm plating up my dish. I've got the zabaglione, the lovely fluffy cake, the syrup and the mascarpone cream. I really hope I can wow them today.


[User] Please transcribe this text

[Assistant] I really hope I've taken this orange cake to a whole new level. It's not just your typical orange cake. I want it to wow them.


[User] Please transcribe this text

[Assistant] It's an orange cake that is my twist on an Italian cake that my mother used to make many, many years ago.


[User] Please transcribe this text

[Assistant] My orange cake was the best they've seen in the whole 10 years of MasterChef.


[User] Please transcribe this text

[Assistant] I'm doing a flash-fried prawn in vermicelli noodles. Delicious. Gonna take all of my willpower not to eat it before I plate it.


[User] Please transcribe this text

[Assistant] Oven baked fish with a tahini sauce and tabbouleh.


[User] Please transcribe this text

[Assistant] I've cooked a prawn in a vermicelli noodle with tuna.


[User] Please transcribe this text

[Assistant] Dave and Ava. Form Animals Train Part 2.


[User] Please transcribe this text

[Assistant] Mice eat almost anything, but they love cheese. Some people keep mice as pets.


[User] Please transcribe this text

[Assistant] Dog! The dog is barking!


[User] Please transcribe this text

[Assistant] Toad Hall, I'm going that way too, she said. Hop in, I'll take you there.


[User] Please transcribe this text

[Assistant] In this video I want to tell you about an algorithm called gradient descent for minimizing the cost function j.


[User] Please transcribe this text

[Assistant] And later in the class, we'll use gradient descent to minimize other functions as well, not just the cost function j for linear regression.


[User] Please transcribe this text

[Assistant] So in this video I'm going to talk about gradient descent for minimizing some arbitrary function j and then in later videos we'll take this algorithm and apply it specifically to the cost function j that we had defined for linear regression.


[User] Please transcribe this text

[Assistant] We're going to assume that we have some function j of theta 0 comma theta 1. Maybe it's a cost function from the regression. Maybe it's some other function we want to minimize.


[User] Please transcribe this text

[Assistant] And we want to come up with an algorithm for minimizing that as a function of j of theta 0, theta 1. Just as an aside, it turns out that gradient descent actually applies to more general functions. So imagine if you have a function that's a function of j of theta 0, theta 1.


[User] Please transcribe this text

[Assistant] It turns out gradient descent is an algorithm for solving sort of this more general problem, but for the sake of brevity, for the sake of, you know, succinctness of notation, I'm just going to pretend I have only two parameters throughout the rest of this video. Here's the idea for gradient descent.


[User] Please transcribe this text

[Assistant] What we're going to do is we're going to start off with some initial guesses for theta 0 and theta 1.


[User] Please transcribe this text

[Assistant] Doesn't really matter what they are, but a common choice would be a reset


[User] Please transcribe this text

[Assistant] Theta zero to zero and set theta one to zero. Just initialize them to zero.


[User] Please transcribe this text

[Assistant] What we're going to do in gradient descent is we'll keep changing theta 0 and theta 1 a little bit to try to reduce j of theta 0 and theta 1.


[User] Please transcribe this text

[Assistant] Until hopefully we wind up at a minimum, or maybe a local minimum.


[User] Please transcribe this text

[Assistant] So, let's see what, let's see in pictures what Gradient Design does.


[User] Please transcribe this text

[Assistant] Let's say you're trying to minimize this function. So notice the axis. This is a theta 0, theta 1 on the horizontal axis and j is a vertical axis and so the height of the surface shows j and we want to minimize this function. So we're going to start off with theta 0, theta 1 at some point. So imagine picking some value for theta 0, theta 1 and


[User] Please transcribe this text

[Assistant] Imagine this is like the landscape of some grassy park with, you know, two hills like so.


[User] Please transcribe this text

[Assistant] And I want you to imagine that you are physically standing at that point on the hill, right, on this little red hill in your park.


[User] Please transcribe this text

[Assistant] If I were to take a little baby step in some direction, and I want to go downhill as quickly as possible,


[User] Please transcribe this text

[Assistant] What direction do I take that little baby step in if I want to go down, if I want to physically walk down this hill as rapidly as possible?


[User] Please transcribe this text

[Assistant] You find that the best direction to take a little, little step downhill is roughly that direction. Okay?


[User] Please transcribe this text

[Assistant] And now you're at this new point on your hill. You're going to again look all around and then say,


[User] Please transcribe this text

[Assistant] What direction should I step in order to take a little baby step downhill? And if you do that and take another step, you take a step in that direction.


[User] Please transcribe this text

[Assistant] And then you keep going, you know, from this new point, you look around, decide what direction will take you downhill most quickly, take another step, another step, and so on, until you converge to this, uhm, local minimum down here. Gradient descent is an interesting property.


[User] Please transcribe this text

[Assistant] This first time we ran gradient descent, we were starting at this point over here, right? Started at that point over here. Now imagine we had initialized gradient descent


[User] Please transcribe this text

[Assistant] Just a couple steps to the right. Imagine with a naturalized gradient descent with that point on the upper right.


[User] Please transcribe this text

[Assistant] So, if you had started at this first point, you would have wound up at this local optima, but if you started just a little bit in a slightly different location, you would have wound up at a very different local optima.


[User] Please transcribe this text

[Assistant] And this is a property of gradient descent that we'll stay a little bit more about later.


[User] Please transcribe this text

[Assistant] Until convergence, we're going to update my parameter theta j


[User] Please transcribe this text

[Assistant] By taking theta j and subtracting from it, alpha times this term over here. Okay?


[User] Please transcribe this text

[Assistant] So let's see, there are a lot of details in this equation, so let me unpack some of it. First, this notation here, colon equals


[User] Please transcribe this text

[Assistant] This is assignment. And I can also do a colon equals a plus one. This means take a and increase this value by one.


[User] Please transcribe this text

[Assistant] Whereas in contrast, if I use the equal sign, and I write A equals B, then this is a truth assertion.


[User] Please transcribe this text

[Assistant] A equals B, then I'm asserting that the value of A equals to the value of B, right? So the left hand side, that's the computer operation.


[User] Please transcribe this text

[Assistant] And so whereas I can write a colon equals a plus one, that means increment a by one, hopefully I won't ever write a equals a plus one because, you know, that's just wrong, right? A and a plus one can never be equal to the same values.


[User] Please transcribe this text

[Assistant] There's a number that is called the learning rate.


[User] Please transcribe this text

[Assistant] And what alpha does is it basically controls how big a step we take downhill when creating descent. So if alpha is very large,


[User] Please transcribe this text

[Assistant] Then that corresponds to a very aggressive gradient descent procedure where we're trying to take huge steps downhill.


[User] Please transcribe this text

[Assistant] And I'll come back and say more about this later, about, you know, how to set alpha and so on. And finally, this term here,


[User] Please transcribe this text

[Assistant] That's a derivative term. I don't want to talk about it right now, but uhm, I will derive this derivative term and tell you exactly what this is later. Okay? And some of you will be more familiar with calculus than others, but even if you aren't familiar with calculus, don't worry about it. I'll tell you what you need to know about this term here.


[User] Please transcribe this text

[Assistant] There's one more subtlety about gradient descent, which is, uh, in gradient descent, we're going to update, you know, theta 0 and theta 1, right? So this update takes place for j equals 0 and j equals 1. So you're going to update theta 0 and update theta 1.


[User] Please transcribe this text

[Assistant] And the subtlety of how you implement gradient descent is for this expression, right, for this update equation, you want to simultaneously


[User] Please transcribe this text

[Assistant] Theta zero and theta one. What I mean by that is that, you know, in this equation we're going to update theta zero colon equals theta zero minus something and update theta one colon equals theta one minus something. And the way to implement this is you should compute the right hand side, right, compute that thing for theta zero and theta one.


[User] Please transcribe this text

[Assistant] And then simultaneously, at the same time, update theta 0 and theta 1. Okay? So, so let me say what I mean by that. We previously defined the cost function J.


[User] Please transcribe this text

[Assistant] In this video I want to tell you about an algorithm called gradient descent for minimizing the cost function j.


[User] Please transcribe this text

[Assistant] And later in the class, we'll use gradient descent to minimize other functions as well, not just the cost function j for linear regression.


[User] Please transcribe this text

[Assistant] So in this video I'm going to talk about gradient descent for minimizing some arbitrary function j and then in later videos we'll take this algorithm and apply it specifically to the cost function j that we had defined for linear regression.


[User] Please transcribe this text

[Assistant] We're going to assume that we have some function j of theta 0 comma theta 1. Maybe it's a cost function from linear regression. Maybe it's some other function we want to minimize.


[User] Please transcribe this text

[Assistant] And we want to come up with an algorithm for minimizing that as a function of j of theta 0, theta 1. Just as an aside, it turns out that gradient descent actually applies to more general functions. So imagine if you have a function that's a function of j of theta 0, theta 1, theta 2 up to say some theta n.


[User] Please transcribe this text

[Assistant] And you want to minimize, uh, theta zero. You minimize over theta zero.


[User] Please transcribe this text

[Assistant] It turns out gradient descent is an algorithm for solving sort of this more general problem, but for the sake of brevity, for the sake of, you know, succinctness of notation, I'm just going to pretend I have only two parameters throughout the rest of this video. Here's the idea for gradient descent.


[User] Please transcribe this text

[Assistant] What we're going to do is we're going to start off with some initial guesses for theta 0 and theta 1.


[User] Please transcribe this text

[Assistant] Doesn't really matter what they are, but a common choice would be a reset


[User] Please transcribe this text

[Assistant] Theta zero to state zero and set theta one to zero. Just initialize them to zero.


[User] Please transcribe this text

[Assistant] What we're going to do in gradient descent is we'll keep changing theta 0 and theta 1 a little bit to try to reduce j of theta 0 and theta 1.


[User] Please transcribe this text

[Assistant] Until hopefully we wind up at a minimum, or maybe at a local minimum.


[User] Please transcribe this text

[Assistant] So, let's see what, let's see in pictures what gradient descent does.


[User] Please transcribe this text

[Assistant] Let's say you're trying to minimize this function. So notice the axis, this is a theta 0, theta 1 on the horizontal axis and j is a vertical axis and so the height of the surface shows j and we want to minimize this function.


[User] Please transcribe this text

[Assistant] So we're going to start off with theta 0, theta 1 at some point. So imagine taking some value for theta 0, theta 1.


[User] Please transcribe this text

[Assistant] And that corresponds to starting at some point on the surface of this function. Okay, so whatever value of theta 0, theta 1 gives you some point here. I didn't initialize them to 0, 0, but you know, sometimes you can initialize it to other values as well.


[User] Please transcribe this text

[Assistant] Imagine this is like the landscape of some grassy park with, you know, two hills like so.


[User] Please transcribe this text

[Assistant] And I want you to imagine that you are physically standing at that point on the hill, right, on this little red hill in your park.


[User] Please transcribe this text

[Assistant] If I were to take a little baby step in some direction, and I want to go downhill as quickly as possible,


[User] Please transcribe this text

[Assistant] What direction do I take that little baby step in if I want to go down, if I want to physically walk down this hill as rapidly as possible?


[User] Please transcribe this text

[Assistant] You find that the best direction to take a little, little step downhill is roughly that direction. Okay?


[User] Please transcribe this text

[Assistant] What direction should I step in order to take a little baby step downhill? And if you do that and take another step, you take a step in that direction.


[User] Please transcribe this text

[Assistant] And then you keep going, you know, from this new point, you look around, decide what direction will take you downhill most quickly, take another step, another step, and so on, until you converge to this, uhm, local minimum down here. Gradient descent is an interesting property.


[User] Please transcribe this text

[Assistant] This first time we ran gradient descent, we were starting at this point over here, right? Started at that point over here. Now imagine we had initialized gradient descent


[User] Please transcribe this text

[Assistant] Just a couple steps to the right. Imagine with a naturalized gradient descent with that point on the upper right.


[User] Please transcribe this text

[Assistant] If you were to repeat this process, so start from that point, look all around, take a little step in the direction of steepest descent, you would do that. Then look around, take another step, and so on. And if you started just a couple steps to the right, gradient descent would have taken you to this second local optimum over on the right.


[User] Please transcribe this text

[Assistant] So, if you had started at this first point, you would have wound up at this local optima, but if you started just a little bit in a slightly different location, you would have wound up at a very different local optima.


[User] Please transcribe this text

[Assistant] And this is a property of gradient descent that we'll stay a little bit more about later.


[User] Please transcribe this text

[Assistant] Until convergence, we're going to update my parameter theta j


[User] Please transcribe this text

[Assistant] So let's see, there are a lot of details in this equation, so let me unpack some of it. First, this notation here, colon equals


[User] Please transcribe this text

[Assistant] So concretely, if I write A colon equals B, what this means is it means, you know, in a computer, this means take the value in B and use it to overwrite whatever value is A. So this means, you know, set A to be equal to the value.


[User] Please transcribe this text

[Assistant] This is assignment. And I can also do a colon equals a plus one. This means take a and increase its value by one.


[User] Please transcribe this text

[Assistant] Whereas in contrast, if I use the equal sign, and I write A equals B, then this is a truth assertion.


[User] Please transcribe this text

[Assistant] A equals B, then I'm asserting that the value of A equals to the value of B, right? So the left hand side, that's the computer operation.


[User] Please transcribe this text

[Assistant] And so whereas I can write a colon equals a plus one, that means increment a by one, hopefully I won't ever write a equals a plus one because, you know, that's just wrong, right? A and a plus one can never be equal to the same values. Okay? So that's the first part of the definition. Uhm, this alpha here is a


[User] Please transcribe this text

[Assistant] There's a number that is called the learning rate.


[User] Please transcribe this text

[Assistant] And what alpha does is it basically controls how big a step we take downhill when creating descent. So if alpha is very large,


[User] Please transcribe this text

[Assistant] And uh, I'll come back and say more about this later, about, you know, how to set alpha and so on. And finally, this term here,


[User] Please transcribe this text

[Assistant] That's a derivative term. I don't want to talk about it right now, but uhm, I will derive this derivative term and tell you exactly what this is later. Okay? And some of you will be more familiar with calculus than others, but even if you aren't familiar with calculus, don't worry about it. I'll tell you what you need to know about this term here. Now,


[User] Please transcribe this text

[Assistant] There's one more subtlety about gradient descent, which is, uh, in gradient descent, we're going to update, you know, theta 0 and theta 1, right? So this update takes place for j equals 0 and j equals 1. So you're going to update theta 0 and theta 1.


[User] Please transcribe this text

[Assistant] Update theta 1. And the subtlety of how you implement gradient descent is for this expression, right, for this update equation, you want to simultaneously


[User] Please transcribe this text

[Assistant] Theta 1. What I mean by that is that, you know, in this equation we're going to update theta 0 colon equals theta 0 minus something and update theta 1 colon equals theta 1 minus something.


[User] Please transcribe this text

[Assistant] And the way to implement this is you should compute the right hand side, right, compute that thing for theta 0 and theta 1.


[User] Please transcribe this text

[Assistant] This is a correct implementation of gradient descent, meaning simultaneous updates. I'm gonna set temp 0 equals that, set temp 1 equals that, so basically compute the right hand sides, and then having computed the right hand sides and stored them into variables, temp 0 and temp 1, I'm gonna, you know, update theta 0 and theta 1 simultaneously. So that's a correct implementation.


[User] Please transcribe this text

[Assistant] In contrast, here's an incorrect implementation that does not do a simultaneous update.


[User] Please transcribe this text

[Assistant] So in this incorrect implementation, we compute temp zero, and then we update theta zero. We previously defined the cost function J.


[User] Please transcribe this text

[Assistant] In this video I want to tell you about an algorithm called gradient descent for minimizing the cost function j.


[User] Please transcribe this text

[Assistant] And later in the class, we'll use gradient descent to minimize other functions as well, not just the cost function j for linear regression.


[User] Please transcribe this text

[Assistant] So in this video I'm going to talk about gradient descent for minimizing some arbitrary function j and then in later videos we'll take this algorithm and apply it specifically to the cost function j that we had defined for linear regression.


[User] Please transcribe this text

[Assistant] We're going to assume that we have some function j of theta 0 comma theta 1. Maybe it's a cost function from the regression. Maybe it's some other function we want to minimize.


[User] Please transcribe this text

[Assistant] And we want to come up with an algorithm for minimizing that as a function of j of theta 0, theta 1. Just as an aside, it turns out that gradient descent actually applies to more general functions. So imagine if you have a function that's a function of j of theta 0, theta 1.


[User] Please transcribe this text

[Assistant] It turns out gradient descent is an algorithm for solving sort of this more general problem, but for the sake of brevity, for the sake of, you know, succinctness of notation, I'm just going to pretend I have only two parameters throughout the rest of this video. Here's the idea for gradient descent.


[User] Please transcribe this text

[Assistant] What we're going to do is we're going to start off with some initial guesses for theta 0 and theta 1.


[User] Please transcribe this text

[Assistant] Doesn't really matter what they are, but a common choice would be a reset.


[User] Please transcribe this text

[Assistant] Theta zero to zero and set theta one to zero. Just initialize them to zero.


[User] Please transcribe this text

[Assistant] What we're going to do in gradient descent is we'll keep changing theta 0 and theta 1 a little bit to try to reduce j of theta 0 and theta 1.


[User] Please transcribe this text

[Assistant] Until hopefully we wind up at a minimum, or maybe a local minimum.


[User] Please transcribe this text

[Assistant] Let's say you're trying to minimize this function. So notice the axis. This is a theta 0, theta 1 on the horizontal axis and j is a vertical axis and so the height of the surface shows j and we want to minimize this function. So we're going to start off with theta 0, theta 1 at some point. So imagine picking some value for theta 0, theta 1 and


[User] Please transcribe this text

[Assistant] And that corresponds to starting at some point on the surface of this function. Okay, so whatever value of theta 0, theta 1 gives you some point here. I didn't initialize them to 0, 0, but you know, sometimes you initialize it to other values as well.


[User] Please transcribe this text

[Assistant] Imagine this is like the landscape of some grassy park with, you know, two hills like so.


[User] Please transcribe this text

[Assistant] And I want you to imagine that you are physically standing at that point on the hill, right, on this little red hill in your park.


[User] Please transcribe this text

[Assistant] If I were to take a little baby step in some direction, and I want to go downhill as quickly as possible,


[User] Please transcribe this text

[Assistant] What direction do I take that little baby step in if I want to go down, if I want to physically walk down this hill as rapidly as possible?


[User] Please transcribe this text

[Assistant] You find that the best direction to take a little, little step downhill is roughly that direction. Okay?


[User] Please transcribe this text

[Assistant] And now you're at this new point on your hill. You're going to again look all around and then say,


[User] Please transcribe this text

[Assistant] What direction should I step in order to take a little baby step downhill? And if you do that and take another step, you take a step in that direction.


[User] Please transcribe this text

[Assistant] And then you keep going, you know, from this new point, you look around, decide what direction will take you downhill most quickly, take another step, another step, and so on, until you converge to this, uhm, local minimum down here. Gradient descent is an interesting property.


[User] Please transcribe this text

[Assistant] This first time we ran gradient descent, we were starting at this point over here, right? Started at that point over here. Now imagine we had initialized gradient descent


[User] Please transcribe this text

[Assistant] If you were to repeat this process, so start from that point, look all around, take a little step in the direction of steepest descent, you would do that. Then look around, take another step, and so on. And if you started just a couple steps to the right, gradient descent would have taken you to this second local optimum over on the right.


[User] Please transcribe this text

[Assistant] So, if you had started at this first point, you would have wound up at this local optimum. But if you started just a little bit in a slightly different location, you would have wound up at a very different local optimum.


[User] Please transcribe this text

[Assistant] And this is a property of gradient descent that we'll stay a little bit more about later. So that's the intuition in pictures. Let's look at the math. This is the definition of the gradient descent algorithm. We're going to just repeatedly do this.


[User] Please transcribe this text

[Assistant] Until convergence, we're going to update my parameter theta j


[User] Please transcribe this text

[Assistant] By, you know, taking theta j and subtracting from it alpha times this term over here. Okay?


[User] Please transcribe this text

[Assistant] So let's see, there are a lot of details in this equation, so let me unpack some of it. First, this notation here, colon equals


[User] Please transcribe this text

[Assistant] I'm going to use colon equals to denote assignment, so the assignment operator.


[User] Please transcribe this text

[Assistant] In a computer, this means take the value in B and use it to overwrite whatever value is A. So this means you'll set A to be equal to the value of B. This is assignment. And I can also do A colon equals A plus one. This means take A and increase this value by one.


[User] Please transcribe this text

[Assistant] Whereas in contrast, if I use the equal sign, and I write A equals B, then this is a truth assertion.


[User] Please transcribe this text

[Assistant] A equals B, then I'm asserting that the value of A equals to the value of B, right? So the left hand side, that's the computer operation.


[User] Please transcribe this text

[Assistant] And so whereas I can write a colon equals a plus one, that means increment a by one, hopefully I won't ever write a equals a plus one because, you know, that's just wrong, right? A and a plus one can never be equal to the same values.


[User] Please transcribe this text

[Assistant] There's a number that is called the learning rate.


[User] Please transcribe this text

[Assistant] And what alpha does is it basically controls how big a step we take downhill when creating descent. So if alpha is very large,


[User] Please transcribe this text

[Assistant] That's a derivative term. I don't want to talk about it right now, but uhm, I will derive this derivative term and tell you exactly what this is later. Okay? And some of you will be more familiar with calculus than others, but even if you aren't familiar with calculus, don't worry about it. I'll tell you what you need to know about this term here. Now,


[User] Please transcribe this text

[Assistant] There's one more subtlety about gradient descent, which is, uh, in gradient descent, we're going to update, you know, theta 0 and theta 1, right? So this update takes place for j equals 0 and j equals 1. So you're going to update theta 0 and update theta 1.


[User] Please transcribe this text

[Assistant] And the subtlety of how you implement gradient descent is for this expression, right, for this update equation, you want to simultaneously


[User] Please transcribe this text

[Assistant] What I mean by that is that, you know, in this equation we're going to update theta zero, code equals theta zero minus something and update theta one code equals theta one minus something.


[User] Please transcribe this text

[Assistant] Equals that, set temp1 equals that, so basically compute the right hand sides. And then having computed the right hand sides and stored them into variables, temp0 and temp1, I'm gonna, you know, update theta0 and theta1 simultaneously. So that's the correct implementation.


[User] Please transcribe this text

[Assistant] In contrast, here's an incorrect implementation that does not do a simultaneous update. So in this incorrect implementation, we compute 10, 0,


[User] Please transcribe this text

[Assistant] And then we update theta 0, and then we compute 10th 1, and then we update 10th 1.


[User] Please transcribe this text

[Assistant] And the difference between the right hand side and the left hand side implementations is that if you look down here, you look at this step.


[User] Please transcribe this text

[Assistant] If by this time you've already updated theta 0, then you would be using the new value of theta 0, you know, to compute this derivative term. And so this gives you a different value of 10th 1 than the left hand side.


[User] Please transcribe this text

[Assistant] Right, because you've now plugged in the new value of theta zero into this equation and so this on the right hand side is not a correct implementation of gradient descent.


[User] Please transcribe this text

[Assistant] So I don't want to say why you need to do the simultaneous updates. Uh, it turns out that, you know,


[User] Please transcribe this text

[Assistant] The way gradient descent is usually implemented, uh, which we'll say more about later, it actually turns out to be more natural to implement a simultaneous update. And when people talk about gradient descent, they always mean simultaneous updates.


[User] Please transcribe this text

[Assistant] If you implement the non-simultaneous update, it turns out it will probably work anyway, but this is, this algorithm on the right is not what people refer to as Bayes, and this is some other algorithm with different problems.


[User] Please transcribe this text

[Assistant] For various reasons, this, this behaves, this can behave in slightly stranger ways. And so, you know, what you should do is really implement the simultaneous update of gradient descent. So, that's the outline of the gradient descent algorithm.


[User] Please transcribe this text

[Assistant] In the next video, we're going to go into the details of the derivative term, which I wrote up but didn't really define.


[User] Please transcribe this text

[Assistant] And, uh, if you've taken a calculus class before and if you're familiar with partial derivatives and derivatives, it turns out that's exactly what that derivative term is.


[User] Please transcribe this text

[Assistant] But in case you aren't familiar with calculus, don't worry about it. The next video will give you all the intuitions and will tell you everything you need to know to compute that derivative term even if you haven't seen calculus or even if you haven't seen partial derivatives before.


[User] Please transcribe this text

[Assistant] Uhm, and uh, with that, with the next video, hopefully we will be able to give you all the interventions you need to apply grading descent.


[User] Please transcribe this text

[Assistant] Today I'm making it into the taco. It's like a take on like a churros, which I kind of just shape like a taco.


[User] Please transcribe this text

[Assistant] I'm gonna fight so hard to stay out of that pressure test. If you're in the bottom three, then someone's going home. The idea of a pressure test is terrifying.


[User] Please transcribe this text

[Assistant] I'm doing a chocolate creme caramel with a sesame nougatine and then I'm going to do some white chocolate domes which will have the sesame nougatine inside. The way this dish is going to look on the plate is there is going to be a chocolate creme caramel, I'm going to have a sesame nougatine which is like a crisp that comes across, I'm going to make some little white chocolate spheres and fill them with the nougatine and that's going to be my surprise.


[User] Please transcribe this text

[Assistant] I know that I can nail this dish. I've done it so many times for my family and it's beautiful. The first thing I do with the creme caramel is I make up my custard and I get that into a mould and I get that in the oven. Once I'm happy with that I start making the nougatine.


[User] Please transcribe this text

[Assistant] If I can pull this off in time, I really do think top three is achievable. I've got this!


[User] Please transcribe this text

[Assistant] The dish that I'm making is a chocolate creme caramel with a sesame nougatine, a white chocolate dome filled with the sesame nougatine. That's going to be my surprise. I get the domes out of the blast chiller and now it's time to start assembling them.


[User] Please transcribe this text

[Assistant] And I noticed that the chocolate is so thin around the edge that that's really going to be almost impossible to join together. For some reason I'm just struggling today.


[User] Please transcribe this text

[Assistant] Without these domes, I'm gonna have no surprise element, and that really is gonna put me in the bottom three. I'm gonna have to quickly put some more of these in and hope that I can...


[User] Please transcribe this text

[Assistant] This is distressing me so much because I've made these fears so many times before and it's just so frustrating knowing that what I'm making right now is just a disaster.


[User] Please transcribe this text

[Assistant] The clock's ticking and I need to get something on a plate, but everything is going wrong. Anything that could go wrong is going wrong. I can't get these doughs to go together. If I can at least get something on a plate, I'm hoping somehow I can stay out of the bottom three. And then it's just not gonna be ready.


[User] Please transcribe this text

[Assistant] All I'm thinking is, I just don't even want them to try it. I'm really feeling that bad about the dish. We had some problems.


[User] Please transcribe this text

[Assistant] I'm going into my first pressure test and this could be it for me. I'm really confident that I'm going to go ahead and I'm just going to get the job done.


[User] Please transcribe this text

[Assistant] This week on MasterChef Australia, a massive surprise.


[User] Please transcribe this text

[Assistant] doozy. It's chocolate. Chocolate. And more chocolate.


[User] Please transcribe this text

[Assistant] So, you have 75 minutes to bring us a delicious dish. Obviously, you can use one or as many of those ingredients under that mystery box as you would like. You've got your staples underneath your bench, and what you need to know is we're only going to be tasting the five most appealing looking dishes. And remember what you're cooking for. The winner


[User] Please transcribe this text

[Assistant] We'll go straight into this week's immunity challenge, for a shot at an immunity pen. But, it's also a season full of surprises, remember? And this week, the winner will get something else. Something very, very special. There's not quite enough veris in that. Nodjella, would you like to tell them?


[User] Please transcribe this text

[Assistant] Straight into immunity. Afternoon tea in London with Nigella. Doesn't get any better than that. But only 45 minutes to go.


[User] Please transcribe this text

[Assistant] If all of those elements were not in a bad spot with ten minutes to go.


[User] Please transcribe this text

[Assistant] So 75 minutes to cook a beautiful dish out of the 10 ingredients that Nigella chose in the mystery box. And a grape.


[User] Please transcribe this text

[Assistant] Couple of prizes on offer. First of all, the opportunity to compete for that immunity pin. But maybe best of all, the chance to have afternoon tea with Nigella in London. Oh my goodness.


[User] Please transcribe this text

[Assistant] Not a bad start, is it? Feel weak? Pretty happy.


[User] Please transcribe this text

[Assistant] I think what's very clever is the little dome, so with that crunchy praline on the top, the soft meringue and the cherries, it's a delicious and kind of heady combination which I love.


[User] Please transcribe this text

[Assistant] It looks beautiful. Yeah. Looks really sophisticated, looks really well balanced, and the kind of dish we were looking for out of that mystery box.


[User] Please transcribe this text

[Assistant] You know what the good news is? Not only are you going to go to London and have afternoon tea with Nigella, but you also go straight through to immunity. And that immunity could be yours. Head up to the gantry, the rest of you, back to your benches. Well done.


[User] Please transcribe this text

[Assistant] To deliver us a delicious dish. You've got an open pantry, the garden's in play and we'll be tasting all of your dishes. The three contestants that give us the best dishes


[User] Please transcribe this text

[Assistant] Join Kristen in the immunity challenge later on this week. However, the contestants that deliver the three least impressive dishes will find themselves in tomorrow's pressure test. Are you ready? Yes. Your time starts now.


[User] Please transcribe this text

[Assistant] It had 75 minutes to give us a dish that snapped, crackled or popped.


[User] Please transcribe this text

[Assistant] To cook for the chance at winning that immunity pin. But the bottom three.


[User] Please transcribe this text

[Assistant] Unfortunately find themselves in tomorrow's pressure test.


[User] Please transcribe this text

[Assistant] So, let's get this tasting underway. First dish up is... Ben.


[User] Please transcribe this text

[Assistant] This is just absolutely delicious.


[User] Please transcribe this text

[Assistant] The spices that sing through the coffee and the chai, just absolutely wonderful.


[User] Please transcribe this text

[Assistant] The last dish we'd like to taste today belongs to... Uda.


[User] Please transcribe this text

[Assistant] What a great dessert. Eat the whole thing together is nothing but snap, crackle and pop.


[User] Please transcribe this text

[Assistant] We have to say, and we all agree, we saw some absolutely amazing cooking today.


[User] Please transcribe this text

[Assistant] The contestants that cook the best three dishes join Krista in this week's immunity challenge. However...


[User] Please transcribe this text

[Assistant] The three contestants that cooked the least impressive dishes will find themselves in tomorrow's pressure test.


[User] Please transcribe this text

[Assistant] Well, when you protest something, you go into the streets and show your disagreement.


[User] Please transcribe this text

[Assistant] And the news comes, and if it's not clear now, it'll be clear after the dialogue, I'm sure.


[User] Please transcribe this text

[Assistant] Can't pay its bills, yeaah. Uh, so basically if it's bankrupt, it's probably going to close.


[User] Please transcribe this text

[Assistant] That's true, it's emergency money that will help save the company. Well, these two words are taken directly from our dialogue. And in today's dialogue, we're going to bring you live to a newscast.


[User] Please transcribe this text

[Assistant] Well this is a wonderful phrasal verb that means...


[User] Please transcribe this text

[Assistant] A lot of great words in our dialogue today. Uhm, and also we have some really interesting grammar in today's dialogue.


[User] Please transcribe this text

[Assistant] In this section, Grammar Breakdown, uh, we show you a particular grammar structure and explain a little bit about how that structure affects meaning. So, it's not really about getting the grammar perfectly right, it's about understanding the meaning.


[User] Please transcribe this text

[Assistant] So present perfect is used when we're talking about an event that happened in the past and is continuing on now.


[User] Please transcribe this text

[Assistant] Right, and I think it's really emphasizing the fact that it is no longer affecting the present moment.


[User] Please transcribe this text

[Assistant] Yeah, I mean, I see what you're saying, Marco, but at the same time, we can't support an industry that isn't working properly. I mean, if one of these companies goes bankrupt, it's gonna be terrible, but it will force the other two companies, perhaps, to manage themselves better, to streamline and become more efficient. I just think it will be better in the future.


[User] Please transcribe this text

[Assistant] Yeah, listeners, please come to our website at EnglishPod.com and tell us what you think about this bailout and about this lesson.


[User] Please transcribe this text

[Assistant] See that he was very nervous. He was frightened and I said to him, you don't have to worry. I'm not going to hurt you. And he just laughed it off.


[User] Please transcribe this text

[Assistant] If the police didn't come then probably they would have found the body. On that wasteland.


[User] Please transcribe this text

[Assistant] Why do you come up with stalking young girls, innocent girls as well?


[User] Please transcribe this text

[Assistant] You come up from behind them, they don't have a chance. You're not a man, you're a beast. And I hate you.


[User] Please transcribe this text

[Assistant] I was called to the boss's office. Got a job for you. I want you to take Maureen Long out.


[User] Please transcribe this text

[Assistant] And joking, I says, I'm sorry she's not my type, you know. Says, no, you ain't got a choice.


[User] Please transcribe this text

[Assistant] See if that man's there. If he's out there stalking a potential victim.


[User] Please transcribe this text

[Assistant] It says this, uh, 15 quid expenses for you. It says that ain't gonna last long. It says that won't go beyond the first round.


[User] Please transcribe this text

[Assistant] I thought, I hope nobody sees me that I know. Cause I had to do the pretense, you know, that we're a couple.


[User] Please transcribe this text

[Assistant] She was to tell me I'd have called troops in and arrest him.


[User] Please transcribe this text

[Assistant] My friends used to say, what are you doing? Haven't you caught this guy yet?


[User] Please transcribe this text

[Assistant] There was nothing to indicate that anybody we saw was our man.


[User] Please transcribe this text

[Assistant] Another blind alley. Another dead end. We just got on with a job. To find something that might assist in the inquiry.


[User] Please transcribe this text

[Assistant] We were teamed up with a colleague who was based in Manchester, the Greater Manchester Police.


[User] Please transcribe this text

[Assistant] We would go to factories where the people worked.


[User] Please transcribe this text

[Assistant] I probably interviewed hundreds of people like that. Hundreds.


[User] Please transcribe this text

[Assistant] Of course, there was haulage, stationers, all sorts of businesses. I mean, it was, uh, an overwhelming number of, uh, inquiries to do and interviews to conduct.


[User] Please transcribe this text

[Assistant] The strange thing about that was the Daily Mirror newspaper placed under her arm.


[User] Please transcribe this text

[Assistant] Which said Ripper 8 Police Mill.


[User] Please transcribe this text

[Assistant] Remember what's at stake, pressure test tomorrow, chance at immunity pin, 30 minutes to go! What am I doing?


[User] Please transcribe this text

[Assistant] In your experience, can somebody successfully feign symptoms of an acute psychiatric condition, uh, not just over one doctor, but over three?


[User] Please transcribe this text

[Assistant] West Yorkshire's Chief Constable, Mr Ronald Gregory, is leaving the police. He's retiring in June.


[User] Please transcribe this text

[Assistant] The jury rejected his plea that he was guilty of manslaughter on the ground of diminished responsibility.


[User] Please transcribe this text

[Assistant] I was the first woman to join the forensic science lab. And I was initially received with a large dose of skepticism by the chap who was my boss.


[User] Please transcribe this text

[Assistant] I don't know what quite I was expecting, but she just looked very serene.


[User] Please transcribe this text

[Assistant] We were very keen, obviously, to see if we could find anything at all that the perpetrator might have left behind them. Because rough wood just picks up stuff, you know, it's splintery.


[User] Please transcribe this text

[Assistant] Whoever it was was just so dangerous, uhm, and so likely to kill again.


[User] Please transcribe this text

[Assistant] And probably could be any day, could be quite quickly. And I think that that added an extra impetus for George.


[User] Please transcribe this text

[Assistant] ...was, uh, grouped, and that's where it was discovered that it had come from a man who was a B secretor, which is actually one of the rarest of the ABO groups. It's only existing about 6% of men.


[User] Please transcribe this text

[Assistant] This is a little bit, well, it's a loving love, innit? It's a loving love.


[User] Please transcribe this text

[Assistant] Being one of the first in a pressure test is scary. I possibly can go home today. I would hate to disappoint anyone and particularly my children and myself.


[User] Please transcribe this text

[Assistant] I'm trying to taste every element and then I taste it all together to see what all these flavors create on a palette.


[User] Please transcribe this text

[Assistant] Everything is balancing itself and trying to memorize how it tasted and recreating it is very important.


[User] Please transcribe this text

[Assistant] I have two goals today to impress Maggie B and to stay in the competition.


[User] Please transcribe this text

[Assistant] I mixed butter, almonds, flour, salt and ice cold water.


[User] Please transcribe this text

[Assistant] When I cook with joy, that's when I do my best. So that's what I'm focusing on.


[User] Please transcribe this text

[Assistant] Uhm, I'm just about to start making caramel. Yes. And I'm scared. Oh no, no, don't be scared. I don't want to overcook it and I don't want to undercook it, I still want it to have a flavour.


[User] Please transcribe this text

[Assistant] In Maggie's dish, to get the caramel right is very important for the flavor.


[User] Please transcribe this text

[Assistant] I'm mixing sugar and water, bringing it to a boil. And I need a wet pastry brush.


[User] Please transcribe this text

[Assistant] I'm brushing the sides off, uh, to prevent it from crystallizing. And yeaah, just watching it intently. I don't want to overcook it or undercook it, so.


[User] Please transcribe this text

[Assistant] I see the amber color slowly creeping in from the sides of the pan and once I see that middle is changing I take the pan off the heat quickly and pour in the verges to stop the cooking.


[User] Please transcribe this text

[Assistant] I'm placing the apples cut side down into the caramel and cooking it over a high heat. The caramel looks great. I feel confident. I'm in a very good mood. I'm just trying to make sure all the apples cook through evenly.


[User] Please transcribe this text

[Assistant] And I'm moving them around. And then I'm gonna place the cartridge back on. Because I want them to steam there. And soften.


[User] Please transcribe this text

[Assistant] I put rosemary tips a bit early, but that's alright. At home, I always follow my instincts. Every element is very important in this dish. The apples have to be cooked. The caramel has to be right.


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[Assistant] The pastry has to be flaky and layered. Now it's the time to make the pastry's base.


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[Assistant] Once I'm finished rolling out and folding the dough for the last time. Gently, gently. Just from the memory. And it's gonna puff.


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[Assistant] I am happy with the apples. Next step is putting it all together.


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[Assistant] I've just poured in cream and the juice mixture into the egg and sugar.


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[Assistant] I'm worried about the bubbles. Okay. I'm getting it out now. I am nervous, but it smells right.


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[Assistant] So now I'm looking at my tart and I'm thinking, is it brown enough on top?


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[Assistant] I'm stressing out. Do I put it back and put the temperature up or keep the tartatian out? And if I make a wrong decision, it's the end.


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[Assistant] Okay. I'm getting it out now. I'm worried that my tartar tent isn't brown enough on top.


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[Assistant] Do I put it back and put the temperature up or keep the tartatine out?


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[Assistant] I make the decision. I put it back in the oven and crank the temperature up.


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[Assistant] Cooking for my hero, Maggie Beer, it's a bit daunting. It's like cooking for someone who is so dear. I put all of my skill.


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[Assistant] And my intuition into this cook. And I want to impress her. I want her to enjoy my cooking.


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[Assistant] I don't think I've ever had, uhm, such an experience in the kitchen.


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[Assistant] Waiting for the TV show to come on TV and watch Maggie Cook. It just made me feel at home.


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[Assistant] It smells and looks good. I'm pleased. Fingers crossed it's cooked properly.


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[Assistant] You're simply going to produce public sympathy for these prostitutes and I'm sure that that is not what you want.


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[Assistant] So you know that cheesy old picture of human evolution, with an ape getting more and more upright until it proudly steps forth as a human being? You probably know that's not how evolution works. It doesn't have a plan, organisms aren't all headed on a set trajectory towards some imagined biological perfection.


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[Assistant] Evolution just throws everything at the wall and sees what sticks. Well, it can't even plan ahead that much. A bunch of different things exist,


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[Assistant] The circumstances of life slam them against the wall.


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[Assistant] And the ones that survive wallbanging are the ones that stick. Or more technically, traits arise through random mutation, and natural selection means the more advantageous ones are passed on. And sometimes, as these adaptations arise,


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[Assistant] After all, natural selection also means that as long as there is no lethal disadvantage,


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[Assistant] With the bar always set to good enough, the traits that evolve can be pretty clunky. So it's worth taking the time to appreciate just how inefficient evolution can be sometimes.


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[Assistant] Photosynthesis, the method of making food most highly recommended by cyanobacteria


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[Assistant] The first and still most common form of this process is called C3 photosynthesis. Using water, carbon dioxide, and solar energy,


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[Assistant] Rubisco strings together a molecule with three carbon atoms, hence C3. That molecule subsequently is converted into a sugar. However, this enzyme is notoriously inefficient. Most enzymes can process thousands of chemical reactions a second. Rubisco can handle about three molecules of CO2, where still, it's


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[Assistant] So why hasn't evolution improved rubisco? Being able to photosynthesize at all is kind of impressive. After all, the C3 pathway allowed ancient microbes to make their own food from practically nothing, just some common resources plus the actual sun. So once these early cyanobacteria cracked it,


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[Assistant] That was the way of photosynthesis for the next couple billion years. But it's not the only way. Much, much later in the history of life on Earth, we're talking only a few tens of millions of years ago, plants evolved two new photosynthesis pathways.