[User] Please transcribe this text

[Assistant] The very bottom of ice sheets in places like Greenland and Antarctica have snow that fell before the beginning of the last ice age, 115,000 years ago or more. Just like snow on a sidewalk can get compressed by boots into sheets of slippery ice, the snow on ice sheets is compacted into huge solid masses.


[User] Please transcribe this text

[Assistant] And buried in each layer of ice is evidence of past atmospheric conditions.


[User] Please transcribe this text

[Assistant] Scientists use isotope dating to tell whether the carbon dioxide in those frozen bubbles was released from burning materials like wood or coal in the lithosphere, or if it was airborne during a nuclear explosion, or if it was part of the natural cycling of carbon. Paleoclimatologists have collected polar ice core samples and analyzed historical air bubbles from Greenland and Antarctica.


[User] Please transcribe this text

[Assistant] Tropical glaciers in the mountains of the Andes in Kenya, and mid-latitude glaciers in the Alps and Himalayas. When all these data are lined up, scientists can compare them with each other and see atmospheric trends, which in turn shows climate change over thousands of years. Analysis shows that it can take just a few decades to change from colder to warmer climate patterns.


[User] Please transcribe this text

[Assistant] That might not sound fast, but when you're a 4.5 billion year old planet like the Earth, that's nothing. Thanks Thought Bubble.


[User] Please transcribe this text

[Assistant] By analyzing proxy data like bubbles and ice cores, we know the Earth's climate has changed significantly many times.


[User] Please transcribe this text

[Assistant] Strong winds spread that dust around the world, darken the skies, and reduce the amount of insulation that can reach the surface, which lowers temperatures. This is related to the albedo effect, or the amount of sunlight a surface reflects back into space. Volcanic dust and even the color of surfaces change Earth's albedo. White surfaces, like ice, reflect the sun's energy, creating a cooling effect.


[User] Please transcribe this text

[Assistant] So when there's less ice, the opposite happens. Darker surfaces absorb sunlight and warm the surface of the Earth. Similarly, when something big like an asteroid strikes, it can cause an impact winter. Like volcanic activity, a big impact throws dust and debris into the air, blocking out insulation and lowering temperatures.


[User] Please transcribe this text

[Assistant] Such an impact may have caused the extinction of the dinosaurs 65 million years ago. Another reason that climates change is when continents and oceans get rearranged. Like when the isthmus of Panama formed, it broke the connection between the Atlantic and Pacific Oceans, which redirected ocean circulation and created the Gulf Stream.


[User] Please transcribe this text

[Assistant] This changed how moisture and ocean currents moved around the globe, which as we know, contributes to climate patterns. And finally, climates change when greenhouse gases in the atmosphere change.


[User] Please transcribe this text

[Assistant] We know from ice core data that carbon dioxide levels in the atmosphere decreased when glaciers formed during ice ages and increased during interglacial periods. However, what's happening now is unlike anything in the past. We're in an interglacial period now, and glaciers are melting faster than ever before in the geologic record.


[User] Please transcribe this text

[Assistant] Average annual global temperatures have risen between 0.3 degrees Celsius and 0.6 degrees Celsius, and sea level has risen between 10 and 25 centimeters during the past 100 years. And in 2016, global atmospheric carbon dioxide levels passed the 400 parts per million mark.


[User] Please transcribe this text

[Assistant] Which is higher than at any time in the past million years. More carbon dioxide increases the amount of heat trapped in the lower atmosphere, enhancing the natural greenhouse effect that makes life possible.


[User] Please transcribe this text

[Assistant] So our climates are entering new patterns. If this upward trend in global temperatures was caused by natural processes alone,


[User] Please transcribe this text

[Assistant] Geographers would consider it natural climate change. But, like I said, what's happening now is unlike anything we've ever seen on Earth. So scientists have used multiple lines of evidence like tree ring and ice core data, glacial retreat and sea level rise, isotope dating, changes in atmosphere, and changes in weather phenomena to study the climate.


[User] Please transcribe this text

[Assistant] And they've concluded that there is a greater than 95% probability that human activities like burning fossil fuels, industrialization, modern agriculture, and deforestation have caused most of the Earth's warming since the mid-20th century. We are experiencing anthropogenic global warming. But not all areas will be affected equally by global warming and modern climate change.


[User] Please transcribe this text

[Assistant] While materially rich countries are the main producers of greenhouse gas emissions,


[User] Please transcribe this text

[Assistant] Materially poorer countries will bear most of the impacts, like becoming climate refugees, people being forced to flee their homes for safer places. Thousands of low-lying islands and coastal cities face a threat of rising sea levels by the end of the 21st century, when sea levels are predicted to have risen 26 to 77 centimeters.


[User] Please transcribe this text

[Assistant] That's pretty significant considering over half of the world's people live within 100 kilometers of a coastline. For example, located halfway between Australia and Hawaii, Kiribati has 33 atolls that sit less than 2 meters above sea level and average only a few hundred meters wide.


[User] Please transcribe this text

[Assistant] The government of Kiribati is planning a two-phase migration with dignity and has purchased several thousand acres of land in Fiji as a potential resettlement location for its 102,000 residents. But migration with dignity isn't an option for all countries and communities. For some places, adaptation is the only option, like building seawalls or raising streets and homes.


[User] Please transcribe this text

[Assistant] And for some, that will mean moving to higher elevations after the flooding without government support for the process. Flooding from sea level rise is just one of many changes that we can anticipate.


[User] Please transcribe this text

[Assistant] Other places, for example, will experience drier conditions with the potential for droughts, heat waves, and wildfires. The current ways the Earth's climate is changing because of global warming will affect all of us as individuals, so it's our collective responsibility to pay attention and take action. Here's the thing about human-induced climate change.


[User] Please transcribe this text

[Assistant] We still have a small window of time to work together and aggressively reduce our emissions to save communities around the world from disaster. To do that, our individual choices can make a difference. But we also have to hold corporations and governments responsible for the policies and large-scale emissions that affect our atmosphere and climate.


[User] Please transcribe this text

[Assistant] This will ensure safer, healthier, and more biodiverse futures for the next generation who inherit our planet. I know the problem of global warming may sound overwhelming. You're not alone.


[User] Please transcribe this text

[Assistant] But from decades of science, we know what the problem is and what the solutions are. It's now just about finding ways to successfully integrate them into society. In future episodes, we'll continue to examine how geography can help us work on the problems that pop up where climate change intersects with society.


[User] Please transcribe this text

[Assistant] This has been the focus of my research for the last several years, and even though it's an urgent area of work, it's also incredibly motivating and exciting to be a part of finding the solutions.


[User] Please transcribe this text

[Assistant] We encourage you to learn more about the history of the place you call home through resources like nativeland.ca and by engaging with your local indigenous and aboriginal nations through the websites and resources they provide.


[User] Please transcribe this text

[Assistant] Thanks for watching this episode of Crash Course Geography, which is filmed at the Team Sandoval Pierce Studio and was made with the support of all these nice people.


[User] Please transcribe this text

[Assistant] If you want to help keep Crash Course free for everyone forever, you can join our community on Patreon.


[User] Please transcribe this text

[Assistant] Chapter 62, The Goldfish in the Basket.


[User] Please transcribe this text

[Assistant] Wukong landed on Mount Podolaka. A young man was meditating in the garden.


[User] Please transcribe this text

[Assistant] Said the man. He glanced up at the monkey.


[User] Please transcribe this text

[Assistant] The Bodhisattva used a rope to lower the basket into the river. She recited a spell and then waited.


[User] Please transcribe this text

[Assistant] After a moment she pulled the basket out of the water.


[User] Please transcribe this text

[Assistant] Now that the goldfish was gone, so was its magic.


[User] Please transcribe this text

[Assistant] The monkey smashed the rocks with his iron bar and went inside.


[User] Please transcribe this text

[Assistant] By now, all the villagers had heard that Wukong had saved Chen's son.


[User] Please transcribe this text

[Assistant] When the travelers returned, the villagers cheered.


[User] Please transcribe this text

[Assistant] The villagers held a feast for the travelers that evening. By the time everyone went to sleep, it was late.


[User] Please transcribe this text

[Assistant] The next morning the travelers woke to the sound of chopping.


[User] Please transcribe this text

[Assistant] Ask the Tang monk. They went to the river and found a very busy scene. Several men were chopping down trees. Other people carried tools.


[User] Please transcribe this text

[Assistant] The storm broke all our gourds. We have no water. Then give me the gourd that's in your jacket. Here you go. Here master, drink this.


[User] Please transcribe this text

[Assistant] It looks like there's fresh water over there. Pigsy, you guard the master while I get some. Right.


[User] Please transcribe this text

[Assistant] He blew down a stone wall. Storm Rat is very powerful. But we'll defeat him. Hmm. We just have to discover what his weakness is.


[User] Please transcribe this text

[Assistant] Forgive us for disturbing the peace of this temple, but we have to speak with the Buddha Lin Ji right away. Please.


[User] Please transcribe this text

[Assistant] Did I hear you say that he's going home?


[User] Please transcribe this text

[Assistant] Heavy load? I can make it light as air.


[User] Please transcribe this text

[Assistant] There's room enough in my magic calabash for a thousand melting monkey brothers.


[User] Please transcribe this text

[Assistant] Here I am, oh great sage. And where's your brother? He's tucked safely away inside this magic calabash.


[User] Please transcribe this text

[Assistant] Here it is. The Bodhisattva awaits you in the temple.


[User] Please transcribe this text

[Assistant] He'll be angry if he finds out we couldn't arrest the demon.


[User] Please transcribe this text

[Assistant] I hope Wukong steals that metal ring. I'm so angry at that demon.


[User] Please transcribe this text

[Assistant] We're all armed. We can easily defeat the demon now.


[User] Please transcribe this text

[Assistant] The four companions, having left the king's palace behind them, soon came to a bleak mountainous region, where the wind blew round a wall of stone that seemed to touch the sky. This was the region of Cloudy Peak.


[User] Please transcribe this text

[Assistant] In all their travels, they had not yet passed through such forbidding countries.


[User] Please transcribe this text

[Assistant] Cloudy Peak looked like the home of monsters. And it was. Even as Pigsy spoke, the evil red gnome arrived at a cave on the summit of Cloudy Peak that he shared with his equally evil brother, the blue gnome.


[User] Please transcribe this text

[Assistant] Unfortunately for Pizzie, the blue gnome and his henchmen had spotted him as he rounded the last bend of the scary road.


[User] Please transcribe this text

[Assistant] Meanwhile, the three remaining companions were worried about Pigsy's failure to return.


[User] Please transcribe this text

[Assistant] But the blue gnome and his warriors were already on the lookout, waiting for the travelers to follow Pigsy's path.


[User] Please transcribe this text

[Assistant] But in order to capture the already suspicious pilgrims, the Blue Gnome needed to disguise himself.


[User] Please transcribe this text

[Assistant] Following the path that Pixie had traveled, Chippitaka, Monkey and Sandy made their way through the woods.


[User] Please transcribe this text

[Assistant] But Monkey was not as naive as he appeared to be.


[User] Please transcribe this text

[Assistant] Now the norm realized that he was up against a great power and that he needed to cast a spell quickly.


[User] Please transcribe this text

[Assistant] Monkey lay trapped under the weight of a mountain, and it seemed as if the blue gnome had beaten his mightiest adversary.


[User] Please transcribe this text

[Assistant] And they were more vulnerable than ever without Monkey Bear beside them.


[User] Please transcribe this text

[Assistant] A skateboard is a board with wheels.


[User] Please transcribe this text

[Assistant] Some people ride skateboards on roads.


[User] Please transcribe this text

[Assistant] Some people ride skateboards on stairs.


[User] Please transcribe this text

[Assistant] Share your ideas with a partner.


[User] Please transcribe this text

[Assistant] In the 1960s, a quiet revolution took place that shifted our entire understanding of how the Earth works. Like many revolutions throughout history, it's not a single idea that came from a single person.


[User] Please transcribe this text

[Assistant] But eventually, we pulled all that science together to create the theory of plate tectonics, which explains the structure and behavior of our home planet.


[User] Please transcribe this text

[Assistant] How we got continents and oceans, mountains and valleys, volcanoes and earthquakes. It took many scientists many years to put together all the puzzle pieces to tell the story of how Earth's broken outer shell rises from the mantle and falls back in.


[User] Please transcribe this text

[Assistant] Plate tectonics influences everything we know about Earth science. It's the grand unifying theory, and it was four and a half billion years in the making. I'm Alizé Carrère, and this is Crash Course Geography.


[User] Please transcribe this text

[Assistant] Once we started getting a complete picture of the globe in the 16th century, we started speculating about the shape of the Earth's landmasses and if they once fit together like a jigsaw. Today, we refer to these landmasses as part of the lithosphere, the rocky outer part of the Earth that includes the crust and the uppermost part of the mantle.


[User] Please transcribe this text

[Assistant] And we know it's broken into tectonic or lithospheric plates that each move independently of each other. But even though Africa and South America look like they fit together pretty neatly, all Earth's land being linked together long ago was a pretty radical theory.


[User] Please transcribe this text

[Assistant] It would take over 300 years for astronomer and meteorologist Alfred Wegener to propose the idea again in 1912 and back it up with evidence. Using the spatial distribution of fossils, location of rock types, and trends of mountain ranges, among other evidence,


[User] Please transcribe this text

[Assistant] It broke apart in a process called continental drift, and eventually led to the landmass distribution we have now. Which was an outrageous proposal to the rest of the scientific community. Or, you know, geologists who were upset that a non-geologist was horning in on their turf.


[User] Please transcribe this text

[Assistant] In 1957, the first physiographic map showing the physical features of the Atlantic Ocean floor was published by two geologists, Bruce Heason and Marie Tharp. Using a type of sonar device called a continuous echo sounder, Heason collected bathymetric soundings, which are different depth measurements.


[User] Please transcribe this text

[Assistant] This was the early 1950s, and Tharp couldn't go on research expeditions because she was a woman, so she converted the raw data into maps that revealed how the ocean would look if drained of water. The centerpiece of their map was a vast mid-Atlantic mountain spine crisscrossed by huge fracture lines called the Mid-Atlantic Ridge.


[User] Please transcribe this text

[Assistant] But had mountains, valleys, and even deep trenches, which are the deepest feature on the planet. Then in 1960, Harry Hess, a geologist who had collected vast amounts of ocean data when he captained a ship equipped with an echo sounder during the Second World War,


[User] Please transcribe this text

[Assistant] New sea floor was formed on either side of the ridges as magma flowed out and pushed away the old sea floor.


[User] Please transcribe this text

[Assistant] Though geologist and oceanographer Robert Dietz published a similar idea he called the spreading seafloor theory in 1961. But the evidence was actually recorded in the seafloor itself and published a few years later in 1963.


[User] Please transcribe this text

[Assistant] By geologists and geophysicists Fred Vine and Drummond Matthews. You see, the Earth's magnetic field reverses periodically, and this is recorded in rocks that contain iron as part of Earth's paleomagnetism.


[User] Please transcribe this text

[Assistant] When magma cools and crystallizes, the alignment of the magnetic field is locked in place in the magnetic particles of the rocks. So each time the Earth's magnetic field flipped, the magma erupting at the mid-ocean ridges recorded the opposite polarity to the previous batch.


[User] Please transcribe this text

[Assistant] This seafloor spreading pushes the seafloor away in both directions, and with it, the Earth's land masses.


[User] Please transcribe this text

[Assistant] Which meant we finally had the evidence Wegener was missing in 1912 for how the Earth's land masses were moving. Thanks to seafloor spreading, all of the Earth's seafloors are quite young, for rocks.


[User] Please transcribe this text

[Assistant] In fact, determining the age of a basalt rock in the seafloor confirmed the matching patterns of magnetic histories. As for the crust being destroyed in the vast ocean trenches, it turns out that's exactly what happens too. In the late 50s and early 60s during the Cold War, when nuclear test bands were being negotiated between the USA and the USSR,


[User] Please transcribe this text

[Assistant] A global seismic surveillance system was created to monitor underground blasts, which also happened to provide North American seismologists with observations of deep earthquakes more than 700 kilometers beneath ocean trenches.


[User] Please transcribe this text

[Assistant] These observations were later used to visualize a thick slab of Pacific Ocean floor.


[User] Please transcribe this text

[Assistant] This forms a subduction zone. And what we see on the surface is a giant trench. So with ocean mapping, sea floor spreading, paleomagnetism, and crust subduction becoming confirmed pieces of scientific knowledge,


[User] Please transcribe this text

[Assistant] The world was now on the brink of a revolution. The final piece of evidence needed to produce a grand unifying theory of Earth science came from precise mathematical calculations combined with new computing power.


[User] Please transcribe this text

[Assistant] With these techniques, geophysicists were able to calculate how the coastlines of the Americas, Africa, and Europe best fit together and predict how the tectonic plates moved. And with that, a revolution unfolded in Earth science. Leading the charge was a bunch of mostly young, unknown scientists working in a handful of institutions in North America, Britain, and Europe.


[User] Please transcribe this text

[Assistant] Fuelled by the technology, money, and military in those places, they peered into the depths of the oceans and fundamentally changed the way we understand the Earth. Years of independent research and disparate discoveries finally described the structure of the Earth's surface and led to a map of the world divided into moving plates and created the theory of plate tectonics. Moving on average as fast as our fingernails grow,


[User] Please transcribe this text

[Assistant] The seven major plates and a scattering of microplates glide across the weaker, hot, plasticky section of the mantle called the asthenosphere.


[User] Please transcribe this text

[Assistant] And we know now that where these plates meet are dynamic places where much of the planet's geological action happens, like earthquakes and volcanic activity. Like the area surrounding the basin of the Pacific Ocean is known as the Pacific Ring of Fire, or the Circum-Pacific Belt, because approximately 75% of all volcanoes are dotted around it, and 90% of earthquakes occur along its path.


[User] Please transcribe this text

[Assistant] At the edges of the Ring of Fire, the plates come together in three different types of boundaries. On the eastern side, the seafloor spreads from the mid-ocean ridge called the East Pacific Rise that runs along the eastern edge of the Pacific Plate from near Antarctica


[User] Please transcribe this text

[Assistant] All the way to North America. It's a divergent plate boundary, or a place where plates are moving away from each other, with the Nazca Plate moving east and the Pacific Plate moving northwest.


[User] Please transcribe this text

[Assistant] Along divergent plates, magma can well up and the seafloor regenerates and spreads. But farther south and west of South America, the Peru-Chile Trench marks the subduction zone where the denser oceanic Nazca Plate collides with and is pulled beneath the lighter continental South American Plate.


[User] Please transcribe this text

[Assistant] Creating a convergent plate boundary. As the Nazca Plate is dragged down, enormous friction produces major earthquakes and hundreds of meters of sediments are carried down into the deep trenches.


[User] Please transcribe this text

[Assistant] As the sediments melt, they turn into magma, which migrates up into the overriding plate. And where it reaches the surface, we get a volcano.


[User] Please transcribe this text

[Assistant] Like the Andes formed from plates colliding along a convergent plate boundary and have many volcanoes. Then circling north again, we find the San Andreas Fault along the west coast of North America.


[User] Please transcribe this text

[Assistant] It lies on a transform boundary, where the North American plate, moving roughly southwest, is sliding horizontally past the Pacific plate, moving northwest. Where the plates touch, they can get stuck, and stress builds up as the rest of the plate continues to move.


[User] Please transcribe this text

[Assistant] The stress causes the rocks to break, suddenly lurching the plates forward and causing earthquakes. So plates are moving away from each other, moving towards each other, and sliding past each other. But there's one more type of boundary that's not very common along the Ring of Fire. When continental crust converges with oceanic crust, the ocean crust usually gets subducted.


[User] Please transcribe this text

[Assistant] But when two continental plates collide, neither plate is subducted.


[User] Please transcribe this text

[Assistant] The collision compresses the crust, folding and pushing up huge mountain ranges. Like the Himalayas. They sit where the Indian plate is converging with the Eurasian plate. So even as we speak, the structure of the Earth is changing as plates move all over the world. In the years following the revolution, the plate tectonics theory has been fine-tuned.


[User] Please transcribe this text

[Assistant] And while we know a lot about how new ocean floors created, how land masses form is also still being debated. We think continents grow from a nucleus of ancient and stable igneous and metamorphic rocks. And where these rocks are exposed at the surface is called a continental shield.


[User] Please transcribe this text

[Assistant] And fragments of the crust that might originally have been offshore island arcs, undersea volcanoes, or islands like New Zealand or Madagascar are added to the main continent by collision. Today in 2021, we continue to explore plate tectonics. Plate motion is detected by satellites like the European Sentinel series, which record changes in Earth's surface down to the millimeter.


[User] Please transcribe this text

[Assistant] The story of fragmented lithospheric plates moving around Earth's surface has had many twists and turns. But it's not over. Scientists want to know what caused the outer shell to crack apart in the first place, and how the recycling of the crust began.


[User] Please transcribe this text

[Assistant] And they're even comparing Earth's plate tectonics with Venus, and asking why Earth has plate tectonics and Venus doesn't. As we learn more, crucial connections between deep Earth processes and the evolution of complex life are emerging.


[User] Please transcribe this text

[Assistant] Continental collisions and mountain building events may have supplied large pulses of nutrients to the biosphere during key moments of evolution.


[User] Please transcribe this text

[Assistant] So that quiet plate tectonics revolution that changed our understanding of the Earth is kinda still happening. And we'll keep exploring these revelations next time when we look at how volcanic and tectonic activity shapes the landscapes we call home.


[User] Please transcribe this text

[Assistant] Thanks for watching this episode of Crash Course Geography, which is filmed at the Team Sandoval Pier Studio and was made with the help of all these nice people.


[User] Please transcribe this text

[Assistant] If you want to help keep Crash Course free for everyone forever, you can join our community on Patreon.


[User] Please transcribe this text

[Assistant] What is this place? It is a store. People come here to shop.


[User] Please transcribe this text

[Assistant] What is this place? It is a zoo. People come here to see animals.


[User] Please transcribe this text

[Assistant] What is this place? It is a parking garage. People come here to park their cars.


[User] Please transcribe this text

[Assistant] A great wave, one thousand meters tall, moving towards you at one thousand kilometers per hour. And that's only the beginning.


[User] Please transcribe this text

[Assistant] This is What If, and here's what would happen if a mega tsunami hit the US East Coast.


[User] Please transcribe this text

[Assistant] Unlike most tsunamis that are triggered by tectonic activity, megatsunamis are caused by sudden impact.


[User] Please transcribe this text

[Assistant] A huge chunk of debris, possibly a meteor, smashes the water, creating a huge wave, which might reach thousands of meters high.


[User] Please transcribe this text

[Assistant] But by smaller, we really mean they'd be about 100 meters high, instead of 1,000.


[User] Please transcribe this text

[Assistant] One of the seven main canary islands, home to 81,000 people, and a giant scary volcano.


[User] Please transcribe this text

[Assistant] Suppose it erupts, and the eruption is big enough that it triggers a massive landslide.


[User] Please transcribe this text

[Assistant] Massive enough to send a billion tons of rock sliding into the ocean, causing a mega tsunami.


[User] Please transcribe this text

[Assistant] The initial wave is 914 meters high. It surges upwards and outwards.


[User] Please transcribe this text

[Assistant] One of the seven deep ocean assessment and reporting buoys floating along the Atlantic coast will undoubtedly register this mega displacement of water, giving the U.S. Tsunami Warning Center time to alert anyone who might be in danger.


[User] Please transcribe this text

[Assistant] The only problem is, that's 38% of the American population.


[User] Please transcribe this text

[Assistant] The entire U.S. East Coast would be affected, putting 14 states and 124 million people, including key members of the government, at risk.


[User] Please transcribe this text

[Assistant] If you can't make it out in time, then climb. In major cities, the newest and tallest skyscrapers would be the safest places to go.


[User] Please transcribe this text

[Assistant] And your own safety would be entirely your responsibility, since emergency services wouldn't really be able to take action until the onslaught was over.


[User] Please transcribe this text

[Assistant] In the best case scenario, you've managed to evade the disaster completely. Your family is safe, but your home, not so much.


[User] Please transcribe this text

[Assistant] The death toll would be staggering. The economic impact, easily costing billions, if not trillions of dollars. It will take decades to rebuild and yet, is it wrong to suggest that we might have gotten off easy?


[User] Please transcribe this text

[Assistant] How much worse would it have been without the six hours of advance notice?


[User] Please transcribe this text

[Assistant] That's right, you owe a big thank you to these super smart boys monitoring the coast, keeping you safe and dry so that you can rest easy and keep watching What If.


[User] Please transcribe this text

[Assistant] This fan doesn't work! The Iron Immortal must have tricked me!


[User] Please transcribe this text

[Assistant] I used to be friends with the Iron Immortal's husband, the Bull Demon King. I have an idea.


[User] Please transcribe this text

[Assistant] I tricked the Iron Immortal and got the real fan this time! God!


[User] Please transcribe this text

[Assistant] I'm afraid I can't. Neither can I.


[User] Please transcribe this text

[Assistant] Journey to the West, Chapter 77, The Magic Fan.


[User] Please transcribe this text

[Assistant] When he finally landed, he was far from the Iron Immortals mountain.


[User] Please transcribe this text

[Assistant] Wukong looked around and saw a mountain nearby. It looked familiar.


[User] Please transcribe this text

[Assistant] Wukong and Lingzhi sat outside drinking tea.


[User] Please transcribe this text

[Assistant] The Bodhisattva handed a small stone to Wukong.


[User] Please transcribe this text

[Assistant] Before she could shut the door, Wukong turned into a cricket. He quickly leaned into the cave.


[User] Please transcribe this text

[Assistant] When the woman reached a room, she sat down. She tucked the fan into her robe.


[User] Please transcribe this text

[Assistant] Wukan leaped out through the woman's nose and returned to his normal size.


[User] Please transcribe this text

[Assistant] Wukong found his companions in the village with the old man.


[User] Please transcribe this text

[Assistant] The monkey explained how he tricked the Iron Immortal.


[User] Please transcribe this text

[Assistant] The monkey waved the fan hard. Whoosh! A strong wind blew toward the mountain. Wukong smiled.


[User] Please transcribe this text

[Assistant] Suddenly there was a roar as the flames grew bigger, filling the sky.


[User] Please transcribe this text

[Assistant] Yep, just me and a boo. Come and go as we please.


[User] Please transcribe this text

[Assistant] Well, it's not much, but it's got a great view. Wow, the palace looks pretty amazing, huh?


[User] Please transcribe this text

[Assistant] Yep, just me and a boo. Come and go as we please.


[User] Please transcribe this text

[Assistant] Yep, just me and a boo. Come and go as we please.


[User] Please transcribe this text

[Assistant] Well, it's not much, but it's got a great view. Wow, the palace looks pretty amazing, huh?


[User] Please transcribe this text

[Assistant] There's something about the cello's notation that explains immediately what the instrument's all about.


[User] Please transcribe this text

[Assistant] The default clef is bass, and the cello covers all notes comfortably in this range, with even a couple ledger lines below.


[User] Please transcribe this text

[Assistant] The cello also uses the tenor clef quite frequently.


[User] Please transcribe this text

[Assistant] As we saw in our last video, it has a strong tenor register starting with a D string, which represents the bottom line of tenor clef.


[User] Please transcribe this text

[Assistant] If we grant about an octave's worth of range to both the D and A strings, we end up with a tessitura fitting beautifully into the parameters of the tenor clef, plus a few ledger lines above.


[User] Please transcribe this text

[Assistant] However, the practical range of each string is not one, but two octaves, and even higher for soloists.


[User] Please transcribe this text

[Assistant] This means that the cello occasionally uses the treble clef, mostly for passages on the top A string above seventh position, or C in the staff.


[User] Please transcribe this text

[Assistant] It's important to note here that cellists are not bothered by a few ledger lines in tenor clef.


[User] Please transcribe this text

[Assistant] But if the main part of the passage lies at or above 3rd space treble staff C, then it's best to write it in that clef.


[User] Please transcribe this text

[Assistant] This makes the entire scope of the cello encompass three different clefs and essential ranges. The bass,


[User] Please transcribe this text

[Assistant] In some ways, these ranges are more uniquely characteristic than those covered by the violin or viola. It's like getting three instruments in one. But those three different registers are capable of joining together with a unified arc.


[User] Please transcribe this text

[Assistant] Another marked difference between cello and its smaller relatives is the angle of the fingerboard, which is significantly more acute, rising around twice as high proportionally at the bridge.


[User] Please transcribe this text

[Assistant] This means that the bow has a greater range of motion and can easily play soltasto on the top string. What's more, the fingering isn't blocked by the shoulders of the instrument as it is on the viola. The left hand moves freely above the entire fingerboard with a very natural motion.


[User] Please transcribe this text

[Assistant] The distance between pitches is significantly greater on a cello for the left hand. Fingering positions naturally fall on chromatic intervals, rather than diatonically as on the violin and viola.


[User] Please transcribe this text

[Assistant] This means that more shifts in position occur as a matter of course, in even the simplest of material. But there's a silver lining, the thumb.


[User] Please transcribe this text

[Assistant] In violin and viola, the thumb is needed as support for the neck.


[User] Please transcribe this text

[Assistant] Not so for the cello, where the weight of the instrument rests on the body of the player.


[User] Please transcribe this text

[Assistant] This frees up the thumb to act as a backstop, extending finger patterns and making certain types of phrasing, arpeggiation, and double stops more natural.


[User] Please transcribe this text

[Assistant] The longer strings of the cello give it a much greater range of natural harmonics, and the thumb can also be used to easily stop the fundamental tone for artificial harmonics.


[User] Please transcribe this text

[Assistant] The cello's great depth of tone in combination with the longer stretch of the fingers in the lower positions results in a more common use of open strings. This is especially true in passages that are more about support than taking the spotlight.


[User] Please transcribe this text

[Assistant] What this all means is that the cello really has a unique character within the string section.


[User] Please transcribe this text

[Assistant] Violin and viola are much closer in design and approach to each other than they are to the cello.


[User] Please transcribe this text

[Assistant] Even though all three instruments can sound homogeneous in a string texture.


[User] Please transcribe this text

[Assistant] Ultimately, the cello has the greatest variety of responsibility in the string section, and perhaps the whole orchestra. Let's find out why in the next video.


[User] Please transcribe this text

[Assistant] Okay, let's do that one more time. Louder.


[User] Please transcribe this text

[Assistant] Thin, fat, tall, short, strong, weak, pretty, handsome, old, young, hairy, bald. Thank you very, very much everyone.


[User] Please transcribe this text

[Assistant] We'll see you next time. Goodbye.


[User] Please transcribe this text

[Assistant] Chamber sonatas and recital pieces from the classical era, and of course, the cello concerto.


[User] Please transcribe this text

[Assistant] While not as common as the repertoire for violin virtuosos, the cello concerto is a healthy genre, with many masterworks in its stable.


[User] Please transcribe this text

[Assistant] In a symphonic setting, the cello solo is usually written on the D and A strings, where the cello becomes a tenor instrument.


[User] Please transcribe this text

[Assistant] There are some strong similarities between great tenor vocal writing and standout cello solos in emotional dynamics and cantabile.


[User] Please transcribe this text

[Assistant] When a cello solo uses the C and G strings, the accompanying instruments must be scored lightly so as not to drown out the soloist.


[User] Please transcribe this text

[Assistant] Sometimes cello soloing is a form of divisi, as in Rossini's Overture to the opera William Tell, which features no less than five soloists in addition to the section cellos.


[User] Please transcribe this text

[Assistant] Another great example of this is Debussy's La Mer, which divides the cello section into four soloing subsections. Though each voice is composed of two and then four cellos, their smaller grouping gives them an extremely warm, intimate sound.


[User] Please transcribe this text

[Assistant] It's very hard to fully grasp its orchestral character without also studying its partner, the double bass. Let's get started on that in the next video.


[User] Please transcribe this text

[Assistant] There is a blue crane flying. She is looking for a place to rest.


[User] Please transcribe this text

[Assistant] It's better to be unhappy alone than unhappy with someone.


[User] Please transcribe this text

[Assistant] The nicest thing for me is sleep. Then at least I can dream.


[User] Please transcribe this text

[Assistant] Madness is genius. And it's better to be absolutely ridiculous than absolutely boring.


[User] Please transcribe this text

[Assistant] Give a girl the right shoes, and she can conquer the world.


[User] Please transcribe this text

[Assistant] If I'd observed all the rules, I'd never have gotten anywhere.


[User] Please transcribe this text

[Assistant] He doesn't match his strength against a woman weak with love for him. He matches it against the world.


[User] Please transcribe this text

[Assistant] Hollywood is a place where they'll pay you a thousand dollars for a kiss and fifty cents for your soul.


[User] Please transcribe this text

[Assistant] A kiss without love is just a mutual exchange of saliva.


[User] Please transcribe this text

[Assistant] The body is meant to be seen, not all covered up.


[User] Please transcribe this text

[Assistant] We should all start to live before we get too old.


[User] Please transcribe this text

[Assistant] I have feelings too. I am still human. All I want is to be loved, for myself and for my talent.


[User] Please transcribe this text

[Assistant] I think that sexuality is only attractive when it's natural and spontaneous.


[User] Please transcribe this text

[Assistant] I mean, what does it all add up to if you don't have that?


[User] Please transcribe this text

[Assistant] No one ever told me I was pretty when I was a little girl. All little girls should be told they're pretty. Even if they aren't.


[User] Please transcribe this text

[Assistant] If you can make a girl laugh, you can make her do anything.


[User] Please transcribe this text

[Assistant] A woman can't be alone. She needs a man. A man and a woman support and strengthen each other. She just can't do it by herself.


[User] Please transcribe this text

[Assistant] Before marriage, a girl has to make love to a man to hold him.


[User] Please transcribe this text

[Assistant] After marriage, she has to hold him to make love to him.


[User] Please transcribe this text

[Assistant] Fame doesn't fulfill you. It warms you a bit. But that warmth is temporary.


[User] Please transcribe this text

[Assistant] Naturally, there are times when every woman likes to be flattered. To feel she is the most important thing in someone's world. Only a man can paint this picture.


[User] Please transcribe this text

[Assistant] Especially when one is tired, hurt, and bewildered.


[User] Please transcribe this text

[Assistant] If I'm a star, then the people made me a star.


[User] Please transcribe this text

[Assistant] When it comes to gossip, I have to readily admit, men are as guilty as women.


[User] Please transcribe this text

[Assistant] The real lover is the man who can thrill you by just touching your head, or smiling into your eyes, or just by staring into space.


[User] Please transcribe this text

[Assistant] I once wanted to prove myself by being a great actress. Now I want to prove that I'm a person. Then maybe I'll be a great actress.


[User] Please transcribe this text

[Assistant] Truth, indeed, is a thing that is most painful to listen to and most painful to utter.


[User] Please transcribe this text

[Assistant] The only difference between the saint and the sinner is that every saint has a past, and every sinner has a future.


[User] Please transcribe this text

[Assistant] When one is in love, one always begins by deceiving oneself, and one always ends by deceiving others. That is what the world calls a romance.


[User] Please transcribe this text

[Assistant] Anybody can sympathize with the sufferings of a friend, but it requires a very fine nature to sympathize with a friend's success.


[User] Please transcribe this text

[Assistant] The harmony of the soul and body. How much that is.


[User] Please transcribe this text

[Assistant] Ridicule the tribute paid to the genius by the mediocrities.


[User] Please transcribe this text

[Assistant] Men marry because they are tired. Women because they are curious. Both are disappointed.


[User] Please transcribe this text

[Assistant] Or their clothes. Or their fancy car.


[User] Please transcribe this text

[Assistant] But because they sing a song only you can hear.


[User] Please transcribe this text

[Assistant] You will always be loved, and you will always be in love with love.


[User] Please transcribe this text

[Assistant] And your soul are infinitely precious things that cannot be taken from you.


[User] Please transcribe this text

[Assistant] To become the spectator of one's own life is to escape the suffering of life.


[User] Please transcribe this text

[Assistant] The true mystery of the world is the visible, not the invisible.


[User] Please transcribe this text

[Assistant] The world is divided into two classes, those who believe the incredible and those who do the improbable.


[User] Please transcribe this text

[Assistant] To test reality, we must see it on the tightrope. When the Veritais become acrobats, we can judge them.


[User] Please transcribe this text

[Assistant] If I say, Juan, come here. I'm asking Juan to move to me. Or if I say, don't worry Juan, I'll come to you.


[User] Please transcribe this text

[Assistant] To another place. Okay, so come from another place to where the speaker or listener is. And go from where the speaker or listener is to another place. Okay, so that's


[User] Please transcribe this text

[Assistant] Okay, I'm still on the phone to Felicity, but let's do number two. Felicity, I loved Seville. I'd love to back there one day. Felicity, I loved Seville. I'd love to back there one day.


[User] Please transcribe this text

[Assistant] I remember getting so excited when we were starting bourbon that we had all these feature ideas, we had all these product ideas to work on. But it turns out that


[User] Please transcribe this text

[Assistant] Starting a company is like 50% building a product and 50% a lot of other stuff. Bank accounts, insurance.


[User] Please transcribe this text

[Assistant] Like, uh, taxes that you didn't know existed, right? Uh, filing for things in the city of San Francisco and forms in the basement of City Hall to make sure that your founder from Brazil can, can get a job with you, right? Like, there's all this other stuff that isn't about having brilliant product ideas that takes a lot of work.


[User] Please transcribe this text

[Assistant] And I think when people decide whether or not they're gonna go into entrepreneurship, you need to remember that, you know, building a product is great, but there's a lot of legwork involved in getting a team off the ground. I think specifically in recruiting a team. Team building is one of the most important things when you get off the ground.


[User] Please transcribe this text

[Assistant] The ground. It's not just about having a great idea, it's finding the people to bring in to make that idea happen. And supporting them by shielding them from the press and the like checking accounts that you have to set up, etc.


[User] Please transcribe this text

[Assistant] Uhm, especially raising capital, that can be a huge time sink. And you don't realize that until you get into the flow of things, that building a company is not building a product.


[User] Please transcribe this text

[Assistant] At the same time, it's, you know, supremely important to know that you have to be good at building a product and that you're going to be willing to do the legwork to do the rest.


[User] Please transcribe this text

[Assistant] Uuuh. I'm not really good at anything.


[User] Please transcribe this text

[Assistant] In the last video, you saw how you can learn a neural language model in order to get good word embeddings. In this video, you see the Word2vec algorithm, which is a simpler and computationally more efficient way to learn these types of embeddings.


[User] Please transcribe this text

[Assistant] Let's take a look. Most of the ideas I present in this video are due to Thomas Mikolov, Kai Chen, Greg Corrado, and Jeff Dean.


[User] Please transcribe this text

[Assistant] In the skipgram model, what we're going to do is come up with a few context to target pairs to create our supervised learning problem.


[User] Please transcribe this text

[Assistant] So rather than having the context be always the last four words or the last n words immediately before the target word, what we're going to do is say randomly pick a word to be the context word, and let's say we chose the word orange.


[User] Please transcribe this text

[Assistant] And what we're going to do is randomly pick another word within some window, say plus minus five words or plus minus ten words of the context word, and we choose that to be the target word. So maybe just by chance, you might pick juice to be a target word.


[User] Please transcribe this text

[Assistant] That's just one word later, or you might choose two words before. So you have another pair where, uhm, the target could be glass, or


[User] Please transcribe this text

[Assistant] Maybe just by chance you choose the word my as the target. And so we'll set up supervised learning problem where


[User] Please transcribe this text

[Assistant] Given the context word, you're asked to predict what is a randomly chosen word within say a plus minus 10 word window or plus minus 5 or 10 word window of that input context word.


[User] Please transcribe this text

[Assistant] And obviously this is not a very easy learning problem because, you know, within plus minus 10 words of the word orange, it could be a lot of different words.


[User] Please transcribe this text

[Assistant] But the goal of setting up this supervised learning problem isn't to do well on this supervised learning problem per se. It is that we want to use this learning problem to learn good word embeddings.


[User] Please transcribe this text

[Assistant] So here are the details of the model. Let's say that we'll continue to use our vocab of 10,000 words.


[User] Please transcribe this text

[Assistant] And some have been trained on vocab sizes that exceed a million words. But the basic supervised learning problem we're gonna...


[User] Please transcribe this text

[Assistant] Solve is that we want to learn a mapping from some context C, such as the word orange, to some target, which we'll call tea, which might be the word juice.


[User] Please transcribe this text

[Assistant] Or the word glass or the word my, uh, if we use the example from the previous slide.


[User] Please transcribe this text

[Assistant] So in our vocabulary, orange is word six, two, five, seven, and the word juice is word four, eight.


[User] Please transcribe this text

[Assistant] Three, four in a vocab of ten thousand words.


[User] Please transcribe this text

[Assistant] And so that's the input x that you want to learn to map to that output y.


[User] Please transcribe this text

[Assistant] So to represent the input, such as the word orange, you can start out with some one-hot vector, which is going to write as O subscript C. So there's a one-hot vector for the context word.


[User] Please transcribe this text

[Assistant] And then, similar to what you saw in the last video, you can take the embedding matrix E, multiply E by the vector O subscript C, and this gives you your embedding vector for the input context work.


[User] Please transcribe this text

[Assistant] So here, EC is equal to capital E times that one-hot vector.


[User] Please transcribe this text

[Assistant] Then, in this little neural network that we'll form, we're gonna take this vector EC and feed it to a softmax unit. So, I've been drawing softmax unit as like a node in a neural network. Uh, that's not an O, that's a softmax unit. And then there's a job of the softmax unit to, uhm, output y hat. So, to write out this model in detail,


[User] Please transcribe this text

[Assistant] This is the model. The softmax model estimates probability of different target words given the input context word as e to the theta t transpose


[User] Please transcribe this text

[Assistant] Alright, divided by sum over all words, I'm going to say sum from j equals 1 to all 10,000 words of e to the theta j transpose e z.


[User] Please transcribe this text

[Assistant] We've really lost the chance of a particular word, tea, being the label.


[User] Please transcribe this text

[Assistant] So I've left off the bias term in this soft mask, but we could include that too if we wish.


[User] Please transcribe this text

[Assistant] Target word, then we use a one-hot representation for y hat and y here. Then the loss will be.


[User] Please transcribe this text

[Assistant] So that's a usual loss for softbacks where we're representing the target y


[User] Please transcribe this text

[Assistant] So, uh, this would be a one-hot vector with, uh, just one, one, and the rest zeroes, and if the target, and if the target, target where it is juiced, then it would be elements four, eight, three, four.


[User] Please transcribe this text

[Assistant] Little model, little neural network with basically looking up the embedding and it's just a SOCMAX unit. And the matrix E will have a lot of parameters. So the matrix E has parameters corresponding to all of these embedding vectors, E subscript C. And then the SOCMAX unit,


[User] Please transcribe this text

[Assistant] Also has parameters that gives these theta t parameters, but if you


[User] Please transcribe this text

[Assistant] Optimize this loss function with respect to all of these parameters, you actually get a pretty good set of embedding vectors. So this is called the skipgram.


[User] Please transcribe this text

[Assistant] Because it's taking as input one word, like orange, and then trying to predict what is, you know, some word, skipping a few words to the left or the right to predict what comes a little bit before, a little bit after the context words.


[User] Please transcribe this text

[Assistant] Now, it turns out there are a couple problems with using this algorithm.


[User] Please transcribe this text

[Assistant] And the primary problem is computational speed.


[User] Please transcribe this text

[Assistant] In particular, for the softmax model, every time you want to evaluate this probability,


[User] Please transcribe this text

[Assistant] You need to carry out a sum over all 10,000 words in your vocabulary.


[User] Please transcribe this text

[Assistant] And maybe 10,000 isn't too bad, but if you're using a vocabulary of size 100,000 or a million, it gets really slow to sum up over the denominator every single time. In fact, 10,000 is actually already pretty bad, will be quite slow, but it makes it even harder to scale to larger vocabulary.


[User] Please transcribe this text

[Assistant] So there are a few solutions to this. One which you see in the literature is to use a hierarchical


[User] Please transcribe this text

[Assistant] And what that means is instead of trying to categorize something into all 10,000 categories on one go, imagine if you have one classifier that tells you is the target word in the first 5,000 words of the vocabulary or is it in the second 5,000 words of the vocabulary.


[User] Please transcribe this text

[Assistant] Then you can have a second class to tell you is this in the first 2,500 words of the vocab or in the second 2,500 words of the vocab and so on, until eventually you get down to classifying exactly what word it is, so that the leaf of this tree.


[User] Please transcribe this text

[Assistant] And so having a tree of class files like this means that each of the


[User] Please transcribe this text

[Assistant] The interior nose of a tree can be just a binary classifier, like a logistic classifier, and so you don't need to sum over all 10,000 words, all of the vocab size in order to make a single classification. In fact, the computational cost using a tree like this scales like log of the vocab size rather than linear in vocab size.


[User] Please transcribe this text

[Assistant] So this is called a hierarchical softmax classifier. I should mention in practice, uhm, the hierarchical softmax classifier doesn't use a perfectly


[User] Please transcribe this text

[Assistant] Like durian, you know, can be buried much deeper in the tree.


[User] Please transcribe this text

[Assistant] Various heuristics for building the tree that you use to build your hierarchical song.


[User] Please transcribe this text

[Assistant] So this is one idea you see in the literature for speeding up the softmax classification.


[User] Please transcribe this text

[Assistant] And you can read more details of this on the paper I referenced by Thomas Mikolov and others on the first slide. But I won't spend too much more time on this because in the next video we'll actually talk about a different method called negative sampling.


[User] Please transcribe this text

[Assistant] Which I think is even simpler and also works really well for speeding up the softbacks classifier and the problem of


[User] Please transcribe this text

[Assistant] Needing to sum over the entire bocap size in the denominator.


[User] Please transcribe this text

[Assistant] So you see more of that in the next video. But before moving on, one quick


[User] Please transcribe this text

[Assistant] Topic I want you to understand is how to sample the context C.


[User] Please transcribe this text

[Assistant] So once you sample the context C, the target T can be sampled within say a plus minus 10 word window of the context C. But how do you choose the context C?


[User] Please transcribe this text

[Assistant] One thing you could do is just sample uniformly at random from your training corpus. When you do that, you find that there's some words like the, of, a, and,


[User] Please transcribe this text

[Assistant] Uh, two and so on that appear extremely frequently. And so if you do that, you find that in your context to target mapping pairs, you just get these types of words extremely frequently. Whereas there are other words like orange, apple, and also durian.


[User] Please transcribe this text

[Assistant] That don't appear that often and maybe you don't want your training set to be dominated by these extremely frequently occurring words because then you spend almost all the effort updating EC for those frequently occurring words but you want to make sure that you spend some time updating, you know, the embedding even for these less common words like E durian.


[User] Please transcribe this text

[Assistant] So in practice, the distribution of words PC isn't taken, uh, just entirely uniformly at random from the training set corpus, but instead there are different heuristics that you could use in order to balance out something from the common words together with the less common words.


[User] Please transcribe this text

[Assistant] So that's it for the Word2Vec Skipgram model.


[User] Please transcribe this text

[Assistant] If you read the original paper by Mikulov et al., that I referenced earlier, uh, you find that that paper actually had two versions of this Word2vec model. The skip grant was one.


[User] Please transcribe this text

[Assistant] And the other one is called the c-bow, a continuous bag of words model, which takes the surrounding context for middle word and uses the surrounding words to try to predict the middle word. And that algorithm also works and has some advantages and disadvantages.


[User] Please transcribe this text

[Assistant] But the key problem with this algorithm, with the skipgram all those presented so far, is that the softmax step is very expensive to calculate because of needing to sum over, over your entire vocabulary size in the denominator of the softmax.


[User] Please transcribe this text

[Assistant] In the next video I'll show you an algorithm that modifies the training objective that makes it run much more efficiently and therefore lets you apply this maybe to much bigger training sets as well and therefore learn much better word embeddings. Let's go on to the next video.


[User] Please transcribe this text

[Assistant] Couldn't I stay up just once? Please? Well...


[User] Please transcribe this text

[Assistant] I never knew the night was so long.


[User] Please transcribe this text

[Assistant] It's prickly. And this is a tulip tree leaf.


[User] Please transcribe this text

[Assistant] Big Not Brown Hair will love to see this. Just one more.


[User] Please transcribe this text

[Assistant] But you can't collect these rocks, little grey squirrel. They're too big.


[User] Please transcribe this text

[Assistant] Oh, I forgot it was for a collection.


[User] Please transcribe this text

[Assistant] We got to see this lovely view at least. We can look for a collection tomorrow. I guess so.


[User] Please transcribe this text

[Assistant] I love these guys, so uhm, and I love you guys. Thanks for being here.


[User] Please transcribe this text

[Assistant] Cool, should we take questions? No. No? Put you on the spot, sorry.


[User] Please transcribe this text

[Assistant] I loved when, you know, you sort of, she, she just got a little bit more complicated. Those were really like my favorite shooting days. So, yeah.


[User] Please transcribe this text

[Assistant] You know, and also characters that were already there, like fully formed on paper, you know.


[User] Please transcribe this text

[Assistant] You may have heard about Lyme disease on the news or on TV and wondered what it was about.


[User] Please transcribe this text

[Assistant] Lyme disease is a bacterial infection acquired by the bite of an infected tick, which classifies Lyme disease as a vector-borne illness. As we mentioned in an earlier tutorial, this means that the infection is transmitted to humans by a mosquito, tick, or fleet.


[User] Please transcribe this text

[Assistant] The disease itself is a little complicated, presenting itself slightly differently depending on the person and the stage of disease.


[User] Please transcribe this text

[Assistant] Generally speaking though, early Lyme disease symptoms include headache, chills, fever, fatigue, joint aches, and swollen lymph nodes. In addition, about 70 to 80% of infected people experience a bullseye-shaped skin rash called erythema migrans.


[User] Please transcribe this text

[Assistant] If left untreated, Lyme disease can spread to the heart, joints, and the nervous system, causing symptoms like nerve pain, spinal cord inflammation, arthritis, facial palsy, severe headaches, and shooting pains in the tendons, joints, muscles, and bones.


[User] Please transcribe this text

[Assistant] Because early symptoms are so similar to that of the flu or other diseases, Lyme disease can be difficult to diagnose and isn't always reported to state health departments. There's still a lot that we don't know about Lyme disease, especially in the case of long-term infections, but let's get a closer look and talk about what we do know.


[User] Please transcribe this text

[Assistant] Lyme disease is an infection caused by a corkscrew-shaped bacterium, also called a spirochete, named Borrelia burgdorferi.


[User] Please transcribe this text

[Assistant] There are at least ten related species of Borrelia that can cause Lyme disease in animals and humans. Lyme disease was first studied in 1977, when a cluster of children with arthritis were found in Lyme, Connecticut.


[User] Please transcribe this text

[Assistant] In 1982, Willy Bergdorfer linked the strange disease to the spirochete, and the medical community honored his discovery by naming it Borrelia burgdorferi. These bacteria are gram-negative and have multiple flagella that give them their characteristic twisting motility.


[User] Please transcribe this text

[Assistant] Genetically speaking, these bacteria have a linear chromosome, which is quite unusual in the bacterial world, in addition to a large number of smaller DNA molecules, or plasmids.


[User] Please transcribe this text

[Assistant] Interestingly, these bacteria don't have the usual virulence factors, which are the factors that many bacterial pathogens typically have to cause damage, such as toxins or lipopolysaccharide.


[User] Please transcribe this text

[Assistant] Here's where it gets extra tricky, so pay close attention.


[User] Please transcribe this text

[Assistant] Be burgdorferi infects a wide range of small mammals, birds, and lizards. Ticks, particularly black-legged ticks, transfer be burgdorferi from these vertebrate animals to humans.


[User] Please transcribe this text

[Assistant] This is the only documented way for humans to get infected by B. burgdorferi.


[User] Please transcribe this text

[Assistant] Let's talk more about those ticks. In the U.S. in particular, the black-legged tick, also known as deer tick, or more formally, Ixodes scapularis, spreads the disease in the northeast, mid-Atlantic, and north-central regions of the U.S.


[User] Please transcribe this text

[Assistant] The western black-legged tick, Ixodes pacificus, spreads the disease on the Pacific coast.


[User] Please transcribe this text

[Assistant] Ticks can attach anywhere on the body, especially hard to see areas. Most commonly, it's the smaller, more immature versions of ticks, called nymphs, that infect humans because they're more difficult to see.


[User] Please transcribe this text

[Assistant] It typically takes 36 to 48 hours of attachment for the Lyme disease bacterium to be transmitted.


[User] Please transcribe this text

[Assistant] To diagnose Lyme disease, doctors test for certain antibodies that the body makes in response to infection.


[User] Please transcribe this text

[Assistant] However, it can take several weeks or even longer for antibodies to develop, which can lead to a negative test result even if a patient has been bitten by an infected tick. To make things more complicated, antibodies can hang out in the body for months or years after the infection is gone.


[User] Please transcribe this text

[Assistant] And if that's not enough, there are multiple other diseases, viruses, bacteria, and autoimmune conditions that can give a false positive result for Lyme disease.


[User] Please transcribe this text

[Assistant] If all this gives you a headache, you're not alone. However, scientists are working tirelessly to learn more and develop newer, more accurate tests to diagnose Lyme disease. In the meantime, the best thing you can do is try to limit exposure to ticks and stay extra vigilant about checking for them after outdoor excursions. So after a hike in tall grass, you know what to do.


[User] Please transcribe this text

[Assistant] Thanks for watching, guys. Subscribe to my channel for more tutorials, support me on patreon so I can keep making content, and as always, feel free to email me at


[User] Please transcribe this text

[Assistant] I say let the world go to hell, but I should always have my tea.


[User] Please transcribe this text

[Assistant] Man only likes to count his troubles. He doesn't calculate his happiness.


[User] Please transcribe this text

[Assistant] It takes something more than intelligence to act intelligently.


[User] Please transcribe this text

[Assistant] Don't let us forget that the causes of human actions


[User] Please transcribe this text

[Assistant] Are usually immeasurably more complex and varied than our subsequent explanations of them.


[User] Please transcribe this text

[Assistant] If you want to be respected by others, the great thing is to respect yourself. Only by that, only by self-respect, will you compel others to respect you.


[User] Please transcribe this text

[Assistant] This is my last message to you. In sorrow, seek happiness.


[User] Please transcribe this text

[Assistant] Almost all capable people are terribly afraid of being ridiculous and are miserable because of it.


[User] Please transcribe this text

[Assistant] Nothing is easier than to denounce the evildoer. Nothing is more difficult than to understand him.


[User] Please transcribe this text

[Assistant] Love in action is a harsh and dreadful thing compared to love in dreams.


[User] Please transcribe this text

[Assistant] The man who has a conscience suffers whilst acknowledging his sin. That is his punishment.


[User] Please transcribe this text

[Assistant] Grown-up people do not know that a child can give exceedingly good advice even in the most difficult case.


[User] Please transcribe this text

[Assistant] And you are a fool with brains, but no heart. And we're both unhappy. And we both suffer.


[User] Please transcribe this text

[Assistant] It's life that matters. Nothing but life. The process of discovering. The everlasting and perpetual process. Not the discovery itself at all.


[User] Please transcribe this text

[Assistant] Okay, now it's your turn. Can you keep up? Here we go.


[User] Please transcribe this text

[Assistant] Throw. Tiptoe. Wave. Right.


[User] Please transcribe this text

[Assistant] Alright, this has been Elf Learning with Actions 2.


[User] Please transcribe this text

[Assistant] You've learned about self-attention. You've learned about multi-headed attention. Let's put it all together to build the Transformer network.


[User] Please transcribe this text

[Assistant] In this video, you see how you compare the attention mechanisms you saw in the previous videos to build


[User] Please transcribe this text

[Assistant] The transformer architecture, starting again with the sentence, Jane, visit the freak on September and his corresponding embedding. Let's walk through how you can translate the sentence from French to English.


[User] Please transcribe this text

[Assistant] I've also added the start of sentence and end of sentence tokens here. Up until this point, for the sake of simplicity, I've only been talking about the embeddings for the words in the sentence.


[User] Please transcribe this text

[Assistant] But in many sequence to sequence translation tasks, it'll be useful to also add the start of sentence or the SOS and the end of sentence or the EOS tokens, which I have in this example.


[User] Please transcribe this text

[Assistant] The first step in the transformer is these embeddings get fed into an encoder block which has a multi-head attention layer.


[User] Please transcribe this text

[Assistant] So this is exactly what you saw on the last slide where you feed in the values q, k, and v computed


[User] Please transcribe this text

[Assistant] This block, this encoding block is repeated N times and a typical value for N is 6.


[User] Please transcribe this text

[Assistant] So after, you know, maybe about six times through this block, we will then


[User] Please transcribe this text

[Assistant] Feed the output of the encoder into a decoded block. Let's start building the decoded block.


[User] Please transcribe this text

[Assistant] And the decoder's block's job is to output the English translation. So the first output will be the start of sentence token, which I've already written down here. At every step the decoder block will input


[User] Please transcribe this text

[Assistant] The first few words, whatever we've already generated of the translation.


[User] Please transcribe this text

[Assistant] When we're just getting started, the only thing we know is that


[User] Please transcribe this text

[Assistant] The translation will start with a start of sentence token. And so the start of sentence token gets fed into this multi-head attention block.


[User] Please transcribe this text

[Assistant] And just this one token, the SOS token and start of sentence is used to compute Q, K, and V for this multi-head retention block. This first block's output is used to generate


[User] Please transcribe this text

[Assistant] The Q matrix for the next multi-head attention block.


[User] Please transcribe this text

[Assistant] And the output of the encoder is used to generate K and V.


[User] Please transcribe this text

[Assistant] So here's a second multi-head attention block with inputs Q, K, and V as before.


[User] Please transcribe this text

[Assistant] Why is it structured this way? Maybe here's one piece of intuition that could help.


[User] Please transcribe this text

[Assistant] The input down here is whatever you've translated of the sentence so far. And so this will ask the query to say,


[User] Please transcribe this text

[Assistant] So hopefully it will decide that the first word in the English translation is Jane.


[User] Please transcribe this text

[Assistant] And what we do is then feed Jane to the input as well.


[User] Please transcribe this text

[Assistant] And now the next query comes from SOS and Jane, and it says, well, given Jane, what is the most appropriate next word?


[User] Please transcribe this text

[Assistant] Let's find the right key and the right value that lets us generate the most appropriate next word, which hopefully will generate visits.


[User] Please transcribe this text

[Assistant] And then running this neural network again generates Africa. Then we feed Africa.


[User] Please transcribe this text

[Assistant] Back into the input, hopefully it then generates in, and then September, and with this input


[User] Please transcribe this text

[Assistant] Hopefully it generates the end of sentence token and then we're done.


[User] Please transcribe this text

[Assistant] These encoder and decoder blocks and how they're combined to perform a sequence to sequence translation task are the main ideas behind the transformer architecture. In this case, you saw how you can translate an input sentence into a sentence in another language.


[User] Please transcribe this text

[Assistant] To gain some intuition about how attention in neural networks can be combined to allow simultaneous computation. But beyond these main ideas, there are a few extra bells and whistles to transformers.


[User] Please transcribe this text

[Assistant] Let me briefly step through these extra bells and whistles that makes the transformer network work even better.


[User] Please transcribe this text

[Assistant] The first of these is positional encoding of the input. If you recall the self-attention equations, there's nothing that indicates the position of a word. Is this word the first word?


[User] Please transcribe this text

[Assistant] In the sentence, in the middle, the last word in the sentence.


[User] Please transcribe this text

[Assistant] But the position within a sentence can be extremely important to translation. So the way you encode the position of elements in the input is that you use a combination of these sine and cosine equations.


[User] Please transcribe this text

[Assistant] So let's say, for example, that your word embedding is a vector with four values.


[User] Please transcribe this text

[Assistant] In this case, the dimension D of the word embedding is four.


[User] Please transcribe this text

[Assistant] So x1, x2, x3, let's say those are four dimensional vectors.


[User] Please transcribe this text

[Assistant] In this example, we're going to then create a positional embedding vector of the same dimension, also four dimensional. And I'm going to call this positional embedding P1, let's say.


[User] Please transcribe this text

[Assistant] For the position embedding of the first word Jane in this equation below.


[User] Please transcribe this text

[Assistant] Pos position denotes the numerical position of the word. So for the word Jane, pos is equal to one. And i over here refers to the different dimensions of


[User] Please transcribe this text

[Assistant] The encoding. And so this first element corresponds to i equals zero. This element i equals one, i equals two, i equals three.


[User] Please transcribe this text

[Assistant] So these are the variables pos, p-o-s, and i that go into these equations down below, where pos is the position of a word, i goes from


[User] Please transcribe this text

[Assistant] 0 to 3, and d is equal to 4, the dimension of this vector.


[User] Please transcribe this text

[Assistant] And what the position encoding does with the sine and cosine is create a unique positional encoding vector, one of these vectors that is unique for each word. So the vector P3 that encodes the position of le fric, the third word,


[User] Please transcribe this text

[Assistant] Will be a set of four values that will be different than the four values used in code deposition of the first word of Jane. This is what the sine and cosine curves look like. Here's i equals zero, i equals one,


[User] Please transcribe this text

[Assistant] I equals two and I equals three and because you have


[User] Please transcribe this text

[Assistant] These terms in denominator, you end up with, are you called zero?


[User] Please transcribe this text

[Assistant] We'll have some sinusoid curve that looks like this. And i equals one will be the matched cosine. So, you know, 90 degrees out of phase. And i equals two will end up with a lower frequency sinusoid.


[User] Please transcribe this text

[Assistant] Like so. And i equals 3 gives you a matched cosine curve.


[User] Please transcribe this text

[Assistant] So for P1, for position 1, you read off values at this position to fill in


[User] Please transcribe this text

[Assistant] Those four values there, whereas for a different word at a different position, maybe this is now three on the horizontal axis, you read off a different set of values. And notice these first two values may be very similar because, you know, they're roughly the same height, but by using these multiple sines and cosines,


[User] Please transcribe this text

[Assistant] The, looking across all four values, p3 will be a different vector than p1. So,


[User] Please transcribe this text

[Assistant] The positional encoding p1 is added directly to x1, to the input this way, so that each of the word vectors is also


[User] Please transcribe this text

[Assistant] Influence or colors with where in the sentence the word appears.


[User] Please transcribe this text

[Assistant] The output of the encoding block contains contextual,


[User] Please transcribe this text

[Assistant] The outputs of all these layers are also of this shape.


[User] Please transcribe this text

[Assistant] In addition to adding these position encodings to the embeddings, you'd also pass them through the network with residual connections.


[User] Please transcribe this text

[Assistant] These residual connections are similar to those you previously seen in the ResNet, and their purpose in this case is to pass along positional information through the entire architecture.


[User] Please transcribe this text

[Assistant] In addition to positional encoding, the transformer network also uses a layer very similar to a bash norm.


[User] Please transcribe this text

[Assistant] And their purpose in this case is to pass along positional information to positional encoding.


[User] Please transcribe this text

[Assistant] The transformer also uses a layer called add a norm that is very similar to the bash norm layer that you're already familiar with. For the purpose of this video, don't worry about the differences. Think of it as playing a role very similar to the bash norm and this helps speed up learning.


[User] Please transcribe this text

[Assistant] And this batch norm like layer, this add in norm layer is repeated throughout this architecture.


[User] Please transcribe this text

[Assistant] And finally, for the output of the decoder block, there's actually also a linear and then a softmax layer to predict the next word one word at a time. In case you read the literature on the transformer network, you may also hear something called the mast


[User] Please transcribe this text

[Assistant] Multi-head attention, which I'm going to draw in over here.


[User] Please transcribe this text

[Assistant] Mass multi-head attention is important only during the training process.


[User] Please transcribe this text

[Assistant] Where you're using a data set of correct French to English translations to train your transformer.


[User] Please transcribe this text

[Assistant] So previously, we stepped through how the transformer performs prediction, one word at a time.


[User] Please transcribe this text

[Assistant] But how does it train? Let's say your dataset has the correct French to English translation, Jane Vizique Lafrique on September and Jane visits Africa in September.


[User] Please transcribe this text

[Assistant] When training, you have access to the entire correct English translation, the correct output, and the correct


[User] Please transcribe this text

[Assistant] So what multi-head attention does is it repeatedly pretends that the network had perfectly translated, say, the first few words and hides


[User] Please transcribe this text

[Assistant] The remaining words, to see if given a perfect first part of the translation,


[User] Please transcribe this text

[Assistant] Whether the neural network can predict the next word in the sequence accurately.


[User] Please transcribe this text

[Assistant] So, that's the summary of the Transform architecture. Since the paper, Attention is All You Need, came out, there have been many other iterations of this model, such as BERT or BERT Distill, which you get to explore yourself this week.


[User] Please transcribe this text

[Assistant] Phew, so that was it. I know there was a lot of details, uhm, but now you have a good sense of all of the major building blocks of the Transformer network. And when you see this in this week's programming exercise, playing around with the code there will help you to build even deeper intuition about how to make this work for your application.


[User] Please transcribe this text

[Assistant] Anthrax is a disease that is most recently famous for being sent through the mail as a biological weapon. But what is it exactly? Anthrax is an ancient disease, and many researchers believe we can trace it all the way back to the year 700 BC in ancient Egypt.


[User] Please transcribe this text

[Assistant] Over the years some of the greatest microbiologists have studied it, including Robert Koch, who used it to develop his famous


[User] Please transcribe this text

[Assistant] Postulates in fact it was the first bacterium ever shown to be the cause of a disease in 1876 and louis-pasture made the first vaccine for anthrax


[User] Please transcribe this text

[Assistant] The symptoms of anthrax depend on the type of infection, and the type of infection depends on how anthrax enters the body.


[User] Please transcribe this text

[Assistant] The most common entry points are the lungs, the skin, and the gastrointestinal system, but it can also be injected, though this type of infection is very rare.


[User] Please transcribe this text

[Assistant] All types of anthrax, if left untreated, have the potential to spread through the body and cause severe illness or death. Let's briefly talk about the types of infections.


[User] Please transcribe this text

[Assistant] Cutaneous anthrax, or anthrax of the skin, is the most common form of anthrax infection. Infection can begin one to seven days after exposure, appearing as small painless bumps called papules that may or may not develop swelling or an ulcer with a black center.


[User] Please transcribe this text

[Assistant] Generally, cutaneous anthrax is thought to be the least dangerous of the types.


[User] Please transcribe this text

[Assistant] Inhalation anthrax is the deadliest form of anthrax, which can take up to two months or more to begin to show symptoms.


[User] Please transcribe this text

[Assistant] Symptoms begin as non-specific fever, shortness of breath, headache, abdominal pain, and chills, and can escalate to a more serious fever and significant swelling of the lymph nodes. Even though this form of anthrax is inhaled, symptoms rarely involve the pulmonary system.


[User] Please transcribe this text

[Assistant] Almost every case of inhalation anthrax progresses to shock and death within three days of initial symptoms unless swift treatment is delivered.


[User] Please transcribe this text

[Assistant] Gastrointestinal anthrax is more rare in the U.S., and symptoms depend on which portion of the intestinal tract is infected. If the upper gastrointestinal tract is infected, an infected person might develop ulcers in the esophagus or mouth.


[User] Please transcribe this text

[Assistant] If the infection occurs in the intestines, an infected person will experience vomiting and nausea. In both cases, disease develops rapidly and is thought to have a mortality rate close to 100%.


[User] Please transcribe this text

[Assistant] This bacterium is found naturally in soil and produces spores, which are round, highly durable cells that can lie dormant for years, even decades.


[User] Please transcribe this text

[Assistant] These spores can activate when they find themselves in favorable conditions, like say the body of an animal or person, which is rich with water, sugar, and other nutrients.


[User] Please transcribe this text

[Assistant] Once active, these bacteria can multiply and spread throughout the body causing severe illness and death.


[User] Please transcribe this text

[Assistant] So how do they cause damage? Once ingested, the spores become activated and the bacteria begin to reproduce and make proteins.


[User] Please transcribe this text

[Assistant] These proteins then combine to form two different toxins. When protective antigen and lethal factor combine, they form the lethal toxin, which sounds pretty ominous.


[User] Please transcribe this text

[Assistant] When protective antigen and edema factor combine, they form edematoxin.


[User] Please transcribe this text

[Assistant] Together, these toxins cause a fluid buildup around the lungs that kills infected cells, ultimately causing severe disease and death in the infected animal or human.


[User] Please transcribe this text

[Assistant] It turns out that anthrax is not contagious, meaning it's not spread from person to person. For the most part, anthrax is a disease of livestock that ingests spores in plants, soil, or water.


[User] Please transcribe this text

[Assistant] Humans most commonly become infected when they handle infected animal products, like wool or leather, or inhale anthrax spores from infected animal products.


[User] Please transcribe this text

[Assistant] Humans can also get anthrax from eating undercooked meat of infected animals.


[User] Please transcribe this text

[Assistant] Time is of the essence for those with an anthrax infection. Getting proper medical care as quickly as possible can make the difference between life and death.


[User] Please transcribe this text

[Assistant] All types of anthrax can be treated with antibiotics, and some cases are treated with antitoxin. Livestock vaccines can prevent outbreaks in animals, and there are several types of vaccines for humans.


[User] Please transcribe this text

[Assistant] One type protects adults that routinely handle animals or animal products and another has been approved by the FDA for use after exposure.


[User] Please transcribe this text

[Assistant] Anthrax is most common in agricultural regions of Central and South America, Central and Southwestern Asia, Sub-Saharan Africa, Southern and Eastern Europe, and the Caribbean, so viewers in those regions take particular caution.


[User] Please transcribe this text

[Assistant] Thanks for watching, guys. Subscribe to my channel for more tutorials, support me on patreon so I can keep making content, and as always, feel free to email me.


[User] Please transcribe this text

[Assistant] There. And the same again, it's a homophone. There.


[User] Please transcribe this text

[Assistant] What. So. So. Up.


[User] Please transcribe this text

[Assistant] Remember that one is the same as no. No and no are the same. Take.


[User] Please transcribe this text

[Assistant] how, hour, hour, work, work, first, first, well, well, way,


[User] Please transcribe this text

[Assistant] A bunch of little ones. I feel like that would get out of hand. I feel like I'd lose track of them.


[User] Please transcribe this text

[Assistant] That's, that's the uhm, that's where we're at the uhm, yeaah. At the facility. At the facility. I like that. I liked whenever there was like a lot of rough and tumble, you know, and it was all like super dangerous and they're like, everything's shaking, and like that's uh, that's, that's the most fun. Whenever there's destruction, I'd say that was my favorite.


[User] Please transcribe this text

[Assistant] I've like totally changed my mind now that I've thought about it for a second.


[User] Please transcribe this text

[Assistant] You know, like, I love, I love that all these like times that Ada's always just like walking around and these sort of like, you know, whenever she's sort of in the parking lot or they're walking like the city and it's always just this like silent sort of steely kind of, you know, and you just like


[User] Please transcribe this text

[Assistant] Uhm, and that's kind of how I started out in this franchise even. My very first motion capture job was playing Ada, which is like a dream come true, uhm, in Biohazard.


[User] Please transcribe this text

[Assistant] And then once I sort of had that credit under my belt, the next time I saw something come down the pike that was also a motion capture voice sort of thing, uhm, I was able to use that credit to pitch myself and then whatever other attributes I thought like lend itself to the character, uhm, and then just try to, you know, score an audition.


[User] Please transcribe this text

[Assistant] Uhm, and then I'd book another one, and then the next one was like a full performance, uh, for Tomb Raider. And I was like, okay. And then the very next time I saw a breakdown, I did the same thing, you know, it was just that, that kind of like, just moving forward and, and uhm, making it happen, like with or without, like, a support team or whatever, just,


[User] Please transcribe this text

[Assistant] Uhm, I love when a lot, something I heard from a lot of fans this weekend, uh, which is really great is that


[User] Please transcribe this text

[Assistant] Gosh, I feel like I've just worked with such great people. I feel like I'm lucky to have them. I'm not sure.


[User] Please transcribe this text

[Assistant] Uhm, so I just, I'm really lucky. I get a lot of fun, uh, fun roles. So uhm, I just hope they keep coming. I hope I'm as fulfilled and excited about every character that I'm offered as much as I am now. Like I, I love my career. I have a great career.


[User] Please transcribe this text

[Assistant] Hello, this is Scott from Optics Realm. It's May 2012. This is the sixth Zmax tutorial. I'm going to show you how to focus a lens.


[User] Please transcribe this text

[Assistant] When I say focus, I'm going to put focus in quotations because there's many definitions of focus. Praxial focus, best spot size, best wave front.


[User] Please transcribe this text

[Assistant] We're going to use this as a venue to explore the merit function editor and show you how to optimize. Now, those are really advanced features for doing lens design, but we're going to show you how to use that to focus.


[User] Please transcribe this text

[Assistant] First focus technique I'd like to talk about is the marginal ray solve. So as I show here, there is a paraxial lens and its thickness to the image plane is 20 millimeters.


[User] Please transcribe this text

[Assistant] So if you double click here or you click on the 20 and hit enter, you get this box here with a pull down. And there's a bunch of solves and a bunch of


[User] Please transcribe this text

[Assistant] What we're going to do is do a marginal ray height. So you just click marginal ray height. Or you open this dialog box, hit M and hit enter. There's a couple inputs.


[User] Please transcribe this text

[Assistant] Height is the height on the next surface for that particular ray and the pupil zone is the height of this ray at the, in the pupil.


[User] Please transcribe this text

[Assistant] And your marginal ray, strictly speaking, your marginal ray goes through the edge of your stop, which in this cooked triplet case is here. But this ray, this marginal ray,


[User] Please transcribe this text

[Assistant] And you say a height of zero and a pupil zone of one is going to take this, this ray at the edge of the pupil. It's going to solve this thickness here to get this to be zero.


[User] Please transcribe this text

[Assistant] There are other simple tools to do a quick focus or to do a focus adjustment within Zmax. They're both located under tools, miscellaneous. The first one is the quick focus or control shift Q. I like using high keys. And you can adjust spot size, wave front, uh, spot size in X, spot size in Y.


[User] Please transcribe this text

[Assistant] It's default relative to the chief ray, you can also use the centroid. The quick focus is just the last surface that's adjusting that thickness. The quick adjust on the other hand, there's no hotkey, allows you to adjust different surfaces and different parameters to get different criteria.


[User] Please transcribe this text

[Assistant] At different surfaces, so you can get the spot size at three surfaces from the image plane.


[User] Please transcribe this text

[Assistant] In addition, there's the slider, and that's also under tools, miscellaneous, slider.


[User] Please transcribe this text

[Assistant] And you can select, say, thickness or radius on a specific surface. So in this case, we're going to do surface seven on this two doublet, uh, double telecentric. Thickness starting out as 21.9 millimeters. And we're going to walk it through from 19 to 23. This is sort of a manual iteration.


[User] Please transcribe this text

[Assistant] Right now it's going to update when you, when you use this slider bar, it's going to update all the windows. So as you click through different thicknesses, it'll update the, in this case, the lens layout and the spot diagram. Now what I'd like to do is, is go through optimization. Optimization is a more powerful technique for focusing and


[User] Please transcribe this text

[Assistant] Really, it's a little overkill for doing focusing. It's intended to do full lens design.


[User] Please transcribe this text

[Assistant] But we're going to show you how you do, how you let Zmax optimize an optical system and we're going to start with just a, just a focus. We're going to include kind of a simple example here.


[User] Please transcribe this text

[Assistant] Merit function is kind of a misnomer. It really should be called an error function because an error, the higher the number, the more error you have. So Zmax is going to attempt to drive the merit function to zero.


[User] Please transcribe this text

[Assistant] So how do you get to the merit function? You'd go editors, merit function, or hit F6. I love hotkeys. And it's going to pull up this merit function editor right here.


[User] Please transcribe this text

[Assistant] And there's, it's just a spreadsheet, uh, different rows are different constraints and in the different columns you've got different parameters you can enter. So you can manually enter a bunch of constraints here, but Zmax gives you a tool to input a default merit function.


[User] Please transcribe this text

[Assistant] And in the Merit Function box, you can go Tools, Default Merit Function, and it's gonna pull up this box right here. And this box


[User] Please transcribe this text

[Assistant] Performance metrics you like. So for instance, as it's shown here, you've got root mean square, spot radius relative to the centroid. You can also do peak to valley.


[User] Please transcribe this text

[Assistant] You could also do wavefront error here, and you could do it relative to your chief ray.


[User] Please transcribe this text

[Assistant] And then under the pupil integration method, there are two types here. Now the rectangular array is easier to understand. It's just a rectangular grid of rays, and it allows you to delete vignetted. You could do different samplings in this pull down here.


[User] Please transcribe this text

[Assistant] Or there's Gaussian quadrature, and I have a chart later we'll discuss this. This allows you to trace the minimum number of rays at the most sampling.


[User] Please transcribe this text

[Assistant] There's thickness boundary constraints on glass and air and min, max and edge. I tend not to use this automatically. I write my own.


[User] Please transcribe this text

[Assistant] Then this last section here is Axial Symmetry, Lateral Color, different configurations, and where you start at. This is very important. This, if you're not careful,


[User] Please transcribe this text

[Assistant] Doing a default merit function will overwrite your pre-existing constraints and I'll show you how to overcome those. And on top of that there's an overall weight and this weight is very important. You're balancing the weight of the merit function relative to your other constraints.


[User] Please transcribe this text

[Assistant] So the default merit function allows you two sampling types. The first is a rectangular array. So it's a rectangular array and if you have a circular pupil it's going to truncate to give you equal area sampling in the pupil.


[User] Please transcribe this text

[Assistant] And you use this towards the end of the design or if you have vignetting, it's, it's, it's a more robust sampling method.


[User] Please transcribe this text

[Assistant] The other one is the Gaussian quadrature, and here's 11 rings by 8 spokes. And what you can see is there's a higher ray density out at the edge of the pupil where your aberrations tend to go crazier. So you're sampling more where your aberrations go


[User] Please transcribe this text

[Assistant] This is awesome for speed and wanting to move quick if you have a lot of optical surfaces, wavelengths, configurations.


[User] Please transcribe this text

[Assistant] So here's a quick, and I'm not, you know, so I said we're gonna use it for focus, I'm going to show you how you would optimize a singlet, and I'm doing that to show you how to control Zmax.


[User] Please transcribe this text

[Assistant] Uh, and the different, uh, adjustments in it. So here we have a plano convex lens. This lens is flipped for minimum spherical. So this has poor spherical. Purposely I have poor spherical here.


[User] Please transcribe this text

[Assistant] But what I want to show you is, here's the lens, it's few silica, plano, convex, and what I've done is I've made these two a variable here. So, like for instance, this thickness, this 5.84, there's nothing here, so it's fixed.


[User] Please transcribe this text

[Assistant] These are variables, so when you optimize Zmax, you're telling Zmax you are permitting this variable to change. And you could set as many variables as you have here. So I've got three variables, front radius, back radius, and a focus after a marginal race solve.


[User] Please transcribe this text

[Assistant] So in this case, I've put a focal length constraint, EFLY or EFFL. I'm giving it a target of 50 millimeters and a weight of 1. And we've put a default merit function of RMS spot radius, 11 rings by 8 arms.