[User] Please transcribe this text

[Assistant] Information for each product and then it checks if there's a category if they want any products, so that would be four.


[User] Please transcribe this text

[Assistant] This object for example, and then it gets all of the product information for the products in that category. And it just adds them to this string. And then that's what it returns.


[User] Please transcribe this text

[Assistant] So at this point, we've found the relevant product information to answer the user question. Now it's time for the model to actually answer the question. So let's have our system message. So this is the instruction.


[User] Please transcribe this text

[Assistant] You're our customer service assistant for a large electronic store. Respond in a friendly and helpful tone with.


[User] Please transcribe this text

[Assistant] Let's say with very concise answers, make sure to ask the user relevant follow up questions. So I want this to be an interactive experience for the user. And so just as a reminder, this was our initial user message. I'll just add it again.


[User] Please transcribe this text

[Assistant] So let's go through this. We have our first message, which is the system message as usual. We have the user message. And then we have this additional assistant message.


[User] Please transcribe this text

[Assistant] And this is the message that contains all of the product information that we just looked up and so.


[User] Please transcribe this text

[Assistant] We're saying relevant product information, new line, and then this product information that we just found.


[User] Please transcribe this text

[Assistant] And so now the model has the relevant context it needs to be able to answer this user's question.


[User] Please transcribe this text

[Assistant] So, let's get the final response and print it.


[User] Please transcribe this text

[Assistant] And we're hoping that the model is going to use relevant information from.


[User] Please transcribe this text

[Assistant] The product information in order to answer the user in a helpful way.


[User] Please transcribe this text

[Assistant] So first it tells the user about the smart X pro phone, tells the user about the photo snap camera, and then talks about the different televisions that we have in stock and then asks a follow up question.


[User] Please transcribe this text

[Assistant] So as you can see, by breaking this up into a series of steps, we were able to load information relevant to the user query.


[User] Please transcribe this text

[Assistant] So you might be wondering why are we selectively loading product descriptions into the prompt instead of including all of them and letting the model use the information it needs. And so what I mean by this is


[User] Please transcribe this text

[Assistant] Why didn't we just include all of this product information in the prompt and we wouldn't have to bother with all of those intermediate steps to actually look up the product information? There's a couple of reasons for this.


[User] Please transcribe this text

[Assistant] Firstly, including all of the product descriptions might make the context more confusing for the model, just as it would for a person trying to process a large amount of information at once.


[User] Please transcribe this text

[Assistant] The next reasons are more compelling. So the second reason is that language models have context limitations, i.e. a fixed number of tokens allowed as input and output.


[User] Please transcribe this text

[Assistant] So if you have a large number of products, imagine you had a huge product catalog.


[User] Please transcribe this text

[Assistant] As you pay per token when using language models, so by selectively loading information, you can reduce the cost of generating responses.


[User] Please transcribe this text

[Assistant] In general, determining when to dynamically load information into the models context and allowing the model to decide when it needs more information is one of the best ways to augment the capabilities of these models. And to reiterate, you should think of a language model as a reasoning agent that requires the necessary context to draw useful conclusions and perform useful tasks.


[User] Please transcribe this text

[Assistant] In this case, we had to give the model the product information and then it was able to reason about that product information to create a useful answer for the user.


[User] Please transcribe this text

[Assistant] The models are actually good at deciding when to use a variety of different tools and can use them properly with instructions.


[User] Please transcribe this text

[Assistant] And this is the idea behind ChatGBT plugins. We tell the model what tools it has access to and what they do, and it chooses to use them when it needs information from a specific source or wants to take some other appropriate action.


[User] Please transcribe this text

[Assistant] In our example, we can only look up information by exact product and category name match, but there are also more advanced techniques for information retrieval.


[User] Please transcribe this text

[Assistant] One of the most effective ways to retrieve information is using text embeddings. Embeddings can be used to implement efficient knowledge retrieval over a large corpus to find information related to a given query.


[User] Please transcribe this text

[Assistant] We're planning to create a comprehensive course on how to use embeddings for various applications soon, so stay tuned.


[User] Please transcribe this text

[Assistant] And with that, let's move on to the next video where we're going to talk about how to evaluate the outputs from the language model.


[User] Please transcribe this text

[Assistant] So in my career, this was always really important to me. I always really wanted to work on things that mattered.


[User] Please transcribe this text

[Assistant] And I wanted to make someone else's life better if I was going to go to work all day. And so I started my career working on leprosy in India.


[User] Please transcribe this text

[Assistant] And then later on, I worked at the US Treasury Department, uh, during the Clinton years, during the Asian financial crisis. And while it wasn't the financial crisis of today that's hitting us, it was one that was hitting a lot of people and impoverishing hundreds of millions of people in some of the poorest countries of the world. So it felt very mission-based. And then I completely surprised myself.


[User] Please transcribe this text

[Assistant] By when I was leading the government, so if you're in the government and you're a political appointee in the United States, they elect a new president, so they elect George Bush, you know, George W., and then they kick you out, so you have to find a new job. And I found myself really drawn to technology.


[User] Please transcribe this text

[Assistant] Because when you were sitting at the treasury looking at what was happening in the economy, and I was there from 1996 to 2000.


[User] Please transcribe this text

[Assistant] So you can, beginning of 2001, so you can see that amazing time that was. This was when technology really took off on the consumer side and on the internet side. And it just seems like the companies working out here were making a huge difference even though they weren't non-profit.


[User] Please transcribe this text

[Assistant] So I decided I was wrong and I would come work, work in these, in, in, you know, and try to get a job in technology.


[User] Please transcribe this text

[Assistant] And I went to Google. And my reason for going to Google, Google was a tiny little company, uh, about 250 or so people, people I had worked with told me I was crazy. Because this was after the bubble burst. You're going to it.


[User] Please transcribe this text

[Assistant] Pets.com is over. This is, for those of you who even remember that, this is not, this is not a good idea. But Google had a really compelling mission and a really compelling vision of achieving that mission.


[User] Please transcribe this text

[Assistant] And the mission was to take the world's information and make it universally useful and accessible.


[User] Please transcribe this text

[Assistant] And interestingly enough, there's probably no better example of that than this lecture in this class. Because...


[User] Please transcribe this text

[Assistant] And this apparently, these broadcasts, uh, probably not mine, but certainly Steve Ballmer's, there are about 5 million people who download these things.


[User] Please transcribe this text

[Assistant] So this is information that me and other, myself and other guest speakers are giving that would only have been available to the elite. That is now available widely.


[User] Please transcribe this text

[Assistant] And that was very much the mission of Google. And why I think it's important is to note that it scales. No matter what Google was trying to do, it was about making information accessible.


[User] Please transcribe this text

[Assistant] Mark Zuckerberg, our founder, who I think was one of the guests in this lecture a couple years ago, has a very compelling vision of what needs to change in the world and what was changing.


[User] Please transcribe this text

[Assistant] And his vision is that we used to think that, you know, we ship that information on the web that was very anonymous. You interact with most websites in a very anonymous way. And so sure, the web made a lot of information available. But the information you care most about is actually about you. And your friend. It's what you're doing, your life.


[User] Please transcribe this text

[Assistant] And the web at the time he founded Facebook wasn't sharing that information. And that's the vision and the mission.


[User] Please transcribe this text

[Assistant] And the vision of how to get there is really, really important. And so for both of these companies, they had visions that scaled. And those visions are usually stated in mission statements. They can be or they cannot be.


[User] Please transcribe this text

[Assistant] And that seems completely obvious now because you all have personal computers and it doesn't even occur to you that you wouldn't. But at the time that was not obvious.


[User] Please transcribe this text

[Assistant] It was not obvious that computing power would be something individuals had.


[User] Please transcribe this text

[Assistant] Or think about Starbucks, I just joined the board so it's something I'm familiar with. Starbucks had a pretty compelling mission.


[User] Please transcribe this text

[Assistant] Early on. Which was basically, and they would never say it this way, but god, a lot of the coffee the United States drank at the time was really bad. And there wasn't really this third place.


[User] Please transcribe this text

[Assistant] So Howard Schultz came in and bought this tiny little five, you know, stores of Starbucks and had a really, really compelling vision about something like coffee.


[User] Please transcribe this text

[Assistant] Right, a pretty basic thing that was part of people's lives. But he was going to make it better and he was going to create community along with it.


[User] Please transcribe this text

[Assistant] If you can buy a pair of shoes, you can just do it and came up with that switch and it was inspiring. I think about that when I run and I am not a very good runner. But when I see those ads, I run a little faster that day.


[User] Please transcribe this text

[Assistant] And the interesting thing about all of these visions is that they scaled. They were visions that these companies set out at the beginning that continue to work. And maybe Apple has been brought in not just personal computing but now personal electronics but it's the same vision. Same vision. Make it personal and make it work.


[User] Please transcribe this text

[Assistant] And so if you want to be an entrepreneur and you want to be a leader or you want to be an entrepreneur or a leader or some combination, a vision that scales, that's compelling, is such an integral part of great leadership.


[User] Please transcribe this text

[Assistant] But you have to compel people with the why. And the what you're doing is the why.


[User] Please transcribe this text

[Assistant] Help us build a community. Help us connect the world. Help us make information accessible. That's compelling.


[User] Please transcribe this text

[Assistant] In the previous video, you saw how you can get a neural network to output four numbers, uh, Bx, By, Bh, and Bw, to specify the bounding box of an object you want the neural network to localize.


[User] Please transcribe this text

[Assistant] In more general cases, you can have a neural network just output x and y coordinates of important points in an image, sometimes called landmarks that you want the neural network to recognize. Let me show you a few examples. Let's say you're building a face recognition application and for some reason you want the algorithm to tell you


[User] Please transcribe this text

[Assistant] Where is the corner of someone's eye?


[User] Please transcribe this text

[Assistant] So that point has an x and y coordinate, so you can just have a neural network, have this, you know, final layer, and have it just output two more numbers, which I'm going to call Lx, and


[User] Please transcribe this text

[Assistant] Why? To just tell you the coordinates of that corner of the person's eye.


[User] Please transcribe this text

[Assistant] Now what if you wanted to tell you, you know, all four corners of the eye, uh, really of both eyes. So if we call the points the first, second, third, and fourth points going from left to right, then you could modify the neural network now to output


[User] Please transcribe this text

[Assistant] And so on, so that the neural network can output


[User] Please transcribe this text

[Assistant] The estimated position of all those four points of the person's face.


[User] Please transcribe this text

[Assistant] But what if you don't want just those four points? What if you want to output this point and this point and this point and this point, you know, along the eye, maybe output some key points along the mouth, so you can extract the mouth shape and tell the person is smiling or frowning, uh, maybe extract a few key points along the edges of the nose,


[User] Please transcribe this text

[Assistant] But you could define some number for the sake of argument, let's say 64.


[User] Please transcribe this text

[Assistant] Points or 64 landmarks on the face.


[User] Please transcribe this text

[Assistant] Maybe even some points, you know, that help you define the edge of the face defines the jaw line.


[User] Please transcribe this text

[Assistant] But by selecting a number of landmarks and generating a label training set that contains all of these landmarks, you can then have the neural network tell you where are all the key positions or the key landmarks on a face. So what you do is you have this image


[User] Please transcribe this text

[Assistant] No, person's face as input, have it go through a confnet, and have a confnet, then


[User] Please transcribe this text

[Assistant] Output units, 1 for is there a face or not? And then if you have 64 landmarks, that's 64 times 2, so 128, uh, plus 1 output units. And this can tell you if there's a face as well as where all the key landmarks on the face.


[User] Please transcribe this text

[Assistant] So, you know, this is a basic building block for, uhm, recognising emotions from faces, and if you played with the Snapchat and the other entertainment, you know, so AR augmented reality filters, like the Snapchat filters can


[User] Please transcribe this text

[Assistant] I don't know, draw a crown on the face and have other special effects. Being able to detect these landmarks on the face is also a key building block for the computer graphics effects that warp the face or draw on various special effects like put a crown or a hat on a person.


[User] Please transcribe this text

[Assistant] Of course, in order to trade a network like this, you will need a label trading set. We have a set of images, as well as labels Y, where people, where someone will have had to go through and laboriously annotate all of these landmarks. One last example.


[User] Please transcribe this text

[Assistant] If you are interested in people pose detection, you could also define a few key positions, like the midpoint of the chest, the left shoulder, left elbow, the wrist, and so on, and just have a neural network, you know, sort of annotate key positions


[User] Please transcribe this text

[Assistant] Uhm, in the person's pose as well. And by having a neural network output all of those points that I'm annotating, you could also have the neural network output the pose of the person.


[User] Please transcribe this text

[Assistant] And of course, to do that, you also need to specify, uhm, these key landmarks, like maybe L1X and L1Y is the midpoint of the chest, down to maybe L32X, L32Y, if you use 32 coordinates to specify the pose of the person.


[User] Please transcribe this text

[Assistant] So this idea might seem quite simple of just adding a bunch of output units to output the XY coordinates of different landmarks you want to recognize. Uhm, to be clear,


[User] Please transcribe this text

[Assistant] The identity of landmark 1 must be consistent across different images. Like maybe landmark 1 is always this corner of the eye, landmark 2 is always this corner of the eye, landmark 3, landmark 4, and so on. So the labels have to be consistent across different images.


[User] Please transcribe this text

[Assistant] If you hire labelers or label yourself a big enough dataset to do this, then a neural network can output, you know, all of these landmarks which you can use to carry out other interesting effects such as estimate the pose of a person, maybe try to recognize someone's emotion from a picture, and so on.


[User] Please transcribe this text

[Assistant] So that's it for landmark detection. Next, let's take these building blocks and use it to start building up towards object detection.


[User] Please transcribe this text

[Assistant] There's so much text in today's world, pretty much none of us have enough time to read all the things we wish we had time to.


[User] Please transcribe this text

[Assistant] So one of the most exciting applications I've seen of large language models is to use it to summarize text. And this is something that I'm seeing multiple teams build into multiple software applications.


[User] Please transcribe this text

[Assistant] So with that, let's dig into the code to see how you could use this yourself to summarize text.


[User] Please transcribe this text

[Assistant] So let's start off with the same starter code as you saw before, import open AI, load API key, and here's that get completion helper function. I'm going to use as the running example, the task of summarizing this product review.


[User] Please transcribe this text

[Assistant] And there's just a large volume of reviews. Having a tool to summarize the lengthy reviews could give you a way to very quickly glance over more reviews to get a better sense of what all your customers are thinking. So here's a prompt for generating a summary.


[User] Please transcribe this text

[Assistant] Your task is generally a short summary of a product review from your commons website. Summarize the review below and so on. And at most, 30 words.


[User] Please transcribe this text

[Assistant] And so this is Soft and Cute Panda Crush Toys Love My Daughter.


[User] Please transcribe this text

[Assistant] The small of the price arrived early. Not bad, it's a pretty good summary.


[User] Please transcribe this text

[Assistant] And as you saw in the previous video, you can also play with things like controlling the character count or the number of sentences to affect the length of this summary.


[User] Please transcribe this text

[Assistant] Now, sometimes when creating a summary, if you have a very specific purpose in mind for the summary,


[User] Please transcribe this text

[Assistant] For example, if you want to give feedback to the shipping department, you can also modify the prompt to reflect that so that it can generate a summary that is more applicable to one particular group.


[User] Please transcribe this text

[Assistant] Shipping and delivery of the product. And if I run this, then


[User] Please transcribe this text

[Assistant] Again, you get a summary, but instead of starting off with soft and cute panda plush toy, it now focuses on the fact that it arrived a day earlier.


[User] Please transcribe this text

[Assistant] Uhm, then expect it. And then the store has, you know, other details.


[User] Please transcribe this text

[Assistant] Or as another example, if we aren't trying to give feedback to the shipping department, but let's say we want to give feedback to the pricing department.


[User] Please transcribe this text

[Assistant] So the pricing department is responsible for determining the price of the product and I'm going to tell it to focus on


[User] Please transcribe this text

[Assistant] Any aspects that are relevant to the price and perceived value, then this generates a different summary.


[User] Please transcribe this text

[Assistant] That it says, maybe the price may be too high for a size.


[User] Please transcribe this text

[Assistant] Now, in the summaries that I've generated for the shipping department or the pricing department,


[User] Please transcribe this text

[Assistant] Focus a bit more on information relevant to those specific departments and in fact feel free to pause the video now and


[User] Please transcribe this text

[Assistant] Maybe ask it to generate information for


[User] Please transcribe this text

[Assistant] The product department responsible for the customer experience of the product.


[User] Please transcribe this text

[Assistant] Or for something else that you think might be interesting to an e-commerce site.


[User] Please transcribe this text

[Assistant] But in these summaries, even though it generated the information relevant to shipping, it had some other information too, which you could decide may or may not be hopeful.


[User] Please transcribe this text

[Assistant] So depending on how you want to summarize it, you can also ask it to


[User] Please transcribe this text

[Assistant] Extract information rather than summarize it.


[User] Please transcribe this text

[Assistant] So here's a prompt that says your task extract relevant information.


[User] Please transcribe this text

[Assistant] Uhm, to give feedback to the shipping department and now it just says product arrived the day earlier.


[User] Please transcribe this text

[Assistant] Less specific to the shipping department if all it wants to know is what happened with the shipping.


[User] Please transcribe this text

[Assistant] Lastly, let me just share with you a concrete example for how to use this in a workflow to help summarize multiple reviews to make them easier to read. So here are a few reviews. This is kind of long, but


[User] Please transcribe this text

[Assistant] You know, here's the second review for a standing lamp, needle lamp on the bedroom. Here's the third review for an electric toothbrush. My dental hygiene is recommended. Kind of a long review about the electric toothbrush.


[User] Please transcribe this text

[Assistant] This is a review for Blender when they said, so that 17-piece system on seasonal sale and so on and so on. This is actually a lot of text. If you want, feel free to pause the video and read through all this text. But what if you want to know what these, uhm,


[User] Please transcribe this text

[Assistant] Review as wrote without having to stop and read all this in detail. So I'm going to set review one to be just the product review that we had up there.


[User] Please transcribe this text

[Assistant] And I'm going to put all of these reviews into a list. And now if I implement a for loop over the reviews,


[User] Please transcribe this text

[Assistant] So here's my prompt, uhm, and here I've asked it to summarize it in at most 20 words. Then let's have it.


[User] Please transcribe this text

[Assistant] Uh, websites where you have hundreds of reviews, you can imagine.


[User] Please transcribe this text

[Assistant] How you might use this to build a dashboard to take huge numbers of reviews, generate short summaries of them so that


[User] Please transcribe this text

[Assistant] You or someone else can browse the reviews much more quickly, and then if they wish, maybe click in.


[User] Please transcribe this text

[Assistant] To see the original longer review and this can help you.


[User] Please transcribe this text

[Assistant] Alright, so that's it for summarizing, and I hope that you can picture, if you have any applications with many pieces of text, how you can use prompts like these to summarize them to help people quickly get a sense of what's in the text, the many pieces of text, and perhaps optionally dig it more if they wish.


[User] Please transcribe this text

[Assistant] In the next video, we'll look at another capability of large language models, which is to make inferences using text. For example, what if you had, again, product reviews and you wanted to very quickly get a sense of which product reviews have a positive or a negative sentiment? Let's take a look at how to do that in the next video.


[User] Please transcribe this text

[Assistant] In this section we will take a look at web content frameworks in Dalby, Intraweb and UniGUI frameworks and also we will learn some fundamentals of HTML, CSS, JavaScript. So let's get started.


[User] Please transcribe this text

[Assistant] In this video, I will cover the following topics, which are Frontend Technologies and the Dolphin Web Framework.


[User] Please transcribe this text

[Assistant] You can design your app using CSS. Also, there are plenty of CSS frameworks that provide many enhancements to your website. You can use XML for marking up data. JavaScript is a scripting language that provides functionality on the web.


[User] Please transcribe this text

[Assistant] It's very similar to XML, but very simpler.


[User] Please transcribe this text

[Assistant] Popular JavaScript frameworks. Let's take a brief look at these ones. JavaScript frameworks fundamentally change the functionality and the development experience. For instance, if you're developing Node.js web app, you don't use JavaScript technically. It doesn't change the syntax, but it's not very similar.


[User] Please transcribe this text

[Assistant] Node.js is a server-side framework using Google's V8 engine. Node.js includes Packet Manager and is supported on multiple platforms. It's a very big system.


[User] Please transcribe this text

[Assistant] AngularJS by Google. It extends from HTML. Vue.js is a library for building web interfaces. Accenture XJS is a cross-platform JavaScript web framework.


[User] Please transcribe this text

[Assistant] But these frameworks may allow customization through HTML, CSS, or JavaScript.


[User] Please transcribe this text

[Assistant] And this web-frontend framework supports REST and database development. And you don't need to special client plugins to deploy or run your application. Delphi web-frontend frameworks use Pascal to JavaScript transpilers. So transpilers convert or translate one programming language into another one.


[User] Please transcribe this text

[Assistant] And this platform specific library is not supported. Frequently used to convert higher level type-safe language into JavaScript.


[User] Please transcribe this text

[Assistant] These two ones uses a Delphi-like IDE to design UI and Object Pascal to JavaScript transpilers to create HTML, CSS and JavaScript.


[User] Please transcribe this text

[Assistant] So, what is Intraweb? Intraweb is a web application framework for Delphi and C++ Builder. Development partnership is very close to desktop applications and it's very easy to get started and it runs Windows Server, IS API, Series or standalone versions.


[User] Please transcribe this text

[Assistant] Interweb is not very new technology to build web apps. The core of this framework built many years ago, but it still works and also is reliable and used by many developers, and you can create enterprise web applications.


[User] Please transcribe this text

[Assistant] Interweb Standard Edition ships with Delphi ID or C++ Builder and it's designed front-end and it runs functionality in the backend.


[User] Please transcribe this text

[Assistant] We can learn more about introweb by looking at this website, hoz.com and the slash introweb. So, it's time to demo.


[User] Please transcribe this text

[Assistant] So, here is demo application for you to demonstrate Intro Web Webwork MOOC. Here is the intro web form. We got plenty of properties, for example content files, JavaScript, and building color, and background color, and we got a few events on our project.


[User] Please transcribe this text

[Assistant] So I got, eh, interweb tap control. Yes it is, and we got two pages, page zero, page


[User] Please transcribe this text

[Assistant] In the page 2, tab 2, we got InterwebImage component, and I have loaded picture to this picture property. This is it.


[User] Please transcribe this text

[Assistant] And in tab 1, we got intro web list and intro web edit and one button, intro web button. And I have created own quick event, so let's open it.


[User] Please transcribe this text

[Assistant] So here is the button one click event, intro web list, one, items, add, intro web edit, text, and next, intro web edit, clear.


[User] Please transcribe this text

[Assistant] So here is our IntraWeb application code.


[User] Please transcribe this text

[Assistant] In the user session, you can connect or you can manipulate the data. Project Intro Web 1 is a main part of this web application. So, you can save and run it without debugging.


[User] Please transcribe this text

[Assistant] So I have created this application as a standalone server. It's very cool. It's very easy to debug and create your web service using the intro. So this is the server. We can click this button to open up our web service on our localhost.


[User] Please transcribe this text

[Assistant] Here is the web app. Tap one, tap two. In the tap two, we got image. In the tap one, we can write something. Hello, intro web. And click the add item button.


[User] Please transcribe this text

[Assistant] And it's added to our intro web list. You can also view the page source. This all code is generated by intro web framework to run our application on the web browser.


[User] Please transcribe this text

[Assistant] So, this was the introduction of web development in Delphi. In other video tutorials, we will cover more complex things and we will create real web services.


[User] Please transcribe this text

[Assistant] In this video, we'll go through an example of how you can use a language model to generate a personalized email based on some information. The email is kind of self-proclaimed to be from an AI bot, which, as Andrew mentioned, is very important.


[User] Please transcribe this text

[Assistant] We're also going to use another one of the models input parameters called temperature and this kind of allows you to vary the kind of degree of exploration and variety in the kind of models responses. So let's get into it.


[User] Please transcribe this text

[Assistant] So before we get started, we're going to, uhm, kind of do the usual setup. So set up the OpenAI Python package and then also define our helper function getCompletion.


[User] Please transcribe this text

[Assistant] And now we're going to write a custom email response to a customer review.


[User] Please transcribe this text

[Assistant] And so given, uhm, a customer review and the sentiment, we're going to generate a custom response.


[User] Please transcribe this text

[Assistant] Now we're going to use the language model to generate a custom email, uhm, to a customer based on a customer review and the sentiment of the review. So we've already extracted the sentiment, uhm, using the kind of prompts that we saw in the empowering video. And then this is the customer review for a blender.


[User] Please transcribe this text

[Assistant] And now we're going to customize the reply based on the sentiment.


[User] Please transcribe this text

[Assistant] And so here the instruction is, you are a customer service.


[User] Please transcribe this text

[Assistant] AI assistant, your task is to send an email reply to a valued customer. Given the customer email delimited by three backticks, generate a reply to thank the customer for their review. If the sentiment is positive or neutral, thank them for their review. If the sentiment is negative, apologize and suggest that they can reach out to customer service.


[User] Please transcribe this text

[Assistant] Make sure to use specific details from the review, write in a concise and professional tone and sign the email as AI customer agent. And when you're using a language model to generate text that you're going to show to a user, it's very important to have this kind of transparency and let the user know that the text they're seeing was generated by AI.


[User] Please transcribe this text

[Assistant] And then we'll just input the customer review and the review sentiment. And also note that this part isn't necessarily important because we could actually use this prompt to also extract the review sentiment and then in a follow-up step write the email. But just for the sake of the example, well, we've already extracted the sentiment from the review.


[User] Please transcribe this text

[Assistant] And so here we have a response to the customer. It kind of addresses details that the customer mentioned in their review and kind of as we instructed.


[User] Please transcribe this text

[Assistant] Next we're going to use a parameter of the language model called temperature that will allow us to, uhm,


[User] Please transcribe this text

[Assistant] So you can kind of think of temperature as the degree of exploration or kind of randomness of the model.


[User] Please transcribe this text

[Assistant] And so, for this particular phrase, my favourite food is...


[User] Please transcribe this text

[Assistant] It will kind of also choose one of the less likely words and at even higher temperature it might even choose tacos which only kind of has a 5% chance of being chosen. And you can imagine that kind of, uhm,


[User] Please transcribe this text

[Assistant] The first response, which is my favourite food is tacos. And so as the kind of model continues, these two responses will become more and more different.


[User] Please transcribe this text

[Assistant] In general, when building applications where you want a kind of predictable response, I would recommend using temperature zero throughout all of these videos.


[User] Please transcribe this text

[Assistant] And let's try generating an email, but let's use a higher temperature.


[User] Please transcribe this text

[Assistant] So in our getCompletion function that we've been using throughout the videos.


[User] Please transcribe this text

[Assistant] And so with temperature zero, every time you execute the same prompt, you should expect, uhm, the same completion. Whereas with temperature 0.7, you'll get a different output every time. So here we have, uhm,


[User] Please transcribe this text

[Assistant] Our email, and as you can see, it's different to the email that we kind of received previously. And let's just execute it again to show that we'll get a different email again.


[User] Please transcribe this text

[Assistant] And here we have another different email. So I recommend that you kind of play around with temperature.


[User] Please transcribe this text

[Assistant] In the next video, we're going to talk more about the chat completions endpoint format and how you can create a custom chatbot using this format.


[User] Please transcribe this text

[Assistant] You've learned about object localization as well as landmark detection. Now let's build out toward an object detection algorithm. In this video you learn how to use a ConvNet to perform object detection using something called the sliding windows detection algorithm.


[User] Please transcribe this text

[Assistant] Let's say you want to build a car detection algorithm. Here's what you can do. You can first create a label training set, so x and y, with closely cropped examples of cars. So this is a image x, there's a positive example, there's a car, there's a car, there's a car, uhm, and then there's not a car, there's not a car.


[User] Please transcribe this text

[Assistant] And for our purposes in this training set, you can start off with what I'm going to call closely cropped images, meaning that X is, you know, pretty much only the car. So you can take a picture and crop out and just cut out anything else that's not part of a car, so you end up with a car, uh,


[User] Please transcribe this text

[Assistant] An image, like one of these closely cropped images, uhm, and then the job of the confnet is to output why 0 or 1 is a zekara knot.


[User] Please transcribe this text

[Assistant] Once you've trained up this confnet, you can then use it in sliding windows detection. So the way you do that is if you have a test image like this,


[User] Please transcribe this text

[Assistant] What you do is you start by picking a certain window size that I've shown down there, and then you would input into this confnet a small rectangular region. So take just this little


[User] Please transcribe this text

[Assistant] Red square, and put that into the confnet, and have a confnet make a prediction. And presumably, for that little region in the red square, I'll say no, that little red square does not contain a car.


[User] Please transcribe this text

[Assistant] In the sliding windows detection algorithm, what you do is you then process input


[User] Please transcribe this text

[Assistant] A second image, now bounded by this red square, shifted a little bit over, and feed that to the confinet, so you're feeding just the region of the image, uhm, in the red square to the confinet, and run the confinet again, and then you do that, uh, with a third image, and so on, and you keep going.


[User] Please transcribe this text

[Assistant] Until you've, you know, slid the window across every position in the image. And, uh, I'm using a pretty large stride in this example just to make the animation go faster. But the idea is you basically go through every region of this size and


[User] Please transcribe this text

[Assistant] Running this was called a sliding window through the image.


[User] Please transcribe this text

[Assistant] You then repeat it, but now use a larger window. So now you take a slightly larger region and run that region. So resize this region into whatever input size the confinet is expecting and feed that to the confinet and have it output 0 or 1. And then slide the window over again using some stride, uhm, and so on. And you run that throughout your entire image.


[User] Please transcribe this text

[Assistant] Uh, until you get to the end. And then you might do it a third time using even larger windows, uhm, and so on. Right.


[User] Please transcribe this text

[Assistant] And the hope is that if you do this, then so long as there's a car somewhere in the image, that there will be a window where


[User] Please transcribe this text

[Assistant] For example, if you are passing in this window into the confnet, hopefully the confnet will have output 1 for that input region, so then you detect that there is a car there.


[User] Please transcribe this text

[Assistant] So this algorithm is called sliding windows detection because you take these windows, these uhm, square


[User] Please transcribe this text

[Assistant] Now, there's a huge disadvantage of sliding windows detection, which is the computational cost.


[User] Please transcribe this text

[Assistant] Because you're cropping out so many different square regions in the image and running each of them independently through a confinet.


[User] Please transcribe this text

[Assistant] All these little regions, they're passing through the confnet, means that there's a very high computational cost.


[User] Please transcribe this text

[Assistant] So before the rise of neural networks, people used to use much simpler classifiers, like a simple linear classifier over hand engineered features in order to perform object detection.


[User] Please transcribe this text

[Assistant] And in that era, because each classifier was relatively cheap to compute, it was just a linear function, sliding windows detection ran okay. It was not a bad method.


[User] Please transcribe this text

[Assistant] But with ConfNet, now running a single classification task is much more expensive, and sliding windows this way is infeasibly slow.


[User] Please transcribe this text

[Assistant] And unless you use a very fine granularity or a very small stride, you end up not able to localize the objects that accurately within the image as well.


[User] Please transcribe this text

[Assistant] Fortunately, however, this problem of computational cost has a pretty good solution. In particular, the sliding windows object detector can be implemented convolutionally or much more efficiently. Let's see in the next video how you can do that.


[User] Please transcribe this text

[Assistant] Lesson 92. Asking for trouble.


[User] Please transcribe this text

[Assistant] It must have been about two in the morning when I returned home. I tried to wake up my wife by ringing the doorbell, but she was fast asleep, so I got a ladder from the shed in the garden, put it against the wall, and began climbing towards the bedroom window. I was almost there, when a sarcastic voice below said,


[User] Please transcribe this text

[Assistant] I don't think the windows need cleaning at this time of night.


[User] Please transcribe this text

[Assistant] I looked down and nearly fell off the ladder when I saw a policeman. I immediately regretted answering in the way I did, but I said, I enjoy cleaning windows at night. So do I, answered the policeman in the same tone.


[User] Please transcribe this text

[Assistant] Excuse my interrupting you. I hate to interrupt a man when he's busy working.


[User] Please transcribe this text

[Assistant] But would you mind coming with me to the station? Well, I'd prefer to stay here, I said. You see, I've forgotten my key. Your what? he called. My key! I shouted. Fortunately, a shouting woke up my wife, who opened the window.


[User] Please transcribe this text

[Assistant] Just as the policeman had started to climb towards me.


[User] Please transcribe this text

[Assistant] One of the problems with object detection as you've seen it so far is that each of the grid cells can detect only one object. Whatever grid cell wants to detect multiple objects, here's what you can do. You can use the idea of anchor boxes. Let's start with an example.


[User] Please transcribe this text

[Assistant] Let's say you have an image like this, and for this example I'm going to continue to use a 3x3 grid.


[User] Please transcribe this text

[Assistant] Notice that the midpoint of the pedestrian and the midpoint of the car are in almost the same place and both of them fall into the same grid cell. So for that grid cell, if y


[User] Please transcribe this text

[Assistant] Outputs this vector, where you are detecting three classes, pedestrians, cars, and motorcycles. It won't be able to output two detections, so have to pick one of the two detections to output.


[User] Please transcribe this text

[Assistant] With the idea of anchor boxes, what you're going to do is pre-define two different shapes called anchor boxes or anchor box shapes. And what you're going to do is now


[User] Please transcribe this text

[Assistant] Be able to associate two predictions with the two anchor boxes. And in general, you might use more anchor boxes, maybe five or even more. But for this video, I'm just going to use two anchor boxes just to make the description easier. So what you do is you define the class label to be


[User] Please transcribe this text

[Assistant] Instead of this vector on the left, you basically repeat this twice. So you would have PC, uhm,


[User] Please transcribe this text

[Assistant] So because the shape of the pedestrian is more similar to the shape of anchorbox 1 than anchorbox 2, you can use these 8 numbers to encode that PC is 1. Yes, there is a pedestrian. Uh, use this.


[User] Please transcribe this text

[Assistant] The box around the car is more similar to the shape of AnchorBox2 than AnchorBox1. You can then use these to encode that the second object here is the car.


[User] Please transcribe this text

[Assistant] And have the bounding box and so on be all the parameters associated with the detected car.


[User] Please transcribe this text

[Assistant] So to summarize, previously, before you were using anchor boxes, you did the following, which is for each object in the training set, in the training set image, it was assigned to the grid cell that corresponds to that object's midpoint. And so the output y was 3 by 3


[User] Please transcribe this text

[Assistant] You now do the following. Now each object is assigned


[User] Please transcribe this text

[Assistant] to the same grid cell as before, assigned to the grid cell that contains that object's midpoint, but is assigned to an anchor cell, but is assigned to a grid cell and anchor box.


[User] Please transcribe this text

[Assistant] With the highest IOU with the object's shape. So you have two anchor boxes. You would take an object and see. So if you have an object with uhm, this shape.


[User] Please transcribe this text

[Assistant] What you do is take your two anchor boxes, maybe one anchor box is this shape, that's anchor box one. Maybe anchor box two is this shape.


[User] Please transcribe this text

[Assistant] And then you see which of the two anchor boxes has a higher IOU with the ground-true bounding box. And whichever it is, that object then gets assigned not just to a grid cell, but to a pair. It gets assigned to a grid cell, comma, anchor box pair.


[User] Please transcribe this text

[Assistant] And that's how that object gets encoded in the target label. And so now, the output


[User] Please transcribe this text

[Assistant] Y is going to be 3 by 3 by 16 because as you saw on the previous slide Y is now 16 dimensional or if you want you can also view this as 3 by 3 by 16.


[User] Please transcribe this text

[Assistant] Two by eight, because they're now two anchor boxes, uhm, and Y is eight dimensional.


[User] Please transcribe this text

[Assistant] Oh, and the dimension of y being 8 was because we have three object classes. If you have more objects, then the dimension of y will be even higher.


[User] Please transcribe this text

[Assistant] Pedestrian is more similar to the shape of anchor box 1.


[User] Please transcribe this text

[Assistant] So for the pedestrian, we're going to assign it to the top half of this vector. So yes, there is an object, there will be some bounding box.


[User] Please transcribe this text

[Assistant] Uh, associated with a pedestrian, and I guess if a pedestrian is cost 1, then it would be C1 is 1, and then 0, 0.


[User] Please transcribe this text

[Assistant] And then the shape of the car is more similar to anchor box 2, and so the rest of this vector will be 1. Uhm, and then the bounding box associated with the car.


[User] Please transcribe this text

[Assistant] And then the car is, you know, C2, so that's 010. And so that's the


[User] Please transcribe this text

[Assistant] Label Y for that, uh, lower middle grid cell, right, that, that I, that this arrow is pointing to.


[User] Please transcribe this text

[Assistant] Now, what if this grid cell only had a car and had no pedestrian? If it only had a car,


[User] Please transcribe this text

[Assistant] Then assuming that the shape of the bounding box around the car is still more similar to anchor box 2, then the target label Y, if there was just a car there and the pedestrian had gone away, it would still be the same for the anchor box 2,


[User] Please transcribe this text

[Assistant] Component, right, remember that, uh, this is a part of the vector corresponding to anchor box 2.


[User] Please transcribe this text

[Assistant] And for the part of the vector corresponding to anchor box one, what you do is you just say there is no object there, so PC is zero, uhm, and then the rest of these will be don't cares.


[User] Please transcribe this text

[Assistant] Now, just some additional details. What if you have two anchor boxes but three objects in the same grid cell? Uhm, that's one case that this algorithm doesn't handle as well. Uhm, you know, if you, hopefully it won't happen, but if it does,


[User] Please transcribe this text

[Assistant] This algorithm doesn't have a great way of handling it. I would just implement some default tiebreaker for that case. Or what if you have two objects associated with the same grid cell, but both of them?


[User] Please transcribe this text

[Assistant] In your data set it won't happen much at all and so it shouldn't affect performance much.


[User] Please transcribe this text

[Assistant] So that's it for anchor boxes. And even though I've motivated anchor boxes as a way to deal with what happens if two objects appear in the same grid cell,


[User] Please transcribe this text

[Assistant] Uhm, in practice that happens quite rarely, especially if you use a 19 by 19 rather than a 3 by 3 grid. You know, the chance of two objects having the same midpoint out of these 261 cells, it does happen, but it doesn't happen that often.


[User] Please transcribe this text

[Assistant] The maybe even better motivation or even better results that Anchor Bosses gives you is it allows your learning algorithm to specialize better.


[User] Please transcribe this text

[Assistant] In particular, if your data set has some tall, skinny objects like pedestrians and some wide objects like cars, then this allows your learning algorithm


[User] Please transcribe this text

[Assistant] How do you choose the anchor boxes? People used to just choose them by hand. Choose maybe 5 or 10 anchor box shapes that spans a variety of shapes that seems to cover the types of objects you seem to detect.


[User] Please transcribe this text

[Assistant] As a much more advanced version, just an advanced comment for those of you that have other knowledge in machine learning, an even better way to do this in one of the later YOLO research papers is to use a k-means algorithm.


[User] Please transcribe this text

[Assistant] But that's a more advanced way to automatically choose the anchor boxes. And if you just choose by hand a variety of shapes that reasonably spans the set of object shapes you expect to detect, some tall skinny ones, some fat white ones, that should work reasonably as well.


[User] Please transcribe this text

[Assistant] So that's it for anchor boxes. In the next video, let's take everything we've seen and tie it back together into the YOLO algorithm.


[User] Please transcribe this text

[Assistant] I was afraid someone was going to do that. Okay, so let's just see where I got this equation from.


[User] Please transcribe this text

[Assistant] Let's do the present value of the discounted dividend. What would it be?


[User] Please transcribe this text

[Assistant] So, first of all, we have to know what unit testing is, what is a unit, and its benefits from using the unit testing parameter in your applications. So, let's get started.


[User] Please transcribe this text

[Assistant] So unit testing frameworks, drivers, stops and mocks or the fixed objects are used to assist in unit testing. Unit testing benefits.


[User] Please transcribe this text

[Assistant] So here we got this demo, and here we got unit one pass, so.


[User] Please transcribe this text

[Assistant] This unit finds the parameter p of the square, this formula here, and now we have t square class and function square, a integer and integer here, and we have not implemented completely it, so now we can


[User] Please transcribe this text

[Assistant] So next, and you can select the GUI for test runner. So finish. So here is the test project. Now we have to again open the other here and


[User] Please transcribe this text

[Assistant] And we got test t square. It's class t-case.


[User] Please transcribe this text

[Assistant] So, we got here F square, it's a private, and set up teardown test square here, set up here, we're creating F square from T square, create from the unit 1.


[User] Please transcribe this text

[Assistant] And we are freeing up the memory here. Here is the test t square. So return value, a. So now we can write our test here.


[User] Please transcribe this text

[Assistant] So we can go to the Unit 1 and write the ABS to Modeling and save it and run it.


[User] Please transcribe this text

[Assistant] So, in the unit testing, you don't need to create test cases for everything. For example, if you have two parameters or three parameters and you don't need to create all of the test cases for this parameter, for example, negative a, negative b, negative c, or the


[User] Please transcribe this text

[Assistant] Whatever they think of you, do what you think is fair.


[User] Please transcribe this text

[Assistant] Above all things, respect yourself.


[User] Please transcribe this text

[Assistant] Ability and necessity dwell near each other.


[User] Please transcribe this text

[Assistant] Even as truth, does error have its lovers?


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[Assistant] Who ought to aid each other to persevere on the road to a happier life.


[User] Please transcribe this text

[Assistant] Nearly 30,000 kilograms of thread are produced at this factory every day. But this thread isn't spun from traditional yarn.


[User] Please transcribe this text

[Assistant] Demand for this type of recycled polyester is growing as brands become more conscious about waste.


[User] Please transcribe this text

[Assistant] The cost of recycled polyester is about 10 to 20 percent higher than traditional thread.


[User] Please transcribe this text

[Assistant] That's good news for brands like Adidas. It's already making the switch to recycled thread.


[User] Please transcribe this text

[Assistant] The sports company promised to remove all single-use plastic from its supply chain by 2024.


[User] Please transcribe this text

[Assistant] India has one of the biggest plastic problems in the world, with trash spilling out onto beaches and piling up in cities.


[User] Please transcribe this text

[Assistant] Commitments like the one made by Adidas might be a step in the right direction. But experts say more brands and consumers will need to buy into products like these to have a meaningful impact on our problem with plastic.


[User] Please transcribe this text

[Assistant] Nearly 30,000 kilograms of thread are produced at this factory every day.


[User] Please transcribe this text

[Assistant] Demand for this type of recycled polyester is growing as brands become more conscious about waste.


[User] Please transcribe this text

[Assistant] Hello and welcome to the last video tutorial about backend and client development section. So in this video we are going to learn BAS providers.


[User] Please transcribe this text

[Assistant] In this video, we're going to take a look at Kinway BAS Provider, and we got BAS with Delphi, the sample application. So, what is BAS? It means Backend Accessories.


[User] Please transcribe this text

[Assistant] These services have their own APIs allowing them to be integrated into applications with relative ease and providing a constant way to manage backend data means that developers don't have to develop another backend for each service that the application use or access to it.


[User] Please transcribe this text

[Assistant] Some PAS features are similar to those of other services models like CAS, IAS, and also PAS.


[User] Please transcribe this text

[Assistant] Although the VES features a lot of benefits, it's also important to take the construction of the user interface into consideration because it's in direct communication with end-users. The user interface's job is to connect the applications to any third-party or pre-operated APIs connected to the backend.


[User] Please transcribe this text

[Assistant] And in this app we will use the Kinway as a service. So, it's time to demo.


[User] Please transcribe this text

[Assistant] First of all, we have to take a look at the Kinway webpage. Here is the Kinway BAService webpage. Again, explore this website.


[User] Please transcribe this text

[Assistant] So why we have to choose Kinue?


[User] Please transcribe this text

[Assistant] You can just create your app, configure the application parameters and easily you can connect the service. So here is the console of the Kinvay.


[User] Please transcribe this text

[Assistant] You can just click this add an app button to create your new app. But already I have created a test demo. You can click development button here. So as you can see we got


[User] Please transcribe this text

[Assistant] One user, and we have used it only approximately 2 kilobytes.


[User] Please transcribe this text

[Assistant] So here we got only one user for our sample application. This username is tester, and the password is tester04 here. So now I'll open the RAD Studio.


[User] Please transcribe this text

[Assistant] So here is the BES StudioList application.


[User] Please transcribe this text

[Assistant] So as you can see, we got three modules. Main form.


[User] Please transcribe this text

[Assistant] So we can open the design and we got kinwave provider here and backend storage. You can see it is a kinwave provider and you have to include your app key, app secret, master secret and password here and one username. And backend storage here, the provider is kinwave provider. And we can open the code here.


[User] Please transcribe this text

[Assistant] We have the lots of components for the UI. For example, tap control and four tabs here, buttons for creating, brushing, and here editing, and saving here.


[User] Please transcribe this text

[Assistant] So in the users class, we got data module unit, to the item types, res backend convey, api, res types, and fmx style of service for showing the messages in the application.


[User] Please transcribe this text

[Assistant] So here we got some additional procedures and functions for our application. This view item click it pushes and opens the details of the item.


[User] Please transcribe this text

[Assistant] In the form grid, we are using the tab item list for active tab and the tab position properties in none. In the form show,


[User] Please transcribe this text

[Assistant] And get title fields, get content fields, it gets the data from the, this, to do the names. And cancel execute here.


[User] Please transcribe this text

[Assistant] Clears the components. Add execute. Here, you can open the push view. Here is it. And show view. This one. You can open the design here.


[User] Please transcribe this text

[Assistant] And, we can see this action list here, and we cut action, add, save, castle, edit, edit, back, prior, next, refreshing. For example, add here.


[User] Please transcribe this text

[Assistant] So here is the to-do list application that is running on my Android device. And we can click the refresh button here. And it gets the data from the server. Because I have tested it already.


[User] Please transcribe this text

[Assistant] And then we can close the application. And we run the app. And we can click this fresh button.


[User] Please transcribe this text

[Assistant] So this is the end of this section. We have learned DataSnap, WebBroker, REST client library, and backend as-a-service providers, for instance, Kinvay. I hope you have enjoyed this section.


[User] Please transcribe this text

[Assistant] In the coming section, we are going to learn the Delphi with IOT, Internet of Things. It will be a more interesting section ever. So, see you in the next section!


[User] Please transcribe this text

[Assistant] Experiments have proved that children can be instructed in swimming at a very early age.


[User] Please transcribe this text

[Assistant] At a special swimming pool in Los Angeles.


[User] Please transcribe this text

[Assistant] Children become expert at holding their breath underwater even before they can walk. Babies of 2 months old.


[User] Please transcribe this text

[Assistant] Do not appear to be reluctant to enter the water


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[Assistant] It is not long before they are so accustomed to swimming that they can pick up weights from the floor of the pool.


[User] Please transcribe this text

[Assistant] A game that is very popular with these young swimmers is the underwater tricycle race.


[User] Please transcribe this text

[Assistant] Tricycles are lined up on the floor of the pool, seven feet underwater.


[User] Please transcribe this text

[Assistant] The children compete against each other to reach the other end of the pool. Many pedal their tricycles.


[User] Please transcribe this text

[Assistant] But most of them prefer to push or drag them.


[User] Please transcribe this text

[Assistant] Some children can cover the whole length of the pool without coming up for breath even once.


[User] Please transcribe this text

[Assistant] Whether they will ever become future Olympic champions, only time will tell. Meanwhile,


[User] Please transcribe this text

[Assistant] They should encourage those among us who cannot swim five yards before they are gasping for air.


[User] Please transcribe this text

[Assistant] The violin is the primary instrument of the string section.


[User] Please transcribe this text

[Assistant] From which all others were developed, except for the double bass.


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[Assistant] It was invented by merging the size and style of the veal family with the shallower body of the rebab.


[User] Please transcribe this text

[Assistant] The result was an instrument that was far more powerful and resonant than either of its parents, with technical possibilities that were unprecedented in the history of music.


[User] Please transcribe this text

[Assistant] In a way, the whole history of concert music seems to start with the violin. Before this, groups of musicians tended to be more miscellaneous, getting together whoever was available and improvising to a set group of tunes.


[User] Please transcribe this text

[Assistant] But from the 15th century onwards, music became ever more structured, with vocal works dominating church music, and works with string groups leading the way for secular music. Both developments required careful instructions to the players, which evolved into our current idea of the musical score.


[User] Please transcribe this text

[Assistant] The advantages of a string ensemble were evident from the beginning. Violinists don't require breath in order to form a tone, and could play for long periods without wearing themselves out, unlike brass or wind players.


[User] Please transcribe this text

[Assistant] The violin family was built as if to encourage virtuosity, with an action that spoke very easily when bowed, and a finger position that could navigate with startling rapidity.


[User] Please transcribe this text

[Assistant] Soon, expert showmen started touring from town to town, dazzling audiences with feats of brilliance. The effort to preserve these showpieces helped to push at the possibilities of music notation and score preparation more than any other instrument of that time.


[User] Please transcribe this text

[Assistant] The fathers of some of the greatest composers were such itinerant violinists, like Bach or Vivaldi.


[User] Please transcribe this text

[Assistant] The most important development was the essence of the string group's sound as the basic building block for concert music.


[User] Please transcribe this text

[Assistant] The patience of the listener was not exhausted by hours of music for strings, and a variety of forms sprang up to fill the need, like the concerto grosso, in which many or all players had a chance to show off.


[User] Please transcribe this text

[Assistant] Strings also made an ideal accompaniment group for singers and soloists, and worked their way into both cantatas and operas.


[User] Please transcribe this text

[Assistant] They could sound effective with a dozen players, or with five dozen. Their dramatic scope was something that each successive style of music helped to expand. From Baroque to Classical, and eventually Romantic and Modern music, the strings were able to express the spirit of the passing eras.


[User] Please transcribe this text

[Assistant] Even as additional instruments were gradually added to the ensemble, the string section stayed supreme, growing in size to balance the newcomers, until the orchestra attained the awesome numbers of today, between 50 to 100 musicians, most of them violinists, violists, and cellists.


[User] Please transcribe this text

[Assistant] If you pick apart a classical symphony bar by bar, you'll see that the strings are everywhere, introducing major themes, leading developments, underpinning other instrumental sections, and hammering home finales.


[User] Please transcribe this text

[Assistant] It's interesting to read the critical reaction to Beethoven's First Symphony. Some listeners felt that it was more of a wind band composition than an orchestral work.


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[Assistant] Nobody thinks that today, but it's an indication of how firm the heritage of symphonic music is founded upon the string group.


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[Assistant] That means composers have to base their understanding of how the orchestra works by looking through that lens, even if your own eventual style is more egalitarian. Right now, orchestrations work because the strings have a primary role much of the time and listeners respond with recognition to that fact.


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[Assistant] Let's meet the family and see what they can do in our next video.


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[Assistant] Tire Town School Episode 3 The Race


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[Assistant] You like to race? said Dipper. You must be fast, Eddy.


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[Assistant] I'm slow, he said. I just wish I was fast. I bet you're fast, said Dipper. Let's all race.


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[Assistant] Poppy looked at everyone. Ready, set, honk, honk. Eddie took off.


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[Assistant] A race car drove over. He had fancy stripes.


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[Assistant] You're not fast, said the car. You're just a slow dump truck.


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[Assistant] And here we are using hash object and misc object for hashing this keys. Here is the TMS cryptography pack. I'm using for elevation of this pack here. If you want to buy, you can come up here and order it from here.


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[Assistant] If edit.ge text equals to HDD serial number, which gets on the form create event, and here we are creating this object here, setting the properties here, and assigning new file called activation.sbs.


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[Assistant] So here is a generator application. We click this and get activation key. It generates activation key here from this text.


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[Assistant] Anyhow, so today I'm going to try and wrap up a few loose ends. So,


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[Assistant] Uh, I'm going to try and talk about the high watermark of finance, which is basically CAPM and Black Shoal, both of which we've almost talked about, almost finished talking about, and


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[Assistant] I'm going to talk in the middle about Social Security, which we never finished, my Social Security plan. Okay, so I hope to deal with all three of these things today. So just to wrap up the CAPM.


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[Assistant] And the second main idea, as we said, was the trade-off between risk and return.


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[Assistant] Shakespeare had both of these ideas. He understood that you're safer if you've got each of your boats on a different ocean. And he understood that if you want, if you're gonna take a high risk,


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[Assistant] You'd better get a high return, otherwise nobody's willing to take it. And so he made that very clear. In fact, I think the whole point of the play is about economics, the merchant of Venice, and he made both of these principles very clear. But he couldn't quantify them and he couldn't mathematize them.


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[Assistant] And the capital asset pricing model gives a mathematical quantifiable form to both and a quite shocking recommendation in both cases.


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[Assistant] If you do Tobin's famous picture, this is the Markowitz-Tobin, Markowitz-Tobin Capital Market Line, they called it.


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[Assistant] You put, it's very important to keep straight what's on the diagram. There's the standard deviation of any asset per dollar. So here I'm writing the price. So it's per dollar, and here's the expectation of the asset.


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[Assistant] Per dollar, so pi of y, I'm calling that the price. Okay, so this is per dollar.


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[Assistant] And I'm not gonna go over what we derived before, but we noticed that if you take in, if you tip all the risky assets, you know, any risky asset's gonna have per dollar, if it's priced, some standard deviation and some expectation, you can write down all the risky assets and you can combine them and you'll get a possibility set that looks like this.


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[Assistant] Okay, so the diversification already shows up in this picture because, you know, if you combine this asset and this asset, you're gonna get possibilities by putting half your money in each, not on the straight line between them, but on some curved line that's much better because you're reducing your risk.


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[Assistant] Okay, and so, uh, Tobin, so Markowitz already had that curve in this picture in mind. Tobin added the riskless asset, which he put here.


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[Assistant] And then he said, well, if there's a riskless asset, everybody's gonna do the same thing, and they're gonna, everyone's gonna choose the same point.


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[Assistant] It's better for everybody, so this is the optimal risky portfolio.


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[Assistant] And it has to be equal to the market. Because if everybody's smart enough to figure out what to do, they're all gonna do the same thing, and then everyone's gonna, whatever everybody holds, that's by definition the market.


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[Assistant] Okay, so Tobin showed that everybody should choose something on this capital market line and so everybody should


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[Assistant] Everybody should split his money between the market


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[Assistant] And a riskless asset. Put your money in the bank or own the entire market.


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[Assistant] That was his recommendation and that was the beginning of the whole idea of index investing.


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[Assistant] Okay, so that was the first idea, first most important idea. Now how could you invest things? How could you test this? Well, you could simply test


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[Assistant] Is worse than the slope of that, which is the Sharpe ratio at this point. The Sharpe ratio, remember, is the expectation per dollar minus what you can get in a riskless asset per dollar divided by the standard deviation of Y per dollar, let's say. Okay, so that's the, that's just the slope of this line.


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[Assistant] So we can measure performance of a portfolio manager by the Sharpe ratio. Everyone should try and get the highest Sharpe ratio. The upshot of diversification is you should buy a little bit of every stock in the economy. No matter how small, no matter how big, no matter how safe, no matter how risky, you should spread your money across everything.


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[Assistant] That's the recommendation of Tobin. And it relies, of course, on everybody else being rational, so everybody else is choosing the right thing. Then you can just piggyback and do what everybody else is doing.


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[Assistant] Okay, so that's the first thing. Very shocking. As I said, at first glance you'd think someone who is love's risk


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[Assistant] would just invest in risky stocks and people who's very, people very risk averse would just invest in safe stocks. Tobin says no no no no, if you're very risk tolerant,


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[Assistant] And people who borrow money to invest, that's called leveraging.


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[Assistant] The crucial idea, there are a lot of ideas mixed in that, and as I said, you can test it by seeing if anyone can beat the market. Is there anyone who historically has had a higher Sharpe ratio than the market?


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[Assistant] And in 1967, when all this was being tested, you know, there was a famous book called, uhm, A Random Walk Down Wall Street.


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[Assistant] Written by Malkew, walked down Wall Street, which I recommend you read, Random Walk Down Wall Street.


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[Assistant] Of course, many people beat the market for short periods of time, then they totally collapse.


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[Assistant] And, uh, and then some people beat the market for 20 years in a row and then retire, but there's so few of those people that it looks like, you know, just by random dumb luck you could get on with those people. On the other hand, there's some people like Warren Buffett who seem to beat the market for 40 or 50 years. It's really hard to believe that that's all just luck. And, uh, so, that's one test of the thing. The second idea,


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[Assistant] Of the capital asset pricing model is risk and return. And so now, if we assume, so all this graph, by the way, assumed quadratic utilities, quadratic or mean variance utilities, quadratic utility and, uh, common probabilities.


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[Assistant] So everybody knew what the probabilities were of the different states. So you know how to measure the covariance and the variance and all that because you know what the probabilities are to measure over. So here, uh, having assumed that, it turns out


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[Assistant] That you just apply the principle that we saw the first few days of class. That the price is going to equal the marginal utility.


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[Assistant] Okay, not the total utility, price is going to equal margin utility, and if the price equals the margin utility, the margin utility is going to be, uh, depends


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[Assistant] According to this assumption of quadratic utilities, as we showed, depends on expectation


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[Assistant] So this is the price of y, let's just call it pi of y, like I did before. Okay, it depends on the expectation of y, and it depends on the covariance


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[Assistant] Okay, so it doesn't depend on the, we've assumed with quadratic utilities that people only care about the mean and the variance. So you'd have expected the, the marginal utility of any single asset to depend on its mean and its variance.


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[Assistant] Changes according to the covariance of the new thing with what you already had. And everybody already had the market.


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[Assistant] Okay, so given that, you know that the price, mathematically therefore, the price


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[Assistant] In particular, it's true for the riskless asset. So the riskless asset pays one, for sure, has no covariance with anything like the market.


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[Assistant] Okay, B could be whatever it is. The covariance, by the way, is gonna be bad, so this is minus.


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[Assistant] And then B, you have to figure B out. So you don't know what B is, a priori. You don't know what the interest rate is either. You had to see that in the market. Once you know the price of one asset, and you know its correlation with the market, if you knew this price,


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[Assistant] And you know the expectation of Y, and you know the covariance of the market, that will tell you B. So once you've given one price of a risky asset, like of the market as a whole, you're gonna be able to figure out the price of every other asset, because from that one price you can deduce B, and once you have B you can price everything.


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[Assistant] So that's the second idea, risk and return, and you can see it graphically and more dramatically.


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[Assistant] Because if I take that same equation, this is called the covariance pricing formula, you take that and divide by pi of y, you get one equals


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[Assistant] So that just tells me the expectation and the covariance of a dollar's worth, just like this was a dollar's worth. This was per dollar, and this is per dollar, so if you put the covariance


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[Assistant] of y and the pi of y down here and m over here and here you put expectation y over pi of y.


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[Assistant] This just says, this is just a constant, and this is a constant, so it says this and this are linearly related. These are all securities that have price one. There has to be a linear relationship between their expectation per dollar and their covariance per dollar with the market. And as covariance gets higher, the return gets higher. So only people, so if you take a stock with a high covariance,


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[Assistant] It's gonna have to have a high expected return. That's exactly what Shakespeare said. If it's more risky, people won't hold it unless it's got a higher return. Which means you can buy it for a cheap price. So its price is less than its expectation.


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[Assistant] But the difference with Shakespeare is we've quantified risk. It's not what you would have thought, variance, it's covariance.


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[Assistant] And that's it. Those are the two big ideas. And you could test, so this is called the security market line.


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[Assistant] And you could test this by asking is every stock. So if you look at every stock last year, you look at its covariance.


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[Assistant] You know, do you get things that lie in a straight line, or close to a straight line? And Sharp, in 1967,


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[Assistant] Found that, amazingly, when you did that, you got


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[Assistant] So I described last night exactly what his test was. You got something that looked incredibly close to a straight line like that. So this was the greatest triumph.


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[Assistant] Uh, it made a huge impression in finance and in economics, and lots of Nobel Prizes were given for the Sharp won a Nobel Prize, Markowitz won a Nobel Prize, Tobin won a Nobel Prize,


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[Assistant] Lindner died, he would have won a Nobel Prize too, uhm.


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[Assistant] Okay, so this is the first major triumph of fi- the most important triumph of finance, the capital asset pricing model. Now as it happens,


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[Assistant] As the theory's been tested over and over again, every year since 1967, it's performed more and more poorly. And so now when you do this, you get points that look like this.


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[Assistant] And like that, and like this, and they don't seem to lie on a straight line at all.


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[Assistant] And it's pretty shocking how up until 67 or the early 70s it seemed to work out perfectly and now it doesn't work very well.


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[Assistant] Okay, and the Markowitz and this business, they've also discovered, according to this, you know, you couldn't beat the market.


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[Assistant] Okay, so how do you test whether something beats the market? You can pick a bunch of people and say, could they beat the market? Or you can actually just make up a strategy yourself, like buy only new stocks. Just every year buy the stocks that have come onto the S&P 500 for the first time. Or just buy the stocks that are the biggest 10%.


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[Assistant] And it turns out some of those strategies actually do beat the market. So I don't have time to go into the details. You know, that is, they lie, uh, above or below here. So the theories come into


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[Assistant] So, theory and doubt. And so what I'm going to talk about next week


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[Assistant] By giving a counter-theory, another theory, which is


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[Assistant] You know, going to explain the crisis and be different from this one. But of course it hasn't been tested over 30 years or 50 years like CAPM. CAPM looked great for the first 20 years. So whatever I say next week will probably turn out to be 90% wrong.


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[Assistant] But at the moment it looks pretty good, and I didn't want to spend half the course on it, in case in 20 years it seemed to be ridiculous. But anyway, I'm going to present it anyway, to those of you who want to hear it, uhm, next week, and it'll be...


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[Assistant] I think understandable and down to earth and it'll invoke


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[Assistant] That they're gonna, they have to pay the money back. And who's gonna trust them to pay the money back? And forcing them to do something to guarantee they're gonna take, pay the money back is gonna change the theory in a critical way, which I'll get to next week. Okay, now one last thing about the theory. Uhm, as I say, you test it by seeing nobody should be above this line.


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[Assistant] And nobody should be above this line. But of course you can find people above this line. Swenson, to take the famous yellow examples above this line. And you can find people above this line. Ellington.


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[Assistant] So, Ellington, let's say, above this line. Alright, so, uh, hedge funds. Let's just call it an anonymous hedge fund since we're on camera now. Hedge fund.


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[Assistant] Alright, well, so, you can measure the, you know, if you believe this theory, and you just say, okay, let's say the theory is 99% true, it's just that there are a few people who really do beat the market, then you can decide, by looking at these graphs, you know, just how good is Swenson relative to the market? How good is this hedge fund relative to the market?


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[Assistant] And, uh, if you have a way of calibrating and measuring how well people do.


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[Assistant] So I don't have time to get into that, but I would measure the quality of a hedge fund by its marginal Sharpe ratio. How much does having access to this hedge fund, if you added it to your portfolio, increase your portfolio hedge ratio? That's I think the right measure of how good a hedge fund is. And the right measure of how good Swenson is, is


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[Assistant] What's the Sharpe ratio? And so there's a different criterion. I keep emphasizing this. Swenson is managing an entire portfolio. So you have to measure him.


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[Assistant] On, how's he done for Yale? You know, not, cause Yale's not gonna combine him with something else. It's just holding what he holds.


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[Assistant] So it's his Sharpe ratio. But Ellington, you're gonna combine Ellington with some other stuff. So Swenson might invest in Ellington, but 10 million other things. And so you have to say, how much is Ellington gonna help Swenson's Sharpe ratio?


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[Assistant] So instead of Swinson's Sharpe Ratio, how much is Ellington going to help the market Sharpe Ratio? So that's how you should measure a hedge fund. It's marginal Sharpe Ratio and, and, uh,


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[Assistant] Swensen by his Sharpe ratio. Okay, so I'd say, you know, uhm, measure Swensen performance. Swensen performance by Sharpe ratio.


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[Assistant] Hedge fund. The only people who do it are the partners of the hedge fund who are sort of forced to do it because that's how they can persuade


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[Assistant] Investors that they're really paying attention to their fun because all their money's at risk. But uhm,


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[Assistant] Okay, so that's it. That's the basic bottom line. So are there any questions before I now move on from this?


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[Assistant] Okay, we talked about this last night, we talked about it for the last two lectures.


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[Assistant] Prediction and incredibly close to the facts, at least in 1967, but not after that. Now I want to move to an aside of social security before I come back to Black Shoals, which is the next high, the last high watermark of the standard theory.


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[Assistant] So remember the upshot of what we've talked about so far. The upshot of this is that uhm, the market


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[Assistant] Oh, by the way, I say one other thing. What is the standard critique?


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[Assistant] Of this theory. So of course everybody else has seen that the theory doesn't work. So what is, what is their explanation for why, what's the old explanation for why the theory isn't working so well now? The explanation is that in the old days, what was thought of as the market was just all American stock.


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[Assistant] But now it's pretty obvious, in fact it should have been obvious from the beginning, and so Swenson realized it early on, that


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[Assistant] Markowitz's advice is to diversify, diversify, diversify, not just into all American stocks, into everything, all over the world, and all kinds of assets that aren't stocks at all.


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[Assistant] So what you measure as the market shouldn't just be American stock, it should be the world portfolio. But how do you measure the world portfolio? That's hard to do. And so people have tried to maintain that the reason the theory doesn't work is because we've got the long M in all these equations.


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[Assistant] Okay, so that would be a nice way of fixing the theory. I doubt if that's going to...


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[Assistant] Help in the end, so my problem about collateral is a different look at it.


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[Assistant] But anyhow, so the market, according to this theory, will get a higher return, okay, than bonds. And of course that's true.


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[Assistant] High covariances itself, so a low price compared to its expectation, the price is low, so you're paying much less than the expectation. So then when you see what actually happens, it's gonna look much higher than that, it's gonna be the ratio, you know, it's not just this.


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[Assistant] If you had two possibilities which you knew were equally likely, a half and a half, and the market, you know, paid off something good here and something bad here,


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[Assistant] Okay, so everybody's rich here and everybody's poor here, the price of the arrow security of this state is going to be much lower than the price of the arrow security of this state. So this state is going to have a price


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[Assistant] You know, uh, let's say 0.8 and this one might have a price 0.2 because


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[Assistant] Here, the bad state, that's when everybody's poor, so you're going to, you know, this formula basically tells you that if you're paying off here, you're negatively correlated with the market, you're gonna have to pay a very high price for it, because you're hedging. You know, by buying this security, you're balancing off, uh, what you have with the market. By buying the market, your, you know, your, uhm,


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[Assistant] Okay, so I wanna now, alright, so that's the conclusion of what we've done. I wanna now connect this to Social Security. If you remember, in our Social Security model, we had land,


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[Assistant] That went here, here, here, you know, paid one, one, one, one, one.


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[Assistant] And we had these people, overlapping generations of people like that. Okay, and we discovered


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[Assistant] A very important thing. The reason why Social Security seems to be broke


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[Assistant] It's not that Social Security wastes money like George Bush seems to imply whenever he talks about it. It's not like they just throw it away. It's that we gave a huge gift to the people in the 40s, the 50s, and the 60s, and so everyone after that has to pay for that, uh, pay for that gift.


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[Assistant] But there's a little bit more to George Bush's story than I've allowed so far, which is that he doesn't, he wants to put the money into stock. And we know that stocks are going to get a higher return than bonds.


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[Assistant] Okay, that's the whole point of this theory. You put money in stocks, the expected return on stocks is higher than the expected return on bonds.


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[Assistant] If you want to make this mathematical, we have to add the possibility of risk and stocks and it looks like it's going to be hopelessly complicated.


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[Assistant] But we can do it very easily. So I'm gonna just put, I'm gonna reproduce the same picture with uncertainty and stocks in it.


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[Assistant] And then, we're gonna come back to Social Security and spend 15 minutes talking about


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[Assistant] How could I take this simple model, which already seemed complicated, it's already a pretty complicated model, had no uncertainty, and now put uncertainty in it? Okay, well I'm gonna suppose that land


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[Assistant] By the way, I had people with endowments here. They were 3-1 and 3-1. Remember, this is their endowments. We didn't say where those endowments came from.


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[Assistant] So I'm going to take the land dividends. I'm going to suppose the dividend of the land today is one.


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[Assistant] You know, things might get better or worse in the future. Maybe the dividend goes to two, or down to a half, say.


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[Assistant] You know, I'm taking some extreme case where it doubles or halves, and maybe after that it could double again, or it could go back to one. And here it could go to one, or it could go to a quarter.


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[Assistant] You know, what dividends are doing is a random walk. If the economy starts getting more productive, uhm,


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[Assistant] You know, then, then it's in a better position, and from that point, things get even more productive, or maybe go back to where they were. If things get worse, you know, they're worse, but maybe things will start to get better again, or continue to get worse.


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[Assistant] This is what I imagine as my process of dividends.


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[Assistant] Okay, so if you own the land, you get all these dividends in the whole infinite future.


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[Assistant] I'm gonna assume only the young work, and I'm gonna assume that


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[Assistant] You know, and by the way, you might think you never go backwards. Maybe it goes from 1 to 2, or 1 to 1.1. So it's always growing, but never


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[Assistant] But, you know, but, but random. But I'm, I'm just gonna keep it here since I solved this example.


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[Assistant] Last night, I might as well stick to that one, but there's no reason why I could have made the thing


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[Assistant] Always, no matter what, get, either stay the same or get better. That might have been a better example to do. Okay, more realistic, because we don't really have tremendous productivity decline. Uhm, but anyway, all I mean to do is to put some randomness here. So, only young work and wages are proportional to dividends. So let's just assume this.


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[Assistant] This is what's called, uh, neutral technical change.


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[Assistant] So presumably the reason why productivity is going up, the dividends are getting higher, is there are more discoveries improving the product of the land, but they're probably also improving the product of labor, and so let's say they have the same effect on both.


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[Assistant] Let's, uh, say labor always gets three times what dividends are. So this is a six, this will be three halves, and this will be twelve, and three again, and three quarters. Okay, so that's what labor is getting. So labor income. I shouldn't switch colors. Labor, income.


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[Assistant] Okay, so that's my complicated world. Way more complicated than it was before.


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[Assistant] So if you own the land, you get all the dividends in the future, people live two periods, so I'm going to assume that utility, so you're going to be


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[Assistant] He's young here, she's young here, she can work here, or she can, uhm, and then she's gonna be old here and here, but when she's old...


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[Assistant] You don't discount the future, let's say. You just take log of consumption today plus a half, you know, probability of half of log.


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[Assistant] Consumption of the upstate, half log of consumption of the downstate. So the probabilities here are a half. These are the objective probabilities.


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[Assistant] Okay, so maybe I should have written it as utility. Maybe better it would have been to write the utility like that. Okay, that's what people know what the probabilities are. Okay, so that's it. That's my setup.


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[Assistant] What should the economy do? What should people do? You see, if you buy, you know, if you're some person like this


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[Assistant] She could decide to hold bonds and she could get whatever the interest rate turns out to be, which we're gonna compute in a minute. Or she could hold stock, in which case she's gonna get, let's start her off here.


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[Assistant] Maybe she could buy land, okay, buy stock, now we don't know what the price is yet, we have to compute that, but if she buys the stock, she'll get a dividend of two here and be able to sell the land for a high price, because from here on it's obviously pretty productive, better than it was back here. Or she might get unlucky and the price might


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[Assistant] You know, the dividend might be really terrible, just a half, and not only that, but the land she sells when she's old is also going to have a really lousy price.


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[Assistant] So what should she do? And, and uh, you know, will that be...


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[Assistant] A good thing for the economy or can we make everybody better off by using social security? So, how can we solve this?


[User] Please transcribe this text

[Assistant] Okay, so it looks like a really complicated model to solve, because we've got a generation born here, another one born here, the same generation might be born up here under different circumstances, and then this generation here could be born under any of three different circumstances, so it gives us a lot to solve for.


[User] Please transcribe this text

[Assistant] But what we know is that when we compute the arrow price starting from any point, this arrow price


[User] Please transcribe this text

[Assistant] Uh, you know, it's gonna be less than a half because there's so much that they have to end up holding. And that'll encourage them to...


[User] Please transcribe this text

[Assistant] How do we solve all this? So everybody following the problem? It's quite a complicated problem.


[User] Please transcribe this text

[Assistant] Right, so, well, so if you start