Machine Learning and Neural Networks - page 45

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Trying out Code Interpreter for ChatGPT

Trying out Code Interpreter for ChatGPT

The Code Interpreter for ChatGPT is demonstrated in this video by analyzing air traffic passenger statistics data from data.gov. The tool can recognize and load a CSV file into a pandas data frame, guess column name headers, and infer information about the columns. The tool can then perform basic descriptive statistics and create simple bar charts and pie charts to visualize the patterns in the data. The tool is also tested by modifying a bar chart, combining data of two levels, filtering domestic-only flights or tracking the passenger count changes over time. The video creator discusses the potential of the tool in self-service data analysis but warns of the potential dangers of unknown consequences and the development of super-intelligence, emphasizing the importance of regulating the technology with guardrails and educating oneself through data literacy channels.

• 00:00:00 In this section of the video tutorial on how to use Code Interpreter, a new Alpha plugin for ChatGPT by OpenAI, the demonstrator walks through how to use the tool to analyze data using a CSV file of air traffic passenger statistics from data.gov. They show how to upload the CSV file into ChatGPT and how the tool recognizes the file name, imports pandas and loads the data into a pandas data frame. The demonstrator also shows how the tool can guess the column name headers and infer information about the columns by just looking at the column data itself, although this might be susceptible to error. Finally, they show how to prompt the plugin to perform some basic descriptive statistics and create simple bar charts and pie charts to visualize the patterns in the data.
  
  
• 00:05:00 In this section, the user interacts with a code interpreter for ChatGPT and requests modifications to a bar chart showing the top 10 operating Airlines by passenger count. The interpreter successfully creates a modified chart with horizontal bars, gray color, and data labels showing thousands separated. The accuracy of the interpreter is then tested by comparing the numbers with a dataset uploaded in tableau, and it is found to be correct. The user then requests a combination of two levels of the operating airline variable, and the interpreter again successfully modifies the data and produces the correct result. The user realizes that this type of modification is common in analysis workflow, and the ease with which the interpreter completes the task is fascinating.
  
  
• 00:10:00 In this section, the speaker tries different analysis using the Code Interpreter for ChatGPT tool, starting with a command to filter domestic-only flights for the top 10 airlines. The tool is able to correctly recognize the variables and perform the filtering without explicit instructions. The speaker then tests the tool by asking for a time series analysis showing passenger count changes over time, and the program infers and displays the significant drop caused by the COVID-19 pandemic. The speaker is impressed with the tool's ability to create Python code, modify charts, combine data, and output data in different formats, making it a groundbreaking tool for business intelligence.
  
  
• 00:15:00 In this section, the video creator discusses the potential of the Code Interpreter for ChatGPT, which allows for quick data analysis and visualization with an almost conversational approach. While it can be error-prone, it represents a groundbreaking step towards self-service data analysis. However, the technology is also terrifying because it raises concerns about the potential development of super-intelligence and other unknown consequences. The creator emphasized the importance of implementing guardrails to regulate the technology. Finally, the creator recommends following data literacy channels to stay up-to-date with new developments, such as the ChatGPT Basics course.

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How to Use GitHub Copilot for Data Science (Python + VS Code)

How to Use GitHub Copilot for Data Science (Python + VS Code)

The video is a tutorial on how to use GitHub Copilot, an AI-powered tool that provides code suggestions for data science projects. The speaker demonstrates using Copilot to group data, create plots, and generate a function based on repeated code. They also show how to use GitHub Copilot Labs to improve code readability and generate test cases, as well as incorporating custom machine learning algorithms. The speaker believes that using tools like GitHub Copilot will be vital for programmers to stay relevant in the future and suggests checking it out.

• 00:00:00 In this section, the speaker introduces GitHub Copilot, a tool that provides AI-powered code suggestions, and explains how it can be used for data science. Using the example of calculating the average rating for each product in a dataset, the speaker demonstrates how GitHub Copilot suggests code to group the data and create a plot, and then generates a function based on the repeated code. The speaker then provides tips on how to get started with GitHub Copilot, including signing up for a free trial, installing the VS Code extension, and using shortcuts to toggle between suggestions and generate more results.
  
  
• 00:05:00 In this section, the speaker demonstrates how to use GitHub Copilot Labs in VS Code, a free extension that can be synced to your GitHub account. The extension provides a language translation feature, brushes for code readability, and test generation. The speaker shows how to use these features by demonstrating how to add comments, data types, handling bad code, and bug fixing using the brushes option. The speaker also shows how you can use the custom option to generate code for a specific machine learning algorithm, such as an SVM regressor. The speaker notes that GitHub copilot labs is an experimental feature of GitHub copilot and that GitHub copilot-X is the future of programming.
  
  
• 00:10:00 In this section, the speaker discusses the potential of GitHub copilot combined with GitHub go by Labs. They explain how they added Support Vector Machine by selecting it and how GitHub copilot produces the syntax and code reliably. The speaker believes that this is the future of coding because with AI assistance programmers can focus on creative thinking, while AI takes care of importing modules and libraries. They encourage the audience to check out GitHub copilot and suggest that learning to work with these tools will be vital to stay relevant in a decade because other programmers would be much more productive in comparison.

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GitHub Copilot in 7 Minutes

GitHub Copilot in 7 Minutes 👨‍💻🤖🚀

GitHub Copilot is an AI-powered autocomplete tool that generates suggestions based on the context of the code being written, thus reducing the amount of code written and speeding up development. It can also generate code based on comments made by the developer, making the code more understandable even for those who are new to the project. GitHub Copilot also includes a feature that allows for the toggling of suggestions and generates additional suggestions of code to optimize performance and improve code efficiency. The transcript discusses the various brushes available in GitHub Copilot, such as the clean brush, list steps brush, make robust brush, chunk code brush, and document code brush. While acknowledging that the tool still produces errors, the speaker encourages viewers to try the two-month free trial and see if it can be helpful in their coding.

• 00:00:00 In this section, the benefits and limitations of GitHub Copilot are discussed, along with real-world examples of how it can be used. The autocomplete feature of Copilot generates suggestions based on the context of the code being written, thus reducing the amount of written code and speeding up the development process. Additionally, Copilot can generate code based on comments made by the developer, making the code more understandable even for those who are new to the project. Copilot also includes a feature that allows for the toggling of suggestions and another that generates additional suggestions of code to optimize performance and improve code efficiency. Finally, the GitHub Copilot Labs initiative is introduced, which offers experimental features such as language translation, brushes for specific use cases, and test generation. These features, while experimental, can greatly enhance productivity and efficiency when used effectively.
  
  
• 00:05:00 In this section, the transcript discusses the various brushes available in GitHub Copilot. The clean brush removes unused variables and functions, the list steps brush helps you create step-by-step instructions for tasks, the make robust brush adds error handling to make your code more robust, the chunk code brush helps you split your code into smaller, more manageable chunks, and the document code brush generates comments and documentation for your code. The custom brush is particularly useful as it allows you to come up with custom commands for specific pieces of code, directly within your editor. Overall, the speaker gives GitHub Copilot a positive review but notes that it still produces some errors. However, the speaker encourages viewers to try the two-month free trial and see if it can be helpful in their coding.

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GitHub Copilot X Explained | A big step forward...

GitHub Copilot X Explained | A big step forward...

The YouTube video "GitHub Copilot X Explained | A big step forward..." discusses the latest development of GitHub Copilot, an AI tool that generates code for developers. The new version, GitHub Copilot X, includes various capabilities such as tailored documentation, pull request analysis, auto-testing suggestions, and ghost text for better auto-suggestions. It also has chat-aware conversations for instant help, and AI-powered pull request completion and review responses. The video further highlights the features of GitHub Copilot CLI, Voice, and Code Brushes, which allows users to use their voice to code and modify code to make it more readable. The YouTuber encourages developers to embrace AI and sign up for GitHub Copilot, which is reasonably priced at $10 per month.

• 00:00:00 In this section, Travis discusses GitHub Copilot and its latest development, GitHub Copilot X. He highlights that Microsoft is dominating the AI industry this year with its various AI tools. He then explains how GitHub Copilot works by generating code for developers and how it has been highly successful in improving developer productivity and satisfaction based on GitHub's research. Travis also notes that the X in GitHub Copilot X stands for various capabilities like docs, PRs, blocks, and CLI. The product vision for GitHub Copilot X is the future of AI-powered software development, and it uses the more accurate and faster GPT-4 model.
  
  
• 00:05:00 In this section, the transcript summarizes the various features of GitHub Copilot X, including its 25,000-word count limit and chat-aware conversations that allow developers to get instant help through a chat window. The tool also offers tailored documentation, where users can ask questions and receive personalized answers based on integrated documentation from GitHub, React.js, MDN, and Azure. Another significant feature is pull requests, where the tool analyzes the code and builds PR descriptions for developers. Additionally, it offers auto-testing suggestions and ghost text for better auto-suggestions. The video highlights how these new features will be beneficial for the developer community.
  
  
• 00:10:00 In this section, the GitHub Copilot's ability to resolve issues with AI is discussed, where it can suggest changes to be made automatically to solve an issue. Another feature mentioned is AI-powered pull request completion, repair, and review responses, which can help users understand changes in a pull request more easily. The GitHub Copilot CLI is also explained, where users can ask for assistance on how to run particular commands in the terminal, and it will generate the command for them. Finally, GitHub Copilot Voice and Code Brushes are introduced, which allows users to use their voice to code and adds a toolbox to assist users in coding.
  
  
• 00:15:00 In this section, the YouTuber discusses the code brushing feature of GitHub Copilot's Visual Studio code extension, which can modify code to make it more readable, add types, fix simple bugs, add debugging statements, and make code more robust. They also mention the GitHub Copilot Labs extension for experimental applications, including test generation, code explanation, and code translation. Another feature is GitHub blocks, an interactive tool for building rich documentation with charts and graphs. They encourage developers to embrace AI and sign up for GitHub Copilot, which is available as a paid service with a reasonable monthly cost of $10.

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A Comprehensive Guide to GitHub Copilot: From Beginner to Expert | VS Code Demo

A Comprehensive Guide to GitHub Copilot: From Beginner to Expert | VS Code Demo

The video provides a comprehensive guide to GitHub Copilot and its capabilities. The presenter shows how Copilot can suggest code to improve efficiency, illustrates its ability to solve complex coding problems and adapt to personal coding styles, and demonstrates its usefulness in learning new libraries like SkiaSharp for 2D drawing. While highlighting the benefits of Copilot, the presenter emphasizes that it is not a substitute for critical thinking and understanding of the code. Overall, the video is an excellent resource for beginners and experts looking to understand how to use GitHub Copilot.

• 00:00:00 In this section of the video, the presenter provides a brief introduction to GitHub Copilot as a coding assistant that can help programmers write code more efficiently and avoid typical errors. He mentions a 60-day free trial period, which users can use to test the tool before deciding whether to use it professionally. The video will feature three demos of increasing complexity, illustrating the tool's capabilities. The presenter demonstrates the tool by showing how, as he writes a function to test whether a number is prime, Copilot provides useful suggestions with performance optimizations he may not have thought of. Copilot's suggestions are not always perfect, so sometimes comments may help guide the suggestions. The presenter then goes on to show how Copilot can solve more complicated programming problems like reading the content of a file from the disk and calculating the average line length inside the file.
  
  
• 00:05:00 In this section of the video, the presenter demonstrates how to use GitHub Copilot to implement a feature that calculates the average line length of a given file. They show how the tool can suggest multiple ways to implement the feature and how it can be leveraged to learn a new library like SkiaSharp for 2D drawing. They emphasize that while Copilot can be helpful in suggesting code, it is not a substitute for thinking through the implementation thoroughly and understanding what the code is doing. The presenter also notes that Copilot can adapt to the user's coding style.
  
  
• 00:10:00 In this section, the speaker demonstrates how GitHub Copilot can recognize and adapt to a coder's personal style and potentially prevent coding mistakes. The speaker also praises Copilot's ability to generate code with minimal effort and research, such as generating colors or fill shapes, and even understanding the "painter's algorithm." Additionally, the speaker showcases the final product with colors and overlapping circles, with Copilot adapting to each iteration of the loop with ease.
  
  
• 00:15:00 In this section, the speaker emphasizes that the best part of the demo is that it does not require any prior knowledge of the SkiaSharp library, demonstrating the ease of use of Copilot when writing code. They also mention that they use Copilot every day and find it particularly helpful when letting the AI guess what they're doing, although they occasionally use comments as well. The speaker encourages viewers to like the video, subscribe to their channel, and check out their other videos and courses.

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Working with GitHub CoPilot

Working with GitHub CoPilot

The video discusses the development and functionality of GitHub CoPilot, which is based on AI and is trained on public repositories. The tool provides suggestions and functions to improve developer productivity and is available for individuals and businesses. CoPilot has the ability to suggest code based on the context of the project and allows users to turn off the IDS or opt-out of Telemetry. The video discusses potential uses of CoPilot, including building user interfaces, testing, and fixing bugs. The speakers emphasize the importance of maintaining secure coding practices and ensuring the quality of the code. Additionally, they discuss the technical limitations of CoPilot and upcoming features, such as chat on your IDE and full review assisted by AI. The video also mentions using CoPilot as an assistant or a pair programmer and recommends using CoPilot for 60 days to adjust and improve coding style.

In this video, the speaker shares their experience of using GitHub CoPilot to write code and address common questions about the tool. They explain that the tool learns from what the user is currently coding and provides helpful hints and nudges in the correct direction. The speaker also gives examples of using CoPilot with Azure cognitive services and for low-level C++ programming. They note that the tool updates with more up-to-date training data and smaller increments of updates to accommodate new versions of frameworks. The speaker praises CoPilot for its utility in helping developers learn new technologies and experiment with APIs to extract useful data.

• 00:00:00 In this section, Tanya, a solution engineer at GitHub, explains the history and development of GitHub CoPilot. She discusses the progression of AI from image recognition to natural language processing, culminating in the development of CoPilot. Tanya explains that the main goal of CoPilot was to bring emerging technologies to developers and improve developer experience in the ideation process. She credits the collaboration between Open AI and GitHub through Microsoft for the creation of the tool. With the recent launch of CoPilot, Tanya sees it becoming a globally recognized brand under the Microsoft umbrella.
  
  
• 00:05:00 In this section, the speaker discusses the functionality of GitHub Copilot and how it can be used by individuals and businesses. It can be used with any source controller and is based on local AI that has been trained on all public repositories from GitHub. The tool supports all languages, including the less popular ones, and provides successful suggestions based on the context of the project and what is around the cursor. It offers more than just completing a single line and can suggest full functions. Individuals can use Copilot for free with a GitHub account and credit card, while businesses have additional features and the ability to manage access to it through policies and configurations. Privacy and VPN proxy support are also available for businesses.
  
  
• 00:10:00 In this section, the speaker discusses the features of working with GitHub CoPilot, such as the ability to turn off or on the IDS or use of copilot and opt-out of Telemetry. The speaker also emphasizes that even though CoPilots generates new suggestions, the code received is built on the model, and sometimes it may happen that the suggested block is identical to some public code. However, users have the configuration option to filter the suggestions and block identical public code suggestions. The speaker also talks about the space framework that talks about developer productivity, and they have surveyed around 2000 people who said they were faster with repetitive tasks using CoPilot. They also discussed the metrics for efficiency and flow of productivity, satisfaction, and developer well-being. Finally, they demonstrate how to use CoPilot on a brand new application.
  
  
• 00:15:00 In this section, the user demonstrates using GitHub CoPilot to write code for a quick website using the Express framework. The tool is able to make suggestions based on the commands given by the user and is able to understand the context of the application. The user is also able to generate code line by line or by using functions, and CoPilot will propose ways to consume the function. Additionally, the user shows how CoPilot can understand the context of different applications and suggest data based on the project's name. Overall, CoPilot makes it easier and faster for developers to write code while also learning from their coding patterns.
  
  
• 00:20:00 In this section, the speaker discusses their experience using GitHub Copilot to quickly generate code in their personal project. They demonstrate how Copilot is able to understand their code and generate suggestions based on the context of their project. They show how Copilot generates blocks of data to print based on their data set and even guesses the codes that match their next step. The speaker notes that Copilot is able to improve developer productivity by interacting with the tools and the developer in the context of the project.
  
  
• 00:25:00 In this section, the speaker gives examples of how GitHub CoPilot can enhance productivity in various use cases. One example is when building user interfaces, as CoPilot suggests inline suggestions and automates repetitive tasks. Another use case is in testing and generating data or schema, as CoPilot can generate large amounts of code quickly and improve code coverage. Additionally, the speaker shows how CoPilot can be used to fix bugs and improve code quality using context-sensitive suggestions. Although CoPilot is still an experimental plugin, it has the potential to greatly improve productivity and efficiency in software development.
  
  
• 00:30:00 In this section of the video, the speakers discuss the importance of maintaining secure coding practices when using GitHub CoPilot, emphasizing that CoPilot is there to assist with coding, not to replace the developer. They note that if a developer is writing insecure code, CoPilot may inadvertently generate more insecure code. To avoid this issue, CoPilot has added filters to prevent suggestions for SQL injections without proper context. Additionally, developers are responsible for reviewing and testing their code for security vulnerabilities. They also touch on how CoPilot adapts to different library versions and frameworks by updating its training data sets, but note that it may not always suggest changes based on new practices if there is not enough data available.
  
  
• 00:35:00 In this section, the video discusses the safety and reliability of GitHub CoPilot's suggestions, as well as how to ensure the quality of the code. GitHub CoPilot's AI generates code based on information inside the GitHub database, without copying code from anywhere else. The developers are responsible for testing and ensuring the safety and security of the code generated by CoPilot. Furthermore, the video explains how quality control is maintained through voting and ranking on resources like Stack Overflow. Finally, the video addresses concerns about CoPilot's ability to maintain context for prolonged conversations, which is currently limited to about two to four thousand tokens.
  
  
• 00:40:00 In this section of the video, the speakers discuss the technical limitations of GitHub CoPilot and how it resends data for every single query. They also talk about the evolving context feature that helps to keep track of where the user is typing. They answer a user's question about whether CoPilot can be used for bulk changes in code and finding existing issues. They mention several upcoming features, including a chat on your IDE and full review assisted by AI. They also discuss the newly announced GitHub Copilot for Teams.
  
  
• 00:45:00 In this section, the speaker mentions that there are many functions available on GitHub Copilot for testing and error handling. The technical preview which began in November 2021 had one million users. The speaker also says that using Copilot's generated code can be a learning tool because developers can check if they are using code and frameworks correctly and if they are writing the code in the right way. The speaker recommends trying Copilot for 60 days and adjusting the coding style according to the suggestions provided. Additionally, the speaker recommends doing a Google search for the Stack Overflow workflow to try and understand what the code is doing and to add basic log and debug information for troubleshooting purposes.
  
  
• 00:50:00 In this section, the speakers discuss how GitHub CoPilot can be used to help with PR reviews by providing context from the business logic involved. They give an example of creating a new table and inserting data using SQL, and show how CoPilot can use the context of a business to generate suggestions for code reviews. They also touch on the importance of syntax and styling when comparing code to the rest of the code base. The speakers mention that while CoPilot may not always know the schema of a database, it can be helpful in many cases.
  
  
• 00:55:00 In this section, the speaker discusses how GitHub CoPilot can be used as an assistant or a pair programmer when developing code. It can provide assistance by suggesting the next block of code that needs to be written based on the context of the project. While it may not be able to fully understand the business logic and requirements behind a project, it can still provide helpful suggestions. The speaker also mentions other tools and extensions that users can experiment with to test new features and provide feedback.
  
  
• 01:00:00 In this section, the speaker asks how scalable is the retraining model of GitHub Copilot. They explain that it's not a smooth process to retrain the model and that they don't have a fixed rhythm for doing so. The model updates with more up-to-date training data as well as smaller increments of updates to accommodate new versions of frameworks. The speaker shares another use case involving creating a new feature very quickly using Copilot to generate suggestions for code. They created a simple function to save an image as a screenshot in a few minutes, which they validated as a starting point for building a new feature.
  
  
• 01:05:00 In this section, the speaker shares their experience of using GitHub Copilot with Azure cognitive services, specifically the form recognizer tool, to extract text from an image. They were able to use Copilot to write the code for the API and test its functionality. The speaker also shares an example of using Copilot to facilitate low-level C++ programming. While Copilot did not solve their problem directly, it provided helpful hints and nudges in the correct direction. Overall, the speaker praises Copilot for its utility in helping developers learn new technologies and experiment with APIs to extract useful data.
  
  
• 01:10:00 In this section, the speaker addresses some common questions about using GitHub CoPilot. They explain that the tool learns from what the user is currently coding and only has context within the current workspace or project. Even if the user switches projects, CoPilot won't have context for the new project until they begin coding in it. The speaker also notes that opting out of telemetry will only prevent suggestion data from being sent to the server for further use, but it won't affect local context. For enterprise users, CoPilot is accessible through a team/group in GitHub, and access to the service can be managed at the top level with different policies for blocking or unloading features. Finally, the speaker offers some time for further discussion and thanks attendees for joining.

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GitHub Copilot - First Look

GitHub Copilot - First Look

GitHub Copilot is a Chrome extension that helps developers manage their to-do lists, syncing changes to the cloud and providing live feedback on progress. The video introduces GitHub Copilot, a new feature in GitHub that automates common tasks for developers. The feature is based on React, a popular programming language. The video shows how to create a row in the table of contents, create an index row, and send the index html to public. The video also shows how to change the contents of the table of contents, and how to create a react component to handle state.

• 00:00:00 GitHub Copilot is a codex-based A.I. system that helps developers by suggesting solutions to code snippets. It is available as a free trial and can be used to create functions and data.
  
  
• 00:05:00 In this video, GitHub Copilot is introduced and demonstrated. The program allows users to create arrays, objects, and functions, and to sort and filter data. The video then shows how to use a third-party API with GitHub Copilot.
  
  
• 00:10:00 GitHub Copilot is a Chrome extension that helps developers manage their to-do lists, syncing changes to the cloud and providing live feedback on progress.
  
  
• 00:15:00 The video introduces GitHub Copilot, a new feature in GitHub that automates common tasks for developers. The feature is based on React, a popular programming language. The video shows how to create a row in the table of contents, create an index row, and send the index html to public. The video also shows how to change the contents of the table of contents, and how to create a react component to handle state.

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GitHub Copilot X tested with REAL scenarios

GitHub Copilot X tested with REAL scenarios

The YouTube video discusses the potential of Copilot X, a tool that can fundamentally change how software is written by assisting developers in building applications from scratch, understanding existing code, and refactoring code. The video demonstrates how Copilot Chat can assist in navigating and understanding code and explain syntax and grammar of programming languages. However, the tool's prompts are not always precise enough, and it needs more context to understand some codebases fully. Despite this, the tool shows promise in assisting with refactoring and modifying existing code. Overall, the speaker is impressed with the accuracy and usefulness of Copilot in navigating and understanding code and believes it will change how software is written.

• 00:00:00 In this section, the speaker discusses Copilot X, a new version of Copilot that has garnered a lot of attention due to its potential to fundamentally change how software is written. They explain that they will demonstrate Copilot X's capabilities by testing its ability to help build an application from scratch, understand existing code, and refactor code. The speaker proceeds to try to create a GitHub CLI extension using Go, relying on Copilot Chat to guide them through the process. Copilot Chat provides useful prompts and suggestions, guiding the speaker to use pre-existing packages to build the extension, rather than starting from scratch. The speaker is impressed with Copilot X's capabilities and notes that it has the potential to introduce a paradigm shift in the industry.
  
  
• 00:05:00 In this section, a software developer uses a tool called Copilot to try and build a Go package. However, the tool suggests installing packages from unknown authors, which could be an attack vector if leveraged by malicious actors. The developer attempts to use Copilot chat to clarify what is going on, but the prompts are not precise enough. This is clear evidence that these kinds of tools are not ready to replace developers entirely but can be helpful in starting to build something. The developer realizes there are better ways to leverage the GitHub API with CLIs that have published packages, which handle pagination, API rate limits, and tabulation.
  
  
• 00:10:00 In this section, the YouTube video discusses an experiment to use Copilot X to understand an existing codebase that they don't have any prior knowledge about. They use an open source Twitter algorithm repository to see if Copilot can help them make sense of the codebase. While the YouTuber is not confident that Copilot can analyze the folder structure, they ask it to describe what the code is doing. Copilot responds by defining an object called home mix alert config with some nested objects and a method, but the YouTuber notes that they need more context. They then ask Copilot more specific questions about the class and method and learn new things about Scala language. However, they conclude that Copilot needs more context, and just reading the code is sometimes more helpful.
  
  
• 00:15:00 In this section, the transcript discusses the potential of Copilot Chat, which can explain the syntax and grammar of programming languages and offer valuable feedback on custom-defined elements within a code base. The transcript highlights the usefulness of Copilot Chat for beginner programmers or those unfamiliar with a particular language, as it can distinguish between language features and custom-built elements, offering a clearer understanding of the code. The video also demonstrates how Copilot Chat can assist in understanding a project's Readme file by summarizing key concepts and identifying relevant code sections, making it a powerful navigational tool.
  
  
• 00:20:00 In this section of the video, the speaker discusses using Copilot to navigate and refactor an existing codebase. They demonstrate how Copilot can assist in understanding an existing codebase by explaining the code in a simple language. They also mention the potential of Copilot to assist with refactoring code, but caution that there is a learning curve to using the tool effectively. They then proceed to use Copilot to refactor a small utility written in node.js that searches through a GitHub repository in the terminal. Overall, the speaker is impressed with the accuracy and usefulness of Copilot in navigating and understanding code.
  
  
• 00:25:00 In this section of the video, the speaker demonstrates how to refactor existing code to stop using Axios and use Fetch instead. He also introduces async/await and Node Fetch, explaining that Fetch is available natively for modern web browsers, but not for Node.js. He goes on to explain that upgrading the Node runtime along with some adjustments to the function calls will make the code work seamlessly. The speaker expresses excitement for GitHub Copilot and believes it will change how we write software.
  
  

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GitHub Copilot for R - First impressions

GitHub Copilot for R - First impressions 

The video showcases a user's experience learning about and using GitHub Copilot, an AI-powered pair programmer designed to suggest code and write functions in real-time. The user tries to enable Copilot for R programming in Visual Studio Code and explores the possibility of using it to save time on UI tasks. They also discuss their troubleshooting experience with Copilot and the potential availability and cost of using Copilot in RStudio. Overall, the user expresses cautious optimism about Copilot's potential to assist with R programming tasks and invites viewers to share their experiences and recommendations.

• 00:00:00 In this section, the YouTuber tries a new format of creating a video where he learns something new as he records, instead of heavily scripting and planning everything out beforehand. Specifically, he wants to teach himself about GitHub Copilot, an AI pair programmer that uses the open AI codex model to suggest code and write entire functions for you in real-time. He signs up for the 60-day free trial and provides his preferences, such as allowing suggestions matching public code and allowing code snippets to help improve GitHub Copilot's model. Unfortunately, GitHub Copilot is not compatible with RStudio, so the YouTuber decides to use Visual Studio Code instead. The video showcases how a user can sign up for GitHub Copilot and how to set it up in VS Code.
  
  
• 00:05:00 In this section, the speaker discusses their experience using GitHub Copilot for R programming in VS Code. They walk through the process of adding the necessary extensions and enabling Copilot for R. After encountering some initial difficulty getting suggestions to populate, they try generating code suggestions based on comments and successfully receive a suggestion from Copilot. Overall, the speaker seems cautiously optimistic about the potential for Copilot to assist with R programming tasks in the future.
  
  
• 00:10:00 In this section, the user tests out GitHub Copilot's capabilities on creating an R script. They discover that it works best for tedious tasks, such as creating the user interface side of a Shiny application. The user is particularly impressed by Copilot's ability to swiftly create the UI and server logic components of a simple Shiny app. Although not entirely sure if VS code could launch a Shiny app, they were able to run the whole app. They also explore the possibility of using Copilot to save them time on UI tasks so that they can focus on the more complex aspects of their work.
  
  
• 00:15:00 In this section, the speaker describes their troubleshooting experience while using GitHub Copilot for R. They initially face issues running their code and suspect that they need an R tool for Visual Studio. However, they eventually realize that they need to use the "run app" function. The speaker is impressed with the functionality of GitHub Copilot and appreciates its ability to suggest code directly in their IDE. They express interest in integrating it into RStudio, but a discussion on a GitHub issue suggests that there are philosophical debates on whether or not this integration should happen. The speaker also comes across a video that converts scripts into functions, which they find to be a similar concept to Copilot.
  
  
• 00:20:00 In this section, the speaker discusses the availability and potential cost of GitHub Copilot for R in RStudio. They note that while Copilot is not currently available in RStudio, an alternative called GPT Studio could be used to add chat GPT functionality. The speaker also notes that Copilot is affordable at $100 per year, while GPT Studio uses a pay-as-you-go model based on tokens. The speaker acknowledges that the decision between these options may come down to personal preference and intended usage, and invites viewers to share their experiences and recommendations.

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David Smith - Copilot for R

David Smith - Copilot for R

David Smith discusses the use of copilot for R, a service provided by GitHub that uses generative AI to suggest the next steps in coding by looking at the context of the code being developed. He provides a demo of copilot and goes into detail about how it works, discussing its limitations while also showcasing the benefits of using predictive AI models for generating complex code and even images from text prompts. He also covers other topics, such as how these models are trained, how they generate text, images, and code, and how they are not intelligent but can be used to extract information and create new content. Additionally, he discusses the licensing considerations and usage of Co-Pilot for commercial work.

He also discusses the limitations of Copilot for R, including its lack of active R evaluation and information about the R environment. He explains how he modifies the context and prompt if he gets incorrect suggestions and addresses privacy concerns related to using Copilot for proprietary code. Smith also provides instructions on how to configure VS code to use Copilot and discusses upcoming features, including GitHub labs and a version for shell prompts. The talk touches on the history of R and innovations made by its users. Copilot's responses are not creative and are an amalgamation of what it's trained on conditioned on the prompt given, so careful consideration is necessary to ensure useful code is generated.

• 00:00:00 In this section, the speaker welcomes everyone to the virtual February 2023 New York open statistical programming Meetup and mentions that they will be moving to a hybrid format as soon as they are able to find speakers and venues to host them. The speaker encourages attendees to post job openings on the NY hack R slack channel and talks about their own job openings for part-time and full-time data scientists, data engineers, and sales roles. They also discuss the pizza they are eating and encourage attendees to share where they are getting food from. The speaker then announces some upcoming conferences and offers a discount code for attendees, as well as a chance for free tickets to be given away at the end of the event.
  
  
• 00:05:00 In this section, the speaker discusses upcoming conferences, including Data Council, D4con in Tampa, Mir, Arc, and ODSC, and notes that they try to provide discount codes for these events to their email subscribers. They also ask for help finding a venue in New York City to host their Meetup and finding a speaker for their May Meetup. The speaker encourages attendees to join their NY Hack R Slack channel to ask questions about R, Python, Julia, SQL, and other topics and notes that the NY Hacker website has 13 years' worth of talks and resources available for learning.
  
  
• 00:10:00 In this section, the speaker introduces himself and talks about the use of copilot for R. He explains that copilot is a service provided by GitHub that uses generative AI to suggest the next steps in coding by looking at the context of the code being developed. The speaker also provides a demo of copilot and goes into detail about how it works. He mentions that copilot is best used within an editor environment and provides a link for users to get started with copilot in Visual Studio code.
  
  
• 00:15:00 In this section, David Smith live codes an analysis of the pumpkins dataset using Github's copilot. He reads in the dataset and uses the Tidy verse to prepare the data. Copilot helps him suggest the janitor package to clean up the column names. David then uses the sample_n function to display random rows from the dataset. He creates a table to show the average high price by package color and then models an analysis of variance using the aov function. However, David notes that copilot can be non-deterministic, as sometimes it passes the solution into Knitter to generate a nicely formatted table, but sometimes it doesn't.
  
  
• 00:20:00 In this section, David Smith explains how copilot works using generative AI models such as GPT-3 and Codex. These models generate suggestions of code from prompts, which are the previous lines of code in a script. Copilot uses a generative AI model in the same way to suggest code idioms and functions for statistical analysis in R. These models are built using vast amounts of training data, and GPT-3, for example, has several billion parameters and was trained on literal zettabytes of data. These models are powerful tools that can generate complex code and even images from text prompts.
  
  
• 00:25:00 In this section, David Smith discusses how generative AI models, such as OpenAI's neural networks, can generate texts, images, and code. These models are trained on different types of data, such as medical literature, which enables them to generate human-like content. While they can extract information and create new content, it’s important to note that they are not intelligent and do not learn. Additionally, these models are not reliable, as they can hallucinate facts and provide different responses to the same prompt. These models only make predictions based on their training data and are essentially black boxes that do not contain all of the information from their training set.
  
  
• 00:30:00 In this section, David Smith discusses generative AI and its limitations, emphasizing that it doesn't understand language, math, facts, manners, emotion, or ethics. However, he notes that prompt engineering can be used to mitigate some of these downsides. He also mentions that Microsoft has partnered with OpenAI to make its models, such as GPT-3, available within the Azure service. Co-pilot, which utilizes the OpenAI Codex model and provides suggestions for code in Visual Studio, is an example of this collaboration.
  
  
• 00:35:00 In this section, David Smith demonstrates the use of generative AI in action and how to interface with the OpenAI service using code. He shows how to manually set up an interaction with the API and defines the URL and payload to send to the API. Additionally, he shares a function that encapsulates the code and error checking. He demonstrates how to ask for a joke and points out some potential problems with the AI model due to it being a black box and not updated in real-time.
  
  
• 00:40:00 In this section, David Smith demonstrates how different AI models generate responses to prompts. Using examples with Copilot for R and Codex, he shows that the models are frozen in time, and non-deterministic, meaning the same prompt can produce different results. When prompted to write a limerick, the latest version of GPT-3 is able to make a good rhyming Limerick, while an older version generates one that doesn't even rhyme. David also explains how prompts are generated using tokens, which are probabilities for potential tokens that the AI might generate, and the model picks from the top few higher probability of things.
  
  
• 00:45:00 In this section, David Smith explains how tokens are used by GPT models to generate human-like text, and demonstrates how to generate a sequence of tokens in R using the OpenAI service. He mentions that programs which utilize GPT models save time, dedicated thinking and ultimately allow for more fulfilling coding sessions. Smith also notes that while GitHub Copilot is not free, the OpenAI service is, and both can be utilized in Azure.
  
  
• 00:50:00 In this section, David Smith fielded questions from viewers, including whether Co-pilot could be used in other editors besides the four presented in the talk (unfortunately, no); whether he had tried using the native pipe instead of magrittr, to which he admitted he hadn't, but speculated that changing his habits now might impact Co-pilot's usefulness; and how often the underlying model of Co-pilot was updated to reflect more recent developments, which he said wasn't very often due to the time and money it takes, but fine-tuning was a possibility left to individual users where the upper layers of the model can be retrained with a new Corpus of data.
  
  
• 00:55:00 In this section, David Smith discusses the licensing considerations and usage of Co-Pilot for commercial work. He emphasizes that the code generated by Co-Pilot is owned by the person who generated it. While Co-Pilot can be useful in generating AI models, users should verify the generated code and perform security and correctness tests to ensure it is reliable. David also shares his experience of using Co-Pilot, finding it good at surfacing idioms and functions he wasn't aware of, but it tends to gravitate back towards the training data when attempting to create complicated or unique functions. Additionally, he discusses the possibility of comparing the Tidy verse and data table code generated by Co-Pilot and requests a pull request for anyone who is interested.
  
  
• 01:00:00 In this section, David Smith explains the limitations of Copilot for R. He notes that Copilot doesn't do any active R evaluation and it doesn't get any information about the R environment. Additionally, Copilot generates tokens based on what it has done before, which means it might generate absolute nonsense. While it's doing its best to generate tokens, one needs to be careful to ensure that the generated code is actually useful. Additionally, David explains that Copilot's responses are not creative and that it's really an amalgamation of what it's been trained on conditioned on the prompt given.
  
  
• 01:05:00 In this section, David Smith discusses how he modifies the context and prompt if he gets something that doesn't look like what he writes using Copilot for R. He also provides insight into the privacy implications of using Copilot for proprietary code. While code snippets are sent to the Copilot server for generating prompts, they are discarded right after the session. David points out that Microsoft is sensitive to these concerns and has designed Copilot with this in mind. Additionally, David provides a link to the GitHub FAQs that address many of the questions around Copilot’s licensing and code completion.
  
  
• 01:10:00 In this section, David Smith discusses how all code completion in his demo was achieved using Copilot instead of traditional intelliSense. He also provides his VS code configuration for using Copilot and R, including instructions on how to turn off intelliSense and other unnecessary features. When asked about how Copilot handles complex coding tasks like debugging or optimization, he admits to not being experienced in that area but mentions the usefulness of Copilot in generating tests for debugging processes. He also notes that the next generation of models being developed for Copilot and GPT-3 are being trained without AI-generated content to avoid problematic feedback loops.
  
  
• 01:15:00 In this section, the speaker mentions some new features coming to Copilot, including GitHub labs which allows users to highlight code and receive an English description of what the code does. Additionally, there will be a version of Copilot for shell prompts, which will suggest code when typing commands. The discussion also briefly touches on data table packages and the history of the R language, which was derived from the programming language S invented at Bell Labs by John Chambers in 1974. Overall, the talk focused on the long history of R and the various contributions and innovations made by users like the speaker.