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We had a deal, programmers are supposed to automate everyone else's jobs, not automate our jobs.



Machine Learning vs Traditional Modeling

Major differences:

  1. More emphasis on information pipeline management; data collection, preparation, feature determination, and pipeline configuration management.
  2. Developing a machine learning application is more iterative and explorative process than traditional software engineering. Learning / Testing / Validation of models is an upfront task

Developing a machine learning application is even more iterative and explorative process than software engineering. Machine learning is applied on problems that are too complicated for humans to figure out (that is why we ask a computer to find a solution for us!). Differences between machine learning and software engineering | Antti Ajanki - Futurice

AI Pair Programming Tools


These tools assist software developers by generating code snippet suggestions based on the context and intent of the code being written. Today, AI-powered software development tools are allowing people to build software solutions using the same language that they use when they talk to other people. These AI-powered tools translate natural language into the programming languages that computers understand. How AI makes developers’ lives easier, and helps everybody learn to develop software | John Roach - Microsoft


Image to Code

Text to Code

ChatGPT | OpenAI for Development

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ChatGPT to create a Chatbot

Website Development with ChatGPT

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Chatgpt4.ai Copilot

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Codex | OpenAI

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Codex, a Generative_Pre-trained_Transformer_(GPT) language model fine-tuned on publicly available code from Microsoft's GitHub. Codex requires that you access it via their API, or Playground. Create your own fine-tuned OpenAI model by feeding it training data from files; then would be able to generate much more accurate and detailed responses; understanding the context of a file and generate very accurate, but specific code completion based on the file. Codex powers Microsoft's GitHub Copilot.


Copilots | Microsoft

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An AI Copilot is an artificial intelligence-powered assistant designed to help users with various tasks, often providing guidance, support, and automation in different contexts. These copilots can assist with tasks like writing a sales pitch or generating images for a presentation. Microsoft has launched Azure AI Studio, a new capability within Azure that allows developers to build their own AI-powered “copilots” using tools on Azure and machine learning models from its close partner OpenAI.

  • Copilot in Power BI (in preview): is an AI-powered assistant that harnesses the capabilities of advanced language models to help users uncover and share insights rapidly. Users can now create and customize reports in seconds, generate and modify DAX calculations, summarize data narratives in conversational language, and even inquire about their data. Copilot in Power BI is currently available for preview.
  • Copilot in Power Pages (in preview): is an AI-powered tool that is revolutionizing how to build and launch data-centric business websites. As a maker, your tasks are simplified using Copilot. These enhancements enable you to construct forms, incorporate text, and embed chatbots into your Power Pages sites, all through a user-friendly, dialogue-like interface, bypassing the need for complex coding proficiency. Copilot in Power Pages is currently available for preview.
  • Copilot in Microsoft Fabric (available in preview soon): is an AI-powered tool that will be available for preview soon. It combines advanced generative AI with your data to help everyone uncover and share insights faster. Simply describe the insights you need or ask a question about your data, and Copilot will analyze and pull the right data into a stunning report—turning data into actionable insights instantly.
  • Windows Copilot: is an AI-powered personal assistant that makes using your PC easier and more intuitive. It provides centralized AI assistance to help people easily take action and get things done. Windows Copilot will be available in preview to beta testers in June and will be available to the general public later this year.
  • Microsoft’s 365 Copilot (will get plug-ins): is an AI-powered tool that brings the power of next-generation AI to Microsoft 365 products like Microsoft Teams, Outlook, and more. Organizations that have joined the Microsoft 365 Copilot Early Access program will soon get access to over 50 plugins from companies like Atlassian and Adobe. In the coming months, the Microsoft 365 Copilot will add support for two new types of plugins, Teams message extensions and Power Platform connectors.



GitHub Copilot


GitHub Copilot is an AI-powered code completion extension for Visual Studio that leverages a vast dataset of publicly available code to provide context-aware code suggestions, snippets, and even entire functions. It works with many programming languages and offers more advanced assistance compared to IntelliCode and IntelliSense. GitHub Copilot is trained on billions of lines of code and turns natural language prompts into coding suggestions across dozens of languages. It draws context from comments and code to suggest individual lines and whole functions instantly. GitHub Copilot is powered by OpenAI Codex, a generative pretrained language model created by OpenAI. Copilot writes code alongside you in your text editor. The extensions for Copilot are available for Noevim, JetBrains, Visual Studio Code, and in the cloud on GitHub Codespaces. GitHub Copilot is really only usable in Visual Studio Code, Microsoft’s IDE, or GitHub Codespaces if you’re into the whole Cloud IDE. Model trained on the GPT-3 language prediction model created by OpenAI. GitHub Copilot supports several programming languages and frameworks, including but not limited to: C#, C++, Python, Java, JavaScript, TypeScript, Ruby, Go, React, Angular, and Vue. It integrates with popular code editors like Visual Studio Code, JetBrains IDEs, Neovim, and Visual Studio.


Word, PowerPoint and Excel

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Amazon

CodeWhisperer

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Amazon CodeWhisperer, a machine learning (ML)–powered service that helps improve developer productivity by generating code recommendations based on developers’ comments in natural language and their code in the IDE. Imagine being a software developer with an AI-powered coding companion, making your coding faster and easier. Amazon CodeWhisperer does just that. It uses generative AI under the hood to provide code suggestions in real time, based on a user’s comments and their prior code. It’s now available in preview as part of the AWS IDE Toolkit, which means developers can immediately use it right inside their preferred IDEs, including Visual Studio Code, IntelliJ IDEA, PyCharm, WebStorm and Amazon’s own AWS Cloud 9. Support for the AWS Lambda Console is also coming soon. Tool can autocomplete entire functions based on only a comment or a few keystrokes. Amazon trained the system, which currently supports Java, Javascript and Python, on billions of lines of publicly available open source code and its own codebase, as well as publicly available documentation and code on public forums.

AI code assistants are software tools that use artificial intelligence to help developers write code faster and more accurately. They can generate code, suggest code completions, detect errors, and more. Here are some of the AI code assistants available today:


AskCodi

An AI code assistant that excels in writing documentation and unit tests. It can generate documentation from code comments or natural language queries, and create unit tests from code snippets or test cases. It supports Python, Java, JavaScript, C#, and PHP. It works as a web app or a browser extension.

Codiga

An AI code assistant that helps ensure code quality and security. It can analyze code for errors, bugs, vulnerabilities, performance issues, style violations, and best practices. It can also suggest code improvements and refactorings. It supports Python, Java, JavaScript, TypeScript, C#, PHP, Ruby, Go, and Swift. It works as a web app or a VS Code extension.

Tabnine

An AI code assistant that helps developers ship code faster without exposing intellectual property. It uses deep learning to provide code completions for any language in any editor. It can also suggest relevant documentation and code examples. It works offline and does not send any code to the cloud.

Sourcegraph Cody

An AI code assistant that helps developers find and reuse code snippets from GitHub. It can search for code snippets based on natural language queries or keywords, and provide context and explanations for the results. It supports over 20 programming languages, such as Python, Java, JavaScript, C#, Ruby, Go, and Rust. It works as a web app or a VS Code extension.

Captain Stack

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This feature is somewhat similar to GitHub Copilot's code suggestion. But instead of using AI, it sends your search query to Google, then retrieves StackOverflow answers and autocompletes them for you.


GPT-Code-Clippy (GPT-CC)

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an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub.

Second Mate

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An open-source, mini imitation of GitHub Copilot using EleutherAI GPT-Neo-2.7B (via Huggingface Model Hub) for Emacs.

Clara-Copilot VSCode

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This feature is somewhat similar to GitHub Copilot's code suggestion. But instead of using AI, it sends your search query to Google, then retrieves StackOverflow answers and autocompletes them for you.


DeepCode

No-code AI

No-code AI, or codeless AI, refers to using a development platform with a visual, code-free, and often drag-and-drop interface to deploy AI and machine learning models. No-code AI solutions aim to allow non-technical users to create ML models without delving into details of every stage of creating ML models. No-code AI uses graphical user interfaces (GUIs) and pre-built machine learning models to build AI-based applications. It lets users input data, configure the model, and quickly create intelligent applications without coding expertise. The no-code AI toolset includes the following features:

  • Data preparation: This feature allows users to prepare their data for machine learning by cleaning, transforming, and featurizing it.
  • Model building: This feature allows users to select the right machine learning algorithm for their problem and train a model on their data.
  • Model evaluation: This feature allows users to evaluate the performance of their models and compare different models to find the best one for their needs.
  • Model deployment: This feature allows users to deploy their models to production so that they can be used to make predictions.
  • Model monitoring: This feature allows users to monitor the performance of their models over time and make adjustments as needed.

DataRobot

a cloud-based platform that provides a complete solution for machine learning, from data preparation to model deployment. Its no-code AI toolset makes it easy for business users and non-technical professionals to create and deploy machine learning models.

Google Cloud AutoML

a suite of tools that automate the machine learning process for different types of tasks, such as image classification, text classification, and natural language processing. Its no-code interface makes it easy for anyone to create and deploy machine learning models without having to write any code.

Amazon SageMaker Autopilot

a fully managed service that automates the machine learning process for a variety of tasks. Its no-code interface makes it easy for anyone to create and deploy machine learning models without having to write any code.

Microsoft Azure Machine Learning Designer

a graphical tool that makes it easy to create and deploy machine learning models without having to write any code. It supports a variety of machine learning tasks, including image classification, text classification, and natural language processing.

H2O Driverless AI

a cloud-based platform that automates the machine learning process for a variety of tasks. Its no-code interface makes it easy for anyone to create and deploy machine learning models without having to write any code.

Domino Data Lab

a cloud-based platform that provides a variety of tools for machine learning, including a no-code AI toolset. Its no-code interface makes it easy for anyone to create and deploy machine learning models without having to write any code.

Clarizen

helps businesses automate their workflows and make better decisions. It offers a variety of AI-powered tools, including a no-code AI builder that lets users create and deploy machine learning models without having to write any code.

Obviously AI

AI platform that helps businesses build and deploy machine learning models without having to hire data scientists. Its no-code AI builder makes it easy to create and deploy machine learning models for a variety of tasks, such as customer segmentation, fraud detection, and product recommendations.

MonkeyLearn

a no-code AI platform that helps businesses extract insights from text data. It offers a variety of pre-trained AI models that can be used for tasks such as sentiment analysis, topic modeling, and named entity recognition.

Akkio

a no-code AI platform that helps businesses build and deploy machine learning models for a variety of tasks, such as fraud detection, customer segmentation, and product recommendations. Its no-code AI builder makes it easy to create and deploy machine learning models without having to write any code.

Teachable Machine

a free web app from Google that lets you create machine learning models for image classification, sound classification, and object detection without having to write any code. Teachable Machine no-code AI toolset

Lobe

a no-code AI platform that helps businesses create and deploy machine learning models for image classification, text classification, and natural language processing. Its no-code AI builder makes it easy to create and deploy machine learning models without having to write any code.

Appen

crowdsourcing platform that offers a variety of no-code AI tools, such as its Image Labeler tool, which can be used to label images for machine learning models.

Scale AI

a crowdsourcing platform that offers a variety of no-code AI tools, such as its Natural Language Processing tool, which can be used to label text for machine learning models.

AI Builder

a no-code AI platform from Microsoft that helps businesses build and deploy machine learning models for a variety of tasks, such as customer segmentation, fraud detection, and product recommendations. Its no-code AI builder makes it easy to create and deploy machine learning models without having to write any code.

Amazon Lex

a cloud-based service that helps businesses build conversational AI experiences. It offers a no-code interface that makes it easy to create chatbots that can understand and respond to natural language queries.

Google Dialogflow

a cloud-based service that helps businesses build conversational AI experiences. It offers a no-code interface that makes it easy to create chatbots that can understand and respond to natural language queries.

RunwayML

Supports a wide range of AI applications, including image synthesis, style transfer, natural language processing, and object detection

Glide

Makes it easy to build and deploy custom tools with clicks, not code

Noogata

Comes with pre-built AI blocks that can be used for a variety of tasks, such as predictive maintenance, fraud detection, and customer segmentation

Replit

  • Ghostwriter | Replit ... your partner in code. Harness the power of Replit’s AI to boost your productivity and creativity

Replit is an online integrated development environment (IDE) platform that provides a cloud-based workspace for coding, debugging, and deploying applications. It also offers a number of features that can help developers to be more productive, including a built-in code editor, a debugger, a terminal, and a file manager. In addition, Replit offers a number of integrations with other services, such as GitHub, Bitbucket, and Heroku. One of the most interesting features of Replit is its integration with AI.

Replit's AI assistant can help developers with a variety of tasks, such as code completion, debugging, and documentation. The AI assistant can also be used to generate code, which can be helpful for developers who are new to a particular programming language or who are looking for inspiration. Overall, Replit is a powerful and versatile IDE platform that can be used by developers of all levels of experience. The integration with AI makes Replit a particularly interesting option for developers who are looking for ways to be more productive and to learn new programming languages. It can generate code from natural language prompts or voice commands, and provide interactive feedback and guidance. It supports over 50 programming languages and frameworks, such as Python, JavaScript, HTML, CSS, React, TensorFlow, and PyTorch. It works as a web app or a mobile app.

Here are some examples of how AI can be used in Replit:

  • Code completion: The AI assistant can help developers to complete code by suggesting relevant functions, variables, and keywords.
  • Debugging: The AI assistant can help developers to debug their code by suggesting possible solutions to errors.
  • Documentation: The AI assistant can help developers to generate documentation for their code by extracting information from the code itself.
  • Generating code: The AI assistant can be used to generate code for a variety of tasks, such as creating web applications, mobile applications, and data science projects.

TerpreT Framework

TerpreT is a framework for building and deploying Natural Language Processing (NLP) models. It is designed to be easy to use and scalable, making it ideal for a variety of Natural Language Processing (NLP) tasks, such as text classification, question answering, and Natural Language Generation (NLG). TerpreT is based on the TensorFlow framework, which provides a powerful and flexible platform for Machine Learning (ML). TerpreT also includes a number of features that make it well-suited for NLP, such as:

  • A built-in library of NLP pre-trained models
  • A simple API for training and deploying NLP models
  • A variety of tools for evaluating NLP models
  • A community of developers and users who are actively contributing to the framework

TerpreT is a powerful and versatile framework that can be used to build and deploy a variety of NLP models. It is easy to use, scalable, and well-supported by a community of developers.

Here are some of the benefits of using TerpreT:

  • Ease of use: TerpreT is designed to be easy to use, even for those with no prior experience with NLP or TensorFlow. The API is simple and intuitive, and the documentation is comprehensive.
  • Scalability: TerpreT is designed to be scalable, so you can use it to train and deploy NLP models on large datasets.
  • Flexibility: TerpreT is a versatile framework that can be used to build and deploy a variety of NLP models. It includes a built-in library of pre-trained models, and it can be used to train custom models from scratch.
  • Community support: TerpreT is a actively supported framework with a large and growing community of users and developers. This means that you can get help and support if you need it.

Automating Web Applications

Full-cycle Deep Learning Projects

Software Development Life Cycle (SDLC)

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Reduce Bugs

Agile

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AI has changed software development by exposing human perception, definition, and execution of programming. ...Future programmers won’t maintain complex repositories, analyze running times or create intricate programs. They’ll collect, sanitize, label, analyze, and visualize data feeding neural networks. 9 Ways To Implement Artificial Intelligence and Agile-Powered Management in Software Development | Chandresh Patel - DZone

...Introduce machine Learning (ML) techniques into your Software Development Life Cycle (SLDC) as follows:

1. Coding Assistants: Most of a developer’s time is spent debugging code and reading the documentation. With smart coding assistant implemented using ML, developers can get quick feedback and recommendations based on the codebase, saving a lot of time. Great examples include Java’s Codota and Python’s Kite.

2. Automatic Coding Refactoring: It is important to have clean code because it makes collaboration a lot easier. Maintenance of clean code is also orders of magnitude easier than unclean code. Here's the deal; whenever an organization scales, refactoring becomes a painful necessity. With ML, it is easy to analyze code and optimize for performance by identifying potential areas for refactoring.

3. Making Strategic Decisions: A large chunk of a developer’s time is spent debating the features and products to prioritize. An AI model trained with data from past development projects can assess how applications perform, helping business leaders and engineering teams to identify methods of minimizing risk and maximizing impact.

4. Providing Precise Estimates: The profession of software development is known for exceeding budgets and timelines. To make a good estimate, it’s important to have a deep understanding of both the context and the development team. You can train an ML model using data from past projects like user stories, cost estimates, and feature definitions. This can prove very helpful in predicting effort and budget.

5. Analytics and Error Handling: Coding assistants based on ML can identify patterns in historical data and identify common errors. If the engineer makes such an error during development, the coding assistant will flag this. And that’s not all…after deployment, ML can be used to analyze logs and flag errors that can then be fixed. This makes the software developer proactive in solving errors. Who knows? Maybe in the future ML will correct software based on errors without the need for human intervention.

6. Rapid Prototyping: Converting business requirements into technology takes months at best or years to turn into technology. Today, however, ML is reducing development time by helping individuals with less technical knowledge to develop technologies.

7. Using AI for Project Planning: The human brain is an astonishingly great knowledge powerhouse. And what’s even more surprising is that we all have different cognitive abilities from one another. No two project managers will have the exact same thoughts on the same project. Enter ML. By replicating human intelligence, ML can create various permutations of a situation similar to the human brain.

8. Risk Estimation: Making informed decisions on risk estimation in software development is complex and factors in budgeting and scheduling constraints. In the beginning, healthy completion levels appear likely for every project. But here’s the kicker, when you start the project, the external environment and project interdependencies alter the probabilistic scenarios. Our limitation as humans is limited by the capacity to store and reproduce information. ML allows you to retrieve parameterized information on demand. You can train the AI model with past data of project start and end dates. This way, it will give you a realistic timeline for the current development project.

9. Project Resource Management: Delivering a software product depends on having the right people working on the project. Again, AI goes deep into the data on the history of past projects. It can give you information in real time on which developers are engaged in other projects. This makes it easy for you to know which developers are ready for deployment. Based on the ML prediction, you can either increase or reduce the number of developers.


Miro

  • Miro ... your team's visual platform to connect, collaborate, and create — together.

Miro has rich, ready-to-use native capabilities for teams of every size to build out their vision with a creative, collaborative edge. That means supporting workflows in every corner of your business.

Miro AI is an AI collaboration tool that is integrated with the Miro platform. It uses artificial intelligence to help teams collaborate more effectively and efficiently. Miro AI is still under development, but it has the potential to significantly improve the way teams collaborate. Some of the features of Miro AI include:

  • Automatic translation: Miro AI can automatically translate text into multiple languages, making it a valuable tool for teams with members from different countries.
  • Optical character recognition (OCR): Miro AI can recognize text in images and PDFs, making it easier to organize and find information.
  • Text analysis: Miro AI can analyze the text in your boards to identify trends and hidden insights.
  • Recommendations: Miro AI can recommend ideas and resources that are relevant to your work.
  • Clustering: Miro AI can cluster sticky notes by keywords or sentiment, making it easier to find related information.
  • Ideas wall: Miro AI can create an "ideas wall" where team members can post their ideas and feedback.
  • Discussions: Miro AI can facilitate discussions by summarizing the key points and identifying areas of agreement and disagreement.


Here are some specific examples of how Miro AI can be used to improve collaboration:

  • A marketing team can use Miro AI to translate their marketing materials into multiple languages.
  • A product development team can use Miro AI to analyze customer feedback and identify trends.
  • A sales team can use Miro AI to create presentations and proposals.
  • A project management team can use Miro AI to track progress and identify risks.

A design team can use Miro AI to brainstorm ideas and collaborate on designs.