Recommendations: PLEASE ADD YOuR IDEAS
- Create recommendations for community:
- Automated Content Generation: Creating standard documents, reports, or templates automatically.
- Document Classification and Organization: Sorting and categorizing documents based on content, relevance, and context.
- Real-Time Collaboration Support: Providing intelligent suggestions and corrections during collaborative writing efforts.
- Content Translation: Instantly translating documents between different languages to enhance global collaboration.
- Chatbots and Virtual Assistants: Providing 24/7 support to answer customer queries.
- Sentiment Analysis: Monitoring and analyzing customer feedback to improve service.
- Personalized Marketing: Offering personalized recommendations and content based on user behavior.
- AI assisting in Best Practice Awarding
Who we are
Why we are here
Slide 1 AI for Enterprises
What Is TYPES of AI tools
AI why (enterprise advantage)
Slide 2 AI for Enterprise Documentation
What is it
Slide 4 This is what we are doing at Hyperledger / What we want to get out of the tools Techniques
Slide 5 Deliverables
Slide Action Slide
subcommittees and how they will incorporate
Slide 3 Tools
** need time to evaluate vast tools available
Open Source Model for adopting AI tools
My Favorite tool DEMO ( all use same prompt with different tools)
Educate Us (MIT course)
A&A work on presentation
DEMO – let us know your tool, let us know the prompt
Gianluca Capuzzi 9:21 AM
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Tripur 9:40 AM
Tripur 9:41 AM
Potential Project Plan for presenting possible AI engines
How do you see AI fitting into the overall enterprise solution, general observation from 10,000 feet
Bobbi Big Picture: white label ChatGBT for Linux with a learning engine specific to the community, each "marketing effort" should incorporate common Template, presentation and videos Akanksha Enterprises can leverage NLP to analyze unstructured data, extract valuable information from text, and enhance communication with customers and employees. Arunima As a tool to improve our productivity. But at the same time, we should use our creativity, thinking power and research. Gianluca Soon AI will pervade production process starting from routine activities Tripur Reducing the workload and shifting the focus to more important areas. Could also add AI to our existing tools and get ahead in the field.
- How do you see AI fitting into the Documentation task force?
Bobbi Overall it will enable us to meet the community needs by creating professional guides. Akanksha By using specific AI tools to reduce time and accuracy of our work templates , documentation ,user guides Arunima Automate some monotonous tasks which take up a lot of time. This way we will have more time to focus on complex tasks that require more thinking. Gianluca One idea is to integrate a ChatBot or Large Language Model that replies to user questions Tripur Creating the first draft of user guides, and presentations, and also keeping the log updated, intelligent search recommendations.
- How do you see AI fitting into each sub-committee
Bobbi , this is where we get into specific workflows and actually applying Akanksha Using or creating specific AI tool for specific work, for example talking about onboarding , we need to know the particular interests of different personas. We can use different ML models to exract the particular needs Arunima Figure out what are some of the tasks that can be automated using AI. This way we can focus our energy on more brainstorming and discussing ideas Gianluca Two example: could analize comments from users, could propose intellisense in github Tripur Division of workflow, setting a pseudo deadline to get the idea.
- How do you envision the best way to accomplish this goal?
Bobbi Present a message, maybe even a catch phrase for our endeavor. Present an overall goal and the focus on a Specific use case (solana) and use it for all examples Akanksha Aligning and planning our requirments with currently present tools and figure out the way to accomplish our goals Arunima Having proper knowledge of AI tools and how to properly use them to boost our productivity Gianluca Integrating AI engine and AI API Tripur Testing different AI- tools.
- What supporting products are needed for each implementation?
Bobbi See list below Akanksha Data preprocessing tools -Google Colab , Jupyter notebook, Pytorch , Libraries , AI tools Arunima ChatGPT, Gamma app Gianluca Machine Learning and AI libraries like TensorFlow Tripur Phind, Copyai
- What is the best way to present this info on Thursday Format and use cases
Bobbi AI generated presentation with overall strategy for AI and thebn demo with use case for user guides in Solana Akanksha Introducing ALL the available solutions and aligning them with Hyperledger needs and make the presentation with examples Arunima AI-Generated presentation + some demo videos of using AI tools Gianluca Let me think..I have a presentation and a simple software in python but is not appliable to blockchain at the moment Tripur Let everyone present their favorite AI tools and live demo on them
What would we need to make it happen?
MIT online course 8/15
Overall Community Analysis
SWOT Analysis for Incorporating AI Tools into the Hyperledger Community:
In conclusion, incorporating AI tools into the Hyperledger community presents significant potential to enhance efficiency, security, and scalability. However, it also comes with challenges related to complexity, resource allocation, and finding the right expertise. Properly managing these aspects can help maximize the benefits and opportunities while mitigating the associated weaknesses and threats.
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1. Workflows for Userguides
2. Templates and Graphics Libraries
3 API / Tokens System for updates
BLOGS AND PRESENTATIONS
Strategies for incorporating AI into the current workflow
Identify Use Cases
|Help Model an overall enterprise solution
Create a Whitelabel AI dashboard for your company
AI (Artificial Intelligence) workflow
refers to the process or series of steps involved in developing, deploying, and maintaining AI systems or models. The specifics can vary depending on the complexity of the project, the tools and technologies used, and the team's preferences, but generally, an AI workflow includes the following steps:
Problem Definition: The first step in any AI workflow is defining the problem. What is the task that the AI is meant to solve? This includes understanding the business or scientific context, setting the goals for the AI project, and specifying the metrics that will be used to evaluate the model's performance.
Data Collection: Once the problem is defined, the next step is to gather the data that the AI will learn from. This could be from various sources like databases, APIs, web scraping, or even manually collected and labelled data.
Data Preprocessing: Raw data often requires cleaning and formatting before it can be used for machine learning. This step might involve removing or filling missing data, handling outliers, normalizing numerical data, encoding categorical data, and splitting the data into a training set and a test set.
Model Selection and Training: Choose an appropriate machine learning model or models for your problem. You'll then train the model on your training data set. The specifics of this step will vary depending on the type of problem you're solving and the kind of model you're using.
Model Evaluation: After the model has been trained, it's time to test it on unseen data to evaluate how well it performs. This involves using the test data set and the metrics defined in the problem definition stage.
Model Optimization: If the model's performance is not satisfactory, you might need to tweak its parameters, choose a different model, collect more data, or preprocess your data in a different way. This is often an iterative process that continues until the model's performance reaches a satisfactory level.
Deployment: Once you're satisfied with your model's performance, it can be deployed to a production environment where it can start doing useful work. This could be on a server, in the cloud, or embedded in a device.
Monitoring and Maintenance: After deployment, the model needs to be monitored to ensure it continues to perform as expected as new data comes in. It may need to be retrained or tweaked over time.
Documentation and Explanation: Throughout this process, it's important to document your work so that others can understand it, reproduce it, and maintain it. Depending on the application, you might also need to provide explanations of the model's decisions or predictions.
In addition, depending on the project, there may be other steps, such as gathering business or user requirements, conducting ethical reviews, or complying with regulations and standards related to data privacy and AI systems.
Testing different generative AI engines that can propagate changes from GitHub repositories to other applications requires thorough planning and systematic steps.
Below are the steps you can follow:
Identify Your Goals and Objectives: Clearly identify what you want to achieve with the AI engines. Which type of changes are you interested in and what other applications should these changes be propagated to?
Research AI Engines: Research the available generative AI engines that are capable of the task at hand. Understand their functionality, strengths, weaknesses, and requirements. Some potential engines you might consider are GPT-3, GPT-4, BERT, or T5 from OpenAI and Google, respectively.
Plan Your Project Structure: Before starting any coding, plan out your project. This includes planning the architecture of your project, identifying the necessary components, and deciding on the programming languages and tools you'll use.
Set Up a GitHub Repository: Create a new GitHub repository for your project. This will be the place where you will be making changes that need to be propagated to other applications. Make sure to understand and configure the repository's settings to fit your project's needs.
Set Up Your Development Environment: Install necessary tools, libraries, and dependencies needed for the project. This could include Python, TensorFlow, PyTorch, and specific libraries for the AI models you plan to use. Ensure you have access to the APIs of the AI engines you'll be testing.
Code Integration with GitHub: Create a script that can monitor changes in your GitHub repository. This can be done by using GitHub's Webhooks or GitHub API. The script should be able to detect changes, categorize them (e.g., commits, pull requests), and parse necessary information.
AI Model Training: Depending on your chosen AI engines, you might need to train your models to interpret the changes made in GitHub and generate appropriate responses. Consider using a large dataset of example GitHub changes and their corresponding actions in other applications.
Propagate Changes to Other Applications: Develop scripts that can take the output of your AI engine and perform necessary actions on the target applications. This will heavily depend on the applications you are targeting and their respective APIs.
Testing: Set up testing procedures to ensure your system works as expected. This could be unit tests for individual components and integration tests for the system as a whole.
Debug and Refine Your System: Based on your test results, debug and refine your system to improve performance and accuracy.
Documentation: Document your project properly, including the setup process, usage, results, and any potential issues or limitations. This will help others understand and potentially contribute to your project.
Monitor and Update Your Project: Keep monitoring the performance of your project, especially in response to changes in the GitHub repo. Regularly update your AI models and scripts as necessary to adapt to any changes or improvements in the AI engines or APIs you're using.
Remember to consider ethical implications and privacy issues while working on this project, as you'll potentially be dealing with data that could be sensitive. Make sure to comply with all the necessary regulations and guidelines.