KnowledgeFace AI Platform Prototype

Summary: Documentation for the “KnowledgeFace” (Scholar AI) web application prototype, designed as a comprehensive learning, contribution, and community platform for AI/ML.

Resource: KnowledgeFace Live Prototype GCP Console Overview

1. Prototype Overview

KnowledgeFace is structured around three core pillars: Learning, Contribution, and Community. The interface is designed for intuitive navigation and seamless access to AI research resources, inspired by platforms like Kaggle.

2. Platform Structure & Navigation

A. Public View

  • Registration: Allows signing up via email/password, Google OAuth, or GitHub OAuth.
  • Login: Directs authenticated users to the personalized “Scholar” dashboard.

B. User Dashboard (“Scholar” View)

A prominent Create button provides immediate access to contribution workflows:

  1. Notebook: Create a new interactive code environment.
  2. Import Notebook: Upload an existing Jupyter/Colab notebook.
  3. Dataset: Upload a new data resource.
  4. Model: Link or upload a pre-trained ML model.
  5. Benchmark: Define a new task for model evaluation.
  6. Benchmark Task: Submit a model’s performance against an existing benchmark.

C. Persistent Navigation Panel

  • Home: Personalized feed and suggested content.
  • Datasets: Browse and search public/private data.
  • Models: Explore pre-trained models.
  • Benchmarks: Leaderboards and evaluation metrics.
  • Code: Access user-created notebooks.
  • Discussions: Community Q&A forums.
  • Learn - Research: Dedicated educational hub.
  • More: Access sub-menus for Progression, Documentation, Blog, and Educator Resources.

3. Learn - Research Filters

The “Learn - Research” page uses a horizontal navigation bar to filter content formats:

  • Math Concept: Theoretical explanations of core mathematical principles (e.g., Linear Algebra).
  • Notes: Study notes and reading summaries.
  • Paper: Direct access to external research papers (e.g., Attention Is All You Need).
  • Code: Associated implementation code or notebooks.
  • Contribute (GitHub): Guidelines on submitting pull requests.
  • Research: Summaries of current trends (e.g., Quantum ML).
  • Applications: Real-world case studies (e.g., Geospatial Reasoning).

4. Platform Contribution Guidelines

  • Datasets: Must include a description, dataset license, and a detailed data dictionary.
  • Notebooks: Must be fully executable and document step-by-step with clear markdown.
  • Models: Must provide validation metrics, an inference script, and an appropriate license.
  • Benchmarks: Must define measurable target metrics and provide a public validation dataset.

5. Computing Quota (Free Tier)

  • CPU: 10 hours of computation per week.
  • GPU: 5 hours of compute per month.
  • Monitoring: Real-time quota metrics are available via the profile menu.

6. Development Roadmap

Phase Description Status / Target
Phase 1: MVP Registration, Login, and basic Notebook execution Done
Phase 2: Data & Models Complete CRUD operations for Datasets and Models Planned
Phase 3: Community & Learning Launch Discussions forum and populate initial learning entries Planned
Phase 4: Benchmarks Deploy leaderboards and first benchmark evaluation task Planned