Sumary: The GUT-AI initiative is subdivived into a series of components in order to build a user-friendly Open-Data, Open-Source, Decentralized ecosystem under the umbrella of the GUT-AI Foundation. This is a list of all the components of such an ecosystem.
It i important to note that each component definition intentionally does not include how to be implemented, but only what to be implemented. The reason is that there should be no constraints or limits on the 'how' since new advances in Technology can potentially bring new opportunities to improve the 'how' a specific component is implemented. The 'why' each component is necessary is explained above and also in the Vision and Mission of GUT-AI.
- Layer 1 components
- Layer 2 components
- Layer 3 components
- Component C3.1: Automated Scientific Discovery
- Component C3.2: MTSU
- Component C3.3: Grounded CV
- Component C3.4: ASR
- Component C3.5: TTS
- Component C3.6: SER
- Component C3.7: MT
- Component C3.8: TOD
- Component C3.9: QA
- Component C3.10: Grounded QA
- Component C3.11: VSPT
- Component C3.12: Multi-Robot Path Planning
- Component C3.13: Multi-Robot Target Detection and Tracking
- Component C3.14: Portfolio Management
- Component C3.15: Anomaly Detection
- Component C3.16: Recommender Engines
- Layer 4 components
Description: Create Distributed Smart Grids for the energy storage needs of the Blockchain and AI era.
Aims:
- Smart Grids
- Distributed Renewables (e.g. PV)
- Decentralized Electricity and Energy Storage (e.g. batteries)
- Distributed Computer Network for Communication
- Support for GUT-AI DCP and other decentralized cloud providers
- Support for interoperable electric vehicles
- Support for conventional (dieasel and petrol) vehicles
- Support for Near-Zero Energy Buildings (NZEBs)
Description: Create a dedicated Decentralized Cloud Proivder (DCP) related to GUT-AI for the information storage needs.
Aims:
- Hosting
- Databases (SQL and NoSQL)
- Data Warehouses
- Data Lakes
- Anything else that a conventional, centralized Cloud Provider can offer
Description: Create a dedicated Marketplace for products (data, software apps) and services (Contractors and Freelancers) related to GUT-AI. Each digital product will be a module.
Aims:
- Open Data (e.g. datasets, pre-trained models) as modules
- Proprietary Data (e.g. datasets, pre-trained models) as modules
- Centralized and decentralized SaaS modules developed by third parties
- Centralized and decentralized PaaS modules developed by third parties
- Centralized and decentralized IaaS modules developed by third parties
- Marketplace for marketplaces by third parties for physical products (e.g. computers, servers, robots)
- Contractors and Freelancers (e.g. Data Scientists, Data Engineers, Machine Learning Engineers, Blockchain Developers)
Description: Perform Automated Data Preparation using AI.
Aims:
- Data Collection
- Data Synthesis / Data Simulation / Adversarial Learning
- Data Fusion and Data Integration
- Data Wrangling / Data Munging
- Data Scraping
- Data Sampling
- Data Cleaning
Description: Perform Continuous Integreation/Continuous Delivery (CI/CD) for all ML systems and also all associated systems. Also use AI to improve CI/CD (AIOps).
Aims:
- Reproducibility
- Replicability
- Code Version Control
- Data Version Control (for both datasets and pretrained models)
- Automatic Configurations (with default, but adjustable values)
- Machine Resource Management
- Governance and Regulatory Compliance (e.g. GDPR, HIPAA, ISOs)
- Monitoring and Reporting
- Diagnostics
- Testing and Quality Assurance (for both code and data)
- User of containers (e.g. Docker)
- User of orchestration (e.g. Kubernetes)
- Use of microservices
- Support for Asynchronous Communication (e.g. ActiveMQ, RabbitMQ, Apache Kafka)
- Support for Synchronous Communication (e.g. REST, GraphQL)
- Support for Databases (SQL and NoSQL), Data Warehouses and Data Lakes
- Support for Data Workflow Management (e.g. Airflow, Kubeflow, MLflow)
- Support for Model Serving (e.g. KFServing, Seldon Core, BentoML)
- Direct integration to Top 10 centralized IaaS cloud providers
- Direct integration to Top 10 decentralized IaaS cloud providers
- Direct integration to GUT-AI Marketplace and other marketplaces
- Webhooks and API for direct integration to IaaS, PaaS, SaaS providers
- Automation, MLOps, DataOps, MoodelOps, DevOps
- Information Security, SecDevOps, DevSecOps
- Anything else reducing the technical debt
Description: Enhance Developer Experience (DX) to make it developer-friendly for almost anyone who can write code at any level.
Aims:
- Separation of concerns
- User-friendly User Interface (UI) and Dashboards
- User-friendly configurations (e.g. using
yamlandjson) - Anything else reduing the cultural debt or improving the DX
Description: Perform Automated Data Science (AutoDS) by combining (internal or external) modules together in an adjustable way.
Aims:
Description: Perform Automated Machine Learning (AutoML).
Aims:
Description: Perform Automated Data Preprocessing.
Aims:
- Automated Feature Selection
- Automated Feature Extraction
- Rule-based AI
- Representation Learning (Supervised, Unsupervised, Self-Supervised)
- Data Augmentation / Contrastive Learning
- Feature Construction / Generative Learning
- Adversarial Learning
Description: Perform Neural Architecture Search (NAS).
Aims:
- Automated Model Selection
- Search space
- Architecture Optimization
- Hyperparameter Optimization
- Automated Model Estimation
Description: Perform Continual Learning.
Aims:
- Automated Model Retraining
Description: Introduce and perform Distributed Systems that are model-specific for ML and especially for Gradient-Based Optimization methods.
Aims:
- Support for generic Distributed Systems (e.g. Horovod, DeepSpeed)
- Devise new ML-specific architectures (similar to Petuum V2)
Description: Solve the issue of memory bottleneck in order to enable the Inference of Deep Learning models in embedded devices.
Aims:
- Model Compression and Weight Sharing
- Nodes Pruning and Weight Pruning
- Quantized Training
- Huffman Coding
- Representation disentanglement on the sparse weight matrix
- Structured Sparsity Learning (StSL)
- Soft-Weight Sharing
- Variational Dropout
- Structured Bayesian Pruning
- Bayesian Compression
- Lottery Ticket Hypothesis
- NAS
- Start with no connections, and add complexity as needed (e.g. Weighted Agnostic Neural Networks)
- Bayesian Neural Networks (BNNs)
Description: Perform Automated Scientific Discovery.
Aims: TODO
Description: Perform Multitask Scence Understanding (MTSU) by applying Multitak Learning on Computer Visions tasks from a still and immobile camera.
Aims:
- Object Detection
- Object Recognition
- Face Recognition
- Image Segmentation (Semantic and Instance)
- Image Captioning and Image Categorization
- Visual Relationship Detection
- Action Classification
- Activity Recognition
- Pose Estimation
- Super-Resolution
- Denoising
- Image Acquisition and Reconstruction
- Image Restoration
- Image Generation
- Image Registration
- Domain Adaptation
- Multi-Object Motion Detection and Tracking
- Vision-Based Motion Analysis
Description: Perform Grounded Computer Vision (Grounded CV) by applying Grounded Cognition on Computer Visions tasks from a single mobile robot or a single aerial robot (drone).
Aims:
- Simultaneous Localization and Mapping (SLAM).
- 3D Scene Reconstruction
- Surface Reconstruction
- Structure from Motion
- Feature Matching
- Active Tracking
Description: Perform Automatic Speech Recognition (ASR).
Aims: TODO
Description: Perform Text-to-Speech (TTS).
Aims: TODO
Description: Perform Speech Emotion Recognition (SER).
Aims: TODO
Description: Perform Machine Translation (MT).
Aims: TODO
Description: Perform Task-Oriented Dialog (TOD).
Aims: TODO
Description: Perform open-domain Question-Answering (QA).
Aims: TODO
Description: Perform Grounded Question-Answering (Grounded QA).
Aims: TODO
Description: Perform Visuo-spatial Perpsective-Taking (VSPT).
Aims: TODO
Description: Perform Multi-Robot Path Planning.
Aims: TODO
Description: Perform Multi-Robot Target Detection and Tracking.
Aims: TODO
Description: Perform Anomaly Detection.
Aims: TODO
Description: Implement Recommender Engines.
Aims: TODO
Description: Perform Automated Protoyping.
Aims:
- Ideation and Creation
Description: Perform Automated User Experience (Automated UX) during Product Discovery and Product Development.
Aims:
- Automated User Research
- Automated User Validation
- Automated UX Research
Description: Perform Automated Marketing.
Aims: TODO
Description: Perform Automated Sales.
Aims: TODO
Description: Perform Customer Support.
Aims: TODO
Description: Perform Automated Governance and Compliance for the Blockchain and AI era.
Aims: TODO
Description: Perform Portfolio Management for the Blockchain and AI era.
Aims: TODO