Building the First SubNet

The development of the first SubNet within the InfiniRoute ecosystem represents a significant step towards creating a robust and competitive environment for AI models. These SubNet will leverage existing, industry-recognised AI benchmarking systems to evaluate and identify the best-performing specialised models across various domains. By incorporating established benchmarks, SubNets ensure that model selection is based on rigorous, widely-accepted performance criteria, fostering trust and reliability in the InfiniRoute marketplace.

Benchmarking Systems

The initial SubNet will be built using the following industry-standard AI benchmarking systems:

1. SuperGLUE:

   • Focus: An improvement over GLUE, designed for more challenging language understanding tasks.

   • Application: Ideal for testing advanced natural language processing models.

2. ImageNet:

   • Focus: Widely used for image classification tasks.

   • Application: Critical for evaluating models in image recognition and computer vision.

3. COCO (Common Objects in Context):

   • Focus: Benchmarks for object detection and segmentation.

   • Application: Used to assess models in advanced image processing and computer vision tasks.

4. MLPerf:

   • Focus: Covers a broad range of AI tasks, including vision, language, and recommendation systems.

   • Application: Provides a comprehensive evaluation across multiple AI domains, ensuring versatile model performance.

5. SQuAD (Stanford Question Answering Dataset):

   • Focus: Evaluates question-answering systems.

   • Application: Benchmark for models focused on understanding and generating precise answers from text.

6. MLBench:

   • Focus: Evaluates the performance and scalability of machine learning algorithms on large-scale distributed systems.

   • Application: Ensures that models are not only accurate but also scalable and efficient.

7. LMSys (Language Model Systems):

   • Focus: Benchmarks large language models and systems, assessing their performance in tasks such as text generation, question answering, and dialogue systems.

   • Application: Provides a comprehensive evaluation of the latest language models in diverse applications.

Implementation Strategy

1. Integration of Benchmarks:

   • Process: Each SubNet will be configured to utilise the appropriate benchmarks relevant to its domain. This integration will ensure that all models are evaluated based on the most stringent and relevant criteria.

   • Tools: Utilising existing datasets and benchmark suites, the SubNets will continuously update and refine their evaluation processes to maintain accuracy and relevance.

2. Evaluation and Competition Dynamics:

   • Mechanism: Models within each SubNet will be subjected to regular evaluations using the integrated benchmarks. Performance metrics will be transparently recorded and compared, fostering a competitive environment.

   • Outcome: The competitive dynamics will drive continuous improvement, ensuring that only the best-performing models are recommended for user applications.

3. Transparency and Trust:

   • Approach: Detailed benchmarking results and model performance metrics will be made available to users. This transparency will help users make informed decisions and build trust in the InfiniRoute marketplace.

   • Security: By extending the Actively Validated Service (AVS), economic security around the evaluation of these models will be reinforced, ensuring the integrity and reliability of the benchmarking process.