2023-02-21-IDC-2022中国大模型发展白皮书⸺元能力引擎筑基智能底座_39页_6mb

2022 China Large Model Development White Paper: Ability Engine for Building AI Infrastructure

1. Technical and Market Highlights

1.1 Key Challenges & Drivers

• Multi-dimensional bottlenecks：
  Data: China's data scale will reach 56.16ZB by 2026 (CAGR 24.9%). Challenges include high-dimensional, multi-modal data and data silos.
  Algorithm: Language ambiguity, facial recognition bias, and lack of explainability in medical imaging.
  Compute: Distributed training requires GPUs/FPGs/ASICs, with training resource costs hinder COGS.
• AI development shifts: Trend from task-specific algorithms to generic large models, reduced training costs through transfer learning and prompt engineering.

1.2 New AI Development Paradigm

• Large models overcome limitations of traditional ML approaches by:
  Raw data: Extensive self-supervised learning on massive unlabeled data (e.g., BERT, GPT, AlphaFold)
  Performance: Improved accuracy across multiple dimensions (e.g., GLUE scores > 80%, medical imaging)
  Versatility: Capabilities spanning natural language processing, computer vision, 3D/generative AI.

1.3 Reduction of AI Development Threshold

• “Pre-train + fine-tuning” paradigm allows downstream tasks to be solved with minimal task-specific data.
• Deep learning platforms (DLPs) provide standardized tools for:
  Infrastructure: Hardware acceleration, auto-parallel training, under-the-hill optimization
  Engineering: Pipeline orchestration, model compression, interface/toolkits
• Benefits:
  Reduced sample dependency
  Standardized development process
  *Lowered threshold/efficiency for enterprise users.

2. Other AI Trends

2.1 Next-Generation Model Development

• Monolayer models for speech processing
• Small-scale models
• Multimodal models: CLIP/DALL-E advances text-image fusion, multi-modal understanding
• Scientific computing: Bioinformatics, protein folding (AlphaFold mimic)

2.2 Role of Deep Learning Platforms

• Full-stack features:
  Architecture layer: Open-source models/APIs, processors for multi-task efficiency
  Application layer: Development tools, one-stop deployment, developer ecosystem
  Infrastructure layer: Support for distributed training, multi-engine compatibility, deployment acceleration.

2.3 Natural Language Processing（NLP）

• Leadership: China is home to multiple top-tier models like ERNIE, Transformer-based systems.
• Performance: Significant improvements across tasks like literature summarization, medical QA, machine reading.

3. Conclusion/Guidance

• Industry guidance:
  Accelerate adoption of large models now to secure competitive advantage.
  Consider security/ethics when implementing these systems.
• Electronic ecosystem development recommendations:
  Infrastructure: Focus on inter-engine compatibility and scaling capabilities.
  New models: Investigate multi-dimensional control for outputs, fine-tuning mechanisms for sustainable performance.
  Business/medical applications: Improve user-friendliness, increase intelligence depth, enhance loyalty.

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