Enterprise Trends for Generative AI

Key Trends in Generative AI

• Machine learning advancements redefine computational capabilities
• Evolving computation and hardware requirements
• Scaling (compute, data, model size) improves results

Progress in AI Capabilities

• Image Recognition
  • Example: “Leopard” classification, 90.88% accuracy (ImageNet)
  • AlexNet initial performance: 63.3%
• Speech Recognition
  • Improved performance on LibriSpeech test-other dataset

Transformers and Foundation Models

• Key techniques
  • Autoregressive training
  • Pre-training with trillions of tokens
  • Example: "The cat sat on the mat"
• Optimization
  • Supervised Fine-Tuning (SFT)
  • Reinforcement Learning from Human Feedback (RLHF)

Gemini Models

• Project started February 2023
• Gemini 1.0 release: December 2023
• Gemini 1.5 release: February 2024
• Features
  • Multimodal reasoning across text, image, and video
  • Long context capabilities (up to 10M tokens)
  • Reduced hallucination rates

Enterprise AI Trends

1. Accelerating AI development as data requirements decrease
2. Transition from single modality to multimodal systems
3. Shift from dense to sparse model architectures
4. Importance of scalable and flexible platforms
5. Declining API costs
6. Integration of LLMs and search

Customization and Efficiency

• Techniques
  • Fine-tuning and parameter-efficient tuning (e.g., LoRA)
  • Distillation for performance and latency optimization
• Challenges
  • Balancing cost, latency, and performance in deployment
• Function Calling
  • Integrates APIs, databases, and external systems
  • Applications: data retrieval, workflows, customer support

Addressing Limitations

• Issues
  • Frozen training data causing outdated knowledge
  • High hallucination rates
  • Inconsistent structured outputs
• Solutions
  • Retrieval-Augment-Generation (RAG) frameworks
  • Grounding in private, fresh, and authoritative data
  • Structured outputs with citations

Future of Generative AI

• Enhanced multimodal reasoning and extended context capabilities
• Optimization to reduce costs and improve scalability
• Improved grounding and factual accuracy in outputs