5 New Trends in Generative AI Highlighting Web3 Opportunities
Generative AI is rapidly evolving, necessitating a shift in focus for Web3 to integrate effectively into this landscape. Key developments in 2024 have transitioned the Web3-AI narrative from speculation to potential utility.
The following trends are shaping AI and presenting opportunities for Web3:
1. Reasoning Capabilities
Recent large language models (LLMs) prioritize reasoning, allowing complex inference breakdowns. This creates a need for traceability that Web3 can fulfill by verifying AI-generated content on-chain.
2. Synthetic Data Training
Synthetic data enhances model training efficiency. A decentralized Web3 framework could incentivize nodes to generate and share synthetic data, fostering a collaborative AI data economy.
3. Post-Training Workflows
Models are shifting from extensive pretraining to mid and post-training processes, enabling distributed computing across decentralized networks. This democratizes AI development through Web3 contributions.
4. Distilled Small Models
Smaller, distilled AI models are increasingly viable, suitable for decentralized inference networks. Web3 marketplaces could emerge, allowing participants to utilize their computing resources for executing these models.
5. Transparent AI Evaluations
Current evaluation methods for generative AI lack reliability. Blockchain-based solutions could introduce transparency in performance verification and foster community-driven standards.
The advancements in generative AI signal a potential shift towards decentralized architectures. The challenge remains for Web3 to adapt swiftly to capitalize on these emerging opportunities in the AI sector.