In 2024, the field of large AI models continued to develop rapidly, bringing forth many important trends and breakthroughs. Here are the top ten keywords for large AI models in 2024: competition between open source and closed source, industry concentration and polarization, innovation in multimodal and intelligent agents, architecture optimization and energy efficiency bottlenecks, exploration of artificial general intelligence (AGI), AI ethics and explainability, financing and mergers and acquisitions, cross-industry integration (AI+X), and self-supervised learning.
1. The Battle between Open Source and Closed Source: Open source large models and closed source large models are in fierce competition, forming profound differences in business models and ecosystem construction, and promoting diversified technological development.
2. Emergence of Industry Polarization: Leading enterprises receive more resource allocation, and star enterprises cluster. The “hundred-model war” subsides, exacerbating industry polarization.
3. The Trend of Multimodal AI and Agents: Multimodal AI and intelligent agents lead product innovation, enhancing user experience and expanding application scenarios.
4. Architecture Optimization and Generalization of Scaling Law: Innovation in large models is reflected in architecture optimization and large-scale development. The Scaling Law promotes research on the relationship between model scale and performance, enhancing reasoning ability.
5. Exploration of AGI and Spatial Intelligence: The exploration of AGI is a long-term goal. The progress of video generation technology ignites the development boom of world models. Spatial intelligence unifies virtual and reality, promoting the evolution of intelligent systems.
6. Energy Efficiency Bottleneck of Large AI Models: Energy consumption is a bottleneck in training large models. Enterprises focus on energy efficiency optimization and adopt efficient algorithms and hardware to reduce energy consumption.
7. Explainability and AI Ethics: Model transparency and ethical considerations ensure the responsible development of AI. Improving explainability enhances user trust.
8. Financing and Mergers and Acquisitions: The investment boom in the field of large models continues to heat up. The Matthew effect of investment and financing and national support drive the development of the AI ecosystem, and merger and acquisition activities intensify.
9. Growth of AI Applications and Empowerment of AI+X: Cross-industry integration promotes the rapid growth of AI applications, emphasizing the importance of cross-industry cooperation and customized solutions.
10. Self-Supervised Learning and Data-Driven Innovation: Self-supervised learning has become a key technology to improve the performance of large models, reducing dependence on labeled data and accelerating training efficiency. In 2024, the development of large model technology brings both opportunities and challenges.
