When discussing the support and propulsion of progress in artificial intelligence (AI), language modeling languages (LLMs) indeed play an essential role. Models such as OpenAI’s GPT-3 and GPT-4 are fundamental in the development of AI technologies, providing impressive capabilities in text generation, natural language understanding, and more. However, to maintain and accelerate the current pace of innovation in AI, other critical components are also necessary:

1. Innovative Hardware: Advances in hardware, such as the development of faster and more energy-efficient processors, are crucial. Specialized processors, such as GPUs and TPUs (Tensor Processing Units), enable the acceleration of training for large AI models. Emerging technologies in quantum and neuromorphic hardware can also provide significant leaps in computational performance.
2. High-Quality Data and Datasets: AI models, especially those in machine learning and deep learning, require massive amounts of data for training. Access to diverse, extensive, and accurately labeled datasets is essential for the development of efficient algorithms and for minimizing bias.
3. Research and Collaboration: Ongoing innovation in AI theory and learning algorithms is vital. Collaborations between universities, research institutes, and industry can accelerate the development of new technologies and AI applications. It is also important to foster an open and inclusive global community in AI research.
4. Regulations and Ethics: As AI becomes more integrated into everyday life, ethical regulations become imperative to ensure responsible use of the technology. These may include standards for transparency, privacy, and safety, as well as directives to prevent discriminatory or unfair use of AI.
5. Education and Resources: Education in the field of AI must be accessible and diverse to prepare future generations of researchers, developers, and users of AI. Investments in educational resources and training programs can help demystify AI and cultivate a framework of understanding and ethical use of this technology.
6. Interdisciplinarity: Integrating knowledge from fields such as psychology, neuroscience, philosophy, and social sciences can provide new perspectives and methods in AI development, contributing to the creation of more robust and adaptable systems to the complexity of the real world.

In conclusion, although LLMs are powerful tools, supporting and accelerating innovation in AI requires a holistic approach that includes advanced technology, extensive collaboration, ethical regulations, educational resources, and an interdisciplinary perspective. These elements combine to continually improve AI capabilities and ensure its responsible and sustainable development.

(Article generated and adapted by CorpQuants with ChatGPT)