AI Race Heats Up 🤖🤯: US vs. China?

AI

🎧

AI’s Unexpected Alliance: US-China Collaboration Fuels Innovation
Despite fierce competition, the United States and China are deeply intertwined in the world of artificial intelligence, with companies vying for dominance in algorithms, models, and specialized silicon. A recent analysis by WIRED, examining over 5,290 AI research papers presented at the Neural Information Processing Systems (NeurIPS) conference last month, revealed a significant degree of collaboration between the two nations’ research labs, particularly concerning cutting-edge research.

A Surprising 3% Collaboration Rate Revealed
The WIRED analysis identified 141 of the 5,290 total papers – approximately 3 percent – as involving co-authorship between institutions affiliated with the United States and China. This surprising figure highlights the extent to which the two countries’ AI research communities are interconnected. The analysis underscores that even amidst competitive pressures, substantial knowledge sharing is occurring.

Transformer Architecture: A Bridge Between East and West
Notably, the transformer architecture, initially developed by researchers at Google and now prevalent in the industry, featured in 292 papers with authors from Chinese institutions. This demonstrates how global innovations are rapidly disseminated and adapted across different research groups, regardless of geopolitical tensions.

Llama and Qwen: Models Shared Across Borders
Meta’s Llama family of models was a key component of 106 research papers, while the increasingly popular large language model Qwen, from Chinese tech giant Alibaba, appeared in 63 papers with authors from US organizations. This exchange of models showcases the practical application of collaborative research.

Decoding Collaboration: Automating the Research Process
To analyze NeurIPS papers, I utilized Codex, OpenAI’s code-writing model. Specifically, I created a script to download all the papers, then used the model to systematically analyze each one. This involved having Codex generate a script to search for US and Chinese institutions within the author field of each paper, providing a fascinating glimpse into the potential for coding models to automate research tasks.

Trial and Error: Ensuring Accuracy in AI-Driven Analysis
I initially began by writing scripts and having Codex modify them, ultimately transitioning to simply requesting the model perform the analysis itself. This process entailed Codex importing Python libraries, testing various tools, and generating scripts before producing reports for my review. It was a fairly iterative process, involving considerable trial and error, and required a cautious approach due to the surprisingly frequent errors made by AI models despite their apparent intelligence. To ensure accuracy, I meticulously incorporated a mechanism for reviewing the results into each report, and I manually checked as many reports as possible.

This article is AI-synthesized from public sources and may not reflect original reporting.