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There are many pain points that warrant discussions between the two nations but AI could be the thing that brings them to the table.


Relations between the United States and China have been downward recently. Topics like artificial intelligence (AI) and such technology in automated weapons could be common points of interest to get the two countries talking again.

Tensions between the two nations have been on the rise for a host of issues. Recently, the origins of the COVID-19 pandemic, China’s burgeoning presence in the South China Sea, and the supply of powerful chips in the technology space have been areas of disagreement on both sides.

The lack of dialogue on such issues has led to tensions between the two countries, and the imposition of restrictions on both sides has further strained relations. While addressing these points is expected to be a long-drawn affair for the two countries, common areas like AI could offer a starting point, experts told the South China Morning Post (SCMP).

Generative AI has emerged as the next wave of innovation amidst the ongoing evolution of the technological landscape, attracting the attention of both researchers and investors.


Even as vector databases and Retrieval-Augmented Generation models become mainstream, offering innovative ways to handle and process data, traditional ETL processes retain their importance in the data management ecosystem. Traditional ETL is fundamental for preparing and structuring data from diverse sources into a coherent, standardized format, making it accessible and usable for various applications. This structured data is crucial for maintaining the accuracy and reliability of information within vector databases, which excel at handling similarity searches and complex queries by converting data into vector space.

Similarly, RAG models, which leverage vast databases to augment content generation with relevant information retrieval, depend on well-organized, high-quality data to enhance their output’s relevance and accuracy. By ensuring data is accurately extracted, cleaned and loaded into databases, traditional ETL processes complement the capabilities of vector databases and RAG models, providing a solid foundation of quality data that enhances their performance and utility. This symbiotic relationship underscores the continuing value of traditional ETL in the age of AI-driven data management, ensuring that advancements in data processing technologies are grounded in reliable and well-structured data sources.

The rise of generative AI has indeed shifted the technological focus, overshadowing some of the core technologies that have been instrumental in our digital progress.

There’s an episode of the show “Black Mirror” where a woman, trapped by grief, starts a relationship with an AI trained on her dead boyfriend’s data.

“You’re not enough of him,” she eventually decides. “You’re nothing.”

But even an empty happily-ever-after is tantalizing in the bleakness of 2024. AI platforms like ChatGPT claim to offer infinite solutions to infinite problems, from parking tickets to homework — and apparently now heartbreak as well. That’s right: if you’re still hung up after a breakup, now you can plug your ex’s emails and texts into a large language model, and date the simulacrum instead of moving on.