在今年的全球图形技术大会(GTC)上,一场别开生面的圆桌论坛吸引了业界的目光。英伟达创始人黄仁勋亲自上阵,邀请了Transformer论文的六位核心作者共聚一堂,遗憾的是,Niki Parmar因故未能出席。这场论坛是Transformer之父们首次集体公开对话,他们对于Transformer的未来和人工智能的发展提出了深刻见解。
在对话中,作者们表达了对于Transformer改进的期待,他们认为当前的技术仍有待突破,期望能有新的模型超越Transformer,带领AI技术攀登新的性能高峰。他们坦诚,Transformer的初衷是模拟Token的动态演化,而非简单的线性生成,这一目标至今尚未完全实现。
讨论中还涉及到了模型效率的问题。面对大模型在解决简单问题时可能过度消耗资源的现象,作者们提出了自适应计算的概念,即根据问题的复杂度动态调整计算资源,以实现更高效的运算。
此外,他们还对当前模型的经济性和规模进行了反思,认为尽管模型的成本已经相当低廉,但相比传统信息获取方式,如购买书籍,模型的性价比仍有巨大的提升空间。他们指出,目前模型的价格大约是1美元百万Token,这比买一本平装书便宜了100倍,暗示未来模型的规模化和成本优化将是重要趋势。
这场论坛不仅揭示了Transformer背后的研发故事,也为AI领域的未来发展方向提供了宝贵的思考。随着技术的不断演进,人工智能的潜力和挑战并存,我们期待更多创新的火花在这样的对话中碰撞而出。
英语如下:
News Title: “Transformer Pioneers Gather at GTC, Unveiling a New AI Vision: The Quest Beyond Transformers”
Keywords: Transformer creators, roundtable discussion, large model evolution
News Content: At this year’s Global Graphics Technology Conference (GTC), a unique roundtable discussion captured the industry’s attention. NVIDIA founder Jensen Huang took the stage, hosting a reunion of the six core authors of the Transformer paper, with the exception of Niki Parmar, who was unable to attend due to unforeseen circumstances. This marked the first public collective dialogue of the Transformer fathers, who shared profound insights into the future of Transformers and artificial intelligence.
During the conversation, the authors expressed their anticipation for Transformer enhancements, acknowledging that current technology still has room for breakthroughs. They hoped for new models to surpass Transformers, propelling AI performance to new heights. They explained that the original intention of Transformers was to emulate the dynamic evolution of tokens, a goal that has yet to be fully realized.
The discussion also touched on model efficiency. Acknowledging the potential overconsumption of resources by large models when solving simple problems, the authors introduced the concept of adaptive computation, adjusting computational resources dynamically based on the complexity of the task to achieve more efficient operations.
Moreover, they reflected on the economics and scale of current models. Despite their relatively low cost, they pointed out that compared to traditional information sources like purchasing books, models still offer significant room for improving cost-effectiveness. They mentioned that models currently cost around 1 USD per million tokens, a fraction of the price of a paperback book, suggesting that scalability and cost optimization of models will be crucial trends in the future.
This forum not only shed light on the development stories behind Transformers but also provided valuable food for thought on the future direction of the AI field. With technology continually evolving, AI holds both potential and challenges, and we look forward to more innovative sparks emerging from such dialogues.
【来源】https://new.qq.com/rain/a/20240321A00W5H00
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