@artesia please summarise the linked article
The Forbes article “Transformers Revolutionized AI. What Will Replace Them?” discusses the significant impact of transformer architecture in AI since its introduction in the 2017 paper “Attention Is All You Need.” Transformers have become the gold standard in AI, powering models like ChatGPT and advancements in various fields, including computer vision and robotics.
Despite their dominance, transformers come with drawbacks, particularly their high computational costs and difficulties processing long sequences. As a result, researchers are exploring alternatives that might replace or improve upon transformers, focusing on architectures that scale more efficiently and can handle extensive data sequences better.
Key emerging technologies include:
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Hyena Architecture: Utilizes long convolutions and element-wise multiplication for improved efficiency and performance, especially with long sequences.
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Liquid Neural Networks: Inspired by biological systems, these models adapt their weights based on input, offering a smaller and more transparent alternative to transformers.
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Sakana AI: Founded by a transformer co-inventor, it aims to develop nature-inspired AI based on collective intelligence principles.
Although promising, these new approaches remain largely unproven, and the future of AI could see either continued dominance of transformers or a diversification into specialized architectures suitable for specific tasks. Overall, the field is dynamic, with potential for rapid evolution.