In addition, the math that transformers use lends itself to parallel processing, so these models can run fast. By finding patterns between elements mathematically, transformers eliminate that need, making available the trillions of images and petabytes of text data on the web and in corporate databases. No Labels, More Performanceīefore transformers arrived, users had to train neural networks with large, labeled datasets that were costly and time-consuming to produce. That’s a radical shift from a 2017 IEEE study that reported RNNs and CNNs were the most popular models for pattern recognition. Indeed, 70 percent of arXiv papers on AI posted in the last two years mention transformers. Transformers are in many cases replacing convolutional and recurrent neural networks (CNNs and RNNs), the most popular types of deep learning models just five years ago. “Transformers made self-supervised learning possible, and AI jumped to warp speed,” said NVIDIA founder and CEO Jensen Huang in his keynote address this week at GTC. Stanford researchers say transformers mark the next stage of AI’s development, what some call the era of transformer AI. Created with large datasets, transformers make accurate predictions that drive their wider use, generating more data that can be used to create even better models. That enables these models to ride a virtuous cycle in transformer AI. The Virtuous Cycle of Transformer AIĪny application using sequential text, image or video data is a candidate for transformer models. People use transformers every time they search on Google or Microsoft Bing. Transformers can detect trends and anomalies to prevent fraud, streamline manufacturing, make online recommendations or improve healthcare. Transformers, sometimes called foundation models, are already being used with many data sources for a host of applications. They’re helping researchers understand the chains of genes in DNA and amino acids in proteins in ways that can speed drug design. Transformers are translating text and speech in near real-time, opening meetings and classrooms to diverse and hearing-impaired attendees. The “sheer scale and scope of foundation models over the last few years have stretched our imagination of what is possible,” they wrote. Stanford researchers called transformers “foundation models” in an August 2021 paper because they see them driving a paradigm shift in AI. They’re driving a wave of advances in machine learning some have dubbed transformer AI. Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other.įirst described in a 2017 paper from Google, transformers are among the newest and one of the most powerful classes of models invented to date. So, What’s a Transformer Model?Ī transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. They’re not the shape-shifting toy robots on TV or the trash-can-sized tubs on telephone poles. If you want to ride the next big wave in AI, grab a transformer.
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