Charting a New Course of Neural Networks with Transformers - RTInsights

A "transformer model" is a neural network architecture consisting of transformer layers capable of modeling long-range sequential dependencies that are suited to leveraging modern computing hardware to reduce the time to train models. State-of-the-art machine learning and artificial intelligence (AI) systems have achieved significant technological advancements in recent years alongside the technology's growing interest and widespread demand. We've seen the general hype around AI fluctuate with media cycles and new product developments, with the buzz of implementing AI for the sake of implementing it wearing off as companies strive to demonstrate its positive impact on business--emphasizing AI's ability to augment, not replace. Emerging now is the concept of transformer-based models. There is speculation surrounding whether transformers, which have gained considerable traction in natural language processing (NLP), will be positioned to "take over" AI, leaving many to wonder what this approach can achieve and how it could transform the pace and direction of technology.