In this video, Andrea Volpini, CEO of Wordlift, dives deep into the world of Transformers and how they are changing Natural Language Processing (NLP). Introduced in 2017 by a team at Google Brain, Transformers have become the model of choice for NLP problems, replacing Recurrent Neural Networks (RNN) models like long short-term memory (LSTM). Pre-trained systems like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) have been trained with large language datasets and can be fine-tuned for specific tasks.
The heart of the Transformer architecture is the Attention Mechanism and the new concept called multi-headed Attention. Using this new architecture, the models can be trained using data and then used as a kind of 'generator' to generate new information. Transformers are designed to take any sequential data and turn them into something else. For example, you could use a transformer model to analyze your Twitter feed and see if there were any patterns in how often you tweet about specific topics.
Anomaly detection is often applied to security systems but could also monitor factory processes or other business activities. Transformer technology makes it easy to distinguish anomalies from normal behavior. As Andrea explains, this revolutionary milestone is changing how we think about Natural Language Processing and enabling us to achieve more accurate and efficient results.
