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The Future of AI: The Transformer and How We Got There
- AI is being integrated into every sector possible, powered by the chatbot- Transformer, which has been around since 2017, and broke the field at the time. The biggest implication of the technology has to do with the field of Natural Language Processing(NLP).
- Recurrent Neural Networks (RNNs) introduced in 1985, is a type of neural network that processes sequential data by storing information across time-steps.
- Long-Short Term Memory networks (LSTM) is a type of RNN that was specifically made to solve the vanishing gradient problem. LSTMs are computationally more intensive and sequential in nature which can hinder their performance.
- The transformer architecture introduced a mechanism known as self-attention that relates different positions of a sequence to compute a representation of that sequence.
- The transformer architecture is the first of its kind to utilize parallelization when analyzing sequential data, allowing it to process huge corpora of text quickly and efficiently.
- Transformers have limitations and complexities such as expense and concerns over excessive carbon footprints.
- By being able to “see” all the data at once, the Transformer architecture allows human-computer interaction in a way never seen before.
- This groundbreaking discovery has accelerated the machine learning field, transforming the field of Natural Language Processing and allowing for human-computer interaction in a way never seen before.
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