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Google Transformer AI algorithm

Updated: Jul 31, 2023

Introduction


The Transformer architecture is based on the attention mechanism, which allows the model to learn long-range dependencies between different parts of a sequence. This makes it well-suited for tasks such as machine translation, text summarization, and question-answering. This neural network architecture was used for natural language processing (NLP) tasks and was first introduced in a paper by Vaswani et al. (2017).


The architecture consists of an encoder and a decoder. The encoder takes a sequence of input tokens and produces a sequence of hidden states. The decoder then takes these hidden states and produces a sequence of output tokens.


The encoder is composed of a stack of self-attention layers. Each self-attention layer takes the hidden states from the previous layer and computes a weighted sum of them. The weights are learned during training and represent the importance of each hidden state for the current layer.


The decoder is also composed of a stack of self-attention layers. However, the decoder also has an attention layer that attends to the output of the encoder. This allows the decoder to take into account the context of the entire input sequence when generating the output sequence.


The Google Transformer AI algorithm:


Input: A sequence of input tokens, x1,x2,...,xn.

Output: A sequence of output tokens, y1,y2,...,yn.


1/. Initialize the encoder and decoder.


2/. For each layer in the encoder:


- For each token in the input sequence:

  • Compute the attention weights for the current token.

  • Attend to the other tokens in the input sequence, weighted by the attention weights.

  • Update the hidden state of the current token.

3/. For each layer in the decoder:


- For each token in the output sequence:

  • Compute the attention weights for the current token.

  • Attend to the output tokens that have already been generated, weighted by the attention weights.

  • Attend to the hidden states of the encoder, weighted by the attention weights.

  • Update the hidden state of the current token.

  • Generate the next output token.

4/. Output the sequence of output tokens.


The attention mechanism is a key component of the Transformer architecture. It allows the model to learn long-range dependencies between different parts of a sequence. The attention weights are computed using a function that takes into account the similarity between the current token and the other tokens in the sequence. The more similar two tokens are, the higher their attention weight will be.


The Transformer architecture has been shown to be very effective for NLP tasks including:

  • Machine translation: The Transformer has been shown to achieve state-of-the-art results on machine translation tasks. For example, the Transformer-based model Google Translate uses has been shown to outperform previous models by a significant margin.

  • Text summarization: The Transformer has also been shown to be effective for text summarization tasks. For example, the Transformer-based model BART has been shown to achieve state-of-the-art results on the CNN/Daily Mail dataset.

  • Question answering: The Transformer has also been shown to be effective for question answering tasks. For example, the Transformer-based model NQ has been shown to achieve state-of-the-art results on the SQuAD dataset.

The Transformer architecture has many directions for future work including:

  • Improving the efficiency of the Transformer architecture

  • Extending the Transformer architecture to other tasks

  • Developing new applications for the Transformer architecture

The Google Transformer AI algorithm is a powerful tool for NLP tasks. It has been shown to achieve state-of-the-art results on a variety of tasks, including machine translation, text summarization, and question answering. The Transformer architecture is still a relatively new research area, and there are many potential directions for future work.


Reference:

  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Kaiser, L. (2017). Attention is all you need. arXiv preprint arXiv:1706.03762.

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