How AI chat GPT works ?
As an AI language model, it works using a combination of different AI algorithms and techniques, including natural language processing (NLP), deep learning, and machine learning.
For example, open AI GPT uses a neural network architecture called a transformer to process and understand natural language text. The transformer architecture enables me to generate responses to questions and provide contextual information that is relevant to the user's query.
It also uses various NLP techniques such as word embedding, named entity recognition, and part-of-speech tagging to understand the meaning of text and extract important information.
Additionally, it utilizes machine learning techniques such as supervised and unsupervised learning to continuously improve its ability to understand and respond to user queries more accurately and efficiently over time.
What is Transformer architecture in AI ?
The transformer architecture is a type of neural network architecture that was introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017. The transformer is specifically designed for processing sequential data, such as natural language text.
The main innovation of the transformer architecture is the attention mechanism, which allows the model to focus on different parts of the input sequence at different times. This is in contrast to traditional recurrent neural network (RNN) architectures, which process the input sequence one step at a time in a fixed order.
The transformer consists of an encoder and a decoder, each of which is composed of multiple layers of self-attention and feed forward neural networks. In the encoder, the input sequence is processed by multiple layers of self-attention and feed forward neural networks to create a sequence of hidden representations that capture the meaning of the input sequence. In the decoder, the hidden representations from the encoder are used to generate an output sequence.
The self-attention mechanism in the transformer allows the model to weigh different parts of the input sequence differently depending on their relevance to the current processing step. This enables the transformer to handle long-range dependencies more effectively than RNNs, which can struggle with vanishing gradients when processing long sequences.
Overall, the transformer architecture has been shown to achieve state-of-the-art performance on a variety of natural language processing tasks, including machine translation, language modeling, and question answering.
Architecture of Transformer Neural network
The transformer neural network architecture is composed of an encoder and a decoder, each consisting of multiple layers of self-attention and feedforward neural networks. Here is a brief overview of the components of the transformer architecture:
Input Embeddings: The input sequence of words is first transformed into a sequence of continuous vector representations called word embeddings.
Positional Encodings: In addition to word embeddings, positional encodings are added to provide information about the relative position of words in the input sequence.
Encoder: The encoder is composed of multiple identical layers, each of which consists of two sub-layers: a self-attention layer and a feedforward neural network layer. The self-attention layer computes the attention weights between all the words in the input sequence and generates a weighted sum of the embeddings. The feedforward neural network layer then applies a non-linear activation function to the output of the self-attention layer.
Decoder: The decoder is also composed of multiple identical layers, each of which consists of three sub-layers: a self-attention layer, an encoder-decoder attention layer, and a feedforward neural network layer. The self-attention layer in the decoder computes the attention weights between all the words in the decoder input sequence. The encoder-decoder attention layer computes the attention weights between the output of the encoder and the current input of the decoder. The feedforward neural network layer then applies a non-linear activation function to the output of the two previous layers.
Output Layer: The output layer takes the final hidden representation of the decoder and applies a softmax activation function to generate a probability distribution over the vocabulary of possible output words.
The transformer architecture is trained using backpropagation and gradient descent to minimize a loss function that measures the difference between the predicted outputs and the true outputs. The training process involves updating the weights of the neural network to improve its ability to make accurate predictions on new input sequences.
What is continuous vector representation ?
The input sequence of words is transformed into a sequence of continuous vector representations using an embedding layer. This layer maps each word in the input sequence to a fixed-size vector representation called a word embedding. The word embeddings are learned during the training process of the neural network and are updated iteratively to improve the performance of the model.
The embedding layer can be initialized randomly or pre-trained using external data sources such as word2vec or GloVe. These pre-trained embeddings capture general semantic relationships between words, and initializing the embedding layer with these embeddings can help the model learn more quickly and generalize better.
Each word in the input sequence is represented by a vector with a fixed number of dimensions. The number of dimensions is a hyperparameter of the neural network and can be adjusted based on the complexity of the task and the size of the training dataset.
Once the input sequence has been transformed into a sequence of word embeddings, the embeddings are passed through the rest of the layers in the neural network, including the encoder and decoder, to generate the final output sequence. The embeddings help to capture the meaning and context of each word in the input sequence and enable the neural network to learn relationships between words and their corresponding outputs.
