Transformers

- Overview

Transformers are undoubtedly one of the most important neural architectures in recent years and are considered the core of fundamental models in many complex AI tasks. Therefore, every AI researcher wants to know every detail of it and how to implement it.

Transformers were developed to solve the problem of sequence transduction, or neural machine translation. That means any task that transforms an input sequence to an output sequence. This includes speech recognition, text-to-speech transformation, etc..

Transformers are a type of neural network architecture that transforms or changes an input sequence into an output sequence. They do this by learning context and tracking relationships between sequence components. 

Transformers, sometimes called foundation models, are already being used with many data sources for a host of applications.

A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence.

Please refer to the following for more information:

• Wikipedia: Transformers

- Transformer Models

Transformer models are a type of deep learning neural network that use sequential data to learn context and meaning by tracking relationships. They are a combination of convolutional neural networks (CNNs) and attention, and use modern mathematical techniques called self-attention. 

Transformer models were introduced in 2017 and are fundamental to natural language processing (NLP). They are also used in a wide range of other machine learning and artificial intelligence tasks, including:

• Translation: Transformers can translate text and speech in near real-time.
• Science and healthcare: Transformers can help researchers understand DNA and proteins, and extract insights from clinical data to speed up medical research.
• Finance and security: Transformers can detect anomalies and prevent fraud.
• Prediction, summarization, and question answering: Transformers can learn long-range dependencies between words in a sentence, making them powerful for these tasks. For example, when translating "kicked" in the sentence "I kicked the ball", a transformer can pay different attention to each word based on the type of question being asked, such as "Who kicked?" .

- Transformers in Deep Learning

In deep learning, transformers are a type of neural network architecture that use mathematical techniques to change an input sequence into an output sequence. 

Transformers learn context and meaning by analyzing the relationships between different elements in sequential data. This allows transformers to handle sequence-to-sequence (seq2seq) tasks while removing the sequential component, which enables greater parallelization and faster training. 

Transformers use a mathematical technique called attention or self-attention to detect how data elements in a series influence each other. 

For example, a transformer might take a sequence of tokens, such as words in a sentence, and predict the next word in the output sequence. The transformer does this by iterating through encoder layers, which generate encodings that define which part of the input sequence are relevant to each other. The encoder then passes these encodings to the next encoder layer, the decoder, which uses their derived context to generate the output sequence. 

• Transformers can be used in any application that uses sequential text, image, or video data. For example, they can:
• Translate text and speech in near real-time
• Help researchers understand the chains of genes in DNA and amino acids in proteins
• Extract insights from clinical data to accelerate medical research 

Transformers are considered the evolution of the encoder-decoder architecture, which relies mainly on Recurrent Neural Networks (RNNs) to extract sequential information.

Transformers lack this recurrency, and instead are specifically designed to comprehend context and meaning by analyzing the relationship between different elements.

- Applications in NLP

The transformer has had great success in natural language processing (NLP), for example the tasks of machine translation and time series prediction. Many large language models such as ChatGPT demonstrate the ability of transformers to perform a wide variety of such NLP-related tasks, and have the potential to find real-world applications. 

These may include:

• machine translation
• document summarization
• document generation
• named entity recognition (NER)
• biological sequence analysis
• writing computer code based on requirements expressed in natural language.
• video understanding.

In addition to the NLP applications, it has also been successful in other fields, such as computer vision, or the protein folding applications (such as AlphaFold).