What is Transformers Architecture?

Have you ever wondered what makes AI machines, robots, and agents interact like a human? 

It is because of the transformers- the revolutionary technology behind AI interaction- that understand the accurate context of a conversation. The research paper titled "Attention is All You Need," published in 2017 by Google researchers, introduced this groundbreaking deep learning architecture that has revolutionized the way AI models process and generate data, particularly in Natural Language Processing and Generative AI. 

Before the discovery of this model, Generative AI was struggling to grasp the relationships between words far apart in a sentence and lacked fluency in the output. As transformers introduced the concept of attention and allowed the parallel processing of the entire input sentence simultaneously, Generative AI became more accurate, relevant, and responsive. 

Businesses willing to master AI development need to understand the significance of transformer architecture, its role in AI and NLP solutions, and how it works. 

Must Read: Fine-Tuning Large Language  Modals (LLMS) In 2024

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What are Transformers in Artificial Intelligence?

Transformers in Artificial Intelligence refers to a neural network architecture type enabling facilitating AI models to process and interpret sequential data. 

Just as a student grasps the content of a teacher's lecture before jotting down notes, a transformer model interprets an input sequence to create a suitable output sequence. 

They make it happen via their proficiency in learning the context and deciphering the relationship between the sequences. 

They are vital for all kinds of sequence-based conversions such as speech recognition, machine translation, and protein sequence analysis. 

Transformer architecture was first introduced in the 2017 “Attention Is All You Need” research paper. The research was carried out by 8 Google scientists who proposed replacing conventional Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) with an attention-based mechanism. 

These researchers trained this model using over 1 billion words in just 3.5 days. It has features such as positional encoding, the ability to process unlabeled data, and self-attention formed a strong foundation for modern-day language models such as GPT and BERT. As of 2024, the paper has been cited more than 100,000 times, underscoring its profound influence on the development of advanced AI systems. 

Transformers are moderately new and progressive models used in machine learning, Natural Language Processing (NLP), and AI. These deep learning models are far better than early deep learning models that focused on assuming the next word in a given sequence after reviewing the previous word. 

For example, the auto-complete feature in your phone is based on early deep-learning models. If you’re typing ‘I want to’ then your phone will auto-suggest the word ‘go to the park ’ because you’ve often typed this phrase in the past. However, it failed to generate a meaningful paragraph and process the inputs in parallel. 

Conversely, the new generation transformer models in AI are now capable of processing long dependencies, using self-attention mechanisms, and reviewing the importance of each input data separately. 

They can process long-form sequences in parallel, cutting down their training and processing time excessively.  Large-scale language models like BERT and GPT can produce long-form paragraphs in complex language because of transformer models only. 

Also Read: How LLMs are Transforming Enterprise Applications? 

In addition, transformer models allow you to use transfer learning and retrieval augmented generation (RAG) techniques to customize it according to industry-specific requirements. 

You can train these models for distinctive tasks like protein synthesis or language translation using huge datasets. This characteristic of a transformer model makes it suitable for diverse domains. 

Because Recurrent Neural Networks (RNNs) are also able to process sequential input data, transformer models are often considered their counterparts. 

However, transformers are different from recurrent neural networks (RNNs): 

• They can process the input sequence all at once while RNN process them in fragments 
• They promote faster computation and better utilization of GPUs because of parallel processing whereas RNNs are not able to perform parallelised computations
• They’re based on attention mechanisms and can focus only on the most relevant input part which makes them ideal for long-range dependencies. 
• RNNs are not able to perform par excellently in long-range dependencies as they lack an attention mechanism 

These exceptional characteristics of transformer models contribute heavily towards their sudden yet desirable popularity. They’re now considered as the best choice for complex NLP problems over recurrent neural networks (RNNs).  

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How does Transformers Work?

Transformers are neural networks featuring multiple layers of well-integrated computing nodes or neurons. These neurons work like the human brain. They try to communicate and transfer information to resolve complex issues. Now, let’s try to understand the functioning of transformer models using an example. 

Suppose “The cat sat on the mat" is the input sequence for a transformer model, it reaches the encoder of the transformer architecture where the input sequence is processed word by word. 

The encoder then creates an easy-to-understand mathematical representation of the input sequence so that the model can understand it. Each input is converted into a vector, featuring information about the word itself and its position in the sentence.

In the next stage, the self-attention mechanism of a transformer model comes into play. Through the use of this mechanism, the model assigns the weight to each part of the sequence and processes only the most crucial part of it. In the above phrase, The cat sat on the mat, the self-attention mechanism might assign the word ‘cat’ a higher weightage as compared to the words ‘ the’ or ‘sat’. 

The decoder component of the model will review the prioritization, done by the self-mechanism model, and will start generating the output sequence. The output could be a translation or an image analysis, based upon the user’s requirements. 

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Top 5 Use Cases of Transformer Models

Transformer models can be trained according to specific needs and this leads to their widespread applications across various industries and domains such as: 
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Natural Language Processing (NLP)

Traditional models like Hidden Markov Models (HMMs) struggle to perform in complex and long-form dependencies. Transformer models have revolutionized NLPs by integrating unparalleled capabilities in language translation, text generation, sentiment analysis, and text classifications. Their ability to capture long-term dependencies accurately in text form and a sense is making NLP more accurate. 

NLP solutions, based on transformer models, are likely to interpret the input accurately, learn the context, and deliver optimized output. They are also useful in cases where NLP solutions need to summarize the lengthy text. They can extra key information and create crisp content summaries. 

Also Read: Top 15 Use Cases Of AI Agents In Business

Computer Vision  

Current in-use computer vision systems lack dependency in accurately detecting and classifying objects in images with varying lighting conditions. Through the use of techniques such as Local Contrast Normalization and colour space transformation, transformer models can empower computer vision in more than one way.  

They are useful for: 

• Converting images into flattened image  patched sequences  
• Retaining spatial information, present in the images, with the help of positional embeddings 
• Generating and segmenting images
• Accurate object detection 

Multi-Modal Learning

Due to the inability of traditional AI models to handle multiple modalities, the current landscape of multi-model learning is erroneous, slow-processing, and has limited scope. 

Transformer models are capable of collecting and combining information through sources such as images, text, and audio, enabling multi-modal learning. Their underlying self-attention mechanism makes them flexible enough to integrate seamlessly with various modalities.  

Audio and Speech Processing

Time-worn audio & speech processing fails to eliminate environmental noises and speech disfluencies, resulting in mistaken processing. As transformer models can effectively model sequential data and capture long-range dependencies within speech sequences, they have diverse use cases in audio and speech processing. 

They can process the entire speech at once, find relationships between distant parts of a speech sequence, process data in parallelization, generate music, and promote speech synthesis. 

The use of hybrid models where transformers are merged with conventional acoustic modeling techniques is likely to reduce word errors and computation complexities. 

Healthcare

Traditional clinical text generation and medical image analysis demand uncompromised accuracy and unbiased data interruption. Transformers' ability to handle complex relationships in language can lead to more accurate and factually correct clinical text generation.

In addition, they are useful for generating informative medical reports describing medical images (X-rays, MRIs) with high accuracy, assisting doctors in diagnosis.

Drug discovery is another very common use case of a transformer model where it can help medical professionals identify potential drug targets and generate summaries of clinical trials. 

In addition, this model is useful in protein structure analysis as it can process sequential data. They are capable of modelling the long amino acid chains and decoding them for better understanding. 

The use of transformer models in healthcare applications can help professionals to predict the 3D protein structure. 

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What are the Components of Transformer Architecture?

A transformer architecture is made up of multiple software layers, integrated and working together to generate accurate outputs. Let’s break down this architecture for you. 

Input Embeddings

Input embedding is the first layer of transformer architecture and is responsible for breaking input into a series of tokens that are later transformed into a mathematical vector sequence. 

These vectors feature syntax details in the form of numbers that software considers while correlating mathematical terms and human language. 

This component is crucial because it supplies continuous tokens that a transformer model can process to extract outputs. 

Positional Encoding

Next, we have positional encoding where each token is embedded with information related to its position in the sequence. A set of functions is assigned the task of generating distinct positional signals exclusively for each token. 

Without positional encoding, a transformer model will fail to understand which toker should be processed and when. This transformer architecture component makes token processing a streamlined task. 

Transformer Block

In a transformer architecture, multiple blocks are stacked together, each containing two main sub-layers, so that accurate token representation is generated. These two sub-layers are: 

Self-Attention Layer that enables the transformer blocks to consider the most relevant part of an input sequence. 

Feed-Forward Neural Network: This is a simple feed-forward neural network that processes each element independently. This layer has some extra elements that enable a transformer model to deliver efficient and relevant outputs. 

These additional components are: 

• Connections that join self-attention and feed-forward comments and behave like shortcuts. These connections promote smooth information flow between two end-points of a network while skipping low-priority operations. 
• Layer normalization aids in model training by keeping the output numbers, generated by different layers of the network, within a certain range. 
• Linear transformation functions are crucial for allowing the transformer models to adjust the provided values so that they can perform the tasks in a better manner. 

Linear and Softmax Blocks

To make sure that a transformer model can generate coherent outputs, the transformer architecture features linear and softmax blocks. 

The linear block is the part of a highly integrated layer, a dense layer, and is responsible for performing a learning linear mapping. 

The decision-making takes place in the linear block and it generates outputs in the form of scores of logits for each token. 

The softmax block is the last stage of a transformer block and it’s responsible for accepting logit scores and converting them into a probability distribution. 

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Real-Life Transformer Models

There are multiple deep-learning transformer architecture models available for usage. 

BERT

BERT or Bidirectional Encoder Representations from Transformers is a deep learning model that Google developed in 2020 and is currently empowering Google  Search Engine.  It’s extensively used in natural language processing(NLP) tasks. 

It’s a “bidirectional” model, meaning that it can understand the context from the left and right of a word, resulting in better machine translation. 

LaMDA

Language Model for Dialogue Applications or LaMDA is also a transformer model developed by Google. It’s mainly designed for dialogue applications and creates a more human-like response against a given input. 

As it was trained using the actual conversation between humans, it can perform translation and text summarization with added accuracy. It’s mainly used in the development of chatbots, language translation systems, and virtual assistants. 

GPT3

Developed by OpenAI, GPT-3 or Generative Pre-trained Transformer 3 is a well-trained natural language processing (NLP) model used in generating coherent human-like text. It’s widely used for generating text, codes, and commands. 

As it’s trained using wide datasets of text & codes, it’s able to provide quality human-like output. 

This transformer-based model has an unmatched ability to adjust according to various types of tasks and writing styles using its ‘few-shot learning’ technique. This process allows the model to learn from a few examples and perform extensive tasks with less training. 

The success of GPT-3 paved the path for the development of more advanced models like GPT-4 and GPT-4 Turbo. GPT-4 data handling capacity is beyond text. It can handle images, codes, and visual information with the same ease and accuracy. 

GPT-4 Turbo is an ultra-modern transformer model that supports a 128K context window, has multi-model capacities such as image creation (DALL·E 3), and text-to-speech (TTS), and can perform tasks requiring strict adherence with the instructions with more efficacy.

MegaMolBART 

MegaMolBART is an advanced deep learning model developed using seq2seq transformers and designed specifically for drug discovery and cheminformatics. It uses the transformers to understand and manipulate molecules. It can predict the properties of new molecules and can even generate new candidate molecules based on desired properties. 

Healthcare industry can use this model on any hardware, running on NVIDIA GPU and having memory greater than 8 GB. Its usage is leading to new drug discovery and identifying promising drug candidates with specific traits. 

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How Ampcome Can Help You Discover the True Power of Transformer Models? 

Transformer models have become a necessity in  AI applications and software solutions because of their unmatched capabilities such as parallel data processing, better capture of long-term dependencies, high scalability, and effective use of transfer learning. 

Wondering how you can leverage the power of transformer models in your generative AI solutions? Let Ampcome lend you a helping hand. With a team of proficient AI developers who know the nitty-gritty of transformer models in AI, Ampcome can assist you develop customized AI apps and software tailored to your specific use case. Whether it's text generation, machine translation, sentiment analysis, or any other NLP task, we can provide you with visionary AI solutions, using transformer models. 

With a strong understanding of transformers and AI, our team can help you have AI products that can execute tasks competently, address industry-specific challenges, and evolve according to current needs. 

We, at Ampcome, combine the latest transformer models and our user-centric approach to devise resourceful AI products that deliver real value and a competitive edge to our customers.

So, stop using slow neural networks and design sub-standard AI products with outdated transformer models. Start experiencing the power of transformer models with Ampcome. Get in touch today for a better tomorrow.