Summary

• Transformers have revolutionized AI by enabling parallel processing of large-scale data sets, overcoming the limitations of sequential data processing inherent in previous models like RNNs and LSTMs. 
• This has led to significant enhancements in the handling of long-range dependencies within texts, which is crucial for tasks such as language translation and content generation. 
• The transformer’s ability to scale efficiently by leveraging self-supervised learning from unlabeled data has catalysed its adoption across various fields, leading to breakthroughs in natural language processing (NLP).
• The versatility of transformers allows for their application in a wide range of linguistic tasks without substantial modifications to their architecture.
• They have demonstrated superior capability in generating context-sensitive and nuanced language interpretations and responses, facilitating advanced language understanding and generation. 
• Additionally, the comparison between transformers and convolutional neural network approaches indicates a potential for more efficient processing through convolution operations, especially in handling longer sequences. 
• This ongoing evolution of AI models continues to push the boundaries of machine capabilities in understanding and manipulating human language, setting the stage for future innovations in AI.

Today’s AI can correctly answer complex medical queries—and explain the underlying biological mechanisms at play. It can craft nuanced memos about how to run effective board meetings. It can write articles analyzing its own capabilities and limitations, while convincingly pretending to be a human observer. It can produce original, sometimes beautiful, poetry and literature.

Language and AI

Language is at the heart of human intelligence and our efforts to build artificial intelligence. No sophisticated AI can exist without mastery of language. The field of language AI—also referred to as natural language processing, or NLP—has undergone breathtaking, unprecedented advances over the past few years. 

Two related technology breakthroughs have driven recent progress: 

• (i) self-supervised learning and 
• (ii) a powerful new deep learning architecture known as the transformer.

It is through language that we formulate thoughts and communicate them to one another. Language enables us to

Given language’s ubiquity, few areas of technology will have a more far-reaching impact on society in the years ahead.

The Transformer

The invention of the transformer, a new neural network architecture that has unleashed vast new possibilities in AI.

The transformers’ great innovation is to make language processing parallelized, meaning that all the tokens in a given body of text are analyzed at the same time rather than in sequence. 

• Very large training datasets are possible because transformers use self-supervised learning, meaning that they learn from unlabeled data. 
• Previous NLP models had to be trained with labeled data. 
• Today’s self-supervised models can thus train on far larger datasets than ever previously possible: there is more unlabeled text data than labeled text data in the world by many orders of magnitude.
• This is single most important driver of NLP’s dramatic performance gains in recent years, more so than any other feature of the transformer architecture.
• Training models on massive datasets with millions or billions of parameters requires vast computational resources and engineering know-how. 
• This makes large language models prohibitively costly and difficult to build. 
• GPT-3, for example, required several thousand petaflops/second-days to train—a staggering amount of computational resources.
• These massive models are not specialized for any activity. 
• They have powerful generalized language capabilities across functions and topic areas. Out of the box, they perform well at the full gamut of activities that comprise linguistic competence: 
• • language classification, 
  • language translation, 
  • search,
  • question answering, 
  • summarization,
  • text generation, 
  • conversation.

How Transformers work

The transformer architecture and its underlying attention mechanism have been central to the evolution of artificial intelligence, particularly through their application in large language models (LLMs) like OpenAI’s GPT series and others. 

These technologies have dramatically enhanced the ability of AI systems to understand and generate human language, opening up new possibilities across various domains such as translation, content generation, and conversational AI. 

Transformers and the attention mechanism have enabled this through four distinct features::

1. Handling Sequential Data

Traditional Challenges:

Previous models, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs), processed data sequentially. This made them slow and also limited their ability to handle long-range dependencies within the text because of issues like vanishing and exploding gradients.

Transformer Solution:

Transformers eliminate the need for processing data sequentially. Instead, they process all parts of the input data simultaneously, thanks to the attention mechanism. This allows them to capture dependencies between elements in the input data regardless of their distance in the sequence. For instance, a transformer can effectively link a subject at the beginning of a paragraph with its corresponding verb several sentences later, which is crucial for tasks like translation and sentence structure understanding.

 2. Scalability and Efficiency

Attention Mechanism:

The core component of the transformer, the attention mechanism, enables the model to focus on relevant parts of the input data for each element of the output. This mechanism computes relevance scores across all parts of the input, which are then used to weight the input elements dynamically. It provides a context-sensitive representation of the input at every output step, enhancing the model’s understanding and generation capabilities.

Efficient Training:

Transformers are highly parallelizable, unlike their predecessors. This parallelization significantly reduces training times, making it feasible to train on vast datasets and subsequently scale up to models with billions of parameters (e.g., GPT-3). The efficiency of training also allows for continuous updates and improvements to the models without the need for exhaustive retraining.

 3. Versatility Across Tasks

Unified Architecture:

The general architecture of transformers, facilitated by the attention mechanism, makes them versatile across different tasks without significant modifications to the model. For example, the same transformer model architecture can be used for language translation, text summarization, sentiment analysis, and more, merely by changing the training data and fine-tuning some parameters. This versatility has led to widespread adoption of transformers in various fields beyond language processing, such as in image recognition and generation tasks.

 4. Advanced Language Understanding and Generation

Contextual Representations:

Transformers generate deep contextual word representations by considering both the left and right context of each word in a sentence, across the entire dataset. This is a significant improvement over earlier models like word2vec or GloVe, which provided static word embeddings. Contextual embeddings allow transformers to understand the nuanced meanings of words based on their usage in specific contexts, leading to more accurate interpretations and responses.

Enabling LLMs:

Large language models built on transformer architectures can store and utilize vast amounts of world and linguistic knowledge, enabling them to generate coherent and contextually appropriate text over extended passages. They can also perform “few-shot” or “zero-shot” learning, where they generalize to new tasks not seen during training, based merely on a few examples or instructions given at runtime.

In summary, transformers and the attention mechanism have not only solved significant technical challenges inherent in model architectures before them but also provided a flexible and powerful framework that underpins the current generation of AI applications. These technologies have set the stage for the ongoing evolution of AI, pushing the boundaries of what machines can understand and accomplish with human language.

 Simple Explanation of the Transformer and Attention Mechanism

Transformer:

The transformer is a type of model architecture that relies heavily on an attention mechanism to handle sequential data like text. Unlike its predecessors (RNNs and LSTMs), which processed data sequentially, the transformer processes all data points simultaneously. This parallel processing makes transformers very efficient for tasks involving large datasets and complex input-output mappings, such as language translation and text generation.

Attention Mechanism:

The attention mechanism is a critical component of the transformer model. It allows the model to focus on different parts of the input sequence while generating each word of the output sequence. This mechanism works by assigning a weight to each input data point, determining how much each part of the input should influence each part of the output. The weights are determined through a trainable scoring function, making the attention mechanism adaptable to the specifics of the task at hand.

1. Alignment Scores: First, alignment scores between input and output positions are calculated to determine the relevance of input elements to the output.

2. Softmax Layer: These scores are normalized using a softmax function, which helps in highlighting the most relevant inputs while dampening the less relevant ones.

3. Context Vectors: Finally, the context vector for each output element is computed as a weighted sum of all input vectors, where weights are the softmax-normalized scores.

This process allows the transformer to dynamically focus on different parts of the input for each part of the output, facilitating better handling of long-range dependencies and complex input patterns.

 What Next? Why Convolution May Be Quicker than Transformers.

Convolution is a mathematical operation that combines two functions to produce a third function.

– It is used to determine how the shape of one function is modified by another.

– Mathematically, it involves shifting one function over another and integrating their product.

Convolution operations could offer a faster alternative to the self-attention mechanism for several reasons:

1. Computational Efficiency: Convolution operations are generally less computationally intensive than the self-attention mechanism of transformers, which involves computing pairwise interactions between all elements in the sequence. The computational complexity of self-attention grows quadratically with the sequence length, making it inefficient for very long sequences.

2. Parallelization: Convolution operations can be easily parallelized on modern hardware architectures. They are well-supported by existing deep learning frameworks and hardware accelerators like GPUs, which have highly optimized routines for performing convolutions efficiently.

3. Simpler Operations: Convolutions apply the same filters across different parts of the input, capturing local dependencies effectively. This often requires fewer parameters than the fully flexible pairwise interactions in self-attention, potentially reducing the training time and complexity.

4. Subquadratic Scaling: Techniques like the Hyena model utilize convolutional approaches that scale subquadratically with sequence length. This approach maintains the expressiveness and flexibility of attention mechanisms while being computationally more efficient, especially for longer sequences.

In essence, while transformers with attention mechanisms have been highly successful, especially for NLP tasks, convolutional approaches are emerging as potentially more efficient alternatives, especially as the need for processing longer sequences and larger datasets grows. 

These methods promise to maintain or even enhance model performance while reducing computational costs and resource requirements. We will explore this in the next few pages.

Convolution

Convolutional neural networks (CNNs) are typically used for spatial data processing, such as images, where there is a spatial relationship between the data or temporal data such as audio. For example, neighbouring pixels (in X or Y direction) are related to each other. A convolutional filter is applied to such data to extract features, such as edges, textures, etc., in images.

Attention

Transformers on the other hand are typically used for sequential data processing, such as text, natural language, where short-term and long-term dependencies are present. The actual dependencies are not explicit in this case. For example, in the sentence “Alice had gone to the supermarket to meet Bob”, one of the verbs “meet”, is located far-away from the subject “Alice” and this dependency is not spatial but differs a lot. This is even more for longer inputs with multiple paragraphs where the final sentence could have had a dependency to a sentence somewhere in the beginning. Transformers are based on the so-called attention mechanisms which learns these relationships between the elements in the sequence.

Basic Self-attention

The basic idea of self-attention is to assign different importance to the inputs based on the inputs themselves. In comparison to convolution, self-attention allows the receptive field to be the entire spatial locations.

Convolution vs. Attention:

Although attention-based models such as vision transformers have shown to outperform CNN based methods, a careful analysis of the two shows comparable performance. 

In early layers of a neural network for images, spatial relations can be captured by convolutions and the later layers could benefit from long-range receptive fields offered by attention. Hence, both can be combined.⁴

As we discussed, the Transformer is a model architecture that eschews recurrence and instead relies entirely on an attention mechanism to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favour of attention mechanisms allows for significantly more parallelization than methods like RNNs and CNNs.

The Convolutional vision Transformer (CvT) is an architecture which incorporates convolutions into the Transformer. The CvT design introduces convolutions to two core sections of the ViT architecture.

First, the Transformers are partitioned into multiple stages that form a hierarchical structure of Transformers. The beginning of each stage consists of a convolutional token embedding that performs an overlapping convolution operation with stride on a 2D-reshaped token map (i.e., reshaping flattened token sequences back to the spatial grid), followed by layer normalization. This allows the model to not only capture local information, but also progressively decrease the sequence length while simultaneously increasing the dimension of token features across stages, achieving spatial down sampling while concurrently increasing the number of feature maps, as is performed in CNNs. 

Second, the linear projection prior to every self-attention block in the Transformer module is replaced with a proposed convolutional projection, which employs a s × s depth-wise separable convolution operation on an 2D-reshaped token map. This allows the model to further capture local spatial context and reduce semantic ambiguity in the attention mechanism. It also permits management of computational complexity, as the stride of convolution can be used to subsample the key and value matrices to improve efficiency by 4× or more, with minimal degradation of performance.