Bespoke Transformers:

Beyond Language

Into Broader Applications

In the realm of deep learning the Transformer architecture, initially introduced in the groundbreaking paper "Attention is All You Need", has fundamentally changed how we approach various problems. At AIVOSI we've taken this innovation a step further, tailoring transformers for applications beyond language, with unique modifications to tokenisation techniques and loss functions.

The Transformer Architecture: A Technical Overview

The Transformer thrives on a self-attention mechanism, enabling the model to emphasise different parts of the input data. With multiple layers of attention heads and feed-forward networks, it captures complex relationships, making it perfect for diverse applications.

  • Dalle-3 Image: A detailed diagram of the transformer architecture as described in the paper 'Attention Is All You Need'. The architecture showcases the multi-head attention mechanism, feed-forward neural networks, position-wise feed-forward networks, and the positional encoding. The encoder and decoder stacks are prominently displayed with their respective layers. Arrows and labels explain the flow of data and the operations taking place at each stage.

Novel Tokenisation Techniques

Tokenisation, the process of converting input data into tokens that the model can understand, is fundamental. Traditional methods might not always be suitable for non-language applications. At AIVOSI, we've developed unique tokenisation strategies for various data types:

  1. Time Series Data: Converting continuous data points into discernible tokens for the model.

  2. 2D Layouts: Through novel tokenisation techniques we have transformers that have learnt 2D layouts and the ‘rules’ between elements within that 2D environment.

Customised Loss Functions

The choice and adaptation of loss functions play a pivotal role in model training. Standard loss functions might not always deliver optimal results for unique applications. We've ventured into creating bespoke loss functions that specifically cater to the nuances of different data types and applications. By tweaking the loss functions, we can ensure that our models prioritise learning certain patterns or characteristics, further refining their predictive and analytical capabilities.

Predictive Prowess

By feeding our transformers with vast data sets, they exhibit unparalleled predictive abilities, generating new data or forecasting future trends, making them indispensable tools in various sectors.

Compute Power

GPUs are the powerhouse of AI.

AIVOSI’s partnership with Reset Data provides us with direct access to high performance NVIDIA GPUs.

Conclusion

By leveraging the power of the Transformer architecture and introducing novel tokenisation techniques and custom loss functions, AIVOSI offers solutions tailored to specific industry needs. This ensures our clients remain at the forefront of their respective fields.