Sequence is a logical order in which related events, ideas, or objects are arranged. It can involve the chronological order of items, numerical order, alphabetical order, and any other kind of ordered arrangement. Sequence is often used to give structure to stories or concepts so that readers can understand them more easily. For example, a linear sequence might be used to list the steps in a process from start to finish. It can also be used to organize data for analysis and comparison. In mathematics and computer science, sequences are often discussed in terms of patterns and algorithms. The use of sequence allows us to better comprehend complex topics and create meaningful connections between different elements.  

The length of a sequence is an important factor in machine learning applications. Longer sequences generally contain more data points, which provide better information for learning algorithms to process. This can lead to improved accuracy and performance in machine learning models. On the other hand, shorter sequences can be processed more quickly and require fewer resources. Furthermore, longer sequences may have unnecessary or redundant data that could hinder the model’s ability to find meaningful patterns. Therefore, it is important to choose a sequence length that balances the need for sufficient data with the constraints on computing resources and time. The optimal sequence length will depend on the specific application and should be chosen based on considerations such as desired model accuracy, computational resources available, and expected processing time.  

Using Cerebras Systems’ CS-2, customers can now rapidly train Transformer-style natural language AI models with 20x longer sequences than is possible using traditional computer hardware. This new capability is expected to lead to breakthroughs in natural language processing (NLP). By providing vastly more context to the understanding of a given word, phrase or strand of DNA, the long sequence length capability enables NLP models a much finer-grained understanding and better predictive accuracy.