In the news

In conversations with AI chatbots like ChatGPT, the text the AI can “see” or “read” at any given moment is determined by its context window.
The context window, measured in tokens, defines the amount of conversation the AI can process and respond to during a chat session.

What are Context Windows?

Tokens: Basic units of data processed by AI models, tokens represent words, parts of words, or characters.
Tokenisation: The process of converting text into vectors (format suitable) for input into machine learning models.
Example: For English text, one token is roughly equivalent to four characters. Thus, a context window of 32,000 tokens translates to around 128,000 characters.

Recall and Understanding: Context windows enable AI models to recall information from earlier in the conversation and understand contextual nuances.
Generating Responses: They help AI models generate responses that are contextually relevant and human-like in nature.

Sliding Window Approach: Context windows work by sliding a window over the input text, focusing on one word at a time.
Scope of Information: The size of the context window determines the scope of contextual information assimilated by the AI system.

Advancements: Recent AI models like GPT-4 Turbo and Google’s Gemini 1.5 Pro boast context window sizes of up to 128K tokens and 1 million tokens, respectively.
Benefits: Larger context windows allow models to reference more information, maintain coherence in longer passages, and generate contextually rich responses.

Computational Power: Larger context windows require significant computational power during training and inference, leading to higher hardware costs and energy consumption.
Repetition and Contradiction: AI models with large context windows may encounter issues such as repeating or contradicting themselves.
Accessibility: The high resource requirements of large context windows may limit access to advanced AI capabilities to large corporations with substantial infrastructure investments.

Context windows play a vital role in enabling AI chatbots to engage in meaningful conversations by recalling context and generating relevant responses.
While larger context windows offer benefits in terms of performance and response quality, they also pose challenges related to computational resources and environmental sustainability.
Balancing these factors is essential for the responsible development and deployment of AI technologies.