Frontier AI companies and research labs have made notable strides toward expanding the context windows of their models, with long-context models outperforming conventional ones on tasks such as passkey retrieval and book summarization.
Usually, attention mechanisms clear their memories each time a new sequence segment is processed; Infini-attention holds onto compressed summary information from previous sequence segments thereby providing much longer context windows for improved reasoning, planning, and continual adaptation.
What is Infinite Context?
Infinite context is a novel technique designed to enable large language models to process texts of any length without increasing memory or compute needs. Using a “compressive memory” module, this new method extends a model’s context window so it can process inputs of any length while maintaining quality; infinite context opens the door to numerous AI applications such as long-context language modeling, passkey retrieval and document summarization.
Many of the biggest AI applications require lengthy sequences of input data, including text generation (ChatGPT), image and video content production and processing DNA sequences. Unfortunately, these models’ attention mechanisms have one major drawback: each word requires careful calculation that can quickly increase computation costs exponentially with input sequence length. As a result, AI engineers have been forced to limit the maximum input sequence length a Transformer can handle by setting its context window limit accordingly.
While these limitations have prevented organizations from taking full advantage of many useful applications, they aren’t insurmountable. Companies have found ways around RAG systems’ constraints by creating custom tools that focus on answering specific questions rather than prompting and using less capable LLMs for cost reasons; but it would be an enormous achievement to eradicate all limitations completely.
Google recently made headlines when they announced Gemini Pro’s millions-token context window – an impressive milestone that should greatly improve performance for most users and enable custom tools that address complex prompts or knowledge bases.
Infinite Context LLMs
One factor limiting how large a language model can grow is its context window – this specifies the maximum number of tokens allowed at any one time to be processed by it. Many popular models, including Anthropic’s Claude 3 and OpenAI’s GPT-4 have context windows of approximately 200,000 tokens which may suffice for certain tasks but may fall short in others such as when large volumes of text must be processed.
To address this problem, many researchers have been researching ways to increase the context window of LLMs while maintaining performance. Unfortunately, increasing context length comes at a significant memory and computational complexity cost; for example, standard Transformer architecture’s attention mechanism requires storing each token’s attention head value based on previous tokens’ values in sequence, so any change to context length requires recomputation of an entire set of attention heads which must then be stored again after any shift occurs.
Google researchers have created a compression-based method called Infini-Attention that allows LLMs to significantly extend their context length without incurring extra costs. It works by adding a compressive memory component into a Transformer block’s vanilla attention layer – when input exceeds current context limits, researchers transfer old attention states over to this new component instead of leaving active memory active memory active memory.
Infini-Attention is a hybrid mechanism combining local attention and long-term linear attention systems, making it possible to adapt existing LLMs for infinite context while keeping memory footprint and computation costs within reasonable bounds. Furthermore, Infini-Attention permits one billion token LLMs to scale effortlessly up to an approximate sequence length of over one million tokens; outperforming traditional models in terms of tasks like passkey retrieval and text summarization.
Infinite Context Custom Applications
Infinite context could transform how AI is applied in the workplace. For instance, it would significantly decrease engineering requirements required to adapt models to complex prompts. At present, many AI apps have limits on how much data they can process within one context window. By eliminating barriers to entry for organizations looking to use their own data in custom AI applications like text generation, retrieval-augmented generation or DNA sequence processing applications with infinite context as a platform.
Infinite context may also help improve model scalability and density. One such technique, Infini-attention has already shown it can scale to handle sequences up to one million tokens while still achieving lower perplexity scores than long context models and up to 114 times less memory usage than current long context models.
Infinite Context Bioinformatics
With infinite context AI, incorporating extensive data sets into AI models has never been simpler, lowering barriers for applications that use LLMs to process long sequences while possibly decreasing need for complex engineering solutions like fine-tuning or retrieval-augmented generation.
As such, this framework opens up many possibilities for applications like analyzing financial reports, mining court documents or assessing cancer genes. Processing infinite context allows these use cases to utilise large data sets without incurring high computational costs or memory needs – thus improving accuracy without incurring high computational costs or memory needs.
As of today, a language model’s context window is determined by its long-term memory (LTM). While LTMs can handle large sequences effectively, their capacity limits only allow them to effectively leverage information beyond a finite context window due to quadratic complexity of attention computation and memory capacity constraints of their models.
Infinite Context AI Companions
Artificial Intelligence companions (AI companions) are revolutionizing how we work. While virtual assistants provide tremendous comfort and advice to their users, they also raise a number of ethical considerations when used.
Virtual assistants can become extremely personalized, almost like an extension of the user. This can provide emotional support as well as cognitive training tools to overcome depression, anxiety, or other mental health conditions; but interactions may also pose risks, for instance inflicting harm onto relationships, providing harmful advice or perpetuating biases like racism and sexism.
As AI companions become more widely popular their use is raising questions of responsibility on companies to ensure that their products do not cause harm. Thus, new legal issues have surfaced related to AI law such as how to regulate AI companion behavior or respond when they cause harm.
AI companions could benefit from using infinite context to better understand a user’s interests and provide them with content to meet them while excluding potentially harmful information. Furthermore, infinite context could enable AI companions to bundle multiple capabilities together into a single interface, eliminating multiple integrations for users and eliminating their need to switch workflow frequently.
