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In scenarios with continuously growing contexts, such as streaming data, a common and effective strategy is to employ fixed-size memory models. This approach is advantageous because it ensures that the computational cost remains constant at each processing step, irrespective of the overall length of the sequence.

Fixed-Size Memory Models for Streaming Contexts

A company is developing a customer service chatbot designed for long, continuous conversations. Two architectures are proposed. Evaluate which architecture is more suitable for this task and explain why, focusing on performance as the conversation length increases.

Chatbot Architecture for Long Conversations

A company is building a system to perform real-time analysis on a continuous, high-volume stream of data. They are considering two architectural approaches:

- **Approach 1:** For each new piece of data, the system re-processes the entire sequence of all data received up to that point to maintain context.
- **Approach 2:** The system maintains a condensed, fixed-size summary of the information from past data and updates this summary with each new piece of data.

Which approach is more suitable for this task, and why? Explain your reasoning in terms of computational cost as the stream of data grows over time.

Comparing Architectures for Real-Time Data Streams

Recurrent models and their variants exemplify the fixed-size memory approach for processing continuously expanding context. They are inherently suited for modeling time series problems, where the effects of past inputs continue indefinitely, by summarizing sequences into a fixed-size set of hidden states and maintaining a constant computational cost per step.

Recurrent Models for Continuously Expanding Context

An engineering team develops a real-time monitoring system to process a continuous, unending stream of user activity events. During testing, they observe that the system processes the first few thousand events quickly. However, as the system continues to run for several hours, the time it takes to process each new, individual event steadily increases, leading to significant delays. Which of the following design principles is the most likely cause of this performance degradation?

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