That's an interesting question. I don't know if there is an AI that can do exactly what you are looking for, but I can tell you about some related research and applications that might be relevant.

One of the challenges of creating an AI that can give cumulative answers based on a yearlong history of interaction is the **memory**. Most AI models have a limited capacity to store and recall information from previous inputs, especially when the inputs are long or complex. For example, ChatGPT⁵⁶, a popular chatbot based on GPT-3⁶, can only process about 3000 words at a time⁶, which means it might forget some parts of the conversation if it is too long. However, some researchers have found a way to make ChatGPT **compress** the previous conversation into a shorter form that it can later **expand** and use as a history⁴. This compressed form is called **Shogtongue**⁴, and it looks like gibberish to humans, but it helps ChatGPT remember more context.

Another challenge of creating an AI that can give cumulative answers based on a yearlong history of interaction is the **quality**. Even if an AI model can remember the past conversations, it might not be able to use them effectively to generate accurate, relevant, and coherent answers. For example, ChatGPT might sometimes repeat itself, contradict itself, or say something inappropriate or nonsensical⁶. This is because ChatGPT was trained on a large corpus of text from the internet, which might contain noise, errors, or biases⁶. Moreover, ChatGPT does not have any real understanding of the meaning or logic behind the words it generates; it only tries to predict the most likely word based on the statistical patterns it learned from its training data⁶. Therefore, ChatGPT might not be able to connect data points from different conversations or give helpful directives based on them.

However, there are some ways to improve the quality of AI models that can give cumulative answers based on a yearlong history of interaction. One way is to use **question answering** models that are specifically designed to answer questions based on a given text or knowledge source³. For example, Natural Questions (NQ) is a dataset and a challenge that requires AI models to read and comprehend an entire Wikipedia article that may or may not contain the answer to a question³. This way, the AI models can leverage more structured and reliable information than just random text from the internet. Another way is to use **feedback** mechanisms that allow users to correct, rate, or guide the AI models based on their performance². For example, in online learning settings, AI systems can use feedback from students and instructors to improve their learner-instructor interaction and provide more personalized and context-aware responses².

In summary, there are some existing AI models that can give cumulative answers based on a yearlong history of interaction, but they face some limitations in terms of memory and quality. However, there are also some promising research directions and applications that aim to overcome these limitations and provide better AI models for this task.

Source: Conversation with Bing, 14/06/2023

(1) How does ChatGPT retain the context of previous questions?. https://ai.stackexchange.com/questions/38150/how-does-chatgpt-retain-the-context-of-previous-questions.

(2) OpenAI GPT-3 API: How to make a model remember past conversations?. https://stackoverflow.com/questions/75501276/openai-gpt-3-api-how-to-make-a-model-remember-past-conversations.

(3) ChatGPT made up its own language to expand its memory. https://www.digitaltrends.com/computing/chatgpt-made-up-language-shogtongue-to-expand-memory/.

(4) Natural Questions - Google AI. https://ai.google.com/research/NaturalQuestions/.

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(7) Does My AI remember our past conversations? – Snapchat Support. https://help.snapchat.com/hc/en-us/articles/15586639078804-Does-My-AI-remember-our-past-conversations-.

(8) pyqt-openai · PyPI. https://pypi.org/project/pyqt-openai/.