What's the most important capability of AI agents?

@kissonlin Yes, several AI models we use have been developed with memory capabilities or have been adapted to include memory functions. Here are some notable examples: Grok (by xAI) - Has a form of memory where it can keep track of the conversation context to provide more relevant and coherent responses. GPT-3, GPT-4 (by OpenAI) - While the initial models didn't have explicit memory, with recent updates and through APIs, developers can implement conversation history mechanisms which effectively give these models memory for the session. BlenderBot 3 (by Meta) - This model includes memory modules that help in maintaining conversation context over longer dialogues, learning from past interactions within a session. LaMDA (by Google) - Designed for conversational applications, LaMDA uses context from previous interactions to make responses more relevant and coherent. MemN2N (Memory Networks by Google) - An earlier model specifically designed for tasks requiring memory, such as question answering over a document or maintaining context in dialogue systems. Transformers with Memory Augmentation - There are research models like Memory Augmented Transformers where memory mechanisms are explicitly integrated into the architecture, enhancing their ability to handle tasks requiring sequential understanding or long-term dependencies. BERT (Bidirectional Encoder Representations from Transformers) - While not traditionally considered for having memory, BERT uses context from both directions (left and right) of a word in a sentence, which can be thought of as a form of memory within the sentence level. RAG (Retrieval-Augmented Generation) - This isn't a model per se but an approach where models like those from the Transformer family are augmented with access to external memory or knowledge bases, effectively giving them a form of memory for factual recall. When considering "memory" in AI: Short-term memory typically refers to the ability to retain information from the immediate context of the conversation or task. Long-term memory might involve models learning from previous interactions or having access to a knowledge base or database to retrieve information for use in responses. These models or techniques are particularly useful for applications like chatbots, personal assistants, or any scenario where maintaining context or learning from past interactions is beneficial. However, the exact implementation details and the effectiveness of these memory systems can vary widely depending on how they are integrated into the broader AI system.