Talking to ai often develops it, and its input will gradually adapt using machine learning — we use the term ‘contextual analysis’ not ‘learned,’ meaning different from humans. When you use an AI system, like the ones from OpenAI e.g. GPT (1/2/3), both do not remember any of the previous conversations where you talked to this chatbot beyond one interaction. It analyzes your words, looks for patterns, and then attempts to spit out a response based on billions of pieces of text that it has been trained on. For instance, GPT-3, the predecessor of GPT-4 was trained on 570 gigabytes of text data which allows it to understand and answer various types of queries with great accuracy.
Having AI at the receiving end makes these interactions more habitual, resulting in better responses that seem human-like and relevant. For example, if you talk about a particular thing all the time, it can change its output according to that and maintain the conversation logically in one session. An AI has access to a limited amount of context, say 4,096 tokens (around 3,000 words), during any given session so it can remember prior questions and adapt its responses/train them if necessary. The immediate context allows AI to maintain context within a flowing conversation, yet does nothing for learning in the long-term.
Though AI systems of such caliber, like GPT-4, do not remember users individually or even your personal information, these systems are nevertheless improved by the responses they get from users. For example, OpenAI continuously builds improvements to its models by looking at aggregated feedback from users and new training data. These updates allow AI systems to improve their responses for similar queries in the future. According to OpenAI’s usage report from 2023, GPT-4 even outperforming its predecessors in responding accurately to nuanced queries by ≈25% compared to GPT-3.
Training AI to “learn” becomes a matter of interaction, such as in the case of Customer Service Bots. Such systems capture data from each question a customer asks, allowing the AI to better learn about the types of problems customers face and suggest more relevant solutions. Amazon and Microsoft have used AI in their customer support systems, enabling the AI to learn from frequent interactions with customers. The AI applies machine learning by identifying trends and then running algorithms on that data to be able to solve the problem more efficiently, thereby faster and with greater accuracy for problems it is used to.
But simply chatting with an AI does not lead to actual personalization. The system can customize answers for the course of a session, but cannot retain detail from one session to another. In other words, if you mention a personal project to an AI during one session, it won’t remember that context when you talk to ai it another time. As Sam Altman, the CEO of OpenAI has stated: AI is not built with a persistent memory; “It doesn’t have any memory about what you said last time unless you remind it again.”
As a result, AI can only learn in the present using current data during one interaction. It lacks the ability to remember or learn like humans really, but can offer context-based responses that are more accurate and relevant. This makes consistent access to AI an asset, especially for things like real-time data analysis or problem-solving. As AI continues to emerge, these systems will learn quicker and become more efficient in adapting the user input, however it will never have a learning process when compared with human thought processes.