The focus of AI is, the more you talk to it, the better it gets because each conversation carries contextual data. AI models like GPT-4 then make use of natural language processing (NLP) in real time to understand your input and create responses that meet your language style along with the subject matter. However, the more you talk to ai it, the more of a ‘sample-set’ it has; and thus it adapts accordingly – picking up on patterns in how you communicate – so that over time (in-line with an individual session at least), it becomes better able to synthesise appropriate & contextually-related statements. For instance, GPT-4 has a 4,096-token context window so it can keep track of conversations and respond appropriately. This implies that when an AI is in a particular session, it would be able to contextualise and remember the topics being spoken about during that session, use the appropriate tone and modulate itself around what you have been saying.
The more frequent the interaction, the larger amount of data AI receives to increase accuracy. OpenAI, and the companies like it keep updating their models as they training their models again and again on users feedback to make such types of responses. AI systems have displayed identical behavior levels when it comes to nuanced queries with 25% better performance compared to earlier models in 2023. That is done through incremental training using relatively new data where AI can iterate on the new ways meaning of complex or different questions that have already been asked.
A good example of AI adapting itself would be your virtual assistants, for example Siri or Alexa which learns from the way you use it over aperiod of time. These assistants adjust and get better at identifying commands suited to consumers, whether it be setting reminders or controlling smart home devices. Between 2021 and 2022, Amazon recorded a massive 40 percent increase in how users interacted with Alexa, attributing much of the difference to enhanced adaptability powered by AI.
AI can adapt within the context of a single conversation, but it does not have long-term memory. Now if you keep asking an AI about a specific topic, it might change its responses in the same session but once the session is over, there's no retention of that information. To quote the OpenAI CEO Sam Altman, "AI doesn't remember what it told you last week." Though it may not remember past conversations, it can use its training data to provide more relevant answers.
If you interact with AI frequently at work, it can give you more personalized and accurate responses for specific tasks such as data analysis or customer service. Specific use cases Watson AI has been used for include diagnostic decisions in healthcare. Though every session with Watson is independent of the one before, by analyzing patterns in medical data it can adapt to offer more accurate treatment recommendations based on previous cases.
Speak to ai and see how it adjust its responses according to your language, questions and the latest context of the keeping conversation for example. Based on the more you interact with AI, the more it will catch your writing style and real-time messages to calibrate its response for your convenience.