Agent-Learning in Real Time

In our fast-paced world, wouldn’t it be amazing to have an AI assistant that knows us as well as we know ourselves? Imagine an AI agent trained on our habits and behaviors integrating into our lives to anticipate our needs and preferences.

By training an AI agent with data on our habits and behaviors, it learns the patterns unique to each of us. Through reinforcement learning, the agent is rewarded for aligning with our habits in different situations, effectively emulating our decision-making process.

This AI assistant is equipped with multimodal sensory inputs, like vision and audio, allowing it to perceive and interpret our environment in real time. With this understanding, the agent can predict our likely actions and desires, providing proactive assistance with our daily activities, and always being one step ahead. This AI companion would be able to enhance our efficiency and convenience.

A fitting analogy to illustrate what it would resemble to possess such a thing is akin to a reflection of yourself, but one step ahead.

The originality of this idea, or so I hope, stems from its multimodal aspect. LLMs themselves can only be so effective, but leveraging multimodal capabilities that interact with each other could realize the proposed idea. Only time will tell, but even simulating this would be difficult because of the vast array of data types you would need to train the network.

Another idea I have thought about in the past was artificially generating data presented in visual, auditory, and language forms for the model to train on. Creating realistic representations of this data would be sufficient to address the issue of lack of data.

Thoughts?