AI agents simulating "Thinking Fast and Slow"

Artificial intelligence is constantly evolving, and with the arrival of advanced language models, AI agents no longer just interact with users through natural conversations but are also capable of planning and reasoning to achieve specific goals. To address this challenge, a new architecture of AI called Talker-Reasoner has been proposed, inspired by the theory "Thinking Fast and Slow" by Daniel Kahneman. This approach divides an agent's mind into two systems: one fast and intuitive, and another slow and analytical.

Subscribed

The two systems: The Speaker and the Reasoner

In this architecture, the Talker behaves like Kahneman's "System 1," processing information quickly, intuitively, and responding immediately through natural conversation. The Reasoner, on the other hand, acts like "System 2," being slower, analytical, and capable of carrying out complex reasoning processes and long-term planning.

The Speaker: Fast Thinking

The Talker is designed to generate coherent and fluid responses during a conversation with the user. It operates in real-time, accessing the agent's memory to retrieve relevant information that has already been processed. Its main goal is to maintain a smooth and natural interaction without stopping to perform complex calculations.

The Reasoner: Slow Thinking

The Reasoner is activated when a deeper level of processing is required, such as when solving complicated problems or creating a step-by-step plan. This system can access external information sources, such as databases, to perform more detailed calculations, formulate updated beliefs, and plan actions in a structured manner.

Why is this architecture important?

The separation between conversation and reasoning offers several key benefits:

1. Modularity: The components of conversation and reasoning can be optimized separately, which provides greater flexibility to the system.

2. Low latency: The Talker can respond quickly to user queries, while the Reasoner performs complex tasks in the background.

3. Improved reasoning: The Reasoner handles tasks that require logic and planning, allowing for greater accuracy in executing complex actions.

Practical example: A sleep coaching agent

To illustrate the usefulness of this architecture, let’s consider a sleep coaching agent. The Talker interacts with the user through empathetic dialogues, gathering information about their sleep habits and concerns. Meanwhile, the Reasoner analyzes the collected data, consults studies on sleep, and generates a personalized improvement plan for the user.

For example, the Talker might start the conversation by asking about the user’s bedroom environment, while the Reasoner, in the background, prepares a detailed plan to reduce distractions during the night, based on scientific knowledge about the impact of noise and light on sleep quality.

Challenges and future advancements

Despite the benefits of this architecture, there are challenges. For example, the Talker may operate with an outdated view of the world if the Reasoner has not yet finished updating the relevant information. However, in scenarios where deep reasoning is not needed, this approach proves effective for maintaining quick and fluid conversations.

In the future, it is expected that the Talker-Reasoner architecture will evolve to include multiple Reasoners, each specialized in different types of reasoning, which will allow for a more robust and adaptable AI.