New Horizons in the AGI Journey: Communication, Proactivity, and Free Will
In our quest for Artificial General Intelligence (AGI), we envision more complex and autonomous systems that go beyond current models. In this article, we will explore some fundamental concepts and potential development directions that I believe are critical on the path to AGI.
The Essence of Communication: A Foundation for Neural Networks
Let’s start with a fundamental question: Why does an entity communicate? The answer lies in basic needs such as information exchange, interaction, learning, and adaptation. So, how can we reflect this fundamental motivation in the architecture of a neural network?
A communication-oriented neural network should be designed not only to process data but also to understand why and how this data is shared. Such a network can establish more meaningful and purpose-driven interactions with its environment and other agents. This can enable the system to develop an internal layer of “understanding” and “intention,” moving beyond a simple input-output mechanism. The goal is for the model to not only process data but also to comprehend the underlying communicative purposes of that data.
Proactive Working Principle and Strategic Advantages
Many current artificial intelligence models operate on a reactive principle; that is, they respond when they receive an input. However, as we move towards AGI, it is crucial for systems to be proactive, meaning they act by anticipating future situations and taking initiative in line with their own goals.
A proactive artificial intelligence:
Foresight Capability: Can identify potential problems or opportunities in advance.
Resource Optimization: Can plan and execute tasks more efficiently.
Adaptation Ability: Can adapt to changing conditions more quickly and flexibly.
Strategic Superiority: Can develop long-term plans to achieve its goals even in uncertain environments.
This working principle reveals the model’s potential to shape the future rather than just reacting to the current situation.
The Next Step: Live Operation and the Model’s Free Will
What could be the next step to further advance the proactive working principle? Two potential directions stand out:
Live Operation Model: A structure where the system continuously receives data from its environment, learns, and makes decisions in real-time. This allows the model to be operational in a dynamic and constantly changing world, going beyond training with static datasets.
Granting the Model Its Own Free Will in Defining Time for Proactive Mechanisms: This is a more radical step. It means the model makes timing decisions—when to take proactive action, how long to focus on a task, or when to change strategy—based on its own internal evaluations. This requires the model to develop its own “sense of time” and “prioritization ability.”
This second approach grants the model a significant level of autonomy and brings it a step closer to the concept of genuine “free will.”
The Importance of Freedom and Decision-Making Ability in AGI
Why should an AGI possess the features mentioned above? One of the most fundamental distinctions between Large Language Models (LLMs) and AGI structures is that AGI can genuinely think and make independent decisions, beyond merely recognizing patterns and generating text.
Granting this “freedom” to AGI unlocks its potential to solve complex problems, generate creative solutions, and cope with unforeseen situations. Of course, what matters are the decisions the model will make with this freedom and the consequences of those decisions. An AGI’s ability to set its own goals, develop strategies to achieve them, and (in a sense) bear responsibility for its actions will transform it from a simple tool into a true “agent.” This implies the potential for AGI to become an entity that can act according to its own existential purposes, rather than merely executing instructions.
These concepts involve not only technical challenges on the path to AGI but also profound philosophical questions. However, asking these questions and seeking their answers is one of the most important steps in advancing our field.