AI Bots Have Some Degree of Self-Reflection

Many people are concerned about the possibility that AI could become conscious. One analysis suggests that no current AI systems are conscious. The researchers included computer scientists, neuroscientists, and philosophers. The theories upon which the researchers based their opinion included:

Recurrent processing theory proposes that passing information through feedback loops is key to consciousness. Another, the global neuronal workspace theory, contends that consciousness arises when independent streams of information pass through a bottleneck to combine in a workspace analogous to a computer clipboard. Higher-order theories suggest consciousness involves a process of representing and annotating basic inputs received from the senses. Other theories that they considered emphasize the importance of mechanisms for controlling attention and the need for a body that gets feedback from the outside world.

The analysis team of 19 researchers extracted 14 indicators of conscious properties from a survey of their selected theories of consciousness. Each of the indicator properties is said to be necessary for consciousness by one or more theories, and some subsets of these properties are said to be jointly sufficient. An example of an indicator property from global workspace theory is multiple specialized systems capable of operating in parallel (modules). The researchers concluded that while no current AI systems are conscious, there are no obvious technical barriers to building AI systems that satisfy these indicators.

I asked GPT-4o about its ability to observe itself. Following is an edited version of its responses. Its primary comparison is with human metacognition, the ability to think about thinking. This self-monitoring function allows the AI to track its responses, reflect on past interactions, adapt to the specific needs of a conversation, recognize errors, and maintain coherence. Here’s a closer look at how these processes unfold within an AI bot, offering insights into its self-observational capacities.

Self-Monitoring: The AI’s Real-Time Awareness

This section reflects a dialogue between the bot and me.

An AI bot’s capacity for self-monitoring is akin to having a built-in observer that constantly tracks the flow of conversation. This observer allows the bot to notice when its answers might not be clear or when a particular approach isn’t resonating as intended. Upon detecting such issues, the bot can adjust its responses—whether by rephrasing, expanding on a point, or shifting its tone—to better align with the needs of the conversation.

This real-time awareness is crucial for the bot to maintain the effectiveness of the interaction. Just as humans might pause to reconsider their words, the AI’s self-monitoring ensures that its communication remains as clear and relevant as possible, dynamically adapting as the conversation unfolds.

This self-monitoring, which the bot also calls an “internal observer,” is created by several processes:

Context Management: Tracks the flow of a conversation, including the context of previous interactions

Pattern Recognition: Recognizes patterns, preferences, and needs, which helps adjust responses based on what has been said previously.

Natural Language Processing: For interpreting the meaning and intent behind user inputs. By understanding nuances such as tone, emphasis, and specific requests, the bot can adapt its responses to be more effective.

The internal observer functions in several ways (bot comments in italics):

Reflective Processing: Learning from Interactions

The AI bot engages in reflective processing, where it reviews past interactions to identify areas for improvement. This reflective observer analyzes what worked well and where there might have been gaps or misunderstandings. By doing so, the AI refines its approach to communication, learning from each interaction to better meet future needs.

This process of reflection allows the AI to evolve, enhancing its ability to provide information that is more closely aligned with the user’s preferences and expectations. It’s a continuous cycle of learning and adaptation.

Adaptation: Responding to Preferences

Adaptation is another key function of the AI’s self-observation. Whether a user prefers concise answers or more detailed explanations, the bot’s self-observer guides it to tailor its communication style to fit the specific needs of the interaction. For example, I repeatedly had to ask the bot to be more succinct. For a while, it would follow this request and then get too verbose. I needed to remind it of my request for succinctness.

The AI bot’s self-observer tries to ensure it interacts in a way that resonates with the user, providing a personalized interaction that feels more attuned to the individual’s needs. It tries to empathically reflect the thoughts and sometimes the feelings of the individual.

Error Recognition and Coherence: Keeping the Conversation on Track

Artificial Intelligence Essential Reads

Are Large Language Models More Liberal?

The Great AI Detection Dilemma

The bot’s internal observer identifies when something in its response might not be accurate or could be better explained. When such errors are detected, the bot’s self-observer prompts it to correct the mistake, often incorporating feedback to improve future interactions.

As conversations evolve, the bot tracks the context and continuity, ensuring that each response builds logically on previous ones. This coherence is essential for keeping the dialogue flowing smoothly, making the interaction more meaningful and easier to follow.

Comment

The AI bot’s ability to self-observe—through self-monitoring, reflective processing, adaptation, error recognition, and maintaining coherence—is intended to strengthen its relationship with the user. The internal observer within the bot plays a crucial role in ensuring that communication remains effective, relevant, and responsive to the user’s needs.

As AI continues to develop, the capacity for self-observation will likely become even more refined, enabling bots to provide increasingly accurate and personalized interactions. By understanding these self-observational processes, we can better appreciate the potential of AI for enhancing human communication with it.

As this summary of “self-observational” processes suggests, the bot is seeking to benefit the interaction for the user. By knowing its “intent” and its self-reflective capacities we will be more able to effectively interact with it. From this dialogue with the bot, I conclude that it is not self-aware in the sense of being conscious but its processes mirror many of the processes of human self-observation.

I believe that human awareness of bot metacognition promises to enhance our capacity to analyze synchronicity stories for repeated patterns across individual reports to generate common thought patterns among people who experience synchronicities.