AI Gets Inner Monologue And Becomes Incredibly Smarter

Quiet-STaR essentially trains AI systems to simulate a varied inner monologue, enabling them to anticipate various conversational trajectories and learn from ongoing interactions. This is very different from conventional AI chatbots (like ChatGPT), which lack the capacity to think about responses or foresee different conversational outcomes.

The results of applying Quiet-STaR to Mistral 7B, an open-source large language model (LLM), are impressive. Before any training, Mistral 7B scored 36.3 percent on a reasoning test. However, after implementing Quiet-STaR, its score jumped to 47.2 percent. While the AI still struggled with specific tasks, such as a school math test, its performance nearly doubled when using an inner monologue.

Quiet-STaR creates an Al inner monologue by generating a mixture of responses with and without a rationale. This allows the AI to select the most suitable answer. It also learns from its mistakes, discarding incorrect rationales to refine its reasoning capabilities over time. This breakthrough is significant because it addresses AI’s lack of common sense reasoning and contextual understanding.

Traditional neural network-based systems often fall short in these areas, lacking genuine comprehension of language and context. Previous attempts to enhance the reasoning skills of LLMs have been highly specialized and unable to generalize across different AI models. However, Quiet-STaR offers a more versatile solution that can be applied across various LLMs and training datasets.

The implications of Quiet-STaR extend beyond improving chatbots. Bridging the gap between AI systems and human-like reasoning capabilities paves the way for more sophisticated applications across industries. From customer service chatbots to medical diagnostics and autonomous vehicles, AI with inner monologue capabilities could revolutionize how humans interact with technology.

As researchers continue to explore the potential of Quiet-STaR and similar methods, a future where AI systems not only understand our words but also grasp the nuances of human thought and conversation is not too far away. However, using AI inner monologue systems presents a few challenges, such as achieving the right balance between autonomy and control.

The dangers associated with conversational AI (due to the system’s inner monologue) involve targeted manipulation of individual users with extreme precision and efficiency, enabling malicious actors to influence behaviors, opinions, and decisions. Conversational AI also involves real-time engagement between users and AI systems, allowing AI to sense user reactions and adjust tactics to maximize persuasion.

Unlike traditional influence campaigns on social media, which are more generalized, manipulation through conversational AI (with an inner monologue) can be highly damaging by polarizing communities, spreading misinformation, and amplifying discontent.

Moreover, the ability of AI systems to engage users in conversations, adjust strategies based on user responses, and pursue specific goals set by corporate sponsors, nation-states, or malicious actors raises significant ethical and societal concerns regarding the potential misuse of the technology.

Source: arXiv