Black implies white, life implies death, self implies other, fear implies courage, action implies time, and Yin implies Yang. In the world of artificial intelligence (AI), Large Language Models (LLMs) imply knowledge graphs (KG). (Paraphrasing Alan Watts.)
This current “AI summer” is dominated by LLMs such as ChatGPT, Gemini, Grok, and LLama. But the dominant technologies from past AI summers, Prolog and Knowledge Graphs (specifically the Semantic Web), complement each other like the complementary forces of Yin and Yang. To fully appreciate their roles and the power they bring when combined, let’s take time to understand their individual characteristics and how they align with Daniel Kahneman’s concepts of System 1 and System 2 thinking.
Large Language Models (LLMs)
LLMs are advanced AI systems trained on vast amounts of text data – humanity’s corpus of our written knowledge, books, articles, and all sorts of public correspondence. They work by predicting the next word or phrase in a sequence, based on the patterns they’ve learned from the data they’ve been exposed to. This predictive nature makes LLMs incredibly powerful for generating human-like text, answering questions, and even engaging in creative tasks. However, LLMs are not deterministic; they don’t follow strict rules. Instead, they operate probabilistically, offering different possible outputs depending on the context and data they’ve processed.
LLMs are trained and molded to reflect the knowledge, biases, and structures of the data they’ve been fed, which often mirrors what humanity has written or recorded throughout history. This means LLMs are good at reflecting the collective “is”—the way things are or have been understood based on vast datasets.
Knowledge Graphs (KGs)
Knowledge Graphs, on the other hand, are deterministic and structured. They are databases that model relationships between entities in a way that is transparent, intentional, and logically sound. KGs are designed to store and retrieve factual knowledge, where each piece of information and its connections are explicitly defined. This makes them highly reliable for tasks that require precision, such as answering specific queries, reasoning about complex relationships, or supporting logical decision-making processes.
Unlike LLMs, which generate predictions based on learned patterns, KGs provide a structured representation of knowledge where every connection is deliberate and traceable. This deterministic nature aligns KGs with the idea of being fully intentional, ensuring that the data they store is used in a way that reflects clear logic and purpose.
Kahneman’s System 1 and System 2 Thinking
Daniel Kahneman, introduced the concepts of System 1 and System 2 thinking to describe two modes of human thought. System 1 is fast, automatic, and often subconscious, relying on intuition and gut feelings. It’s the type of thinking that happens when we recognize a face, react quickly to danger, or answer simple questions.
System 2, by contrast, is slow, deliberate, and logical. It’s engaged when we solve complex problems, make reasoned decisions, or learn new skills that require concentration. System 2 is about careful analysis and logical deduction, requiring effort and conscious thought.
LLMs as Yin and Knowledge Graphs as Yang
In the context of AI, LLMs embody Yin—the passive, intuitive, and flexible aspect. Just as Yin represents darkness and fluidity, LLMs operate in a realm of ambiguity, generating responses based on a vast sea of data without relying on strict, predefined rules. They are excellent at tasks requiring creativity, pattern recognition, and rapid response, much like how System 1 operates in our brains.
Conversely, KGs represent Yang—the active, logical, and structured side. Yang is about brightness, clarity, and structure, which aligns perfectly with the deterministic and rule-based nature of KGs. They provide the framework for clear reasoning and logical analysis, embodying the essence of System 2 thinking.
The Symbiotic Relationship
Just as Yin and Yang are interdependent, LLMs and KGs complement each other in AI. LLMs bring the flexibility and intuition needed to handle unstructured data and generate creative solutions, while KGs provide the logical structure necessary for precise reasoning and factual accuracy.
By integrating these two technologies, we can create more holistic AI systems that leverage both structured knowledge and intuitive flexibility. For instance, an AI system could use an LLM to generate a hypothesis or creative solution and then use a KG to verify the logical consistency of that hypothesis or to provide factual support.
This symbiosis not only enriches our understanding of AI but also highlights the importance of balancing these two approaches in data-driven decision-making. Just as in life, where Yin implies Yang, in the world of AI, LLMs imply KGs, each bringing out the strengths of the other to create a more powerful and versatile system.
Conclusion
The symmetry of opposites, as exemplified by the interplay between Knowledge Graphs and Large Language Models, offers a profound lesson: the coexistence of contrasting forces can be a powerful guide. Just as Yin and Yang together create harmony, the integration of logical structure with intuitive flexibility in AI shows us that recognizing and leveraging these opposites can lead us in the right direction. By understanding how these contrasting approaches complement each other, we can navigate the complexities of AI and data-driven decision-making more effectively, ensuring that our journey is both innovative and rooted in deep understanding.
Faith and Patience,
Reverend Dukkha Hanamoku
Bodhi Day 