I asked DeepSeek: «Can the computer language PROLOG remain useful in today’s LLM-based A.I.?»
Here is DeepSeek’s answer:
Yes, Prolog can still remain useful in today’s AI landscape, even with the rise of large language models (LLMs) like GPT, Gemini, and others. While LLMs excel at natural language understanding, generation, and pattern recognition, Prolog offers unique strengths in symbolic reasoning, rule-based systems, and logical inference. Here’s how Prolog can complement LLM-based AI:
- Strengths of Prolog in AI
Symbolic Reasoning: Prolog is designed for symbolic manipulation and logical inference, making it ideal for tasks requiring precise reasoning, such as theorem proving, expert systems, and knowledge representation.
Rule-Based Systems: Prolog’s declarative nature allows developers to define rules and relationships clearly, which is useful for encoding domain-specific knowledge (e.g., medical diagnosis, legal reasoning).
Backtracking and Search: Prolog’s built-in backtracking mechanism is powerful for solving combinatorial problems, such as scheduling, planning, and constraint satisfaction.
Transparency and Explainability: Prolog programs are inherently interpretable, making it easier to trace and explain decisions compared to the “black-box” nature of LLMs.
- Complementing LLMs
LLMs are excellent at handling unstructured data (e.g., text, images) and generating human-like responses, but they often struggle with:
Logical Consistency: LLMs can produce plausible but incorrect or inconsistent answers.
Precise Reasoning: Tasks requiring strict logical inference (e.g., mathematical proofs, symbolic logic) are better handled by Prolog.
Domain-Specific Knowledge: Prolog can encode structured knowledge bases that LLMs can query or use for grounding.
Hybrid Approaches:
Use LLMs for natural language understanding and generation, and Prolog for logical reasoning and rule-based decision-making.
For example, an LLM could parse a user’s query into a structured format (e.g., a logical statement), which Prolog then evaluates to produce a precise answer.
- Use Cases for Prolog in Modern AI
Expert Systems: Prolog can power expert systems in domains like healthcare, finance, and law, where precise rule-based reasoning is critical.
Knowledge Graphs: Prolog can query and reason over knowledge graphs, enabling complex inferences that LLMs alone cannot perform.
Formal Verification: Prolog is useful for verifying the correctness of systems, such as software or hardware designs, using logical rules.
Education: Prolog is still widely used in teaching logic, AI, and computational thinking due to its simplicity and clarity.
Chatbots and Assistants: Prolog can handle structured, rule-based interactions (e.g., answering FAQs, processing forms), while LLMs manage free-form conversations.
- Challenges and Limitations
Scalability: Prolog struggles with large-scale data and computationally intensive tasks, which are better suited for neural networks or statistical methods.
Integration with Modern Tools: Prolog is not as widely used as Python or other mainstream languages, so integrating it into modern AI pipelines may require additional effort.
Learning Curve: Prolog’s declarative paradigm can be challenging for developers accustomed to imperative or object-oriented programming.
- Future of Prolog in AI
Symbolic-Neural Integration: There is growing interest in combining symbolic AI (e.g., Prolog) with neural networks (e.g., LLMs) to create hybrid systems that leverage the strengths of both approaches.
Explainable AI (XAI): Prolog’s transparency makes it a valuable tool for building explainable AI systems, which are increasingly important in regulated industries.
Niche Applications: Prolog will continue to thrive in domains where symbolic reasoning and rule-based systems are essential.
Conclusion:
Prolog remains a valuable tool in the AI toolkit, especially for tasks requiring logical reasoning, rule-based systems, and explainability. While LLMs dominate areas like natural language processing, Prolog’s strengths in symbolic AI make it a natural complement to modern AI systems. By combining Prolog with LLMs, developers can build more robust, interpretable, and capable AI systems.
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NOTES:
DeepSeek is the Chinese alternative to western A.I. chatbots such as ChatGPT; much cheaper to train, typically with more intelligent answers, etc.:
A.I. programming in Prolog and Assembler 
