Symbolic AI today
Being the first major revolution in AI, Symbolic AI has been applied to many applications – some with more success than others. Despite the proven limitations we discussed, Symbolic AI systems have laid the groundwork for current AI technologies. This is not to say that Symbolic AI is wholly forgotten or no longer used. On the contrary, there are still prominent applications that rely on Symbolic AI to this day and age. We will highlight some main categories and applications where Symbolic AI remains highly relevant.
Expert systems
Symbolic AI has been predominantly used to design and develop ESs. IBM’s Representation, Ontology, Structure, Star (ROSS) is a great example. You can refer to https://arxiv.org/pdf/1411.4192.pdf for further reading. ROSS is an expert system platform for legal research, much like an AI lawyer. Given a natural language prompt, ROSS can sift through the law, complete court cases, and other documents, and return relevant structured data and evidence based on the query. Symbolic AI is the core method behind several other expert systems, with additional examples being decision-making systems, process monitoring, and logistics.
Natural language processing
Symbolic AI was also seriously successful in the field of NLP systems. We can leverage Symbolic AI programs to encapsulate the semantics of a particular language through logical rules, thus helping with language comprehension. This property makes Symbolic AI an exciting contender for chatbot applications. Symbolical linguistic representation is also the secret behind some intelligent voice assistants. These smart assistants leverage Symbolic AI to structure sentences by placing nouns, verbs, and other linguistic properties in their correct place to ensure proper grammatical syntax and semantic execution.
Moreover, Symbolic AI allows the intelligent assistant to make decisions regarding the speech duration and other features, such as intonation when reading the feedback to the user. Modern dialog systems (such as ChatGPT) rely on end-to-end deep learning frameworks and do not depend much on Symbolic AI. Similar logical processing is also utilized in search engines to structure the user’s prompt and the semantic web domain.
Constraint satisfaction
Naturally, Symbolic AI is also still rather useful for constraint satisfaction and logical inferencing applications. The area of constraint satisfaction is mainly interested in developing programs that must satisfy certain conditions (or, as the name implies, constraints). Through logical rules, Symbolic AI systems can efficiently find solutions that meet all the required constraints. Symbolic AI is widely adopted throughout the banking and insurance industries to automate processes such as contract reading. Another recent example of logical inferencing is a system based on the physical activity guidelines provided by the World Health Organization (WHO). The knowledge base of this AI is the guidelines themselves. Since the procedures are explicit representations (already written down and formalized), Symbolic AI is the best tool for the job. The researchers were able to provide the guidelines as logical rules. When given a user profile, the AI can evaluate whether the user adheres to these guidelines.
Explainable AI
Symbolic AI is also highly interpretable. Since the program has logical rules, we can easily trace the conclusion to the root node, precisely understanding the AI’s path. For this reason, Symbolic AI has also been explored multiple times in the exciting field of Explainable Artificial Intelligence (XAI). A paradigm of Symbolic AI, Inductive Logic Programming (ILP), is commonly used to build and generate declarative explanations of a model. This process is also widely used to discover and eliminate physical bias in a machine learning model. For example, ILP was previously used to aid in an automated recruitment task by evaluating candidates’ Curriculum Vitae (CV). Due to its expressive nature, Symbolic AI allowed the developers to trace back the result to ensure that the inferencing model was not influenced by sex, race, or other discriminatory properties.
These limitations of Symbolic AI led to research focused on implementing sub-symbolic models.
