Symbolic AI and Expert Systems: Unveiling the Foundation of Early Artificial Intelligence by Samyuktha jadagi

DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can also be viewed as an ontology. YAGO incorporates WordNet as part of its ontology, to align facts extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology currently being used.

We observe its shape and size, its color, how it smells, and potentially its taste. In short, we extract the different symbols and declare their relationships. With our knowledge base ready, determining whether the object is an orange becomes as simple as comparing it with our existing knowledge of an orange. An orange should have a diameter of around 2.5 inches and fit into the palm of our hands. We learn these rules and symbolic representations through our sensory capabilities and use them to understand and formalize the world around us. Symbolic AI algorithms have played an important role in AI’s history, but they face challenges in learning on their own.

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Life Sciences have long been one of the key drivers behind progress in AI, and the vastly increasing volume and complexity of data in biology is one of the drivers in Data Science as well. Life Sciences are also a prime application area for novel machine learning methods [2,51]. Similarly, Semantic Web technologies such as knowledge graphs and ontologies are widely applied to represent, interpret and integrate data [12,32,61].

Problems that can be drawn as a flow chart, with every variable accounted for, are well suited to symbolic AI. Symbolic AI simply means implanting human thoughts, reasoning, and behavior into a computer program. Symbols and rules are the foundation of human intellect and continuously encapsulate knowledge. Symbolic AI copies this methodology to express human knowledge through user-friendly rules and symbols.

The Rise and Fall of Symbolic AI

Neuro-Symbolic AI enjoins statistical machine learning’s unsupervised and supervised learning techniques with symbolic reasoning methods to redouble AI’s enterprise worth. This total expression of AI realizes its full potential for cognitive search, textual applications, and natural language technologies. It’s the means of resolving the tension between the connectionist and symbolic approaches that have widely prevented them from working together in modern organizations’ IT systems. Symbolic AI theory presumes that the world can be understood in the terms of structured representations.

Alessandro holds a PhD in Cognitive Science from the University of Trento (Italy). The only way to solve real language understanding problems, which enterprises need to tackle to obtain measurable ROI on their to combine symbolic AI with other techniques based on ML to get the best of both worlds. Being the first technology created and widely used to mimic human understanding of language, it is not a limitation but a significant value addition because it is well-known and can be used in predictable and explainable ways (no “black boxes” here). It uses explicit knowledge to understand language and still has plenty of space for significant evolution.

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Samuel’s Checker Program[1952] — Arthur Samuel’s goal was to explore to make a computer learn. The program improved as it played more and more games and ultimately defeated its own creator. In 1959, it defeated the best player, This created a fear of AI dominating AI. This lead towards the connectionist paradigm of AI, also called non-symbolic AI which gave rise to learning and neural network-based approaches to solve AI. The difficulties encountered by symbolic AI have, however, been deep, possibly unresolvable ones.

Symbolic AI, GOFAI, or Rule-Based AI (RBAI), is a sub-field of AI concerned with learning the internal symbolic representations of the world around it. The main objective of Symbolic AI is the explicit embedding of human knowledge, behavior, and “thinking rules” into a computer or machine. Through Symbolic AI, we can translate some form of implicit human knowledge into a more formalized and declarative form based on rules and logic. Seddiqi expects many advancements to come from natural language processing. Language is a type of data that relies on statistical pattern matching at the lowest levels but quickly requires logical reasoning at higher levels.

Natural language processing

A knowledge graph consists of entities and concepts represented as nodes, and edges of different types that connect these nodes. To learn from knowledge graphs, several approaches have been developed that generate knowledge graph embeddings, i.e., vector-based representations of nodes, edges, or their combinations [15,36,47,48,50]. Major applications of these approaches are link prediction (i.e., predicting missing edges between the entities in a knowledge graph), clustering, or similarity-based analysis and recommendation.

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