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TDWI Upside - Where Data Means Business

How Symbolic AI Yields Cost Savings, Business Results

"Good old-fashioned AI" experiences a resurgence as natural language processing takes on new importance for enterprises.

Thinking involves manipulating symbols and reasoning consists of computation according to Thomas Hobbes, the philosophical grandfather of artificial intelligence (AI). Machines have the ability to interpret symbols and find new meaning through their manipulation -- a process called symbolic AI. In contrast to machine learning (ML) and some other AI approaches, symbolic AI provides complete transparency by allowing for the creation of clear and explainable rules that guide its reasoning.

For Further Reading:

Self-Supervised Learning's Impact on AI and NLP

How to Judge a Training Data Set

The Future of Machine Learning: Models as APIs

Commonly used for segments of AI called natural language processing (NLP) and natural language understanding (NLU), symbolic AI follows an IF-THEN logic structure. When an IF linguistic condition is met, a THEN output is generated. Symbolic AI works best when rules are straightforward. By using the IF-THEN structure, you can avoid the "black box" problems typical of ML where the steps the computer is using to solve a problem are obscured and non-transparent.

Originating in the 1950s, symbolic AI was the original approach to AI, such that it received the nickname "Good Old-Fashioned AI (GOFAI)" in the 1980s book, Artificial Intelligence: The Very Idea by John Haugeland.

However, in the 1980s and 1990s, symbolic AI fell out of favor with technologists whose investigations required procedural knowledge of sensory or motor processes. Today, symbolic AI is experiencing a resurgence due to its ability to solve problems that require logical thinking and knowledge representation, such as natural language.

The reason money is flowing to AI anew is because the technology continues to evolve and deliver on its heralded potential. For instance, through NLP, computers can now elaborate human language. Examples of NLP systems in AI include virtual assistants and some chatbots. In fact, NLP allows communication through automated software applications or platforms that interact with, assist, and serve human users (customers and prospects) by understanding natural language. As a branch of NLP, NLU employs semantics to get machines to understand data expressed in the form of language. By utilizing symbolic AI, NLP models can dramatically decrease costs while providing more insightful, accurate results.

Play by Your Rules

Yet, it is not always understood what takes place between inputs and outputs in AI. A system that performs functions and produces results but that cannot be explained is of grave concern. Unfortunately, this black-box scenario goes hand in hand with ML and elevates enterprise risk. After all, an unforeseen problem could ruin a corporate reputation, harm consumers and customers, and by performing poorly, jeopardize support for future AI projects.

When you build an algorithm using ML alone, changes to input data can cause AI model drift. An example of AI drift is chatbots or robots performing differently than a human had planned. When such events happen, you must test and train your data all over again -- a costly, time-consuming effort. In contrast, using symbolic AI lets you easily identify issues and adapt rules, saving time and resources.

Insufficient language-based data can cause issues when training an ML model. Such models require an immense amount of data to run. This differs from symbolic AI in that you can work with much smaller data sets to develop and refine the AI's rules. Further, symbolic AI assigns a meaning to each word based on embedded knowledge and context, which has been proven to drive accuracy in NLP/NLU models.

A Focus on the Data

The ability to cull unstructured language data and turn it into actionable insights benefits nearly every industry, and technologies such as symbolic AI are making it happen.

  • The insurance industry manages volumes of unstructured language data in diverse forms. With symbolic AI, insurers can extract specific details for policy reviews and risk assessments. This streamlines workflows, allowing underwriters to process four times the claims while cutting risk significantly.

  • Banking's slow adoption of digitization has made it difficult for institutions to handle high volumes of customer service calls, online requests, and email. However, some AI platforms have successfully overcome this issue with knowledge-based FAQs, email automation, and classification to collection processes via the services supply chain.

  • The shift to digital has made media and publishing competitive, too. Users now decide what content they will explore at a glance. Outlets can successfully process, categorize, and tag more than 1.5 million news articles each day with symbolic AI, making it simple for readers and viewers at scale to identify keywords and topics of interest.

A Hybrid Approach

Symbolic AI and ML can work together and perform their best in a hybrid model that draws on the merits of each. In fact, some AI platforms already have the flexibility to accommodate a hybrid approach that blends more than one method.

When applied to natural language, hybrid AI greatly simplifies valuable tasks such as categorization and data extraction. You can train linguistic models using symbolic AI for one data set and ML for another. Then, combining them both in a pipeline achieves even greater accuracy.

In the paper "The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence," scientist and entrepreneur Gary Marcus proposes a "hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models that could provide the substrate for a richer, more robust AI than is currently possible."

Today, across industries, it's all about the data. Symbolic AI is strengthening NLU/NLP with greater flexibility, ease, and accuracy -- and it particularly excels in a hybrid approach. As a result, insights and applications are now possible that were unimaginable not so long ago.

Enterprises that fail to focus their efforts accordingly may find themselves a thing of the past, too.

About the Author

Luca Scagliarini is chief product officer of and is responsible for leading the product management function and overseeing the company’s product strategy. Previously, Luca held the roles of EVP, strategy and business development and CMO at and served as CEO and co-founder of semantic advertising spinoff ADmantX. During his career, he held senior marketing and business development positions at Soldo, SiteSmith, Hewlett-Packard, and Think3. Luca received an MBA from Santa Clara University and a degree in engineering from the Polytechnic University of Milan, Italy.

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