Symbolic AI, a transparent artificial intelligence

As an alternative to logic, Roger Schank introduced case-based reasoning . The CBR approach outlined in his book, Dynamic Memory, focuses first on remembering key problem-solving cases for future use and generalizing them where appropriate. When faced with a new problem, CBR retrieves the most similar previous case and adapts it to the specifics of the current problem.

Now that AI is tasked with higher-order systems and data management, the capability to engage in logical thinking and knowledge representation is cool again. 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.

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Maybe in the future, we’ll invent AI technologies that can both reason and learn. But for the moment, symbolic AI is the leading method to deal with problems that require logical thinking and knowledge representation. Knowledge-based systems have an explicit knowledge base, typically of rules, to enhance reusability across domains by separating procedural code and domain knowledge. A separate inference engine processes rules and adds, deletes, or modifies a knowledge store. The automated theorem provers discussed below can prove theorems in first-order logic.

But symbolic AI starts to break when you must deal with the messiness of the world. For instance, consider computer vision, the science of enabling computers to make sense of the content of images and video. Say you have a picture of your cat and want to create a program that can detect images that contain your cat.

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NSCL uses both rule-based programs and neural networks to solve visual question-answering problems. As opposed to pure neural network–based models, the hybrid AI can learn new tasks with less data and is explainable. And unlike symbolic-only models, NSCL doesn’t struggle to analyze the content of images. The full value of Neuro-Symbolic AI isn’t just in its elimination of the training data or taxonomy building delays that otherwise impede Natural Language Processing applications, cognitive search, or conversational AI. Nor is it only in the ease of generating queries and bettering the results of constraint systems, all of which it inherently does.

There are two fields dealing with creating high-performing AI models with reasoning capabilities, which usually requires combining components from both symbolic and subsymbolic paradigms. While XAI aims to ensure model explainability by developing models that are inherently easier to understand for their users, NSC focuses on finding ways to combine subsymbolic learning algorithms with symbolic reasoning techniques. The two biggest flaws of deep learning are its lack of model interpretability (i.e. why did my model make that prediction?) and the large amount of data that deep neural networks require in order to learn. And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge.

From Philosophy to Thinking Machines

These algorithms along with the accumulated lexical and semantic knowledge contained in the Inbenta Lexicon allow customers to obtain optimal results with minimal, or even no training data sets. This is a significant advantage to brute-force machine learning algorithms which often requires months to “train” and ongoing maintenance as new data sets, or utterances, are added. Neural networks are almost as old as symbolic AI, but they were largely dismissed because they were inefficient and required compute resources that weren’t available at the time. In the past decade, thanks to the large availability of data and processing power, deep learning has gained popularity and has pushed past symbolic AI systems.

Knowledge base question answering is a task where end-to-end deep learning techniques have faced significant challenges such as the need for semantic parsing, reasoning, and large training datasets. In this work, we demonstrate NSQA, which is a realization of a hybrid “neuro-symbolic” approach. Allen Newell, Herbert A. Simon — Pioneers in Symbolic AIThe work in AI started by projects like the General Problem Solver and other rule-based reasoning systems like Logic Theorist became the foundation for almost 40 years of research. Symbolic AI is the branch of artificial intelligence research that concerns itself with attempting to explicitly represent human knowledge in a declarative form (i.e. facts and rules). If such an approach is to be successful in producing human-like intelligence then it is necessary to translate often implicit or procedural knowledge possessed by humans into an explicit form using symbols and rules for their manipulation.

Agent-Based Model Visualization

This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science and high-school math. There have been several efforts to create complicated symbolic AI systems that encompass the multitudes of rules of certain domains. Called expert systems, these symbolic AI models use hardcoded knowledge and rules to tackle complicated tasks such as medical diagnosis. But they require a huge amount of effort by domain experts and software engineers and only work in very narrow use cases. As soon as you generalize the problem, there will be an explosion of new rules to add (remember the cat detection problem?), which will require more human labor.

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. Foo, Y.P.S. and Takefuji, Y., “Integer linear programming Neural Networks for Job-Shop Scheduling”, Proc. Of the Second IEEE International Conference on Neural Networks, Vol.2, pp.341–348, San Diego, July 1988. Generalization of the solutions to unseen tasks and unforeseen data distributions.

Data Hungry Models

Regarding implementing symbolic AI, one of the oldest, yet still, the most popular, logic programming languages is Prolog comes in handy. Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages. That is, to build a symbolic reasoning system, first humans must learn the rules by which two phenomena relate, and then hard code those relationships into a static program. A second flaw in symbolic reasoning is that the computer itself doesn’t know what the symbols mean; i.e. they are not necessarily linked to any other representations of the world in a non-symbolic way. Again, this stands in contrast to neural nets, which can link symbols to vectorized representations of the data, which are in turn just translations of raw sensory data. So the main challenge, when we think about GOFAI and neural nets, is how to ground symbols, or relate them to other forms of meaning that would allow computers to map the changing raw sensations of the world to symbols and then reason about them.

• Here, instead of clearly defined human-readable relations, we design less explainable mathematical equations to solve problems.
• A different way to create AI was to build machines that have a mind of its own.
• With the ability to learn and apply logic at the same time, the system automatically became smarter.
• Our chemist was Carl Djerassi, inventor of the chemical behind the birth control pill, and also one of the world’s most respected mass spectrometrists.
• No explicit series of actions is required, as is the case with imperative programming languages.
• GUIDON, which showed how a knowledge base built for expert problem solving could be repurposed for teaching.

LISP is the second oldest programming language after FORTRAN and was created in 1958 by John McCarthy. LISP provided the first read-eval-print loop to support rapid program development. Program tracing, stepping, and breakpoints were also provided, along with the ability to change values or functions and continue from breakpoints or errors.

We expect it to heat and possibly boil over, even though we may not know its temperature, its boiling point, or other details, Symbolic AI such as atmospheric pressure. “A physical symbol system has the necessary and sufficient means of general intelligent action.”

• You can easily visualize the logic of rule-based programs, communicate them, and troubleshoot them.
• While Symbolic AI is better at logical inferences, subsymbolic AI outperforms symbolic AI at feature extraction.
• For instance, if you ask yourself, with the Symbolic AI paradigm in mind, “What is an apple?
• More advanced knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies.
• At Bosch, he focuses on neuro-symbolic reasoning for decision support systems.
• Instead of manually laboring through the rules of detecting cat pixels, you can train a deep learning algorithm on many pictures of cats.

Backward chaining occurs in Prolog, where a more limited logical representation is used, Horn Clauses. Programs were themselves data structures that other programs could operate on, allowing the easy definition of higher-level languages. This “knowledge revolution” led to the development and deployment of expert systems , the first commercially successful form of AI software. Early work covered both applications of formal reasoning emphasizing first-order logic, along with attempts to handle common-sense reasoning in a less formal manner. 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.

While subsymbolic AI is developed because of the shortcomings of the symbolic AI paradigm, they can be used as complementary paradigms. While Symbolic AI is better at logical inferences, subsymbolic AI outperforms symbolic AI at feature extraction. While this may be unnerving to some, it must be remembered that symbolic AI still only works with numbers, just in a different way. By creating a more human-like thinking machine, organizations will be able to democratize the technology across the workforce so it can be applied to the real-world situations we face every day.