Symbolic AI vs Machine Learning in Natural Language Processing

Expert systems are monotonic; that is, the more rules you add, the more knowledge is encoded in the system, but additional rules can’t undo old knowledge. Monotonic basically means one direction; i.e. when one thing goes up, another thing goes up. Because machine learning algorithms can be retrained on new data, and will revise their parameters based on that new data, they are better at encoding tentative knowledge that can be retracted later if necessary. With this historical basis, early AI

researchers created representations of logic that would allow  computers to perform logical

reasoning. First Order Logic provides a method to store declarations about the world, the robot and everything it knows. There are limits to what it can represent, but you can go a long way before running into them.

In artificial intelligence, the reasoning is essential so that the machine can also think rationally as a human brain, and can perform like a human. Another way the two AI paradigms can be combined is by using neural networks to help prioritize how symbolic programs organize and search through multiple facts related to a question. For example, if an AI is trying to decide if a given statement is true, a symbolic algorithm needs to consider whether thousands of combinations of facts are relevant. This is important because all AI systems in the real world deal with messy data. For example, in an application that uses AI to answer questions about legal contracts, simple business logic can filter out data from documents that are not contracts or that are contracts in a different domain such as financial services versus real estate. Legacy systems often require an understanding of the logic or rules upon which decisions are made.

Reasoning in Artificial intelligence

Emerging in the mid-20th century, Symbolic AI operates on a premise rooted in logic and explicit symbols. This approach draws from disciplines such as philosophy and logic, where knowledge is represented through symbols, and reasoning is achieved through rules. Think of it as manually crafting a puzzle; each piece (or symbol) has a set place and follows specific rules to fit together. While efficient for tasks with clear rules, it often struggles in areas requiring adaptability and learning from vast data. One of the main stumbling blocks of symbolic AI, or GOFAI, was the difficulty of revising beliefs once they were encoded in a rules engine.

“We are finding that neural networks can get you to the symbolic domain and then you can use a wealth of ideas from symbolic AI to understand the world,” Cox said. “Neuro-symbolic modeling is one of the most exciting areas in AI right now,” said Brenden Lake, assistant professor of psychology and data science at New York University. His team has been exploring different ways to bridge the gap between the two AI approaches. Now researchers and enterprises are looking for ways to bring neural networks and symbolic AI techniques together.

Reach Global Users in Their Native Language

The expert system processes the rules to make deductions and to determine what additional information it needs, i.e. what questions to ask, using human-readable symbols. For example, OPS5, CLIPS and their successors Jess and Drools operate in this fashion. Implementations of symbolic reasoning are called rules engines or expert systems or knowledge graphs. Google made a big one, too, which is what provides the information in the top box under your query when you search for something easy like the capital of Germany. These systems are essentially piles of nested if-then statements drawing conclusions about entities (human-readable concepts) and their relations (expressed in well understood semantics like X is-a man or X lives-in Acapulco).

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. Symbols also serve to transfer learning in another sense, not from one human to another, but from one situation to another, over the course of a single individual’s life.

Summarizing, neuro-symbolic artificial intelligence is an emerging subfield of AI that promises to favorably combine knowledge representation and deep learning in order to improve deep learning and to explain outputs of deep-learning-based systems. Neuro-symbolic approaches carry the promise that they will be useful for addressing complex AI problems that cannot be solved by purely symbolic or neural means. We have laid out some of the most important currently investigated research directions, and provided literature pointers suitable as entry points to an in-depth study of the current state of the art. New deep learning approaches based on Transformer models have now eclipsed these earlier symbolic AI approaches and attained state-of-the-art performance in natural language processing. However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and documents.

• There have been several efforts to create complicated symbolic AI systems that encompass the multitudes of rules of certain domains.
• Deep learning is better suited for System 1 reasoning,  said Debu Chatterjee, head of AI, ML and analytics engineering at ServiceNow, referring to the paradigm developed by the psychologist Daniel Kahneman in his book Thinking Fast and Slow.
• Emerging in the mid-20th century, Symbolic AI operates on a premise rooted in logic and explicit symbols.
• For example, a symbolic AI system might be able to solve a simple mathematical problem, but it would be unable to solve a complex problem such as the stock market.
• Despite these limitations, symbolic AI has been successful in a number of domains, such as expert systems, natural language processing, and computer vision.

Symbolic AI, also known as “Good Old-Fashioned Artificial Intelligence” (GOFAI), refers to the approach in artificial intelligence research that emphasizes the use of symbols and rules to solve problems. Neuro-symbolic artificial intelligence can be defined as the subfield of artificial intelligence (AI) that combines neural and symbolic approaches. By symbolic we mean approaches that rely on the explicit representation of knowledge using formal languages—including formal logic—and the manipulation of language items (‘symbols’) by algorithms to achieve a goal. A. Symbolic AI, also known as classical or rule-based AI, is an approach that represents knowledge using explicit symbols and rules.

Two major reasons are usually brought forth to motivate the study of neuro-symbolic integration. The first one comes from the field of cognitive science, a highly interdisciplinary field that studies the human mind. In order to advance the understanding of the human mind, it therefore appears to be a natural question to ask how these two abstractions can be related or even unified, or how symbol manipulation can arise from a neural substrate [1].

Using symbolic AI, everything is visible, understandable and explainable, leading to what is called a “transparent box,” as opposed to the “black box” created by machine learning. Using symbolic AI, everything is visible, understandable and explainable, leading to what is called a ‘transparent box’ as opposed to the ‘black box’ created by machine learning. The logic clauses that describe programs are directly interpreted to run the programs specified. No explicit series of actions is required, as is the case with imperative programming languages.

Furthermore, it can generalize to novel rotations of images that it was not trained for. Unlike other branches of AI, such as machine learning and neural networks, which rely on statistical patterns and data-driven algorithms, symbolic AI emphasizes the use of explicit knowledge and explicit reasoning. It involves the creation and manipulation of symbols to represent various aspects of the world and the use of logical rules to derive conclusions from these symbols.

Another recent example of logical inferencing is a system based on the physical activity guidelines provided by the World Health Organization (WHO). Since the procedures are explicit representations (already written down and formalized), Symbolic AI is the best tool for the job. When given a user profile, the AI can evaluate whether the user adheres to these guidelines. We might teach the program rules that might eventually become irrelevant or even invalid, especially in highly volatile applications such as human behavior, where past behavior is not necessarily guaranteed. Even if the AI can learn these new logical rules, the new rules would sit on top of the older (potentially invalid) rules due to their monotonic nature. As a result, most Symbolic AI paradigms would require completely remodeling their knowledge base to eliminate outdated knowledge.

In contrast, a neural network may be right most of the time, but when it’s wrong, it’s not always apparent what factors caused it to generate a bad answer. Also, some tasks can’t be translated to direct rules, including speech recognition and natural language processing. Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning. Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions.

Many leading scientists believe that symbolic reasoning will continue to remain a very important component of artificial intelligence. Deep learning and neural networks excel at exactly the tasks that symbolic AI struggles with. They have created a revolution in computer vision applications such as facial recognition and cancer detection. The advantage of neural networks is that they can deal with messy and unstructured data. Instead of manually laboring through the rules of detecting cat pixels, you can train a deep learning algorithm on many pictures of cats. When you provide it with a new image, it will return the probability that it contains a cat.

It offers transparency, flexibility, and interpretability in certain domains. Combining Symbolic AI with other AI techniques can lead to powerful and versatile AI systems for various applications. On the other hand, Neural Networks are a type of machine learning inspired by the structure and function of the human brain.

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• A thing that represents a subset of a set “generalizes” it and its relation predicate is genls.
• Analog to the human concept learning, given the parsed program, the perception module learns visual concepts based on the language description of the object being referred to.
• At face value, symbolic representations provide no value, especially to a computer system.
• Since the representations and rules are explicitly defined, it is possible to understand and explain the reasoning process of the AI system.