Neuro Symbolic AI: Enhancing Common Sense in AI

The following chapters will focus on and discuss the sub-symbolic paradigm in greater detail. In the next chapter, we will start by shedding some light on the NN revolution and examine the current situation regarding AI technologies. We also looked back at the other successes of Symbolic AI, its critical applications, and its prominent use cases.

Since antiquity, these issues have been raised by mythology, fiction, and philosophy. Science fiction and futurology also suggest that AI could become an existential threat to humanity with its enormous potential and power. Its origins date back to 1956 when it was established as an academic discipline. As a result, it experienced several waves of optimism, disappointment, and new approaches and successes throughout its development period. In the case of genes, small moves around a symbolic ai genome are done when mutations occur, and this constitutes a blind exploration of the solution space around the current position, with a descent method but without a gradient. In general, several locations are explored in parallel to avoid local minima and speed up the search.

What is Neuro Symbolic AI?

In these cases, the aim of Data Science is either to utilize existing knowledge in data analysis or to apply the methods of Data Science to knowledge about a domain itself, i.e., generating knowledge from knowledge. This can be the case when analyzing natural language text or in the analysis of structured data coming from databases and knowledge bases. Sometimes, the challenge that a data scientist faces is the lack of data such as in the rare disease field. In these cases, the combination of methods from Data Science with symbolic representations that provide background information is already successfully being applied [9,27]. Neuro Symbolic Artificial Intelligence, also known as neurosymbolic AI, is an advanced version of artificial intelligence (AI) that improves how a neural network arrives at a decision by adding classical rules-based (symbolic) AI to the process. This hybrid approach requires less training data and makes it possible for humans to track how AI programming made a decision.

Only few papers address out of distribution issues or learning from small data, and there is hardly any work related to error recovery. Whether the fact that the system is neuro-symbolic is instrumental to enhanced interpretability of system behavior or outputs, e.g. in terms of making system decisions more transparent and explainable for a human user. The remainder of this article will focus on providing an overview of recent research contributions to the NeSy AI topic as reflected in the proceedings of leading AI conferences. In order to provide a structured approach to this, we will group these recent papers in terms of a topic categorization proposed by Henry Kautz at an AAAI 2020 address. We will also categorize the same recent papers according to a 2005 categorization proposal [2005-nesy-survey], and discuss and contrast the two.

Further Reading on Symbolic AI

In the simplest terms Artificial Intelligence is a field of Computer Science working on creating machines that are capable of performing tasks that typically require human intelligence. Computer systems that are able to mimic human behavior, such as the ability to reason, discover meaning, generalize, or learn from past experience are a few examples. It would have been more difficult to use a neural network alone to go directly from the text of the protocol to a risk value, because data is difficult to tag, and far more data would be needed for this approach. Furthermore, human intelligence is helpful to specify what is a sensible rule. If all the high-risk trials contained a particular feature, such as being located in a certain country, a traditional deep learning model might erroneously learn that country is a risk factor and end up discriminating accidentally. Similarly, they say that “[Marcus] broadly assumes symbolic reasoning is all-or-nothing — since DALL-E doesn’t have symbols and logical rules underlying its operations, it isn’t actually reasoning with symbols,” when I again never said any such thing.

In ML, instead of a human programmer manually coding up all the rules, the AI program uses lots of examples or data to “learn” for itself what we want it to do. Symbolic AI is also known as Good Old Fashioned AI (GOFAI) because it has been around for many decades. A human programmer must manually code up all the rules that govern a Symbolic AI system. However, it is still being used for some use cases where we humans need to understand why the AI program makes a specific decision in a given circumstance. For example, if an AI judge sentences someone to prison, it must describe why it decided.

Unleashing the Potential of AI Inference Engines – What is an AI inference engine?

This, in his words, is the “standard operating procedure” whenever inputs and outputs are symbolic. Neural networks (connectionist AI) are usually used for inductive reasoning (i.e. the process of generalizing given a finite set of observations), while symbolic AI is usually used for deduction (i.e. to logically derive conclusions from premises). The more knowledge you have, the less searching you need to do for an answer you need. This trade-off between knowledge and search is unavoidable in any AI system. Symbolic systems acknowledge this and give their algorithms a large amount of knowledge to process. They have been widely applicable to games, as they can model various aspects of game logic, such as blackboard architectures, pathfinding, decision trees, state machines, and more.

• Explicit knowledge is any clear, well-defined, and easy-to-understand information.
• However, this program cannot do anything other than play the game of “Go.” It cannot play another game like PUBG or Fortnite.
• In supervised learning, those strings of characters are called labels, the categories by which we classify input data using a statistical model.
• These include learning and problem-solving, imitating human behavior, and performing human-like tasks.
• 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.
• 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.

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.

Types of common tasks

This is a very important feature, since it allows us to chain complex expressions together. We already implemented many useful expressions, which can be imported from the symai.components file. The way all the above operations are performed is by using a Prompt class. The Prompt class is a container for all the information that is needed to define a specific operation. Our API is built on top of the Symbol class, which is the base class of all operations. This provides a convenient modality to add new custom operations by sub-classing Symbol, yet, ensuring to always have a set of base operations at our disposal without bloating the syntax or re-implementing many existing functionalities.

However, the lack of comprehensive knowledge on the human brain’s functionality has researchers struggling to replicate essential functions of sight and movement. ANI’s machine intelligence comes from Natural Language Processing (NLP). Thus, AI is programmed to interact with people natural, personalized way by understanding speech and text in natural language. Artificial Intelligence can encompass everything from Google search algorithms to autonomous vehicles. As a result, AI technologies have enabled people to automate previously time-consuming tasks and gain untapped insight into data through rapid pattern recognition. “With symbolic AI there was always a question mark about how to get the symbols,” IBM’s Cox said.

Explainable AI (XAI) and Neural-Symbolic Computing (NSC) for Explainable Models

As shown in the above example, this is also the way we implemented the basic operations in Symbol, by defining local functions that are then decorated with the respective operation decorator from the symai/core.py file. The symai/core.py is a collection of pre-defined operation decorators that we can quickly apply to any function. The reason why we use locally defined functions instead of directly decorating the main methods, is that we do not necessarily want that all our operations are sent to the neural engine and could implement a default behavior.

On the other hand, Symbolic AI seems more bulky and difficult to set up. It requires facts and rules to be explicitly translated into strings and then provided to a system. Patterns are not naturally inferred or picked up but have to be explicitly put together and spoon-fed to the system. To better simulate how the human brain makes decisions, we’ve combined the strengths of symbolic AI and neural networks.

Common sense is not so common

The second argument was that human infants show some evidence of symbol manipulation. In a set of often-cited rule-learning experiments conducted in my lab, infants generalized abstract patterns beyond the specific examples on which they had been trained. 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.

• Monotonic means one directional, i.e. when one thing goes up, another thing goes up.
• Fifth, its transparency enables it to learn with relatively small data.
• “Without this, these approaches won’t mix, like oil and water,” he said.
• In DL, the chaining together of multiple layers of artificial neural networks in a “deep” network can approximate any arbitrary mathematical function as per the Universal Approximation Theorem.
• Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions.
• 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.

In-depth studies of these from a deep learning perspective would provide systems with elementary capabilities that can then be composed for more complex solutions, or used as modules in larger AI systems [HarmelenT19]. In a nutshell, metadialog.com Symbolic AI has been highly performant in situations where the problem is already known and clearly defined (i.e., explicit knowledge). Translating our world knowledge into logical rules can quickly become a complex task.