Symbolic AI vs machine learning in natural language processing

A “neural network” in the sense used by AI engineers is not literally a network of biological neurons. Rather, it is a simplified digital model that captures some of the flavor of an actual biological brain. Artificial intelligence has mostly been focusing on a technique called deep learning.

In addition, areas that rely on procedural or implicit symbolic ai such as sensory/motor processes, are much more difficult to handle within the Symbolic AI framework. In these fields, Symbolic AI has had limited success and by and large has left the field to neural network architectures which are more suitable for such tasks. In sections to follow we will elaborate on important sub-areas of Symbolic AI as well as difficulties encountered by this approach. I’m really surprised this article only describes symbolic AI based on the 1950s to 1990s descriptions when symbolic AI was ‘rules based’ and doesn’t include how symbolic AI transformed in the 2000s to present by moving from rules based to description logic ontology based.

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Also, some tasks can’t be translated to direct rules, including speech recognition and natural language processing. Now we turn to attacks from outside the field specifically by philosophers. Multiple different approaches to represent knowledge and then reason with those representations have been investigated. Below is a quick overview of approaches to knowledge representation and automated reasoning.

What happened to symbolic AI?

Some believe that symbolic AI is dead. But this assumption couldn't be farther from the truth. In fact, rule-based AI systems are still very important in today's applications. Many leading scientists believe that symbolic reasoning will continue to remain a very important component of artificial intelligence.

In contrast, a multi-agent system consists of multiple agents that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language . Advantages of multi-agent systems include the ability to divide work among the agents and to increase fault tolerance when agents are lost. Research problems include how agents reach consensus, distributed problem solving, multi-agent learning, multi-agent planning, and distributed constraint optimization.


In the recently developed framework SymbolicAI, the team has used the Large Language model to introduce everyone to a Neuro-Symbolic outlook on LLMs. Insofar as computers suffered from the same chokepoints, their builders relied on all-too-human hacks like symbols to sidestep the limits to processing, storage and I/O. As computational capacities grow, the way we digitize and process our analog reality can also expand, until we are juggling billion-parameter tensors instead of seven-character strings. Read more about our work in neuro-symbolic AI from the MIT-IBM Watson AI Lab. Our researchers are working to usher in a new era of AI where machines can learn more like the way humans do, by connecting words with images and mastering abstract concepts. One of the keys to symbolic AI’s success is the way it functions within a rules-based environment.

learning and symbolic

Agents are autonomous systems embedded in an environment they perceive and act upon in some sense. Russell and Norvig’s standard textbook on artificial intelligence is organized to reflect agent architectures of increasing sophistication. Researchers at MIT found that solving difficult problems in vision and natural language processing required ad hoc solutions—they argued that no simple and general principle would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, since they must be built by hand, one complicated concept at a time. The two biggest flaws of deep learning are its lack of model interpretability (i.e. why did my model make that prediction?) and the amount of data that deep neural networks require in order to learn. 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.

A Beginner’s Guide to Symbolic Reasoning & Deep Learning

And all sort of intermediary positions along this axis can be imagined, if you can introduce some domain specific bias in the probing selection, instead of simply picking randomly. The summer school will include talks from over 25 IBMers in various areas of theory and the application of neuro-symbolic AI. We will also have a distinguished external speaker to share an overview of neuro-symbolic AI and its history.

What is the drawback of symbolic AI?

However, the primary disadvantage of symbolic AI is that it does not generalize well. The environment of fixed sets of symbols and rules is very contrived, and thus limited in that the system you build for one task cannot easily generalize to other tasks. The symbolic AI systems are also brittle.

Alessandro’s primary interest is to investigate how semantic resources can be integrated with data-driven algorithms, and help humans and machines make sense of the physical and digital worlds. Alessandro holds a PhD in Cognitive Science from the University of Trento . To summarize, one of the main differences between machine learning and traditional symbolic reasoning is how the learning happens. In machine learning, the algorithm learns rules as it establishes correlations between inputs and outputs.

IEMS: AI based.

No explicit series of actions is required, as is the case with imperative programming languages. Alain Colmerauer and Philippe Roussel are credited as the inventors of Prolog. Prolog is a form of logic programming, which was invented by Robert Kowalski. Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods. For more detail see the section on the origins of Prolog in the PLANNER article. Neural—allows a neural model to directly call a symbolic reasoning engine, e.g., to perform an action or evaluate a state.


This leads to the establishment of benchmarks against which competing models are compared. As a side note, it’s interesting to see that the requirement of differentiable functions to process input data brings along the requirement to “flatten” data into vectors for input . This process loses the potentially recursive structure of the elements of the input space, which could be easily exploited by a program in a non-differentiable framework .

Differences between Inbenta Symbolic AI and machine learning

You only need 1 percent of data from traditional methods to train the neuro-symbolic AI systems. Machine learning is an application of AI where statistical models perform specific tasks without using explicit instructions, relying instead on patterns and inference. Machine learning algorithms build mathematical models based on training data in order to make predictions. At a more concrete level, realizing the above program for developmental AI involves building child-like machines that are immersed in a rich cultural environment, involving humans, where they will be able to participate in learning games. These games are not innate , but must be learned from adults and passed on to other generations. There is an essential dissymmetry here between the “old” agents that carry the information on how to learn, and the “new” agents that are going to acquire it, and possibly mutate it.

In a nutshell, symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. As you can easily imagine, this is a very time-consuming job, as there are many ways of asking or formulating the same question. And if you take into account that a knowledge base usually holds on average 300 intents, you now see how repetitive maintaining a knowledge base can be when using machine learning. Must-Read Papers or Resources on how to integrate symbolic logic into deep neural nets. Comparing SymbolicAI to LangChain, a library with similar properties, LangChain develops applications with the help of LLMs through composability.


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