COMP_SCI 496: AI Perspectives: Symbolic Reasoning to Deep Learning Computer Science Northwestern Engineering

In addition, areas that rely on procedural or implicit knowledge 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 (discussed in a later chapter) 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. But the benefits of deep learning and neural networks are not without tradeoffs. Deep learning has several deep challenges and disadvantages in comparison to symbolic AI.

• Blazek said the new technique could have practical commercial applications in the next few years.
• For industries where stakes are high, like healthcare or finance, understanding and trusting the system’s decision-making process is crucial.
• Companies like Bosch recognize this blend as the next step in AI’s evolution, providing a more comprehensive and context-aware approach to problem-solving, which is vital in critical applications.
• This work complements previous research on neurosymbolic methods such as MIT’s Clevrer, which has shown some promise in predicting and explaining counterfactual possibilities more effectively than neural networks.
• Advantages of multi-agent systems include the ability to divide work among the agents and to increase fault tolerance when agents are lost.

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.

Unleashing the Power of Machine Learning: Empowering Computers to Learn and Adapt

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).

As such, initial input symbolic representations lie entirely in the developer’s mind, making the developer crucial. Recall the example we mentioned in Chapter 1 regarding the population of the United States. It can be answered in various ways, for instance, less than the population of India or more than 1. Both answers are valid, but both statements answer the question indirectly by providing different and varying levels of information; a computer system cannot make sense of them. This issue requires the system designer to devise creative ways to adequately offer this knowledge to the machine. The premise behind Symbolic AI is using symbols to solve a specific task.

Combining Deep Neural Nets and Symbolic Reasoning

While ML models and other practical applications of data science are the eaiser parts of AI strategy consulting, legal reasoning is a lot more tricky. Moreover, Symbolic AI allows the intelligent assistant to make decisions regarding the speech duration and other features, such as intonation when reading the user. Modern dialog systems (such as ChatGPT) rely on end-to-end deep learning frameworks and do not depend much on Symbolic AI. Similar logical processing is also utilized in search engines to structure the user’s prompt and the semantic web domain.

Symbolic AI and Neural Networks are distinct approaches to artificial intelligence, each with its strengths and weaknesses. The weakness of symbolic reasoning is that it does not tolerate ambiguity as seen in the real world. One false assumption can make everything true, effectively rendering the system meaningless. This attribute makes it effective at tackling problems where logical rules are exceptionally complex, numerous, and ultimately impractical to code, like deciding how a single pixel in an image should be labeled.

IBM Hyperlinked Knowledge Graph

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.

• 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.
• COLTRANE combines machine learning and symbolic reasoning so AI systems can adapt to real-world changes whenever and wherever they occur.
• It is one form of assumption, and a strong one, while deep neural architectures contain other assumptions, usually about how they should learn, rather than what conclusion they should reach.
• In previous topics, we have learned various ways of knowledge representation in artificial intelligence.
• I will discuss some of the approaches that have been taken to legal AI over the years.

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].

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.

In the following subsections, we will delve deeper into the substantial limitations and pitfalls of Symbolic AI. Relations allow us to formalize how the different symbols in our knowledge base interact and connect. Explicit knowledge is any clear, well-defined, and easy-to-understand information. In a dictionary, words and their respective definitions are written down (explicitly) and can be easily identified and reproduced. Building a symbolic AI system requires a human expert to manually encode the knowledge and rules into the system, which can be time-consuming and costly.

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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.

Researchers tried to simulate symbols into robots to make them operate similarly to humans. This rule-based symbolic AI required the explicit integration of human knowledge and behavioural guidelines into computer programs. Additionally, it increased the cost of systems and reduced their accuracy as more rules were added. It uses deep learning neural network topologies and blends them with symbolic reasoning techniques, making it a fancier kind of AI than its traditional version.

Goals of Neuro Symbolic AI

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