Symbolic AI v s Non-Symbolic AI, and everything in between? by Rhett D’souza DataDrivenInvestor

Arguably, human communication occurs through symbols (words and sentences), and human thought – on a cognitive level – also occurs symbolically, so that symbolic AI resembles human cognitive behavior. Symbolic approaches are useful to represent theories or scientific laws in a way that is meaningful to the symbol system and can be meaningful to humans; they are also useful in producing new symbols through symbol manipulation or inference rules. An alternative (or complementary) approach to AI are statistical methods in which intelligence is taken as an emergent property of a system. In statistical approaches to AI, intelligent behavior is commonly formulated as an optimization problem and solutions to the optimization problem leads to behavior that resembles intelligence. Prominently, connectionist systems [42], in particular artificial neural networks [55], have gained influence in the past decade with computational and methodological advances driving new applications [39]. Statistical approaches are useful in learning patterns or regularities from data, and as such have a natural application within Data Science.

If one assumption or rule doesn’t hold, it could break all other rules, and the system could fail. There is also debate over whether or not the symbolic AI system is truly “learning,” or just making decisions according to superficial rules that give high reward. The Chinese Room experiment showed that it’s possible for a symbolic AI machine to instead of learning what Chinese characters mean, simply formulate which Chinese characters to output when asked particular questions by an evaluator.

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By including extensions for input and output, they allow the Expert System to interact with the world. Although expert systems are limited, they have proven themselves to be extremely useful in certain applications. In the future, AI systems will also be more bio-inspired and feature more dedicated hardware such as neuromorphic and quantum devices. “We all agree that deep learning in its current form has many limitations including the need for large datasets.

Problems that can be drawn as a flow chart, with every variable accounted for, are well suited to symbolic AI. However, the current keyword-based search engine approach, for example, can absorb and interpret entire documents with blazing speed, but they can extract only basic and largely non-contextual information. Similarly, automation email management systems are not quite capable of penetrating meaning beyond just product names and other points of information or references.

Types of Learning in ML

Deep neural networks are machine learning algorithms inspired by the structure and function of biological neural networks. They excel in tasks such as image recognition and natural language processing. However, they struggle with tasks that necessitate explicit reasoning, like long-term planning, problem-solving, and understanding causal relationships.

• Unfortunately, LeCun and Browning ducked both of these arguments, not touching on either, at all.
• The foundations of description logics are pre-
  sented in Appendix D, whereas a detailed presentation of semantic networks, frames,
  and scripts is included in Chap.
• Instead, the AI we have today is a subset of Artificial Intelligence called Narrow AI.
• In this article, we will explore five key characteristics of modern customer service.

While symbolic AI used to dominate in the first decades, machine learning has been very trendy lately, so let’s try to understand each of these approaches and their main differences when applied to Natural Language Processing (NLP). Constraint solvers perform a more limited kind of inference than first-order logic. They can simplify sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, along with solving other kinds of puzzle problems, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic programming can be used to solve scheduling problems, for example with constraint handling rules (CHR).

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Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more limited logical representation is used, Horn Clauses. The automated theorem provers discussed below can prove theorems in first-order logic. Horn clause logic is more restricted than first-order logic and is used in logic programming languages such as Prolog. Extensions to first-order logic include temporal logic, to handle time; epistemic logic, to reason about agent knowledge; modal logic, to handle possibility and necessity; and probabilistic logics to handle logic and probability together.

• Hybrid AI can also free up data scientists from cumbersome and tedious tasks such as data labelling.
• If you show a child a picture of an elephant — the very first time they’ve ever seen one — that child will instantly recognize that a) that is an animal and b) that this is an elephant next time they’ll come across that animal, either in real life or in a picture.
• These symbols can easily be arranged through networks and lists or arranged hierarchically.
• “Our vision is to use neural networks as a bridge to get us to the symbolic domain,” Cox said, referring to work that IBM is exploring with its partners.

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