16 Feb 2023

What is symbolic artificial intelligence?

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Symbolic AI: The key to the thinking machine

symbolic learning

Bruner (1966) was concerned with how knowledge is represented and organized through different modes of thinking (or representation). VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. An exclusive invite-only evening of insights and networking, designed for senior enterprise executives overseeing data stacks and strategies.

  • A second flaw in symbolic reasoning is that the computer itself doesn’t know what the symbols mean; i.e. they are not necessarily linked to any other representations of the world in a non-symbolic way.
  • An example episode with input/output examples and corresponding interpretation grammar (see the ‘Interpretation grammars’ section) is shown in Extended Data Fig.
  • The decoder vocabulary includes the abstract outputs as well as special symbols for starting and ending sequences ( and , respectively).
  • These permutations are applied within several lexical classes; for examples, 406 input word types categorized as common nouns (‘baby’, ‘backpack’ and so on) are remapped to the same set of 406 types.

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. Notably, deep learning algorithms are opaque, and figuring out how they work perplexes even their creators. And it’s very hard to communicate and troubleshoot their inner-workings. Neural networks are almost as old as symbolic AI, but they were largely dismissed because they were inefficient and required compute resources that weren’t available at the time. In the past decade, thanks to the large availability of data and processing power, deep learning has gained popularity and has pushed past symbolic AI systems.

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A rule’s right-hand side was generated as an arbitrary string (length ≤ 8) that mixes and matches the left-hand-side arguments, each of which are recursively evaluated and then concatenated together (for example, ⟦x1⟧ ⟦u1⟧ ⟦x1⟧ ⟦u1⟧ ⟦u1⟧). The last rule was the same for each episode and instantiated a form of iconic left-to-right concatenation (Extended Data Fig. 4). Study and query examples (set 1 and 2 in Extended Data Fig. 4) were produced by sampling arbitrary, unique input sequences (length ≤ 8) that can be parsed with the interpretation grammar to produce outputs (length ≤ 8).


At this stage, your child will play alone or side-by-side with other children their age. Instead, he sees a gradual development of cognitive skills and techniques into more integrated “adult” cognitive techniques. Although Bruner proposes stages of cognitive development, he doesn’t see them as representing different separate modes of thought at different points of development (like Piaget). There are similarities between Piaget and Bruner, but a significant difference is that Bruner’s modes are not related in terms of which presuppose the one that precedes it. Scaffolding involves helpful, structured interaction between an adult and a child with the aim of helping the child achieve a specific goal. On the surface, Bruner’s emphasis on the learner discovering subject content for themselves seemingly absolves the teacher of a great deal of work.

Preperation: Data and Pre-trained Backbone Models

Examples of common-sense reasoning include implicit reasoning about how people think or general knowledge of day-to-day events, objects, and living creatures. This kind of knowledge is taken for granted and not viewed as noteworthy. During the first AI summer, many people thought that machine intelligence could be achieved in just a few years. By the mid-1960s neither useful natural language translation systems nor autonomous tanks had been created, and a dramatic backlash set in. During SCAN testing (an example episode is shown in Extended Data Fig. 7), MLC is evaluated on each query in the test corpus. For each query, 10 study examples are again sampled uniformly from the training corpus (using the test corpus for study examples would inadvertently leak test information).

symbolic learning

And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge. This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science and high-school math. This, in turn, enables AI to be trained using multiple techniques, including semantic inferencing and both supervised and unsupervised learning, which will ultimately create AI systems that can reason, learn, and engage in natural language question-and-answer interactions with humans.

The role of symbols in artificial intelligence

A,b, Based on the study instructions (a; headings were not provided to the participants), humans and MLC executed query instructions (b; 4 of 10 shown). The four most frequent responses are shown, marked in parentheses with response rates (counts for people and the percentage of samples for MLC). The superscript notes indicate the algebraic answer (asterisks), a one-to-one error (1-to-1) or an iconic concatenation error (IC). The words and colours were randomized for each participant and a canonical assignment is therefore shown here.

symbolic learning

First of all, every deep neural net trained by supervised learning combines deep learning and symbolic manipulation, at least in a rudimentary sense. Because symbolic reasoning encodes knowledge in symbols and strings of characters. In supervised learning, those strings of characters are called labels, the categories by which we classify input data using a statistical model.


1) Hinton, Yann LeCun and Andrew Ng have all suggested that work on unsupervised learning (learning from unlabeled data) will lead to our next breakthroughs. Symbolic artificial intelligence, also known as Good, Old-Fashioned AI (GOFAI), was the dominant paradigm in the AI community from the post-War era until the late 1980s. Deep learning has its discontents, and many of them look to other branches of AI when they hope for the future.

symbolic learning

Symbolic artificial intelligence showed early progress at the dawn of AI and computing. You can easily visualize the logic of rule-based programs, communicate them, and troubleshoot them. Being communicate in symbols is one of the main things that make us intelligent.

Extended Data Fig. 4 Example meta-learning episode and how it is processed by different MLC variants.

So how do we make the leap from narrow AI systems that leverage reinforcement learning to solve specific problems, to more general systems that can orient themselves in the world? Enter Tim Rocktäschel, a Research Scientist at Facebook AI Research London and a Lecturer in the Department of Computer Science at University College London. Much of Tim’s work has been focused on ways to make RL agents learn with relatively little data, using strategies known as sample efficient learning, in the hopes of improving their ability to solve more general problems. In fact, rule-based AI systems are still very important in today’s applications.

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Therefore, symbols have also played a crucial role in the creation of artificial intelligence. This creates a crucial turning point for the enterprise, says Analytics Week’s Jelani Harper. Data fabric developers like Stardog are working to combine both logical and statistical AI to analyze categorical data; that is, data that has been categorized in order of importance to the enterprise. Symbolic AI plays the crucial role of interpreting the rules governing this data and making a reasoned determination of its accuracy.

However, Bruner would argue that understanding of this concept would be much more genuine if the child discovered the difference for themselves; for instance, by playing a game in which they had to share various numbers of beads fairly between themselves and their friend. This means students are held back by teachers as certain topics are deemed too difficult to understand and must be taught when the teacher believes the child has reached the appropriate stage of cognitive maturity. The main premise of Bruner’s text was that students are active learners who construct their own knowledge. Bruner argues that language can code stimuli and free an individual from the constraints of dealing only with appearances, to provide a more complex yet flexible cognition. This may explain why, when we are learning a new subject, it is often helpful to have diagrams or illustrations to accompany the verbal information. This mode continues later in many physical activities, such as learning to ride a bike.

  • The study and test items always differed from one another by more than one primitive substitution (except in the function 1 stage, where a single primitive was presented as a novel argument to function 1).
  • Bruner views symbolic representation as crucial for cognitive development, and since language is our primary means of symbolizing the world, he attaches great importance to language in determining cognitive development.
  • By creating a more human-like thinking machine, organizations will be able to democratize the technology across the workforce so it can be applied to the real-world situations we face every day.
  • The use of words can aid the development of the concepts they represent and can remove the constraints of the “here & now” concept.
  • The power of human language and thought arises from systematic compositionality—the algebraic ability to understand and produce novel combinations from known components.
  • The query ordering was chosen arbitrarily (this was also randomized for human participants).

Note that an earlier version of memory-based meta-learning for compositional generalization used a more limited and specialized architecture30,65. To provide new readers with a comprehensive understanding of neural-symbolic learning systems, this paper surveys representative research and applications of these systems. For example, Andrews et al. (1995) and Townsend et al. (2019) center around knowledge extraction techniques, which aligns with the first category discussed in Section 2. While surveys (Besold et al., 2017, Garcez and Lamb, 2020) also cover neural-symbolic learning systems comprehensively, their focus remains primarily theoretical, lacking a thorough introduction to specific techniques and related works. Therefore, an urgent need arises to provide a comprehensive survey that encompasses popular methods and specific techniques (e.g., model frameworks, execution processes) to expedite advancements in the neural-symbolic field. Distinguishing itself from the aforementioned surveys, this paper emphasizes classifications, techniques, and applications within the domain of neural-symbolic learning systems.

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Neither the study nor query examples are remapped; in other words, the model is asked to infer the original meanings. Finally, for the ‘add jump’ split, one study example is fixed to be ‘jump → JUMP’, ensuring that MLC has access to the basic meaning before attempting compositional uses of ‘jump’. OOP languages allow you to define classes, specify their properties, and organize them in hierarchies.

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). The instructions were as similar as possible to the few-shot learning task, although there were several important differences. First, because this experiment was designed to probe inductive biases and does not provide any examples to learn from, it was emphasized to the participants that there are multiple reasonable answers and they should provide a reasonable guess. Second, the participants responded to the query instructions all at once, on a single web page, allowing the participants to edit, go back and forth, and maintain consistency across responses. By contrast, the previous experiment collected the query responses one by one and had a curriculum of multiple distinct stages of learning.

symbolic learning

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