Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant[1] and others,[2][3] the effective construction of rich computational cognitive models demands the combination of symbolic reasoning and efficient machine learning. Gary Marcus, argued, "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning."[4] Further, "To build a robust, knowledge-driven approach to AI we must have the machinery of symbol manipulation in our toolkit. Too much useful knowledge is abstract to proceed without tools that represent and manipulate abstraction, and to date, the only known machinery that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation."[5]
Henry Kautz,[6] Francesca Rossi,[7] and Bart Selman[8] also argued for a synthesis. Their arguments attempt to address the two kinds of thinking, as discussed in Daniel Kahneman's book Thinking Fast and Slow. It describes cognition as encompassing two components: System 1 is fast, reflexive, intuitive, and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is used for pattern recognition. System 2 handles planning, deduction, and deliberative thinking. In this view, deep learning best handles the first kind of cognition while symbolic reasoning best handles the second kind. Both are needed for a robust, reliable AI that can learn, reason, and interact with humans to accept advice and answer questions. Such dual-process models with explicit references to the two contrasting systems have been worked on since the 1990s, both in AI and in Cognitive Science, by multiple researchers.[9]
Approaches for integration are diverse. Henry Kautz's taxonomy of neuro-symbolic architectures,[10] along with some examples, follows:
These categories are not exhaustive, as they do not consider multi-agent systems. In 2005, Bader and Hitzler presented a more fine-grained categorization that considered, e.g., whether the use of symbols included logic and if it did, whether the logic was propositional or first-order logic.[14] The 2005 categorization and Kautz's taxonomy above are compared and contrasted in a 2021 article.[10] Recently, Sepp Hochreiter argued that Graph Neural Networks "...are the predominant models of neural-symbolic computing[15]" since "[t]hey describe the properties of molecules, simulate social networks, or predict future states in physical and engineering applications with particle-particle interactions."[1]
Marcus argue that "...hybrid architectures that combine learning and symbol manipulation are necessary for robust intelligence, but not sufficient",[16] and that there are:
"...four cognitive prerequisites for building robust artificial intelligence:
This echoes earlier calls for hybrid models as early as the 1990s.[18][19]
Garcez and Lamb described research in this area as ongoing at least since the 1990s.[20][21]
A series of workshops on neuro-symbolic AI has been held annually since 2005 Neuro-Symbolic Artificial Intelligence.[22] In the early 1990s, an initial set of workshops on this topic were organized.[18]
Many key research questions remain,[23] such as:
Implementations of neuro-symbolic approaches include: