Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing

The Disease Ontology is an example of a medical ontology currently being used. At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to accelerate the development of AI applications and research. In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were selling expert system shells, training, and consulting to corporations. For now, neuro-symbolic AI combines the best of both worlds in innovative ways by enabling systems to have both visual perception and logical reasoning. And, who knows, maybe this avenue of research might one day bring us closer to a form of intelligence that seems more like our own.

This makes them inherently unwieldy and uninterpretable, and in many ways unsuited for “augmented cognition” in conjunction with humans. Hybrids that allow us to connect the learning prowess of deep learning, with the explicit, semantic richness of symbols, could be transformative. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep learning in one of science’s most important journals, Nature.20 It closed with a direct attack on symbol manipulation, calling not for metadialog.com reconciliation but for outright replacement. Later, Hinton told a gathering of European Union leaders that investing any further money in symbol-manipulating approaches was “a huge mistake,” likening it to investing in internal combustion engines in the era of electric cars. Where people like me have championed “hybrid models” that incorporate elements of both deep learning and symbol-manipulation, Hinton and his followers have pushed over and over to kick symbols to the curb.

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The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. In the history of the quest for human-level artificial intelligence, a number of rival paradigms have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks.

Similarly, scientists have long anticipated the potential for symbolic AI systems to achieve human-style comprehension. And we’re just hitting the point where our neural networks are powerful enough to make it happen. We’re working on new AI methods that combine neural networks, which extract statistical structures from raw data files – context about image and sound files, for example – with symbolic representations of problems and logic.

The benefits and limits of symbolic AI

Knowledge representation is used in a variety of applications, including expert systems and decision support systems. Symbolic AI, also known as rule-based AI or classical AI, uses a symbolic representation of knowledge, such as logic or ontologies, to perform reasoning tasks. Symbolic AI relies on explicit rules and algorithms to make decisions and solve problems, and humans can easily understand and explain their reasoning. When considering how people think and reason, it becomes clear that symbols are a crucial component of communication, which contributes to their intelligence. Researchers tried to simulate symbols into robots to make them operate similarly to humans.

In particular, a human observer would not be able to readily recognize what is being represented. In a deep learning context, these distributed representations are called embeddings, are learned during training, and are thus an explicit part of the architecture of the deep learning system. On a fundamental level, these two types of representing information are very different indeed.

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Symbols can represent abstract concepts (bank transaction) or things that don’t physically exist (web page, blog post, etc.). Symbols can be organized into hierarchies (a car is made of doors, windows, tires, seats, etc.). They can also be used to describe other symbols (a cat with fluffy ears, a red carpet, etc.).

In order to make machine think and perform like human beings, researchers have tried to include symbols in them. Learning games involving only the physical world can easily be run in simulation, with accelerated time, and this is already done to some extent by the AI community nowadays. While this may be unnerving to some, it must be remembered that symbolic AI still only works with numbers, just in a different way. It helps AI recognize objects in videos, analyze their movement, and reason about their behaviors and not only to understand have casual relationships but applied common sense to solve problems. Not only these industries but also Intel, Google, Facebook, and Microsoft, and researchers like Josh Tenenbaum, Anima Anandkumar, and Yejin Choi, are starting to apply this technique in 2022.

Natural Language Processing

LISP is the second oldest programming language after FORTRAN and was created in 1958 by John McCarthy. LISP provided the first read-eval-print loop to support rapid program development. Program tracing, stepping, and breakpoints were also provided, along with the ability to change values or functions and continue from breakpoints or errors. It had the first self-hosting compiler, meaning that the compiler itself was originally written in LISP and then ran interpretively to compile the compiler code.

An early boom, with early successes such as the Logic Theorist and Samuel’s Checker’s Playing Program led to unrealistic expectations and promises and was followed by the First AI Winter as funding dried up. A second boom (1969–1986) occurred with the rise of expert systems, their promise of capturing corporate expertise, and an enthusiastic corporate embrace. Neuro-symbolic systems have already demonstrated the capability to outperform state-of-the-art deep learning models in domains such as image and video reasoning. While neural networks are the most popular form of AI that has been able to accomplish it, symbolic AI once played a crucial role in doing so. Language is a type of data that relies on statistical pattern matching at the lowest levels but quickly requires logical reasoning at higher levels. Pushing performance for NLP systems will likely be akin to augmenting deep neural networks with logical reasoning capabilities.

Cultivating Joy in Science

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. 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.

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