Using symbolic AI for knowledge-based question answering

Integrating this form of cognitive reasoning within deep neural networks creates what researchers are calling neuro-symbolic AI, which will learn and mature using the same basic rules-oriented framework that we do. 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.

For that, however, researchers had to replace the originally used binary threshold units with differentiable activation functions, such as the sigmoids, which started digging a gap between the neural networks and their crisp logical interpretations. With this paradigm shift, many variants of the neural networks from the ’80s and ’90s have been rediscovered or newly introduced. Benefiting from the substantial increase in the parallel processing power of modern GPUs, and the ever-increasing amount of available data, deep learning has been steadily paving its way to completely dominate the (perceptual) ML. The tremendous success of deep learning systems is forcing researchers to examine the theoretical principles that underlie how deep nets learn.

The second AI summer: knowledge is power, 1978–1987

Symbolic AI was the dominant paradigm from the mid-1950s until the mid-1990s, and it is characterized by the explicit embedding of human knowledge and behavior rules into computer programs. The symbolic representations are manipulated using rules to make inferences, solve problems, and understand complex concepts. These dynamic models finally enable to skip the preprocessing step of turning the relational representations, such as interpretations of a relational logic program, into the fixed-size vector (tensor) format. They do so by effectively reflecting the variations in the input data structures into variations in the structure of the neural model itself, constrained by some shared parameterization (symmetry) scheme reflecting the respective model prior.

For instance, while it can solve straightforward mathematical problems, it struggles with more intricate issues like predicting stock market trends. Symbolic AI was the dominant approach in AI research from the 1950s to the 1980s, and it underlies many traditional AI what is symbolic ai systems, such as expert systems and logic-based AI. Symbolic AI works by using symbols to represent objects and concepts, and rules to represent relationships between them. These rules can be used to make inferences, solve problems, and understand complex concepts.

What is an example of symbolic artificial intelligence?

Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning. Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions. Multiple different approaches to represent knowledge and then reason with those representations have been investigated. Below is a quick overview of approaches to knowledge representation and automated reasoning. Expert systems can operate in either a forward chaining – from evidence to conclusions – or backward chaining – from goals to needed data and prerequisites – manner.

Despite this, symbolic AI is often integrated with other AI techniques, including neural networks and evolutionary algorithms, to enhance its capabilities and efficiency. Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets to work effectively, entailing that they are slow to learn even when such datasets are available.

It is of course impossible to give credit to all nuances or all important recent contributions in such a brief overview, but we believe that our literature pointers provide excellent starting points for a deeper engagement with neuro-symbolic AI topics. “Neuro-symbolic [AI] models will allow us to build AI systems that capture compositionality, causality, and complex correlations,” Lake said. During training and inference using such an AI system, the neural network accesses the explicit memory using expensive soft read and write operations.

Third, it is symbolic, with the capacity of performing causal deduction and generalization. Fourth, the symbols and the links between them are transparent to us, and thus we will know what it has learned or not – which is the key for the security of an AI system. We present the details of the model, the algorithm powering its automatic learning ability, and describe its usefulness in different use cases.

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). 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. Already, this technology is finding its way into such complex tasks as fraud analysis, supply chain optimization, and sociological research. In response to these limitations, there has been a shift towards data-driven approaches like neural networks and deep learning. However, there is a growing interest in neuro-symbolic AI, which aims to combine the strengths of symbolic AI and neural networks to create systems that can both reason with symbols and learn from data. For other AI programming languages see this list of programming languages for artificial intelligence.

• 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.
• Deep Reinforcement Learning combines neural networks with a reinforcement learning architecture that enables software-defined agents to learn the best actions possible in virtual environment scenarios to maximize the notion of cumulative reward.
• Such causal and counterfactual reasoning about things that are changing with time is extremely difficult for today’s deep neural networks, which mainly excel at discovering static patterns in data, Kohli says.
• Neurosymbolic AI is also demonstrating the ability to ask questions, an important aspect of human learning.