Neuro Symbolic AI: Enhancing Common Sense in AI
Since ancient times, humans have been obsessed with creating thinking machines. As a result, numerous researchers have focused on creating intelligent machines throughout history. For example, researchers predicted that deep neural networks would eventually be used for autonomous image recognition and natural language processing as early as the 1980s. We’ve been working for decades to gather the data and computing power necessary to realize that goal, but now it is available. Neuro-symbolic models have already beaten cutting-edge deep learning models in areas like image and video reasoning. Furthermore, compared to conventional models, they have achieved good accuracy with substantially less training data. This article helps you to understand everything regarding Neuro Symbolic AI.
Table of Contents
- What is Neuro Symbolic AI?
- Need for Neuro Symbolic AI?
- Goals for Neuro Symbolic AI?
- Key terminologies used in Neuro Symbolic AI
- Key components of Neuro Symbolic AI
- Writing program in Neuro Symbolic AI
- Use cases of Neuro Symbolic AI
This article was published as a part of the Data Science Blogathon.
What is Neuro Symbolic AI?
It uses deep learning neural network topologies and blends them with symbolic reasoning techniques, making it a fancier kind of AI than its traditional version. We have been utilizing neural networks, for instance, to determine an item’s type of shape or color. However, it can be advanced further by using symbolic reasoning to reveal more fascinating aspects of the item, such as its area, volume, etc.
Need for Neuro Symbolic AI
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. This rule-based symbolic AI required the explicit integration of human knowledge and behavioral guidelines into computer programs. Additionally, it increased the cost of systems and reduced their accuracy as more rules were added.
Researchers investigated a more data-driven strategy to address these problems, which gave rise to neural networks’ appeal. While symbolic AI requires constant information input, neural networks could train on their own given a large enough dataset. Although everything was functioning perfectly, as was already noted, a better system is required due to the difficulty in interpreting the model and the amount of data required to continue learning.
Deep learning fails to extract compositional and causal structures from data, even though it excels in large-scale pattern recognition. While symbolic models aim for complicated connections, they are good at capturing compositional and causal structures.
Due to the shortcomings of these two methods, they have been combined to create neuro-symbolic AI, which is more effective than each alone. The goal is to make systems smarter by combining logic and learning. According to researchers, deep learning is expected to benefit from integrating domain knowledge and common sense reasoning provided by symbolic AI systems. For instance, a neuro-symbolic system would employ symbolic AI’s logic to grasp a shape better while detecting it and a neural network’s pattern recognition ability to identify items.
A neuro-symbolic system employs logical reasoning and language processing to respond to the question as a human would. However, in contrast to neural networks, it is more effective and takes extremely less training data.
Goals of Neuro Symbolic AI
To fill the remaining gaps between the current state of the art and the fundamental goals of AI, Neuro-Symbolic AI (NS) seeks to develop a fundamentally new approach to AI. It specifically aims to balance (and maintain) the advantages of statistical AI (machine learning) with the strengths of symbolic or classical AI (knowledge and reasoning). It aims for revolution rather than development and building new paradigms instead of a superficial synthesis of existing ones.
The main objectives of NS are to show that it is capable of
- Solve even more difficult issues
- Learn with far less data, eventually for many different tasks instead of just one specific one.)
- Make judgments and behaviors that are naturally understandable and within your power.
Key Terminologies Used in Neuro Symbolic AI
Here are some key terminologies used in neuro-symbolic AI:
- Hybrid Architecture: Refers to integrating neural networks and symbolic reasoning components in a neuro-symbolic AI system.
- Symbolic Representation: Refers to using symbolic representations, such as logic, ontologies, and knowledge graphs, to represent knowledge and perform reasoning tasks.
- Neural-Symbolic Integration: Refers to integrating neural and symbolic reasoning in a hybrid architecture.
- Perception: Refers to the ability of a neuro-symbolic AI system to process and interpret sensory input, such as images, speech, and text.
- Reasoning: Refers to the ability of a neuro-symbolic AI system to perform logical inference, theorem proving, and planning based on symbolic representations.
- Explanation: Refers to the ability of a neuro-symbolic AI system to provide human-understandable explanations for its predictions and decisions.
- Knowledge Graph: Refers to a graph-based representation of knowledge, where nodes represent entities and edges represent relationships between entities.
- Ontology: Refers to a formal representation of a set of concepts and relationships within a specific domain.
Key Components of Neuro Symbolic AI
The key components of a neuro-symbolic AI system are:
- Neural Network: A component that performs perceptual tasks using deep learning algorithms, such as image recognition and natural language processing.
- Symbolic Reasoning Engine: A component that performs logical inference, theorem proving, and planning using symbolic representations, such as logic and knowledge graphs.
- Integration Layer: A component that integrates the neural network and symbolic reasoning engine to form a hybrid architecture. This component is responsible for mapping the symbolic and neural representations and enabling communication between the two components.
- Knowledge Base: A component that stores knowledge in a structured form, such as ontologies, knowledge graphs, and relational databases.
- Explanation Generator: A component that generates human-understandable explanations for the predictions and decisions made by the neuro-symbolic AI system.
- User Interface: A component that allows human users to interact with the neuro-symbolic AI system, for example, to provide input and receive output.
These components work together to form a neuro-symbolic AI system that can perform various tasks, combining the strengths of both neural networks and symbolic reasoning.
How to Write a Program in Neuro Symbolic AI?
Writing a program in neuro-symbolic AI can be a complex task requiring symbolic reasoning and deep learning expertise. Here is a high-level overview of the steps involved in developing a neuro-symbolic AI program:
1. Define the problem: Determine what the AI system is expected to accomplish and what kind of knowledge or data it will work with.
2. Determine the knowledge representation: Choose a suitable representation for the problem domain, such as first-order logic, graphs, or probabilistic models.
3. Preprocess the data: Prepare the data for use by the AI system, such as transforming it into the chosen knowledge representation format.
4. Train the deep learning component: Train a deep neural network to learn the relationships and patterns in the data.
5. Integrate the symbolic reasoning component: Integrate the symbolic reasoning component into the AI system, such as using a rule-based system or a theorem prover, to perform logical inference and make decisions based on the knowledge representation.
6. Evaluate the performance: Test the AI system to determine how well it solves the problem and make any necessary adjustments.
7. Deploy the system: Deploy the AI system to the desired environment and monitor its performance.
Note: This is a high-level overview. The steps involved in developing a neuro-symbolic AI program may vary depending on the problem domain and the tools used.
What is a Logical Neural Network?
Logical Neural Networks (LNNs) are neural networks that incorporate symbolic reasoning in their architecture. In the context of neuro-symbolic AI, LNNs serve as a bridge between the symbolic and neural components, allowing for a more seamless integration of both reasoning methods.
An LNN consists of a neural network trained to perform symbolic reasoning tasks, such as logical inference, theorem proving, and planning, using a combination of differentiable logic gates and differentiable inference rules. These gates and rules are designed to mimic the operations performed by symbolic reasoning systems and are trained using gradient-based optimization techniques.
By combining symbolic and neural reasoning in a single architecture, LNNs can leverage the strengths of both methods to perform a wider range of tasks than either method alone. For example, an LNN can use its neural component to process perceptual input and its symbolic component to perform logical inference and planning based on a structured knowledge base.
Overall, LNNs is an important component of neuro-symbolic AI, as they provide a way to integrate the strengths of both neural networks and symbolic reasoning in a single, hybrid architecture.
Symbolic AI Vs. Neural Network
Symbolic AI and Neural Networks are distinct approaches to artificial intelligence, each with its strengths and weaknesses.
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.
On the other hand, Neural Networks are a type of machine learning inspired by the structure and function of the human brain. Neural networks use a vast network of interconnected nodes, called artificial neurons, to learn patterns in data and make predictions. Neural networks are good at dealing with complex and unstructured data, such as images and speech. They can learn to perform tasks such as image recognition and natural language processing with high accuracy.
In summary, symbolic AI excels at human-understandable reasoning, while Neural Networks are better suited for handling large and complex data sets. Integrating both approaches, known as neuro-symbolic AI, can provide the best of both worlds, combining the strengths of symbolic AI and Neural Networks to form a hybrid architecture capable of performing a wider range of tasks.
The following image shows how Symbolic AI might define a mango:
The following image shows how a neural network might define a mango:
Use Cases of Neuro Symbolic AI
Neuro-symbolic AI has a wide range of potential use cases in various domains due to its ability to handle both symbolic reasoning and complex, non-linear relationships in data. Here are a few examples:
- Knowledge-based AI Systems: Neuro-symbolic AI can be used to develop knowledge-based AI systems that use logical inference to reason about and manipulate symbols representing real-world objects and concepts.
- Decision Making: Neuro-symbolic AI can be used to develop decision-making systems that consider both symbolic knowledge and data-driven predictions, such as in finance, medicine, or autonomous vehicles.
- Natural Language Processing: Neuro-symbolic AI can be used to improve natural language processing tasks, such as question answering, machine translation, and text generation, by combining the strengths of deep learning and symbolic reasoning.
- Robotics: Neuro-symbolic AI can be used to develop intelligent robots that can reason about and interact with their environment based on both symbolic knowledge and sensor data.
- Planning and Scheduling: Neuro-symbolic AI can be used to develop planning and scheduling systems that can handle complex, real-world constraints and make decisions based on both symbolic knowledge and data-driven predictions.
These are just a few examples, and the potential applications of neuro-symbolic AI are constantly expanding as the field of AI continues to evolve.
In conclusion, neuro-symbolic AI is a promising field that aims to integrate the strengths of both neural networks and symbolic reasoning to form a hybrid architecture capable of performing a wider range of tasks than either component alone. With its combination of deep learning and logical inference, neuro-symbolic AI has the potential to revolutionize the way we interact with and understand AI systems.
By integrating neural networks and symbolic reasoning, neuro-symbolic AI can handle perceptual tasks such as image recognition and natural language processing and perform logical inference, theorem proving, and planning based on a structured knowledge base. This integration enables the creation of AI systems that can provide human-understandable explanations for their predictions and decisions, making them more trustworthy and transparent.
The development of neuro-symbolic AI is still in its early stages, and much work must be done to realize its potential fully. However, the progress made so far and the promising results of current research make it clear that neuro-symbolic AI has the potential to play a major role in shaping the future of AI.
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