What is Neural-Symbolic Integration? by Gustav Šír

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 what is symbolic ai 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.

This method of problem-solving is particularly beneficial in complex systems where optimization involves multifaceted decision-making, directly impacting AI Interpretability. In optimization, Symbolic AI applies its rule-based logic to identify the most efficient solutions. This methodology is rooted in traditional AI, where the focus is on explicitly encoding knowledge and logic into systems. 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. The universe is written in the language of mathematics and its characters are triangles, circles, and other geometric objects.

Say you have a picture of your cat and want to create a program that can detect images that contain your cat. You create a rule-based program that takes new images as inputs, compares the pixels to the original cat image, and responds by saying whether your cat is in those images. 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.

Programs were themselves data structures that other programs could operate on, allowing the easy definition of higher-level languages. Early work covered both applications of formal reasoning emphasizing first-order logic, along with attempts to handle common-sense reasoning in a less formal manner. Google last week stopped allowing users of its Gemini chatbot technology to generate images of humans. The move came after Gemini users produced pictures of Black Founding Fathers in American history as well as other imagery.

A gentle introduction to model-free and model-based reinforcement learning

First, symbolic AI algorithms are designed to deal with problems that require human-like reasoning. This means that they are able to understand and manipulate symbols in ways that other AI algorithms cannot. Second, symbolic AI algorithms are often much slower than other AI algorithms.

• I’m speaking primarily about artificial general intelligence, which many roboticists believe is about five years out — though that could well prove optimistic.
• Planning is used in a variety of applications, including robotics and automated planning.
• In contrast, a multi-agent system consists of multiple agents that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML).
• One of the most successful neural network architectures have been the Convolutional Neural Networks (CNNs) [3]⁴ (tracing back to 1982’s Neocognitron [5]).

The two biggest flaws of deep learning are its lack of model interpretability (i.e. why did my model make that prediction?) and the large amount of data that deep neural networks require in order to learn. In NLP, symbolic AI contributes to machine translation, question answering, and information retrieval by interpreting text. For knowledge representation, it underpins expert systems and decision support systems, organizing and accessing information efficiently. In planning, symbolic AI is crucial for robotics and automated systems, generating sequences of actions to meet objectives. And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge.

Natural Language Processing

Their arguments are based on a need to address the two kinds of thinking discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having two components, System 1 and System 2. System 1 is the kind used for pattern recognition while System 2 is far better suited for planning, deduction, and deliberative thinking. You can foun additiona information about ai customer service and artificial intelligence and NLP. In this view, deep learning best models the first kind of thinking while symbolic reasoning best models the second kind and both are needed. Since some of the weaknesses of neural nets are the strengths of symbolic AI and vice versa, neurosymbolic AI would seem to offer a powerful new way forward. Roughly speaking, the hybrid uses deep nets to replace humans in building the knowledge base and propositions that symbolic AI relies on.

It wasn’t until the 1980’s, when the chain rule for differentiation of nested functions was introduced as the backpropagation method to calculate gradients in such neural networks which, in turn, could be trained by gradient descent methods. 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. He is worried that the approach may not scale up to handle problems bigger than those being tackled in research projects. This video shows a more sophisticated challenge, called CLEVRER, in which artificial intelligences had to answer questions about video sequences showing objects in motion.

This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science and high-school math. The advantage of neural networks is that they can deal with messy and unstructured data. Instead of manually laboring through the rules of detecting cat pixels, you can train a deep learning algorithm on many pictures of cats. When you provide it with a new image, it will return the probability that it contains a cat. Henry Kautz,[18] Francesca Rossi,[80] and Bart Selman[81] have also argued for a synthesis.

What the ducklings do so effortlessly turns out to be very hard for artificial intelligence. This is especially true of a branch of AI known as deep learning or deep neural networks, the technology powering the AI that defeated the world’s Go champion Lee Sedol in 2016. Such deep nets can struggle to figure out simple abstract relations between objects and reason about them unless they study tens or even hundreds of thousands of examples.

Historically, the community targeted mostly analysis of the correspondence and theoretical model expressiveness, rather than practical learning applications (which is probably why they have been marginalized by the mainstream research). Symbolic AI has been used in a wide range of applications, including expert systems, natural language processing, and game playing. It can be difficult to represent complex, ambiguous, or uncertain knowledge with symbolic AI. Furthermore, symbolic AI systems are typically hand-coded and do not learn from data, which can make them brittle and inflexible. The second module uses something called a recurrent neural network, another type of deep net designed to uncover patterns in inputs that come sequentially.

The current state of symbolic AI

In Symbolic AI, Knowledge Representation is essential for storing and manipulating information. It is crucial in areas like AI History and development, where representing complex AI Research and AI Applications accurately is vital. Logic Programming, a vital concept in Symbolic AI, integrates Logic Systems and AI algorithms. It represents problems using relations, rules, and facts, providing a foundation for AI reasoning and decision-making, a core aspect of Cognitive Computing. Marvin Minsky first proposed frames as a way of interpreting common visual situations, such as an office, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has attempted to capture useful common-sense knowledge and has «micro-theories» to handle particular kinds of domain-specific reasoning.

While warehouses tend to be fairly structured environments, any number of things can occur in the real world that will knock a task off-kilter. And the less structured these tasks become, the larger the potential for error. A lot of questions remain, including how many takes it took to get this right.

A research paper from University of Missouri-Columbia cites the computation in these models is based on explicit representations that contain symbols put together in a specific way and aggregate information. In this approach, a physical symbol system comprises of a set of entities, known as symbols which are physical patterns. Search and representation played a central role in the development of symbolic AI.

Advancements in Knowledge Representation:

And, the theory is being revisited by Murray Shanahan, Professor of Cognitive Robotics Imperial College London and a Senior Research Scientist at DeepMind. Shanahan reportedly proposes to apply the symbolic approach and combine it with deep learning. This would provide the AI systems a way to understand the concepts of the world, rather than just feeding it data and waiting for it to understand patterns. This approach could solve AI’s transparency and the transfer learning problem. Shanahan hopes, revisiting the old research could lead to a potential breakthrough in AI, just like Deep Learning was resurrected by AI academicians. The GOFAI approach works best with static problems and is not a natural fit for real-time dynamic issues.

Once trained, the deep nets far outperform the purely symbolic AI at generating questions. It’s possible to solve this problem using sophisticated deep neural networks. However, Cox’s colleagues at IBM, along with researchers at Google’s DeepMind and MIT, came up with a distinctly different solution that shows the power of neurosymbolic AI. Each of the hybrid’s parents has a long tradition in AI, with its own set of strengths and weaknesses.

As a subset of first-order logic Prolog was based on Horn clauses with a closed-world assumption—any facts not known were considered false—and a unique name assumption for primitive terms—e.g., the identifier barack_obama was considered to refer to exactly one object. 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. And while these concepts are commonly instantiated by the computation of hidden neurons/layers in deep learning, such hierarchical abstractions are generally very common to human thinking and logical reasoning, too.

It is one form of assumption, and a strong one, while deep neural architectures contain other assumptions, usually about how they should learn, rather than what conclusion they should reach. The ideal, obviously, is to choose assumptions that allow a system to learn flexibly and produce accurate decisions about their inputs. There are several flavors of question answering (QA) tasks – text-based QA, context-based QA (in the context of interaction or dialog) or knowledge-based QA (KBQA).

The expert system processes the rules to make deductions and to determine what additional information it needs, i.e. what questions to ask, using human-readable symbols. For example, OPS5, CLIPS and their successors Jess and Drools operate in this fashion. Symbolic artificial intelligence, also known as symbolic AI or classical AI, refers to a type of AI that represents knowledge as symbols and uses rules to manipulate these symbols. Symbolic AI systems are based on high-level, human-readable representations of problems and logic. The hybrid artificial intelligence learned to play a variant of the game Battleship, in which the player tries to locate hidden “ships” on a game board.

Using OOP, you can create extensive and complex symbolic AI programs that perform various tasks. Symbolic Artificial Intelligence continues to be a vital part of AI research and applications. Its ability to process and apply complex sets of rules and logic makes it indispensable in various domains, complementing other AI methodologies like Machine Learning and Deep Learning.

Symbolic Approach in Semantic Knowledge Processing:

Moreover, they lack the ability to reason on an abstract level, which makes it difficult to implement high-level cognitive functions such as transfer learning, analogical reasoning, and hypothesis-based reasoning. Finally, their operation is largely opaque to humans, rendering them unsuitable for domains in which verifiability is important. In this paper, we propose an end-to-end reinforcement learning architecture comprising a neural back end and a symbolic front end with the potential to overcome each of these shortcomings.

Crucially, these hybrids need far less training data then standard deep nets and use logic that’s easier to understand, making it possible for humans to track how the AI makes its decisions. Other ways of handling more open-ended domains included probabilistic reasoning systems and machine learning to learn new concepts and rules. McCarthy’s Advice Taker can be viewed as an inspiration here, as it could incorporate new knowledge provided by a human in the form of assertions or rules. For example, experimental symbolic machine learning systems explored the ability to take high-level natural language advice and to interpret it into domain-specific actionable rules. For the first method, called supervised learning, the team showed the deep nets numerous examples of board positions and the corresponding “good” questions (collected from human players).

By optimizing decision-making processes, it not only conserves resources but also ensures higher accuracy, a key aspect in the realm of Algebra and Invariant Theory within AI. The application of Symbolic AI in complex systems underscores its ability to enhance operational efficiency. Despite facing challenges and criticisms, especially in handling complex, real-world scenarios, Symbolic AI experienced a resurgence in the 21st century. In terms of application, the Symbolic approach works best on well-defined problems, wherein the information is presented and the system has to crunch systematically.

Examples of common-sense reasoning include implicit reasoning about how people think or general knowledge of day-to-day events, objects, and living creatures. In contrast to the US, in Europe the key AI programming language during that same period was Prolog. Prolog provided a built-in store of facts and clauses that could be queried by a read-eval-print loop. The store could act as a knowledge base and the clauses could act as rules or a restricted form of logic.

They have created a revolution in computer vision applications such as facial recognition and cancer detection. Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside. However, as imagined by Bengio, such a direct neural-symbolic correspondence was insurmountably limited to the aforementioned propositional logic setting.

Mechatronics are easier to judge in a short video than AI and autonomy, and from that perspective, the Figure 01 robot appears quite dexterous. In fact, if you look at the angle and positioning of the arms, you’ll notice that it’s performing the carry in a manner that would be quite uncomfortable for most people. It’s important to note that just because the robot looks like a person doesn’t mean that it has to behave exactly like one.

For more detail see the section on the origins of Prolog in the PLANNER article. Follow Reinhardt Krause on X, formerly called Twitter, @reinhardtk_tech for updates on artificial intelligence, cybersecurity and cloud computing. What sets OpenAI’s ChatGPT, Google’s Gemini and other large language models apart is the size of data sets, called parameters, used to train the LLMs. The more data a large language model is trained upon, the more powerful its capabilities can become.

• But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside.
• A hybrid approach, known as neurosymbolic AI, combines features of the two main AI strategies.
• LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since they can remember previous information in long-term memory.
• Due to the explicit formal use of reasoning, NSQA can also explain how the system arrived at an answer by precisely laying out the steps of reasoning.
• If you ask it questions for which the knowledge is either missing or erroneous, it fails.

Despite its strengths, Symbolic AI faces challenges, such as the difficulty in encoding all-encompassing knowledge and rules, and the limitations in handling unstructured data, unlike AI models based on Neural Networks and Machine Learning. Neural Networks display greater learning flexibility, a contrast to Symbolic AI’s reliance on predefined rules. Symbolic AI’s logic-based approach contrasts with Neural Networks, which are pivotal in Deep Learning and Machine Learning. Neural Networks learn from data patterns, evolving through AI Research and applications. In contrast, a multi-agent system consists of multiple agents that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). Advantages of multi-agent systems include the ability to divide work among the agents and to increase fault tolerance when agents are lost.

A paper on Neural-symbolic integration talks about how intelligent systems based on symbolic knowledge processing and on artificial neural networks, differ substantially. A key challenge in computer science is to develop an effective AI system with a layer of reasoning, logic and learning capabilities. But today, current AI systems have either learning capabilities or reasoning capabilities —  rarely do they combine both. Now, a Symbolic approach offer good performances in reasoning, is able to give explanations and can manipulate complex data structures, but it has generally serious difficulties in anchoring their symbols in the perceptive world.