Symbolic AI: The key to the thinking machine

So, while naysayers may decry the addition of symbolic modules to deep learning as unrepresentative of how our brains work, proponents of neurosymbolic AI see its modularity as a strength when it comes to solving practical problems. “When you have neurosymbolic systems, you have these symbolic choke points,” says Cox. These choke points are places in the flow of information where the AI resorts to symbols that humans can understand, making the AI interpretable and explainable, while providing ways of creating complexity through composition. Lake and other colleagues had previously solved the problem using a purely symbolic approach, in which they collected a large set of questions from human players, then designed a grammar to represent these questions. “This grammar can generate all the questions people ask and also infinitely many other questions,” says Lake.

A single nanoscale memristive device is used to represent each component of the high-dimensional vector that leads to a very high-density memory. The similarity search on these wide vectors can be efficiently computed by exploiting physical laws such as Ohm’s law and Kirchhoff’s current summation law. VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. The universe is written in the language of mathematics and its characters are triangles, circles, and other geometric objects.

Getting AI to reason: using neuro-symbolic AI for knowledge-based question answering

Even if you take a million pictures of your cat, you still won’t account for every possible case. A change in the lighting conditions or the background of the image will change the pixel value and cause the program to fail. Error from approximate probabilistic inference is tolerable in many AI applications. But it is undesirable to have inference errors corrupting results in socially impactful applications of AI, such as automated decision-making, and especially in fairness analysis.

The Disease Ontology is an example of a medical ontology currently being used. Neural networks are almost as old as symbolic AI, but they were largely dismissed because they were inefficient and required compute resources that weren’t available at the time. In the past decade, thanks to the large availability of data and processing power, deep learning has gained popularity and has pushed past symbolic AI systems.

Integration with Machine Learning:

One promising approach towards this more general AI is in combining neural networks with symbolic AI. In our paper “Robust High-dimensional Memory-augmented Neural Networks” published in Nature Communications,1 we present a new idea linked to neuro-symbolic AI, based on vector-symbolic architectures. To better simulate how the human brain makes decisions, we’ve combined the strengths of symbolic AI and neural networks. Symbolic AI has numerous applications, from Cognitive Computing in healthcare to AI Research in academia. Its ability to process complex rules and logic makes it ideal for fields requiring precision and explainability, such as legal and financial domains.

• Symbolic AI’s adherents say it more closely follows the logic of biological intelligence because it analyzes symbols, not just data, to arrive at more intuitive, knowledge-based conclusions.
• For instance, if you take a picture of your cat from a somewhat different angle, the program will fail.
• MIT researchers have developed a new artificial intelligence programming language that can assess the fairness of algorithms more exactly, and more quickly, than available alternatives.
• DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can also be viewed as an ontology.
• These explainable aspects make this hybrid approach a good fit for many use cases, but best suited for those that are either internal or not mission critical (e.g., categorization of highly complex documents, anti-money laundering processes, etc.).

This kind of meta-level reasoning is used in Soar and in the BB1 blackboard architecture. In games, a lot of computing power is needed for symbolic ai example graphics and physics calculations. Thus the vast majority of computer game opponents are (still) recruited from the camp of symbolic AI.

The new SPPL probabilistic programming language was presented in June at the ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI), in a paper that Saad co-authored with MIT EECS Professor Martin Rinard and Mansinghka. Symbolic AI, a subfield of AI focused on symbol manipulation, has its limitations. Its primary challenge is handling complex real-world scenarios due to the finite number of symbols and their interrelations it can process. For instance, while it can solve straightforward mathematical problems, it struggles with more intricate issues like predicting stock market trends. During training and inference using such an AI system, the neural network accesses the explicit memory using expensive soft read and write operations.

We chose to focus on KBQA because such tasks truly demand advanced reasoning such as multi-hop, quantitative, geographic, and temporal reasoning. In the CLEVR challenge, artificial intelligences were faced with a world containing geometric objects of various sizes, shapes, colors and materials. The AIs were then given English-language questions (examples shown) about the objects in their world. 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.

It operates by manipulating symbols to derive solutions, which can be more sophisticated and interpretable. This interpretability is particularly advantageous for tasks requiring human-like reasoning, such as planning and decision-making, where understanding the AI’s thought process is crucial. The efficiency of a symbolic approach is another benefit, as it doesn’t involve complex computational methods, expensive GPUs or scarce data scientists. Plus, once the knowledge representation is built, these symbolic systems are endlessly reusable for almost any language understanding use case.

The output of the recurrent network is also used to decide on which convolutional networks are tasked to look over the image and in what order. This entire process is akin to generating a knowledge base on demand, and having an inference engine run the query on the knowledge base to reason and answer the question. The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks. In pursuit of efficient and robust generalization, we introduce the Schema Network, an object-oriented generative physics simulator capable of disentangling multiple causes of events and reasoning backward through causes to achieve goals.

Fulton and colleagues are working on a neurosymbolic AI approach to overcome such limitations. The symbolic part of the AI has a small knowledge base about some limited aspects of the world and the actions that would be dangerous given some state of the world. They use this to constrain the actions of the deep net — preventing it, say, from crashing into an object. 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 separate inference engine processes rules and adds, deletes, or modifies a knowledge store. 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. It does this especially in situations where the problem can be formulated by searching all (or most) possible solutions. However, hybrid approaches are increasingly merging symbolic AI and Deep Learning. The goal is balancing the weaknesses and problems of the one with the benefits of the other – be it the aforementioned “gut feeling” or the enormous computing power required.

If exposed to two dissimilar objects instead, the ducklings later prefer pairs that differ. Ducklings easily learn the concepts of “same” and “different” — something that artificial intelligence struggles to do. A new approach to artificial intelligence combines the strengths of two leading methods, lessening the need for people to train the systems.

The knowledge contained in these terms, definitions, and hierarchy of terms is explicit and always used transparently. Such a lack of ambiguity about what words mean and their relation to each other is optimal for explaining the results of symbolic AI systems. Explainability is the means of logically explaining—in words—the reasons AI applications produce their specific results. It’s similar to (but ultimately distinct from) interpretability which is the ability to understand what numerical outputs of models mean for business problems.

Symbolic AI is 100% based on explicit knowledge at every level, which makes it an excellent means of explaining every language understanding use case. Already, this technology is finding its way into such complex tasks as fraud analysis, supply chain optimization, and sociological research. Next, we’ve used LNNs to create a new system for knowledge-based question answering (KBQA), a task that requires reasoning to answer complex questions. Our system, called Neuro-Symbolic QA (NSQA),2 translates a given natural language question into a logical form and then uses our neuro-symbolic reasoner LNN to reason over a knowledge base to produce the answer.

Hatchlings shown two red spheres at birth will later show a preference for two spheres of the same color, even if they are blue, over two spheres that are each a different color. Somehow, the ducklings pick up and imprint on the idea of similarity, in this case the color of the objects. The words sign and symbol derive from Latin and Greek words, respectively, that mean mark or token, as in “take this rose as a token of my esteem.” Both words mean “to stand for something else” or “to represent something else”. Similar axioms would be required for other domain actions to specify what did not change. Cognitive architectures such as ACT-R may have additional capabilities, such as the ability to compile frequently used knowledge into higher-level chunks.