Mimicking the brain: Deep learning meets vector-symbolic AI

Limitations were discovered in using simple first-order logic to reason about dynamic domains. Problems were discovered both with regards to enumerating the preconditions for an action to succeed and in providing axioms for what did not change after an action was performed. 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.

But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside. 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. 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.

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Production rules connect symbols in a relationship similar to an If-Then statement. 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.

• A different way to create AI was to build machines that have a mind of its own.
• 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.
• Its primary challenge is handling complex real-world scenarios due to the finite number of symbols and their interrelations it can process.
• However, it can be advanced further by using symbolic reasoning to reveal more fascinating aspects of the item, such as its area, volume, etc.
• NSCL uses both rule-based programs and neural networks to solve visual question-answering problems.

For example, Figure 3 shows the steps of geographic reasoning performed by LNN using manually encoded axioms and DBpedia Knowledge Graph to return an answer. Expert systems can operate in either a forward chaining – from evidence to conclusions – or backward chaining – from goals to needed data and prerequisites – manner. More advanced knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies. This creates a crucial turning point for the enterprise, says Analytics Week’s Jelani Harper. Data fabric developers like Stardog are working to combine both logical and statistical AI to analyze categorical data; that is, data that has been categorized in order of importance to the enterprise.

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Similarly, LISP machines were built to run LISP, but as the second AI boom turned to bust these companies could not compete with new workstations that could now run LISP or Prolog natively at comparable speeds. We use symbols all the time to define things (cat, car, airplane, etc.) and people (teacher, police, salesperson). 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.).

Cognitive architectures such as ACT-R may have additional capabilities, such as the ability to compile frequently used knowledge into higher-level chunks. Our chemist was Carl Djerassi, inventor of symbolic ai examples the chemical behind the birth control pill, and also one of the world’s most respected mass spectrometrists. We began to add to their knowledge, inventing knowledge of engineering as we went along.

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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. Symbolic artificial intelligence showed early progress at the dawn of AI and computing.

• In fact, rule-based AI systems are still very important in today’s applications.
• Better yet, the hybrid needed only about 10 percent of the training data required by solutions based purely on deep neural networks.
• Implementations of symbolic reasoning are called rules engines or expert systems or knowledge graphs.
• This hybrid approach enables machines to reason symbolically while also leveraging the powerful pattern recognition capabilities of neural networks.

Take, for example, a neural network tasked with telling apart images of cats from those of dogs. The image — or, more precisely, the values of each pixel in the image — are fed to the first layer of nodes, and the final layer of nodes produces as an output the label “cat” or “dog.” The network has to be trained using pre-labeled images of cats and dogs. During training, the network adjusts the strengths of the connections between its nodes such that it makes fewer and fewer mistakes while classifying the images.