Symbolic vs Subsymbolic AI Paradigms for AI Explainability by Orhan G. Yalçın
Deep learning, in contrast to symbolic AI, has several deep challenges. Deep learning algorithms are so opaque that even their creators are perplexed by how they work. Neural networks, as opposed to symbolic AI, do not have symbols or hierarchies of knowledge. According to some, symbolic reasoning will continue to be an important component of artificial intelligence in The deep learning hope—seemingly grounded not so much in science, but in a sort of historical grudge—is that intelligent behavior will emerge purely from the confluence of massive data and deep learning.
First, it is universal, using the same structure to store any knowledge. Second, it can learn symbols from the world and construct the deep symbolic networks automatically, by utilizing the fact that real world objects have been naturally separated by singularities. Third, it is symbolic, with the capacity of performing causal deduction and generalization.
Improving Neural Networks with Neuroscience
Symbolic AI algorithms are designed to deal with the kind of problems that require human-like reasoning, such as planning, natural language processing, and knowledge representation. There are now several efforts to combine neural networks and symbolic AI. One such project is the Neuro-Symbolic Concept Learner (NSCL), a hybrid AI system developed by the MIT-IBM Watson AI Lab. NSCL uses both rule-based programs and neural networks to solve visual question-answering problems. As opposed to pure neural network–based models, the hybrid AI can learn new tasks with less data and is explainable. And unlike symbolic-only models, NSCL doesn’t struggle to analyze the content of images.
To think that we can simply abandon symbol-manipulation is to suspend disbelief. Qualitative simulation, such as Benjamin Kuipers’s QSIM,[89] approximates human reasoning about naive physics, such as what happens when we heat a liquid in a pot on the stove. We expect it to heat and possibly boil over, even though we may not know its temperature, its boiling point, or other details, such as atmospheric pressure. Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture[17] and the longer Wikipedia article on the History of AI, with dates and titles differing slightly for increased clarity.
Understanding the impact of open-source language models
LISP provided the first read-eval-print loop to support rapid program development. Program tracing, stepping, and breakpoints were also provided, along with the ability to change values or functions and continue from breakpoints or errors. It had the first self-hosting compiler, meaning that the compiler itself was originally written in LISP and then ran interpretively to compile the compiler code. The main limitation of symbolic AI is its inability to deal with complex real-world problems.
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