Could Symbolic AI Unlock Human-like Intelligence?

"Will computers ever match or surpass human-level intelligence and, if so, how? When the Association for the Advancement of Artificial Intelligence (AAAI), based in Washington DC, asked its members earlier this year whether neural networks the current star of artificial-intelligence systems alone will be enough to hit this goal, the vast majority said no. Instead, most said, a heavy dose of an older kind of AI will be needed to get these systems up to par: symbolic AI."

"Sometimes called good old-fashioned AI', symbolic AI is based on formal rules and an encoding of the logical relationships between concepts. Mathematics is symbolic, for example, as are ifthen' statements and computer coding languages such as Python, along with flow charts or Venn diagrams that map how, say, cats, mammals and animals are conceptually related. Decades ago, symbolic systems were an early front-runner in the AI effort."

"However, in the early 2010s, they were vastly outpaced by more-flexible neural networks. These machine-learning models excel at learning from vast amounts of data, and underlie large language models (LLMs), as well as chatbots such as ChatGPT. Now, however, the computer-science community is pushing hard for a better and bolder melding of the old and the new. Neurosymbolic AI' has become the hottest buzzword in town."

Most AI researchers believe neural networks alone will not achieve human-level intelligence and favor integrating symbolic AI to fill gaps. Symbolic AI uses formal rules and encoded logical relationships between concepts, exemplified by mathematics, if-then statements, programming languages, flow charts and Venn diagrams. Neural networks surged in the early 2010s by learning from vast datasets and underpinning large language models and chatbots like ChatGPT. The field is now pursuing neurosymbolic approaches that combine neural learning with symbolic reasoning. Interest in neurosymbolic AI spiked around 2021 and continues rising, with researchers framing it as a counterbalance to neural-network dominance.

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