Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Even as many enterprises are just starting to dip their toes into the AI pool with rudimentary machine learning (ML) and deep learning (DL) models, a new form of the technology known as symbolic AI is emerging from the lab that has the potential to upend both the way AI functions and how it relates to its human overseers. 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. It's most commonly used in linguistics models such as natural language processing (NLP) and natural language understanding (NLU), but it is quickly finding its way into ML and other types of AI where it can bring much-needed visibility into algorithmic processes. The technology actually dates back to the 1950s, says expert.ai's

Even as many enterprises are just starting to dip their toes into the AI pool with rudimentary machine learning (ML) and deep learning (DL) models, a new form of the technology known as symbolic AI is emerging from the lab that has the potential to upend both the way AI functions and how it relates to its human overseers. 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. It's most commonly used in linguistics models such as natural language processing (NLP) and natural language understanding (NLU), but it is quickly finding its way into ML and other types of AI where it can bring much-needed visibility into algorithmic processes. The technology actually dates back to the 1950s, says expert.ai's Luca Scagliarini, but was considered old-fashioned by the 1990s when demand for procedural knowledge of sensory and motor processes was all the rage.

Even as many enterprises are just starting to dip their toes into the AI pool with rudimentary machine learning (ML) and deep learning (DL) models, a new form of the technology known as symbolic AI is emerging from the lab that has the potential to upend both the way AI functions and how it relates to its human overseers. 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. It's most commonly used in linguistics models such as natural language processing (NLP) and natural language understanding (NLU), but it is quickly finding its way into ML and other types of AI where it can bring much-needed visibility into algorithmic processes. The technology actually dates back to the 1950s, says expert.ai's Luca Scagliarini, but was considered old-fashioned by the 1990s when demand for procedural knowledge of sensory and motor processes was all the rage.