Symbolic AI: The key to the thinking machine
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.
Because they are bound by rules, however, symbolic algorithms cannot improve themselves over time, which is, after all, one of the key value propositions that AI brings to the table, says Jans Aasman, CEO of knowledge graph solutions provider Franz Inc. This is why symbolic AI is being integrated into ML, DL, and other forms of rules-free AI to create hybrid environments that provide the best of both worlds: full machine intelligence with logic-based brains that improve with each application.
Read the full article at VentureBeat.