Artificial intelligence is increasingly delivering on its potential. For instance, through natural language processing (NLP), a segment of AI, computers can now interpret human language.
Sounds great, but such advancements come at a steep cost and consume a lot of data, time and processing resources when driven by machine learning (ML). And after all this investment, you still run the risk of less-than-accurate results. Yet, equipped with the right AI strategy, enterprises can sidestep these challenges. Known as symbolic AI, this approach for NLP models delivers both lower computational costs and more insightful and accurate results.
