There has been a revolution of technologies. Emerging technologies like deep learning and machine learning have resulted in tools capable of advancements never before seen. Forbes brought this interesting topic to our attention in their article, “Why Machine Learning Needs Semantics Not Just Statistics.”
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions like humans, but with minimal human intervention.
The problem with likening machine learning to human learning is that when humans learn, they connect the patterns they identify to high order semantic abstractions of the underlying objects and activities. Our background knowledge and perspective give us the necessary context to reason about those patterns and identify the ones most likely to represent robust actionable knowledge. Machines aren’t there just yet.
Melody K. Smith
Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.
