That’s what Jason Bloomburg writes in his article on Forbes, ‘Semantic Technology: Building the HAL 9000 Computer?’.
While, as Bloomburg points out, we’re not building HAL quite yet, we are moving in the right direction with a “fascinating combination of innovations” in semantic technology. This technology adds a layer of context that allows for information to be processed in a more ‘human’ way. It’s not AI, and it’s not Data from Star Trek, but it does have valuable real-world applications.
Real World Applications
Lexalytics is an example of that. Our ability to perform sentiment analysis, entity extraction, and ambiguity resolution are all examples of semantic technology at work. It’s because of that added layer of context that our technology can distinguish between different categories, such as bats (animal) and bats (baseball), and determine the sentiment expressed towards an entity.
Not Quite HAL 9000
Semantic technology allows for computers to get closer than ever to making sense of certain aspects of human language. However, that’s still a far cry from what fiction would have us believe that means. Just because computers can determine sentiment, for example, doesn’t mean they understand or express emotion.
Although it might lack the theatricality of the big screen, that doesn’t mean semantic technology doesn’t possess huge value. The ability to analyze text the way that we do has an astounding ability to affect everything from the stock market to healthcare, and right down to to the way businesses interact with customers.
Real world applications for semantic technology are here. We know because we’re helping provide them. The bounds of what semantic technology can do are limited only by human imagination.