In some use cases, it is impossible for humans to replicate the performance of artificial intelligence. But businesses will need a lot of data for AI systems to be effective.
Maybe you’ve seen an artificial intelligence (AI) system like Watson at work on “Jeopardy!” or have heard of its successes in medical diagnoses or other fields. Maybe you’ve only heard about other similar systems working through incredibly complex and large sets of data to produce results that even non-experts can understand, through visualizations or natural language. Either way, AI systems are impressing many on their march toward becoming essential business processes.
How does artificial intelligence work? AI systems “seem so intensely magical, but they’re not. At the bottom of these systems is hardcore data analytics, and in order for them to work, the data needs to be there,” said Kristian Hammond, a faculty member of the International Institute for Analytics (IIA), durin a recent webinar. Hammond, who is also chief scientist of Narrative Science,insists we live in a world absolutely brimming with data—power comes in knowing how to process it.
Watson, developed in IBM’s DeepQA project, is just one example of an AI system capable of churning through terabytes of information in search of an answer to a natural language question. If one asks it, “Who ruled Spain in 1829?” it will turn the language into a number of similar search queries, such as “was king of Spain” or “ruled Spain.”
Hammond says that with these queries, Watson searches through its various networks of sources to find patterns in historical documents or reputable websites, such as Wikipedia. From there, it’s capable of aggregating the evidence into a best guess: for example, 87 percent of the evidence points toward Ferdinand VII, and 17 percent for Maria Christina. At this point, it’s not a stretch to trust in Watson’s analysis, which would be impossible to replicate in human terms.
It’s not unlike a visual recognition engine, which examines millions of images, and all their individual pixels, to understand what patterns indicate a picture of cat versus a picture of anything else.
IBM reported that Watson used more than 100 different techniques for analyzing natural language with Watson, but “what is far more important than any particular technique we use is how we combine them in DeepQA such that overlapping approaches can bring their strengths to bear and contribute to improvements in accuracy, confidence, or speed,” researchers stated.