I can guarantee that anybody reading this blog uses machine learning dozens of times each day without even realizing it. When you perform a web search usingGoogle or Bing, for instance, the search engine works so well because their software has figured out how to predict searches and rank pages for you. When Facebook or iPhoto recognize the faces of the people in your pictures, they too are using machine learning. Each time you read your email and there is a SPAM filter that saves you tons of time by not needing to sort through massive amounts of SPAM emails, that is because your computer has learned to distinguish SPAM from non-SPAM emails.
It is exciting to think that someday there will be artificial intelligence machines that are built as smart or smarter than you or me. Don’t be nervous…yet. We are a long way away from that reality. What will help us speed up the process of achieving this artificial intelligence goal? Learning algorithms that try to mimic how the human brain learns.
Machine learning is the science of getting computers to learn, without being explicitly programmed. Turns out that knowing the math and knowing the algorithms isn’t that much good, if you don’t also know how to get the machines to work on problems that you care about. There is a great course offered by coursera that you may sign-up for to learn how to use algorithms and math to solve the problems that interest you.
The engineers at Extract Systems have been hard at work applying the math and algorithms to our software, to make time-consuming, error-prone, mundane manual task automated. Our software is constantly learning, which means constantly improving. With machine learning we can guarantee a 99% or greater accuracy rating, as well as having the ability to index, redact, or extract information from unstructured documents. Our software is smart enough that it doesn’t need the documents to be all in the same format or file type.
If you would like to learn more about Machine Learning, we encourage you to sign-up for our free webinars, or to email our PS team.