Man wearing tech goggles

Machine Learning: Friend or Foe?

When people think of machines and machine learning in the late 1990 era of robot featured films jump to most people’s minds. The Terminator or the Matrix to name a few. Both movies portray machines as scary because they hurt us or try to alter our reality so they can feed off of us. If you were to ask Brittney Muller, led industry researcher and developer at Moz, what she thinks of machine learning, you would hear a totally different answer. “Machine Learning is the subset of AI that combines programming with stats” Brittney exclaimed. “If we want to do better we have to think differently and machine learning can help us do that.” The example she used that I thought was so perfect for her argument in support of machine learning was about Bill Gates’s efforts to eradicate malaria in Africa. As most people know the malaria virus is carried by mosquitoes. To eradicate malaria we would have to find a solution to eliminate the pesky bugs, but how? They are so small and there are far too many of them to go after every single one. But Gates had an idea. Using machine learning software, Gates worked with Nathan Myhrvold, former Microsoft CTO, and general genius, to develop a laser beam that could detect mosquitoes that carried the malaria virus through infra-red readings. Once the laser found what it was looking for it does what it is programmed to do, kill the bug and thus the virus it carries. Fences are to be set up around the communities in Africa with these machine learning lasers to help reduce the probability of the virus passing through and infecting the community. While malaria is still around, taking the first steps towards developing a way to remove the life-threatening virus is the premise behind machine learning. To assist us in creating a safer and more efficient world and society. “Machine learning will free us up to do more strategic work,” says Brittney and she couldn’t be more right about it. So how does this playing into marketing? In a variety of ways. Today we see subtle uses of machine learning from face recognition software to algorithms on Spotify to find what new songs you may like based on what you already listen to. All of these things are minor compared to fighting malaria, but it is the first step towards developing software that can ultimately advance how we manage our day-to-day tasks. Currently, software engineers are developing software that can help you write the perfect SEM caption for Google, software to find a picture that perfectly represents your written content, and software that takes data in spreadsheets and creates a visual representation of the data. There is so much machine learning software that is being created, and some of them may never move past the beta stage during development. It is this idea of continually thinking outside the box that will push how we live into the next century. As of right now, we are creating machine learning technology to better advance society and to help us, not hurt us. And while change is scary and there will be a lot of debate behind machine learning in future years, it is exciting to see what the future holds with the help of a machine. If you are interested in machine learning or want to explore that variety of machine learning experiences that are out there check out these links. They are a fun way people are developing machine learning software.
  • Google facets
  • Spark Toro
  • Monkey Learn
  • Paul Shapiro
  • Lumen 5
  • Vision API
  • Natural Language API
  • Algorithmia
  • Kaggle
  • SEM Rush
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