In part 1 of this two part blog post I talked about why you should start using Machine Learning. In particular, I discussed that the barriers to entry in Machine Learning are going down, and although applying it to your business problems may not be easy, it is definitely within reach and can yield great benefits. In part 2, I would like to offer three guiding principles on how to start using Machine Learning.
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“We believe that every successful new application built today will be an intelligent application,” says Soma Somasegar, venture partner at Madrona Venture Group and former head of Microsoft’s Developer Division. Indeed, we are in a transition period where the barriers to entry in Machine Learning are going down dramatically. And at the same time, more individuals and businesses are seeing the potential of Machine Learning to improve existing products and services and to enable completely new applications. The time is now for both software developers and businesses of any size to start using Machine Learning to create more powerful user experiences and bring new ideas to the marketplace.
It's been a few weeks now since EntreFEST 2017 took over downtown Iowa City with the largest gathering of Iowa's entrepreneurial and innovation community. I was there to connect with like-minded people and get energized alongside fellow innovators. I was also there to speak to entrepreneurs about how to capitalize on trends in Artificial Intelligence and Machine Learning to create more engaging experiences for their customers. This conference did not disappoint: I met a lot of fantastic people that are pouring their energy into new ideas with great potential, I rekindled old friendships, and I learned new things from some top-notch speakers. So with the conference now behind us, and some time to process it all, I thought I'd share some of my takeaways.
If you were using email in the early 2000’s, you may remember it was around that time that spam was becoming a serious problem. Think of roughly 80% of your email being unsolicited garbage from unprincipled marketers and con artists. Imagine having to sort through that on your own, every day. There were various attempts at addressing the problem, and it was around 2002 that Machine Learning techniques started to be applied to identify and filter out spam. Over the years more sophisticated Artificial Intelligence techniques have been applied, such that the difficult, yet indispensable task of spam filtering is done for us, executed pretty well, and is nearly invisible to us.