Machine Learning in 2016
The world of data has really boomed in the last few years. When I first joined Packt Hadoop was The Next Big Thing on the horizon and what people are now doing with all the data we have available to us was unthinkable. Even in the first few weeks of 2016 we’re already seeing machine learning being used in ways we probably wouldn’t have thought about even a few years ago — we’re using machine learning for everything from discovering a supernova that was 570 billion times brighter than the sun to attempting to predict this year’s Super Bowl winners based on past results.
So what else can we expect in the next year for machine learning and how will it affect us? Based on what we’ve seen over the last three years here are a few predictions about what we can expect to happen in 2016 (With maybe a little wishful thinking mixed in too!)
Machine Learning becomes the new Cloud
Not too long ago every business started noticing the cloud, and with it came a shift in how companies were structured. Infrastructure was radically adapted to take full advantage that the benefits that the cloud offers and it doesn’t look to be slowing down with Microsoft recently promising to spend over $1 billion in providing free cloud resources for non-profits. Starting this year it’s plausible that we’ll see a new drive to also bake machine learning into the infrastructure. Why? Because every company will want to jump on that machine learning bandwagon! The benefits and boons to every company are pretty enticing — ML offers everything from grandiose artificial intelligence to much more mundane such as improvements to recommendation engines and targeted ads; so don’t be surprised if this year everyone attempts to work out what ML can do for them and starts investing in it.
The growth of MLaaS
Last year we saw Machine Learning as a Service appear on the market in bigger numbers. Amazon, Google, IBM, and Microsoft all have their own algorithms available to customers. It’s a pretty logical move and why that’s not all surprising. Why? Well, for one thing, data scientists are still as rare as unicorns. Sure, universities are creating new course and training has become more common, but the fact remains we won’t be seeing the benefits of these initiatives for a few years.
Second, setting up everything for your own business is going to be expensive. Lots of smaller companies simply don’t have the money to invest in their own personal machine learning systems right now, or have the time needed to fine tune it. This is where sellers are going to be putting their investments this year — the smaller companies who can’t afford a full ML experience without outside help.
Smarter Security with better protection
The next logical step in security is tech that can sense when there are holes in its own defenses and adapt to them before trouble strikes. ML has been used in one form or another for several years in fraud prevention, but in the IT sector we’ve been relying on static rules to detect attack patterns. Imagine if systems could detect irregular behavior accurately or set up risk scores dynamically in order to ensure users had the best protection they could at any time?
We’re a long way from this being fool-proof unfortunately, but as the year progresses we can expect to see the foundations of this start being seen. After all, we’re already starting to talk about it.
Machine Learning and Internet of Things combine
We’re already nearly there, but with the rise in interest in the IoT we can expect that these two powerhouses to finally combine. The perfect dream for IoT hobbyists has always been like something out of the Jetsons or Wallace and Gromit –when you pass that sensor by the frame of your door in the morning your kettle suddenly springs to life so you’re able to have that morning coffee without waiting like the rest of us primals; but in truth the Internet of Things has the potential to be so much more than just making the lives of hobbyists much easier. By 2020 it is expected that over 25 billion ‘Things’ will be connected to the internet, and each one will be collating reams and reams of data. For a business with the capacity to process this data they can collect the insight they could collect is a huge boon for everything from new products to marketing strategy. For IoT to really live up to the dreams we have for it we need a system that can recognize and collate relevant data, which is where a ML system is sure to take center stage.
Big things are happening in the world of machine learning, and I wouldn’t be surprised if something incredibly left field happens in the data world that takes us all by surprise, but what do you think is next for ML?
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