When we interact with digital devices and services, they almost always generate data about our location. Smartphone users rely on location services to look up driving directions and weather reports and set up geo-fenced alerts. For service providers, this information is equally useful: It can reveal a lot about customers, competitors, and opportunities to expand or improve their services.
Applying machine learning will make location-based services even better attuned to our needs and preferences. Let’s take a look at what this might mean in practice.
The greatest triumph of the smartphone was the democratization of the Global Positioning System. Once just a tool for governments and militaries, GPS now empowers people all over the globe with insights into where they’re going and how to get there. We can complain all we like that nobody knows how to read a paper map anymore, but does anybody really want to go back to those days?
Thanks to machine learning, our smartphones, and mobile apps are going to get better at delivering uncannily accurate predictions about where we need to go, when we need to leave and how to get there, all based on pattern recognition and historical user data. We can already add a “time to leave” modifier to the entries we make in our calendars, but machine learning will take this to the next level.
Next time you’re getting ready for work, imagine receiving an unprompted notification on your phone screen indicating a traffic snarl-up along your usual route. You know how to get to the office — you haven’t needed directions to get there in a long time. However, thanks to machine learning, your phone is helpfully pinging you with an alternate route, because it knows your usual commute is going to slow you down and it doesn’t want you to be late.
From Apple and Google to Nokia and lots of startups you haven’t even heard of yet, there’s a lot of money being poured into intelligent navigation systems. The future, according to Uber, Lyft, Tesla and others, is autonomous cars with smart navigation that can change in response to real-time events. Getting there means our technology needs to get a lot better at studying and visualizing user patterns for thousands of customers at one time. It also needs to take into account congestion, weather, time of day, planned and unplanned events, and much more.
Many of us rely on smartphones to remind us about upcoming items on our to-do lists, to keep track of which groceries we’re running low on, and whether our next dental cleaning is coming up. If reminders and calendar items are the bread and butter of the mobile operating system experience, machine learning is the secret sauce that could take stock smartphone apps and deliver performance that makes it feel like we’re living in the future.
Suppose we’ve been adding items to our grocery lists like toilet paper, milk, and eggs. Before too long, our favorite apps will be smart enough to plan our shopping trips and even entire days with uncanny accuracy and help us make the most of our limited time. They’ll know what’s on our shopping or to-do lists and where we’ve been in the past when we checked those items off. The next time we’re driving by the store, they’ll let us know about it — all without being asked — or maybe even suggest an alternative that’s running a sale or promotion.
We tell ourselves that smartphones are like digital personal assistants, but we have to perform a lot of the logic for them. Thanks to a combination of machine learning and location services, we can expect far more intuitive and automated performance soon.
Read the source article in TechiExpert.com.