Today is a pretty exciting time to be alive. Sometimes I think about how nice it is that I’m living in the future, when I can video chat with a dozen people without any expensive hardware or complex software.
As it turns out, another thing that happens in the future is that the electronics that you carry around can learn from what you do with them.
“Machine learning is such a huge opportunity,” says Justin Rattner, Intel’s chief technology officer. His statement was part of an announcement about Intel’s research on smart applications and devices, such as small, wearable computers that can enhance daily life.
The combination of machine learning and mobile devices is incredibly potent. Part of the reason for this is that computing platforms like phones and tablets are increasingly replete with sensors like microphones, cameras, accelerometers, gyroscopes and GPS chips. More and more devices have the capability to collect data about where they are and how they’re being used. Machine learning can turn this data into insights about user behavior.
We believe machine learning is a big part of the future of mobile computing, and a small but growing community of developers around BigML agrees. One of them has started an Open Source Library called ML4iOS that uses BigML’s REST API to help iOS developers integrate machine learning into any iOS application.
The library is very well done, includes a test suite, and has support for synchronous and asynchronous requests. With this library, you can easily integrate BigML’s cloud-based machine learning service into native iPhone and iPad applications with a few simple lines of code.