The roles of big data and machine intelligence are becoming increasingly visible in our day-to-day lives. That doesn't necessarily mean that tomorrow we'll all be riding around in driverless cars but it does mean that we're seeing a major increase in the availability of these sorts of applications.
Two major examples of this were seen at the end of July, with the news that Apple acquired Swell, an app similar to Pandora with an emphasis on talk radio as opposed to music, and BookLamp, a platform that is most well-known for their Book Genome Project which suggests books for users to read by scanning the writing style of previous books they've read.
While Swell monitors human behavior to deliver curated, relevant playlists without much user input, BookLamp utilizes big data-style book analytics services to provide relevant recommendations to end-users - both taking a very intelligent approach to activities integrated in our daily lives.
But Apple isn't the only one investing in machine intelligence to make more personalized and relevant mobile experiences for end-users. The potential uses for big data and machine intelligence are endless particularly when it comes to how our mobile lifestyles have just become part of the norm.
It's not beyond the imagination to consider (and in fact, it's already happening) grocery shopping, dating and even personal health applications taking more of a data-led approach. Personalization technology will no longer be something that we merely appreciate but something users will begin to expect from their mobile experiences and companies should consider incorporating this into their apps.
In Machines We Trust
Big data and machine intelligence applications are providing incredibly relevant information to end-users by employing algorithms based off of data-gathering techniques from app usage. The benefit of machine learning is that it can gather ambient data such as time of day and geo-location as well as user data like past preferences, wants and needs in the background offering a very seamless experience for the user.
However, it is an incredibly complicated and complex relationship to utilize machine learning algorithms to create a solution that simplifies the users' interaction and reduces the manual interaction, in turn, benefitting the user. But it is exactly this type of interaction that is needed to create a valued, personalized application for users.
The more personalized a solution is, the more that end-users will see things they want to see, when they want to see them, automatically. This relevance will in turn mean that users will begin to establish a trust with the device and increases the usage with these intelligent solutions.
Real World Apps - Not Just Apple's Swell or BookLamp
Beyond managing our social lifestyle such as the music we listen to or the books we read, in the two recent Apple acquisition examples, there are many other examples that we will see in the near future that impact of our daily lives in significant ways as machine learning is applied to new areas. For example, at the Google I/O conference last month, education and scientific exploration were referenced as industries that could be impacted by an intelligent alert system. Google is going full-steam ahead with investing in and knowing the importance of providing its users with intelligent alerts.
Other examples lie within the retail industry, where brands and retailers are starting to roll out these types of smart machine learning. While it is not yet live in stores, IBM's Watson has demo-ed an "expert personal shopper"that utilizes machine learning for this exact usage for shoppers of The North Face retail brand. This cognitive device utilizes knowledge of the brand's product database to provide recommendations to the end-user. For example, an end-user can tell this device that they are planning on going on a trip and need a tent, sharing the location of where they would like to go. The voice-recognition technology takes into account the user's request, as well as the device's knowledge about the location (including weather and other ambient information) in order to recommend the shopper the best product for their request.
Machine learning and Artificial Intelligence technology will continue to enhance our daily lives - whether it be via our grocery shopping experiences, picking movies to consume, or in other more unique daily environments. The possibilities are endless and are even more accessible than ever before.
Man & Machine
As Bob Dylan once sang, "The times they are a changin'..." Machine learning is no longer the plot of science fiction novels. The future of mobile and the machine is now. Apps such as Pandora, Netflix and other recommendation engine-powered mobile applications, have made us grow accustomed to the luxury of relevance using sophisticated data gathering and modeling techniques and machine learning. The two recent Apple acquisitions are continued testaments of this and show real-world examples that leading organizations have recognized this - and are doing something about it. With the real world uses providing endless possibilities, companies need to pick up machine intelligence to build intelligent apps in order to flourish.
The potential uses for big data and machine intelligence are endless particularly when it comes to how our mobile lifestyles have just become part of the norm.