Machine learning continues to grow as a new frontier in the modern technological landscape, with its impact felt in areas ranging form streamlined customer analytics to advanced auditory technology. At the same time, our overall digital activity has become increasingly facilitated by mobile applications found on smartphones and wearable devices.
Naturally, these two prevalent variables have become married in recent years, producing apps capable of tracking and utilizing user data — namely in-app tendencies and preferences — to create an experience that is personalized and unique.
Now, machine learning apps stand as their own hybridized benchmark in technological achievement.
A significant, and perhaps the most fascinating, detail of machine learning apps is their ability to learn at a nonstop rate, so long as they are used regularly. Many of these apps have grown capable of learning from the seemingly minuscule details of their users’ day-to-day lives. An easy example is the advancement of recommendation services within social media; a Facebook user, for instance, now has a vast pool of personalized options at his or her finger tips, from user suggestion based on location and mutual workplace to photo tagging via facial recognition. Other apps, especially entertainment-based tools like Netflix and Spotify, have become smart enough to not only compile suggested content based on interest and thematic preference, but they can also subdivide these preferences based on different genres, artists, and moods. In this sense, these apps respond to our feelings and passions at an almost sentient level.
Expanding boundaries in the workplace
Compared to other relevant facets of Artificial Intelligence (AI) technology, machine learning apps have gained traction as a leading recipient of funding. This notion alone is indicative of the working world’s increasing trust in this expansive technology.
As the boundaries continue to expand in terms of machine learning potential, they have grown to include corporate ventures in data mining, robotics, and finance algorithms — and now, app technology can be included as a crucial highlight in this list of uses. With many workplaces moving to a hybrid brick-and-mortar/digital layout, the ability to remain uniformly rooted in automated analytics has become vital. Subsequently, many businesses now rely on machine learning app intelligence to develop a stronger notion of customer feedback. Services like Yelp, for instance, have become a significant consideration for essentially any business capable of being reviewed, as widespread ratings can quickly make or break revenue figures based on digital reputation alone. Analytics compiled via machine learning help facilitate this process for all involved, keeping a potentially overwhelming chunk of data organized and digestible. The ability to take these offerings on the go has only increased convenience.
As these factors are already noteworthy in early 2018, they will surely continue to gain momentum throughout the year and beyond — especially in the mobile sector.