Software and tools that utilize machine learning in particular have become the primary differentiator in the smartphone market as manufacturers seek to make their brands and devices stand out. For the most part, that’s down to the fact that smartphones, with and despite all of their different names and brands, have grown increasingly more similar in terms of features and hardware. The level of ubiquitousness does, of course, have some variances from one price range to the other. However, over time and with both increasing frequency and quickness, even premium features such as fingerprint scanners or dual, high-resolution cameras end up on budget devices. Not only has machine learning and associated software become the best way for OEMs to set themselves apart, but because those learn and become more advanced on their own over time, they are more difficult to replicate as time passes. Consequently, they become more important to both the tech companies behind them and their customers.
It bears mention that as more companies get involved in machine learning, with big names such as Qualcomm investing heavily in the development of A.I.-specific chipsets and other components, those features could become widespread as well. Some instances of those, in fact, are deliberately going to be pushed to all Android devices. Google’s A.I. driven Assistant is one example and Amazon’s Alexa is another, with both vying to offer the same functions as the competition and receiving new features all the time in an attempt to gain and retain market share. Bearing that in mind, individual manufacturers such as Huawei, Samsung, and Xiaomi, are also in the process of developing their own, in-house machine learning platforms and hardware. So it isn’t likely that there will be no difference between any of the major manufacturers soon, even if they all eventually offer the same core functionality.
Meanwhile, the software goes far beyond simple search mechanisms, home control, or reminders. Machine learning has become a mainstay in everything from camera management to overall hardware performance improvements. One major example of that is Google’s own Pixel devices, specifically those devices’ cameras. In spite of the fact that those devices only house a single sensor, they are equipped with nearly all of the same features that the competition has created, and then some. Google has gone further still with its Clips camera, which uses machine learning to capture those images that would ordinarily keep a user behind the lens instead of in the moment. The company does, on the other hand, have some related projects that will go or have gone much more mainstream. Those include innovations such as Google Assistant, recent efforts to secure the Google Play Store against malware using A.I., and its TensorFlow platform. However, those kinds of services are more a result of Google’s maintenance of Android itself, rather than an attempt to transition to a full-fledged smartphone company.
Samsung’s Bixby is another example similar to Google Assistant or Amazon Alexa but, like Alexa, it is designed to only work with specific devices. It is also debatable how successful Samsung’s efforts have been. Others, in the meantime, are understandably taking things in a different direction since it wouldn’t necessarily be feasible for the companies to try and recreate what’s been done by Google. Huawei is developing a platform for improving device performance over time, in terms of battery life and strain on other components. The Neural Processing Unit (NPU) responsible for making that happen made its first appearance in the company’s Mate 10 Pro. It will, of course, be limited until more applications are optimized to run on it, but it could eventually be included in all Huawei-made devices. Since machine learning, as its name implies, learns over time, that could eventually result in phones that last far longer on average.
LG has taken a different route entirely, including an A.I.-driven biometrics security feature that was developed by Sensory in its latest run of flagship smartphones. That feature will almost certainly arrive on plenty of other devices since it is software-based and can use any camera but, as with so many other machine learning advancements, LG’s headstart will likely help set it apart. At the same time, LG and the majority of large players in the mobile industry are also directly involved with machine learning projects outside of that sphere or are rumored to have related mobile projects in the works. Those external efforts will undoubtedly lead to new announcements in the mobile markets as the companies better learn how machine learning can best be utilized on a smartphone.
Although the larger players definitely have an advantage in terms of money and brand recognition, consumers are also looking more fervently for the latest innovations to help streamline their lives and make the most out of every day. Unlike with previous mobile enhancements, the steady stream of improvements could mean a serious shakeup in top manufacturers. That’s because the innovations themselves will primarily come down to software engineering and success will largely hinge on which company or companies manage to meet those needs the best. Moreover, mobile devices are arguably better suited for such applications than other devices or platforms, thanks to the fact that they are – mobile. That could greatly increase the level of competitiveness and result in some very interesting ideas over the next several years, even if many of them ultimately fail.