Author: Paul Matthews
Back in 2010, no one thought that machines running complex tasks could have become reality in 8 years. Instead, the whole Machine Learning industry (which we will shorten as ML in this article) developed to a point where the hardware must keep up to its evolution. Given the fact that most of our lives are "controlled" by smartphones and mobile devices, it's pretty simple to understand how such technology moved towards the mobile industry. Let's break down the main routes ML is taking to conquer the mobile world.
Google Maps: Not Just A TomTom
Back when it was launched, everyone was screaming at Google Maps, saying that it was basically just a big TomTom rip-off. Instead, Google's developers took an already solid platform, developing it to a point at which they were able to implement their own ideas and technologies. Google Maps is rapidly moving towards an augmented reality-based approach, by using SLAM applications in combination with cloud-based scans. SLAM (Simultaneous Localization And Mapping) is a VR and AR technology which translates information from the real world into a digital environment. Given the fact that the entire process is led by the sole computing power of the hardware and applications, the ML part of the code is definitely important to consider.
From an OS Point Of View
iOS and Android are the two biggest OS in the world, that is a fact. We can expect more and more autonomous learning features in the near future, probably and mostly related to user security. Machine learning is applied to an OS by running several instances of Python coded pieces at once. These are all learning from each other, which is where the "learning" part of "machine learning" comes from. There are many different factors that must be taken into consideration, although: for example, developing or upgrading an OS with some particular Machine Learning features could be impacting the hardware requests, given the fact that these applications are requiring a lot of processing power. Many mobile app development companies are, in fact, trying to break down the overall requirements, but this is still miles away from being done.
Are The Machines Taking Over Us?
Although ML made significant progress in the last couple of years, there are several areas that are still at a very embryonic stage of development. For now, the infamous Terminator scenario is not likely to happen, and probably won't for the next 50 years.
Machine Learning is an incredibly bulky and expensive development area, which is why most of the apps that are developed on mobile are part of a more "startup-related" field.
Machine learning is set to be the biggest technology focus in the next decade, therefore being a Python savvy developer should and could be a big advantage for the younger generations.
With this in mind, we must also keep in consideration the fact that the industry will adapt to such technologies, by creating new job positions and by eventually removing others. An interesting case study was related to bridging loans, which are now enrolled online with a Type1 Machine Learning Algorithm.
About the Author
Paul Matthews is a Manchester based business writer who writes in order to better inform business owners on how to run a successful business. You can usually find him at the local library or browsing Forbes' latest pieces.
The views, positions and opinions expressed by the guest writer are those of the writer alone and do not reflect those of the iHub or any employee thereof. The accuracy and validity of the information supplied by the guest writer are not guaranteed by the iHub. The copyright of this content belongs to the author and any liability with regards to infringement or intellectual property rights remains with them.