Machine Learning is one of the hottest trends in technology right now, and with due reason. An important branch within the field of Artificial Intelligence, it has the potential to disrupt entire industries as well as to create others that we can’t even conceive. It is a powerful tool thanks in part to the computational potential it has for the automation of complex tasks. However, its real strength lies, as its name infers, in its capability to learn.
The capability to learn in terms of Machine Learning is the possibility to recognize patterns as well as to learn from trial and error. This is not something new, but the possibility to do it through powerful code frameworks and processing tools like the ones available today has no precedent in the history of computer science.
The algorithmic process in Machine Learning resembles an inferential analysis, that is, one in which missing data is built from the available one. Thus, data is an important part of Machine Learning. As a matter of fact, one may even say that its learning curve is only as good as the available data.
As we push the boundaries of technology, it seems like Machine Learning is evolving so fast that we can’t keep up to date with the latest trends. Web and mobile apps are not exempt from this phenomenon. It happens often that a new Machine Learning feature in an app revolutionizes how certain things are done in a given industry. In this post we discuss five app features you probably didn’t know were based in Machine Learning.
This is probably the most common use of Machine Learning in apps. With the proper amount of data, an app is able to provide a user with all sorts of recommendations. There is no single approach to how this should be done, and the possibilities for designing a recommendation algorithm vary according to what the app wants to prioritize for the user.
This feature is very important in retail and eCommerce apps, but it also plays a crucial role in video, image, and music streaming apps. Apps with this feature have the possibility to give users valuable insights into goods or services they might like based on historical data. Better recommendations mean a win-win situation for the app and its users.
One of the major challenges this feature faces is in introducing new recommendations to users with consolidated historical data. Not doing so might result in giving users the same recommendations over and over again, therefore limiting the scope of what is offered.
The future is hard to predict, especially since the concept of a black swan, an unlikely but impactful event, took over the business literature. However, there are many things that can be predicted when the right tools are at hand.
Machine Learning has a great potential to deliver precise and accurate inferential data thanks to its potent predicting power. This can be used in a variety of ways but is particularly interesting for its capability to understand time series in order to improve logistic related matters.
As for the recommendation feature, retail and eCommerce apps can benefit from better predictions. The pattern recognition feature can help understand how certain parameters in consumer behavior change throughout time. This will definitely influence how companies plan their inventories as well as how they react to changes in supply and demand.
The possibility to detect outliers and abnormal patterns is another of Machine Learning’s features from which web and mobile apps can benefit. This is particularly useful in a variety of industries. For the case of the biometric industry, anomaly detection through IoT devices is revolutionizing how certain diseases are detected based on readings of user’s bodies.
This feature is also used in the FinTech industry. As digital banking becomes stronger, so do criminals’ skills for deceiving. The anomaly detection feature can help identify if a user’s financial behavior presents an irregular pattern as a result of a crime. It can also help detect whether money laundering is taking place. The potential for this last is particularly interesting for governments.
The Deep Learning feature is one that will surely impact quality control methods throughout a variety of manufacturing industries. Thanks to the power of pattern recognition, a Machine Learning algorithm can detect through Deep Learning when a defective product is found. Having the potential to do so will help automate complex value chains, reducing costs and increasing efficiency.
Most people think that this feature works by comparing a good product against a defective one, but in reality, it uses the algorithm’s learning ability to recognize a pattern so that it can identify something as defective.
Natural Language Processing (NLP)
This last feature seeks to process and analyze language. Although there is still a long way to go before computers are able to process language in the same way humans do, there are many practical uses for this technology.
Two of the most common uses of this technology are grammar and spelling checks as well as speech recognition. As technology allows for a better understanding of language it is very likely that powerful apps appear to reap the benefits. There is great potential for personal assistants like Amazon’s Alexa to perform ever more complex tasks.
Wrapping It Up
These are only a few of the Machine Learning features that HiTech apps can use to deliver a great experience and value to users. Because Machine learning is constantly evolving, how these features are used within apps depends not only on the quality of the algorithms used, but also on how creative developers can be to solve problems. At Koombea we not only have a team of developers with strong expertise in programming. Our team consists, above all, of creative problem solvers with a passion to drive technology to the next level.
Interested in Machine Learning? Contact us so we can help you build that great app!