Machine learning and AI have been a focus of discussion since the 70s’, but they have only become widespread in the past few years. We all know Artificial Intelligence. And even if you are not some hardcore computer scientist sitting in a secret lab testing AI techniques, you probably still know it through Arnold Schwarzenegger’s Terminator movies.

Everybody is wondering what will be next for AI. Will the machines take over?

Yes, they will, but not in Terminatorish way.


Some statistics about Machine Learning and Mobile App Development 

39% of businesses today utilize some form of  AI and ML technologies.

A survey conducted by McKinsey and Co. showed that AI and ML projects’ investment tripled over the last 5 years.

According to a research, by 2020, AI technologies will be an investment priority for CIOs.

It’s pretty clear what’s in store for AI in the future. Machine learning allows AI technology to learn from experience and complete tasks in an efficient way. ML essentially allows AI technologies to consistently grow which generates opportunities for higher intelligence.

Before we go any further, let’s just recall how machine learning and AI relate to each other.

Artificial intelligence refers to computer systems that are capable of performing tasks that require human intelligence. These tasks can include decision-making, translation, visual perception, and speech recognition. Artificial intelligence is designed to make critical tasks easy to accomplish for an increased and fast-paced development in tech and business.

Machine learning is a part of artificial intelligence. It can be defined as the application that enables the AI to improve through experience for enhanced learning that does not require any pre-programming. Machine learning aims to enable AI to gather data and learn on its own.

The Applications of ML Today and Tomorrow 

Machine learning is a multidisciplinary domain that can be applied to various services. There are a couple of industries where you can utilize machine learning to create productive, revenue-generating applications.

ECommerce Web Solutions

Today we are experiencing digitalization that possibly impacts each and every aspect of our lives. E-commerce has already replaced (to a significant degree) the conventional means to buy and sell services. As an app developer, you can use machine learning in e-commerce industry in the following ways:

  • Offer relevant information to the potential customers as they search for the products on a company’s website. Many companies are already utilizing artificial intelligence and machine learning to understand consumers’ preferences and give them suggestions accordingly. Most users are usually not aware that the search suggestions provided on the website are actually coming from an artificial intelligence.
  • Recommend customers products based on their preferences or past shopping experiences.
  • Gather data regarding trends and sales from various resources and making predictions in real-time to improve quality of the service.

Social media applications 

If you have used Snapchat then you are probably aware of its technology and its usage among both adults and teens. You can use machine learning to expand the capabilities of the artificial intelligence to improve social media applications and bring a customizable solution. There is a great opportunity for mobile app developers who are looking to create something that has significant longevity and the potential to generate tremendous revenue.

Healthcare apps

Google is a pioneer in developing healthcare applications that allow physicians and surgeons to understand complex diseases in a timely manner to find the right treatment option for the patient. There is an incredible opportunity for mobile app developers to work on Healthcare applications that contribute to the industry by providing unprecedented solutions through machine learning.

Finance market management

This is probably the most beneficial segment of an industry for mobile app developers. Machine learning can be utilized to understand the financial trends to protect market crashes that can damage investments. Artificial Intelligence can track the previous transactions of customers and make appropriate suggestions for investments and savings that are most profitable.

Enterprise applications

Artificial intelligence utilizes intelligent algorithms that can automate various enterprise processes to manage hundreds and thousands of customers’ emails, feedback, and comments.

Other machine learning applications include:

  • Image Recognition
  • Speech Recognition
  • Statistical Arbitrage
  • Learning Associations between different products
  • Classification of subjects based on various attributes
  • Make weather Predictions
  • Extraction (extracting structured data from unstructured information)
  • Regression
  • Fraud Detection
  • Data security
  • Personal security

AI and machine learning make a big part of app development in the tech industry. If you are a mobile app developer, this is something you need to consider. Did you know that Google offers free courses to learn AI and machine learning? Check it out here.

Final thoughts: Machine learning is here to stay. And although they won’t take over us, they will help us do things faster. In fact, you shouldn’t be worried about the machines taking over us, you should, however, worry about them taking over our jobs. Machines have already replaced so many jobs in factories and it is very likely that machine learning will increase the capabilities of artificial intelligence to an extent that it may take over various tasks that humans are just not fast to finish.

In the coming 50 years, machine learning will probably expand to more applications, giving app developers tremendous opportunities for growth.

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