It’s no secret that AI and machine learning are shaping many of the things we indulge in on a regular basis. From online shopping to watching TV shows on your favorite content platform, machine learning (ML) has a hand in personalizing our user experience in more ways than most of us realize. And, mobile applications are no exception to the influence of ML. From New York to Dallas, organizations across the country are now addressing how to break into the machine learning market.
So, you want to design an ML-integrated app?
Fortunately, due to this push for advanced automation, there is an inflated demand for apps that utilize machine learning. However, with new mobile apps optimized with AI constantly hitting the market, it can be hard to pinpoint areas that are in high-demand for ML integration.
Don’t worry, we have you covered!
Throughout this article, we will discuss a few ideas for application designs that implement machine learning within their core infrastructure. It’s our hope that with this information your business is able to explore the progressing demand for ML-integrated mobile apps and pursue one of our design strategies that cater to this need.
Machine Learning for Financial Success
Let’s face it, most of us could use some help when it comes to spending habits and saving money. Luckily, an application integrated with machine learning and optimized to promote financial literacy might help solve these problems.
Just like artificial intelligence is used by Amazon to monitor consumer transactions and spending behavior to recommend the best-fit products for purchase, an ML-integrated financial tracking application could be mirrored to monitor the same consumer activity, but for a different purpose.
By studying the financial practices and spending habits of a user, ML would have the ability to construct a financial success model for said user to follow. Different features of a user’s financial model might include:
- Financial management and budgeting
- A financial goal tracker
- Investment strategies
Personalized Cyber Protection Courtesy of ML
Did you know that the banking industry uses artificial intelligence to monitor for suspicious activity? Barclays, for example, is one bank that uses AI to look for and mitigate potential instances of fraud. Similarly, more advanced financial institutions like PayPal use ML-integrated neural networks that can process over 10 million data attributes in real time.
Now imagine having access to this technology on a personal level… pretty cool, right? But, what if we take it a step further? What if instead of only observing suspicious activity, your application simultaneously performed routine security checks on the user’s device to ensure their data is well-protected? Even cooler!
By monitoring the various web pages and applications accessed by a user on a daily basis, machine learning would enable your application to not only warn users of compromised data and potential malicious activity, but also provide suggestions on how to optimize their data protection. Some potential security enhancement suggestions might include:
- Stronger passwords
- Downloading a VPN
- Using Encryption
- OS updates
An ML-Integrated Personal Medical Assistant
As a result of the COVID-19 pandemic, it’s easier now more than ever to consult a doctor online and have medication prescribed and delivered without even having to leave your home.
However, just as with a regular trip to the doctor’s office, online consultations typically have to be scheduled prior to the day of the checkup. And, while this system works for the majority of medical cases, some instances require faster help.
For example, let’s say you wake up with a sinus headache on the day you’re supposed to host a staff meeting and you have no idea what type of medicine to take. Finding an online appointment before noon will likely be a struggle, and you can forget about scheduling an in-person checkup. So, what do you do?
Machine learning may be the answer to your dilemma!
Studying symptoms, measuring vital signs, and comparing that data against data from other patients to determine the appropriate self-care are all tasks that can be accomplished through the use of machine learning. By incorporating ML into a healthcare application, patients would in turn have access to a highly intelligent medical chatbot at a moment’s notice to help alleviate their medical complications.
It’s our hope here at Dogtown Media that this article provided you with some valuable design strategies for your business to think about when exploring the increasing demand for ML-based mobile applications. And, whether you’re creating an app centered around personalized medical care, financial success, or advanced cyber protection, we believe that with the incorporation of AI and machine learning your application will garner consumer support and satisfaction in no time.Tags: adversarial machine learning, AI and machine learning, AI app development Dallas, app developers Dallas, app development Dallas, Dallas, Dallas AI app developers, Dallas app developers, Dallas app development, Dallas cybersecurity, Dallas drone app developers, Dallas iOS app developers, Dallas IoT app developer, Dallas machine learning app development, drone app developers Dallas, Google machine learning, machine learning