A 2021 Guide To Machine Learning Apps
Modern AI powered by machine learning (ML) has been creeping up in our daily and business technology – to grow this and following years at a substantially higher rate than the past few combined. Already we see machine learning in healthcare using and showcasing promising results.
For business, backend machine learning frameworks Pytorch and TensorFlow are drastically improving how various industries are conducting business. Every industry from healthcare to entertainment is incorporating these systems as the developer cost to do so has dropped significantly, often dramatically improving their level of service. We’re going to explore some of the underlying components of and look at some of the best examples of machine learning apps on the market today.
How is machine learning in apps being used?
Before we look at specific, tangible examples it is important to understand what is going on in the backend of a system. We recently covered some of these aspects in relation to iOS machine learning between iOS’s CoreML, the Bionic Chip A13 and programming frameworks like Pytorch and Tensorflow. These mobile deep learning systems can be used to help apps in a few ways:
Understand imaging at a granular level with computer vision.
Neural networks using AI-powered nodes have the capability to learn how to comprehend images through supervised and unsupervised input. A great example is healthcare in AI – computer vision can learn to recognize visual information that reveals everything from broken bones, growths and especially incredibly subtle visual elements in various medical imaging technologies even the best-trained human eye can miss.
Pattern recognition of user behavior.
By analyzing tapping and clicking patterns, how a smart device is held, the physical location and virtually every other bit of information devices collect are all becoming a staple in measuring user engagement. This determines how effective a platform is (or isn’t) at delivering the optimal user experience.
Natural language processing enables more human interaction with digital services.
Remember the 90s and early 2000s when you had to haphazardly plug in keywords to try and find content of the web? Voice recognition platforms like Amazon’s Alexa, Siri, Cortana, and Google Voice Search have been frontrunners in mobile deep learning at collecting and interpreting natural language (sometimes called computational linguistics). This is continuously evaluated for accuracy and effectiveness through each system’s ML algorithms in conjunction with reinforced learning either through a backup system or human intervention.
Specific examples of machine learning apps in 2020
Several apps that use machine learning might be a part of your daily life. The backends are often subtle, meaning the underlying AI isn’t a glaring component.
Contemporary machine learning apps and platforms
This fun article does a good job of compiling different apps that use machine learning. Since Snapchat is one of biggest go-to’s for explaining augmented reality (i.e. AR, referring to superimposing of images over what a device camera is capturing in real-time), the GIF below tells you what you need to know.
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One of our marketers in the act of “research” sometimes called “wasting time.”
One of the machine learning apps I find most interesting is LeafSnap which uses computer vision-powered AI to identify leaves. Collecting leaves may not be a big part of your life (likely, not at all) but for students and those in the biology field, this demonstrates a powerful, effective application of mobile deep learning.
Students in entry-level biology classes are usually required to complete projects like insect or leaf collections. Prior to smart devices, identifying either required use of field guides like those from the National Audobon Society. Today, these projects can be completed much faster and with great accuracy, thanks to computer vision and machine learning in apps.
Even more interesting is the application that mobile deep learning can have for scientists. Over generations, most species tend to evolve to adapt to changes in the environment. By monitoring living things over time, the data can prove valuable in identifying and monitoring species of plants and animals as they evolve to the world around them.
Amazon’s use of machine learning in physical storefronts
Another well-known example – and perhaps one of the most ubiquitous – is international retailer and cloud service provider, Amazon. Beneath their digital storefront and within their data centers that power AWS are machine learning engines that are perhaps the most widely discussed (or at least best publicized) of any major digital entity.
If you’ve used the store you know the AI shopping system often recommends other items based on what it has learned from other user’s purchase patterns. But even more impressive is how they’re using machine learning at their brick-and-mortar locations, such as the Chicago Amazon Go stores.
One of the evolutions that we’ve seen in the home, business and “smart” location’s security (i.e. large business campuses, airports, smart cities, etc.) is the use of AI-based camera systems that use computer vision machine learning functions to recognize individuals (currently referred to as reidentification or re-id) not just on facial features alone. Modern systems recognize other details from clothing, posture, and objects being carried as well as create movement profiles based on how they move.
Optimize the store layout
This kind of camera system has other benefits, too, which Amazon uses in their AI for other retail endeavors. For example, heat mapping allows Amazon to see how customers move through the store and where they stop, which shows areas that can be popular either because of the items and sometimes simply by virtue of the store layout.
Pairing with the Amazon Go machine learning app
These stores work in conjunction with machine learning apps (technically, the backend) and the Amazon Go app on a customer’s mobile device. You simply open the app that produces a QR code which allows you or designated family member on the account (i.e. spouse, child, trustworthy roommate, etc.) to shop in what is perhaps the most uninhibited retail model to date. Any time you pick something off a shelf, an item is added to your cart and automatically deducted from your preferred payment when you exit the store.
Blue Label Labs uses machine learning to make intelligent apps
The modality of creating modern AI-driven platforms ultimately geared toward creating convenient, user-friendly solutions that the lives of users more convenient. We use mobile deep learning to build powerful machine learning apps that drive results through smart algorithms and sophisticated analytics that provide concise feedback on the UX. Feel free to get in touch with us at Blue Label Labs to learn more about how AI can leverage the success of your application.