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AI Use Cases Transforming Your Future

A deep dive into just some of the AI use cases that we foresee breaking new ground in 2022.

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How is AI revamping the world we live in? If you haven’t noticed already then you’re in for a shock. From transporting avatars between virtual spaces to decongesting data architectures to creating hologram fitness instructors in our homes, artificial intelligence has already propelled us forward to an exciting new era of living.

We may not be living in a Star Trek sci-fi fantasy yet but we are getting closer. Below, we’re going to discuss novel AI use cases that include object detection technology in fitness, object detection in edge computing, and examine how edge computing with object detection is improving data transmission between digital devices.

Let’s take a deep dive into just some of the AI use cases that we foresee breaking new ground in 2022.

Computer Vision AI in The Metaverse

Metaverse AI Application

Ever since Mark Zuckerburg rebranded Facebook to Meta, short for Metaverse, the term has been hot on everybody’s lips. But what exactly is it? In short, the metaverse is a blanket term that refers to the digital realms which are meant to extend the real world.

Imagine attending virtual events, concerts, meet-ups and you’ll get the right idea. But the metaverse also includes simpler ‘virtual’ interactions such as logging into social media and scrolling through your news feed.

Although there is no definitive end goal, scientists are moving mountains to try and make the metaverse as immersive as possible by using computer vision AI - a field of artificial intelligence that trains computers to make sense of valuable information from visual inputs and provide recommendations based on the data gathered.A crucial element of computer vision AI in the metaverse is interoperability. This fancy, slightly intimidating term is basically the process of seamlessly transferring avatars and digital items from one virtual realm to another.

Machine learning (ML) algorithms in interoperability have already empowered the healthcare industry. For example, when you get a CT scan, large volumes of data will be processed, gathered, and stored in a medical database.

Doctors will take a different approach by manually entering your healthcare information into a database. Interoperability is then used to integrate these two data analyses to provide a fast diagnosis of illness.

Vision AI in Fitness

Object detection in 2022 is an exciting prospect and is already making waves in the fitness industry. Mirror and Tonal are both examples of successful companies promoting AI in fitness - both offering an interactive home device that can stream over 10,000 workouts and project them onto your mirror all for the purpose of improving your health and exercise.

Metaverse AI Application

Many of us find fitness more of a chore than a hobby and are even reluctant to set foot inside a gym. But from the comfort of your home, Mirror allows you to track your progress, form, and other metrics through stance detection.

This highly-advanced application critiques the posture and pose of people on video by using Human Pose Estimation - a process that predicts the poses of human body parts and joints in images or videos.

It differs from object detection by differentiating people from a human box and developing an understanding of human body language through machine-learning algorithms. But by merging Human Pose Estimation with deep learning, Mirror will have conceptualized models of how each exercise should be executed by having analyzed millions of different workouts.

During exercise, the app uses an algorithm to compare the position of your joints. Any deviations will be detected and highlighted, reducing the risk of injury and promoting a safer, more optimal way of working out without a personal trainer.

Vision AI in fitness has already taken a quantum leap in recent times through innovative applications such as Mirror, which only makes you wonder…what will the fitness industry look like in 2023?

Object Detection in Edge Computing

The world is drowning in data. Although data has been labeled as ‘the new oil,’ the reality is that too much of it causes a problem. Not all data is created equal. Gathering, organizing, and sifting through what’s been collected eats away at the clock.

Edge computing with object detection has relieved us of this heavy burden of extracting data away from the main data center and onto the edges of its architecture. But what is edge computing and how does it work?

Imagine an orbit of technical devices that transmit data to and from the main database. That’s a lot of information for it to process. The database’s speed processing capabilities will be hampered, causing lags and disruptions that will degrade performance.

But with edge computing, much of this data will be spread out onto the periphery. Machine learning algorithms put each edge device in charge of training an analytical model with the data that’s stored locally.

Each device will do its heavy lifting by filtering out the most valuable bits of data, which will then be sent to the main database for a holistic analysis. Think of a scientist taking on a project that’s densely filled with research. Instead of analyzing all of the data of every single experiment they delegate this responsibility to other researchers who’ll report back with a summary.

Vision AI is changing the world as we speak and the AI use cases we’ve covered here are only the tip of the iceberg. But, what’s even more exciting is that you can also tap into the wonders of vision AI with our ML deployment platform, Ultralytics HUB.

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