Hi world, my name is Rachael and I am a “people” person here at SpringML. This translates to: I am not one of the many technically talented team members who understand the ins and outs of how to create data structures or code. If you’re like me, then the many terms of the tech world can not only be confusing, but downright intimidating. So today, I’m going to take a little time to translate Vision AI into terms that are a bit more approachable to those of us who aren’t in the technical track.
First of all, what is Vision AI?
Vision AI implements artificial intelligence (don’t panic, the robots are not taking over the world!) to digital images, videos, and other visual inputs to process the images and help all kinds of individuals, from manufacturers to marketers, make decisions.
The information derived from Vision AI is used to train machine learning models to recognize patterns and those patterns can contribute to predictions for real life use. One of the major upsides of implementing artificial intelligence in these cases is that accuracy of reporting and interventions increases without having to strain current staff or take on new staff.
For instance, one SpringML customer was concerned with keeping their customers and staff safe and socially distanced, during COVID-19. They collaborated with SpringML to take the data from their existing security cameras, apply AI models, and then create an alert when social distancing measures were not being adhered to. Thus, creating a safer environment for everyone, while not needing to hire more staff to monitor customer and staff proximity.
How can different industries use Vision AI?
Construction & Infrastructure
Nowadays, most vehicles come equipped with multiple cameras or can easily have a camera installed. These cameras can then be used to spot maintenance or jobsite safety issues. For example, damaged highway guardrails
Healthcare & Life Sciences
Arguably, this industry is very cutting-edge and full of highly trained individuals. But let’s admit it, no one is perfect. Vision AI can help cut out human error and improve patient outcomes because what the human eye may miss, the machine learning models VERY rarely do. One real-life example was a SpringML customer who leveraged vision machine learning to evaluate pictures of patient eyelids and help predict their likelihood of degeneration for a specific disease
Imagine a world where there are fewer production line jams or defective products coming off of those production lines. Vision AI can be trained to spot any variation of issues that would cause the production line to stop or prevent a bad run of product. If you want to take it one step further, predictive maintenance can even tell manufacturers when their lines will need to be serviced to prevent production downtime
Telecom, Media, Entertainment, and Gaming (TMEG)
All of these industries process HUGE amounts of digital images. To have an individual manually classify these would not only be a full- time job, it would take a massive full-time team. Vision AI models can quickly identify images and classify them- thank Vision AI the next time you Google image search pictures of cute cats!
What’s worse than popping into a store for something in particular, only to realize the item is not available? The answer is nothing. One SpringML customer implemented Vision AI to allow shoppers to have the ability to see if their product is available on the shelf before they drive to the store. After all, if you’re going to put on real pants, you want to be sure you only need to go to one store to get your desired item
How do we continually innovate?
SpringML has been a premier partner of Google Cloud since 2015. And, as Google Cloud does, we do – “Deliver meaningful, transformational business insights” to empower customers and partners to explore the latest cutting-edge AI tools for developing, deploying, and managing ML models at scale
With our partnership, SpringML is excited to be invited as a signature sponsor to the Google Cloud Applied ML Summit on June 9 2022, to further help our customers make the most of integrated data and ML solutions like Vision AI, Vertex AI, Bigquery ML, AutoML and Contact Center AI. Register today for the Google Cloud Applied ML Summit.
Additionally, you don’t want to miss our on-demand session exploring real-world use cases using unstructured visual data to make automated decisions. Our Machine Learning Practice Manager, Andrew Larimer will guide you through building a high-quality dataset for training vision models while considering the following:
- Camera and compute platform selection
- The balance between recording formats and network considerations
- How datasets can be built iteratively over time to lower the labeling burden
- Guidelines for using early versions of a model to assist in labeling additional data
Already have a use case to leverage Vision AI? Contact us today at [email protected] to set up your customized demo.