When Speed is Key, MediaMonks Labs Enables Swift, Proactive AI Prototyping

As the COVID-19 pandemic spreads throughout the world and people retreat into their homes to practice social distancing, ingenuity and the need to digitally transform have become more apparent now than ever. Always looking for ways to jump-start innovation, the MediaMonks Labs team has experimented with ways to speed up the development of machine learning-based solutions from prototype to end product, cutting out unnecessary hours of coding to iterate at speed.

“Mental fortitude and being used to curveballs are skills and ways of working that come to the foreground now,” says Geert Eichhorn, Innovation Director at MediaMonks. “We see those eager to adapt come out on top.” Proactively aiming to solve the challenges faced by brands and their everyday audiences, the team recently experimented with a faster way to build and iterate artificial intelligence-driven products and services.

Fun Experiments Can Lead to Proactive Value

The idea behind one such experiment, the Canteen Counter, may seem silly on the surface: determine when the office canteen is less busy, helping the team find the optimal time to go and grab a seat. But the technology behind it provides some learnings for those who aim to solve challenges quickly with off-the-shelf tools.

Here’s how it works. The Canteen Counter’s camera was pointed at the salad bar, capturing the walkway from the entrance to the dishwashers—the most crowded spot in the canteen. The machine learning model detects people in the frame and keeps a count of how many are there to determine when it’s busy and when it isn’t—much like how business listings on Google Maps predict peak versus off-peak hours.

Of course, now that the team is working from home, there’s little need to keep an eye on the canteen. But one could imagine a similar tool to determine in real time which spaces are safe for social distancing, measured from afar. Is the local park empty enough for some fresh air and exercise? Is the grocery store packed? Ask the AI before you leave!

“I would like to make something that is helpful to people being affected by COVID-19 next,” says Luis Guajardo, Creative Technologist at MediaMonks. “I think that would be an interesting spinoff of this project.” The sentiment shows how such experiments, when executed at speed, can provide necessary solutions to new problems soon after they arise.

Off-the-Shelf Tools Help Teams Plug In, Play and Apply New Learnings

Our Canteen Counter is powered by Google’s Coral, a board that runs optimized TensorFlow models using an Edge TPU chip. To get the jargon out of the way, it essentially lets you employ machine learning offline—a process that typically connects to a cloud, which is why you need a data connection to interact with most digital assistants. The TPU chip (which stands for tensor processing unit) is built to handle the neural network-trained machine learning directly on the hardware.

This not only allows for faster processing, but also increased privacy because data isn’t shared with anyone. Developers may simply take an existing, off-the-shelf machine learning model to quickly optimize to the hardware and the goals of a project. While the steps behind this process are simpler than training a model of your own, there’s still some expertise required in discovering which model best suits your needs—a point made clear with another tool built by Labs that compares computer vision models and the differences between them.

What the team really likes about Coral is how flexible it is thanks to the TPU chip, which comes in several different boards and modules to easily plug and play. “That means you could use the Coral Board to build initial product prototypes, test models and peripherals, then move into production using only the TPU modules based on your own product specs and electronics and create a robust hardware AI solution,” says Guajardo.

Quicken the Pace of Development to Stay Ahead of Challenges

For the Labs team, tools like Coral have quickened the pace of experimentation and developing new solutions. “The off-the-shelf ML models combined with the Coral board and some creativity can let you build practical solutions in a matter of days,” says Eichhorn. “If it’s not a viable solution you’ll find out as soon as possible, which prevents you from wasting any valuable time and resources.” Eichhorn compares this process to X (formerly Google X), where ideas are broken down as fast as possible to stress test viability.

“At Labs, we jump on new technologies and apply them in new creative ways to solve problems we didn’t know we had, so any project or platform that has as much flexibility as the Canteen Counter is very much up Labs’ alley,” says Eichhorn. “What is a canteen counter today could become a camera that tells you something about your posture tomorrow. Anything goes, and it changes by the day.” He notes that more is being worked on behind the scenes as the team ponders the trend toward livestreaming, the need for showing solidarity, play and interaction while working from home.

It’s worth reflecting on how dramatically the world has changed since we settled on the idea to keep an eye on our workplace canteen through a fun, machine learning experiment. But Eichhorn cautions that in a rush for much-needed solutions, “innovation” can often begin to feel like a buzzword. “What we do differently is that we can actually build, be practical, execute, and make it work.”

Extraordinary times call for extraordinary solutions.