What a Formula E Race Team’s AI Means for People
Auto racing is a sport that combines technological and human ingenuity. Skilled teams make a small modification to an engine or change a car’s aerodynamics and the result is sometimes victory. The world’s first fully electric international auto league launched in 2014, known as the ABB FIA Formula E Championship. This season Formula E is making tech headlines with a second-generation car that’s significantly faster and safer than the first, and runs twice as long without recharging.
But a different kind of tech-aided innovation is thriving inside the league, at the Envision Virgin Racing team. There, race strategists are working with artificial intelligence specialists from Genpact to structure and analyze massive amounts of data on car performance, track designs, weather surveys, and more. AI returns pre-race insights and recommendations that, when paired with the judgment of engineers and drivers, give the team an edge against its rivals.
This human/machine collaboration is a promising example of how sophisticated AI can create an advantage in an intensely competitive arena. AI isn’t replacing the racing team’s engineers, strategists, or drivers. It’s not doing the thinking for them. Quite the opposite. It’s sharpening their focus on the variables that matter most and enabling people involved to make better, quicker decisions.
Machines excel at data processing and analysis tasks. We are using AI to digest the Formula E rulebook and annotate it with unstructured data from race videos and other sources, to help the team make better decisions and avoid costly infractions and penalties. Machines are incomparably proficient at reading, codifying, and adhering to rules in highly regulated environments, from race courses to global financial markets.
But machines don’t care about winning. They can be taught how to value choices and make winning decisions, but they are incapable of caring the way humans do. People bring the context and motivation that matter just as much as data in a complex competitive environment.
Humans care about eliminating every last inefficiency and minimizing waste, because winners make the most of their constraints. AI is assessing massive amounts of data to unearth patterns on and off the racetrack, making it perfectly suited to analyze constrained systems, test numerous possibilities, present choices, and recommend a course of action.
This year, AI is closely analyzing and tracking the telemetry from sensors mounted all over the race cars to better predict performance and improve replacement schedules. The insights derived from the vehicles on the track in any given race are important for the high-performance race team, and they also have implications for managed fleets of autonomous and semi-autonomous vehicles, so they can help make journeys safer and enhance the travelling experience.
Down the road
Even the most sophisticated machines don’t have imagination the way we understand it. But they open up new territory for business leaders by augmenting our imagination and sharpening our instinct. By cutting through the data and simulating the future, machines help us make faster course corrections to secure more wins.
Even as machines get better at interpreting the Formula E rules, the team’s judgment and temperament will work to address gray areas and exceptions, and prevent bias. Any monoculture will start to think of the world through its own lens, so to combat human biases, AI needs objective algorithms. At the same time, people bring ethics and compassion to AI-assisted processes when they are making decisions based on insights from the data.
As human/machine partnerships flourish, the combination presents an exciting new area for worker training and reskilling experts. Workers will develop the skills to ‘listen’ to what the AI is telling them and understand the strengths and weaknesses of AI contributors. It’s not that different from how they would assess the best tasks for a new hire and know where best to apply them. And visualization dashboards can make clear how even high-speed algorithms are interpreting and acting on data and rules. That allows for the team’s insight and business expertise to make course corrections or retrain the AI as needed.
A team’s winning advantage doesn’t come from automating people out of the equation. The real magic begins when humans and machines amplify each other’s potential.
Dr. Armen Kherlopian is chief science officer at Genpact, a global professional services firm focused on delivering digital transformation.