Data of Neural Network Can't Be Trusted


Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis. These networks are good at recognizing patterns in large, complex datasets to aid in decision-making. But how do we know they’re correct? Alexander Amini and his colleagues at MIT and Harvard University wanted to find out.

Current methods of uncertainty estimation for neural networks tend to be computationally expensive and relatively slow for split-second decisions. But Amini’s approach, dubbed “deep evidential regression,” accelerates the process and could lead to safer outcomes. “We need the ability to not only have high-performance models, but also to understand when we cannot trust those models,” says Amini, a PhD student in Professor Daniela Rus’ group at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

After an up-and-down history, deep learning has demonstrated remarkable performance on a variety of tasks, in some cases even surpassing human accuracy. And nowadays, deep learning seems to go wherever computers go. It fuels search engine results, social media feeds, and facial recognition. “We’ve had huge successes using deep learning,” says Amini. “Neural networks are really good at knowing the right answer 99 percent of the time.” But 99 percent won’t cut it when lives are on the line.

Neural networks can be massive, sometimes brimming with billions of parameters. So it can be a heavy computational lift just to get an answer, let alone a confidence level. Uncertainty analysis in neural networks isn’t new. But previous approaches, stemming from Bayesian deep learning, have relied on running, or sampling, a neural network many times over to understand its confidence. That process takes time and memory, a luxury that might not exist in high-speed traffic.

Their network’s performance was on par with previous state-of-the-art models, but it also gained the ability to estimate its own uncertainty. As the researchers had hoped, the network projected high uncertainty for pixels where it predicted the wrong depth. “It was very calibrated to the errors that the network makes, which we believe was one of the most important things in judging the quality of a new uncertainty estimator,” Amini says.

Deep evidential regression is “a simple and elegant approach that advances the field of uncertainty estimation, which is important for robotics and other real-world control systems,” says Raia Hadsell, an artificial intelligence researcher at DeepMind who was not involved with the work. “This is done in a novel way that avoids some of the messy aspects of other approaches —  e.g. sampling or ensembles — which makes it not only elegant but also computationally more efficient — a winning combination.”

Shara Rose
Managing Editor
International Journal of Swarm Intelligence Evolutionary Computation.