Join JAAGNet and Group

SIgn up for JAAGNet & the Artificial Intelligence Group its FREE!!

 

Member Benefits:
_____________________

 

Again signing up for JAAGNet & Group Membership is FREE and will only take a few moments!

Here are some of the benefits of Signing Up:

  • Ability to join and comment on all the JAAGNet Domain communities.
  • Ability to Blog on all the Domain communities 
  • Visibility to more pages and content at a group community level, such as Community, Internet, Social and Team Domain Community Feeds.
  • Make this your only content hub and distribute your blogs to LinkedIn, Reddit, Facebook, Twitter, WhatsApp, Messenger, Xing, Skype, WordPress Blogs, Pinterest, Email Apps and many, many more (100+) social network and feed sites. 
  • Opportunity to collaborate (soon to be  released) with various JAAGNet Business communities and other JAAGNet Network members.
  • Connect (become friends), Follow (and be Followed) and Network with JAAGNet members with similar interests.
  • Your Content will automatically be distributed on Domain and JAAGNet Community Feeds. Which are widely distributed by the JAAGNet team.

Join Us!

Bronze Level Contributor
A neural network learns when it should not be trusted A faster way to estimate uncertainty in AI-assisted decision-making could lead to safer outcomes.
MIT researchers have developed a way for deep learning neural networks to rapidly estimate confidence levels in their output. The advance could enhance safety and efficiency in AI-assisted decision making.      Image: iStock image edited by MIT News
 

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.

They’ve developed a quick way for a neural network to crunch data, and output not just a prediction but also the model’s confidence level based on the quality of the available data. The advance might save lives, as deep learning is already being deployed in the real world today. A network’s level of certainty can be the difference between an autonomous vehicle determining that “it’s all clear to proceed through the intersection” and “it’s probably clear, so stop just in case.” 

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).

“This idea is important and applicable broadly. It can be used to assess products that rely on learned models. By estimating the uncertainty of a learned model, we also learn how much error to expect from the model, and what missing data could improve the model,” says Rus.

Amini will present the research at next month’s NeurIPS conference, along with Rus, who is the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, director of CSAIL, and deputy dean of research for the MIT Stephen A. Schwarzman College of Computing; and graduate students Wilko Schwarting of MIT and Ava Soleimany of MIT and Harvard.

Efficient uncertainty

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.

“One thing that has eluded researchers is the ability of these models to know and tell us when they might be wrong,” says Amini. “We really care about that 1 percent of the time, and how we can detect those situations reliably and efficiently.”

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.

The researchers devised a way to estimate uncertainty from only a single run of the neural network. They designed the network with bulked up output, producing not only a decision but also a new probabilistic distribution capturing the evidence in support of that decision. These distributions, termed evidential distributions, directly capture the model's confidence in its prediction. This includes any uncertainty present in the underlying input data, as well as in the model’s final decision. This distinction can signal whether uncertainty can be reduced by tweaking the neural network itself, or whether the input data are just noisy.

Confidence check

To put their approach to the test, the researchers started with a challenging computer vision task. They trained their neural network to analyze a monocular color image and estimate a depth value (i.e. distance from the camera lens) for each pixel. An autonomous vehicle might use similar calculations to estimate its proximity to a pedestrian or to another vehicle, which is no simple task.

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.

To stress-test their calibration, the team also showed that the network projected higher uncertainty for “out-of-distribution” data — completely new types of images never encountered during training. After they trained the network on indoor home scenes, they fed it a batch of outdoor driving scenes. The network consistently warned that its responses to the novel outdoor scenes were uncertain. The test highlighted the network’s ability to flag when users should not place full trust in its decisions. In these cases, “if this is a health care application, maybe we don’t trust the diagnosis that the model is giving, and instead seek a second opinion,” says Amini.

The network even knew when photos had been doctored, potentially hedging against data-manipulation attacks. In another trial, the researchers boosted adversarial noise levels in a batch of images they fed to the network. The effect was subtle — barely perceptible to the human eye — but the network sniffed out those images, tagging its output with high levels of uncertainty. This ability to sound the alarm on falsified data could help detect and deter adversarial attacks, a growing concern in the age of deepfakes.

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.”

Deep evidential regression could enhance safety in AI-assisted decision making. “We’re starting to see a lot more of these [neural network] models trickle out of the research lab and into the real world, into situations that are touching humans with potentially life-threatening consequences,” says Amini. “Any user of the method, whether it’s a doctor or a person in the passenger seat of a vehicle, needs to be aware of any risk or uncertainty associated with that decision.” He envisions the system not only quickly flagging uncertainty, but also using it to make more conservative decision making in risky scenarios like an autonomous vehicle approaching an intersection.

“Any field that is going to have deployable machine learning ultimately needs to have reliable uncertainty awareness,” he says.

Originally published by
Daniel Ackerman | MIT News Office | November 20, 2020
MIT

This work was supported, in part, by the National Science Foundation and Toyota Research Institute through the Toyota-CSAIL Joint Research Center.

original article

E-mail me when people leave their comments –

You need to be a member of JAAGNet to add comments!

Join JAAGNet

JAAGNet AI Feed

AI and Big Data Expo Europe 2020 - Hybrid Event

AI For Good Summit 2021 - Seattle

JAAGNet AI Blog Archive

See Original | Powered by elink

JAAGNet Channel AI Playlist