New tool might aid the adoption of technologies such as autonomous vehicles
From: University of Southern California
August 31, 2020 -- One of the biggest
impediments to adoption of new technologies is trust in artificial intelligence
(AI). Now, a new tool generates automatic indicators if data and predictions
generated by AI algorithms are trustworthy.
Now, a new tool developed by USC Viterbi
Engineering researchers generates automatic indicators if data and predictions
generated by AI algorithms are trustworthy. Their research paper, "There
Is Hope After All: Quantifying Opinion and Trustworthiness in Neural
Networks" by Mingxi Cheng, Shahin Nazarian and Paul Bogdan of the USC
Cyber Physical Systems Group, was featured in Frontiers in Artificial
Intelligence.
Neural networks are a type of artificial
intelligence that are modeled after the brain and generate predictions. But can
the predictions these neural networks generate be trusted? One of the key
barriers to adoption of self-driving cars is that the vehicles need to act as
independent decision-makers on auto-pilot and quickly decipher and recognize
objects on the road -- whether an object is a speed bump, an inanimate object,
a pet or a child -- and make decisions on how to act if another vehicle is
swerving towards it. Should the car hit the oncoming vehicle or swerve and hit
what the vehicle perceives to be an inanimate object or a child? Can we trust
the computer software within the vehicles to make sound decisions within fractions
of a second -- especially when conflicting information is coming from different
sensing modalities such as computer vision from cameras or data from lidar?
Knowing which systems to trust and which sensing system is most accurate would
be helpful to determine what decisions the autopilot should make.
Lead author Mingxi Cheng was driven to
work on this project by this thought: "Even humans can be indecisive in
certain decision-making scenarios. In cases involving conflicting information,
why can't machines tell us when they don't know?"
A tool the authors created named
DeepTrust can quantify the amount of uncertainty," says Paul Bogdan, an
associate professor in the Ming Hsieh Department of Electrical and Computer
Engineering and corresponding author, and thus, if human intervention is
necessary.
Developing this tool took the USC
research team almost two years employing what is known as subjective logic to
assess the architecture of the neural networks. On one of their test cases, the
polls from the 2016 Presidential election, DeepTrust found that the prediction
pointing towards Clinton winning had a greater margin for error.
The other significance of this study is
that it provides insights on how to test reliability of AI algorithms that are
normally trained on thousands to millions of data points. It would be
incredibly time-consuming to check if each one of these data points that inform
AI predictions were labeled accurately. Rather, more critical, say the
researchers, is that the architecture of these neural network systems has
greater accuracy. Bogdan notes that if computer scientists want to maximize
accuracy and trust simultaneously, this work could also serve as a guidepost to
how much "noise" can be in testing samples.
The researchers believe this model is
the first of its kind. Says Bogdan, "To our knowledge, there is no trust
quantification model or tool for deep learning, artificial intelligence and
machine learning. This is the first approach and opens new research
directions." He adds that this tool has the potential to make
"artificial intelligence aware and adaptive."
Story Source:
Materials provided by University of Southern
California. Original written by Amy Blumenthal. Note: Content
may be edited for style and length.
Journal Reference:
Mingxi Cheng, Shahin Nazarian, Paul
Bogdan. There Is Hope After All: Quantifying Opinion and
Trustworthiness in Neural Networks. Frontiers in Artificial
Intelligence, 2020; 3 DOI: 10.3389/frai.2020.00054
https://www.sciencedaily.com/releases/2020/08/200827105937.htm
No comments:
Post a Comment