The breakthrough could lead the way in developing a framework suitable for safe implementation in high-risk environments
copyright by www.techhq.com
Researchers from top US colleges have devised a method for predicting failure rates of safety-critical machine learning systems
The neural bridge sampling method provides the ability to assess risks associated with deploying complex machine learning systems in safety-critical environments
The breakthrough could lead the way in developing a framework suitable for safe implementation in high-risk environments
Artificial intelligence (AI) is coming at us fast. It’s being used in the apps and services we plug into daily without us really noticing, whether it’s a personalized ad on Facebook, or Google recommending how you sign off your email. If these applications fail, it may result in some irritation to the user in the worst case. But we are increasingly entrusting AI and machine learning to safety-critical applications, where system failure results in a lot more than a slight UX issue.
One of the most significant examples of this is in autonomous vehicles, where the safety of AI systems is paramount to the technology’s adoption and acceptance in society.
In 2018, Elaine Herzber was hit and killed by an Uber self-driving car on a pilot test when the backup driver (who has this week been charged) failed to intervene. A Tesla vehicle’s fatal crash in California on March 23 of this year raised a further cloud over the safety and readiness of self-driving technology.
Safety-critical machine learning systems are occupying wider roles in anything from robotic surgery, pacemakers and autonomous flight systems. Any kind of failure in these cases could lead to injury, death or, perhaps at best, serious financial or reputational damage.
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According to researchers from MIT, Stanford University, and the University of Pennsylvania, this could all be about to change.
A breakthrough
In a recent paper titled Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems, published on arXiv, the neural bridge sampling method specified in the paper draws on decades-old statistical techniques and builds upon a simulation testing framework that evaluates black box autonomous vehicle systems.
[…]
Read more: www.techhq.com
The breakthrough could lead the way in developing a framework suitable for safe implementation in high-risk environments
copyright by www.techhq.com
The neural bridge sampling method provides the ability to assess risks associated with deploying complex machine learning systems in safety-critical environments
The breakthrough could lead the way in developing a framework suitable for safe implementation in high-risk environments
Artificial intelligence (AI) is coming at us fast. It’s being used in the apps and services we plug into daily without us really noticing, whether it’s a personalized ad on Facebook, or Google recommending how you sign off your email. If these applications fail, it may result in some irritation to the user in the worst case. But we are increasingly entrusting AI and machine learning to safety-critical applications, where system failure results in a lot more than a slight UX issue.
One of the most significant examples of this is in autonomous vehicles, where the safety of AI systems is paramount to the technology’s adoption and acceptance in society.
In 2018, Elaine Herzber was hit and killed by an Uber self-driving car on a pilot test when the backup driver (who has this week been charged) failed to intervene. A Tesla vehicle’s fatal crash in California on March 23 of this year raised a further cloud over the safety and readiness of self-driving technology.
Safety-critical machine learning systems are occupying wider roles in anything from robotic surgery, pacemakers and autonomous flight systems. Any kind of failure in these cases could lead to injury, death or, perhaps at best, serious financial or reputational damage.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
According to researchers from MIT, Stanford University, and the University of Pennsylvania, this could all be about to change.
A breakthrough
In a recent paper titled Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems, published on arXiv, the neural bridge sampling method specified in the paper draws on decades-old statistical techniques and builds upon a simulation testing framework that evaluates black box autonomous vehicle systems.
[…]
Read more: www.techhq.com
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